esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization of HVSR and VRSR in the Loess Plateau of China Based on Strong-Motion Data
## 4 Results and Discussion
### Characterization of the Power Spectrum
#### 4.2.2 Characterization of the Power Spectra at Different Epicentral Distances  
Based on the epicentral distance groupings in Section 4.1.2, the power spectra for the EW, NS, and UD components were calculated for each group. The mean power spectral curves are shown in Figure 10. The \(P_{max}\) and \(T_{p}\) results for each epicentral distance range are summarized in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{\(M_{avg}\)} & \multicolumn{2}{c}{**EW**} & \multicolumn{2}{c}{**NS**} & \multicolumn{2}{c}{**UD**} \\ \cline{2-7}  & \(P_{max}\) **(gal2/s)** & \(T_{p}\) **(s)** & \(P_{max}\) **(gal2/s)** & \(T_{p}\) **(s)** & \(P_{max}\) **(gal2/s)** & \(T_{p}\) **(s)** \\ \hline
3.1–4 & 0.03 & 0.11 & 0.02 & 0.11 & 0.02 & 0.10 \\
4.1–5 & 0.02 & 0.18 & 0.02 & 0.16 & 0.02 & 0.12 \\
5.1–6 & 0.03 & 0.18 & 0.02 & 0.13 & 0.01 & 0.12 \\
6.1–7 & 0.04 & 1.58 & 0.03 & 1.62 & 0.03 & 1.61 \\
7.1–8 & 0.17 & 3.00 & 0.17 & 3.00 & 0.19 & 3.00 \\ \hline \end{tabular}
\end{table}
Table 3: List of \(P_{max}\) and \(T_{p}\) values for different magnitude subgroups.  
Figure 10 and Table 4 indicate that, on average, the power spectra of the three components are notably influenced by the earthquake magnitude. As the magnitude increases, both \(P_{max}\) and \(T_{p}\) exhibit a distinct upward trend. Within the same magnitude range, except for the 7.1-8.0 group, the \(P_{max}\) and \(T_{p}\) values of the horizontal components are not significantly different from those of the vertical components.
## 3 Pulsed Fibre Lasers Using 2D Materials
### Q-Switched Fibre Lasers  
Q-switching results from the SA modulating the cavity Q factor to periodically emit light as a pulse train, typically with a kHz repetition rate, ns-\(\upmu\)s pulse durations and high pulse energies (nJ-mJ level). Such lasers have become important tools in machining and materials processing [1]. Q-switched fibre lasers have been demonstrated using a variety of 2D materials, initially with graphene in 2011 [63] and subsequently with the topological insulator (TI) Bi\({}_{2}\)Se\({}_{3}\)[64], TMD MoS\({}_{2}\)[38] and, most recently, BP [65].  
Focusing on TMDs, greatest progress has been demonstrated with MoS\({}_{2}\). The first MoS\({}_{2}\) Q-switched fibre laser was a Yb-doped fibre ring cavity, producing \(\sim\)2.7 \(\upmu\)s pulses at 1068 nm with a repetition rate of 67 kHz and 0.5 mW of output power [38]. Since this initial report, further MoS\({}_{2}\)-based lasers have been demonstrated utilizing Yb active fibre at \(\sim\)1 \(\upmu\)m [32; 38; 40], in addition to using Er fibre at \(\sim\)1.5 \(\upmu\)m [35; 36; 40; 41; 48] and Tm fibre for \(\sim\)2 \(\upmu\)m operation [40]. These results, in addition to reports of continuously tunable operation from 1030-1070 nm [32] and 1520-1568 nm [41], confirm the promising wideband saturable absorption of the material. The highest output power from MoS\({}_{2}\) Q-switched lasers to date is 47 mW [40], corresponding to a pulse energy of 1.2 \(\upmu\)J.  
Q-switched lasers using other TMD materials are also emerging. For example, WS\({}_{2}\)[42; 50; 66; 67] and WSe\({}_{2}\)[42] have been demonstrated in passively Q-switched Er:fibre lasers. Additionally, a single MoSe\({}_{2}\) SA was used to Q-switch Yb, Er and Tm-doped fibre lasers [55]. These MoSe\({}_{2}\) Q-switched lasersall employed ring-cavity designs and generated self-starting stable pulse trains at 1060 nm, 1566 nm and 1924 nm, respectively, as shown in Figure 5.
