esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Real-Scale Vehicle Tests and Result Analysis
### Testing Program  
In order to verify the identification accuracy and robustness of the ACS-SSI algorithm under complex operating conditions and a strong noise background, tests under various suspension faults were carried out. Suspension faults were identified by analyzing the vibration response caused by random excitation of the road surface roughness. The real-scale vehicle testing programs under the condition of leaf spring failure (stiffness weakening) and shock absorber failure (damping reduction) are designed respectively. The two sets of testing cases are as follows:  
In the first case, named Case 1, the left rear suspension spring was replaced by two springs with 80% and 90% stiffness of the original spring to simulate a suspension failure. It should be noted that, corresponding to the simulation results, the sampling length of the test data for each case is 200 s/times \(\times\) 100 times = 20,000 s.  
In the second case (Case 2), the left and right dampers of the front suspension were replaced by adjustable dampers. The sampling length of each test is the same 20,000 s. Table 6 summarizes the changes of damping in the experiments. The damping of the adjustable damper can be set to three states, and State 1 is the baseline state. The damping value of State 1 to State 3 decreases from 100% to 60%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**State** & **Change of Left Front Damping** & **Change of Right Front Damping** & **Vehicle Speed** \\ \hline
**1** & 0\% & 0\% & 30 km/h \\
**2** & 20\% & 0\% & 30 km/h \\
**3** & 40\% & 0\% & 30 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 6: Changes of damping in the test.  
Figure 12: Position of accelerometers.
# A Novel Visual System for Conducting Safety Evaluations of Operational Tunnel Linings
## 4 Engineering Case Analysis
### Source of Monitoring Data  
K+050-K+350 in the right line was selected as the evaluation object. Fiber optic sensors were used to monitor lining cracks (Figure 6a,b).  
A ZC3-A rebound tester was used to test the lining strength (Figure 7a), and ground penetrating radar was used to detect the lining thickness and the presence of caves (Figure 7b), etc.  
Consequently, 30 sections were divided and numbered from 1 to 30. Each evaluation section was approximately 10 m in length, as shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Section**} & \multicolumn{2}{c}{**Crack**} & \multirow{2}{*}{\begin{tabular}{c} **Crack** \\ **Length** \\ **/m** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Crack** \\ **Width/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Lining** \\ **Deformation** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Depth** \\ **/mm** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Strength** \\ **Index** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Thickness** \\ **Index** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Leakage** \\ **State** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Depth** \\ **/mm** \\ \end{tabular} } \\ \hline
1 & 2.3 & 0.4 & 0.00 & 8 & 0.90 & 0.70 & drip & 30 \\
2 & 0.0 & 0.0 & 0.07 & 0 & 1.00 & 0.80 & seepage & 41 \\
3 &  
Please note that the lining deformation in the Table above refers to the ratio of the deformation amount to the internal limit distance; the strength and thickness indexes are the ratios of the measured values to the design values. Therefore, these three indexes are dimensionless and unitless.
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 3 Experiments and Results
### Data Preparation  
Following the proposed fault diagnosis model in Section 2, we convert the time-domain waveforms of the source and target domains into time-frequency images using CWT. Under the condition that [PERSON]'s sampling theorem is satisfied, the waveforms are first sliced into vibrational snippets of equal length, 1200 points in our experiments. Each vibration snippet is transformed into one time-frequency image of size 224 \(\times\) 224 via downsampling. The images are then extended to three channels to fit the input dimension of ResNet-50. Figure 7 shows an example of the time-frequency images in our target domain.  
