esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Network and VoD Content Modeling
### VoD Content Catalog Modeling  
#### 3.2.1 Catalog Size and Popularity  
We consider a catalog size of 20.000 items assumed to follow a _Zipf_-like VoD-content popularity distribution, as in [16,18,19], meaning that a small percentage of the content accounts for a high portion of the total requests. The popularity of an item \(i\) is  
\[P(i)=\frac{\frac{1}{i^{\alpha}}}{\sum_{j=1}^{N}\frac{1}{j^{\alpha}}} \tag{1}\]  
and the cumulative probability of the most popular \(f\) items is  
\[\sum_{i=1}^{f}P(i)=\frac{\sum_{i=1}^{f}\frac{1}{\pi}}{\sum_{j=1}^{N}\frac{1}{j^ {\alpha}}} \tag{2}\]  
where N represents the total number of items and \(\alpha\) is the popularity distribution skew parameter. Note that the quantity \(\sum_{j=1}^{N}\frac{1}{j^{\alpha}}\) represents the normalization constant of the _Zipf_ distribution. As an example, we show a calculation of the cumulative probability utilizing Equation (2) with \(N\) = 20,000 and \(\alpha\) = 0.8. For \(f\) = 2000 (the most popular 10% of the content catalog), _Pr.(f =_ 2000) is 0.6, showing that most popular 10% of video content accounts for 60% of the requests, whereas the rest of the content (the remaining 90% of the content catalog) accounts for only 40% of the requests. In fact, the popularity distribution of VoD content is the main driving force behind caching popular content near end-users, as a small cache permits serving a significant number of the VoD-content requests. Note also that \(\alpha\) defines the skewness of the curve, and thus the popularity distribution itself. As an example, Figure 2 plots the popularity distribution curves for a content catalog of 100 items for \(\alpha\) = 0.8, 0.9 and 1, showing that for lower values of \(\alpha\), popular items become slightly less popular, whereas items belonging to the long tail have more popularity. It follows that \(\alpha\) is a decisive parameter to be considered when finding the optimal storage-capacity distribution.  
Figure 1: Network topology considered in our study.
# An Equilibrium Strategy-Based Routing Optimization Algorithm for Wireless Sensor Networks
## Network Model and Related Definitions  
### Network Model  
In this paper, we focus on the typical information collection application in event-driven wireless sensor networks. The sensor nodes periodically perceive the data and transmit them to the sink node in a multi-hop manner. In order to simplify the network, it is assumed that:  
1. All sensor nodes are isomorphic and randomly deployed in a certain monitoring area. The sensor nodes have only one sink node, and the location of sensor nodes and the sink node will not be changed after being deployed.
2. The node can change the transmission power according to the distance to the receiver, and the distance from one sensor node to another can be estimated based on the received signal strength.
3. In a practical application, the packet size and the data generation rate can be determined according to different scenarios. As for this paper, in order to simplify the model, the size of the packet is fixed and the data generation rate is the same for all nodes.
## 1 Introduction
### Salt Tolerance Mechanism of Halophytes: Role of Secondary Metabolites
#### 1.3.3. Succulence Mechanism and Epidermal Bladder Cells  
Halophytes exhibit selective Na\({}^{+1}\) transport; if the concentration is high, then it is sequestered in vacuoles. Na\({}^{+1}\) sequestration is possible though the NHX family Na/H antiporter in the tonoplast and V-H-ATPase, which create a pH gradient between the cytoplasm and vacuole, allowing Na\({}^{+1}\) to enter into the vacuole against the electrochemical gradient. The compartmentalization of Na\({}^{+1}\) in the vacuoles contributes not only to ion homeostasis and cell turgidity but also protects metabolic enzymes from ion toxicity. Some halophytes exhibit the succinate property as they have thick leaves and stems to accumulate excess Na\({}^{+1}\) and Cl\({}^{-1}\) in vacuoles, increasing the size of mesophyll cells, and have smaller intercellular spaces to enhance water content and cause a high turgor pressure.  
