esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Polymer Synthesis  
In our work, we synthesized two alternating copolymers comprising benzothiadiazolearboxylic imide (BTDI) with varying substituents flanked by two thienyl repeat units as electron-accepting moieties and DTS as electron donor moieties. The preparation of polymers PDTSDTBTDI-DMO and PDTSDTBTDI-8 is presented in Scheme 1. Both polymerizations were performed for 72 h, and dark blue precipitates were obtained, which were then dissolved in chloroform with the addition of an NH\({}_{4}\)OH solution. The mixture was stirred overnight to eliminate the residue of the Pd metal catalyst which yielded Pd(NH\({}_{3}\))\({}_{4}\)(OH)\({}_{2}\) soluble complexes. The polymers were achieved by precipitation from methanol accompanied by filtration. Finally, the polymers were purified through Soxhlet extraction using methanol, acetone, hexane, toluene, and chloroform. The methanol, acetone, and hexane fractions separated small molecules, oligomers, and some impurities. Subsequently, the toluene and chloroform fractions were collected then re-precipitated in methanol accompanied by filtration to obtain the purified polymers. \({}^{1}\)HNMR, FT-IR and elemental analyses were carried out to confirm the structures of the PDTSDTBTDI-DMO and PDTSDTBTDI-8. The \({}^{1}\)HNMR spectra for the polymers are available in the Supplementary Materials (see Figures S4 and S5).  
Reagents and conditions: (i) Pd(OAc)\({}_{2}\), P(\(o\)-tol)\({}_{3}\), toluene, 110 \({}^{\circ}\)C, 72 h.
# Non-Stationary Stochastic Global Optimization Algorithms
## 3 Materials and Methods
### Arithmetic between Measurable Functions  
Arithmetic operations can be found in schemes of mutation and recombination [25]. So to characterize an algorithm with kernels in its entirety we must characterize all methods that can alter the generation of new populations.  
According to Theorem 22 in [11], to characterize arithmetic methods as deterministic kernels it is enough to prove that these methods are measurable. Proposition 1 provides the sufficient conditions for a function \(f\colon\Omega\to\mathbb{R}\) to be measurable.  
Proposition 1: _Let \((\Omega,\Sigma)\) be a measurable space, then \(f\colon\Omega\to\mathbb{R}\) is \(\Sigma-\mathcal{B}(\mathbb{R})\) measurable if and only if one of the following conditions holds:_  
1. \(\{x\in\Omega\colon f(x)<b\}\in\Sigma\) _for every_ \(b\in\mathbb{R}\)__
2. \(\{x\in\Omega\colon f(x)\leq b\}\in\Sigma\) _for every_ \(b\in\mathbb{R}\)__
3. \(\{x\in\Omega\colon f(x)\geq b\}\in\Sigma\) _for every_ \(b\in\mathbb{R}\)__
4. \(\{x\in\Omega\colon f(x)>b\}\in\Sigma\) _for every_ \(b\in\mathbb{R}\) _._  
Proof.: Note that \(\{x\in\Omega\colon f(x)<b\}=f^{-1}((-\infty,b))\), and use \(\{(-\infty,b)\colon b\in\mathbb{R}\}\) family to generate \(\mathcal{B}(\mathbb{R})\). Details see [27] proposition 1 chapter 3.  
Lemma 4 gives a useful equality between sets to characterize arithmetic methods.  
**Lemma 4**.: _Let \((\Omega,\Sigma)\), be a measurable space and \(f\colon\Omega\to\mathbb{R}\) and \(g\colon\Omega\to\mathbb{R}\) be \(\Sigma-\mathcal{B}(\mathbb{R})\) measurable, then_  
\[\{(x,y)\in\Omega\times\Omega\colon f(x)+g(y)<c\}=\bigcup_{q\in \mathbb{Q}}L_{q,c}\times R_{q,c}, \tag{16}\]  
_where_  
\[L_{q,c}=\{x\in\Omega\colon g(x)<c-q\}\]  
_and_  
\[R_{q,c}=\{y\in\Omega\colon f(y)<q\}.\]  
Proof.: \([\supseteq]\) Consider a tuple \((x,y)\) such that:  
\[(x,y)\in\bigcup_{q\in\mathbb{Q}}L_{q,c}\times R_{q,c}\]  
then, for some \(q\in\mathbb{Q}\) we have that \(g(x)<c-q\) and \(f(y)<q\). Hence, applying an arithmetic operation we have that \(f(y)+g(x)<c\). So \((x,y)\in\{(x,y)\in\Omega\colon f(y)+g(x)<c\}\).  