Using the above approach, we construct two data repositories of time-frequency images: one is the source-domain CWRU dataset for pretraining; the other is the target-domain gearbox dataset for finetuning and model validation. The former consists of nine faulty states (three fault locations and three sizes), each of which has 1203 time-frequency domain images; approximately 20% of them (240) are used for model selection. The latter has 2808 samples, and each of the nine working conditions includes 312 samples. To investigate how the number of samples in the target domain affects the diagnostic accuracy, we split the target domain into a training set and a testing set using different ratios: 90:10, 50:50, 10:90, 5:95, and 1:99. This is summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Working Conditions** & **Sampling Frequency (HZ)** & \
\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Length of Each** \\ **Sample** \\ \end{tabular} \\ \hline Healthy & 20k & 104 & 3600 \\ Missing & 20k & 104 & 3600 \\ Crack & 20k & 104 & 3600 \\ Spalling & 20k & 104 & 3600 \\ Chip5a & 20k & 104 & 3600 \\ Chip4a & 20k & 104 & 3600 \\ Chip3a & 20k & 104 & 3600 \\ Chip2a & 20k & 104 & 3600 \\ Chip1a & 20k & 104 & 3600 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Metadata of the gearbox dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Splitting Ratios** & \
\begin{tabular}{c} **Total Number of** \\ **Samples** \\ \end{tabular} &
\begin{tabular}{c} **Number of Samples for Each Working State** \\ **Training Set** \\ \end{tabular} \\ \hline
90:10 & 2808 & 2529 & 279 \\
50:50 & 2808 & 1404 & 1404 \\
10:90 & 2808 & 279 & 2529 \\
5:95 & 2808 & 144 & 2664 \\
1:99 & 2808 & 27 & 2781 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Target-domain training/testing set division under different splitting ratios.  
Figure 7: Vibration signals of nine working conditions and their corresponding time-frequency images in the target domain.
## 3 Performance Evaluation
### Anti-Multipath Performance  
The anti-multipath performance of navigation signals is usually evaluated with the code-tracking multipath error envelope and the average multipath envelope error based on an NELP DLL [41; 42]. The multipath error envelope is the maximum deviation caused by the multipath effect, which reflects the worst case of multipath error corresponding to a specific geometric delay. The average multipath envelope error is the accumulated average of the multipath error envelope with the geometric delay, which reflects the overall situation of the multipath deviation within a certain geometric delay.  
The multipath error envelope is quantified as a function of the relative delay between two rays, with the approximate expression being  
\[\varepsilon\approx\frac{\pm a\int_{-B_{r}/2}^{B_{r}/2}G_{n}(f)\sin(2\pi f \tau)\sin(\pi fd)df}{2\pi\int_{-B_{r}/2}^{B_{r}/2}fG_{n}(f)\sin(\pi fd)[1\pm a \cos(2\pi f\tau)]df} \tag{28}\]  
where \(a\) is the multipath-to-direct ratio (MDR), generally below \(-6\) dB, \(\varepsilon\) is the delay estimation error, and \(\tau\) is the geometric path delay.  
The average multipath error envelope is calculated by the cumulative sum of the absolute values of the multipath error envelope, whereby the values are added together to give a total. The relationship between the average multipath error envelope can be written as follows:  
\[\varepsilon_{a}\approx\frac{1}{\tau}\int_{0}^{\tau}\Bigg{[}\frac{\left| \varepsilon(\tau)\right|_{\Delta\phi_{1}=0^{\circ}}\left|+\left|\varepsilon( \tau)\right|_{\Delta\phi_{1}=180^{\circ}}\right|}{2}\Bigg{]}d\tau \tag{29}\]  
where \(\Delta\phi_{1}\) is the carrier phase offset between multipath and line of sight (LOS) path, typically \(0^{\circ}\) or \(180^{\circ}\).  
Figure 11 shows the multipath error envelope and average multipath error envelope curves of all signals with \(d=1\) chip, \(a=-6\) dB. The multipath error envelopes of Pulse-OTFS(10,1023) and OTFS(10,1023) are significantly lower than that of Pulse-BPSK(5) and BPSK(5) at a geometric path delay of less than 84 m and slightly worse thereafter. For the average multipath error envelope, Pulse-OTFS(10,1023) and OTFS(10,1023) are the best at all geometric path delays. Hence, the proposed signal has better anti-multipath performance than that of the Pulse-BPSK and BPSK signals.  
