esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Engineering Application  
### Fracturing Design and Construction Process  
A test was conducted at a coal mine in the Shendong mining area, China, to assess the impact of underground directional long-hole segmented fracturing on weakening hard roof strata. The test involved six directional hydraulic fracturing long holes, spaced 60 m apart, as shown in Figure 14. Transient electromagnetic responses, and microseismic and mine pressure variations in the fractured zone, were monitored to evaluate the roof-weakening effect and improvements in mine pressure.  
Figure 12: The distribution of Misses stress with different segment length. (**a**) 12 m; (**b**) 20 m; (**c**) 30 m; (**d**) 40 m.  
Figure 13: The fracture propagation lengths with different segment lengths.  
The process of segmented hydraulic fracturing for coal seam roofs are as follows: The water injection pump is first activated to slowly expand the packer. The pressure gradually increases with the injection volume. Once the packer is fully seated, pumping pressure is continuously increased to fracture the target formation. During segmented hydraulic fracturing, remote computers are used to automatically monitor and record fracturing data. This enables the real-time measurement of water injection pressure at each segment. The water injection pressure-time curves of single segments during fracturing are shown in Figure 15. Pressure curves show multiple sharp drops, signaling fracture initiation and propagation.  
Figure 14: Hydraulic fracturing directional long borehole trajectories.  
Figure 15: Typical hydraulic fracturing pump injection pressure curves. (**a**) Segment 6 of hole No. 1 at No. 1 drilling sit; (**b**) Segment 8 of hole No. 2 at No. 2 drilling sit; (**c**) Segment 6 of hole No. 2 at No. 2 drilling sit.  
In the fracturing of segment 6 of hole No. 1 at drilling site No. 1 (Figure 15a), the initial pump pressure spiked to 25 MPa before sharply dropping to 14.9 MPa. With continued injection, the pressure quickly rebounded to 17 MPa, stabilized, then dropped again to 14.3 MPa. After a high-rate injection, pressure rapidly recovered to 17 MPa, held steady, then dropped to 10 MPa before rising back above 17 MPa until fracturing concluded. In the fracturing of segment 8 of hole No. 2 at drilling site No. 2 (Figure 15b), the pressure initially surged to 15 MPa, then increased in a saw tooth pattern to 18 MPa. After stabilization, it dropped abruptly to 15.1 MPa. With continued injection, the pressure rapid recovery to 18 MPa, followed by another drop to 15.2 MPa. Then the pressure rebounded to 19 MPa, held for 10 min, and dropped again before recovering above 18 MPa until the target volume of water was injected. During the fracturing of segment 6 of hole No. 2 at drilling site No. 2 (Figure 15c), two significant pressure drops were recorded. The initial pressure spiked to 20.6 MPa before sharply dropping to 10.9 MPa. The pressure then rebounded and stabilized at 18 MPa before falling again to 11.7 MPa. Subsequently, the pressure fluctuated upward to 19 MPa and remained stable until the process was complete.
## 3 Research Progress of High-Temperature Rock Mechanics in Large-Buried Tunnels
### Thermo-Mechanical Properties of Shotcrete
[PERSON] [72] carried out relevant tests using the two-factor influence method and found that appropriate reduction of the water-binder ratio of concrete and the use of appropriate amount of silica fume could improve the internal bonding of concrete in high-temperature environments from the micro-level observation. On this basis, [PERSON] [73] conducted further research on silica fume and high-temperature concrete, and concluded that replacing 10-20\(\%\) cement with silica fume and 3-6\(\%\) aggregate with rubber could improve the high-temperature performance of cement concrete. [PERSON] et al. [74] observed the changes of aggregate volume in concrete under high temperature through the principle of thermal phase formation, and found that the different expansion coefficients of cement and coarse aggregate resulted in great expansion stress, causing the reduction of concrete strength under high temperature. [PERSON] et al. [75] studied the influence of thermal conductivity and moisture resistance factors of different types of concrete in high temperature, and deduced the second-order simplified equation with high correlation (Equation (10)). \[Y=a\times T^{2}+b\times T+c \tag{10}\]  
where \(Y\) indicates the thermal conductivity or moisture resistance factor; \(T\) indicates the exposed temperature; \(a\), \(b\), and \(c\) indicate constants.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 3 The Data Storage and Exchange Method Based on Superscalar Pipeline
### The Three-Dimensional Cross-Mapping Method Based on Equal Division of Sub-Matrix with Dual-Channel  
The three-dimensional cross-mapping scheme based on the equal division of a sub-matrix with a dual-channel takes full advantage of the parallel advantages of FPGA. Unlike the method used to block a matrix and map to a three-dimensional memory array, the data of each sub-matrix are equally divided according to their sub-matrix size and mapped to a row in dual-channel DDR3A and DDR3B, respectively. When DDR3A and DDR3B are read or written synchronously, the read operation reads data from DDR3A and DDR3B in the azimuth or range direction, enters the compute core for processing, and writes data to DDR3A and DDR3B after processing, and performs circular read and write access according to the operation flow. Figure 5 shows the method's complete schematic diagram.
