esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Three-Hole Simultaneous Blasting Technology
### Characteristics of the Three-Hole Simultaneous Blasting Technology  
In general, the three-hole simultaneous blasting technology has the following advantages:  
(1) Increasing the energy of rock mass interaction  
The three-hole simultaneous blasting technology was applied to detonate three blast holes simultaneously. In this process, the amount of explosives used in one section is three times that required for hole-by-hole blasting. This increased the blasting energy in the same section while improving the outcome of the rock mass fragmentation. Compared to hole-by-hole blasting, the vibration induced by blasting fell within a controllable range [17; 18].  
(2) Reducing the ratio of large blocks  
As mentioned above, the three holes within the same section were detonated simultaneously. In this case, the distribution of charge was relatively extensive, and the explosive load borne by the rock mass was relatively uniform [2; 19]. This enabled sufficient fragmentation of the rock mass. After the detonation of the previous blast hole, a new free surface developed for the blasting of the subsequent blast hole. The free surface became more abundant, reducing the rate of large blasting blocks.  
(3) Reducing the susceptibility of rear-hole blasting to overturning and backlashing  
Considering the bench blasting, the large resistance line of the rear hole chassis caused a significant clamping effect and insufficient blasting energy. This phenomenon hindered the rock mass from moving horizontally. As a result, the rock mass tended to overturn or recoil in the rear holes [20]. On this basis, combining three holes into one can increase the blasting energy in the rear holes. Furthermore, detonating the three holes at the same time was beneficial to the formation of a neat fracture surface in the rear holes, thereby avoiding the risk of upward blasting and backlash in the rear holes.  
Figure 1: Schematic diagram of the three-hole simultaneous blasting.
## 2 Materials and Methods  
The FE model of the rat cervical spinal cord was developed based on a previously validated model [31]. The geometry of the rat spinal cord was obtained from an in vivo imaging study [34]. The developed model consists of white and gray matter, the dura mater, and the CSF (cerebrospinal fluid) layer (Figure 1). The length of the spinal cord was 14 mm, the same as in a previous modeling study [23]. In total, the model comprised 201,826 nodes and 172,716 hexahedron elements except the CSF layer. The CSF layer was modeled as fluid elements surrounding the spinal cord using ABAQUS/Explicit (ABAQUS Inc., Providence, RI, USA). The details of the fluid modeling technique can be found in the previous study [31]. Furthermore, the simplified spinal canal was modeled based on anatomical measurements of the rat cervical spinal canal to mimic the experiment [35]. The frictionless contact was applied between the cord, the CSF layer, and the dura mater due to the lubricating effect of fluid inside the dural sheath, while the friction coefficient between the dura mater and impactor as well as the spinal column was 0.15 based on a previous study [23]. The material properties of the white and gray matter and dura mater were hyperelastic-viscoelastic [23]. The CSF layer was modeled with a Newtonian fluid with a viscosity of 0.001 pa-s [36].  
Two different (NYU and IH impactor) hemicontusion SCI models were simulated with the four differently sized impactor rods. The rods were modeled with diameters of 0.8 mm, 1.2 mm, 1.6 mm, and 2.0 mm and placed 1.0 mm to the right side of the spinal cord to simulate the hemicontusion injury models (Figure 2), which was approximately centered over the right side of the spinal cord based on previous experimental studies [12; 16]. In the NYU model, the four different impactor rods were the same weight of 10 g and placed in direct contact with the spinal cord [31]. Then, the predefined initial velocities were applied to the impactor rods for drop heights of 6.25 and 12.5 mm cases [31]. In the IH model, the impactor rods were the massless cylindrical body and loaded with forces of 100 kdyn and 200 kdyn with a velocity of 125 mm/sec to replicate the experimental condition [5]. In total, eight replicates were simulated in each impactor. The von-Mises stress and maximum principal strain for the right (ipsilateral) and left (contralateral) sides of the spinal cord, and spinal cord displacement were analyzed for two different (NYU and IH impactor) models with four differently sized impactors using FE analysis. The right and left sides of the spinal cord were determined by dividing the spinal cord at the midline. The spinal cord displacement was estimated from the impactor rod motion [31]. A two-way ANOVA was performed to compare the stresses and strains on the right and left sides of the spinal cord. A one-way ANOVA was also employed to analyze the maximum displacement of the spinal cord. The significance level was set at \(p<0.05\).