\([\subseteq]\)  
Let \((x,y)\in\{(x,y)\in\Omega\colon f(y)+g(x)<c\}\) so there exists some \(q\in\mathbb{Q}\) that by the density of rational numbers holds: \(f(y)<q<c-g(x)\) applying some arithmetic \(f(y)<q\) and \(g(x)<c-q\). From,  
\[(x,y)\in\bigcup_{q\in\mathbb{Q}}[\{x\in\Omega\colon g(x)<c-q\} \times\{y\in\Omega\colon f(y)<q\}\]  
follows that  
\[\{(x,y)\in\Omega\times\Omega\colon f(x)+g(y)<c\}\subseteq\bigcup_{q\in \mathbb{Q}}L_{q,c}\times R_{q,c}.\]
# Sequential-Fault Diagnosis Strategy for High-Speed Train Traction Systems Based on Unreliable Tests
## 2 Problem Statement  
### Traction System  
In this paper, the era electric traction system of a certain China Railway High-speed (CRH) train is adopted, and the main circuit of traction is shown in Figure 2.  
Figure 1: The construction process of a sequential-fault diagnosis strategy for high-speed train traction systems based on unreliable tests.  
Figure 2: Main circuit for car traction of CRH.  
The traction system is the core component of urban rail transit vehicles. It provides traction and braking force for the train according to traction and braking needs. It converts the electric energy provided by the power grid into three-phase AC energy for the motor, and then the traction motor converts the electric energy into mechanical energy to drive the train. According to the system maintenance manual and related materials, the main components included in the system are high-voltage electrical box HV011/HV02, reactor box L, Variable-Voltage-Variable-Frequency (VVVF) inverter box, etc. Via the analysis of the traction system, it can be seen that the electrical relationship of the traction system is very complex, and there are inevitably some uncertain factors in the test process.
## 2 Results and Discussion  
### Chemical Composition of the Main Gas Species  
A representative composition of the main gas components in fluid discharges from the volcanic and geothermal systems investigated in the present study is listed in Table 1. Gas concentrations are expressed in umol/mol and referred to the dry gas phase. Gas samples from Teide (Spain), Turrialba (Costa Rica), Vulcano (Italy), Lascar and Tacora (Chile) volcanoes have outlet temperatures varying in a wide range (from 72 to 405 \({}^{\circ}\)C) and show a chemical composition dominated by CO\({}_{2}\) and characterised by variable amounts of SO\({}_{2}\) (from 1.2 to 569394 umol/mol).  
