esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In recent decades, VLC gained more attention from both commercial and academic areas. Hence, the efforts to integrate and standardize VLC to wireless infrastructures are remarkable when considering the novelties from applications (underwater, localization) to physical perspectives (LED technology, modulation). In recent scenarios, VLC-based applications have many concerns like node mobility, obstacles, shadowing, and LEDs directionality and field of view. To highlight these concerns, a novel cross layer routing protocol is proposed named MGOA for WAVLCN. In this research paper, the routes were maintained and generated on the basis of the network demands. The experimental analysis  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Number of Nodes**} & \multicolumn{2}{c}{**Throughput (bit/s)**} & \multicolumn{2}{c}{**PDR (\%)**} & \multicolumn{2}{c}{**Routing Overhead**} & \multicolumn{2}{c}{**End-To-End Delay**} \\  & **DYRP-VLC** & **MGOA-WAVLCN** & **DYRP-VLC** & **MGOA-WAVLCN** & **DYRP-VLC** & **MGOA-WAVLCN** & **DYRP-VLC** & **MGOA-WAVLCN** \\ \hline
50 & 546 & 594 & 90.74 & 93.28 & 279 & 262 & 2.39 & 2.09 \\
100 & 507 & 589 & 86.33 & 90.12 & 283 & 269 & 2.46 & 2.12 \\
250 & 486 & 553 & 80.74 & 88.90 & 296 & 278 & 2.52 & 2.19 \\
500 & 439 & 544 & 78.11 & 84.10 & 304 & 283 & 2.64 & 2.31 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the MGOA-WAVLCN and DYRP-VLC.  
Figure 6: Graphical analysis of MGOA-WAVLCN in terms of end-to-end delay.  
showed that the developed routing protocol MGOA significantly deals with obstacles and shadows, and creates alternative paths between source and destination nodes for effective data transmission. The performance metrics, such as PDR, end-to-end delay, throughput, and routing overhead, confirm that the routing protocol MGOA obtained significant performance in WAVLCN. Compared to the existing protocol, DYRP-VLC, the proposed protocol MGOA-WAVLCN almost showed 3% to 8% improvement in PDR and reduced 0.3 s of end-to-end delay in data transmission. In future work, a new hybrid optimization algorithm could be developed to further improve WAVLCN performance. In addition, the proposed protocol MGOA-WAVLCN could be applied in real-world scenarios and its performance evaluated in terms of scalability, security, integrity, and confidentiality.  
The paper investigation, resources, data curation, writing--original draft preparation, writing--review and editing, and visualization were done by [PERSON] and S.K. The paper conceptualization, software, validation, and formal analysis were done by K.R.B. Methodology, supervision, project administration, and final approval of the version to be published were conducted by A.S. and B.D.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Tumor Characterization by Radiomics Signature  
The ROI Hunter locates lesions without giving further information. The second and last part of the process concerns the characterization of the found ROIs, so that a decision-making system can correctly discriminate in situ from infiltrative lesions. This step consists of Radiomics feature extraction from the selected ROIs and classification. As the calculation was performed in 3D, before proceeding, 2D ROIs were grouped on the basis of continuity from slice to slice, so as to form 3D ROIs.  
In order to discriminate the tumor volumes so obtained, we investigated a large dataset of Radiomics features. Overall, 1820 features comprising shape, first order, and higher order features were generated for each detected ROI, with original and filtered intensity. We computed 18 first-order statistic features describing the distribution of voxel intensities within the image region defined, and 68 textural features quantifying intra-tumor heterogeneity (22 from gray-level co-occurrence matrices (GLCM), 16 from gray-level run length matrices (GLRLM), 14 from gray level dependence matrices (GLDM), and 16 from gray level size zone matrices (GLSZM)) [48]. In addition to calculating the features on the original ROI volumes, we applied several preprocessing filters to each ROI, before computing the Radiomics signatures--Laplacian of Gaussian filter for edge enhancement, Wavelet filters yielding 8 subfilters (all possible combinations of applying either a High or a Low pass filter in each of the three dimensions), Square and SquareRoot filters that take the square and the square root of the image intensities, and linearly scale them back to the original range, Logarithm, Exponential, Gradient filters, the Local Binary Pattern filter (in a by-slice operation, i.e., 2D, and using spherical harmonics, in 3D). After this step, we applied recursive feature elimination to remove redundant and irrelevant features.
