esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 2 Mega-Constellation Design Model
### Prerequisite Conditions for the Model  
Before going into details, some prerequisite conditions need to be clarified to ensure the rationality of the model. The specific content is as follows:  
1. In general, the relative distances of two satellites with different orbital altitudes vary continuously with time, which will eventually lead to the two satellites not satisfying the geometric visibility condition. In other words, a stable ISL cannot be established between satellites with different orbital altitudes. Therefore, to establish durable and stable ISLs within the mega-constellation system, all satellites are required to be at the same orbital altitude.
2. For LEO satellites, the non-spherical gravitational perturbation and atmospheric drag perturbation will have a noticeable impact on satellite orbit. In particular, the drift of the ascending node caused by the \(J_{2}\) perturbation is significant. In the long run, the constant change in the relative drift of the ascending nodes between satellites with different inclinations will prevent the constellation from operating in an orderly manner according to the initial configuration. Though the effects of perturbation can be circumvented by placing satellites with different orbital inclinations at different orbital altitudes, the different orbital altitudes can bring trouble to the establishment of ISLs, as illustrated in (1). For heterogeneous constellations with satellites of the same orbital altitude and different orbital inclinations, the long-term impact of perturbation on the constellation configuration cannot be solved by the constellation configuration design. Therefore, this paper focuses on the configuration design of the mega-constellations with a two-body orbit model. The long-term maintenance of the constellation configuration will be studied in depth in the subsequent work.
## 2 Materials and Methods
### Film Characterization  
A Bruker FastScan-Bio(tm) AFM (Bruker, Billerica, MA, USA) was used to characterize the morphology of the deposited layers and measure film thicknesses. The thickness of the MEH-PPV layers was also measured using a profilometer (Veeco DEKTAK 150, Veeco Instruments, Oyster Bay, NY, USA). HR-TEM (Jeol-Jem 2100, Jeol, Akishima City, Japan) images were acquired on cross-sections of the film stack, which were prepared by focused ion beam milling. The TEM was also used to perform position-dependent Energy Dispersive Spectroscopy (EDS) to investigate the extent of layer intermixing. Optical transmission measurements were obtained using a UV-Vis spectrophotometer (Cary 100 Bio UV-Vis, Agilent, Santa Clara, CA, USA).
## 4 Results and Discussion
### Overpressure Distribution Characteristics
#### 4.1.2 Pressure Prediction Results  
Measured pressure values provide only discrete data points; in contrast, logging-based methods can yield continuous pressure profiles [36, 37, 38, 39]. Therefore, this study employed the equivalent depth method, the Eaton method, and the Bowers method to calculate the pressure of the Qiongzhusi Formation. To obtain continuous formation pressure profiles, we refined the logging prediction models using measured values and assessed the accuracy of these models. The results are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods/Wells**} & \multicolumn{2}{c}{**Equivalent Depth Method**} & \multicolumn{2}{c}{**Eaton Method**} & \multicolumn{4}{c}{**Bowers Method**} \\ \cline{2-9}  & **Error Rate/\%** & \(C\) & **Error Rate/\%** & \(A\) & \(B\) & \(U\) & **Error Rate/\%** \\ \hline ZJ2 & 5.33 & 6.85 & 1.84 & 0.122 & 0.723 & 1.19 & 2.99 \\ ZZ01 & 5.66 & 3.48 & 2.9 & 0.025 & 1.142 & 1.09 & 5.4 \\ GS17 & 6.18 & 4.33 & 2.65 & 0.31 & 0.55 & 1.04 & 2.89 \\ DS1 & 5.54 & 7.7 & 4.77 & 0.26 & 0.963 & 1.22 & 6.31 \\ WY1H & 9.38 & 2.91 & 2.8 & 0.93 & 0.29 & 0.58 & 4.7 \\ MX9 & 6.23 & 4.71 & 0.18 & 1.39 & 0.22 & 1.21 & 0.07 \\ W207 & 4.63 & 2.13 & 2.94 & 2.17 & 0.17 & 0.98 & 3.05 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predicting the parameters and error rate of the pressure model for the Qiongzhusi Formation.  
Figure 2: Characteristics of Pressure in the Qiongzhusi Formation: (**a**) Pressure vs. depth; (**b**) Pressure coefficient vs. depth.  
