esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Statistical Analysis  
Alpha and beta diversity statistics were recorded using the QIIME 2 scripts diversity plugin, which supports metrics for calculating and exploring the community alpha and beta diversity through statistics and visualizations in the context of sample metadata. In the calculation of alpha diversity metrics, normalization was performed using the \"rarefaction\" QIIME 2 process with standard parameters, setting the max_rare_depth (upper limit of rarefaction depths) to the mean sample size. Alpha diversity metrics were calculated using 'observed species', 'Chao1 index' (species richness estimator), 'Shannon's diversity index', and 'Good's coverage'. An alpha-rarefaction plot was created for each metric. The alpha diversity values at the same rarefaction level were calculated.  
The normality and homogeneity of variance of all weight, length, and alpha diversity indices obtained from fish gut microbiome analysis were checked by [PERSON]'s and [PERSON]'s test. Next, one-way ANOVA followed by [PERSON]'s post-hoc test (\(p\) = 0.05) was used to check the statistical differences between fish of different tanks (STATISTICA v.13.1 (StatSoft, Inc, Tulsa, OK, USA)).  
The number of reads across samples was normalized by the sample size and the relative abundance (%) of each taxon was calculated. All taxa found in the gut microbiome were considered for statistical analysis. Statistical analysis of intestinal microbial profiles was performed using the Statistical Analysis of Metagenomics Profiles (STAMP) program ([[http://kiwi.cs.dal.ca/Software/STAMP](http://kiwi.cs.dal.ca/Software/STAMP)]([http://kiwi.cs.dal.ca/Software/STAMP](http://kiwi.cs.dal.ca/Software/STAMP))), retaining unclassified reads [41]. To conduct a reliable statistical analysis, one sample (from TS_0%o) was rejected from the analysis due to the low number of reads assigned to taxon levels. All \(p\)-values were calculated by ANOVA followed by [PERSON]'s post-hoc test and corrected for multiple comparisons using the Benjamini-Hochberg method for a False Discovery Rate (FDR) of 5% [42].  
The beta diversity metric is an estimation of the between-sample diversity of the microbial profile and it was calculated by the QIIME 2 \"diversity beta-group-significance\" script. Both weighted (abundance matrix) and unweighted (presence/absence matrix) UniFrac distances [43; 44] were used. The distance matrices were graphically visualized by three-dimensional principal coordinates analysis (PCoA) representations.  
Differences in the beta diversity of bacterial communities were verified using a nonparametric Permutational Multivariate Analysis of Variance (PERMANOVA) test with 999 permutations (\(p=0.05\)). A pairwise significance test was also performed by comparing groups from tanks with different salinity levels using the same distance matrix metrics (weighted and unweighted UniFrac distances). These tests were available in QIIME 2.
# Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks
## 4 Performance Evaluation  
Simulations were conducted using Castalia v3.2 [29] running on top of OMNET++ [31] to evaluate the performance of EDDS. The system parameters and their settings used in the experiments are summarized in Table 4. The network model (illustrated in Section 3.1) was tested in four rectangular fields with different parameter settings. Sensor MAC (SMAC) [32] and Sector Based Distance Routing (SBD) [15] are used in MAC and routing layer. Simulations were run 30-40 times with varying-channel affecting seeds to provide results that included average and 95% confidence interval. In Section 4.1, possible distribution of data throughout the network is presented. Section 4.2 evaluated EDDS in terms of energy consumption, latency and accuracy in a network of 180 nodes in a 90 m \(\times\) 90 m (8100 m\({}^{2}\)) rectangular field. In Section 4.3, the performance of EDDS was tested using four different rectangular fields with four different distributions respectively. In Section 4.4, the performance of EDDS is evaluated against SkySensor in terms of data loss, data uniformity, success rate and resilience to node failure.
# Seismic Stability of Dual Tunnels in Cohesive-Frictional Soil Subjected to Surcharge Loading
## 7 Conclusions  
This study employed self-developed AFELA code to investigate the effect of several influential factors on the seismic bearing of double unlined parallel tunnels in cohesion-frictional soil. Rigorous UB and LB results were obtained, which agree well with previous studies and the relative errors of which are less than 7%. The failure patterns and the scope of collapse with different influential factors are discussed. Additionally, detailed design tables were presented to facilitate the utilization of engineering practices. After investigations by AFELA code, several conclusions can be drawn as follows:  
1. The greater the \(\gamma D/c\) is, the greater the seismic bearing capacity becomes. Additionally, the variations in \(\gamma D/c\) and \(H/D\) linearly affect the seismic bearing capacity.