[PERSON] et al. [35] reported that _Glycine max_ showed a metabolic response to salt stress by producing isoleucine, serine, and aspartic acid. Similarly, three halophytes (_Frankenia pulverulenta_, _Atriplex prostrata_, and _Plantago coronopus_) showed increased content of alkaloid derivative, polyamines (spermidine + spermine ratio) against 400 mM NaCl stress [36]. Epidermal bladder cells, also known as salt bladders, contain vacuoles that store excess Na\({}^{+}\) and Cl\({}^{-}\). They also store ROS-scavenging metabolites and organic osmoprotectants.
# Topology Inference and Link Parameter Estimation Based on End-to-End Measurements +
## 1 Introduction
* We design and implement a practical application of the proposed NN-extended clustering algorithm that combines the NT-based monitoring with change point analysis for performance anomaly detection. The respective source code is also publicly available [14]. The remainder of this article is organized as follows. In Section 2, we introduce the network and probing model, along with the considered inference problem. Section 3, describes in detail the proposed algorithm, analyzes the extensions needed for inferring physical routing trees, and explores a variety of alternative reduction update formulas. In Section 4, we present the employed experimental setup and we report the respective performance evaluation results. Finally, Section 5 presents the practical application of the clustering algorithm and Section 6 concludes the paper.
## 3 The Proposed Method for MSB Identification and Verification Against Deployed Controls
The method is formed of 4 main steps (schematic method representation is provided on Figure 3):  
1. represent information security standards' requirements to be mapped as separate graphs;
2. generate a new graph by linking requirements of N subgraphs (representing different information security standards);
3. add a vertex to the vertexes with a single edge;
4. apply minimum vertex cover algorithm. After the vertex cover algorithm is applied, we have to ensure that vertexes without a direct connection to other standards that were removed from the graph are restored. The outcome of this process will be the MSB graph. The formal MSB identification method described with the help of a Business Process Model and Notation (BPMN) diagram is presented in Figure 4.  
In Table 1 a detailed description of actions defined in Figure 4 is provided. Figure 4: Minimum security baseline identification method. Figure 3: Schematic MSB identification method representation. PCI DSS is payment card industry data security standard. When the MSB graph is identified in the next step, we perform its verification against controls already deployed by the organization. As stated earlier, subgraph isomorphism algorithms are used for that task. In our case, it is not significantly important which subgraph isomorphism algorithm will be used, since our primary goal is to perform a feasibility study of such an approach and its practical applicability. In this step, controls implemented by the organization are presented as a deployed control graph (DCG), which is compared to the received MSB graph to verify their alignment. It is important to mention that the DCG graph may have stand-alone vertexes, i.e., not connected with any other vertexes, which is usually caused by inconsistency while developing the information security management system (ISMS). As such, it is necessary to ensure that all controls (even stand-alone) are verified, which is achieved by introducing two additional conditions. For simplicity reasons, while implementing the subgraph isomorphism algorithm, any other vertex verification properties (e.g., name matching or properties matching) will not be used. Usage of additional verification properties potentially could make the approach more effective. However, it is not so important at this stage when just method feasibility is evaluated. Because of the fact that the DCG graph could have stand-alone vertexes or small subgraphs as separate parts of the DCG graph, we have to ensure that all of them are compared against the MSB graph. The formal MSB verification against deployed controls method description with the help of the BPMN diagram is presented in Figure 5.  
In Table 2 a detailed description of actions defined in Figure 5 is provided. The method concept was tested experimentally in order to prove its feasibility for real-life applications. The test results are presented and discussed in the \"Experimental Method Verification Results and Discussion\" section.
# Estimating Forest Carbon Stock Using Enhanced ResNet and Sentinel-2 Imagery
## 3 Methodology
### Regression Strategy and Optimization
#### 3.3.3 Dataset Split  
The dataset is divided into training (70%), validation (15%), and test (15%) sets. Stratified sampling ensures proportional representation of forest types and elevation zones in each subset, supporting fair performance evaluation and generalization.  
Figure 4: Structure of the channel-wise Squeeze-and-Excitation (SE) module, which adaptively reweights spectral features through global average pooling and two fully connected layers.  
Figure 3: Structure of the proposed Multiscale Residual Block (MSRB), combining standard, large-kernel, and dilated convolutions to capture spatial features at multiple receptive fields.