Figure 11: _Cont._
# Application of Polymers as a Tool in Crystallization--A Review
## 2 Basic Research  
### Confined Crystallization  
[PERSON] et al. documented a specific method for combining calcium ions and alginate molecules in mineralization [12]. The presented results could contribute to the understanding of the mineralization process of calcium carbonate (CaCO\({}_{3}\)) in hydrogel systems. The same research group reported experiments dealing with CaCO\({}_{3}\) crystals' growth in silk fibroin hydrogel by a simple ion diffusion method [13]. The study gave a better explanation of the influence of silk fibroin concentration and its structure on CaCO\({}_{3}\) crystals' growth. [PERSON] et al. investigated the influence of the porosity of the growth medium on the crystallization of CaCO\({}_{3}\) in hydrogels with different gelatin solid contents [14], whereas [PERSON] et al. crystallized CaCO\({}_{3}\) polymorphs in alginate and xanthan hydrogels in which the degree of entanglement was altered by the polysaccharide concentration [15]. Crystallization experiments performed in alginate hydrogels indicated the initial formation of a mixture of calcite, vaterite and amorphous CaCO\({}_{3}\). In turn, [PERSON] et al. tested the formation of single or two coexisting crystalline phases of calcium phosphate (Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\)), hydroxyapatite, hydroxyapatite/dicalcium phosphate dehydrate or octacalcium phosphate polyacrylamide hydrogels containing phosphate ions by diffusion of calcium ions from calcium nitrate solutions [16]. They found that the dense hydroxyapatite layer and hydroxyapatite/dicalcium phosphate dehydrate layer prevented the diffusion of calcium ions from the calcium nitrate solution, thus inhibiting the formation of Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\) in the gel phase. [PERSON] et al. discussed the theory and practice used for the preparation of polymorphs, use of gels into crystallogenesis of different substances, particularities of protein crystal polymorphism and also modern trends in gel growth for high-resolution X-ray crystallography [17]. In turn, [PERSON] et al. provided evidence for synthetic biomimicry similar to bio-polymerization mechanisms to steer disorder-to-order transitions via solvent plasticization-like behavior [18]. In addition, [PERSON] et al. demonstrated a new method to control nucleation with polymer microgels [19]. They found that the polymer microstructure had a significant impact on nucleation kinetics. Moreover, they demonstrated that by tuning the polymer-solute interactions, solute nucleation kinetics was significantly promoted [20]. Finally, [PERSON] et al. studied crystallization of two types of zeolites in glutaraldehyde crosslinked chitosan hydrogels [21]. They showed that chitosan hydrogen is a promising space-confinement medium for the synthesis of zeolite nanocrystals.
## 3 Results and Discussions
### Effects of Reduced Frequency on Aerodynamic Coefficients  
Three cases when \(k=0.026\), \(0.502\) and \(0.106\) were simulated, and the results are shown in Figure 7. The dynamic characteristics of the airfoil undergoing pitch oscillation were strongly dependent on the reduced frequency. The hysteresis loop increased with the increasingly-reduced frequency. In a loop, as can be seen, the maximum lift coefficient in upstroke and the corresponding angle of attack were much higher than those in the static condition. Previous researchers have observed this phenomenon already as mentioned in Section 1. Contrarily, the lift coefficient in downstroke was smaller than that in upstroke and sometimes even smaller than that in the static condition. On the lift curves, the intersection point in a loop was named the \"reunion point\" by the authors. An increase in the reduced frequency made the angle of reunion point smaller. This also means that the effect of delay was more significant when the frequency reduction was greater. On the other hand, the increasingly-reduced frequency would cause the increase of the drag coefficient. This effect is clearly revealed in the lift-drag-ratio curve in Figure 7c.  
The lift-to-drag ratio is an important parameter that indicates the aerodynamic efficiency of the airfoils. The aerodynamic performance of the airfoils is better with a larger lift-to-drag ratio. The angle of attack corresponding to the maximum lift-to-drag ratio is called a favorable angle of attack. In general, from the minimum angle to the favorable angle of attack, lift increases fast, but  
Figure 7: Comparison of the different reduced frequencies \(k=0.105\), \(0.052\), \(0.026\) of the computed (**a**) lift coefficient \(C_{l}\), (**b**) drag coefficient \(C_{d}\) and (**c**) lift-to-drag ratio \(C_{l}/C_{d}\) with \(\alpha_{0}=10^{\circ}\), \(\alpha_{amp}=10^{\circ}\).  
