esa-sceva/satcom-chunk-collection huggingface dataset:
# Landslide Susceptibility Assessment in Hong Kong with Consideration of Spatio-Temporal Consistency
## 5 Discussion
### Assessment Factor Importance Analysis  
Each decision tree in the RF is trained using different samples and subsets of features to avoid overfitting issues. After the prediction results of all decision trees are integrated through methods such as voting or averaging, the final prediction is obtained. During this process, the frequency of use and prediction effectiveness of each feature across multiple decision trees are recorded. By summing up the feature importance scores of all trees, the importance of each feature within the entire RF model is determined.  
Based on the discussion in Section 5.1, the performance of the RF model was specifically analyzed. The importance levels of assessment factors for different study time ranges were calculated. Figure 12 demonstrates that the lithology factor is the most significant driving factor in all tasks, which is consistent with the conclusion of [PERSON] et al. [47] that lithology is a classic variable that controls landslide susceptibility. Additionally, AMN and curvature play a negligible role in driving landslides in this study, and AMN's importance level is even zero during the 2010-2014 period.  
By comparing the assessment factors with the prediction results of the RF model in Figure 4, a certain degree of overlap is observed between the spatial distribution of middle-to-high susceptibility areas and regions with high rainfall. In other words, high susceptibility areas often correlate with greater rainfall, which is consistent with the work by [PERSON] and [PERSON] [48], [PERSON] [49], [PERSON] and [PERSON] [50], and [PERSON] et al. [51]. But this does not necessarily mean that places with high rainfall will always have middle-to-high susceptibility. Regarding the lithology factor, taking the high-susceptibility area of Lantau Island as an example, its stratigraphy mainly comprises Jurassic volcanic rocks with an age ranging from approximately 147.5 \(\pm\) 0.2 to 146.6 \(\pm\) 200,000 years. The lithology is primarily composed of fine-grained glassy tuff and flow-banded rhythmic lava, with a small amount of euhedral coarse-grained crystal tuff. Given the analysis results from the past 20 years, Lantau Island has consistently been a high-risk area for landslides. Therefore, there is reason to believe that Lantau Island will continue to be a prime focus for landslide disaster prevention and control in Hong Kong for the foreseeable future.  
Figure 11: Jenks classification statistics: (**a**) 2000–2004; (**b**) 2005–2009; (**c**) 2010–2014; (**d**) 2015–2019. Note: this displays the distribution of each category across different landslide susceptibility levels.
# Mechanisms of Sweep on the Performance of Transonic Centrifugal Compressor Impellers
## 6 Conclusions  
Mechanisms by which the sweep feature affects the performance of a transonic centrifugal compressor impeller with twin splitters have been discussed through numerical method. Sweep is defined as scaling up or down the shroud chord while keeping the blade angle and the thickness distribution along the relative chord length unchanged. The variation range of the sweep angle has been set at \(-25^{\circ}\) to \(+25^{\circ}\). Several conclusions can be drawn, as follows:  
1. Sweep feature has a great impact on the choke mass flow rate, efficiency and pressure ratio of the impeller. Its effect on compressor efficiency will be stronger if the centrifugal compressor has a higher pressure ratio or a higher front loading.
2. Sweep influences the flowfields through the effects on the front loading, the shock structure, the tip leakage vortex, and the flow separation. On the shroud section, forward sweep tends to restrict the front loading, the shock strength and the tip leakage vortex, which reduces the loss near the casing. On the hub section, aft sweep suppresses the front loading and the flow separation, which reduces the loss near the hub.
3. The mechanism for the optimal sweep angle is deduced to be a trade-off between the effects of reduced tip leakage loss and the enhanced hub endwall loss. Because the spanwise front loading distribution varies in different cases, the optimal sweep angle, which in the investigated case occurs near \(+11^{\circ}\), will be different in other cases. Forward sweep is expected to be more efficient in heavy tip loading cases, while in heavy hub loading ones aft sweep has the potential to increase efficiency.  
This research was supported by the National Natural Science Foundation of China (Grant No. 51176087). The authors would like to thank [PERSON] from the University of Strathclyde for the enlightening comments and discussions. Special thanks to the anonymous reviewers who offered valuable suggestions and comments for improving the manuscript.  
[PERSON] acquired the data and wrote the paper; [PERSON] revised the paper.  
The authors declare no conflict of interest.
## 2 Fault Diagnosis Method Based on System Characteristics
### Mechanism Analysis of the Influence of Valve Leakage on the RC  
Based on the construction and working process, a two-stage and single acting RC can be represented, as in Figure 2. It consists of four parts: an induction motor, a reciprocating compressing unit, a compressed air storage tank, and safe-assistant device.  