2. The critical interaction distance between the double tunnels would be stronger with the increase in \(H/D\) and \(k_{h}\) and the decrease in \(\varphi\), \(\gamma D/c\). Additionally, the further the distance between the tunnels is, the weaker the interaction becomes.
3. Three typical failure patterns can be summarized as follows: (a) single side-wall failure, which is the most stable condition, meaning that there is no interaction between the dual tunnels; (b) half-cross-shaped failure, meaning that there is a moderate interaction between the dual tunnels, and its stability is lower than the former one; and (c) cross-shaped failure, which means that there is an intense interaction between the dual tunnels, and the stability of this failure pattern is the worst.  
* Before the failure pattern transforms to the single side-wall failure, the scope of collapse would be broader with the increase in \(S/D\), \(H/D\) and \(k_{h}\), while the strength of soil (\(\varphi\) and \(\gamma D/c\)) has almost no influence on the scope of collapse.  
Funding acquisition, G.W.; Investigation, Y.Z.; Software, Y.Z. and G.W.; Writing--original draft, G.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (No. 51978255), the Shuimu Tsinghua Scholar Project of Tsinghua University (No. 2021 SM007) and the Postgraduate Scientific Research Innovation Project of Hunan Province, China (No. CX20200394).  
The data presented in this study are available on request from the corresponding author.  
The authors would like to acknowledge the financial support of the National Natural Science Foundation of China (No. 51978255), the Shuimu Tsinghua Scholar Project of Tsinghua University (No. 2021 SM007) and the Postgraduate Scientific Research Innovation Project of Hunan Province, China (No. CX20200394).  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Backbone Models and Construction of Basic Natural Gas Purchase Forecasting Models
#### 3.2.3 SVR and Its Four Base Model Constructions  
SVR is a regression method based on the support vector machine (SVM) principle, aiming to find a function that minimizes the deviation within a certain interval for the training dataset while also minimizing model complexity [34]. SVR introduces the \(\epsilon\)-insensitive loss function, which only considers the training samples whose deviations exceed \(\epsilon\), thereby constructing a robust regression model [35].  
The goal of the SVR is to find a function \(f(x)\) that keeps the deviation within \(\epsilon\) while ensuring smoothness. Specifically, SVR solves the following optimization problem:  
\[\min_{w,b}\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{n}(\xi_{i}+\xi_{i}^{*}) \tag{11}\]  
S.t.  
\[\begin{cases}y_{i}-(w\cdot x_{i}+b)\leq\epsilon+\xi_{i}\\ (w\cdot x_{i}+b)-y_{i}\leq\epsilon+\xi_{i}^{*}\\ \xi_{i},\xi_{i}^{*}\geq 0\end{cases}\]  
where \(w\) denotes the weight vector, \(b\) denotes the bias term, \(C\) denotes the regularization parameter, and \(\xi_{i}\) and \(\xi_{i}^{*}\) denote slack variables. \(\epsilon\) denotes the tolerance of the \(\epsilon\)-insensitive loss function.  
The SVR can be transformed into the following form through the Lagrange multipliers \(\alpha_{i}\) and \(\alpha_{i}^{*}\):  
\[\hat{Y}_{SVR}\ =\ \sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(x_{i},x)+b \tag{12}\]  
where \(K(x_{i},x)\) denotes the kernel function, \(n\) represents the number of support vectors, and \(b\) denotes the bias term of the model.  
For SVR, four base models were developed to assess the impacts of various feature combinations, as detailed below:  
1. SVR: \[\hat{Y}_{SVR}\ =\ \sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(X_{basic},x)+b\] (13)
2. SVR-RB: \[\hat{Y}_{SVR-RB}\ =\ \sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(X_{basic}+X_{ region},x)+b\] (14)
3. SVR-UB: \[\hat{Y}_{SVR-UB}\ =\ \sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(X_{basic}+X_{ usage},x)+b\] (15)
4. SVR-UB-RB: \[\hat{Y}_{SVR-RB-UB}\ =\ \sum_{i=1}^{n}(\alpha_{i}-\alpha_{i}^{*})K(X_{basic}+X_{ region}+X_{usage},x)+b\] (16)  
The SVR implementation from the sklearn library in Python was used to construct the support vector regression models. The parameters for the model are shown in Table 4.  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameter** & **Description** & **Value** \\ \hline fit\_intercept & Whether to calculate the intercept & True \\ normalize & Whether to normalize the input data & False \\ copy\_X & Whether to copy the input data or overwrite it & True \\ \hline \end{tabular}
\end{table}
Table 3: MLR model training parameters.