## 2 System Modelling  
### Multilevel Inverter  
The MLI topology from [13] was chosen in the proposed system, as stated in Section 1. The topology's structure is shown in Figure 1. The packed H-Bridge unit was the central part of the topology, which consisted of six switches (S\({}_{1}\), S\({}_{2}\), S\({}_{3}\), S\({}_{4}\), S\({}_{5}\), and S\({}_{6}\)). The basic units were located on the bridge's right and left sides. On one side, the basic unit was positioned in the opposite direction to the basic unit on the other side. Each basic unit contained two DC sources (V\({}_{1/2}\) and V\({}_{3/4}\)) and two unidirectional switches (S\({}_{7/8}\) and S\({}_{9/10}\)).  
The topology could generate 17 output levels (17 L) using the configuration of asymmetrical sources. There were differences in the values of the DC sources in this arrangement. The DC sources ratio should follow V\({}_{1}\):V\({}_{2}\):V\({}_{3}\):V\({}_{4}\) = 1V\({}_{\text{DC}}\):1V\({}_{\text{DC}}\):3V\({}_{\text{DC}}\):3V\({}_{\text{DC}}\). Table 1 presents all the possible source combinations for generating the 17 L output. Although the topology could be cascaded into as many stages as needed, this paper only considered its 17 L operation.  
\begin{table}
\begin{tabular}{c c c} \hline
**Levels** & **Combinations** & **Outputs** \\ \hline
1 & None & 0 \\
2 \& 3 & \(\pm\)(V\({}_{1}\)) & \(\pm\)V\({}_{\text{DC}}\) \\
4 \& 5 & \(\pm\)(V\({}_{1}\) + V\({}_{2}\)) & \(\pm\)2V\({}_{\text{DC}}\) \\
6 \& 7 & \(\pm\)(V\({}_{3}\)) & \(\pm\)3V\({}_{\text{DC}}\) \\
8 \& 9 & \(\pm\)(V\({}_{1}\) + V\({}_{3}\)) & \(\pm\)4V\({}_{\text{DC}}\) \\
10 \& 11 & \(\pm\)(V\({}_{1}\) + V\({}_{2}\) + V\({}_{3}\)) & \(\pm\)5V\({}_{\text{DC}}\) \\
12 \& 13 & \(\pm\)(V\({}_{3}\) + V\({}_{4}\)) & \(\pm\)6V\({}_{\text{DC}}\) \\
14 \& 15 & \(\pm\)(V\({}_{1}\) + V\({}_{3}\) + V\({}_{4}\)) & \(\pm\)7V\({}_{\text{DC}}\) \\
16 \& 17 & \(\pm\)(V\({}_{1}\) + V\({}_{2}\) + V\({}_{3}\) + V\({}_{4}\)) & \(\pm\)8V\({}_{\text{DC}}\) \\ \hline \end{tabular}
\end{table}
Table 1: Voltage source combinations for 17 L operation.  
Figure 1: Selected MLI topology.
# Self-Organizing Control of Mega Constellations for Continuous Earth Observation
## 3 Results and Discussion  
In this section, the self-organizing control under continuous coverage constraints is simulated. The GW-2 constellation is designated as the mega constellation example. The control consumption is assessed, and the coverage performance is analyzed.
## Mathematical Modeling and Performance Evaluation  
Several works have applied queueing theory to model performance degradation in cloud systems [15] introduced the use of M/M/1 models to analyze average waiting times and system congestion under varying arrival and service rates. More recent applications in IoT scenarios [16] show how security filters and firewall rules increase processing time per packet, thus affecting throughput.  