# Performance Analysis of a PEMFC-Based Grid-Connected Distributed Generation System
## 4 Operation Results  
The PEMFC-based grid-connected DG system at Marmara University, Faculty of Technology, was analyzed in detail in this study. It was found that 53.53% of the total energy demand was met by the utility grid, while 46.47% of the demand was met by the DG system, as can be seen in Figure 15. As seen in Table 4, the annual average energy demand was calculated as 3791.04 kWh; 2029.497 kWh that could not be met by production could be supplied from the grid via the hybrid inverter. The remaining energy demand was met by the distributed generation, 1761.543 kWh. To this end, we can clarify that 53.53% of the total energy demand is met by the utility grid, while 46.47% of the demand is met by the DG system.  
Energy supplied from the utility grid, production of DG systems, and energy demands for the system are given in Table 5. With respect to the energy values of DGs in kWh, most of the demands are met by PV, 35.22% of total production, i.e., nearly 76% of the DG system, as can be seen in Figure 16. The contributions of the wind and PEMFC, (nearly 24% of the DG system) are similar in percentage at 6.02% and 5.23%, respectively.  
Figure 14: (**a**) Start-up battery _SoC_ levels; (**b**) PEMFC’s DC/DC converter output voltages.  
Figure 15: Grid support and DG energy production.
# On Compressed Sensing of Binary Signals for the Unsourced Random Access Channel
## 4 Conclusions and Additional Discussion
Consider a model \(\mathbf{y}=AH\mathbf{x}+\sigma\mathbf{z}\) where \(H=\text{diag}(h_{1},\ldots,h_{m})\) is a diagonal matrix of (random) fading coefficients. We would like to sample from the posterior of \(\mathbf{x}\) given \(\mathbf{y}\):  
\[\Pr(\mathbf{x}=\widehat{\mathbf{x}}|\mathbf{y})\propto\mathbb{E}_{H}\exp\left\{-\frac{1}{ 2\sigma^{2}}\|\mathbf{y}-AH\widehat{\mathbf{x}}\|^{2}+\lambda\|\widehat{\mathbf{x}}\|_{1} \right\},\]  
where notice that we now need to marginalize over \(H=\text{diag}(h_{1},\ldots,h_{M})\). This marginalization appears to complicate things considerably: in particular, in contrast to the case where \(H\) is the identity matrix, in the general case, it is not so straightforward to sample \(x_{i}\) conditioned on all other coordinates. Devising an MCMC decoder that can handle fading is an interesting problem for future research. **Author Contributions:** Conceptualization, E.R. and O.O. ; methodology, E.R. and O.O. ; software, E.R. ; validation, E.R. ; formal analysis, E.R. and O.O. ; investigation, E.R. and O.O. ; writing--original draft preparation, E.R. and O.O. ; writing--review and editing, E.R. and O.O. ; visualization, E.R. ; supervision, O.O. ; project administration, O.O. ; and funding acquisition, O.O. Both authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported in part by ISF under Grant 1791/17 and in part by the GENESIS Consortium via the Israel Ministry of Economy and Industry. The work of [PERSON] was supported in part by an [PERSON] fellowship from the Hebrew University of Jerusalem. **Data Availability Statement:** Python code implementing the proposed algorithm is available in the following url: [[https://github.com/dalevonamor/BinaryCompressedSensingEntropy](https://github.com/dalevonamor/BinaryCompressedSensingEntropy)]([https://github.com/dalevonamor/BinaryCompressedSensingEntropy](https://github.com/dalevonamor/BinaryCompressedSensingEntropy)). **Acknowledgments:** We thank [PERSON], [PERSON] and [PERSON] for valuable discussions and for kindly sharing with us their code for the scheme described in [11] and [PERSON] for valuable discussions. **Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Methodology
### Model Establishment
#### 2.3.2 Viscous Terms  
The dam-break debris flow studied in this paper is a typical two-phase fluid problem. The sediment concentration in the slurry at the initial stage is quite low, which can be approximately regarded as Newtonian fluid. Its constitutive equation meets the following requirements:  
\[\tau=\mu\mathbf{D} \tag{7}\]  
where \(\mu\) is the dynamic viscosity coefficient of water, usually taken as. \(\mu=1.01\times 10^{-6}kpa\cdot s\). When the particles are initiated by water and mixed with it, the non-Newtonian fluid with higher sediment concentration will become more viscous, and the deformation to the dam will produce greater shear stress.  