\begin{table}
\begin{tabular}{l l l l l l l l l l l l l l} \hline
**n\({}^{\circ}\)** & **sample** & **Location** & **T \({}^{\circ}\)C** & **CO\({}_{\rm 2}\)** & **HCI** & **SO\({}_{\rm 2}\)** & **H\({}_{\rm 5}\)S** & **N\({}_{\rm 1}\)** & **CH\({}_{\rm 4}\)** & **Ar** & **O\({}_{\rm 2}\)** & **H\({}_{\rm 2}\)** \\ \hline  
ulcano Island crater 1 & Italy & 311 & 930405 & 31870 & 213218 & 8307 & 27345 & 1.5 & 42 & 1679 & 336 \\
30 & Vulcano Island crater 2 & Italy & 317 & 933226 & 39026 & 107870 & 852  
**Table 1.**_Cont._  
\begin{tabular}{c c c c c c c c c c c c c} \hline
**n\({}^{*}\)** & **sample** & **Location** & **T \({}^{*}\)C** & **CO\({}_{2}\)** & **HCl** & **SO\({}_{2}\)** & **H\({}_{3}\)S** & **N\({}_{3}\)** & **CH\({}_{4}\)** & **Ar** & **O\({}_{3}\)** & **H\({}_{3}\)** \\ \hline  
schia Island 1 & Italy & 101 & 913479 & & & 2259 &  
**Table 1.**_Cont._  
\begin{tabular}{c c c c c c c c c c c c} \hline
**n\({}^{\circ}\)** & **sample** & **Location** & **T \({}^{\circ}\)C** & **CO\({}_{2}\)** & **HCl** & **SO\({}_{2}\)** & **H\({}_{5}\)S** & **N\({}_{2}\)** & **CH\({}_{4}\)** & **Ar** & **O\({}_{2}\)** & **H\({}_{5}\)** \\ \hline  
& 19 & 14 Such features, coupled with relatively low concentrations of CH\({}_{4}\) (<690 \(\upmu\)mol/mol) and high concentrations of HCl (from 351 to 74540 \(\upmu\)mol/mol) and H\({}_{2}\) (up to 32591 \(\upmu\)mol/mol), indicate that the gas chemistry of these systems is strongly controlled by magma degassing [16]. This hypothesis is in agreement with previous studies [17; 18; 19; 20] that investigated the source of fluids produced by the intense fumarolic activity recently observed at these volcanic systems. A different chemistry characterises gases from (1) El Tatio (Chile), Larderello (Italy) and Tendaho (Ethiopia) geothermal systems [21; 22; 23] (samples #48-64), and (2) volcanoes whose degassing activity is considered to be mainly related to boiling of extended hydrothermal reservoirs (Copahue, Argentina; Deception, Antarctica; El Chichon, Mexico; Ischia, Pantelleria, Phlegrean Fields and Vesuvio, Italy; Nisyros, Greece; Tatun, Taiwan; Yellowstone, USA) [24; 25; 26; 27; 28; 29; 30; 31] (samples #65-139). This group shows SO\({}_{2}\) below the instrumental detection limit (\(\approx\)0.01\(\upmu\)mol/mol: samples #48-138), relatively low outlet temperatures <118 \({}^{\circ}\)C, high CH\({}_{4}\) concentrations (up to 64103 \(\upmu\)mol/mol), and HCl not exceeding 500 \(\upmu\)mol/mol.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 4 Performance Evaluation
### Overall Rate-Distortion Performance Evaluation  
The key frame coding has a strong effect on the overall performance of a DVC codec, since the image quality of the reconstructed key frame directly affects the accuracy of side information (SI) creation [7], the reliable of a correlation noise model (CNM) [8], and finally, the quality of the decoded WZ frames. As can be seen in Figure 7, the coding efficiency of the key frame coding in the proposed JEM-DVC consistently outperforms the H.265-DVC and DISCOVERY DVC. Subsequently, it is expected that the overall rate-distortion or PSNR performance of the proposed JEM-DVC, including the PSNR performance of both the key and WZ frames is also higher than that of other conventional codecs.  
Figure 11 shows the overall PSNR performance of the H.265-DVC, DISCOVERY DVC, and JEM-DVC codecs obtained for _Foreman_, _Soccer_, _Coastguard_, and _Hall Monitor_ test sequences. As can be seen in Figure 11, for all of the test video sequences, the proposed JEM-DVC consistently provides better performance than the H.265-DVC and DISCOVERY DVC codecs. For example, as indicated in Figure 11c, for the _Hall Monitor_ sequences, the proposed JEM-DVC provides up to 0.4 dB and 2.1 dB gains as compared to the H.265-DVC, DISCOVER DVC, respectively.  
Figure 10: Fluctuation in the Peak signal-to-noise ratio (PSNR) performance between the key and WZ frames obtained by DISCOVER DVC and the proposed JEM-DVC with different test sequences: (**a**) _Foreman_, (**b**) _Soccer_, (**c**) _Coastguard_, and (**d**) _Hall Monitor_.  