# NOMA-Based Rate Optimization for Multi-UAV-Assisted D2D Communication Networks
## 4 Problem Solution
### MDQN-Based Trajectory Design and Power Control
#### 4.2.1 MDP Model  
In the NOMA-based UAV-assisted D2D communication model, each UAV is considered as an intelligent body for maximum system performance. The three key elements of the agent \(u\) are constructed in detail as follows: (1) State space: The state space should be composed of the 3D spatial position of the UAV and the position information of the cellular users and D2D cluster users. The 3D position \(h_{u}(t)\) of the UAV is an important feature of the current state because the position information of the cellular users and D2D cluster users is difficult to obtain in real time, so according to Equations (5), (6), and (8)-(10), this study adopts the known channel gain among the UAV, the cellular users, and the D2D cluster users to characterize the state of the cellular users and the D2D cluster users. The known channel gains between the three are used to characterize the states of cellular users and D2D cluster users, and the input array \(s_{u}(t)\) of the final state space is expressed as  
\[s_{u}(t)=\{h_{u}(t),g_{i}^{u}(t),g_{DT_{j}}^{u}(t),g_{i,DT_{j}}^{u}(t),g_{i,DR_ {j,u}}^{u}(t),g_{i,DT_{j},DR_{j,u}}^{u}(t)\} \tag{34}\]  
In order for multiple UAVs to share the same neural network, the data in the state space need to be scalarized and normalized. Specifically, there are UAV 3D coordinates \(h_{u}(t)\) which are decomposed into three scalars that serve as independent inputs to the neural network. When a drone is connected to the neural network, its position information is input into the first neuron; when another UAV is accessed, the position information of that drone must also be input into the same neuron, as shown in Figures 3 and 4. Compared with the way that drones are trained separately and independently, the MDQN is able to significantly improve the convergence speed compared with the DQN.  
(2) Action space: The action space in this section consists of two subsets, the direction of motion of the UAV and the power allocation factor, which are discretized due to the infinite action complexity of the continuous action space and in order to adapt the discrete action output of the MDQN model.  
In the direction of UAV movement, the UAV can perform horizontal back-and-forth, vertical up-and-down, and stationary-in-flight operations; in terms of the power factor, the transmission power is preset to a number of fixed slots; and at each movement execution, the UAV selects and maintains a power for its associated D2D cluster until the next movement update.  
Figure 3: MDQN neural network connection.  
(3) Reward: As stated in Equation (25a), the objective function of this paper is to maximize the system throughput, so the reward function is set as  
\[r_{u}(t)=R \tag{35}\]
## 3 Mathematical Model and Boundary Conditions  
### Mathematical Model  
The design of the microchannel heat dissipation system in this paper was based on the relevant principles of fluid-solid coupling, and the thermal analysis was based on the three laws of fluid mechanics. The governing equations of mass, momentum, and energy can be written as follows:  
Continuity equation:  
\[\frac{\partial u}{\partial x}+\frac{\partial v}{\partial y}+\frac{\partial w}{ \partial z}=0 \tag{1}\]  
where \(u\), \(v\), and \(w\) are velocity components in \(x\), \(y\), and \(z\) directions, respectively.  
Momentum equations:  
\[u\frac{\partial u}{\partial x}+v\frac{\partial u}{\partial y}+w\frac{\partial u }{\partial z}=-\frac{1}{\rho_{f}}\frac{\partial p}{\partial x}+\frac{\mu_{ f}}{\rho_{f}}\bigg{(}\frac{\partial^{2}u}{\partial x^{2}}+\frac{\partial^{2}u}{ \partial y^{2}}+\frac{\partial^{2}u}{\partial z^{2}}\bigg{)} \tag{2}\]  
\[u\frac{\partial v}{\partial x}+v\frac{\partial v}{\partial y}+w\frac{\partial v }{\partial z}=-\frac{1}{\rho_{f}}\frac{\partial p}{\partial y}+\frac{\mu_{ f}}{\rho_{f}}\bigg{(}\frac{\partial^{2}v}{\partial x^{2}}+\frac{\partial^{2}v}{ \partial y^{2}}+\frac{\partial^{2}v}{\partial z^{2}}\bigg{)} \tag{3}\]  
\[u\frac{\partial w}{\partial x}+v\frac{\partial w}{\partial y}+w\frac{\partial w }{\partial z}=-\frac{1}{\rho_{f}}\frac{\partial p}{\partial z}+\frac{\mu_{f}}{ \rho_{f}}\bigg{(}\frac{\partial^{2}w}{\partial x^{2}}+\frac{\partial^{2}w}{ \partial y^{2}}+\frac{\partial^{2}w}{\partial z^{2}}\bigg{)} \tag{4}\]  
where \(\rho_{f}\) and \(\mu_{f}\) are the density and dynamic viscosity of the coolant, respectively, and p is the coolant pressure.  
