esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Coherent Noises Associated with the Impact of Medium Inhomogeneities during Hologram Recording
#### 4.2.1 Averaging over Several Digital Holograms  
The averaging over several digital holograms was performed by recording the holograms of stationary model particles (Figure 1) placed in water containing a suspension of smaller scattering particles--hydrosol or small-scale turbulence. In total, 70 holograms were recorded at different time intervals, with further averaging. The obtained results (Figure 15) showed that starting from the averaging of three digital holograms, the boundary intensity jump \(\Delta\)I of the reconstructed images of model particles does not change within the error limit and exceeds the threshold value of the criteria for acceptable quality of particle images [29] (\(\Delta\)I = 0.25 in Figure 15). For boundary contrast \(K\), such a condition [29] is already fulfilled during the averaging of two digital holograms (\(K\) = 2 in Figure 15). The averaging of three digital holograms makes it possible to improve the quality of reconstructed images of model particles compared to reconstructed images obtained without hologram averaging: \(K\) increased by 1.6 times and \(\Delta\)I increased by 1.7 times. These estimates are consistent with the theoretical estimate of the coherent noise reduction by \(\sqrt{N}\) times [38; 40; 41], where \(N\) is the number of statistically averaged independent digital holograms.  
Figure 15: Dependence of boundary contrast K and boundary intensity jump \(\Delta\)I of the reconstructed image of model particles on the number of averaged digital holograms.  
The results of the study of the dependence of quality indicators on the number of averaged images of particles reconstructed from digital holograms also showed that the averaging over three digital holograms is sufficient to achieve the given quality indicators of the particle image boundary. Such averaging is possible for a stationary object and can be used to form a reference \"zero\" frame, which is used to eliminate photometric inhomogeneities in the reconstructed holographic image.  
In a real dynamic situation, a multispectral source with a laser diode fiber multiplexer can be used for the same purpose. We conducted an experiment to record three digital holograms of a model particle at wavelengths of 668.7 nm, 640.9 nm, and 660.2 nm.  
The averaging was performed over two or three reconstructed images of the model particle. Figure 16 shows the dependencies of quality indicators on the number of averaged images of a model object. As in the previous case, the averaging of three digital holograms improves the quality of the reconstructed holographic image of a particle by at least 1.7 times in terms of the distinctive ability and the boundary intensity jump.  
Moreover, in the considered case of digital holography, this averaging can be performed at the stage of hologram reconstruction by modeling a polychromatic source, which makes the technical implementation much easier.
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 5 Conclusions  
A buffer aided multi-UAV relaying network with joint trajectory and communication design was studied in this paper. Specifically, based on the information causality, aiming at maximizing the average throughput of the network, the relay selection, UAV transmit power, and UAV trajectory were jointly optimized. Since the maximization problem was a mixed integer non-convex problem, the problem was re-described and divided into iterations of the two sub-problems through the block  
Figure 4: Average throughput versus flight period T.  
Figure 5: Convergence behavior of Algorithm 1.  
coordinate descent and the successive convex optimization techniques. Finally, the convergence of the proposed algorithm was testified analytically. Numerical results demonstrated that Algorithm 1 could significantly improve the average throughput of the considered network. Furthermore, in this paper, we considered the transition of buffer states, and the proposed algorithm can avoid the collision of UAVs in multi-UAV relaying networks.  
[PERSON] and [PERSON] conceived of the model; [PERSON] performed the simulation results and wrote the paper; and [PERSON], [PERSON] provided some suggestions and revised the paper.  
This work is supported by the National Natural Science Foundations of China under Grant 61771487 and Grant 61371122, and the Research Program of the National University of Defense Technology under Grant No.ZS18-02-04.  
The authors declare no conflict of interest.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 3 Method
### Spatially Correlative Loss
Two different forms of \(d(\cdot)\) are considered: the \(L1\) distance \(\parallel\ S_{x}-S_{\hat{y}}\parallel_{1}\) and the cosine distance \(\parallel\ 1-cos(S_{x},S_{\hat{y}})\parallel\). The \(L1\) distance promotes the spatial similarity across all points within a patch that is uniform, emphasizing the similarity in magnitude. Meanwhile, the cosine distance supports pattern correlation, irrespective of magnitude differences, focusing more on the overall pattern rather than specific magnitudes. In the generator, the spatially correlative loss is consistently defined by the loss in Equation (9). By incorporating spatially correlative loss, we can constrain the reconstruction outcomes by calculating the correlation between features. This method emphasizes maintaining the structural consistency and capturing fine details of the image, thereby improving the accuracy and realism of the reconstructed image.
