esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Study Area and Data
### Ground Gauge Data  
The China Gauge-based Daily Precipitation Analysis (CGDPA), operated by the National Meteorological Information Center (NMIC) of the China Meteorological Administration (CMA), was used as the reference dataset in this study. All the gauge data used in CGDPA are manually recorded by bucket rain gauges and underwent a strict quality control procedure [54]. Using the optimal interpolation (OI) method, daily precipitation data from about 2400 gauge stations in mainland China were interpolated to obtain the gridded CGDPA product (0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\)). The CGDPA is considered as a high-quality ground precipitation dataset and was previously adopted to evaluate satellite precipitation products over China [55; 56; 57].  
For the hydrological evaluation, the daily meteorological observations from 2009 to 2015 at the upper Yellow River basin were obtained from CMA, including daily maximum and minimum temperature, and daily average wind speed. The daily streamflow data at the Tangnaihai hydrological station from 2009 to 2015 were primarily collected from the Chinese Hydrology Almanac, which is published by the Hydrological Bureau of the Ministry of Water Resources in the People's Republic of China.
## 4 Experimental Research  
### Coal-Rock Image Dataset  
Since it is difficult to obtain a sufficiently large number of paired low- and high-illumination coal-rock images in underground environments, a public dataset was used for training, and the proposed model was subsequently tested using real coal-rock images. Specifically, the BrighteningTrain dataset was employed for training, which contains 1000 high-illumination and 1000 corresponding low-illumination images encompassing a variety of object structures. This diversity ensures the generalization capability of the image-enhancement network. All images are in PNG format with a resolution of \(384\times 384\) pixels. Among them, 900 image pairs were used for training and 100 for validation. The experimental test set included 500 low-illumination coal-rock images collected under dim underground mining conditions, as well as 1000 synthetically degraded coal-rock images generated by adding 0.02% Gaussian noise and impulse (salt-and-pepper) noise. Manual annotations were created using the LabelMe version 5.3.1 software to generate pixel-level ground truth labels for evaluating segmentation performance. As shown in Figure 9, the red regions represent coal seams, while the black regions denote rock seams.  
Although the proposed model is trained using the publicly available BrighteningTrain dataset, which primarily consists of synthetic low- and high-illumination image pairs, special consideration was given to addressing the potential domain shift between synthetic training data and real-world underground coal mine images. To evaluate the generalization ability of the model, we constructed a testing dataset composed of 500 authentic coal-rock images captured in actual underground environments under poor lighting conditions. Furthermore, we manually augmented this dataset with 1000 additional coal-rock images by injecting Gaussian and salt-and-pepper noise to simulate the diverse interference patterns encountered in real scenarios.  
The model architecture is inherently designed to improve robustness against domain discrepancies. The multi-scale luminance-enhancement module adaptively enhances brightness across multiple perceptual fields, mitigating local over- or underexposure under non-uniform lighting conditions. Meanwhile, the NR-Transformer, incorporating long-range attention with convolutional refinement, effectively suppresses structural noise and enhances salient features. These components jointly contribute to enhancing the model's ability to generalize beyond the training domain.  
Although explicit domain adaptation methods, such as adversarial domain alignment, domain-specific normalization, or discrepancy-based losses, are not currently implemented in our framework, we acknowledge their importance and plan to integrate them in future iterations of our work. Specifically, strategies such as fine-tuning with a small amount of labeled underground data, domain-specific feature alignment, or leveraging style transfer for domain translation can be considered to further reduce the performance gap across domains.