# A Novel Spatiotemporal Analysis Framework for Air Pollution Episode Association in Puli, Taiwan
## 3 Proposed Methods
### Air-Quality Data Acquisition and Conversion  
The field study conducted in this paper is located in Puli, central Taiwan. Puli is a small town with an area of 13 \(\times\) 13 km\({}^{2}\) and it is centered at 23\({}^{\circ}\)58\({}^{\prime}\)12\({}^{\prime\prime}\) north latitude and 120\({}^{\circ}\)58\({}^{\prime}\)12\({}^{\prime\prime}\) east longitude. The geography of Puli is basin terrain. There are three rivers flowing to the west coast of Taiwan and creating three valleys, which induce the transportation of air pollutants yielded in the west metropolis into the Puli basin. It is desired to analyze the main patterns of pollution emergence, including those resulting from external sources or local emissions. As such, appropriate pollution-free actions can be enforced by the public. Our studied field is the interior of the basin geography, which is resolved in 10 \(\times\) 10 grids with an area of 8 \(\times\) 8 km\({}^{2}\). Within each grid, we acquire the PM\({}_{2.5}\) concentration every hour. In our previous research [28], we had deployed 32 PM\({}_{2.5}\) sensors in some of the grids. The sensors are model G7 PMS7003, which uses a laser beam  
Figure 1: Framework architecture of the proposed methods.  
to illuminate the suspended air particles and generate light scattering. The scattered light is then analyzed to estimate the particle size and the number of particles. For those grids where there are no sensors, the concentration intensity can be simulated by smoothing the concentration intensity from nearby sensors or applying machine-learning imputation techniques such as the one suggested in [PERSON] et al. [21]. Hence, for every hour, we obtain an image frame of n \(\times\) n pixels, and the PM\({}_{2.5}\) concentrations in the grids are considered the gray values of the image. If we continuously monitor the PM\({}_{2.5}\) concentrations in Poli for 365 straight days, we will produce a time series of 365 \(\times\) 24 = 8760 image frames, which is a video. In this paper, we acquired the hourly PM\({}_{2.5}\) concentrations from the PM\({}_{2.5}\) sensors from 1 January 2018 until 31 December 2018, so the collected data involve four seasons. To extract salient and meaningful PM\({}_{2.5}\) episodes, the concentrations of image pixels are converted to those with 10 air-quality alert levels, as shown in Figure 2. As the original six air-quality alert categories defined by the Taiwan Environmental Protection Administration (EPA) may not be sufficient to describe the dynamic variations of air pollution activities, we refine the six-level categories to 10-level categories. Next, we propose pollution episode segmentation methods with the alert category time series video.
## 1 Introduction
To correctly describe the effect of the martensite content on the material model, the parameters related to martensite can be associated with kinematic hardening and isotropic hardening in the constitutive model [29; 30]. The constitutive models used in commercial software, such as ANSYS and ABAQUS, to predict ratcheting strain are relatively simple; however, these models are not ideal for predicting ratcheting strain. Many scholars [31; 32; 33; 34] have written user subroutines by using the secondary development function provided by ANSYS and ABAQUS, and embedded some new constitutive models into the software to predict ratchet deformation, and achieved good prediction results. In this paper, we tested the ratcheting of this material under different loading conditions. Based on the Ohno-Wang II model, the content of martensite is related to the isotropic hardening and kinematic hardening of materials, and we propose a constitutive model that can reasonably describe the martensitic transformation induced by S30408 at cryogenic temperatures. Then, in the numerical algorithm, we utilized the radial return method to realize the internal equilibrium iteration and determined the tentative strain after each equilibrium iteration by the consistent tangent matrix. Finally, we applied the constitutive model with martensitic transformation to the finite element analysis to simulate the ratcheting strain of S30408 round bar. We examined the capability of the proposed constitutive model to predict the uniaxial ratcheting strain of the specimen.