## 3 Analysis of the Penetration Test Results
### Analysis of the Influence of Confining Pressure on the Permeability Coefficient, \(k_{v}\)  
#### 3.3.1 Direct Loading to Constant Confining Pressure  
The fitting curve between the \(k_{v}\) of low- and high-confined water and the \(\sigma_{r}\), after the clayey sand was stabilized under different \(\sigma_{r}\) values and the same osmotic pressure differences, is shown in Figure 11. From the diagram analysis, it can be seen that the \(k_{v}\) of clayey sand decreased nonlinearly with an increasing \(\sigma_{r}\), regardless of whether in a low- or high-confined water state. When the \(\sigma_{r}\) was 1 MPa, the \(k_{v}\) was obviously greater than that under a high \(\sigma_{r}\). According to preliminary analysis, this phenomenon is related to the structural characteristics of the internal permeability pores of the sample, and this phenomenon will be analyzed and discussed by using the low-field NMR testing system later, so it will not be repeated here.  
Figure 10: Curves of the high-confined water permeability coefficient with time under different \(\sigma_{r}\) values.  
With an increase in the \(\sigma_{r}\), the pore structure in the sample changed, and the seepage pore gradually narrowed and closed, leading to a decrease in its permeability coefficient. In order to show the nonlinear decreasing relationship of the permeability coefficient of the clayey sand sample with an increasing \(\sigma_{r}\), the power function was used to fit the test results:  
\[k_{v}=k_{0}\sigma_{r}{{}^{-\alpha}} \tag{1}\]  
where \(k_{0}\) is the initial permeability coefficient, \(\alpha\) is the coefficient, and \(\sigma_{r}\) is the confining pressure.  
According to the test data and Formula (1), the fitting curves and equations of the relationship between the \(\sigma_{r}\) and permeability coefficient of low- and high-confined water, as shown in Figure 11, could be obtained, and the fitting correlation coefficients, \(R^{2}\), were all above 0.9, which shows that the power function reliably fits the relationship between the \(\sigma_{r}\) and permeability coefficient of clayey sand.
## 3 Numerical Calculation Application and Experimental Setup
### Unsteady State CFD Model Setup  
The unsteady CFD model was set basically consistent with the steady-state CFD model except for solving settings and the processing of the impeller rotating regions.  
In the unsteady CFD model, the frame of the impeller region was changed into a stationary reference one, and the sliding meshes were applied to grids in the impeller region. In other words, all grids in the impeller region make counterclockwise rotation around the impeller center at the rate of 1480 rev min\({}^{-1}\).  
With respect to solving, the calculated results of steady-state CFD model were used as the initial conditions for unsteady CFD model under the same flow rate to assure convergence performance. In this study, the time step for solution was \(1.1261\times 10^{-4}\) s, which equals to the time for the impeller to rotate by 1\({}^{\circ}\). And the whole solving time is 0.2432 s, corresponding to six revolutions of the impeller. The convergence standard adopted by each solving step was that the maximum residual values reached \(10^{-4}\) of the initial values of mass and momentum, but the maximum iteration number for each time step was controlled to be less than 20. Besides, various monitoring settings were added during the unsteady CFD solving process, aiming to get time series data of calculated parameters such as mean pressure and flow rate at entrance and exit of the pump, torque on impeller and hydraulic forces. The computation was carried out on the HP workstation (CPU: E5-2695V2, Memory: 48 GB), and it took about 6 days to complete one case.
# Aging Alters Cervical Vertebral Bone Density Distribution: A Cross-Sectional Study
## 1 Introduction  
Osteoporosis is an age-dependent systematic disease that results in bone loss, increasing the risk of fracture [1, 2, 3]. The vertebra is one of the most common anatomical sites where osteopororotic fractures were observed [4, 5]. While thoracic and lumbar fractures have been frequently reported, recent clinical observations indicated that fall-induced severe cervical vertebral injuries steeply rose more than eight times for elderly population over the 47 years [6]. However, relatively limited studies have focused on osteoporosis of cervical spine.  
