esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relevant Theory
### Image Decomposition Module  
Retinex theory suggests that the human visual perception of color is largely influenced by the object's reflectance properties across different wavelengths, rather than by the absolute level of incoming illumination. This effect, referred to as color constancy, indicates that an object's perceived color tends to remain consistent under varying lighting conditions. Based on this principle, the observed image \(S(x,y)\) can be mathematically decomposed as follows:  
\[S(x,y)=R(x,y)\times L(x,y) \tag{1}\]  
where \(L(x,y)\) represents the light intensity image irradiated onto the object, which determines the pixel intensity range of the final captured image; \(R(x,y)\) represents the object's intrinsic ability to reflect light, which is unaffected by external factors and directly determines the color, texture, and other visual characteristics of the final image. To enhance the quality of the original image, the low-illumination coal-rock image is decomposed into its reflectance and illumination components. However, traditional enhancement networks are inadequate for handling severely degraded images caused by the complex and harsh underground environment (Figure 2). In the proposed method, the low-light coal-rock image is first processed by an image decomposition module. The resulting reflectance map is then passed into a reflectance denoising module, which incorporates a Transformer-based structure to remove noise artifacts that may affect subsequent segmentation. Meanwhile, the illumination component is input into a multi-scale brightness-enhancement module that adaptively enhances brightness at different spatial scales, effectively preventing local overexposure resulting from uneven lighting in the original coal-rock image. Finally, the enhanced coal-rock image is reconstructed by performing element-wise multiplication between the outputs of the reflectance denoising module and the multi-scale brightness-enhancement module.
In addition, what these investigations have in common is that the samples synthesized by mimicking HBS were used for water impingement, such as the sand soil model, the pure ice model, and the cement model. The primary reason is that it is difficult to carry out water jet erosion of HBS test under high-pressure and low-temperature coexisting conditions (hydrate stable existence), and the test requires high operation capacity of the device. Compared with the experimental method, numerical simulation is a common research method which is not susceptible to the limitation of experimental conditions [21; 22; 23]. Furthermore, the stress distribution of HBS and the flow situations can be obtained through numerical simulation [22; 24], which is conducive to revealing the water jet erosion mechanism. Therefore, by using the numerical simulation method, this paper attempts to study the water jet erosion performance of HBS under different operation parameters. The high-pressure water jet erosion of HBS is a fluid-solid coupling problem and simulations of fluid-solid coupling is complicated. Fortunately, LS-DYNA software has been developed specifically for such complex and intricate analyses [25]. The Arbitrary Lagrangian-Eulerian (ALE) method is available in this software and is considered by many scholars to be an effective method for analyzing the fluid-solid coupling problem [21; 22; 23; 24; 26]. Therefore, based on LS-DYNA software, this paper adopted the ALE method to establish numerical models for the water jet erosion of HBS in the Shenhu area of the South China Sea. However, it should be noted that the material model selection for HBS is crucial to establishing the numerical models. The hydrates of the Shenhu area of the South China Sea are hosted in unconsolidated clay or sandy clay in a dispersion state and it is cemented together with marine sediments [27; 28; 29; 30]. Compared with original marine sediments, HBSs have greater cohesion and internal friction angle, as well as higher consolidating strength [31; 32; 33]. As a consequence, this paper simplified the HBS as the soil body with improved mechanical properties, and the material model of *MAT_FHWA_SOIL [34] was adopted to describe the HBS. Within this framework, based on an orthogonal experimental design of a numerical simulation, the sensitivity of work parameters (jet velocity, standoff distance, and nozzle diameter) on the water jet erosion of HBS was analyzed through range analysis. Meanwhile, for the erosion depth and erosion  
Figure 2: Schematic diagram for the trial production of marine natural gas hydrates (NGHs) through fluidization (modified from Zhou [20]). HBS: hydrate-bearing sediment. volume of HBS, the corresponding optimal work parameter combination was obtained. In addition, through the establishment of different numerical models, the influence of the work parameters on the efficiency of HBS erosion by a water jet was studied, in which the mechanism by which the water jet eroded HBS was shown through analyzing the erosion depth, erosion volume, and effective stress distribution of HBS.
## 3 Results and Discussion
### Resistivity Variation Characteristics of Frozen Clayey Sand  
#### 3.2.1 Influence of Unfrozen Water Content on the Resistivity of Clayey Sand  
The variations in electrical resistivity and unfrozen water content with soil temperature during the freezing process are shown in Figure 7. As depicted in Figure 6, with the decrease in soil temperature, water within the soil gradually freezes, the unfrozen water content decreases, and the electrical resistivity increases accordingly. There exists a strong negative correlation between soil resistivity and unfrozen water content. Previous studies have confirmed an inverse relationship between the two. The results of this study yield a fitted equation of y = 8833.14x\({}^{-1.58}\) (R\({}^{2}\) = 0.98), where x represents the unfrozen water content and y denotes the resistivity, which aligns with prior findings, thereby verifying the accuracy of the experimental data [48; 49].  