From a statistical standpoint, performance comparisons pre- and post-security implementation are often evaluated using hypothesis testing. For instance, the work [17] applied two-sample t-tests to determine the significance of latency differences in encrypted versus non-encrypted IoT traffic flows.  
**Gap and Contribution.** While existing literature extensively covers IoT security measures and performance modeling independently, few studies provide a detailed mathematical and empirical assessment of their joint effect on smart city platforms using real implementation data [18,19]. This paper addresses that gap by providing a quantitative evaluation of the trade-off between security and performance in the specific context of the UBT Smart City platform, leveraging Azure-native tools and configurations. By combining queueing theory, statistical hypothesis testing, and cost modeling, this work contributes a reproducible framework for secure and efficient IoT Cloud system design.
# A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion
## 4 Experiments  
### Datasets  
The DOTA-v1.0 dataset [36] is a large-scale aerial image object detection dataset comprising 2806 images across fifteen categories: Basketball Court (BC), Roundabout (RA), Harbor (HA), Swimming Pool (SP), Helicopter (HC), Plane (PL), Baseball Diamond (BD), Bridge (BR), Ground Track Field (GTF), Soccer Field (SBF), Large Vehicle (LV), Ship (SH), Tennis Court (TC), Small Vehicle (SV), and Storage Tank (ST). Collected from diverse sensors and platforms, this dataset features images with resolutions ranging from 800 \(\times\) 800 to 20,000 \(\times\) 20,000 pixels, presenting objects with significant variations in scale, orientation, and shape.  
The HRSC2016 dataset [37] was released by Northwestern Polytechnical University in 2016, focusing primarily on ship detection. This dataset contains 1061 images. The objects within it are categorized into three major categories and 27 subcategories, encompassing 2976 targets in total. This makes it of great research value for studying ship detection, especially detailed classification of detection tasks.  
The UCAS-AOD dataset [38] is a dataset for aircraft and vehicle detection constructed from Google Earth imagery. This dataset contains 2420 images and 14,596 instances, covering two types of samples (aircraft and vehicles) along with some negative samples (background).  
For these three datasets, during the preprocessing stage we normalized the image pixel values to the range [0,1], and removed extreme noise pixels using the threshold of mean pixel count \(\pm\) 3 times the standard deviation. In addition, during the training phase we adopted data augmentation strategies to enhance the generalization ability of the model, specifically including random horizontal/vertical flipping, which means randomly performing horizontal flipping or vertical flipping on training images. In order to truly reflect the model's performance on the original data, the validation and test sets were not augmented. The dataset division methods were tailored to the characteristics of each dataset: DOTA-v1.0 was randomly divided into training, validation, and test sets in an 8:1:1 ratio; for HRSC2016, we adopted the official fixed division (436 images in the training set, 181 in the validation set, and 182 in the test set, focusing on ship detection); finally, UCAS-AOD was randomly divided in a 7:1:2 ratio.
## 1 Introduction
They inferred that there would be no ice formation if the impingement area was covered with a superhydrophobic coating and the temperature was maintained above 6 \({}^{\circ}\)C.  
The literature suggests two main perspectives: (1) Coatings with better superhydrophobic behavior, characterized by larger contact-angles and smaller roll-off-angles, achieve superior anti-icing performance. To address this perspective, novel coatings must be developed. (2) Combining thermoelectric and superhydrophobic coatings represents an efficient and low-energy consumption method for preventing ice formation. This perspective requires research into the design methods for the combined use of these coatings, which is a focus of this study. The literature has focused on the anti-icing mechanisms of superhydrophobic coatings, the reason why it reduces energy consumption, the effectiveness of different superhydrophobic coatings and the reduction of energy consumption. However, less attention has been paid to the engineering problems associated with the application of superhydrophobic coatings, such as their layout and internal heating area. To address these issues, similar to the design of thermoelectric and hot-air anti-icing systems, it is crucial to investigate the effects of various parameters of the anti-icing system, including the heating range and the layout of superhydrophobic coatings, under different icing conditions. This research provides valuable insights into the design of anti-icing system parameters specific to different icing conditions and offers actionable methods for the design and development of anti-icing systems that combine thermoelectric and superhydrophobic coatings. This study provides fundamental research data to guide the future design of the Ice Protection System with a superhydrophobic coating (IPS). The data include the sensitivities of IPS to icing environmental parameters, as well as protective effects at different IPS parameters. The IPS parameters include the superhydrophobic coating range, heating range and power. Moreover, the environment parameters include median volumetric diameter (MVD), liquid water content (LWC) and environment temperature. These data can help guide the design of this type of IPS in the future.