In terms of structural similarity (SSIM) index, which is an efficient metric for visual quality measurement [28], the proposed JEM-DVC can also provide higher SSIM performance than the conventional DVCs. As shown in Figure 12, for all the test sequences, the proposed JEM-DVC always achieve higher SSIM values for the reconstructed key and WZ frames as compared to the H.265-DVC and DISCOVERY DVC.  
In terms of the Bjontegaard metric [29], which can more precisely present the bitrate and PSNR gains; Table 2 shows the BD-Rate and BD-PSNR of the proposed JEM-DVC, H.265-DVC, and DISCOVER DVC codecs. As reported in Table 2, for the BD-Rate, the proposed JEM-DVC can gain about 5% and 17% bitrates saving as compared to the H.265-DVC and DISCOVER DVC, respectively. Additionally, for the BD-PSNR, the proposed JEM-DVC can achieve up to 1.21 dB higher coding efficiency when compared to the DISCOVER DVC codec.  
Figure 11: Comparisons of overall rate-distortion (RD) performance obtained by H.265-DVC, DISCOVER DVC, and the proposed JEM-DVC with different test sequences: (**a**) _Foreman_, (**b**) _Soccer_, (**c**) _Coastguard_, and (**d**) _Hall Monitor_.
## 3 Case Study (Quseir Road)
### Flood Plain Analysis of Quseir Road  
1. **Hydrological Considerations:** The catchment area, covering 2260 km\({}^{2}\), experiences periodic flash floods during the winter, posing risks to road infrastructure. Analysis of rainfall data from Quseir rainfall stations spanning 34 years revealed that the maximum daily rainfall depth for a 100-year return period is recorded at 28.5 mm.
2. **Topographic Diversity:** The road encounters a diverse topography characterized by distinct landforms and elevations. The road navigates through a series of wadis, or dry river valleys, that cut through the plateau, creating deep gorges and canyons. As the road progresses, the elevation of the terrain steadily rises, reaching a maximum of approximately 540 m above sea level near Qeft, the inland terminus of the road.  
The floodplain simulation and visualization were conducted using HEC-RAS software, producing water depth and velocity maps essential for assessing flood hazards and devising mitigation strategies. Figure 9a shows the water depth distribution map for the case study, while Figure 9b illustrates the velocity distribution map. Water depth and velocity maps were superimposed, creating the flood hazard map as shown in Figure 9c.
## 4 Test Results and Analyses
### Bond Properties of Single Steel Fiber
#### 4.3.2 Energy Ratios  
Figure 14 displays the energy ratios of different deformed steel fibers. Hooked-end and crimped steel fibers have a low toughening capacity before the concrete cracking, with the debonding energy ratios \(R_{\text{d}}\) no more than 6%, while the indentation steel fiber IF2 has an excellent toughening capacity with an \(R_{\text{d}}\) of 35.28%.  
Suitable toughening capacities in the normal serviceability of concrete were observed in hooked-end, crimped, and indentation steel fibers. The slipping energy ratios \(R_{\text{dp}}\) of hooked-end steel fibers are 28.61-44.78%, while those of CF, CF2, and IF2 are 56.84%, 32.71% and 40.10%, respectively.  
Figure 14: The energy ratios of different deformed steel fibers: (**a**) \(R_{\text{d}}\); (**b**) \(R_{\text{dp}}\); (**c**) \(R_{\text{pr}}\).  
Figure 13: The strength ratios of different deformed steel fibers: (**a**) \(u_{\text{de}}\); (**b**) \(u_{\text{res}}\).  
Excellent toughening capacities in the ultimate bearing capacity of concrete were observed in hooked-end steel fibers and crimped steel fiber CF2, with the pull-out energy ratios \(R_{\text{pr}}\) higher than 50%. The indentation steel fiber IF2 has a low toughening capacity in the ultimate bearing capacity of concrete with \(R_{\text{pr}}\) of 24.62%.