## 3 Molecular Characterization of Coat Color Affecting Genes in Rabbits
### Mutations in the Melanophilin (MLPH) Gene and the Dilute Locus  
An altered distribution of eumelanin and pheomelanin in the hairs is determined by the _Dilute_ locus that produces a coat color dilution. The murine _Dilute_ locus is caused by mutations in the in the myosin Va (_Myo5a_) gene, which encodes for an actin-binding protein that is involved in the process of melanosome transport [71]. Considering the potential homology between the mouse and rabbit _Dilute_ loci, the rabbit _MYO5A_ gene was first investigated to evaluate if its mutations could be responsible for the coat color dilution observed in a few rabbit breeds, such as Vienna Blue. [PERSON] et al. [72] excluded that the rabbit _MYO5A_ gene was involved in determining the _Dilute_ locus in this lagomorph species. Then, the rabbit melanophilin (_MLPH_) gene that in mice is responsible for a similar coat color phenotype determined by the _Leaden_ locus [73] was investigated. A family-based segregation analysis that followed the transmission of the mutated allele \(d\) revealed that the rabbit _MLPH_ gene determines the _Dilute_ locus in _Oryctolagus curiculus_[74]. Sequence and gene expression analyses identified two candidate mutations for the \(d\) allele [74; 75]: the c.111-5C > A mutation, located within intron 2 in an acceptor site for splicing, leading to the skipping of exons 3 and 4 from the normal rabbit _MLPH_ transcript; and a 1-bp deletion in exon 6 (c.585 delG, indicated also g.549853 delG, if referred to its position on the scaffold GL018840 of the oryCun2.0 genome version) that leads to a frameshift and an altered amino acid sequence, with a premature stop codon in a downstream exon. Subsequent segregation and gene expression investigations strongly supported the causative role of the frameshift mutation (c.585 delG) and tended to exclude the intron 2 mutation as being the responsible variant of the \(d\) allele in most coat color diluted breeds [76]. All self blue breeds and lines (Vienna Blue, Castor Rex, and Chinchilla diluted) and spotted blue rabbits of different breeds (Californian, Checkered Giant, and English Spotted rabbits) and light grey or cream rabbits (Fairy Marburg and Fairy Pearly rabbits) were homozygous for the c.585 delG frameshift mutation [74; 76]. This recessive allele was also identified in a few other breeds in which it was masked by the wild type \(D\) allele [74]. A complete list of breeds in which the c.585 delG mutation was identified is reported in Table 2.  
These results, which confirmed that the rabbit _MLPH_ gene is involved in this coat color phenotype, make it possible to consider the rabbit as a natural animal model for the human Griscelli syndrome type 3 where mutations in the same gene produce coat color defects in humans since infancy, with unusually hypopignmented skin and light silvery-gray hair [77; 78].
# SatScope: A Data-Driven Simulator for Low-Earth-Orbit Satellite Internet
## 3 SatScope Model Framework
### Ground Segment Modeling
#### 3.2.3 Randomization of User Nodes  
SatScope utilizes the dataset published by Starlink to perform user location simulations. Based on relevant studies, we assume that the distribution of users within the same city follows a normal distribution. Specifically, the distance of users from the city center is modeled using a normal distribution with a mean \(\mu\) of 50 km and a standard deviation \(\sigma\) of 22 km. Consequently, the user distribution can be represented as  
\[f(d)=\frac{1}{\sigma\sqrt{2\pi}}\exp\biggl{(}-\frac{(d-\mu)^{2}}{2\sigma^{2}} \biggr{)}, \tag{1}\]  
where \(f(d)\) denotes the probability density at distance \(d\), \(\mu\) represents the mean, and \(\sigma\) is the standard deviation. SatScope employs random number generation to determine the relative azimuth angle of users from the city center, ensuring that terminal users are uniformly distributed around the city.