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 2 Preliminaries
### Image Segmentation  
There are various medical image segmentation methods widely used at present, and the OTSU algorithm is a classical threshold segmentation method.  
The OTSU algorithm, also referred to as the maximal variance between clusters, is an algorithm that utilizes the image histogram to determine the optimal global threshold. The advantages include simplicity, a fast calculation speed, and independence from image brightness and contrast. The basic principle of the OTSU algorithm is as follows. Firstly, it automatically finds a threshold to divide the image and distinguish the foreground region from the background region. It divides the histogram into two groups at a certain threshold. The threshold is determined by maximizing the variance between these two groups of histograms, thus achieving the optimal segmentation effect.  
Specifically, assume that a grayscale image has a range of gray values of \([0,L-1]\); the pixel number of gray value \(i\) is \(n_{i}\), the number of total pixels is denoted as \(N\), and each gray value has a probability of \(p_{i}\). The first step involves obtaining a threshold value, denoted as \(k\). Subsequently, this value is utilized to partition the gray values into two distinct groups. \(C_{0}\) represents the set of gray values in pixels that are less than or equal to \(k\), while \(C_{0}\) denotes the set of gray values in pixels greater than \(k\). The probabilities of \(C_{0}\) and \(C_{1}\) are denoted as \(m_{0},\ m_{1}\), respectively, so \(m_{0}+m_{1}=1\); the average values of \(C_{0}\) and are denoted as \(w_{0},w_{1}\), respectively; the whole image's average gray value is denoted by \(w\); and \(w(k)\) is the average gray value when the threshold is \(k\). The specific expression is shown in Equation (1).  
\[\left\{\begin{array}{l}N=\sum_{i=0}^{L-1}n_{i}\\ p_{i}=\frac{n_{i}}{N}\\ w=\sum_{i=0}^{L-1}ip_{i}\\ w(k)=\sum_{i=0}^{k}ip_{i}\end{array}\right.\quad\left\{\begin{array}{l}m_{0} =\sum_{i=0}^{k}p_{i}=m(k)\\ m_{1}=\sum_{i=k+1}^{k}p_{i}=1-m(k)\\ w_{0}=\sum_{i=0}^{k}\frac{ip_{i}}{m_{0}}=\frac{w(k)}{m(k)}\\ w_{1}=\sum_{i=k+1}^{L-1}\frac{ip_{i}}{m_{1}}=\frac{w-w(k)}{1-m(k)}\end{array}\right. \tag{1}\]  
The grayscale average of all samples is:  
\[w=m_{0}w_{0}+m_{1}w_{1} \tag{2}\]  
The between-class variance of two sets of gray values can be obtained with the following equation:  
\[\begin{array}{l}\sigma^{2}(k)=m_{0}(w_{0}-w)^{2}+m_{1}(w_{1}-w)^{2}\\ =m_{0}m_{1}(w_{1}-w_{0})^{2}\\ =\frac{[wm(k)-w(k)]^{2}}{m(k)[1-m(k)]}\end{array} \tag{3}\]  
Traversing the gray level \([0,\ L-1]\), the inter-class variance under each threshold \(k\) is calculated in turn, and the \(k\) value that can make \(\sigma^{2}(k)\) obtain the maximum value is the optimal threshold.
## 4 Simulation Analysis  
The proposed method uses the following parameters to build the network. The network is configured with 100 to 200 data points as various scenarios, five access points, five internet gateways and one streaming server. Furthermore, network terminals are built with a beam-forming MM wave antenna for transmission and receiver pockets and a multi-input-multi-output channel installed at each terminal for communication. The movement of the nodes is undertaken in random mobility mode. Each node moves at random speed from 0 to its maximum speed of 5 m in a random direction. As well as stopping for a short period after going a certain distance and then moving again known as pause time, and here pause time is mentioned as 10 milliseconds. In this mode the nodes travel across the network. Accordingly, video packets are converted to multicast routing mode. Table 5 shows the network parameters used.