The piston makes reciprocating motion in the compression cylinder under the joint action of the connecting rod, the crankshaft, the pulley, the belt, and the motor, which constitutes the working process of RC. During the movement of the piston from the top dead center (TDC) to the bottom dead center (BDC), the pressure in the piston cylinder gradually decreases, which causes the suction valve to gradually open and the discharge valve to gradually close. When the piston moves to the vicinity of the BDC, the suction valve is fully opened, the discharge valve is completely closed, and the expansion and suction process of the RC are completed, as shown in Figure 3b. At the moment when the valve is fully opened or closed, it will hit the valve body to generate the impact. During the movement of the piston from the BDC to the TDC, the pressure in the piston cylinder gradually increases, thus causing the suction valve to gradually close and the discharge valve to gradually open. When the  
Figure 1: Quantitative diagnosis method of reciprocating compressors (RC) valve failure based on system characteristics.  
Figure 2: Structure diagram of RC.  
piston moves to the vicinity of the TDC, the suction valve is completely closed, and the discharge valve is fully opened, completing the compression and discharge process of RC, as shown in Figure 3a.  
When the RC suction valve leaks, the pressure difference between the piston cylinder and the intake pipe decreases, which causes the suction valve to not fully open or close, thus reducing the impact of the suction valve on the valve body. When the RC discharge valve leaks, the pressure difference between the piston cylinder and the exhaust pipe decreases, which causes the discharge valve to not completely close or open, reducing the impact of the discharge valve on the valve body. At the same time, the peak pressure in the cylinder will be reduced due to the existence of leakage, thus resulting in the crank torque reduction, that is, the load on the motor is reduced. Therefore, the motor speed will increase, which will lead to an increase in the rotation frequency of the RC.  
In summary, the valve leakage will reduce the energy of the impact component in the RC response signal, and increase the frequency of the impact. In addition, the leakage of the valve will increase the fluctuation frequency of the current signal since the load on the motor is positively correlated with the motor current. Therefore, the response and excitation signals will all contain RC valve leakage fault information, and both have some correlation.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 2 Evolution of C-RAN Architecture, and Its Types, Advantages, and Challenges
### Traditional Base Station  
In cellular networks, the base station is an essential component that facilitates the communication between users and the networks under whose coverage they are located. The base station has two functionalities named as baseband processing and radio functions. The functions of the baseband processing unit include coding, modulation, and Fast Fourier transform (FFT). The radio unit is responsible for digital processing, frequency filtering, and power amplification [5]. In the traditional architecture, both baseband processing and radio functions are carried out inside a base station. The base station is situated at the base of a tower and connected to the antenna located at the top of the tower using coaxial cable. Different base stations are connected to each other through the X2 interface. Each base station is connected with the mobile core network through the S1 interface. This architecture is being used since the 2G mobile network.
## 2 Influences of Residual Topospheric Delays on Single Point Positioning
Equation (6) indicates that the residual ZTD will be projected to the SPP solution with a mapping coefficient vector \(\mathbf{m}=\mathbf{D}_{\hat{x},\hat{x}_{1}}\mathbf{A}^{\text{T}}\mathbf{D}_{ee}^{-1}\mathbf{\alpha}\). With a prior variance model \(\mathbf{D}_{ee}\), \(\mathbf{m}\) is determined by the station geographical location and observing time epoch. Therefore, the SPP solution biases \(\delta\mathbf{\hat{x}}\) can be estimated at anytime and anywhere once residual ZTD (dZTD) is obtained.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 3 Feature Extraction and Decision-Making Parameters
### Communication Decision-Making Parameter
#### 3.2.1 Frequency Domain Decision-Making Parameters  
The frequency domain anti-jamming strategies include adaptive frequency change and adaptive frequency hopping.  
The decision-making parameters for the adaptive frequency change strategy mainly include the available communication frequency gap within the communication time. The method for this decision-making is to calculate the available frequency gap with a duration satisfying the communication time according to the frequency domain parameters of the jamming pattern. According to the theory presented in Section 3.1, we can calculate the frequency gaps between any two adjacent jamming frequency points of the jamming signal. The gap which can last during the entire communication time and has the frequency gap bandwidth that meets the requirements of communication bandwidth will be selected for the communication signal transmission.  
The decision-making parameters for adaptive frequency hopping strategy are its frequency hopping set and dwell time. The method for the decision-making is to calculate the available frequency gap satisfying the lasting time of communication.  