# A Space-Borne Ka-Band Tile-Type Phased Array Transmitter Module Based on HTCC Technology
## 3 Tile-Type Transmitter Module Based on HTCC Technology
### Reliability Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Parameter** & **Furthest Solder Joint** & **Signal Solder Joint** \\ \hline Fatigue ductility index & \(\varepsilon_{\text{f}}\) & 0.325 & 0.325 \\ (for Sn10 Pb90, 2 \(\varepsilon\approx\) 0.65) & \(\varepsilon_{\text{s}}\) & \(1.6132\times 10^{-5}\) & \(1.6132\times 10^{-5}\) \\ Height of the solder joint/mm & h & \(0.45\) & \(0.45\) \\ Experience coefficient (To match the difference between the idea solder and actual solder joint) & F & \(1.35\) & \(1.35\) \\ Distance between the solder joint and the neutral & L\({}_{\text{D}}\) & \(7.53\) & \(5.8\) \\ point/mm & & & \\ Component Substrate thermal expansion & \(\alpha_{\text{c}}\) & \(5.8\times 10^{-6}\) & \(5.8\times 10^{-6}\) \\ coefficient/\({}^{\circ}\)C & \(\alpha_{\text{s}}\) & \(1.6132\times 10^{-5}\) & \(1.6132\times 10^{-5}\) \\ Substrate equivalent thermal expansion coefficient/\({}^{\circ}\)C & \(\alpha_{\text{s}}\) & \(1.6132\times 10^{-5}\) & \(1.6132\times 10^{-5}\) \\ Temperature range of the component & \(\Delta\text{T}_{\text{c}}\) & \(155\) & \(155\) \\ Temperature range of the substrate & \(\Delta\text{T}_{\text{s}}\) & \(155\) & \(155\) \\ Temperature Acceleration Factor & c & \(-0.39949\) & \(-0.39949\) \\ Weibull shape parameter & \(\beta\) & \(4\) & \(4\) \\ Failure rate & X & \(25\%\) & \(25\%\) \\ Predicted cycles under accelerated thermal stress & N & \(554\) & \(1065\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Lifespan prediction calculation process.
## 3 The Fractal Model and the W-M Contact Model
### Optimization of a Single Micro-Asperity Model  
Superimposing micro-asperities of different frequencies to simulate a state of micro-asperities, a single micro-asperity with a frequency exponent of \(n\) undergoes three types of deformation during the contact process: elastic, elastoplastic, and plastic deformation. Different deformation processes are fitted and analyzed using micro-asperities in various states, thereby simulating the true micro-deformation process of a single micro-asperity. Schematic diagrams of multi-frequency micro-asperity synthesis are shown in Figure 11.  
Figure 10: Base curved surface 3D model diagram (the color range from blue to yellow in this image indicates a change in surface height from low to high).  
The structural parameters of a single micro-asperity are as follows.  
Single asperity contact area:  
\[a_{n}=2\pi R_{n}\omega_{n} \tag{17}\]  
Single asperity deformation:  
\[\omega_{nec}=\bigg{(}\frac{\pi KH}{2E}\bigg{)}^{2}R_{n} \tag{18}\]  
Radius:  
\[R_{n}=\bigg{|}\frac{\left(1+z^{\prime 2}\right)^{3/2}}{z^{\prime\prime}}\bigg{|}_{ x=0}=\frac{\left[L/2\gamma^{n}\right]^{D-1}}{2^{3-D}\pi^{2}G^{D-2}ln\gamma^{1/2}} \tag{19}\]  
Height:  
\[\delta_{n}=G^{D-2}ln\gamma^{1/2}\bigg{(}\frac{L}{\gamma^{n}}\bigg{)}^{3-D} \tag{20}\]  
Variable quantity:  
\[\omega_{n}=\delta_{n}-z_{n}(r_{n})=G^{D-2}ln\gamma^{1/2}\frac{L}{\gamma^{n}}^ {3-D}\times\bigg{[}1-cos\bigg{(}\frac{\pi r_{n}^{\prime}}{r_{n}}\bigg{)}\bigg{]} \tag{21}\]  
Parameter explanation:  
\(H\): hardness of materials;  
Figure 11: Schematic diagram of multi-frequency micro\(-\)asperities synthesis: (**a**) \(f=1\); (**b**) \(f=2\); (**c**) \(f=3\); (**d**) \(f=28\).  
\(K\): hardness coefficient, \(K=0.454+0.4\upsilon\);  
\(E\): elastic modulus, \(E=\frac{1}{1-\delta_{1}/k_{1}+1-\delta_{2}/k_{1}}\);  
\(E_{1},\ E_{2}\): elastic modulus of two contacting surfaces;  
\(\upsilon_{1},\ \upsilon_{2}\): [PERSON]'s ratio of two contacting surfaces.  
Using the arc-shaped profile of a cross-section of the base model Figure 10 as the base for the fractal model, as shown in the schematic Figure 12.  
Equations (17)-(21) are from references [9; 17].  