According to Table 1, the error rate of the equivalent depth prediction for the pore pressure of the Qiongzhusi Formation ranges from 4.63% to 9.38% (average = 6.14%). The error rate for the Eaton method model prediction is between 0.18% and 4.77% (average = 2.58%). The error rate for the Bowers method prediction ranges from 0.07% to 6.31% (average = 3.63%). Moreover, the prediction accuracy for well MX9 is higher than that of the Eaton method. This indicates that the Eaton method is the most effective approach for predicting the pore pressure of the Qiongzhusi Formation.  
Based on the [PERSON] and Bowers methods, the pressure distribution characteristics of 8 drilling wells in the study area were predicted. The results indicated that the formation pressure of the Qiongzhusi Formation exhibited a pattern of \"increase-decrease-increase-decrease-increase-decrease-increase-increase-decrease-increase-decrease-increase-decrease-decrease-increase-decrease-decrease-increase-decrease-decrease-increase-decrease-decrease-increase
## 2 New EMI Shielding Materials: Biochars, Scaffolds, Rare Earth, and Ferrite-Based Materials
### Scaffolds as EMI Shielding Material
Due to their exceptional three-dimensional construction and inner network, they show anisotropic thermal conductivity and allow heat dissipation, making them efficient EMI shielding materials [56; 52]. The results from the literature based on biochar and scaffolds in shielding applications are listed in Table 1. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Material** & **Reinforcement and** & **EMI Shielding** & & \\
**Matrix Thickness** & **Effectiveness** & & & \\ \hline Bamboo charcoal/HMWPE/LLDPE composite & 3 mm, 140 mm & 48.7 dB & 1500 MHz & [39] \\ Pine chip/PLA composite & 0.25 mm & \textgreater{}32 dB & 18–26.5 GHz & [4] \\ MXene aerogel/WPC composite & 3 mm & 71.3 dB & 8.5–12.5 GHz & [1] \\  & & \(11.65\pm 16\) dB, 19.2 \(\pm\) 5.7 & & \\ Gypsum–biochar drywall-like composite & \textgreater{}2 mm & dB, and \(19.25\pm 18.8\) dB for & & \\  & & 10, 20, and 40\% \(w/w\) & & \\  & & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 1: Summarized results for biochar and scaffolds as EMI shielding materials.
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 2 Virtual Power Plant (VPP)
### Distributed-Energy Resources (DERs)  
One of the most significant changes in electricity systems worldwide has been the rapid expansion of DERs, which provide an energy-generation solution that has great potential in the global energy sector, and are directly connected to local distribution systems or to aggregated VPPs within those systems, and have the potential to offer substantial benefits to a power system.  
DERs may include electric vehicles (EVs), wind energy, biomass and biogas, photovoltaic production, prosumers, energy storage, fuel cell, flexible consumption (controllable/dispatched loads), and small plants (gas turbines, diesel, etc.), among others. Discussions on how to better integrate and manage these features are under intense development and this should contribute to an increase in the potential benefits gained from adding more DERs.
# Computational Acceleration of Topology Optimization Using Deep Learning
###### Abstract  
Topology optimization is a computationally expensive process, especially when complicated designs are studied, and this is mainly due to its finite element analysis and iterative solvers incorporated into the algorithm. In the current work, we investigated the application of deep learning methods to computationally accelerate topology optimization. We tested and comparatively analyzed three types of improved neural network models using three different structured datasets and achieved satisfactory results that allowed for the generation of topology optimized structures in 2D and 3D domains. The results of the studies show that the improved Res-U-Net and U-Net are reliable and effective methods among deep learning approaches for the computational acceleration of topology optimization problems. Moreover, based on the results, it is evaluated that Res-U-Net gives better results than U-Net for higher iterations. We also showed that the proposed CNN method is highly accurate and required much less training time compared to existing methods.  
topology optimization; deep learning; convolutional neural network; Res-U-Net; U-Net +
Footnote †: journal: Applied sciences
# Per-Pixel Manifold-Based Color Calibration Technique
###### Abstract  
In this paper, we present a method for obtaining a manifold color correction transform for multiview images. The method can be applied in various scenarios, for correcting the colors of stitched images, adjusting the colors of images obtained in different lighting conditions, and performing virtual view synthesis based on images taken by different cameras or in different conditions. The provided derivation allows us to use the method to correct regular RGB images. The provided solution is specified as a transform matrix that provides the pixel-specific color transformation for each pixel and therefore is more general than the methods described in the literature, which only provide the transformed images without explicitly providing the transform. By providing the transform for each pixel separately, we can introduce a smoothness constraint based on the transformation similarity for neighboring pixels, a feature that is not present in the available literature.  