# Creating a Parallel Corpus for the Kazakh Sign Language and Learning
## 3 Materials and Methods  
### Glossing Signs  
Reference [19] presents one of the earliest annotation systems for interpreting sign language. Until then, gestures were considered indivisible units with no internal structure. The Stokoe system for American Sign Language (ASL), which utilized graphic symbols, was among the first methods for recording these elements. Later, additional systems were developed, including HamNoSys [20] and the gloss recording system [21].  
Glossing is a method of annotating sign language by assigning a written form to gestures. While it is not a direct translation, it serves as a structured approximation. Glosses do not provide an exact translation but instead follow the sequential word order of Kazakh, aligning with its equivalent sign language structures. This practice has evolved in linguistics to aid the study and analysis of languages, particularly those without a written form. In this work, we use glosses to describe KSL. The next section presents step-by-step instructions for creating our parallel corpus.  
Our research utilizes modern software tools such as Python 3.10, Stanza 1.10.1, PyTorch 2.1, NumPy 2.2.0, and Pandas 2.2.3. Stanza is used for natural language processing tasks, including morphological and syntactic analysis. PyTorch enables efficient training of deep neural networks, while NumPy and Pandas facilitate working with multidimensional data arrays and tabular structures, streamlining data preprocessing and result analysis.
# Solar Radiation Parameters for Assessing Temperature Distributions on Bridge Cross-Sections
## 6 Influence of Solar Radiation on Temperature Distribution on Bridge Grider Cross-Sections
### Side-by-Side Box Girder Bridge
#### 6.2.1 Finite Element Model
The temperature contour plots obtained from finite element model with or without the consideration of solar radiation are compared in Figure 18 (16:00, 31 July 2014). In Figure 17, it can be observed that the variation trends and the times at which the highest temperatures occurred are similar for measured and calculated results on 31 July 2014. The differences between the highest measured and calculated temperatures when considering the effect of solar radiation were very small (maximum 0.5 \({}^{\circ}\)C for top flanges and 1.8 \({}^{\circ}\)C for webs). However, the highest calculated temperatures without the consideration of solar radiation were lower (maximum 9.3 \({}^{\circ}\)C for top flanges and 1.8 \({}^{\circ}\)C for webs) and had a time delay (2 h for top flanges). For bottom flanges, the differences between the highest measured and calculated temperatures with and without the consideration of solar radiation were very small (maximum 0.6 \({}^{\circ}\)C). The accuracy of the solar radiation calculated by using the estimation models can meet the engineering requirements. In Figure 18, it can be observed that the differences between the highest calculated temperatures with or without the consideration of solar radiation were significant at 16:00 on 31 July 2014 (maximum 10.3 \({}^{\circ}\)C). The vertical temperature variation on the cross-section of side-by-side box girder when considering the effect of solar radiation (15.5 \({}^{\circ}\)C) was significantly larger than that without the consideration of solar radiation (5.3 \({}^{\circ}\)C) in summer. Therefore, the influence of solar radiation should be considered in the analyses of the temperature distribution on side-by-side box girders, especially for top flanges. Figure 16: Finite element model of cross-section of side-by-side box girder. Figure 17: _Cont._  
Figure 17: Influence of solar radiation on temperature distribution on cross-section of side-by-side box girder in summer (31 July 2014): (**a**) top flange (1-T); (**b**) top flange (11-T); (**c**) web (1-W); (**d**) web (11-W); (**e**) bottom flange (1-B); and (**f**) bottom flange (11-B). Figure 18: Temperature contour plots of side-by-side box girder at 16:00 on 31 July 2014: (**a**) with solar radiation; and (**b**) without solar radiation.