Divide the point cloud height data of the contact surface into \(M\times N\) small blocks to approximate the fitting of the contact surface, with the equivalent distance from each point on the contact surface to the contact plane being \(d_{ij}\).  
\[d_{i,j}=r-r\times cos\bigg{(}\frac{i-1}{N-1}\times\beta_{i}\bigg{)} \tag{22}\]  
Let the deformation of the central area be \(\omega_{0,j}\), then the deformation of the area deviating from the central contact area is calculated by Equation (23).  
\[\frac{\omega_{0,j}}{\delta_{0,j}}=\frac{\epsilon_{1\times}\delta_{i,j}-d_{i,j }}{\delta_{i,j}} \tag{23}\]  
The scale factor \(D_{s}\) can determine the relationship between the average distribution quantity and the size of micro-asperities, thereby obtaining the true contact area of the micro-asperities.  
\[\Delta_{i,j}{=z_{i,j}\times d_{i,j}/\left(MN\delta_{max}\right)} \tag{24}\]  
Equations (22)-(24) are from references [4; 16].
# A Review of Cell Operation Algorithm for 3D NAND Flash Memory
## 2 Program Algorithm
### Cell Variation Improvement
In the case of pair-BL PGM, by surrounding the adjacent pattern with two floating channels with boost potential, it improves the program efficiency of the slowest bit and address large cell-to-cell PGM variation caused by high aspect-ratio hole etching in 3D NAND Flash. Furthermore, high aspect-ratio hole etching reduces the hole radius toward the lower WL and strengthens the electric field applied to the gate dielectric [57]. This increases the program speed of the lower WL, but further deteriorates WL-to-WL interference when the aggressor cell becomes the lower WL [58]. To improve this phenomenon, instead of programming from lower WL to upper WL, performing programming from upper WL to lower WL can greatly relieve the cell-to-cell interference between WLs [59]. On the other hand, there have been several system-level optimizations to address temperature and process-variation issues in 3D NAND flash memory. [PERSON] et al. suggested the temperature-aware data management scheme [60] and a process-variation-aware space allocation strategy [61] in the open-channel solid state drive (SSD), a hardware and file system interface that can allocate physical space to relieve the process variation of 3D NAND flash memory. Several optimization strategies have been proposed for open channel SSDs to prevent unreliable physical block usage, demonstrating that uncorrectable bit errors are reduced.
## 3 Results and Discussion  
### Yields under Different Carrier Gas Flow  
Figure 2 shows the yield of nanosized composite under different carrier gas (argon) flows with a certain flow of reaction gas (methane and silane). All the samples were collected under the same  
Figure 1: DC magnetoelectric plasma installation. (**a**) Schematic diagram, (**b**) Discharge device.  
experimental conditions and tested by parallel experiments. When the mixture enters the plasma region under the carrier gas, it first breaks into many electriferous intermediates in the electric field, and these intermediates polymerize at the molecular scale and then nucleate and grow. Finally, the product is carried out of the plasma by the carrier gas. When the carrier gas flow is very low (\(<\)4000 mL/min), it is difficult to rotate the arc, resulting in high concentration of plasma and high temperature of the arc. Excessive temperature will cause the free radicals obtained by pyrolysis of silane and methane to reform into gaseous products instead of solid products. As the carrier gas flow increases, the arc starts to rotate, and the reaction gas/carrier gas ratio is more suitable for pyrolysis. The optimum carrier gas flow appears at 7000 mL/min. Under this condition, the maximum yield reaches 5.6 g/h. However, further increase of the carrier gas flow would lead to a decrease in yield. This is because a higher carrier gas flow shortens the residence time of methane or silane in the plasma [29]. Because of that, methane or silane cannot be pyrolyzed effectively. The experimental results in this work are consistent with previous studies [30].