## 1 Introduction  
In recent years, marine-derived fungi have been demonstrated to be a rich and promising source of novel anticancer, antibacterial, antiplasmodial, anti-inflammatory, and antiviral agents [1]. To date, more than one thousand unique molecular structures have been discovered from marine-derived fungi. Several reviews on marine fungi [2; 3; 4] have shown that a variety of secondary metabolites isolated from marine-derived fungi had not been produced by terrestrial fungi, and these metabolites possibly act as a chemical defense, enabling marine-derived fungi to survive competition with native microorganisms [5]. Thus, marine-derived fungi, which successfully fostered their armamentarium against bacterial competitors for millions of years, can be considered as a potential source of antibiotics.  
In our ongoing pursuit of new natural products with antibacterial activity produced by marine-derived fungi of the genera _Neosartorya_ and _Apergillus_, we have investigated the secondary metabolites of a Thai collection of _Neosartorya takakii_ KUFC 7898, isolated from the marine macrocalga _Amphiroa_ sp., collected from Samaesarn Island in the Gulf of Thailand. The ethyl acetate extract of its culture yielded, in addition to the previously reported aszonapyrone A [6], chevalone B [7; 8], aszonalenin [6], acetylaszonalenin [6], 3'-(4-oxoquinazolin-3-yl) spiro[1\(H\)-indole-3,5'-oxolane]-2,2'-dione [9], tryptoquivalines L, F and H [9], and 6-hydroxymellein [10], three new compounds including a meroditerpene sartorend **(1)**, a prenylated indole alkaloid takakiamide (**2**), and a new tryptoquivaline analog, which we have named tryptoquivaline U **(3**)** (Figure 1). Compounds **1-3** were screened for their antibacterial activity against Gram-positive and Gram-negative bacteria, and multidrug-resistant isolates from the environment as well as for their quorum sensing inhibitory activity.
## 3 Results
### Independent Effects of Atmospheric Drought on GPP  
#### 3.4.1 Contributions of Multi-Factors Dominated by VPD and Spatial Heterogeneity Regulation of GPP  
Integrating the results of partial correlation, ridge regression analysis, and SEM, VPD emerged as the dominant meteorological factor exerting a significant inhibitory effect on GPP (path coefficient\(=-0.58\), \(p<0.05\)). To further elucidate the control mechanisms of atmospheric drought on grassland productivity in Xinjiang, we conducted a coupled analysis of VPD threshold effects and spatial heterogeneity, revealing the underlying regulatory principles.  
Using a ridge regression model, the spatial heterogeneity of meteorological drivers affecting grassland productivity was analyzed (Figure 7). The spatial pattern indicated that the relative contribution of VPD peaked in the central Tianshan Mountains and the eastern Altai Mountains, highlighting the sensitivity of mountainous ecosystems to atmospheric drought stress (Figure 7a). High contribution zones for TEM were distributed in a belt along the western edge of the Junggar Basin, while the maximum contribution area for SSR was concentrated in the Ili River Valley.  
In terms of hierarchical factor contributions, VPD dominated with a relative contribution significantly higher than that of other meteorological variables, confirming atmospheric drought as the key limiting factor for grassland growth in arid regions (Figure 7b). SSR and PRE followed, reflecting their fundamental regulatory roles in controlling light use efficiency and water availability, respectively. TEM's contribution is comparatively weaker.  
Figure 6: The influence of different meteorological factors on the variability of GPP. The red lines represent direct effects, indicating that independent variables directly affect dependent variables, while blue lines indicate indirect effects through one or more mediating variables. R\({}^{2}\) = 0.80 indicates a high model fit. It emphasizes that these paths are not direct but connected through mediating variables. * denotes significant effects of independent variables on dependent variables.
# An Efficient Quantized Message Passing Receiver Design for SCMA Systems
## 4 Simulation Results
### Ber Evolution for the Rayleigh Channel  
In this subsection, numerical results were conducted for different SCMA detectors for the Rayleigh channel. Figure 9 illustrated the BER performance of different SCMA detectors under the Rayleigh channel for \(K=4\) and \(K=5\), respectively. The effectiveness of the Max-log MPA and Sub-log MPA can be observed. Specifically, the BER curves of Max-log MPA and Sub-log MPA are overlapped, and their robust performance can be inferred from a close match to the original Log-MPA detector. Next, we investigated the convergence behavior of different SCMA detectors for the Rayleigh channel in Figures 10 and 11 in terms of \(K=4\) and \(K=5\), respectively. It can observed that the Sub-log MPA only needs four iterations to converge, while six are needed both in the Log-MPA and Max-log MPA schemes.  