Bone mineral density (BMD) is a primary parameter assessed to diagnose osteoporosis [7, 8, 9]. The World Health Organization (WHO) defined an osteopororotic patient as someone who has BMD with 2.5 standard deviation lower than young healthy women [10, 11]. Dual energy X-ray absorptiometry (DEXA) has been used as a standard technique to diagnose BMD [7, 11, 12]. However, DEXA is used at low radiation doses but produces a low resolution with approximately 500 \(\upmu\)m of two-dimensional (2D) image that only provides areal BMD [11, 13] (Table 1). On the other hand, three-dimensional (3D) quantitative computed tomography (QCT) was applied to measure BMD and related parameters of cervical and lumbar vertebrae [7, 8, 9]. However, the QCT was limited due to its relatively high radiation dose and rough resolutions at the range of in-plane pixel size of 0.29 to 0.68 mm and slice thickness of 1.25 to 2.5 mm.  
Cone beam computed tomography (CBCT) has been widely used in a dental clinical setting [14; 15; 16; 17; 18]. Cervical spine, especially C2 and C3, is captured during a routine CBCT scan used in dentistry. This technique uses moderate radiation doses and provides higher resolutions (0.2 to 0.4 mm) of 3D images [17]. While it was indicated that there has been scarcity of studies that examine capability of CBCT to screen patients' BMD [19], a few studies showed that CBCT images of cervical vertebra can be used to detect osteoporosis of patients [20; 21; 22]. However, these studies are limited by using 2D region of interest that provides the relative measures of areal BMD. On the other hand, we successfully calibrated 3D CBCT images by showing the strong positive correlations between hydroxyapatite phantoms with three different densities (1000, 1250, and 1750 mg/cm\({}^{3}\)) and gray levels scanned using three different resolutions (200, 300, and 400 \(\upmu\)m) at full field of view (FOV) of CBCT (Figure 1) [11]. Thus, the objective of the current study is to examine whether dental CBCT can identify age and sex effects on volumetric BMD and morphology of human cervical vertebrae.
# Methods, Progress and Challenges in Global Monitoring of Carbon Emissions from Biomass Combustion
## 1 Introduction
Long-term, high-resolution, and multi-scale biomass burning carbon  
Figure 1: Equilibrium carbon balance between human activities, atmosphere, and natural ecosystems for the 10-year period 2013–2022 [2]. emissions constitute an essential foundation for identifying GHG sources, supporting model simulations, interpreting observations, and formulating emission reduction and control programs. This study provides a comprehensive and systematic review of the current global biomass burning carbon emission monitoring methods, offering a detailed analysis of the latest developments and challenges in their applications. It also present a structured overview and future outlook for biomass burning carbon emission monitoring research while simultaneously providing theoretical insights that can inform a further understanding of the scientific issues related to the carbon cycle in terrestrial ecosystems, remote sensing monitoring and inversion of biomass burning carbon emissions, as well as GHG emission reductions.
## 3 Electron-Phonon Interactions: Theoretical Background
### The Frohlich Hamiltonian
With these simplifications we are left with the _Frohlich Hamiltonian_:  
\[H=\sum_{\mathbf{k}}\varepsilon_{\mathbf{k}}c^{\dagger}_{\mathbf{k}}c_{ \mathbf{k}}+\sum_{\mathbf{q}}\hbar\omega_{\mathbf{q}}a^{\dagger}_{\mathbf{q} }a_{\mathbf{q}}+\sum_{\mathbf{k},\mathbf{k}^{\prime}}\;M_{\mathbf{k}, \mathbf{k}^{\prime}}\;(a^{\dagger}_{-\mathbf{q}}+a_{\mathbf{q}})\;c^{\dagger}_{ \mathbf{k}}c_{\mathbf{k}^{\prime}} \tag{8}\]where the electron-phonon matrix element is defined by:  
\[M_{\mathbf{k},\mathbf{k}^{\prime}}=i\sqrt{\frac{N\hbar}{2M\omega_{\mathbf{q}}}}\;| \mathbf{k}^{\prime}-\mathbf{k}|\;V_{\mathbf{k}-\mathbf{k}^{\prime}} \tag{9}\]  
The interaction \(H_{\mathrm{e-ph}}\) can be considered as being composed of two parts - terms involving \(a^{\dagger}_{-\mathbf{q}}c^{\dagger}_{\mathbf{k}}c_{\mathbf{k}^{\prime}}\) and terms involving \(a_{\mathbf{q}}c^{\dagger}_{\mathbf{k}}c_{\mathbf{k}^{\prime}}\). These terms may be represented by the diagrams shown in Figure 7a,b, respectively. In the first diagram an electron is scattered from \(\mathrm{k}^{\prime}\) to \(\mathrm{k}\) with the emission of a phonon with momentum ( \(\mathrm{k}^{\prime}-\mathrm{k}\)). The second diagram represents the electron being scattered from \(\mathrm{k}^{\prime}\) to \(\mathrm{k}\) with the absorption of a phonon with momentum (\(\mathrm{k}-\mathrm{k}^{\prime}\)).
