esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study established a radial seepage model for coal seam water injection boreholes. Based on this model, computational fluid dynamics software was employed to investigate the radial seepage pressure field, seepage velocity field, and seepage flow distribution around the borehole under different injection times and pressures. The analysis of the numerical simulation results yielded the following main conclusions:  
(1) The study identified that the permeability of the coal seam does not continuously increase with rising water injection pressure. Instead, a specific pressure threshold exists between 15 Mpa and 20 Mpa, beyond which the permeability significantly enhances. This finding is critical for optimizing water injection processes in coal seams and should be considered in the design of field operations.  
(2) The analysis revealed that the radial seepage velocity around the borehole stabilizes after 42 h, regardless of the injection pressure. This indicates that 42 h is the time threshold for achieving an effective wetting effect in the coal seam. Injection beyond this period does not contribute significantly to further wetting, highlighting the importance of time management in water injection practices.  
(3) The identified thresholds for pressure and time are essential for improving the efficiency of water injection in coal mining. These thresholds should be directly applied in field operations to enhance coal seam permeability and wetting effectiveness.  
Figure 7: Cloud plot of the effect of water enhancement around the radial direction of the borehole with time for different injection pressures.  
**Author Contributions:** Writing--original draft preparation, methodology, L.L.; supervision and funding acquisition, G.H.; conceptualization, Y.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is financially supported by grants from the National Science Foundation of China (Grant No.51674049), Chongqing Municipal Education Commission Scientific and Technological Research Project (Grant No.KJQN202304402). The authors are grateful for this support.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in article, further inquiries can be directed to the corresponding authors.  
**Conflicts of Interest:** Author [PERSON] was employed by the company Shanxi Lu'an Mining Group Cilinshan Coal Industry Co. Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# Advancing Sustainable Textile Metrology: Reflectivity Measurement with Controlled Light Sources
## 3 Results and Discussion
### Cluster Analysis  
To demonstrate the proposed measurement method with different types of controlled lighting, we also show the possibility of conducting a cluster analysis. This allows for a deeper understanding of the behavior of the textile samples by grouping them based on their reflectivity under different lighting conditions. Through cluster analysis, samples with similar characteristics are identified, providing valuable information--for example, for the selection and optimization of materials. In the case of this particular study, it is used for the selection of different knitted designs.  
The cluster analysis was performed using the k-means method. The textile samples were assigned to a predefined number of groups based on similarities in their reflective properties. Prior to the analysis, the data were normalized to eliminate scale differences between the different measurement conditions. Standard min-max scaling was used, which transforms the values into a range between 0 and 1.  
The graph in Figure 13 visualizes the cluster analysis of the textile samples based on their reflectivity under three different lighting conditions: daylight (X axis), fluorescent store light (Y axis), and regular store light (Z axis). The samples are color-coded according to the cluster they belong to, as follows:  
* Purple dots (Cluster 0) represent the samples with low reflectivity under all conditions. These are samples D, E, and F.
* Blue dots (Cluster 1) are samples with medium reflectivity. These include samples A and C.
* The yellow dot (Cluster 2) illustrates the most reflective sample B.  
Figure 13: Correlation between samples’ reflectivity under regular store light and fluorescent store lighting. Colour-coded “X” symbols indicate cluster membership: purple—low, blue—medium, yellow—high reflectivity.  
The analysis shows a clear clustering trend, meaning the created knitted designs can be classified based on their reflective properties.  
In this sense, the samples in Cluster 0 likely contain fewer or less integrated reflective threads. Cluster 1 contains samples with medium reflectivity, demonstrating moderate effectiveness under different lighting conditions. Cluster 2 includes the sample with the highest reflectivity, which is sample B. This means that its design makes it the most suitable for applications requiring strong reflectivity.  
Cluster analysis allows the optimization of selected textiles based on reflectivity needs. In this case, it enables the choice between different designs (knit patterns) that contain the same reflective thread. The cluster analysis shows that reflectivity is not evenly distributed and depends on the design, as some samples maintain consistent properties across different lighting environments, while others exhibit greater variation depending on the type of lighting.