## 3 Results
### CARAIB Simulations for the Future
#### 3.2.2 Changes in Soil Water Content  
Future soil water availability simulated by CARAIB varies considerably with the climate scenario (Figure 4) and RCP scenario (Figure 5). Where, with the GCM ensemble mean, the vegetation model simulates important decreases of soil water levels with respect to the reference period (Figure 5), it projects rather minor changes with the best-ranked model (CNRM-CM5, Figure 4). Under the scenario projecting the largest precipitation reduction (CSIRO-MK3.6.0), the model simulates significant soil water reduction in most areas. In case of the largest precipitation increase (CESM1-BGC model), soil water slightly increases in large parts of tropical Africa. Changes in soil water availability are most contrasted for the model projecting largest temperature increase (CanESM2). The GCM ensemble mean clearly shows the difference in soil water induced by RCP scenarios (Figure 5): when considering a rise of external forcing (RCP8.5), soil water decreases with a mean factor of 0.80, while that decrease is limited to 0.90 when considering a stabilization without overshoot (RCP4.5).  
Figure 4: Increase factor of soil water (i.e., the ratio of average available soil water amounts between the annual average over the 2080–2099 period and the reference 1980–1999 period), under the RCP8.5 scenario and a variable CO\({}_{2}\) mixing ratio (CO\({}_{2}\) [var] configuration), for (**a**) the best ranked model CNRM-CM5, (**b**) the most humid model CESM1-BGC, (**c**) the driest model CSIRO-MK3.6.0, and (**d**) the warmest model CanESM2.
## 2 Experimental Methodology  
A series of premixed bluff-body flames were investigated in this study. A schematic of the burner we used is shown in Figure 1. The burner mainly included an inlet, a bluff body, and a quartz liner. The burner exit diameter was 35 mm, and the top surface diameter of the conical bluff body was 25 mm with a half-angle of 45\({}^{\circ}\). The blockage ratio for the burner was approximately 0.51. To construct a combustion chamber, we implemented a 70 \(\times\) 70 \(\times\) 180 mm\({}^{3}\) squared liner equipped with quartz glass at four sides, which allowed for optical access (i.e., laser diagnostics). Methane, hydrogen, and air were premixed upstream in the mixing section at the bottom of the burner. Then, the mixture travelled through a venturi nozzle and two sintered metal plates. The flame was stabilized by the bluff body inside the liner. All experiments were performed at room temperature and under atmospheric pressure conditions (\(T\) = 298 K, \(P\) = 0.1 MPa).  
Four hydrogen ratios were investigated: \(Z\) = 0%, 30%, 60%, and 90%. The hydrogen ratio in fuel is defined as \(Z=\chi_{H2}/(\chi_{H2}+\chi_{CH4})\), where \(\chi_{H2}\) and \(\chi_{CH4}\) are the mole fractions of H\({}_{2}\) and CH\({}_{4}\), respectively. The mixture inlet velocity to the combustion chamber was set at 10 m/s (about 282.74 L/min), as illustrated in Figure 1. The premixed unburned mixtures of the four hydrogen ratios are abbreviated as 00H2, 30H2, 60H2, 90H2, as summarized in Table 1. The equivalence ratio was kept at 0.75 for each hydrogen ratio condition. PIV and PLIF measurements were recorded to investigate the velocity field and flame structure, as shown in Appendix A.  
Figure 1: Schematic of the bluff-body burner (all undefined units: millimeter).
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 3 Advanced Technology of Saline-Alkaline Soil Management
### Emerging Chemical Treatments
#### 3.2.1 Biochar Technology
In saline-alkaline soils, increased microbial activity accelerates the release and transformation of soil nutrients, providing more available nutrients for crops. Despite its numerous advantages, the application of biochar alone has its limitations. For instance, it cannot directly adjust the pH of saline-alkaline soils, which may limit its effectiveness in certain conditions. This reason can be due to the influence of the organic matter from which the biochar is produced. Biochar is known to be typically alkaline, and its pH level depends on the raw materials and how it is produced. When added to neutral or slightly alkaline soils, it might raise the pH too much, which may cause harm to crops by making it harder for them to absorb essential nutrients like iron and zinc, and reduce the availability of phosphorus. In the short term, especially within the first three months, biochar can cause a significant raise in soil pH. However, over the long term, biochar can continue to raise the pH because it slowly releases alkaline substances. This ongoing increase in pH can disrupt the soils microbial community, particularly by harming acid loving bacteria [83]. Therefore, future research should focus on optimizing biochar-based treatments, such as combining biochar with other amendments or developing modified biochar formulations. It is also important to explore how biochar interacts with different soil types and climates, to overcome these limitations and maximize its potential for soil improvement [91].