Energy equation for the coolant  
\[u\frac{\partial T_{f}}{\partial x}+v\frac{\partial T_{f}}{\partial y}+w\frac{ \partial T_{f}}{\partial z}=\frac{k_{f}}{\rho_{f}c_{pf}}\bigg{(}\frac{\partial ^{2}T_{f}}{\partial x^{2}}+\frac{\partial^{2}T_{f}}{\partial y^{2}}+\frac{ \partial^{2}T_{f}}{\partial z^{2}}\bigg{)} \tag{5}\]  
where \(T_{f}\) is the coolant's temperature, \(c_{pf}\) is fluid-specific heat and \(k_{f}\) is fluid thermal conductivity.  
Energy equation for the solid region  
\[0=k_{s}\bigg{(}\frac{\partial^{2}T_{s}}{\partial x^{2}}+\frac{\partial^{2}T_ {s}}{\partial y^{2}}+\frac{\partial^{2}T_{s}}{\partial z^{2}}\bigg{)} \tag{6}\]  
Figure 4: (**a**) Model of the heat dissipation system of the Pin-finned microchannel. (**b**) Schematic diagram of the top-view cross-sectional structure of the Pin-finned microchannel.  
where \(T_{s}\) is solid temperature, and \(k_{s}\) is solid thermal conductivity
## 4 Results and Discussion
### Effect of Sealing Gap  
The effects of the gap size on leakage amount and orifice coefficient are shown in Figure 6a. The results were obtained under the condition that the spacing of 300 mm, the total pressure difference of 5 kPa, the sealing number of 2, and the bending angle of 25\({}^{\circ}\). The abbreviation SIM, EXP, and IDE refer to the leakage results by simulation, experiment, and ideal gas calculation, respectively. The results show that both the leakage amount and the orifice coefficient increase with the leakage gap size. In the operation of RAPH, thermal stress deformation of the rotor would cause an increase in the gap size and lead to a larger leakage rate. The installation of sealing sheet in the flow path facilitates the formation of throttling structure. The well-known continuum theory tells us that when the fluid flows through a gap, the flow area gets smaller and the velocity gets larger at the gap position [25]. In the front of gap, the flow is in a stable state, which means that the fluid particles are in a balanced state. At the gap position, the fluid particles are sped up and fluid velocity is larger than that of the position in the front of gap. It can be clearly seen from the velocity contours (Figure 6b) that the velocity at the gap of 25 mm is greater than that of 15 mm.  
Figure 5: Effect of inlet pressure on leakage in (**a**) different sealing number and in (**b**) different pressure difference.  
Figure 6: _Cont._
## 3 Numerical Results
### Bayesian Update  
In this section, we compare the results of state/parameter update by the assimilation of surface displacements using the classical ES approach and its variation in the form of gPCE-ES. As already mentioned before, the synthetic \"true\" observations \(\mathbf{d}^{i}\) are collected from a geomechanical model run with the \"true\" parameter set and perturbed by a zero-mean Gaussian noise \(\mathbf{\epsilon}\) (Figure 2b). Different \"true\" parameter sets are investigated to test the robustness of the proposed approach.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{\(\mathbf{N=1}\) (\(\mathbf{n_{q}=16}\))} & \multicolumn{2}{c}{\(\mathbf{N=2}\) (\(\mathbf{n_{q}=81}\))} & \multicolumn{2}{c}{\(\mathbf{N=3}\) (\(\mathbf{n_{q}=137}\))} \\ \hline \(\mathbf{t\) [yr]} & \(\mathbf{\mu_{u}\) [m]} & \(\mathbf{\sigma_{u}^{2}\) [m\({}^{2}\)]} & \(\mathbf{Q^{2}}\) & \(\mathbf{\mu_{u}\) [m]} & \(\mathbf{\sigma_{u}^{2}\) [m\({}^{2}\)]} & \(\mathbf{Q^{2}}\) & \(\mathbf{\mu_{u}\) [m]} & \(\mathbf{\sigma_{u}^{2}\) [m\({}^{2}\)]} & \(\mathbf{Q^{2}}\) \\ \hline
1 & \(-2.1\times 10^{-3}\) & \(2.0\times 10^{-6}\) & \(0.820\) & \(-2.3\times 10^{-3}\) & \(3.1\times 10^{-6}\) & \(0.931\) & \(-2.3\times 10^{-3}\) & \(3.0\times 10^{-6}\) & \(0.823\) \\
5 & \(-1.7\times 10^{-1}\) & \(1.4\times 10^{-2}\) & \(0.860\) & \(-1.7\times 10^{-1}\) & \(1.5\times 10^{-2}\) & \(0.995\) & \(-1.7\The gPCE-ES technology is then applied within the iterative ES-MDA framework using either constant and decreasing factors \(\alpha_{MDA}\) to inflate the covariance matrix of measurement error.
