esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To mitigate the prevalence of highway accidents in Southwest China during adverse weather conditions, this study introduces a novel method for identifying accident-prone sections in complex meteorological circumstances. The technique, anchored in data mining's support index, pioneers the concept of meteorological responsiveness, which includes the elucidation of its mechanisms and the development of computational methodologies. Historical meteorological data and accident records from mountainous highways were meticulously analyzed to quantify the spectrum of adverse weather impacts on driving risks. By integrating road geometry, weather data, and accident site information, meteorological events were identified, categorized, and assigned a meteorological responsiveness score. Outlier sections were processed for preliminary screening, enabling the identification of high-risk segments. The Meteorological Response Ratio Index was instrumental in highlighting and quantifying the influence of adverse weather on traffic safety, facilitating the prioritization of critical sections. The case study of the SC2 highway in Southwest China validated the method's feasibility, successfully pinpointing eight high-risk sections significantly affected by adverse weather, which constituted approximately 19.05% of the total highway length. Detailed analysis of these sections, especially those impacted by rain, fog, and snow, revealed specific zones prone to accidents. The meteorological responsiveness method's efficacy was further substantiated by correlating accident mechanisms under adverse weather with the road geometry of key sections. This approach stands to significantly enhance the safety management of operational highways.  
Keywords:road engineering; adverse weather; risk identification; safety assessment; physical element analysis method +
Footnote †: journal: Applied sciences
## 3 Numerical Model and Method
### Materials and Simulation Procedures  
The simulations were carried out on a granular of silicon-si (sand) assembly which constituted the discrete phase provided by EDEM whereas the liquid water of kinematic viscosity \(\
u_{\mathrm{f}}\) = 2.0 \(\times\) 10\({}^{-6}\) m\({}^{2}\cdot\)s\({}^{-1}\), provided by FLUENT database by setting up the materials and operating conditions in Fluent, constituted the continuous phase. The particles of spherical form had a density of 2500 kg\(\cdot\)m\({}^{-3}\) and an average size of 1.610\({}^{-3}\) m while the density of the water was 1000 kg\(\cdot\)m\({}^{-3}\). The initial volume fractions of the particles were fixed at 0.52 and 0.62. The size of the maximum injection di\({}_{\mathrm{max}}\) was taken as practically equal to the width L of the grain layer (di\({}_{\mathrm{max}}\)\(\approx\) L \(\approx\) 250 mm). It is reported that hydrodynamics mechanisms are very sensitive to a change of restitution and friction coefficients [28]. Moreover, some low values of restitution coefficient can lead to a large solids kinetic energy dissipation at the wall region and allow some particles to stay in the wall region [29]. With the fixed simulation parameters in Table 1, the different observations made during the experimental phase were realistically reported.  
For the simulation, we imposed a flow velocity U greater than the fluidization threshold, at the base of the granular layer, and kept constant during the duration of the simulation.
###### Abstract  
The aim of the study was to design microcapsules with a core of blueberry fruit extracts (_Vaccinium myrtillus_ L.) using the ionotropic gelation method and then assess the effect of the type of extracts used and the combination of polymers on the profile of phenolic compounds, their in vitro bioavailability, stability during storage, as well as their antioxidant characteristics and cytotoxic activity against colon cancer cells while assessing biocompatibility against normal colon epithelial cells. Encapsulation efficiency (EE), ranging from 8.79 to 74.55%, significantly depended on the extract used and the type of carrier. It was shown that the addition of pectin (Pect) and whey protein isolate (WPI) to alginate (Alg) improved the efficiency of the encapsulation process. For this version of microcapsules, the highest antioxidant activity, phenolic compound content and their stability during storage were also demonstrated. The estimated content of phenolic compounds ranged from 0.48 to 40.07 mg/g, and the dominant compound was cyanidin 3-\(O\)-glucose. In turn, the highest bioavailability of these compounds and the highest cytotoxic activity against cancer cells were characterized by microcapsules with Alg and WPI. Nevertheless, good biocompatibility with normal colon epithelial cells was demonstrated for all versions of microcapsules. The obtained data indicate that the tested variants of microcapsules protect the bioactive compounds of blueberry fruit extracts, which translates into maintaining their health-promoting properties.  