# A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images
## 5 Conclusions  
This study successfully assessed the benefits of employing a supervised classification approach with two different classifier algorithms within the GEE platform. The examination encompassed the PlanetScope-only bands as well as combinations of multiple bands from Sentinel-1 and Sentinel-2, alongside two proposed indexes, RTVI and RTWI.  
When using only the PlanetScope bands and the RF classifier algorithm, the accuracy reached 84.9%. However, by incorporating multiple image sources, accuracy significantly improved to 95.9%. The CART classifier algorithm, when applied solely to PlanetScope bands, achieved an accuracy of 85.1%, which saw a slight increase to 87.7% with the inclusion of multiple images. The performance of the RF classifier algorithm demonstrated that all the bands used significantly contributed to the classification.  
On average, Jakarta's urban areas cover approximately 33.2% of green spaces. Data and maps from this study can be utilized by urban residents, researchers, and stakeholders interested in urban green spaces. These maps enable the exploration and promotion of a healthier lifestyle, the measurement of landscape metrics, the monitoring of urban green space conditions, and a detailed assessment of existing urban green spaces in Jakarta.  
The urban green belt areas in Jakarta play a crucial role in preserving the city's natural heritage, reducing environmental risks, and promoting sustainable urban development. It is important for academic research and policy initiatives to consistently prioritize the protection and improvement of these green spaces. This ensures a healthier and more resilient future for Jakarta and its residents.  
This research was supported by the University of Tsukuba.  
**Data Availability Statement**: Google Earth Engine was utilized via [[https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)]([https://code.eartheengine.google.com/](https://code.eartheengine.google.com/)) (accessed on 12 December 2023) to query Sentinel-1 and Sentinel-2 images and generate spectral indices (RTVI and RTWI) from the PlanetScope images. Preprocessing and classification were executed within the GEE environment. The GEE codes developed in this research are available upon any reasonable request via email. Additionally, various other tasks were completed, including converting images into polygons, correcting geometries, adding new fields, dissolving features, and calculating areas, all conducted using QGIS 3.22.8-Bialowieza. The data resulting from this study on Jakarta's urban green space, with very high spatial resolution and high accuracy, are publicly available on Zenodo: [[https://doi.org/10.5281/zenodo.10472507](https://doi.org/10.5281/zenodo.10472507)]([https://doi.org/10.5281/zenodo.10472507](https://doi.org/10.5281/zenodo.10472507)).  
**Conflicts of Interest**: The author declares no conflict of interest.
# How Insoluble Inclusions and Intersecting Layers Affect the Leaching Process within Potash Seams
## 1 Introduction  
The role of potash seams within salt deposits can be viewed from different perspectives: on the one hand, they are a valuable resource that has been mined for many decades; on the other hand, they represent potential risks for technical caverns and subsurface waste repositories [1]. However, both perspectives consider the uncontrolled leaching of potash seams as highly critical. Whether water contact is enabled by geological fault zones or mining activities: in both cases, the increased solubility of potash salt causes a preferential expansion of leaching zones, jeopardizing the integrity and mechanical stability of mines or technical caverns [2, 3, 4, 5]. In order to assess the hazard potential of leaching zones in potash seams, their evolution has to be described in space and time.  
[PERSON] et al. [6] developed a reactive transport model complemented by the \"interchange approach\" to reproduce the complex interplay between chemical reactions and density-driven transport of species within potash seams. The authors showed that leaching zones can be classified into four different groups based on the Peclet (Pe) and Damkohler (Da) numbers, which indicate if a system is dominated by reactions (Da < 1) or transport (Da > 1), and if species transport is dominated by diffusion (Pe < 2) or advection (Pe > 2). However, only homogeneous potash seams have been investigated thus far. Studies on technical cavern construction in rock salt reveal that inclusions or intersecting layers of insoluble materials, such as mudstone, gypsum or sandstone, lead to highly irregular cavern shapes [7, 8, 9]. Furthermore, inclined layers can cause an asymmetric expansion of leachingzones [10]. To investigate if similar phenomena occur within potash seams, heterogeneous rock distributions need to be examined. In addition, mineralogical heterogeneity and the associated variations in dissolution rate need to be taken into account, because these can significantly influence dissolution structure, especially at high Peclet numbers [11; 12].  