Figure 7: Relationship curves of electrical resistivity and unfrozen water content with temperature during soil freezing (initial volumetric water content = 18.5%).  
In addition, by analyzing the curves of temperature, unfrozen water content, and resistivity, collectively, it can be observed that near the freezing point, the proportion of unfrozen water in the soil is relatively high and the resistivity is low. As the temperature continues to drop, unfrozen water gradually freezes, and the resistivity increases significantly. From Figure 7, it is evident that when the resistivity ranges from 105.14 \(\Omega\cdot\)m to 914.26 \(\Omega\cdot\)m, the soil is in the progressive freezing stage; when the resistivity exceeds 914.26 \(\Omega\cdot\)m, it can be considered that the soil is completely frozen. This phenomenon can be used to monitor and assess water distribution during the freezing process and has significant practical value in the identification and distribution analysis of frozen soils [50].
## 6 Simulation Results
### Hyper-Parameter Analysis  
In this subsection, we will analyze the effect of the parameters on the proposed framework of the MDRL-UL. For comparison, we assume that there are M = 3 cells, N = 8 channels, and K = 12 users in the NOMA system.  
The impact of the batch size is demonstrated in Figure 13. We can observe that the energy efficiency and the sum rate of the NOMA system increase with the batch size. This is because, with a larger batch size, the proposed framework of the MDRL-UL can explore  
Figure 11: (**a**) the energy efficiency obtained by the proposed MDRL-UL algorithm and the single-agent baseline method under different number of channels. (**b**) the energy efficiency obtained by the proposed MDRL-UL algorithm and the single-agent baseline method in a time-varying dynamic environment.  
Figure 12: The impact of each subchannel power on system performance under unsupervised learning.  
more resource allocation schemes, which helps to discover deeper relationships and obtain a better resource allocation scheme. We can also observe that as the batch size increases, the process of convergence becomes more stable, and the upper bounds are higher.  
Figure 14 shows the effect of the learning rate, where the batch size is set to 50. When the learning rate of MDRLNN and ULNN are both set to 0.1 or 0.01, we can see that the energy efficiency and the sum rate both stabilize at a low level, indicating that the NOMA system cannot obtain a good resource allocation scheme at each time slot. When the learning rate of MDRLNN and ULNN are both set to 0.001, the results converge to a higher level, but the process is not stable enough. If the learning rate is set even lower, e.g., 0.0001, the energy efficiency and the sum rate will continue to improve, but the convergence speed will be too slow. Considering these observations, we set the learning rate of MDRLNN to 0.0008 and the learning rate of ULNN to 0.0001. It can be seen that both the energy efficiency and the sum rate have converged to a higher level, and the testing process is more stable.  
Figure 14: (**a**) Convergence of the energy efficiency of the MDRL-UL framework with different learning rate. (**b**) Convergence of the sum rate of the proposed MDRL-UL framework with different learning rate.  
Figure 13: (**a**) Convergence of the energy efficiency of the proposed MDRL-UL framework under different batch sizes. (**b**) Convergence of the sum rate of the proposed MDRL-UL framework under different batch sizes.
# Fast-RRT: A RRT-Based Optimal Path Finding Method
## 3 Method
### Improved RRT
#### 3.2.2 Random Steering  
In addition to Fast-Sampling, the Random Steering strategy was also proposed to solve the problem of poor performance of the RRT algorithm in narrow channel scenarios. The inferior performance of the RRT algorithm can be attributed to the invalid expansion introduced in Section 3.2.1, and a large number of failed expansions caused by the narrow  
Figure 3: Schematic illustration of the Fast-Sampling sampling strategy. The red circle is the explored area. Since the first sampling point \(x_{rand1}\) falls in the explored area, it is regarded as an invalid sampling. After re-sampling, the second sampling point \(x_{rand2}\) is obtained. \(x_{rand2}\) bootstrap tree expansion to obtain \(x_{new}\), thus avoiding invalid expansion.  
Figure 2: Schematic illustration of the invalid expansion caused by RRT’s random sampling strategy. \(x_{rand}\) is the randomly sampled point, \(x_{rand}\) is the nearest neighbor of \(x_{rand}\), and \(x_{new}\) is the expansion point. Unfortunately, this step of expansion is invalid because the random tree is not brought close to the target point or expanded to a new area.  
passages are shown in Figure 4. In order to pass through the narrow passages, the tree \(T\) should expand toward the right state space. While the expansion guided by \(x_{rand}\) collides with an obstacle, resulting in the failed expansions.  