The instantaneous frequency gaps can be calculated based on the process introduced in Section 3.1. Then, the instantaneous frequency gaps within each dwell time that meets the requirements of communication bandwidth are recorded to form the set of frequencies for adaptive frequency hopping. For specific jamming patterns, there is a simpler decision making method. Taking the frequency hopping strategy against the linear sweep jamming as an example, the linear sweep jamming can be regarded as a narrow band jamming sweeping through the jamming frequency band in a sweep cycle. The sweep jamming is divided into two parts. In each part, the swept frequency band of each part of jamming is regarded as the whole. Linear sweep jamming can be regarded as two partial frequency jamming bands whose dwell time is a half sweep cycle. Then, the decision-making parameters can be acquired as follows: the frequency gaps swept are selected as the available frequency band, respectively, in the first and last half of the frequency sweep period, and the frequency hop set is divided according to the communication bandwidth.
## 2 Background
### Frost Penetration and Hawve
In order to calculate frost depth and frost heave, the air-surface transfer coefficient (n-factor) also needs to be defined. The n-factor is defined as the ratio between the surface temperature and the air temperature in freezing conditions. This value depends on the atmospheric conditions (hours of sunshine, cloud cover, wind, humidity, pollution, etc. ), the absorption capacity of the surface layer (e.g., albedo, colour, texture, sun exposure, inclination, orientation, etc. ), and the thermal regime below the surface. [PERSON] and [PERSON] proposed typical values for the n-factor according to the surface conditions. The authors suggested that, for asphalt pavements, the n-value probably ranges from 0.9 to 0.95, while for concrete pavements this range would be between 0.7 and 0.9 [10]. Lastly, when predicting the frost penetration through the MTQ methodology, the rigorous frost index (Flr) needs to be defined. Flr represents an incremented frost index that accounts for colder-than-average winters. The i3c-me v1.0 software offers two options for determining this value. The first option uses an estimate that represents a winter that would correspond to the average of the three coldest winters over a 30-year period, according to Equation (3). The second option follows a probabilistic approach that considers the history of winters for a given period, over which the standard deviation and average of the freezing index distribution need to be calculated. \[FI_{r}=1.143 FI_{n}+220 \tag{3}\]  
where \(FI_{r}\) is the rigorous freezing index (\({}^{\circ}\)C-days) and \(FI_{n}\) is the normal freezing index (\({}^{\circ}\)C-days), or just freezing index, as previously defined. In the first analysis presented in the current study, the MTQ prediction was compared with field data from a specific year, where the rigorous freezing index was considered to be the same as the normal freezing index. In the second analysis, in which the MTQ prediction model was used to evaluate climate change scenarios, the rigorous freezing index was used, and its value was determined through Equation (3).
## 3 Calculation Results and Analysis
### Influence of Different Rotational Speeds on Internal Fluid Pressure  
Figure 5 shows the influence of different rotational speeds on the internal fluid pressure of the vortex pump when the flow rate of the vortex pump is 0.6Q\({}_{\rm d}\). For ease of comparison, four axial sections at \(z=\) 0, 0.7, 1.4, and 2.0 mm within the impeller flow field are selected as reference interfaces at the same rotational speed. It can be clearly seen from the figure that under the same rotational speed condition, for the radial direction, as the rotational speed of the vortex pump increases, the fluid pressure from the inlet to the outlet along the flow direction shows a gradual increasing trend under the rotation of the impeller. In particular, the fluid pressure at the inlet of the vortex pump is negative, indicating the presence of a negative pressure area. According to the laws of energy conservation and Bernoulli, the kinetic energy of the fluid increases and the static energy decreases, resulting in a negative pressure area. From the pressure trace, it can be seen that as the fluid flows closer to the outer edge of the impeller, the pressure gradually increases. Starting from the center position of the tongue separation area of the vortex pump, it rotates counterclockwise. In the range of 0\({}^{\circ}\) to 180\({}^{\circ}\), the fluid pressure of the vortex pump is negative, and the increasing trend of the internal fluid pressure of the vortex pump is relatively slow. After the fluid  
Figure 4: Distribution of fluid velocity in an internal vortex pump at different rotational speeds.  
flows through 180\({}^{\circ}\), the fluid pressure increases to a positive value. Under the continuous rotation of the impeller, the trend of fluid pressure increase becomes significantly faster, causing the fluid to flow out of the vortex pump at a higher pressure. For the axial direction, the pressure distribution pattern at each section is basically the same. As the fluid flows in from the inlet, the pressure gradually increases. Under the rotation of the impeller, the pressure gradually increases along the flow channel and reaches its maximum at the outlet. The energy accumulated by the impeller's continuous work cycle is accumulated at the outlet, so that the fluid is pressurized and flows out at the outlet, resulting in a decrease in pressure in the tongue area. In addition, it can be observed that as the rotational speed increases from 2700 r/min to 3300 r/min, the pressure distribution of the internal fluid along the flow direction of the vortex pump under the action of impeller rotation is basically similar at different speeds. The increase in rotational speed of the vortex pump gradually increases the internal fluid pressure, especially in the area between the impeller and the outlet where the pressure reaches the maximum value.  
