esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Example and Sensitivity Analysis  
First of all, the utilized parameters for experimental design and their surfaces are described; subsequently the statistical results of modeling with upper and lower limit and the alpha cut method are determined; finally, each parameter's influence on objective function is illustrated by sensitivity analysis.  
Table 1 indicates the utilized parameters for experiments and their numerical surfaces considering the research of [PERSON] et al. in [8],  
\[\Pi\ =\ (O,\ P,\ \mu,\ w_{1}/P_{1},\ldots,w_{m}/P_{m},R_{1},\ldots,R_{m},i_{0}) \tag{40}\]  
where:  
1. \(O\) is an alphabet; its elements are called components;
2. \(P\) is an alphabet; its sets are called enzymes, \(P_{1},\ \ldots,\ P_{m}\) are the collection of enzymes on the \(m\) layers of \(\mu\);
3. \(\mu\) is a layer structure consists of \(m\) layers, \(m\) constitute also the level of \(\Pi\);
4. \(w_{i},\ 1\ \leq\ i\ \leq\ m\), are collection of strings which part of the initial multisets over \(V\) associated with the regions \(1,\ 2,\ \ldots\,\ m\) of \(\mu\);
5. \(R_{i},\ 1\ \leq\ i\ \leq\ m\), are sets of rules in a maximal parallel manner that associated to the regions \(1,\ 2,\ \ldots\,\ m\) of \(\mu\);
6. \(i_{0}\), is a indexing between \(1\) and \(m\) which shown on the output layer of \(\Pi\).  
Employing Design Expert software and response surface methodology, this has shed light on the parameters' impact on the proposed model. Based on [PERSON] et al. in [8] regarding the demand to be fuzzy, considering an alpha \(\alpha=0.1\) tolerance Figure 4 presents objective function as a function of parameters, \(O\) and \(P\), respectively.  
Furthermore, Figure 5 pictures the objective function based on parameter \(\mu\) and wi with lower demand limit and \(\alpha=0.1\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Parameter Surfaces** \\ \hline \(O\) & 2, 8, 14, 20 \\ \(p\) & 0.01, 0.04, 0.07, 0.1 \\ \(\mu\) & 4 M, 14 M, 22 M, 30 M \\ _wi_ & 3 K, 12 K, 21 K, 30 K \\ _Ri_ & 6 M, 14 M, 22 M, 30 M \\ _io_ & 2, 4, 6, 8 \\ \(i\) & 1, 2, 3, 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter levels.  
Figure 4: Objective function diagram as a function of (O and P) parameters with lower demand limit and \(\alpha=0.1\).  
As depicted, escalation in enzymes size has a positive correlation with the objective function. We also noted that a collection of P systems with active layers and enzymes used in this experiment already show the polynomial time \(O(n(n-1))\) (\(n\) is a number of nodes in the graph).  
Moreover, in each problem parameter's coefficient function, response surface prediction of the mentioned issue is explicit which is indicated in Table 2.
###### Abstract  
Domain Generation Algorithms (DGAs) remain a persistent technique used by modern malware to establish stealthy command-and-control (C&C) channels, thereby evading traditional blacklist-based defenses. Detecting such evolving threats is especially challenging in decentralized environments where raw traffic data cannot be aggregated due to privacy or policy constraints. To address this, we present FedSAGE, a security-aware federated intrusion detection framework that combines Variational Autoencoder (VAE)-based latent representation learning with unsupervised clustering and resource-efficient client selection. Each client encodes its local domain traffic into a semantic latent space using a shared, pre-trained VAE trained solely on benign domains. These embeddings are clustered via affinity propagation to group clients with similar data distributions and identify outliers indicative of novel threats without requiring any labeled DGA samples. Within each cluster, FedSAGE selects only the fastest clients for training, balancing computational constraints with threat visibility. Experimental results from the multi-zones DGA dataset show that FedSAGE improves detection accuracy by up to 11.6% and reduces energy consumption by up to 93.8% compared to standard FedAvg under non-IID conditions. Notably, the latent clustering perfectly recovers ground-truth DGA family zones, enabling effective anomaly detection in a fully unsupervised manner while remaining privacy-preserving. These foundations demonstrate that FedSAGE is a practical and lightweight approach for decentralized detection of evasive malware, offering a viable solution for secure and adaptive defense in resource-constrained edge environments.  