## 3 Results
### Fitness Verification of RSM Models and Optimization  
BBD matrix and response values are listed in Table 1, and the results from the analysis of variance (ANOVA) are provided in Table 2. A multinomial regression analysis was used to assess the relationship between sensory score and observed variables, and the second-order polynomial model was mathematically expressed in the following Equation (2):  
\[\mathrm{Y}=-1044.1+68.0\mathrm{A}+16.2\mathrm{B}+327.3\mathrm{C}+0.4\mathrm{ AB}+0.5\mathrm{AC}+(1.7\times 10^{-14})\mathrm{BC}-4.8\mathrm{A}^{2}-1.0\mathrm{B}^{2}-34.0 \mathrm{C}^{2} \tag{2}\]  
Figure 2: The sensory evaluations of prepared yogurt under different conditions.  
The regression results indicated that the model was statistically significant (_p_ < 0.01) and had an F value of 13.47. The predicted value and actual value were found to be in good agreement (R\({}^{2}\) = 0.95). The adjusted-R\({}^{2}\) was 0.87, demonstrating that only 12.48% of the variance could not be predicted by this model. The nonsignificant lack-of-fit further suggests that this model showed a range of satisfaction up to good accuracy when it was used to anticipate the relevant responses. The adequate precision value was 10.27, revealing a significantly adequate signal-to-ratio relationship. The coefficient of variation (C.V.) of yogurt sensory score was 2.83% (<5.00%), indicating high reproducibility. According to the \(p\) value, the linear effect of C, as well as the square effects of A (A\({}^{2}\)) and C (C\({}^{2}\)), were extremely significant, showing that the level of 1%, and B\({}^{2}\) was significant at the 5% level, and indicating that the amount of fermentation time had a significant correlation with the sensory score.  
In order to facilitate the interpretation of the key interactions and gain a better understanding of the complex linkages among the model variables, three response surface plots were figured as shown in Figure 3. The final results showed that the optimal levels of addition formulation and fermentation duration were 8 g of TB, 10 g of sugar, and 5 h of fermentation time, respectively.
# VFLD: Voxelized Fractal Local Descriptor
## 4 Experimentation
### Setting the Number of Search Radii  
Another parameter of VFLD is the number of radii to be used for the computation of the descriptor. This parameter is related to the amount of the neighborhood used to compute the descriptor and also to the dimensionality of the descriptor. For instance, 12 search radii means a descriptor of 12 components. In this experiment, we tested 6, 9, 12, 15, 18, 24, and 36 in order to know the number of radii that is the optimal value. The other parameters were 1 as the starting value and 0.5 for the increment. Regarding the box-counting algorithm parameter, 5 was chosen for the \(nIters\). We also incorporated FPFH for comparison.  
The results of this experiment are shown in Figure 13. As can be seen, in the tested datasets, the numbers of radii yielding the best performance in terms of accuracy, precision, and recall are 18 and 24. The results of both setups are practically the same. In addition, the performance improves as the number of radii increases up to a certain level. If there are too many radii, the accuracy drops, as is the case with 36 components for both datasets. At this point, information on points located too far away are considered, thus reducing the descriptiveness of the feature vector. Finally, it can be concluded that the best number of search radii is 18, as 24 provides virtually the same result but takes more processing time.
# Strategies for Dielectric Contrast Enhancement in 1D Planar Polymeric Photonic Crystals
## 5 Prospects for Polymer Planar Microcavities
For instance, as discussed in this work, perfluorinated polymers show refractive indexes as low as 1.33. On the other hand, their low wettability and chemical stability makes physical surface activation necessary to cast other polymers on their surface [107; 108; 109; 226]. In most cases, plasma treatments are performed to favor the casting of high refractive index layers on these low wettable polymers. However, plasma surface treatments induce a roughness that may lead to scattering phenomena and decrease the optical quality of the DBRs and microcavities [227; 228; 229; 230; 231]. Similarly, employing nanocomposites introduces possible particle aggregation, which would again lead to light scattering and the low quality of the DBR [118; 121; 139; 156; 232]. These aspects further highlight the importance of processing engineering for the fabrication of DBRs with engineered polymers.