drag increases slowly. Therefore, the lift-to-drag ratio increases. Then, from the favorable angle to a critical stall angle of attack, the increase of lift is slow, and the drag increases rapidly. This makes the lift-to-drag ratio relatively small. After the critical stall angle of attack, the lift-to-drag ratio sharply decreases with the sharply increasing pressure drag. The lift-to-drag ratio of the airfoil during a complete hysteresis loop was quite different with a regular pattern. The peak of the lift-to-drag ratio curve in upstroke decreased and even disappeared with the increasingly-reduced frequency because of the faster increase of the drag coefficient. This effect was amplified in the lift-to-drag ratio and showed the tremendous difference between static and dynamic stall. Instead, the lift-to-drag ratio in the downstroke process showed a different form. Its shape was closer to the static form, which also had a peak. The angle of attack corresponding to the peak was smaller with increasingly-reduced frequency. This also shows the degree of the delay effect. Therefore, the aerodynamic efficiency was lower in dynamic stall process though the lift in upstroke was higher than that in the static condition.
## 4 Experiments and Results  
In this section, we evaluate the performance of the proposed method. We also introduce the implementation details of the proposed method.  
The proposed method was evaluated on two datasets: (1) the SPARE challenge dataset [32], which is the clinical sparse-view 4D CBCT dataset, and (2) the lithium polymer battery dataset with the in-house CBCT system. Both datasets provide projection images and the geometry of the tomographic system. The proposed method had promising results in both the SPARE challenge dataset and lithium polymer battery dataset.
## 4 Assessment and Analysis of LeGNSS Precise Positioning
### Assessment and Analysis of LeGNSS PPP Performance
Such a result is very promising, which means that the convergence time would be shortened to a few seconds if more LEO satellites are incorporated in the navigation service.
# Response of NDVI and SIF to Meteorological Drought in the Yellow River Basin from 2001 to 2020
## 4 Discussion  
### Characteristics of Vegetation and Drought  
The MODIS dataset is reconstructed with Savitzky Golay (S-G) filter, which can effectively remove outliers and better preserve the continuity of vegetation changes [34]. From 2001 to 2020, the NDVI and SIF of YRB have increased significantly (\(p<0.001\)) at the rates of 0.496 \(\times\) 10\({}^{-2}\) and 0.345 \(\times\) 10\({}^{-2}\) respectively. From the perspective of temporal and spatial variation trends in different seasons, the trend of vegetation growth monitored by NDVI and SIF is consistent, showing a gradually increasing trend. The significant improvement area of SIF (66.49%, \(p<0.05\)) was higher than that of NDVI (50.7%, \(p<0.05\)). On the one hand, this study confirmed that under the background of global warming, photosynthesis and respiration of vegetation increased, promoting plant growth, and vegetation showed a greening trend [38]. The significant vegetation improvement trend is consistent with the results of other scholars [39; 40]. On the other hand, it also shows that SIF is consistent with the vegetation growth monitored by NDVI. SIF indicates vegetation photosynthesis and has the potential to better monitor vegetation growth and response [24].  
From 2001 to 2020, the SPEI in spring showed a linear downward trend (0.2147/a, R\({}^{2}\) = 0.0171), and the SPEI in spring showed a significant drying trend (95.78%, \(p<0.05\)), showing a gradually increasing trend of drought. Because the Yellow River Basin has scarce precipitation in spring, long sunshine duration, rapid temperature rise, rapid decline in surface soil moisture content, and vigorous evaporation, which exacerbated the development of drought [41; 42]. The recent sixth assessment report of the United Nations Intergovernmental Panel on Climate Change (IPCC) pointed out that the global warming will reach at least 1.5 \({}^{\circ}\)C and the sustained global warming will cause damage to nature, and will aggravate the intensity and frequency of drought, extreme high temperature, sudden changes in ocean circulation, and complex extreme weather events [43]. With the increasing demand of social and economic development for water resources, drought, a natural disaster, has become one of the obstacles to China's agricultural development [3]. The Yellow River is the \"Mother River\" of the Chinese nation. The ecological environment is fragile and the drought is intensifying, which has seriously affected the local people's life, production, and economic development [44; 45].