network security; intrusion detection systems; federated learning; DGA detection 2020
## 2 Materials and Methods  
### Glass Fabrication  
Seven oxides like strontium oxide (SrO), tellurium oxide (TeO\({}_{2}\)), boron oxide (B\({}_{2}\)O\({}_{3}\)), titanium oxide (TiO\({}_{2}\)), zinc oxide (ZnO), barium oxide (BaO), and lead oxide (PbO) were utilized to fabricate four glass samples using the traditional melt-quench technique. The oxides were purchased from different company: SrO (99.9%), TeO\({}_{2}\) (98%), B\({}_{2}\)O\({}_{3}\) (98%), BaO (99%) and (PbO) Aldrich, and ZnO (99.9%) WINLAB, while TiO\({}_{2}\) (98%) from BDH chemical Ltd. No purification was used for these chemicals. The chemical ratios for oxides are enlisted in Table 1. The traditional melt quench method was started by weighting the oxides carefully based on the mole percent for each sample, as shown in Table 1. After that, these chemical oxides were mixed and put in an alumina crucible. The mixture was introduced in an electrical furnace at 1000 \({}^{\circ}\)C for 30 min to perform the melting process, and the mixture was stirred periodically during the melting process. Subsequently, the crucible was poured on a steel plate inside another furnace at 400 \({}^{\circ}\)C to form glasses. The glasses were kept inside the electrical furnace for three hours to minimize the stress and reduce the glass fracture probability; then, the furnace temperature was lowered with a cooling rate of 15 \({}^{\circ}\)C/min.  
The density for current glasses was measured experimentally using Archimedes principle; the special density kit from Rad Wag company was employed for determining the sample weight in the air (\(A\)) and liquid (\(B\)) according to the next relation:  
\[\rho=\frac{A}{A-B}x\rho_{liq} \tag{1}\]  
Here, \(\rho_{liq}\) represents the water density utilized as an immersion fluid in this experiment, this method was utilized in our previous work [22; 23].
## 4 Edge Cloud Infrastructure
### Networking  
As we mentioned before, networking in the edge computing paradigm provides connectivity between edge DCs, user devices, and edge DCs to cloud DCs. The end users can connect to the computing and storage resources through one of the three access technologies: wired LAN, wireless LAN and cellular networks. In general, networks between edge DC to cloud DC do not have many differences from cloud architecture. In this case, connection between edge DC and cloud DC could be established by WAN, cellular, or satellite technology. Deployment of edge servers can reduce operational costs by up to 67% for bandwidth-hungry and compute-intensive applications [27; 30]. Moreover, [PERSON] et al. [32] demonstrated that network delay could be significantly reduced in edge cloud networks.  
As we mentioned before, edge cloud DCs are heterogeneous and deployed in the proximity to customers; therefore, workloads are much more distributed, localized, and variable. To spread load and provide redundancy, the ability to connect inter edge DCs flexibly is very important. Integration of virtualization and SDN controller with edge cloud fits such requirements very well.
## 3 Conventional Frequency-Offset Estimation Method  
In OFDM, post-FFT estimation schemes are in practice performed in the frequency-domain, either in a pilot-aided or blind way. Since many pilot symbols are provided to help acquire synchronization in the DVB-C2 system, we focus our attention on the pilot-based estimation strategy [23; 24; 25; 26; 27; 28; 29], which is introduced as a reference to the proposed scheme. Considering the characteristics of the cable channel,we assume that the CFR is unchanged over several successive OFDM blocks. Therefore, the unknown channel phase can be removed performing correlation between consecutive pilots.
# Building Tree Allometry Relationships Based on TLS Point Clouds and Machine Learning Regression
## 2 Materials and Methods
### Field Data  
A TLS field campaign was conducted in November 2018 over the aforementioned 58 reference plots. Note that the phenological stage of teak at this time in the Coastal region of Ecuador can be qualified as leaf-off conditions.  
The TLS used in the field study was the FARO Focus 3D X330 (FARO Technologies Inc., Lake Mary, FL, USA). This TLS captures nearly a million points per second with millimeter precision and with a range of about 330 m, along with high-resolution RGB images. Pre-tests were carried out to define the optimal parameters in terms of quality and exploration time [14]. Panoramic RGB images were taken to colorize the 3D point cloud and serve as a high-quality visual reference for post-processing tasks. All sensors incorporated in the scanner (i.e., GPS, inclinometer, compass, and altimeter) were activated.  