###### Abstract  
The first Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on Suomi National Polar-orbiting Partnership (S-NPP) satellite in late 2011. Similar to the Moderate resolution Imaging Spectroradiometer (MODIS), VIIRS observes top-of-atmosphere spectral reflectance and is potentially suitable for retrieval of the aerosol optical depth (AOD). The VIIRS Environmental Data Record data (VIIRS_EDR) is produced operationally by NOAA, and is based on the MODIS atmospheric correction algorithm. The \"MODIS-like\" VIIRS data (VIIRS_ML) are being produced experimentally at NASA, from a version of the \"dark-target\" algorithm that is applied to MODIS. In this study, the AOD and aerosol model types from these two VIIRS retrieval algorithms over the North China Plain (NCP) are evaluated using the ground-based CE318 Sunphotometer (CE318) measurements during 2 May 2012-31 March 2014 at three sites. These sites represent three different surface types: urban (Beijing), suburban (XiangHe) and rural (Xinglong). Firstly, we evaluate the retrieved spectral AOD. For the three sites, VIIRS_EDR AOD at 550 nm shows a positive mean bias (MB) of 0.04-0.06 and the correlation of 0.83-0.86, with the largest MB (0.10-0.15) observed in Beijing. In contrast, VIIRS_ML AOD at 550 nm has overall higher positive MB of 0.13-0.14 and a higher correlation (0.93-0.94) with CE318 AOD. Secondly, we evaluate the aerosol model types assumed by each algorithm, as well as the aerosol optical properties used in the AOD retrievals. The aerosol model used in VIIRS_EDR algorithm shows that dust and clean urban models were the dominant model types during the evaluation period. The overall accuracy rate of the aerosol model used in VIIRS_ML over NCP three sites (0.48) is higher than that of VIIRS_EDR (0.27). The differences in Single Scattering Albedo (SSA) at 670 nm between VIIRS_ML and CE318 are mostly less than 0.015, but high seasonal differences are found especially over the Xinglong site. The values of SSA from VIIRS_EDR are higher than that observed by CE318 over all sites and all assumed aerosol modes,with a positive bias of 0.02-0.04 for fine mode, 0.06-0.12 for coarse mode and 0.03-0.05 for bi-mode at 440 nm. The overestimation of SSA but positive AOD MB of VIIRS_EDR indicate that other factors (e.g., surface reflectance characterization or cloud contamination) are important sources of error in the VIIRS_EDR algorithm, and their effects on aerosol retrievals may override the effects from non-ideality in these aerosol models.  
aerosol optical depth; aerosol models; VIIRS; NCP region +
Footnote †: journal: Journal of the American Statistical Association
## 5 Validation of Proposed Method  
Evaluation metrics were established and used to compare the proposed method with the existing method outlined in [12] through numerical simulations conducted in environments similar to those targeted by [12]. This was conducted to confirm that the method proposed in Section 4 satisfies the functional requirements specified in Section 4.1, and to accurately evaluate the impact of adding the new function on the efficiency of autonomous navigation through fair comparisons. In the comparison of the methods, \(t\)-tests were conducted under the same pedestrian density conditions, and a significant difference was identified when the \(p\)-value was less than 0.05.
## 4 Discussion
[32] increased the simulation accuracy of the climate in the Hebei area. [PERSON] et al. [33] used an artificial neural network-based statistical downscaling model to estimate evaporation from the Yarkant River basin and reservoirs under future climate change scenarios, reducing the uncertainty of simulation findings. The second technique is a multi-model ensemble simulation. The use of a multi-model ensemble simulation can reduce the uncertainty of climate model simulation results and improve the simulation capability of climate models due to the differences in the selection, resolution, parameterization, and response processes of physical parameters among climate models. The GCM simulation of global and regional climate has certain uncertainties due to differences in the selection, resolution, parameterization, and response processes of physical parameters among climate models, and the use of multi-model ensemble simulation can reduce the uncertainty of climate model simulation results and improve the simulation capability of climate models. [PERSON] et al. [34] conducted a study on predicting future changes in precipitation in China based on ensemble-averaged simulation results of 16 climate models with equal weight coefficients. The simulation results better reproduced the climatic state distribution characteristics of annual rainfall at the regional scale in China. Through climate model simulations, [PERSON] et al. [35] discovered that equal-weight ensemble simulations outperformed median ensemble simulations. The multi-model ensemble simulation approach does not imply that the primary averaging method is utilized alone to create the model ensemble, and the simulation capabilities of various climate change models vary. The simulation ability of the model ensemble can be effectively improved by using the weighted averaging method instead of the equal-weighted averaging method and assigning different weighting coefficients to other climate models, which is also an important future development direction for using climate models to study climate change [36].