\[\tau=\frac{1}{2}\bigg{(}\frac{\mu_{0}}{|\mathbf{D}|}+\mu_{1}+\mu_{2}|\mathbf{ D}|\bigg{)}\mathbf{D} \tag{8}\]  
where \(\mu_{0}\) refers to the yield stress; \(\mu_{1}\) refers to the coefficient of friction and \(\mu_{2}\) refers to the coefficient of collision stresses.  
Refer to [26] and deal with Equation (8), the viscous term in the x, y direction can be represented by the following discretization:  
\[\frac{du}{dt}=\sum_{b}m_{b}\bigg{\{}\frac{1}{\rho_{a}\rho_{b}}\cdot\frac{1}{2} \bigg{(}\frac{\mu_{0}}{|\mathbf{D}|}+\mu_{1}+\mu_{2}|\mathbf{D}|\bigg{)}\bigg{[} 2\frac{\partial u}{\partial x}\mathbf{i}+\bigg{(}\frac{\partial v}{\partial x }+\frac{\partial u}{\partial y}\mathbf{j}\bigg{)}\bigg{]}\bigg{\}}\cdot\bigg{(} \frac{\partial W}{\partial q}\cdot\frac{x_{ab}}{\mathrm{i}lr}\mathbf{i}+ \frac{\partial W}{\partial q}\cdot\frac{y_{ab}}{hr}\mathbf{j}\bigg{)} \tag{9}\]  
\[\frac{dv}{dt}=\sum_{b}m_{b}\bigg{\{}\frac{1}{\rho_{a}\rho_{b}}\cdot\frac{1}{2} \bigg{(}\frac{\mu_{0}}{|\mathbf{D}|}+\mu_{1}+\mu_{2}|\mathbf{D}|\bigg{)}\bigg{[} \bigg{(}\frac{\partial v}{\partial x}+\frac{\partial u}{\partial y}\mathbf{j }+2\frac{\partial v}{\partial y}\mathbf{j}\bigg{]}\bigg{\}}\bigg{)}\cdot\bigg{(} \frac{\partial W}{\partial q}\cdot\frac{x_{ab}}{hr}\mathbf{i}+\frac{\partial W }{\partial q}\cdot\frac{y_{ab}}{hr}\mathbf{j}\bigg{)} \tag{10}\]  
In this study, the slurry formed after the initiation of the sediment particles is collected through the flume experiment. The slurry rheology coefficient was measured by the MCR301 advanced rotary rheometer, which is an equipment manufactured by [PERSON], Austria [27]. The viscosity parameters collected are the following ones: \(u_{0}=0.00004\), \(u_{1}=0.0048\), \(u_{2}=0.0197\).
# Can Walnut Serve as a Magic Bullet for the Management of Non-Alcoholic Fatty Liver Disease?
## 3 Results and Discussion  
### Pathogenesis of NAFLD and Progression to NASH, Fibrosis, and Primary Liver Cancer  
The liver performs a wide range of body functions essential for maintaining health. A key role of the liver is maintaining metabolic homeostasis by regulating the levels of macronutrients, such as glucose, lipid and protein [12]. Hepatocytes, which are major parenchymal cells in the liver, play pivotal roles in metabolism, detoxification, and protein synthesis. Hepatocytes can accumulate triglyceride by increasing fatty acid uptake and de novo lipogenesis, and metabolize fatty acids through lipolysis [13]. Kupffer cells, the resident macrophage in the liver, play an integral role in the hepatic immune response [14]. Hepatic stellate cells produce collagen and matrix when activated in response to liver injury and play a vital role in hepatic fibrogenesis [15] (Table 1).  