# A Novel Radio Network Information Service (RNIS) to MEC Framework in B5G Networks
## Abbreviations  
B5G Beyond the Fifth Generation RNIS Radio Network Information Service RAN Radio Access Network 5G Fifth Generation of mobile networks eMBB enhanced Mobile Broadband uRLLC Ultra-Reliable Low-Latency Communication mMTC massive Machine Type Communications 6G Sixth Generation of mobile networks IoT Internet of Things MEC Multi-Access Edge Computing NG-RAN Next Generation Radio Access Network UE User Equipment MEC apps MEC applications WS Web Service SOA Service-oriented Architecture ERAB E-UTRAN Radio Access Bearer RRC Radio Resource Control S1 AP S1 Application Protocol X2-AP X2 Application Protocol QoE Quality of Experience CCE Congestion Control Engine MEP MEC Platform MEO Multi-Access Edge Orchestrator PLMN Public Land Mobile Network
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 2 Analysis of Influencing Factors and Feature Selection of TBM Performance  
### Establishing TBM Driving Efficiency Database  
In order to analyze the TBM performance in the south of Qinling tunnel of the Hanjiang-to-Weihe River Diversion Project, the TBM performance database was established based on the data collected from the construction site. As shown in Table 1, the database is mainly composed of two categories: the first category is TBM mechanical parameters. By sorting out the TBM automatically recorded driving data and combining it with TBM construction time records, the corresponding operation parameters of TBM operation data and mileage number are obtained, mainly including cutterhead thrust (TF), penetration (P), torque (T), rotational speed (RPM), penetration rate (PR) and TBM utilization ratio (U), etc.  
The second type is the rock mass parameters corresponding to each driving mileage of TBM, which are composed of a geological investigation in the early stage of construction; field investigation during construction and geophysical prospecting; and rock mass parameters that characterize the physical and mechanical properties of driving rock mass measured in the laboratory, mainly including rock uniaxial compressive strength (UCS), number of rock volume joints (\(J_{v}\)), angle between joint surface and tunnel axis (\(\alpha\)), rock abrasion resistance index (\(A_{\text{b}}\)), specific work of chiseling (\(a\)), and quartz content (\(q\)), etc.  
\begin{table}
\begin{tabular}{c c} \hline
**TBM Mechanical Parameters** & **Rock Mass Parameters** \\ \hline Cutterhead thrust TF (kN) & Rock uniaxial compressive strength UCS (MPa) \\ Penetration \(P\) (mm/r) & Rock abrasion resistance index \(A_{\text{b}}\) (10\({}^{-1}\) mm) \\ Torque \(T\) (N-m) & Specific work of chiseling \(a\) (1/ cm\({}^{3}\)) \\ Rotational speed RPM (r/min) & Number of rock volume joints \(J_{\text{v}}\) (pieces/m\({}^{3}\)) \\ Rotational speed PR (m/h) & Angle between joint surface and tunnel axis \(\alpha\) (\({}^{\circ}\)) \\ Construction speed AR (m/d) & Quartz content \(q\) (\%) \\ TBM utilization ratio \(U\) (\%) & P-wave velocity of rock mass \(V_{\text{pm}}\) \\ Penetration index FPI (kN-r mm\({}^{-1}\)-m\({}^{-1}\)) & P-wave velocity of rock block \(V_{\text{pr}}\) \\ Number of tool change of cutterhead (N/d) & \\ \hline \end{tabular}
\end{table}
Table 1: Structure of the TBM performance database.  
Table 2 lists the basic information of various parameters in the TBM tunneling efficiency database collected in the 8521 m statistical section of the Lingnan Project in the TBM construction section.