Figure 5: Distribution of fluid pressure internal vortex pump at different rotational speeds.
## 2 Study Area and Data
### Cloud Processing Platform and Satellite Data  
In this paper, we take advantage of a cloud platform for accessing Earth observation data, on-demand processing, and the generation of a value-added product called CODE-DE (Copernicus Data and Exploitation Platform--Deutschland) [48]. This platform is a hybrid service that enables the provision of web access to a large number of satellite datasets and a geospatial database over Germany for viewing and downloading, while also offering cloud computing with virtual machines and network services in a highly scalable way. The entire workflows in this platform, including data preprocessing, modelling, and post-processing, were consistently implemented using Python programming language on a virtual machine provided on CODE-DE. The computing resources of this machine included 16 processor cores and 128 gigabytes of RAM.  
The satellite data used in this study consisted of Copernicus Sentinel-2 analysis-ready datasets (ARD) that were built from S2 Level 2A [49], have already undergone a full preprocessing chain (atmospheric correction, cloud masking, etc.), and are consistently tiled into 4212 \(10\times 10\) km\({}^{2}\) non-overlapping square tiles (Figure 1), whereas each tile is made up of a raster dataset that contains 10 atmospherically corrected cloud-masked spectral bands (Blue, Green, Red, RedEdge1, RedEdge2, RedEdge3, NIR 10 m, NIR 20 m, SWIR1, SWIR2) of the S2 sensor. Furthermore, each tile contains specific information that was gleaned from the scene classification layer provided in the Level 2 product regarding the percentage of pixels inside the tile that were impacted by clouds, snow, or other factors [50]. For the present study, a valid pixel filter was considered in the collection of S2-ARD data, in which scenes with less than 10% valid pixels were removed from the analysis. The S2-ARD image collection was used to create \(NDVI\) layers according to (1), where \(NIR\) and \(RED\) represent the near-infrared band and the red band reflectance values (B8 and B4 in S2, respectively). These layers were then used to create an annual time series of \(NDVI\) for each tile in the study region and further to extract crop-specific \(NDVI\) profiles for each field parcel from agricultural parcel data while using a 10 m internal buffer to reduce the influence of heterogeneous pixels at field boundaries. These calculated \(NDVI\) time series will serve as the basis for further processing and analysis.  
\[NDVI=\frac{NIR-RED}{NIR+RED}. \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Crop Type** & **BBCH Code** & \begin{tabular}{c} **DWD Phase** \\ **Description** \\ \end{tabular} & **DWD Phase ID** & \begin{tabular}{c} **DWD Phase** \\ **Distribution** \\ \end{tabular} & \
\begin{tabular}{c} **N\({}^{\circ}\) of DWD** \\ **Obs** \\ \end{tabular} &
\begin{tabular}{c} **N\({}^{\circ}\) of In-Situ** \\ **BCH Obs** \\ \end{tabular} \\ \hline Sugar beet & 10 & emergence & & & 125 & 7 \\
35 & closed stand & 13 & & 120 & 6 \\
99 & harvest & 24 & & 93 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._
## 5 Open Challenges and Future Directions
Furthermore, implementing modular blockchains can address the performance and scalability challenges of monolithic blockchains. Decoupling transaction execution, consensus, data availability, and settlement layers allows modular systems such as Celestia and Avail to provide more flexible and scalable solutions. Future research can focus on further enhancing the modular blockchain layers and improving the existing ones for more efficient and resilient networks. Security and Trust Models--The resilient security frameworks for data availability solutions are critical, which involve improving cryptographic proof techniques to ensure data integrity and developing more robust protocols to protect against different attacks. Moreover, hybrid techniques combining on-chain and off-chain data availability can balance security and efficiency. Advanced cryptographic techniques such as homomorphic encryption and Zero-Knowledge Proofs can improve data integrity and privacy while reducing computing overhead. Future work can also investigate the integration of quantum-resistant cryptographic algorithms in blockchain architectures. Cross-Layer and Cross-Chain Interoperability--The interoperability between different blockchain layers and across various blockchain networks is crucial for the broader adoption of blockchain technology. Future research can investigate developing standardized protocols and frameworks to facilitate the interaction between rollup solutions and mainnet blockchains. Additionally, focusing on developing communication standards and universal data formats to ensure data validation and exchange between different blockchains can mitigate the interoperability issue. Hybrid solutions--Given all the previous considerations, we can also conclude that a hybrid solution integrating on-chain and off-chain data storage with real-time price analysis can significantly advance the data availability landscape. Hybrid frameworks can dynamically assign data to either calldata or blobs based on cost and storage requirements. The concept of shared blobs, in which multiple rollups collaborate to share data slots, provides an innovative approach to high-cost problems while improving transaction finality. Further research on these concepts will be critical for improving the performance and scalability of rollup solutions.