In the early stage of simple steatosis, there is no or minimal damage to hepatocyte function. However, non-alcoholic steatohepatitis (NASH) causes hepatocyte damage that is represented by hepatocellular ballooning, formation of Mallory-Denk bodies, and lobular inflammation [16]. In the spectrum of NAFLD, liver injury results from a chronic inflammatory response by immune cells (Kupffer cells, neutrophils, dendritic cells, B cells, cytotoxic T cells and T-helper cells) and fibrosis by activated stellate cells [17; 18].
## 2 Materials and Methods
### Landslide Inventory  
The precise identification of landslide locations is crucial for assessing landslide susceptibility within a specific area. Hence, it is imperative to create a detailed inventory map of co-seismic landslides. In this study, the detection of landslide locations was achieved by combining existing research findings from [PERSON] et al. [7] and the National Disaster Risk Reduction and Management Authority (NDRRMA) [21] along with manual interpretation of images from Google Earth. Earthquake-induced landslides were verified through a comparative analysis of pre- and post-event imagery using Google Earth's historical timeline feature, as shown in Figure 2. The mapped landslides were then exported to ArcGIS Pro for further processing and spatial analysis.  
Figure 1: Location map of the study area (Gorkha District, Nepal) showing the epicenter of the 2015 Gorkha Earthquake (red star), mapped landslide spots, rivers, and district boundaries. The inset map (top-right) illustrates Nepal’s geographic context within South Asia, while the middle-right panel highlights the study area within Nepal.  
The combination of image analysis and field survey efforts resulted in the identification of a total of 4775 co-seismic landslides, ranging in coverage from 28.7 m\({}^{2}\) to 308,428 m\({}^{2}\), with an average area of 2776 m\({}^{2}\). The dataset containing the identified landslides was randomly divided into two subsets: the training dataset of 3820 items (80%) and the model validation dataset of 955 items (20%), as illustrated in Figure 3. Landslide polygons were converted into 4775 point features using ArcGIS Pro, each labeled as '1' to represent a landslide occurrence.  
To balance the dataset for binary classification, an equal number (n = 4775) of non-landslide points were generated within the same geographic extent and labeled as '0'. A buffer-based sampling method was used to ensure that non-landslide points were spatially independent and not influenced by the same geomorphological or triggering conditions. Specifically, a 100 m buffer was created around each landslide point, and non-landslide samples were randomly drawn from outside these buffer zones. Both landslide and non-landslide point data are presented in Figure 3b.  
Figure 3: (**a**) Earthquake-induced landslide inventory polygon map. (**b**) Landslide points and random non-landslide points.  
Figure 2: Georeferenced Google Earth imagery used for landslide inventory verification: (**a**) pre-earthquake image with earthquake-induced landslides marked by red polygons and (**b**) post-earthquake image showing the co-seismic landslides, marked by red polygons and delineated through visual comparison.  
This approach reduced spatial autocorrelation and ensured distinct class separation between landslide and non-landslide samples. All sampled points--both landslide and non-landslide--were validated to fall within the study area boundary and regions with complete and valid conditioning factor data.