For this purpose, the reactive transport model presented by [PERSON] et al. [6] was extended by mineral-specific, saturation-dependent dissolution rates, and a scenario analysis under density-driven flow conditions was performed. Thereby, six generic mineral distributions were investigated, with insoluble areas ranging from several small inclusions to one continuous layer. The main objective was to determine the influence of insoluble layers and inclusions on the evolution and hazard potential of leaching zones within potash seams, and if a risk assessment based on the Pe and Da numbers is still feasible.
## 2 RO Measurement Requirements  
During an RO, the transmitted GNSS signal undergoes refractive bending, causing a phase shift in the signal due to variations of the atmospheric refractive index. Atmospheric refractivity is directly related to the density of the atmosphere (temperature and pressure) and its water vapor content. Typically, an RO receiver tracks two GNSS carrier frequencies (denoted L1 and L2) to separate the dispersive ionospheric delay from the non-dispersive atmospheric delay (refractive index-based delay induced by the tropospheric constituents). For GPS, L1 = 1.57542 GHz and L2 = 1.22760 GHz.  
The key measurement required from a GNSS-RO instrument is a time series of phase delays relative to an initial arbitrary phase. Knowing the precise positions and clock drifts of the transmitter and receiver, the receiver phase delays are converted to _excess phase delay_ due to the atmosphere as the ray paths cut through successive atmospheric layers [17]. The atmospheric refractive index is obtained successively from each layer from top to bottom, like peeling an onion [16]. The theoretical vertical resolution of the retrieval is limited by diffraction within the atmosphere and estimated to be \(\sim\)60 m [24]. In practice, measurement noise limits the obtainable vertical resolution to \(\sim\)200 m in the  
Figure 1: A graphical depiction of the closely spaced radio occultation (RO) observation strategy. In this depiction, three polarimetric RO-receiving capable satellites orbit in formation with a time separation near two minutes. The short time separation increases the likelihood that all three satellites observe the same occulting GNSS satellite, and one or more ray path profiles transects through the atmosphere containing a region of heavy precipitation (dark grey) and the nearby environment outside of heavy precipitation (light grey).  
lower troposphere [28; 29], providing a horizontal resolution in the range of \(\sim\)100 km, sufficient for the synoptic scale structures addressed in Section 1. In the low to middle troposphere (below \(\sim\)10 km altitude in the tropics), the relevant altitude range for the free troposphere, retrieval of water vapor profile requires a priori information on temperature [16; 30]. Given the reasonable estimates of the temperature and refractivity uncertainties, tropospheric specific humidity can be determined to within 0.5 g/kg per profile [31; 32; 33].
## 6 Experimental Results
### Searching Results via Darts_Tiny
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Architectures** & **Channels** & **Paras** & **FLOPs** & **AP** & **F1** \\ \hline H\_Arch\_32 & \(32\times 4\) & 280 K & 6.89 G & 85.09 & 85.636 \\ H\_Arch\_16 & \(16\times 4\) & 79 K & 1.84 G & 80.76 & 81.868 \\ H\_Arch\_8 & \(8\times 4\) & 28 K & 0.62 G & 82.03 & 82.270 \\ H\_Arch\_4 & \(4\times 4\) & 10 K & 0.21 G & 80.38 & 80.172 \\ \hline H\_Arch\_C\_32 & \(32\times 4\) & 246 K & 5.64 G & 79.726 & 80.515 \\ H\_Arch\_C\_16 & \(16\times 4\) & 79 K & 1.64 G & 82.826 & 83.630 \\ H\_Arch\_C\_8 & \(8\times 4\) & 28 K & 0.58 G & 83.93 & 83.158 \\ H\_Arch\_C\_4 & \(4\times 4\) & 9 K & 0.18 G & 79.35 & 78.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection results on HRSID (the first four are obtained by setting \(ops\_ratio=0\), and the last four are obtained by setting \(ops\_ratio=0.012\)). Compared with HRSID, SSDD has fewer SAR images and simpler scenarios. Consequently, even Darts_Tiny cuts down the number of channels to \(4\times 4\), so the detection accuracy is still competitive. Finally, combined with the results given in Tables 5 and 6, we can notice that the detection accuracy and efficiency are balanced when the number of channels is equal to \(8\times 4\).