Four scans were performed on each reference plot, including a central scan and three scans located around it in order to draw, approximately, an equilateral triangle (Figure 2a). The four scans were later co-registered and merged into the same spatial reference system using nine reference white spheres, with a size of 15 cm in diameter, which Scene(tm) software 7.1 (FARO Technologies Inc., Lake Mary, FL, USA) was able to automatically detect in the point cloud. These spheres were placed with the help of iron rods to ensure that at least three spheres were visible from every two consecutive scans. Note that multi-scan data are usually more accurate to measure stem diameter and tree height than single scans [22].  
Figure 1: Location map of the three teak plantations (Morondava, El Tecal and Allteak).  
The processed TLS point clouds were clipped with a circular shape of 18 m radius. Next, bare earth points were classified by applying the octree search method implemented in the open-source software 3D Forest [23] (Figure 2b). A 20 cm grid spacing DTM was built from ground points to proceed with the calculation of the normalized heights of each TLS point. In addition, each tree in the plot was automatically segmented by using the point clusters method implemented in the software 3D Forest [23]. It should be noted that additional manual editing was also carried out to remove some errors observed in the automatic segmentation of each tree.  
Finally, 3791 leak trees were extracted from the 58 reference plots. Those leak trees with DBH < 5 cm and/or CD < 1 m (underdeveloped trees) were removed from the original dataset. Thus, 2272 trees remained as the final dataset to develop the regression models (DBH = \(\Phi\)(h) and DBH = \(\Psi\)(h, CD)) tested in this study.  
A MATLAB code was developed for the automatic extraction of the dendrometic variables DBH and H [14] from the point clouds corresponding to segmented trees, while CD was manually measured for each tree in the digital environment provided by Fusion/LDV software [24]. A complete description about the methods followed in order to obtain the dataset used in this study can be found in [14].
# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 4 Experiments and Results
### Experimental Results  
#### 4.3.1 Experimental Results of Independent Use of Dual-Emotion Features  
In order to verify the validity of dual emotions alone, two baseline features, Emoratio and EmoCred, were selected for comparison, and the same emotion dictionary was used to extract features. Since the semantic features automatically extracted by the rumor detector will cause interference with the verification results, this paper selected a five-layer MLP to exclude interference factors from the rumor detector. The results of MLP detection of six types of emotional characteristics is shown in Figure 7.  
1. In the rumor text, the performance of publisher's emotional features is better than the Emoratio and Emocred baseline features;
2. The F1 score of social-emotional traits was 12% higher than that of publisher's emotional traits and about 13% higher than that of the two baseline traits;
3. The dual-emotion features significantly enhanced the accuracy of the dataset, and the F1 score was as high as 71.6% when only the emotion gap was used.  
These data indicate the necessity of dual affective features in affective signal modeling. In addition, the improvement from social affective features and the affective gap in the detection results further proves that the independent use of dual affective features can also improve the effectiveness of rumor detection.  
In this paper, five types of emotional features (1 emotion category, 2 emotion vocabulary, 3 emotion intensity, 4 emotion score, and 5 other auxiliary features) are used to extract the emotional features of publishers. To verify the effect of each affective trait, F1 scores were compared before and after one particular type of trait was removed from the dual-emotion features each time. Figure 8 demonstrates that no matter what type of affective feature is removed, the macro F1 score for dual-emotion features decreases. Therefore, it proves the necessity of the existence of the five emotional characteristics, and their combination will achieve the maximum effect.  
Figure 7: The results of MLP detection of six types of emotional characteristics.
# Constraints of Using Conductive Screen-Printing for Chipless RFID Tags with Enhanced RCS Response
## 3 Tags with Enhanced RCS Response  
The main issue of the design of the chipless RFID transponders (for identification as well as sensoring) is ensuring a sufficiently high radar cross-section (RCS) to detect the tag at a sufficient distance [13]. The second important issue is to ensure the proper operation of the tag in any surroundings. This means even in the close presence of metallic or loss dielectric objects, especially, that create relatively strong unwanted reflections and thus mask the desired response [19]. Three chipless RFID transponders based on different approaches to the RCS response level enhancement that enables the reduction of false reflections developed by the authors were analyzed from the screen-printing point of view [33; 34; 35]. These structures were compared with the improved re-arranged U-dipole structure with very good robustness of the RCS response [13]. As for the transponders used for encoding multi-bit information, 20-bit versions were chosen. This is a suitable compromise between the number of bits needed to encode the information and the area occupied by such a structure (space bit density). The number of bits can be arbitrary with respect to a particular application.  