## 3 Experiments and Results
### Experimental Results
The calculation results of the output images of the four real HSIs under the four evaluation standards are shown in Tables 1-4. As can be seen, in comparison with PCA, CMF, and BS, the proposed method, which is based on the application of different manifold algorithms, had higher OIF and \(\lambda\) under most conditions. This means that it obtained the best results in comparison with images generated by the data-oriented approaches. It can be observed that the proposed object-oriented approach could display more information and obtain a better separability between classes. In addition, looking at the results for these two parameters, the location of optimal results was relatively stable, which shows that these two features were not  
Figure 8: Visualization results for SalinasA. (**a**) PCA; (**b**) CMF; (**c**) band selection; (**d**) T-SNE; (**e**) LTSA; (**f**) LE; (**g**) ISOMAP. Figure 7: Visualization result for Salinas. (**a**) PCA; (**b**) CMF; (**c**) band selection; (**d**) T-SNE; (**e**) LTSA; (**f**) LLE; (**g**) LE; (**h**) ISOMAP. consumed longer computing time than the other methods. Therefore, T-SNE and ISOMAP are unsuitable for real-time processing or processing that has requirements regarding the amount of time needed. If no special requirements are needed, the LE algorithm has more advantages.
# Application of Transient Electromagnetic Method for Investigating the Water-Enriched Mined-Out Area
## 2 Materials and Methods
### Field Operation  
A large-loop TEM measurement was carried out along a section of proposed location, data acquisition system was arranged along 23 survey lines, the interval of the lines was 40 m, and the survey sites along the survey line with the interval of 20 m covers an area of 1.53 km\({}^{2}\), and the total of 1975 survey points were recorded. The GDP-32 system that was developed by Zonge Engineering and Research Organization was adopted in our measurements, and a magnetic sensor with effective receiving areas of 10,000 m\({}^{2}\) was used to receive the induced voltage.  
Figure 4: Diagrammatic explanation of the large-fixed loop Transient Electromagnetic (TEM) response.  
In general, the data acquisition areas of a large loop TEM device are located in about one-third of each side length in the loop central (Figure 5). Because in these area, the induced electromagnetic field values distributed in this area can be considered as uniform [33].  
Different fundamental frequencies have different observation time [14; 32]. In general, the lower frequency, the longer observation time, but with lower efficiency. If the fundamental frequency is too high, then the detecting depth cannot be able to reach the depth of the investigate target. So, it is necessary to test the fittest frequency in the survey area. Fundamental frequency of 32 Hz, 16 Hz, and 8 Hz were chosen (Figure 6). It can be seen that, if the decay time \(t\) shorter than 2 ms, all the results of the three different frequencies match each other well, the quality of the acquired raw data is accurate and reliable. But, the observation time of 32 Hz data is shorter than 6 ms, the detecting depth is too shallow [33]. We also can obtain that the when the decay time \(t\) longer than 9 ms, the date of 8 Hz have lower signal to noise ratio, otherwise the fundamental frequency of 16 Hz was chosen in this work (Figure 6).  
Note that the transmitter loop is square shape with the side length size of 480 \(\times\) 480 m, the transmitter voltage is 120 V, and the strength of the transmitting current is 15 A.
## 2 Materials and Methods
### Statistical Analysis and Correlation Assessment  
#### 2.6.1 Analysis of Variance (ANOVA)  
To identify significant differences between the four SCS climatic zones, representative samples were first obtained for each zone. ANOVA, a widely used statistical test that assumes normality, independence, and homoscedasticity [35], was employed. This method tests the null hypothesis that group means are equal. If the False-Positive Risk (FPR) is below a conventional threshold (typically 0.05), the null hypothesis is rejected, indicating significant differences between groups [36].  
The initial step involved verifying three statistical assumptions:  
\begin{table}
\begin{tabular}{l l l l} \hline
**SCS 1** & **SCS 2** & **SCS 3** & **SCS 4** \\ \hline
1.1 Avila & 2.1 Badalona & 3.1 Albacete & 4.1 Alicante \\
1.2 Gión & 2.2 Cuenca & 3.2 Ciudad Real & 4.2 Almería \\
1.3 Leon & 2.3 Ourense & 3.3 Lérida & 4.3 Caceres \\
1.4 Lugo & 2.4 Salamanca & 3.4 Malaga & 4.4 Cordoba \\
1.5 Vigo & 2.5 Segovia & 3.5 Murcia & 4.5 Huelva \\ \hline \end{tabular}
\end{table}
Table 1: Cities considered for UHI model calibration by SCS climate zone.  
* Normality: Checked using the Kolmogorov-Smirnov-Lilliefors test due to the sample sizes exceeding 50.
* Independence: Ensures that sample values from one population were not related to those from another.
* Homoscedasticity: Evaluated using [PERSON]'s test, the most common test for this purpose.  