We also compared the BER performance of different quantized schemes under the Rayleigh channel for \(K=4\) and \(K=5\) in Figures 12 and 13, respectively, in terms of the Max-log MPA detector. The 4-bit, 5-bit, 6-bit, 7-bit, and 8-bit uniform quantization schemes were considered, where the 8-bit case can achieve nearly the same BER performance as compared to the original Log-MPA. An important observation is that the (5 + 1)-bit quasi-uniform quantization has almost the same BER performance as the 8-bit uniform  
Figure 8: The BER performance of the (5+1)-bit quasi-uniform quantizer under the AWGN channel for \(K=4\) and the Max-log MPA detector, under different combinations of \(\Delta\) and \(\mu\).  
Figure 9: The BER performance of different SCMA detectors under the Rayleigh channel for \(K=4\) and \(K=5\), respectively.
# Modeling Open Channel Flows of a Viscous Fluid: Critical Transition and Apparent Bottom
## 2 The Navier-Stokes and the Shallow Water Models  
### Navier-Stokes Model (NSM)  
A stratified flow of a heavy and light fluid with densities \(\rho_{1}\) and \(\rho_{2}\), respectively, is modeled with the NSM. The VOF Method, introduced in [47], is used to describe the stratified flow in this study. The volume fraction \(\gamma\in[0,1]\) is thus used to track the free surface of the heavier fluid such that \(\gamma=1\) is the heavier fluid and \(\gamma=0\) is the lighter fluid, the average density being defined by  
\[\rho=\rho_{1}\gamma+\rho_{2}(1-\gamma). \tag{4}\]  
We consider a two-dimensional domain and the equations are solved in the \((x,z)\) coordinate system, whose axis \(z\) is perpendicular to the bottom wall outside the obstacle and is rotated clock-wise by an angle \(\theta\) with respect to the gravitational axis. The bottom is defined by the equation \(z=Z(x)\) where \(Z(x)=0\) everywhere except at the semi-cylindrical obstacle of radius \(H\).  
The volume averaged velocity field, whose components are \(u\) and \(w\), obey the mass conservation equation  
\[\frac{\partial u}{\partial x}+\frac{\partial w}{\partial z}=0 \tag{5}\]  
and the momentum equations read \[\rho\bigg{(}\frac{\partial u}{\partial t}+u\frac{\partial u}{ \partial x}+w\frac{\partial u}{\partial z}\bigg{)} =-\frac{\partial p}{\partial x}+\rho g\sin\theta+2\sigma\kappa\, \hat{n}_{x}+\frac{\partial}{\partial x}\bigg{[}2\mu\frac{\partial u}{\partial x }\bigg{]}+\frac{\partial}{\partial z}\bigg{[}\mu\bigg{(}\frac{\partial u}{ \partial z}+\frac{\partial w}{\partial x}\bigg{)}\bigg{]} \tag{6}\] \[\rho\bigg{(}\frac{\partial w}{\partial t}+u\frac{\partial w}{ \partial x}+w\frac{\partial w}{\partial z}\bigg{)} =-\frac{\partial p}{\partial z}-\rho g\cos\theta+2\sigma\kappa\, \hat{n}_{z}+\frac{\partial}{\partial x}\bigg{[}\mu\bigg{(}\frac{\partial u}{ \partial z}+\frac{\partial w}{\partial x}\bigg{)}\bigg{]}+\frac{\partial}{ \partial z}\bigg{[}2\mu\frac{\partial w}{\partial z}\bigg{]} \tag{7}\]  
where \(p\) is the pressure, \(g\) is the gravitational acceleration, \(\sigma\) is the surface tension and \(\mu\) is the dynamic viscosity of the mixture defined by  
\[\mu=\mu_{1}\gamma+\mu_{2}(1-\gamma). \tag{8}\]  
The evolution of the volume fraction \(\gamma\), according to [47], is given by the following transport equation  
\[\frac{\partial\gamma}{\partial t}+u\frac{\partial\gamma}{\partial x}+w\frac{ \partial\gamma}{\partial z}=0. \tag{9}\]  
The free surface \(S\) of the heavier fluid can be defined as:  
\[S=\{(x,z)\in\ \Omega:\gamma(t,x,z)-0.5=0\} \tag{10}\]  
whose normal is defined as  
\[\hat{n}=\
abla\gamma \tag{11}\]  
and the curvature  
\[\kappa=-\frac{1}{2}\bigg{(}\frac{\partial n_{x}}{\partial x}+\frac{\partial n _{z}}{\partial z}\bigg{)}. \tag{12}\]  
Surface tension \(\sigma\) is included in the numerical simulations to regularize the interface but the sensitivity of the results presented here is negligible with respect to this force. We assume that \(S\) is sufficiently smooth to be described by the equation \(z=Z_{s}(t,x)\).