# An Efficient One-Dimensional Texture Representation Approach for Lung Disease Diagnosis
## 2 Related Works  
Bacterial and viral pulmonary infections, particularly pneumonia and COVID-19, have affected many individuals, especially in environments with low hygienic living conditions, overcrowding, and an inadequate medical infrastructure. Pneumonia and COVID-19 have a very high mortality rate, with around 65 million people having COVID-19 leading to 3 million deaths; 808,694 children died of pneumonia in 2017, and 10 million people had tuberculosis, with 1.4 million deaths [4; 5; 6; 7]. Thus, early diagnosis of this infectious disease is crucial to providing proper treatment, increasing survival rates, and reducing the spread of infection. Chest X-ray imaging is one of the most frequently used methods for diagnosing these pulmonary infectious diseases. It is considered one of the most efficient diagnostic tools, with over two million procedures performed yearly [8]. However, reading chest X-ray images is challenging and subject to many variabilities, including radiologists' interpretations and experiences and overlap of pulmonary symptoms and signs, causing severe and critical medical complications that require intensive medical care. Chest X-ray interpretations of radiologists' disagreement are substantial and reported as high as 56% [9]. Thus, deep learning techniques have been used to interpret, read, and analyze chest X-ray images to minimize human errors and facilitate prompt and more accurate detection and diagnosis of different lung diseases [10; 11; 12; 13; 14; 15; 16; 17; 18]. Artificial intelligence methods are reported to be effective in 2D chest X-ray image analysis. They are increasingly used to detect lung abnormalities [19; 20; 21]. The large, publicly available 2D chest X-ray image datasets have encouraged scientists to advance the performances of deep learning algorithms to detect and classify various pulmonaria diseases automatically.  
Figure 1: The overall methodology for 1 DTR uses statistical methods and LBP to extract the texture features of the input images.
# The Generalized Mohr-Coulomb Failure Criterion
## 7 Conclusions  
By simply regarding the strength parameters \(c\) and \(\phi\) in the conventional Mohr-Coulomb failure criterion as functions of the coefficient \(b\) of the intermediate principal stress, written as \(c(b)\) and \(\phi\) (\(b\)), respectively, the generalized Mohr-Coulomb failure creation, abbreviated as GMC, is derived. Numerous comparisons with well-established failure criteria, such as Lade-Duncan and SMP, indicate that GMC exhibits superior performance.  
In the simplest way, the GMC can be built by just using a piece of conventional triaxial apparatus, if the true triaxial tests are not applicable. Even so, the GMC still matches test data better than either Lade-Duncan or SMP.  
The Mohr-Coulomb criterion tends to overestimate shear deformation, while the Lade-Duncan criterion tends to underestimate shear deformation.  
Even for an isotropic geomaterial, the failure surface in the stress space has been proved non-smooth per se.  
Conceptualization, D.T. and H.Z.; methodology, D.T. and H.Z.; validation, D.T. and H.Z.; writing--original draft, D.T.; writing--review and editing, D.T. and H.Z.; Funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52130905 and 52079002.  
Not applicable.  
The authors declare no conflict of interest.  
Figure 14: Comparisons of predictions of failure criteria with test data for various soils and rocks (**a**) Geotechnical materials [36; 37; 46]; **(b)** Mizuho trachyte [40]; **(c)** Yuubari shale [41].
## 4 Proposed Routing Protocol
### Routing Decision Phase  
According to the above-constructed reinforcement learning model, we design the DSEGR protocol. The DSEGR protocol mainly contains neighbor table maintenance and routing decision. Each node maintains and updates its neighbor topology table by the periodic hello packet. Whenever a node receives a packet, the routing algorithm will be executed; a detailed description is shown in algorithm 1. We take node \(i\) as an instance. First extract features of node \(i\), which can be described as \(\text{F}_{i}=\Big{\{}\Big{(}L_{i},L_{i},L_{d},L_{x},LS_{i,x},E_{x}^{pre},NF_{x }\Big{)}\mid x\in N_{nbr}(i)\Big{\}}\), from the neighbor topology table and data packet, where \(L_{i},L_{i},L_{d},L_{x}\) is obtained from the data packet. \(L_{i,x},E_{x}^{pre},NF_{x}\) is obtained from the NT table information. Then, all neighbor features are input into the RL model to learn how to make an optimal routing decision.  