# Dynamics Investigation on Axial-Groove Gas Bearing-Rotor System with Rod-Fastened Structure
###### Abstract  
This research report discusses the dynamic behaviors of an axial-groove gas bearings-rotor system with rod-fastened structure. The time-based dependency-compressible Reynolds equation in the gas bearing nonlinear system is solved by the differential transformation method, and the continuous gas film forces of a three-axial-groove gas bearing are obtained. A dynamic mathematical model of the rotor system with rod-fastened structure supported in two- and three-axial-groove gas bearings with eight degrees of freedom is established. The dynamic motion equation of the rod-fastened rotor system is solved by the modified Newmark-\(\beta\) method based on disturbance compensation, which can reduce the computing error and improve computing stability. The dynamic characteristics of the rod-fastened rotor-gas bearing system are analyzed efficiently by the diversiform unbalance responses. The influence of the position angle of the pad on the nonlinear characteristics of the rod-fastened rotor system is also studied.  
rod-fastened rotor; axial-groove gas bearing; Newmark-\(\beta\) method; differential transformation method; disturbance compensation
# Non-Intrusive Load Disaggregation by Linear Classifier Group Considering Multi-Feature Integration
## 3 Methodology
### Event Clustering  
The collection of the registered events \(P_{\textit{ext}}(n)\), \(n\) = 1,2,..., \(N_{\textit{e}}\) is the basis of the event clustering. \(N_{\textit{e}}\) denotes the number of events detected. Each state of appliances has a unique value and only one appliance may have state transition in one sampling interval, it is reasonable to gather events with similar value into one cluster. Each cluster represents one kind of state transition of appliance.  
The proposed clustering algorithm without prior knowledge can adaptively determine the number of clusters. There are two steps:  
Step (1): separate the rising and falling edges of the event candidates into two collections of \(P_{\textit{ext\_up}}\) and \(P_{\textit{ext\_down}}\). Then the rising and falling edges are arranged in descending order according to the absolute value of power, respectively. Set cluster threshold as \(\textit{Thr}_{\textit{c}}\). When the difference between two consecutive rising or falling edges is greater than \(\textit{Thr}_{\textit{c}}\), a new cluster is generated. The value of \(\textit{Thr}_{\textit{c}}\) is set small so that the clustering results are more detailed. However, the event caused by the same appliance is also easy to separate some events into different clusters wrongly because of the power fluctuation. In order to solve this problem, we merge some clusters with the similar average power. The detailed process is illustrated in step 2.  
Step (2): calculate the mean power of each cluster, and the mean power difference between two adjacent clusters is obtained. If the difference is less than a certain value, it can be considered that these two adjacent clusters belong to the same appliance state. Thus, we will merge them and the new cluster candidates will be formed.
## 4 Experimental Simulation Analysis
### Influence of Different Chaotic Maps on Performance  
Set the same initial value and semi\(-\)spread spectrum factor and use different chaotic map equations to explore the influence of different maps on the MC\(-\)ODBR\(-\)DCSK system. Combined with the map balance degree, the special initial value is avoided when determining the initial value. The following experimental results are representative experimental results selected after multiple adjustments of experimental data. The experiment is performed at semi\(-\)spread spectrum factor \(\beta\) = 100 and \(\beta\) = 200, respectively. The simulation results obtained are shown in Figure 9.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bandwidth** & **IFFT Points** & \(N_{sub}\) & \(N_{data}\) & \(N_{pilot}\) \\ \hline
10 & 1024 & 595 & 560 & 35 \\
15 & 1536 & 901 & 848 & 53 \\
20 & 2048 & 1190 & 1120 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some simulation parameters.  