## 3 Results
### Fracture Resistance Values of the Fixed Partial Detture Groups
CM, conventional manufacturing; SM; subtractive manufacturing; AM, additive manufacturing. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Production Method** & \
\begin{tabular}{c} **Aging** \\ **Applied** \\ \end{tabular} &
\begin{tabular}{c} **Not Applied** \\ \end{tabular} \\ \hline Conventional & Mean \(\pm\) SD & 585.06 \(\pm\) 96.77 & 695.60 \(\pm\) 140.97 \\ Manufacturing & Median (Q1–Q3): & 587.99 & 724.68 \\  & (558.60–602.55) & (579.76–815.65) \\  & Min–Max & 432.10–802.09 & 450.27–858.72 \\ Subtractive & Mean \(\pm\) SD & 561.63 \(\pm\) 140.21 & 1291.13 \(\pm\) 564.15 \\ Manufacturing & Median (Q1–Q3): & 592.71 & 993.20 \\  & (457.94–656.56) & (917.29–1692.83) \\  & Min–Max & 277.86–745.65 & 765.62–2447.01 \\ Additive & Mean \(\pm\) SD & 373.29 \(\pm\) 107.24 & 368.87 \(\pm\) 104.04 \\ Manufacturing & Median (Q1–Q3): & 377.54 & 372.11 \\  & (260.90–441.06) & (331.33–459.85) \\  & Min–Max & 214.48–542.61 & 189.48–479.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Descriptive and comparative statistics for the fracture load values (N) of the experimental fixed partial denture groups. The Tukey multiple comparison test results for the combined effect of the manufacturing method and aging application revealed that aging significantly affected subtractively manufactured FPDs, as aged restorations exhibited significantly different fracture resistance values from their non-aged counterparts (\(p<0.001\)). However, no statistically significant differences were found between aged and non-aged FPDs fabricated using additive methods (\(p=1.000\)) and conventional methods (\(p=0.925\)). Among aged FPDs, no significant differences were observed between any of the manufacturing methods. In contrast, for non-aged FPDs, subtractive manufacturing demonstrated significantly different fracture load values compared to both conventional (\(p<0.001\)) and additive methods (\(p<0.001\)), while no significant difference was found between conventional and additive FPDs (\(p=0.062\)).
# Experimental and Mathematical Study of Flexible-Rigid Rail Vehicle Riding Comfort and Safety
## 5 Results and Discussion  
The rigid body dynamics model treats the numerous components of the vehicle system as if they were a single rigid body. This model disregards the local vibration in each element, instead focusing on how the components are connected. The rigid and flexible coupling dynamic model produces a one-of-a-kind dynamic effect. This effect can take into account the coupling between the object's rigid body motion and the component's elastic deformation, in addition to the one-of-a-kind dynamic effect brought about by the coupling. The traditional rigid and flexible coupling model will only consider one member of the vehicle system to be an elastic body, while the other component will remain rigid. This will allow for a more precise study of the wheel abrasion caused by the dynamic performance of the vehicle body, frame, and wheel parts, all considered elastic bodies. This will allow for the combination of a vehicle model with flexible construction. The research establishes a stiff flexible coupled dynamic model of an ICF railway coach vehicle by combining finite element analysis with multi-body dynamics simulation. This model is depicted in Figure 5.  
The comprehensive rigid-flexible coupling model of the ICF railway coach is created using a geometric model formulated in UNIGRAPHICS NX7.5, which is exported in Parasolid format to HYPERMESH 10 to develop the finite element model of the coach. The Parasolid format is preferred for its effective translation of solid models into ANSYS-17 FEA software. HYPERMESH is chosen due to its faster computing ability and interoperability with ANSYS in terms of element types and file extensions. The meshed coach model in HYPERMESH 10 is further exported to ANSYS Mechanical APDL in cdb (card scan database) format to conduct the finite element analysis.  
The idealized railway coach model is obtained by considering the car body shell with various channel sections, two bogie assemblies on either end, a trough floor with cross beams, and body bolsters on front and rear ends, while neglecting remaining parts. The solid components of the coach, bogie frame, body bolsters, wheel axle sets and bogie bolster are discretized using the SOLID 45 element type in HYPERMESH 10. The coach shell with the channels, such as stanchions and sole-bars, is meshed using SHELL 63 in HYPERMESH 10. The suspensions between the car body of the coach and the bolsters are defined using the line element type COMBIN14, accounting for both the helical spring and the damping. For compatibility, the shell and solid elements are integrated using the option 'Coupling' in  
Figure 5: 3D FEA model of rail vehicle.  
ANSYS Preprocessor. After achieving mesh convergence, the finite element model of the railway coach comprises 171,677 elements and 204,027 nodes.