microapsules; blueberry; _Vaccinium myrtillus_ L.; bioavailability; colon cancer; polyphenol content +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### TOPCon Solar Cell Achievement  
Figure 12a-d shows the consequences of the thickness of the oxide layer as well as the oxide layer's work function on the device's performances, whose default parameters are included in Table 1. Generally, V\({}_{\rm oc}\) and J\({}_{\rm sc}\) have the maximum value at a work function below 4.0 eV and progressively decrease with an increase in the work function of the nc-SiO\({}_{\rm x}\) layer. It was observed that the J\({}_{\rm sc}\) would drop rapidly as the tunnel oxide surpassed a specific thickness due to the deficiency of carrier tunneling. The fill factor was also affected by oxide thickness because the tunnel current diminished by \(\sim\)15 times with every 0.2 nm increase in the thickness of the oxide. The highest efficiency of an nc-SiO\({}_{\rm x}\)-based TOPCon solar cell, 27.49%, was accomplished with an oxide layer of \(\sim\)1.2 nm; however, a practical problem that may obstruct the preparation of a high efficiency nc-SiO\({}_{\rm x}\) based TOPCon solar cell remains. The critical complication concerns how to develop a low-D\({}_{\rm it}\) as well as a low pinhole oxide layer with an adjustable thickness. An oxide layer is synthesized using a wet chemical method (such as hot HNO\({}_{3}\)) with an immense density of interfacial defects. The higher annealing temperature varies from 800 \({}^{\circ}\)C to 1100 \({}^{\circ}\)C [10,11] and is needed to exclude the oxide defects and anneal the nc-SiO\({}_{\rm x}\) layer.
## 3 Development of Thermal Condition Monitoring System
### Overview of WSNs and Communication Links Characteristics
#### 3.3.2 Payload Data Rate and Scalability  
Numerous parameters could be used to evaluate the wireless network performances. The assessment expresses technical ideals of network configuration performance and reliability. The considered parameters deem to have the following characteristics to guarantee signal reliability [64; 67]. Throughputs present quantity of data transmitted appropriately from the source to the node sink within a specific time mandates in (secs). The node rate is an indication of estimated data packets received effectively at the node gateway from the routing/coordinator scheme, which proves the received bits number with the total time of signal on transmission. The network performance is an accomplishment of all the nodes rates inferred in the data transmission. Besides, an end-to-end delay is equal to the time taken by the data packets to reach the destination nodes, hence called the packet delay. Consequently, good-put is considered as the data packets rate received functionally of the total transmitted data packets. The lost data packets rate does not take into account the retransmissions of signal during delays. Therefore, the good-put is evaluated as the expression of latency time, which is equal to the message creation time deducted from the message arrival times [69]. Service time represent the message life time from its creation by the transmitter until the positive acknowledgement of message reception. ZigBee uses CSMA/CA and GTS to impact on its real-time characteristics with extremely low duty-cycle of about 0.01%. ZigBee is envisaged to contain about 65,000 nodes in application [64; 65; 71; 72].  
Several techniques are under consideration to involve its deployment of efficient exploitation for WSNs channel diversity as well as in time and performance delivery. However, NB-IoT/LTE offers the advantage of very high scalability than Sigfox and LoRa. NB-IoT/LTE allows connectivity of up to 100,000 end devices per cell site on full deployment as compared to 50,000 nodes for cell site of both Sigfox and LoRa [69]. Whiles the NB-IoT/LTE also offers the advantage of maximum payload length but of limited access of transmission. LoRa, although second to NB LTE with multiple gateway layers receiving signals concurrently, thus causes signal collapse and delays. On the contrary, Sigfox proposes to have the lowest payload length, which puts limit on its utilization for perspective WSN applications that may need to transmit large data sizes on its link for industrial purposes [68; 69; 70; 73].
## 3 Research Method
### Generation of Audio Spectrograms
#### 3.2.2 Wavelet Transform  
The wavelet transform (WT) is an advanced time-frequency analysis tool that employs the idea of multiresolution analysis to obtain proper resolution in different time-frequency regions by non-uniformly dividing the time-frequency space. It uses finitely attenuated wavelet basis functions, consisting of scaled and shifted versions of the parent wavelet and the parent wavelet, to better localize signals in the time and frequency domains. This property makes the wavelet transform particularly suitable for audio signal processing, providing better time resolution in the high-frequency part to detect transient changes and higher frequency resolution in the low-frequency part to accurately track slowly changing features. The continuous WT of the signal s(t) is as follows:  
\[W(a,b)=\frac{1}{\sqrt{a}}{\int_{-\infty}^{\infty}x(t)\psi_{a,b}^{*}(\frac{t-b} {a})dt} \tag{2}\]  
where \(x(t)\) is the original signal, \(\psi^{*}\) is the complex conjugate of the wavelet function, \(a\) is the scale parameter, and \(b\) is the translation parameter.  