```
0:\(L_{i}\): location of node \(i\); \(p\): data packet; \(NT_{i}\): topology table of node i;
0: Select next hop with optimal Q value;
1: Obtain set of neighbor nodes \(A_{i}\) by \(NT_{i}\);
2: Set of optional neighbor is \(A_{i}^{\text{opt}}=A_{i}-HVN\);
3:if\(A_{i}^{\text{opt}}\)=\(\emptyset\)then
4:\(a^{*}=-1\) and packet loss
5:else
6:for\(x\) in \(A_{i}^{\text{opt}}\)do
7: Extract \(F_{i}^{x}=\left(L_{i},L_{i},L_{d},L_{x},LS_{i,x},E_{x}^{pre},NF_{x}\right)\);
8: Input \(F_{i}\) into our RL model and output \(Q_{x}\)
9:if Q network is in training phase then
10: Select next hop according to (13);
11: Store \(e_{t}=(s_{i},a_{i},r_{i},s_{x})\) into experience set \(D_{t}=\left\{e_{1},\cdots,e_{t}\right\}\);
12: Update Q network parameters \(\theta\) and \(\theta^{\prime}\)
13:else
14: State select next hop using (14)
15:endif
16:endfor
17:endif
```  
**Algorithm 1****DSEGR routing protocol**
# Research on the Aerodynamic-Propulsion Coupling Characteristics of a Distributed Propulsion System
###### Abstract  
In recent years, the distributed propulsion system has received extensive attention due to its advantages such as high propulsion efficiency, low noise, high safety redundancy, and good flexibility and maneuverability. However, the interaction between the internal and external flow can limit the aerodynamic performance of the ducted fan. To investigate the influence of the internal and external flow interaction on the aerodynamic-propulsion coupling characteristics of the distributed propulsion system, an over-wing symmetric configuration with five distributed ducted fans was constructed, and numerical simulations were performed using a method based on the body force model. Results show that as the flight Mach number increases, the lift obtained by the wing increases, while the stall angle of attack decreases, and the stall angle of attack at a Mach number of 0.5 is reduced by 15\({}^{\circ}\) compared with a Mach number of 0.2. At large angles of attack, the edge fans have the strongest ability to resist airflow separation, while the middle fan has the weakest ability to resist airflow separation, and its fan performance index drops the fastest. When the Mach number is 0.4, the mass flow rate and thrust of the middle fan are reduced by 16% and 28%, respectively, compared with those when the Mach number is 0.2. The higher the flight Mach number, the larger the intake distortion degree of the ducted fans. The middle fan is most affected by total pressure distortion and least affected by swirl distortion, whereas the edge fans are least affected by total pressure distortion and most affected by swirl distortion.  
distributed propulsion system; body force model; aerodynamic-propulsion coupling characteristics; intake distortion Article  
1
## 2 Materials and Methods
### Fly Ash and Cenospheres Identification  
Physical properties, morphologies, and chemical composition of fly ash and the collected cenospheres were investigated. Apparent density was determined by ultrapvcnometer (Ultrapycrometer 1200e, Quantachrome Instruments, FL, USA). Helium gas is the preferred displacement fluid due to its small dimensions and ideal gas behavior for volume measurement of porous solids and powders [48]. The gas flow purge was set at 1.0 min and five runs were performed, with the set deviation of 0.0050%. Particle size and distribution of the fly ash sample and the collected cenospheres were characterized by laser particle size analyzer with dry dispersion units (Masterizer 2000, Malvern Instrument, Malvern, UK). The measurements were run in air dispersion medium, with air pressure of 2 bar. The average particle size by means of the volume mean diameter is shown in _D [4,3]_. Morphologies of fly ash and cenospheres, wall thickness and hollow structured were studied by scanning electron microscopy (SEM) (Phenom ProX, Eindhoven, Netherlands). The SEM imaging was performed under the accelerating voltage of 15 kV on the samples with no coating. The wall thickness was measured through a random cross-section analysis using SEM technique; three to five measurements were conducted on each cross-sectional view of wall thickness as the pictures were taken from the broken cenospheres shell. The results derived in this study were generally in the form of an average wall thickness of bulk cenosphere sample. ImageJ was employed for the analysis of particle size and wall thickness. The elemental analysis of each ash and cenosphere particle, and the small particles depositing on the cenospheres surface were determined by the SEM equipped with energy-dispersive spectroscopy (EDX); spot analysis was carried out for the specific areas of interest on the particles. The EDX analysis results in terms of the compositional data are expressed in weight-percent (wt.%). Bulk chemical composition of fly ash and cenospheres were characterized by energy dispersive X-ray fluorescence (EDXRF; EDAX Smart Insight, ORBIS PC, NJ, USA). The detection limit for each element is 10 ppm, with intensity error set at 5%. The XRF results were presented in weight percentage of oxides. Concerning the aspect of statistics governing the properties of distribution, the mean and the standard deviation for the characterized results were considered to determine in this study. Powder X-ray diffraction patterns were recorded for the starting fly ash and the bulk cenospheres by a Bruker D8 ADVANCE diffractometer (Bruker, Bremen, Germany) (Cu K\(\alpha\) radiation (1.540 A), scan range 2\(\theta\) = 10-80\({}^{\circ}\), scan step 0.5 s), with high speed data collection with LYNXEYE XE-T detector (Bruker, Germany). Phase analysis and peak interpretation were performed by DIFFRAC.EVA V5.1 software, database PDF2-Release2018.