color correction; multiview; multi-camera; virtual views Article
## 1 Introduction
* Joint Optimization of SUL Admission, TDD Pattern, and Band Slicing Scheme: In the practical implementation of hard radio access network (RAN) slicing, each BS needs to switch the downlink/uplink transmission of all the RAN slices simultaneously, according to different TDD patterns [21]. With SUL-enabled transmission, the admission results of the SUL band will directly affect the TDD pattern and the band slicing scheme. To solve this issue, we formulate a joint optimization problem of SUL admission, TDD pattern, and band slicing scheme, and propose a well-designed DRL-based solution. The activation function of the output layer is well-designed to accommodate the three optimization aspects mentioned above. * Load Balance of Slices for further QoS Guarantee:Traditional load balancing mainly focuses on different nodes of the core network or multiple RANs [22; 23; 24]. Rapidly fluctuating traffic requires a solution with a margin for each slice-level service enabled with SUL. We introduce the slice-level load balancing mechanism into the above DRL-based solution for further QoS guaranteed in the face of fluctuating traffic demands. The rest of this paper is organized as follows. Section 2 introduces the system model and the corresponding wireless resource allocation. In Section 3, we formulate an optimization problem regarding QoS satisfaction rate maximization, as well as load balancing. In Section 4, we transform the original optimization problem into a Markov Decision Process (MDP) and propose a DRL-based solution of the Deep Deterministic Policy Gradient (DDPG) algorithm. In Section 5, we compare the proposed framework with the baselines and analyze the performance, followed by the concluding remarks in Section 6.
## 2 Materials and Methods
### Infrastructure and Functional Components
#### 2.3.3 Life Cycle Assessment  
LCA provides information to decision makers by giving insights into the potential environmental impact of products from raw material extraction or manufacture to end-of-life disposal. LCA, therefore, supports decision making by exposing trade-offs, illuminating system interactions, and showing impact over the whole product life cycle and not a specific phase.  
LCA has been standardized by the International Organization for Standardization (ISO), and it is part of a family of concepts and tools that are being used by organizations to better understand, manage, and reduce the impacts of materials, products, and services across their life cycle.  
The ISO standards 14,040 and 14,044 (International Organization for Standardization, 2006) define LCA as follows:  
\"LCA is the compilation and evaluation of the inputs, outputs, and the potential environmental impacts of a product system throughout its life cycle.\" [38]  
There are three primary aspects or dimensions to a life cycle study:  
1. Consideration of the life cycle stages for the product system in question: The life cycle is the physical sequence of operations in a product system. The primary stages are material acquisition and processing, product manufacturing, use, and end-of-life disposal/recycling.
2. Consideration of multiple environmental and resource issues across the life cycle stages: LCA studies trade-offs by analyzing significant inputs from the earth, the flow of these materials and energy carriers through the system, and outputs to the environment, across the various life cycle stages.
3. Assessment or interpretation of the significance of the results about the goal and scope of the study: The intended application of the study is critical for interpreting the results. For example, comparing the carbon footprint of one material to another requires more rigorous analysis and review than a study aimed at identifying the environmental and resource use _hotspots_ in a system.
## 3 Method
### Multi-Scale Channel Attention Gate  
One of the key reasons for the excellent performance of U-Net in medical segmentation tasks is the skip connection operation, which concatenates low-level features with high-level features along the channel dimension. This fusion assists in feature recovery by incorporating essential low-level spatial information. However, since channels in the feature maps contain redundant and diverse feature information, there is a desire to emphasize the information most crucial for feature recovery. To address this concern, the  
Figure 2: (a) Depthwise separable convolution. (b) Residual depthwise separable convolution.  
SE (Squeeze-and-Excitation) attention mechanism was introduced as a solution [44]. The structure of the SE attention mechanism is depicted in Figure 11 (Appendix A).  