Choosing the appropriate mother wavelet is the key to improve the efficiency and accuracy of wavelet transform analysis. The continuous WT of structural vibration response signals using complex Morlet wavelets can realize arbitrary time-frequency resolution. Therefore, in this paper, the complex Morlet wavelet is selected as the mother wavelet, and the mathematical expressions of its mother wavelet and sub-wavelet are as follows:  
\[\psi(t)=\frac{1}{\sqrt{\pi f_{b}}}e^{-2\pi f_{c}t}e^{-\frac{2}{f_{b}}} \tag{3}\]  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi\left(\frac{t-b}{a}\right) \tag{4}\]  
where \(f_{c}\) is the WT center frequency and \(f_{b}\) is the WT bandwidth frequency.
# Study on Characteristic Strength and Constitutive Model of Red Sandstone under Hydraulic Coupling
## 4 Test Results and Analysis
### Deterioration Mechanism Caused by Water Pressure  
[PERSON]'s circles and stress envelopes were obtained by the M-C strength criterion. The cohesion and internal friction angle are calculated according to the following formula:  
\[c=\frac{b}{2\sqrt{k}},\varphi=\arcsin\left(\frac{k-1}{k+1}\right) \tag{4}\]  
where \(k\) and \(b\) are the slope and intercept of the peak strength--confining pressure curve in Figure 9d.  
As shown in Figure 10, under the effect of 0-3 MPa seepage pressure, the internal friction angle of red sandstone is between 32.3\({}^{\circ}\) and 34.5\({}^{\circ}\), and the cohesion is between 0.8 and 4 MPa. With the increase in seepage pressure, the internal friction angle increases slowly, indicating that water pressure has a reduction on the internal friction angle of weakly cemented sandstone. The seepage pressure will affect the cohesion, which decreases with the increase in seepage pressure; when the seepage pressure increases to 3 MPa, the cohesion is only 20% of that without seepage pressure. This is because Cretaceous red sandstone has the characteristics of poor cementation capacity; under the effect of seepage pressure, the particles are dissolved, and the cements between particles lose their cementation strength, so the cementation is weakened. Moreover, in the loading process, the seepage pressure will inevitably produce expansion tension on the tip of the pore fracture, further reducing the cementation.  
Mineral composition and microstructure essentially affect the macromechanical properties of red sandstone. Most clay minerals are hydrophilic. In the process of water-rock interaction, the cement absorbs water, expands, and disintegrates. The skeleton structure bonded by the cement tends to be loose, indicating that the low content of clay minerals is the direct reason for the weak cementation ability of Cretaceous red sandstone. Under the action of hydraulic coupling, seepage pressure accelerates the dissolution of cementation.  
Figure 9: Variation curve of characteristic strength: (**a**) crack closure strength, (**b**) crack initiation stress, (**c**) dilatation strength, and (**d**) peak strength.  
titious materials, increases the scouring of pores and fissures, and causes the decrease in internal cohesion and loosening of particles. The seepage volume force increases the expansion of pores and fissures and the scouring of particles increases, which speeds up the disintegration of the internal structure of sandstone.