# Hybrid Fuzzy-DDPG Approach for Efficient MPPT in Partially Shaded Photovoltaic Panels
## 2 Materials and Methods
### Reinforcement Learning Fuzzy-DDPG
_The additive noise in the target computation is zero-mean and uniformly bounded._ _Then, the iterative update of the critic network via stochastic gradient descent,_ \[y_{i}=r_{i}+\gamma Q^{\prime}(\phi(s_{i+1}),\mu^{\prime}(\phi(s_{i+1})|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})\]2 (32) _with the loss function defined as_ \[y_{i}=r_{i}+\gamma Q^{\prime}(\phi(s_{i+1}),\mu^{\prime}(\phi(s_{i+1})|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})\]3 (33) _converges almost surely to a parameter set_ \[y_{i}=r_{i}+\gamma Q^{\prime}(\phi(s_{i+1}),\mu^{\prime}(\phi(s_{i+1})|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})\]4 _such that_ \[y_{i}=r_{i}+\gamma Q^{\prime}(\phi(s_{i+1}),\mu^{\prime}(\phi(s_{i+1})|\theta^{ \mu^{\prime}})|\theta^{Q^{\prime}})\]5 (34)

The above lemma rigorously establishes that, under the appropriate conditions on the function approximators and learning parameters, the critic network in the Fuzzy-DDPG framework converges to the optimal action-value function. This result, combined with analogous arguments for the actor network, provides a solid theoretical basis for the overall convergence of our hybrid approach. ## 3 Results

The performance of the proposed hybrid Maximum Power Point Tracking (MPPT) control strategy, which integrates fuzzy techniques with Deep Deterministic Policy Gradient (DDPG), is evaluated on a simulated photovoltaic (PV) panel operating under partial shading conditions. The simulation results indicate that the proposed method yields significant improvements compared to conventional techniques such as Perturbation and Observation (P&O) and Incremental Conductance (IC), both of which tend to exhibit oscillatory behavior around local maxima and are consequently unable to reliably attain the global optimum. Table 1 summarizes the key hyperparameters and network configuration employed in the Fuzzy-DDPG implementation. The partially shaded PV panel exhibits a complex, non-convex power-voltage characteristic with multiple local maxima. Under these challenging conditions, the Fuzzy-DDPG method is capable of converging to the global maximum power point, thereby surpassing baseline methods that are confined to local optima. In the following section, we present a comprehensive analysis of the experimental findings associated with the proposed hybrid MPPT control scheme. This evaluation includes a comparative study against conventional MPPT approaches, namely Perturb and Observe (P&O) and Incremental Conductance (IC). The photovoltaic system utilized in the simulations is based on a commercially available panel. Table 2 details the electrical characteristics of the panel, including its rated power, voltage, and current at the maximum power point (MPP), as well as other essential parameters for precise simulation modeling. ### Reward Function Design

The reward function used in this study is designed to ensure optimal tracking of the Maximum Power Point (MPP) while penalizing undesirable behaviors such as excessive
# OQPSK Synchronization Parameter Estimation Based on Burst Signal Detection
## 4 Conclusions  
In order to reduce the complexity of algorithm implementation, we propose an OQPSK synchronization parameter estimation algorithm based on burst signal detection. First of all, the burst signal detection is performed by adopting a simplified segment correlation algorithm, which greatly reduces the amount of calculation with a very small performance  
Figure 8: Timing error estimation simulation when adding 0.05 normalized frequency offset.  
Figure 9: Timing estimation accuracy under different frequency offset.  
loss. After completing the burst signal detection, on the one hand, we use the results generated during the burst signal detection to estimate the frequency offset and then estimate the phase offset. On the other hand, the results of burst signal detection are also used to estimate the timing error. The simulation results show that the algorithm in this article not only greatly reduces the amount of calculation, but still retains good performance. More precisely, firstly, the estimation accuracy of the frequency offset estimation algorithm in this article is close to the L&R and Fitz algorithms with a much wider estimation range; secondly, the timing estimation algorithm keeps high accuracy with excellent anti-frequency offset ability and is less sensitive to the change of the roll-off factor.  
Conceptualization, Z.L. and K.G.; methodology, Z.L., K.G., and K.Z.; software, Z.L.; validation, Z.L., K.G., and J.D.; writing, original draft preparation, Z.L.; writing, review and editing, Z.L., P.S., and L.D.; project administration, K.G., P.S., and J.D. All authors read and agreed to the published version of the manuscript.  
This research was funded by the Development Program \"Frontier Scientific and Technological Innovation\" Special under Grant 2019 QY0302, the National Natural Science Foundation of China under Grant U1736107, and the National Natural Science Foundation of China under Grant 61901417.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
We sincerely thank the members of the Communication Signal Analysis and Countermeasure Laboratory of the School of Information Engineering of Zhengzhou University and BeiBei Zhang for their help and support in developing this paper.  
The authors declare no conflict of interest.