Figure 8: BER performance curves of the system with different number of IFFT points.  
After many experimental observations and summaries, the following conclusions can be drawn: under the same initial value conditions, the bit error rate changes of the MC\(-\)ODBR\(-\)DCSK system generated by different maps under the same semi\(-\)spread spectrum factor show a consistent change rule.
# A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO
## 3 Results
### Assimilation of the Combined Satellite Datasets
Figure 11: Surface wind speed near the WWB event from (**a**) ASCAT observation around 1448 UTC, and prediction from (**b**) CTRL, and (**c**) DA_com at 1500 UTC on 7 January 2018; and (**d**) ASCAT around 1524 UTC, and surface wind prediction from (**e**) CTRL, and (**f**) DA_com at 1600 UTC on 8 January 2018. The impacts of the combined satellite datasets on the intensity and track of TC Irving are displayed in Figures 7 and 8. As shown in Figure 7, DA_com also underestimated the intensity of TC Irving, but apparently performed better when comparing to CTRL, DA_cyg_fd, and DA_cyg_lf. An intensification was predicted from 999 hPa in MSLP and 17 m s\({}^{-1}\) in maximum surface wind speed at 0000 UTC on 6 January to 985 hPa and 35 m s\({}^{-1}\) at 1800 UTC on 7 January 2018. The weakening after 0600 UTC on 8 January was also captured. With the assimilation of the combined satellite datasets, the track forecast of TC Irving in DA_com was also improved. The center locations in DA_com were very close to the observed ones with the track error ranges from 11 km at 1200 UTC on 6 January 2018. Figure 12: 1-h precipitation around TC Irving from (**a**) IMERG, (**b**) CTRL, (**c**) DA_com at 1500 UTC on 7 January 2018 and (**d**) IMERG, (**e**) CTRL, and (**f**) DA_com at 2100 UTC on 8 January 2018. Figure 13: 1-h precipitation for convection related to the WWB event from (**a**) IMERG, (**b**) CTRL, (**c**) DA_com at 1200 UTC on 7 January 2018 and (**d**) IMERG, (**e**) CTRL, and (**f**) DA_com at 2100 UTC on 8 January 2018.  
to 88 km at 0000 UTC on 9 January 2018. In comparison, the track errors were 125, 93, and 94 km at 1200 UTC on 6 January 2018, and 266, 259, 198 km at 0000 UTC on 9 January 2018 in CTRL, DA_cyg_fd, and DA_cyg_lf, respectively.
## 3 Smart and Secure HIE Systems: SSHES
### Blockchain and Integration into HIE Systems
#### 3.3.3 Consortium Blockchains in HIE Systems  
A consortium blockchain is a form of blockchain network managed and governed by a collective of organizations or entities, rather than being overseen by a singular entity. It is designed to enable collaboration and cooperation among multiple participants while maintaining a certain level of trust and privacy. Unlike public blockchains, where anyone can connect and engage in the network, consortium blockchains have restricted access and are typically permissioned. This means that only allowed patients can connect the network, approve transactions, and contribute to the consensus mechanism. This blockchain type is often used in some scenarios where multiple organizations need to work together and share data, but still require a level of control and privacy over the blockchain. In the last decade, several consortium blockchains have been released e.g., Hyperledger Fabric, R3 Corda, Quorum, Ripple, B3i, etc. Thus, integrating a consortium blockchain into an HIE system can lead to numerous benefits, including improved security and data interoperability, and increased trust among participants. However, such integration requires careful planning, cooperation among stakeholders, and compliance with regulatory requirements.