The input features \(\mathrm{X}\in\mathrm{R}^{\mathrm{H}\times\mathrm{W}\times\mathrm{C}}\) are first turned into features of size \(1\times 1\times\mathrm{C}\) after global average pooling, and then the number of feature channels is reduced to \(1/\mathrm{r}\) through a fully connected layer \(F_{\mathrm{c1}}\). Then, after function ReLU activation, the fully connected layer \(F_{\mathrm{C2}}\) is used to recover the feature channels, the attention coefficients are normalized through the sigmoid layer, and finally, the attention weights are multiplied by the input features \(\mathrm{X}\) to obtain the attention-weighted feature map \(\mathrm{X}\prime\). The SE attention calculation process is shown in the following equation:  
\[\mathrm{X}\prime=X\otimes\mathit{Sigmoid}(F_{\mathrm{C2}}(\mathrm{ReLU}(F_{ \mathrm{c1}}(\mathit{AvgPooling}(\mathrm{X}))))) \tag{3}\]  
Nevertheless, breast tumors exhibit varying sizes and irregular shapes, and simply enhancing attention in the channel dimension may not suffice. To address this, we have designed the MCAG (Multi-scale Channel Attention Gate) module, the structure of which is depicted in Figure 3.  
Our approach starts by conducting a multi-scale fusion of breast tumor features across various spatial locations. Subsequently, we obtain multi-scale coefficients \(\lambda\) after normalization and applying an activation function. These coefficients are then multiplied with the initial features, followed by a residual operation. This process enables the initial features to learn the spatial scale information that is most relevant for the task. Finally, we filter the valuable information within the channel dimension, further enhancing the segmentation network's adaptability to irregular breast tumor lesions. The calculation process for the MCAG module is outlined in Equations (4) and (5).  
\[\lambda=\mathit{Sigmoid}(PConv(\sigma_{1}(concat\{BN\{(DConv(X_{\mathrm{i}})),(PConv(X_{\mathrm{i}})),(SConv(X_{\mathrm{i}}))\}\})) \tag{4}\]  
\[X_{i}^{\prime}=SE(X_{i}+\lambda X_{i}) \tag{5}\]
## 3 Construction of Fault Diagnosis Model
### Fault Feature Extraction Method  
In VMD, the penalty factor \(\alpha\) serves as a crucial parameter that influences the bandwidth of each IMF. Vibration signals from rolling bearings generally encompass both harmonic and pulse components. Harmonic signals necessitate a larger \(\alpha\) due to their narrow frequency bandwidth, whereas fault pulse signals require a smaller \(\alpha\) to adequately capture their broader bandwidth. Conventional methods are often inadequate in simultaneously extracting both harmonic and fault pulse characteristics effectively.  
To address this issue, the IGTO is utilized, with the minimization of envelope entropy serving as the optimization objective to ascertain the optimal values of \(\alpha\) and K for VMD. Following the decomposition, the PE value of each IMF is computed to characterize the fault frequency features of the vibration signal. The process of fault feature extraction using IGTO-VMD-PE is depicted in the accompanying Figure 4.
# Advances in Three Dimensional Bioprinting for Wound Healing: A Comprehensive Review
## 4 3D Bioprinting Techniques
### Extrusion-Based Bioprinting
On the seventh day, the wound treated with CP hydrogel was sealed. After 14 days, the CP group was almost fully healed. Ultimately, the wound healing assay showed CP hydrogels' ability to heal 5 times faster than the control group, which promotes tissue growth, revascularization, and collagen accumulation [181]. The other study conducted with a 1% t-ZnO-VEGF GelMA hydrogel-based dressing caused a significant decrease in the wound by about 24.90% \(\pm\) 4.21% after one week, and this reached 3.48% \(\pm\) 6.03% at the end of two weeks. Additionally, the wound treated with this dressing provided much higher epidermal thickness, with 101.2 \(\upmu\)m \(\pm\) 12.3 \(\upmu\)m, compared to the untreated wound's thickness of 19.7 \(\upmu\)m \(\pm\) 4.1 \(\upmu\)m. GelMA hydrogels involving t-ZnO microparticles adequately decreased bacterial concentrations, and promoted the re-growth of human umbilical vein endothelial cells (HUVEC) and C2C12 myoblasts. The VEGF produced optimal microenvironments on t-ZnO for enhancing endothelial cell growth while retaining antibacterial activity. In vivo tests were performed on mice using non-infected wound models and IHC staining methods. Finally, the printed scaffold containing t-ZnO-VEGF exhibited adequate biological stability and improved angiogenesis, cell proliferation, and anti-inflammatory responses at the wound site, leading to rapid wound healing [182]. The optimization of structures constructed in a 3D bioprinting system complicates the production of traditional approaches due to the variety in bioink type and the geometric complexity of the scaffolds. To address these shortcomings, an artificial intelligence-assisted high-throughput print condition scanning system (AI-HTPCSS) was introduced. It included a programmable pneumatic extrusion bioprinter and an artificial intelligence-assisted image analysis algorithm. The printing conditions were screened efficiently using AI-HTPCSS to produce uniformly organized hydrogel structures. The results reveal that the scaffolds printed under optimal conditions have adequate mechanical properties in vitro and are effective in accelerating diabetic wound healing in vivo. The authors envisage that the unique AI-HTPCSS will provide a platform technology for the future modernization of tissue engineering scaffold manufacturing using 3D bioprinting techniques [183].