## 3 Results
### Regional Subdivisions of the Substantia Nigra
\begin{table}
\begin{tabular}{c c c c} \hline
**Pathway** & **Connection** & **Significance for Neurology** & **Study** \\ \hline Nigrostriatal & SNc \(\rightarrow\) dorsal striatum (putamen, caudate) & A key pathway for motor control; its degeneration leads to the motor symptoms of PD & [11; 12; 14; 57; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70] \\ \hline Nigrothalamic & SNr \(\rightarrow\) VA, VL, VM thalamic nuclei & Influence motor and associative cortical circuits, important for motor and cognitive function, involved in dystonia pathophysiology & [28; 64; 70] \\ \hline SNc \(\rightarrow\) thalamus & Dopaminergic connections from the SNc directly to the thalamus & Possible dopamine influence on thalamocortical circuits; potential mechanism for non-motor symptoms of PD & [28] \\ \hline Cortico–Nigral & Direct projections from the cortex (prefrontal, sensorimotor areas) \(\rightarrow\) SN & Cognitive and sensory modulation of dopamine neurons; SN responds to cortical inputs & [7; 34; 74] \\ \hline SN–ventral striatum (mesolimbic pathway) & SNc/VTA \(\rightarrow\) NAc and orbitofrontal cortex & Regulation of reward, emotion, and impulsiveness; key to addictions and behavioral disorders & [28; 66; 69; 72; 73] \\ \hline SN–hippocampus & SN/VTA \(\leftrightarrow\) hippocampus (direct and indirect connections) & Modulation of motivation, novelty, and learning; relevance to stress; major depressive disorder (MDD) & [47; 67] \\ \hline SN–amygdala, limbic cortex & SN2 (part of SNc) \(\rightarrow\) amygdala, cingulum, orbitofrontal region & Emotion regulation; involved in mood disorders, anxiety, REM atonia & [68] \\ \hline SN–GPI/ansa lenticularis (GPSN) & Globus pallidus \(\rightarrow\) SN (via specific GPSN branch) & Possible feedback effect of pallidum on SN; integration of BG outputs and feedback & [65] \\ \hline SNc–subcortical circuits (reticular formation) & SNc \(\leftrightarrow\) brainstem, VTA, PAG, hypothalamus & Involved in regulation of sleep and autonomic functions; important in MDD, stress, and sleep disorders & [68; 75] \\ \hline \end{tabular}
\end{table} 
Table 1: Connections of the substantia nigra.
## 3 Materials and Methods
### Hydrochemical Survey  
Three groundwater sampling campaigns (1989, 2007, and 2016), carried out in the same area as that of the geophysical surveys, were considered in this study. Groundwater hydrochemical data of the selected campaigns were obtained from Barradas [43], Ordens [33], and Marques [34] in 1989, 2007, and 2016, respectively. Groundwater samples were collected in both holes and pumping wells. It should be noted that some of these wells are now abandoned due to unsafe levels of contamination.  
The physicochemical parameters (temperature, electrical conductivity, and pH) were measured in situ using specific electrodes for each parameter. For chemical analysis, the water samples were filtered through a 0.45 \(\upmu\)m pore size filter and stored in high-density polyethylene bottles, which were kept refrigerated in coolers during transport to the laboratory.  
The analyses of chloride (Cl\({}^{-}\)) and sulphate (SO\({}_{4}\)\({}^{2-}\)) were done by ion chromatography, while major cations and trace elements were determined by atomic absorption spectrometry (after a pre-concentration of waters by lyophilisation) for the 1989 sampling campaign, and by inductively coupled plasma mass spectrometry (ICP-MS) for the samples collected in 2007 and 2016.
## 3 Results and Discussion  
### Model Validation  
The chemical binding kinetics of the analyte (Anti-rabbit IgG) with the ligand (Rabbit IgG) at the reaction surface has been validated with experimental results of [PERSON] et al. [13] without confinement flow and for the case of no AC electric field. Figure 2a presents the transient evolution of the normalized concentration of the complex. We notice good agreement between the simulation results with the experimental data of [PERSON] et al. [13].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Adsorption rate constant \(k_{1}\) & \(10^{4}\) & m\({}^{3}\)/(mol-s) \\ Desorption rate constant \(k_{2}\) & \(2.6\times 10^{-2}\) & s\({}^{-1}\) \\ Ligand concentration \(\theta_{0}\) & \(1.4\times 10^{-8}\) & mol/m\({}^{2}\) \\ Diffusion coefficient \(D\) & \(2.175\times 10^{-11}\) & m\({}^{2}\)/s \\ Inlet concentration \(C_{0}\) & \(6.4\) & umol/m\({}^{3}\) \\ Thermal conductivity \(k\) & \(0.6\) & W/(K-m) \\ Fluid density \(\rho\) & \(1000\) & kg/m\({}^{3}\) \\ Dynamic viscosity \(\mu\) & \(1.08\times 10^{-3}\) & Pa-s \\ Specific heat \(C_{p}\) & \(4.184\) & kJ/(kg-K) \\ Electrical conductivity \(\sigma\) & \(5.75\times 10^{-2}\) & S/m \\ Relative permittivity \(\varepsilon_{r}\) & \(80.2\) & \\ Frequency \(f\) & \(100\) & kHz \\ \hline \hline \end{tabular}
\end{table}
Table 4: Numerical values of the parameters used in the current simulations [12; 23; 25].  