## 1 Introduction
Solving the MCS problem, an NP-Hard challenge, has always been difficult. The most effective current method is based on heuristic algorithms using branch-and-bound techniques [17], albeit time-consuming. Neural networks' advent has opened new pathways for addressing this problem. For instance, [PERSON] et al. [18] integrated deep neural networks into the MCS framework, creating an MCS solution framework with a reinforcement learning (RL) network, which enhances the model's solving efficiency. [PERSON] et al. [19] and [PERSON] et al. [20] have also made significant contributions, applying deep learning networks to the MCS problem and making initial attempts to output MCSs directly using DL-based methods, respectively. Although the graph neural network (GNN) development has provided new insights, a universal model for solving this problem is still in development. To address these problems, we propose a comprehensive framework for digital modelling and information matching of substation secondary panel cabinet terminal block diagrams and physical entities. The main contributions are as follows:  
* To achieve intelligent recognition of secondary screen cabinet terminal block information, we design a multi-layer region segmentation algorithm based on You Only Look At Coefficients (YOLACT) and YOLOv7 with a multi-head self-attention mechanism and the space-to-depth module (YOLOv7-MS) to achieve the extraction of microelementary target regions in drawings. For the recognition of physical information of terminal blocks in the actual field, we also design YOLOv7 based on a differentiable binarization attention head (YOLOv7-DBAH) to solve the interference problem caused by shooting. * To facilitate the digital information storage, we establish the network topology based on the Neo4j database for the diagram and physical system, respectively. * To achieve fast matching of network topology data between terminal block drawings and physical entities, we design an improved branch definition method based on a multi-modular GNN (MGCN) and deep Q-network (DQN) to solve the MCS problem.
## 4 Conclusions  
In\({}_{2}\)O\({}_{3}\)-based gas sensors decorated with three noble metals (Au, Pd, and Pt) to detect carbon monoxide at temperatures near room temperature were fabricated and investigated using XRD, TEM, and XPS. Their sensing properties were measured with specifically designed sensing equipment. It should be noted that Au/In\({}_{2}\)O\({}_{3}\) nanoparticles exhibited far better sensing responses to CO gas at low temperature (50 \({}^{\circ}\)C) than Pd/In\({}_{2}\)O\({}_{3}\) or Pt/In\({}_{2}\)O\({}_{3}\). The sensitizing effect of noble metals on the sensing properties of CO gas sensors was explained by the surface chemical state of Au, Pd, and Pt nanoparticles and their interaction with the In\({}_{2}\)O\({}_{3}\) particles. The concentration of oxygen vacancies in the Inlattice depends on which catalyst is used to decorate the surfaces of In\({}_{2}\)O\({}_{3}\) particles, and it has been shown that the degree of oxidation of the metal particles has a great influence on their detection characteristics. The average crystal size of In\({}_{2}\)O\({}_{3}\) nanoparticles obtained by [PERSON]'s equation was about 10 nm (diameter), which is slightly smaller than the diameter observed in SEM and TEM images (~15 nm). The Au-decorated In\({}_{2}\)O\({}_{3}\) sensor exhibited superior sensitivity to CO gas at 100 \({}^{\circ}\)C, with sensitivity and response times of 1.44 and 77 s, respectively, compared to other Pd- or Pt-decorated In\({}_{2}\)O\({}_{3}\) sensors. This can be explained by two factors. One seems to be the high concentration of oxygen adsorbed on the surface of In\({}_{2}\)O\({}_{3}\) particles due to the high concentration of oxygen vacancies in the In\({}_{2}\)O\({}_{3}\) lattice. Another factor is the improvement in activity due to pure metallic Au particles that are not oxidized, unlike catalysts with Pt or Pd, which become partially or fully oxidized.  
Conceptualization, M.C.; methodology, J.H. validation, M.C, H.J., D.-S.K., D.S.K.; formal analysis, D.S.K., H.-S.S.; investigation, H.-S.S.; writing--original draft preparation M.C.; writing--review and editing, B.-K.J. and M.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Core Technology (20181110200170, Development of Removal of Acid Gas and Fine Particle for Coal Thermal Power Plant). This work was supported by the Ministry of Trade, Industry & Energy, and supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No.2018R1A6A1A03026005).  
Not applicable.  
The authors declare no conflict of interest.