## 2 Overview of the Study Area  
### Geological Conditions and Representativeness of the Baige Landslide  
The Baige landslide is situated within Baige Village, Buluo Township, Jiangda County, Tibet Autonomous Region, at geographical coordinates of E 98\({}^{\circ}\)42\({}^{\prime}\)14.1\({}^{\prime\prime}\) and N 31\({}^{\circ}\)04\({}^{\prime}\)53.21\({}^{\prime\prime}\). This location is positioned on the right bank of the Jinsha River, characterized by its presence within the Alpine and Canyon zones. As elevation increases, a noticeable decline in temperature occurs (Figure 1). The region experiences a distinct rainy season, with a concentration of summer rainfall. The average annual precipitation is about 650 mm. The maximum annual rainfall is about 1070 mm, and the maximum daily rainfall is about 40 mm. Jiangda County has an average altitude of about 3800 m, and the terrain is high in the west and low in the east, with an overall slope from southwest to northeast. There are many mountains and rivers in the area. The terrain is high, and the river valleys are developed and deep, showing a \"V\" shape. It is an example of a tectonic mountain and valley landform. The relative height difference between mountains and valleys is wide, up to 3100 m.  
This paper takes the Baige landslide in the Jinsha River as an example to construct the evolution network of the landslide disaster chain in the west of China for the following reasons [46]: (1) The Baige landslide occurred within the Jinsha River suture zone, characterized by a complex lithology and structure, making it a quintessential example of a landslide disaster caused by mixed structural rocks on the Qinghai-Tibet Plateau. (2) Different from landslides triggered by rainfall or earthquakes, the Baige landslide system was naturally induced, providing valuable insights into the natural evolution process of landform and river valleys in western China. (3) The Baige landslide exhibited distinctive features of high speed and collision accumulation, which are common traits in the context of western China's geological dynamics. (4) The disaster chain effect stemming from the Baige landslide was particularly pronounced. Both landslides led to the inundation of villages and towns, destruction of bridges, and various other related sub-disaster events, encompassing a wide range of consequences associated with the landslide.
## 3 Methodology
### Inversion of the Dispersion and HVSR Curves  
The HVSR and dispersion curves are inverted using the neighborhood algorithm to obtain an optimal estimation of the Vs profiles [14]. The inversion process explores a defined parameter space (number of layers, S-wave velocity, P-wave velocity, density and layer thickness) in search of possible solutions. The results previously obtained from borehole drills and downhole experiments have helped to constrain the number of layers and their corresponding thickness and S-wave velocity ranges. [PERSON]'s ratio and density values have been set up for all the layers in the range of 0.2-0.5 and 1600-2200 kg/m\({}^{3}\), respectively, which correspond to the characteristics of the materials in the studied landslide.  
The ellipticity of the fundamental mode Rayleigh wave or the dispersion curve is estimated for each computed model (forward problem), and then contrasted with the empirical curves using a misfit function [55], which reflects quantitatively the difference between the generated models and the observed results.  
For the calculation of the Vs profiles, the Dinver software from the Sesarray package has been used [44]. This software allows the inversion of dispersion and ellipticity curves. In the case of the dispersion curves, they are obtained directly from the f-k analysis. In the case of the ellipticty curves, they were previously extracted from the experimental HVSR curves [42; 56] and then used as input data of the Dinver software. Alternatively, HVSR curves can also be used as input data if some considerations are taken into account [41; 57]. For large velocity contrast, [PERSON] et al. [58; 59] showed that HVSR curves are dominated by the ellipticity of the fundamental mode Rayleigh wave in the frequency band between the fundamental resonant frequency and the next minimum, with its shape being dominated by the specific layering of the sediments. With regards to these indications, in this work the frequency band around the main resonant peak has been considered for the inversion process. Only in the cases where a secondary peak or hump has also been identified have we broadened the frequency interval to include also this peak, as this could provide information about other velocity contrasts or interfaces.