Figure 2: Numerical results of C-reactive protein (CRP) complex concentration as a function of time (**a**) compared to experimental data from [PERSON] et al. [13] and (**b**) compared to results from [PERSON] et al. [23] with and without electrothermal force.  
Figure 2b compares the results obtained by the present studies with those obtained by [PERSON] et al. [23] in the cases without and with the electrothermal force. A good agreement has been observed. It should be noted that several components of the computation code has also been validated in our previous papers [18; 19; 20; 21; 22].  
In order to optimize microfluidic biosensor performance, several parameters were tested in this work. Two biosensor geometries with two forms of reaction surface were studied.
## 2 Fundamentals
### Continuous Wavelet Transform (CWT)  
The CWT of the signal \(f(t)\in L^{2}(R)\) can be expressed as the following formula [26]:  
\[W\!f(b,s)=f*\frac{1}{\sqrt{s}}\overline{\varphi}(b/s)=f*\overline{\varphi}_{s} (b)=\int_{-\infty}^{+\infty}f(x)\overline{\varphi}_{b,s}dx \tag{1}\]  
where \(W\!f(b,s)\) is the CWT coefficient of \(f(t)\), \(\varphi(x)\) is the wavelet function, \(b\) and \(s\) are the translation and scale parameters, respectively, \(*\) denotes the conjugate operation, and \(*\) denotes the convolution operation. In the field of signal processing, CWT is the most commonly used tool for time-frequency analysis, which can effectively portray the local characteristics of signals.
## 1 Introduction
Using FEA, the surface contact between the outer and inner tubes requires a lot of computational resources and is hard to converge sometimes [27]. We propose the kinematic pair equivalent method to solve this contact problem. Elastodynamic optimization can improve the stiffness, dynamic performance, and lightness of PMs in the initial design stage [28]. The elastodynamic optimization process consists of adjusting the values of the design parameters to derive better elastodynamic performance defined by the designers [29]. One big challenge is the huge computational cost. The conventional structure optimization method under dynamic loading needs to repetitively solve the nonlinear differential equation. This process is time-consuming [30]. What is worse, the optimization process for a parallel mechanism needs to consider mechanism configurations. Usually, the mechanism under different configurations is considered a series of structures. Therefore, the configuration of the mechanism adds another iteration dimension for elastodynamic optimization. One solution to the computational problem is determining a particular trajectory in the workspace and evaluating the performance on the trajectory [26; 31]. However, this cannot reflect the PM characteristics in the whole workspace [32]. We introduce and modify the equivalent static loads (ESLs) [33] in structural optimization to solve this problem. ESLs transfer the dynamic response to a series of equivalent static loads. Then, the dynamic optimization problem becomes the classical static optimization problem. As a result, only the algebraic equation needs to be solved in the sub-optimization procedure. In application, more than one objective function needs to be considered simultaneously, such as the mass, fundamental frequency, energy, displacement, and so on. Therefore, linear static response optimization is a multi-objective optimization process. We introduce the desirability approach [34] to implement the multi-objective optimization process. The desirability approach transfers different objective functions into a single desirability objective function by assigning different weight factors. It is widely used in a multi-objective optimization design since it is easy to implement and has a low computational cost. In this paper, the outer tube-inner tube model is added to the MSA with the kinematic pair equivalent method. Equivalent static loads and the desirability approach are introduced and modified in the elastodynamic optimization of PMs. In Section 2, the elastodynamics of a PM with tubes is constructed with the kinematic pair equivalent method based on matrix structural analysis. In Section 3, a reconfigurable lunar lander is used as an example. The theoretical elastodynamic model is compared to the FEA results to verify the effectiveness of the method. In Section 4, elastodynamic optimization with ESLs and the desirability approach are introduced, modified, and finally, applied to the lander.
# Costs and Benefits of Autonomous Shipping--A Literature Review
## 3 Results
### Basic Costs of Operating Autonomous Slips  
The following section provides a detailed description of the economic performance of autonomous ships in two selected case studies. The analysis is focused on the cost of operating a bulk carrier and a containership.