# Mechanical Performance of Confined Autoclaved Fly-Ash-Brick Masonry Walls under Cyclic Loading
###### Abstract  
In order to solve the limitations of masonry structures, such as poor seismic performance, complicated construction techniques, and energy wastage of wall materials, a new type of confined autoclaved fly-ash-brick wall was proposed and its mechanical performance was analyzed. An axial compression test of autoclaved fly-ash-brick short columns was carried out to analyze the failure mode and obtain the constitutive parameters of the brick. Meanwhile, a low-cyclic loading test of an assembly using an autoclaved fly-ash-brick wall was carried out to prove the correctness of the numerical model. Under multiple influencing parameters, the seismic performance of the assembly of autoclaved fly-ash-brick walls was analyzed by a numerical-simulation method. The results show that (1) the seismic performance of the assembled autoclaved fly-ash-brick walls is better than that of cast-in-place masonry walls; (2) low-strength mortar leads to premature cracking, which is unfavorable to earthquake resistance of the walls; and (3) the bearing capacity of the wall is increased and then decreased with the increase of the vertical compressive stress, so the number of layers of brick masonry structural should be limited. In addition, some construction measures were proposed to improve the mechanical performance of assembled autoclaved fly-ash-brick walls.  
fly-ash-brick; axial compression test; low-cyclic loading test; numerical simulation; mechanical performance
# Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples
## 2 Materials and Methods
### Superpixel-Based Processing Framework
#### 2.3.2 Supervised Classification  
The last stage of the superpixel-based processing framework is composed by a supervised classifier employed to perform the classification of each superpixel centroid. Support Vector Machines (SVMs) were employed to perform the experiments since it has been demonstrated in the literature this algorithm performs well with imbalanced datasets [25]. SVMs are supervised classification algorithms widely employed for classification of HS data since they have shown good performance with high dimensional data, even with a limited number of training samples [25]. [PERSON] proposed the binary classification approach of the SVM algorithm in 1979 [26]. This algorithm tries to separate two classes (\(y_{i}\in\{-1,\ 1\}\)) based on the use of a training set composed by \(N\) data samples from the d-dimensional feature space (\(x_{i}\in\mathbb{R},\ \ \{i=1,2,\ldots,\ N\}\)). The algorithm finds the optimal hyperplane that maximizes the margin defined by a weight vector \(w\in\mathbb{R}\) and a bias \(b\in\mathbb{R}\). The prediction of the class of a new data sample is given by equation (4), where \(\hat{y}\) is the predicted class for the data sample \(x_{i}\). The SVM classifier can be used for data which is not linearly separable by using different kernels to map the data in higher dimensional space. In this work, we selected the linear kernel for the SVM approach, employing two different evaluation metrics to optimize the hyperparameter cost (C). This hyperparameter is the constant of constraint violation, which is in charge of deciding if a data sample is classified on the wrong side of the decision limit [27]. MATLAB(r) (R2019b, The MathWorks Inc., Natick, MA, USA) was employed to perform the experiments and LIBSVM (Library for Support Vector Machines) was used for the classifier implementation [28].  
\[\hat{y}=x_{i}\cdot w+b \tag{4}\]