## 3 Parameter Modeling and Error Analysis of the INS and GNSS Systems
### Inertial Navigation Error Analysis and Modeling
#### 3.1.4 Error Analysis of Inertial Elements  
The random drift error of the gyroscope is generally composed of constant error, fast drift error, and slow drift error. The constant error always remains a constant value and does not change with time. The model is as follows:  
\[\dot{\epsilon}_{b}=0. \tag{24}\]  
The remarkable characteristic of fast varying drift error is random high frequency jitter; specifically, the correlation coefficients of the corresponding values at any two time points are 0, which can be modeled as white Gaussian noise:  
\[E[w_{g}(t)w_{g}(\tau)]=\sigma_{g}\delta(t-\tau), \tag{25}\]  
where \(\sigma_{g}\) is the variance of the white noise.  
The change trend of slow drift is relatively slow, and the value of the later time is closely related to the value of the previous time, which is similar to the change law of the Markov process, so we can model it as a first-order Markov process:  
\[\dot{\epsilon}_{r}=-\frac{1}{T_{r}}\epsilon_{r}+w_{r}, \tag{26}\]  
where \(T_{r}\) is the correlation time constant and \(w_{r}\) is the white noise with a zero mean value.  
To summarize, the error model of the gyroscope is as follows:  
\[\epsilon=\epsilon_{b}+\epsilon_{r}+w_{g}. \tag{27}\]  
The error of the accelerometer is similar to the error of the gyroscope, so we do not need to repeat it here.
## Materials and Methods
### Cell Culture and Toxicity Test  
The cytotoxicity of contact lenses was evaluated referring to ISO standard 10993-5. Mouse embryo fibroblast (3T3) cell lines were purchased from Cell Bank of the Chinese Academy of Sciences (Shanghai, China), and were cultured in Dulbecco's Modified Eagle's medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin (100 IU/mL) and streptomycin (100 ug/mL) at 37 \({}^{\circ}\)C with 5% CO\({}_{2}\). Before measurement, the contact lenses were soaked in 75% aqueous alcohol for 4 h for sterilization, and subsequently washed with PBS three times. The cell viabilities were analyzed with the standard Cell Counting Kit-8 (CCK-8) assay. Briefly, the contact lenses were placed on the bottom surface of a 24-well tissue culture plate (TCP) at a density of 4 \(\times\) 10\({}^{4}\) cells per well, then 3T3 cells were sequentially seeded. After incubation for 1, 2, 3 and 4 days, the culture medium was replaced by fresh DMEM containing CCK-8 and incubated for another 1 h. Then, each sample was transferred into separate wells of a 96-well plate to determine the optical density (OD) value at 450 nm using a microplate reader (Epoch 2 Microplate Spectrophotometer, BioTek Instruments, Inc., Winooski, VT, USA) at an absorbance value of 450 nm. The final results were calculated with \(n=5\).  
For cell morphology observation by confocal laser scanning microscope (CLSM), 3T3 cells were seeded onto a 35 mm glass-bottom dish with covered contact lenses at a density of 5 \(\times\) 10\({}^{4}\) cells per well. After incubating for 1, 2, 3 and 4 days, respectively, the culture medium was removed, and the contact lenses were washed by PBS three times. Afterward, the cells were stained with 10 uM calcein-AM solution for 20 min, and then fixed for 15 min with 4% paraformaldehyde aqueous solution at 37 \({}^{\circ}\)C. Finally, the stained 3T3 cells were observed by CLSM with excitation wavelengths of 499 nm and emission wavelengths of 515 nm, respectively.