## 3 Proposed Method
### Image Segmentation  
The preprocessing stage of the lossless compression algorithm includes segmenting the image into distinct regions with similar color and texture features. The primary objective of this image segmentation phase is to group regions with similar colors or near-identical RGB values, ensuring optimal compression performance and efficiency. The literature offers a wide range of algorithms for image segmentation. In this study, the graph-based segmentation algorithm proposed by [PERSON] and [PERSON] (FH) was chosen [48]. The algorithm basically uses a minimum-spanning-tree-based clustering approach for segmenting RGB images. The algorithm uses Euclidean distance calculation between pixels in color space. The primary reason for selecting this approach was that the FH algorithm generates over-segmented regions that align well with image boundaries, allowing even complex images to be divided into segments with high accuracy and efficient execution time [49]. However, these regions typically have irregular sizes and shapes. Despite appearing as  
Figure 3: Block diagram of proposed framework (S+DEA).  
a disadvantage at first, this irregularity allows for color-based segmentation of the entire image, making it the most appropriate approach for achieving better compression results.  
A notable example is the Baboon image from the USC-SIPI dataset, which is widely utilized in the literature for evaluating image compression algorithms. This image presents a significant challenge for compression due to its intricate textural characteristics and complex color distribution. In Figure 4, the original input image is now explicitly labeled as Figure 4a, ensuring clarity in the segmentation process. The segmented images, derived from the Baboon image through the Felzenszwalb and Huttenlocher (FH) algorithm, are annotated as Figure 4b-k. The FH algorithm generates segments that vary in size, shape, and geometric regularity, yet it effectively clusters these segments based on color tone similarities, fulfilling the intended segmentation criteria. These updates enhance the comprehensibility of Figure 4, ensuring that the segmentation process is accurately represented and that the figure annotations are aligned with the manuscript's descriptive content.  
Lossless compression algorithms must guarantee the exact reconstruction of the original file. However, the irregular sizes and shapes of the image segments generated by the FH algorithm makes direct compression infeasible. The irregularity of the segments generated by the Felzenszwalb and Huttenlocher (FH) algorithm introduces a significant challenge during the reconstruction phase. Specifically, determining the precise placement, orientation, and spatial configuration of each segment within the original image becomes a complex and non-trivial task. This difficulty arises from the heterogeneous sizes and shapes of the segments, which lack a consistent or predictable structure. To address this issue, a specialized data structure was designed to facilitate the lossless storage of image segments.
# Statistical Uncertainty of DNS in Geometries without Homogeneous Directions
###### Abstract  
In this paper, we present uncertainties of statistical quantities of direct numerical simulations (DNS) with small numerical errors. The uncertainties are analysed for channel flow and a flow separation case in a confined backward facing step (BFS) geometry. The infinite channel flow case has two homogeneous directions and this is usually exploited to speed-up the convergence of the results. As we show, such a procedure reduces statistical uncertainties of the results by up to an order of magnitude. This effect is strongest in the near wall regions. In the case of flow over a confined BFS, there are no such directions and thus very long integration times are required. The individual statistical quantities converge with the square root of time integration so, in order to improve the uncertainty by a factor of two, the simulation has to be prolonged by a factor of four. We provide an estimator that can be used to evaluate a priori the DNS relative statistical uncertainties from results obtained with a Reynolds Averaged Navier Stokes simulation. In the DNS, the estimator can be used to predict the averaging time and with it the simulation time required to achieve a certain relative statistical uncertainty of results. For accurate evaluation of averages and their uncertainties, it is not required to use every time step of the DNS. We observe that statistical uncertainty of the results is uninfluenced by reducing the number of samples to the point where the period between two consecutive samples measured in Courant-Friedrichss-Levy (CFL) condition units is below one. Nevertheless, crossing this limit, the estimates of uncertainties start to exhibit significant growth.  
turbulent flow; DNS; statistical uncertainty; passive scalars +
Footnote †: journal: Applied Sciences