## 2 Methodology
### Penalty Applied Loss Function  
In this paper, the loss function used is sparse categorical cross entropy (SCCE). However, despite using SCCE, the trained model sometimes generated sentences typically found in normal CT scans (\"unremarkable finding of brain parenchyma and cerebrospinal fluid space\") when dealing with ICH CT scans. Here, we will refer to this as None-ICH Text. To prevent this, a penalty-applied loss function is proposed, which applies a penalty when the model generates a None-ICH Text for ICH CT scans and is implemented in the model training. Equation (1) represents the categorical cross entropy (\(l\)) loss function.  
\[l=-\frac{1}{\eta}\sum_{j=1}^{n}\sum_{i=1}^{c}t_{ij}\text{log}\left(y_{ij} \right). \tag{1}\]  
Figure 2: Example of medical images. (**a**) Single 2D image from brain CT scan in the in-house data; (**b**) Sequential 2D images from brain CT scan in the in-house data. Images order in (**b**) are left to right and top to bottom.  
In this case, \(n\) represents the number of training samples, \(c\) is the number of classes, \(y\) is the model prediction, and \(t\) is the ground truth. Equation (2) is the loss function \(L\) that applies a penalty when the input is an ICH CT scan, but the output is a None-ICH Text.  
\[L=\left\{\begin{matrix}l_{i}+p,&if\ r_{i}=1\\ l_{i},&if\ r_{i}=0\end{matrix}\right.. \tag{2}\]  
In this case, \(l_{i}\) is the \(i\)-th loss value calculated from Equation (1), and \(p\) is the penalty value we determined. \(r_{i}\) is 1 if the \(i\)-th input is ICH, and the output is a None-ICH Text and 0 otherwise.
## 4 Theoretical Motivation of NBR-Clustering
### NBR Clustering Framework
#### 4.1.4 Identifying and Assigning Overlap Nodes  
The underlying hypothesis is that overlap nodes (nodes that belong to more than one cluster) are a subset of the critical attack set nodes generated by NBR-Clust. Overlap nodes and attack set nodes share a similar characteristic: both lie on inter-cluster boundaries. In addition, an overlap node maintains a \"tight connection\" with all its communities [46]. An overlap node should have equally strong numbers of adjacencies to several communities, while a non-overlap node would display a significantly higher number of adjacencies to one particular community in contrast to others. An example is shown in Figure 5. Both green and cyan nodes have high betweenness centrality and are likely to be part of a resilience measure critical attack set. The cyan node is determined to be an overlap node because it is tightly connected to both the magenta and yellow communities, and those communities share no other connections. The green node is also connected to two communities but much more tightly to the gray community (four adjacencies) than to the magenta community (only one adjacency). The green node is therefore not an overlap node.  
To quantify the strength of a node's connection to multiple communities, different strategies could be applied. For example, in [46], overlap nodes are detected using a two-part criterion based not only on adjacencies, but also on a node's distance from the hubs of the corresponding communities. In [47], overlap is based on the \"distance\" from a node to a community, and it is determined with a heuristic that counts the number of triangles between a node and a community.  
In this work, the Edge Dispersion Variation (EDV) measure is utilized to quantify how tightly a node \(u_{x}\) is connected to a given set \(Q\) of communities. The EDV for \(u_{x}\), given a set of communities  
Figure 5: Identifying overlap. The cyan node overlaps the magenta and yellow clusters. Despite having adjacencies to two clusters, the green node is not an overlap node.  
\(Q=\{C_{1},C_{2},...,C_{n}\}\), is defined as a normalized standard deviation of the percentages of \(u_{x}\)'s edges that link to \(Q\)'s communities:  
\[EDV(u_{x},Q)= \sqrt{\sum_{i=1}^{|Q|}(a_{u_{x},C_{i}}-\mu_{Q})^{2}}, \tag{11}\]  
where \(\mu_{Q}\) is the mean of \(a_{u_{x},C_{i}}\) computed over \(Q\) and \(a_{u_{x},C_{i}}\) (\(0\leq a_{u_{x},C_{i}}\leq 1\)) is the percentage of node \(u_{x}\)'s edges adjacent to \(Q\) that are also adjacent to cluster \(i\):  
\[a_{u_{x},C_{i}}=\frac{|\{(u,v)\in E|u=u_{x},v\in C_{i}\}|}{|\{(u,v)\in E|u=u_{x },v\in Q\}|}. \tag{12}\]  
The measure of the strength of the connection correlates inversely with the EDV value. Nodes (like the green node in Figure 5) that are not overlaps will have a higher EDV value.