To avoid this problem, we adjusted the expansion algorithm and named it as Fast-Expand. As shown in Figure 5, if the expansion guided by \(x_{rand1}\) fails, Fast-Expand will randomly generate a direction \(\theta\) for expansion. If edge \((x_{near},x_{new1})\) is obstacle-free, then they will be added to the tree; otherwise, another random state will be sampled by Fast-RRT. Fast-Expand enables the random tree of the Fast-RRT algorithm to pass through the narrow passage quickly. The details of the Fast-Expand algorithm are shown in Algorithm 6.  
Figure 4: Schematic illustration of invalid expansions in narrow passages. \(x_{rand1}\) and \(x_{rand2}\) are two randomly sampling points. Since the random tree can only pass through a narrow passage, the expansions guided by \(x_{rand1}\) and \(x_{rand2}\) both collided with obstacles.  
Figure 5: Schematic illustration of random expansion strategy. When the tree \(T\) expands towards \(x_{rand1}\), it randomly samples a direction \(\theta\) to expand because it collides with an obstacle. In this way, the Fast-Expand samples another random tree and provides a successful pass through the narrow channel after getting \(x_{rand2}\) in the next sampling.
# High Imperceptible Data Hiding Method Based on Combination Theory for AMBTC Compressed Images
## 1 Introduction
However, these methods are not applicable to complex images because they cannot embed confidential messages in a complex image. Embedding in the complex block distorts the image and results in low imperceptible performance for the data-hiding method. Based on the concerns above, this research proposes an innovative data-hiding scheme for AMBTC-compressed images based on combination theory. The paper's main contributions are highlighted in the following:  
1. Non-block classification. In the proposed scheme, all blocks are embeddable. The threshold value is not established because there is no specification for a smooth and complex block. 2. High embedding capacity in the complex block. By utilizing the concept of combination theory, the proposed scheme can embed at least four secret bits resulting in higher embedding capacity in the complex block compared with the related works. 3. High imperceptible AMBTC data hiding. By applying the concept of combination theory, the proposed scheme only needs to change 1 bit of pixel to embed 4 secret bits. Hence, it has advantages for complex images. The remainder of this work is structured as follows. Section 2 provides an overview of the related study. Our proposed data-hiding method is described in Section 3. Section 4 contains the findings of the experiments. Section 5 finally presents the conclusions.
# Design of a CW BISOL RFQ for Three Kinds of High-Charge-State Ions Simultaneous Acceleration
## 2 Beam Dynamics Design
### Errors Study  
An RFQ has various errors, including alignment errors and installation errors induced by the machine, inter-vane voltage errors, and dipole components caused by the field tuning. The electrode length of the RFQ is 5.7 m, the RFQ needs to be divided into five segments during the machining. It is necessary to study the effect of the alignment error (\(X\) and \(Y\)-directions) between each segment of the RFQ on the transmission efficiency. Figure 5a shows the comparison of the electrodes' envelope with no error and with a deviation of 100 \(\upmu\)m. Toutatis software [17] can be used to simulate the influence of alignment error on beam transmission, and the simulation results are shown in Figure 5b. When the alignment error is 100 \(\upmu\)m, the transmission efficiency of RFQ is still greater than 95%.  
Figure 4: Transmission efficiency of the RFQ versus (**a**) Beam offset, (**b**) Beam tilt, (**c**) Beam current (\({}^{32}\)Sn\({}^{22+}\)), and (**d**) Input Nor. RMS emittance.  
Figure 5: Vane alignment error. (**a**) Electrodes’ envelope, (**b**) Simulation resultsThere are two types of inter-vane voltage errors in our study. The first one is the tilt of the voltage, as shown in Figure 6a. A negative tilt means a higher value of voltage than the design value at the beginning of RFQ. As shown in Figure 6b, there is no significant reduction in the transmission efficiency at +5% voltage tilt; a positive tilt will increase the beam losses because focusing on strength is not enough, so the transmission efficiency will gradually decrease with the increase in positive voltage tilt percentage, but the power of the beam loss is related to the number of lost particles and its energy at the time of loss, so it is not simply a gradual increase in the process. The second perturbation is the residual voltage error, as shown in Figure 7a, the magnitude of which is generated by introducing higher order harmonics, as shown in Equation (2)  
\[V(z)=V_{0}(z)+\text{sgn}(n)\cdot\Delta V\cdot\cos(\frac{n\pi}{2})\cdot\frac{z} {L_{\text{rfq}}} \tag{2}\]  
where \(n\) is the harmonic number (\(n\) is an integer), \(V_{0}\) is the nominal inter-vane voltage, and \(\Delta V\) is the amplitude of residual voltage error, which is set to 3% \(V_{0}\). \(L_{\text{rfq}}\) is the RFQ length. Figure 7b shows that the different voltage harmonics have no obvious influence on beam transmission efficiency.  