# A Real Case of Implementation of the Future 5G City
## 3 Future 5G City Use Cases
### Mec  
A multi-access edge computing paradigm allows cloud-computing capabilities to be deployed closer to the user within the RAN, thus reducing network congestion and improving application performance. MEC allows one to efficiently process data generated from different sources (vehicles and infrastructure) and to deliver locally relevant content for smart driving services. This environment was characterized by ultra-low latency, high bandwidth, and real-time access to the RAN information that can be leveraged by the applications.
## 4 Sample Failure Characteristics
### Failure Angle of Sample  
Fiber content plays an important role in the PCS's failure model. Due to the confining pressure having no significant influence on the failure angle, in order to investigate the effect of fiber on the failure angle of cement subgrade soil samples, PCS samples with typical failure behavior [15] under 300 kPa confining pressure were selected, as shown in Figure 11. According to the failure pattern of these samples, the failure section diagram was drawn as shown in Figure 12. The dimensions are specified in mm, the oblique line is the failure surface of the sample, and the angle between the failure  
Figure 10: Brittleness index against confining pressure.  
surface and the horizontal direction is determined as the failure angle. The relationship between the fiber mass content and the angle can be obtained from the data in Figure 12, as shown in Figure 13.  
Figure 13 indicates that, under the same confining pressure, the failure angle of PCS samples first decreases and then increases as the fiber mass content increases. When the fiber mass content is 6%o, the failure angle reaches its minimal value. This value is 16% less than that of a 0%o PCS sample,  
Figure 12: Profile map of PCS sample failure. (**a**) 0%o PCS; (**b**) 2%o PCS; (**c**) 4%o PCS; (**d**) 6%o PCS; (**e**) 10%o PCS.  
Figure 13: Fiber content and failure angle.  
indicating that the incorporation of fiber can improve the brittle fracture pattern of a sample to some extent. When the fiber mass content further increases to 10%, the failure angle is still less than that of a 0%0 PCS sample, and the modification is significantly improved with the increase in fiber mass content. The main reason is that when the fiber mass content is too large, the fibers are likely to overlap with each other in the soil; as a result, the soil cannot be well compacted. Hence, the modification effect is limited. In addition, when the fiber mass content is within a certain range, the fiber can better fill the voids inside the PCS sample, a more stable structure can be formed between the fiber and soil particles, thereby improving the failure form of the sample. On the contrary, when the fiber mass content is too high, the fibers readily overlap with each other in the soil. In this regard, neither can the soil be well compacted, nor can the fiber be well connected. As such, the failure angle of the sample is improved and the modification effect decreases.
## 4 Feature Extraction Techniques
### Statistical Features: MRE  
This statistical feature extraction method, termed MRE (Mean, Relative Amplitude, and Entropy), is based on the observation of the output of PCA; that is, the principal components. First, it is observed that the principal components are of dimensions \(1\times 28\), which automatically increases the complexity of the EHMM. However, as mentioned earlier, the principal components are arranged with respect to the variance of the eigenvector \(\mathcal{V}\) from the highest to the lowest. This implies that the dimension of the principal components can be reduced to \(1\times k\) (\(k\) is the 'optimal' length of the principal components) without necessarily losing information, as shown in Section 7.1. Yet, the value of \(k\) is still large enough to increase the computational burden of the EHMM. Therefore, the proposed MRE is used to statistically reduce the dimension of \(k\) so as to limit the computational time complexity of the EHMM. Accordingly, given that the principal component is of \(1\times k\) dimensions (\(f=[f_{1},f_{2},\dots,f_{k}]\)), the MRE feature vector can be constructed by merging the following statistical parameters in no specified order.