If any of these assumptions were violated and considering more than two groups, the Kruskal-Wallis H test, a non-parametric ANOVA, was applied to detect significant differences between the four zones. A significant Kruskal-Wallis statistic indicated average differences between at least two climate types.  
Post hoc tests, such as the Holm test (less strict than Bonferroni) [37], were conducted following significant ANOVA results to identify pairwise differences among groups, providing detailed insights into the data.
## 4 Results and Discussion
### Quantitative Interpretation
#### 4.2.1 Euler Deconvolution  
In this study, Euler's solutions of the CBA map are generated using a structural index of 0.0 to delineate faults/lineaments contact with depth estimation based on Euler anomalies. A window size of 5 km \(\times\) 5 km and 20 km \(\times\) 20 km grid cells with a depth of tolerance of 10% were applied (Figure 9). Both Maps were overlayed on top of the Kedah state map with the known location of faults/lineaments adopted from the geological map of Kedah. A high concentration of Euler anomalies was observed along the Bok Bak Fault and northeastern part of the Muda River Basin. A moderate concentration of Euler anomalies can be spotted at the northern, western, southwestern parts of Kedah and south-southwestern parts of the Muda River Basin. The depth range of Euler anomaly sources are ranging from less than 2 km to more than 10 km.  
Bok Bak Fault has the most significant number of Euler anomalies along the fault zone, with a shallow depth ranging from 2 km to 4 km at the northwestern part and increasing towards the southeastern direction with a depth ranging from 6 km to more than 10 km. Another major Euler anomaly observed in the northeastern part of the Muda River Basin is interpreted as a fault boundary separating from the Semanggol Formation and Mahang Formations with granite. This fault boundary has a shallow depth ranging from 4000 m to 6000 m separating Semanggol and Mahang Formations and increases towards the southwestern direction with a depth ranging from 6000 m to 8000 m that separates Semanggol Formation and granite. Similar Semanggol-granite fault boundaries are also observed in the southern part of the Muda River Basin trending in the NE-SW direction near the Bok Bak Fault and the southwestern part of Kedah, trending in the NNE-SSW direction. Euler anomalies are also determined in the western part of Kedah, which might represent a fault contact boundary between the Quaternary sediments and Kubang Pasu Formation.  
Figure 8: Comparison of the rose diagrams between (**a**) Topographic lineament traced; (**b**) THD residual anomaly traced.
# Remote Sensing and Machine Learning Tools to Support
## 3 Results and Discussion  
A detailed report of the systematic review will be provided in this section. We will begin by giving an overview of the general characteristics of wetland publications, including the author affiliations, journals, and the number of publications per year/journal/publisher.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Attribute** & **Type** & **Categories** \\ \hline
1 & Publication Title & Free-text & - \\
2 & Keywords & Free-text & - \\
3 & Authors & Free-text & First Author Name \\
4 & First Author Affiliation & Free-text & University/Organization Country \\
5 & Publication Year & Free-text & Published Year \\
6 & Document Type & Classes & Journal; Conference \\
7 & Source & Free-text & Published Journal or Conference \\
8 & Publisher & Classes & MDPI, IEEE, Elsevier, etc. \\
9 & Citation & Numeric & - \\
10 & Study Focus/Objective & Classes & classification, change detection, vegetation mapping, etc. \\
11 & Study Area Country & Free-text & Countries all around the world \\
12 & Study Area Extent & Classes & Very small, Local, Regional, Provincial, National \\
13 & Data Type & Classes & Multispectral, Hyperspectral, SAR, etc. \\
14 & Sensor type & Classes & Landsat, Sentinel, RADARSAT, etc. \\
15 & Feature Type & Classes & Imagery Features, Spectral Indices, Textural metrics, etc. \\
16 & Methodology & Classes & ensemble learning, decision tree, DL-based, etc. \\
17 & Accuracy Assessment & Numeric & Overall Accuracies analysis based on seven factors \\
18 & Processing Tools & Classes & ArcGIS, ENVI, Python, SNAP, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 2: A listing of extracted sub-fields/attributes considered for the content analysis of included papers.  
Figure 3: Paper selection and the review process flow diagram. A total of 528 papers were found in our primary search in the WoS database.  
Afterward, study objectives and applications, study regions, data and sensor type, and ML algorithms utilized in the literature will be discussed.