The asymmetry of the four quadrants of the RFQ will result in dipole field components. Figure 8 shows the influence of the dipole components on the beam transmission efficiency, which should be controlled to be less than 9% in order to avoid a significant increase in beam loss.  
Figure 6: Inner-vane voltage tilt error. (**a**) Inner-vane voltage tilt, (**b**) Simulation results.  
Figure 7: Inner-vane voltage harmonics error. (**a**) Inner-vane voltage harmonics, (**b**) Simulation results.
# Accounting for Energy Cost When Designing Energy-Efficient Wireless Access Networks
## 3 Results and Discussion
### Results: Strategy 1, Simple Algorithms  
The results obtained with the cost-reducing actions of Strategy 1 (Section 2.1) are shown in Figure 8. Concerning _Buy in advance_, the interval I is set to 23 h. For _Buy and sell_, the energy is sold at the half market price. The remaining parameters are set as previously mentioned. Figure 7: Flowchart when using the _Only Microcell BSs: No Action_ or _Only Microcell BSs: Deactivation of some BSs_ algorithm. Note that the _Buy in advance_ algorithm provides considerable reduction in terms of total energy cost. The scenario in which _Undertake no action_ is used presents the largest reduction (until approximately 30%, Figure 8). In this case, the storage is on average 52% and 65% less full than for _Deactivate as much microcell BSs as needed_ and _Deactivate all microcell BSs_, respectively. Therefore, the algorithm can be more effective since a larger amount of energy can be bought in advance, if the energy is less expensive. This explains also the difference concerning the reduction of cost when the two different energy-reducing actions are used, which provide 27.7% and 26% cost reductions, with _Deactivate as much microcell BSs as needed_ and _Deactivate all microcell BSs_, respectively. With respect to _Buy and sell_, reductions are not large (Figure 8) and not always achieved: the cost increases by 1.5%, when _Undertake no action_ is used. A 3% and less than 0.7% reduction is obtained with _Deactivate all microcell BSs_ and _Deactivate as much microcell BSs as needed_, respectively. These results are summarized in Table 2.  
Drops are limited since the amount of money spent to buy the necessary energy is greater (Figure 9a) than when the energy is not sold. This is due to the fact that the amount of sold energy is actually needed for the base stations' supply. For this reason, it is bought back, often at a higher price than the one at which it has been sold. The amount of money earned thanks to the energy sale (Figure 9b) contributes to the reduction of the total outgoing. The gross income is larger when an energy-reducing action is applied since, in those cases, the network is more often out of the critical situation, and so, it can sell electricity. In case no energy-reducing strategy is used, the sold energy is not enough to make the total outgo lower than the reference scenario. Figure 8: Total outgo obtained using energy- and cost-reducing actions of Strategy 1.
## 5 Conclusions  
Through a series of formula derivation, algorithm implementation, numerical verification, and comparison with three published research results, this study can draw the following conclusions:  
1. The direct calculation method (DCM) is used to study the theoretical explanation and numerical analysis of the support-ground interaction caused by a circular tunnel excavation in the isotropic stress field.
2. The DCM is proposed to provide a special algorithmic process to solve the support-ground interaction solution at the equilibrium state. The roots are obtained by applying the method of simultaneous equations in the elastic region and Newton's recurrence method in the plastic region. DCM investigated the solution for the interaction between GRC and SCC, namely the mobilized support pressure and the radial displacement.
3. To resolve the theoretical analysis into an executable numerical program, a simple spreadsheet of calculations is used to realize the application of DCM.
4. The confinement loss as the incremental factor defining the situation of tunnel advancing excavation is classified by two cases (Case I and II) and proposed to distinguish whether the stress state is in the plastic or the elastic regions.
5. Good validation results are obtained between FEM and DCM for tunnel excavation simulations under support or no support conditions.
6. Three research results, [PERSON] (2004) [36], [PERSON] (2009) [6], and [PERSON] (2012) [23], are used to compare with that obtained by DCM. In the no support condition, the consistency of the results is also very good. In the support condition,  
Figure 7: Percentage error of results at the equilibrium state between DCM and other studies under the support condition. (**a**) the plastic zone radius (\(R_{p}^{s}\)), (**b**) the mobilized support pressure (\(p_{s}\)), and (**c**) the radial displacement (\(u_{R}^{s}\) ).  
the maximum percentage error is 10%, 6%, and 4% for the mobilized support pressure, the radial displacement, and the plastic zone radius, respectively.  
Y.-L.L.: analysis, validation, and main editing and reviewing; W.-K.H. and P.-Y.C.: formula derivation and verification; P.-W.H., C.-H.M. and W.-C.K.: software programming and computation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest. The sponsors had no role in the design, execution, interpretation, or writing of the study.