All tags were re-designed for a Folex Folanorm (polyester film) dielectric substrate (\(\epsilon_{\text{r}}=4\), \(\tan\delta=0.005\)) with a thickness of 125 \(\upmu\)m. The screen-printing of the conductive parts was performed by ESL Electroscience polymer silver conductor paste ESL 1901-S with resistivity 20 m\(\Omega\)/sq and thickness 30 \(\upmu\)m; for details, see in Section 4. The size of all the structures are summarized in Tables 1-4, respectively.
## 4 The Proposed Routing Strategy
### Routing Algorithm  
The GA offers a practical approach to performing a global search through multiple local searches. This algorithm relies on operations such as selection, crossover, and mutation to optimize the solution. In each loop, the GA generates a set of chromosomes, which represent candidate solutions for the optimization problem. The goal is to use the fitness value of each chromosome as a standard to find an approximate optimal solution over several generations. The algorithm process is shown in Figure 1. In each generation, the roulette wheel selection method is used to select elite chromosomes. The crossover operation from elite chromosomes then selects the optimal chromosomes as parents. In this operation, the genesof the two parent chromosomes are randomly exchanged with a predefined probability, giving rise to next-generation chromosomes. To maintain diversity and feasibility in the set of chromosomes, the genes in the next generation of chromosomes undergo mutation based on the following three principles:  
1. Mutation operates on a randomly selected individual gene, i.e., an individual ISL;
2. The mutated gene is only allowed to be replaced with one of the three alternative ISLs on the same path;
3. We ensure that this hop after mutation is feasible. Once the crossover and mutation operations are completed, the next-generation chromosomes undergo evaluation based on the fitness value and problem constraints. This evaluation determines the fitness values of the chromosomes and prepares them for the next loop of operations. This process continues until the optimal chromosome remains unchanged for a specified number of generations. Table 1 summarizes the GA terminology that we will use in the algorithm. The specific execution process of the routing algorithm is displayed in Algorithm 1, and we can see that it is generally executed alternately between two steps:  
1. Solve the linear relaxation problem, where each product can have multiple paths that can forward the flow;
2. Utilize GA to ensure the uniqueness of the solution after linear relaxation, so that each commodity has a unique path. The algorithm continues to execute until all commodities have a unique traffic forwarding path. Due to the fact that solving relaxation is easier than solving the satellite routing problem, our algorithm can achieve faster convergence times. Referring to the worst-fit memory allocation concept, we rank the commodities in descending order of demand. Studies have demonstrated the significant influence of this order on the heuristic's solution quality. After obtaining a linear relaxation solution, each commodity has several candidate paths. Figure 1: GAGR routing strategy. The GA treats the problem's solution set as a population and improves the solution quality through selection, crossover, and mutation operations, ultimately obtaining an approximate optimal solution. Therefore, it serves as a significant method in solving the aforementioned problem.
## 4 Hardware Implementation of the RNCS
### A&F Relay Node Implementation  
In this subsection, we propose our scheme to implement MIMO A&F Relay Nodes with standardized LTE signal by 3 GPP. Here, we present A&F protocol without and with pre-equalization techniques. Therefore, the implemented A&F Relay Node estimates the channel or not, depending on whether this functionality is activated, as shown in Figure 8. In the first phase, the IQ is captured with the MIMO NI-USRP 2944R platform. After acquiring the received information, the signal is saved in the buffer (processing signal block) and, considering the selected functionality, switch 1 will select position 1 (Amplify-&-forward) or 2 (pre-equalization and Amplify-&-forward). If position 2 is selected, before pre-equalization, it must perform a cell search and another step. This involves acquiring slot and frame synchronization, looking up the cell identity, and decoding the Master Information Block (MIB). In the basic structure, a configuration structure taking into account the sample rate of the MIMO NI-USRP 2944R platform is performed, in which a  
Figure 7: Transmitted Signal Demodulation from eNB.  
Figure 8: MIMO Amplify-&-Forward Relay Node Processing Blocks.  
demodulation are passed through the MMSE and ZF equalizers, for which the equalization type is selected using switch 2. Finally, the equalized signal is modulated(MIMO-OFDM) and transmitted through an the RF transmit chains of the MIMO NI-USRP 2944R.
## 3 Datasets and Processing Strategies  
To evaluate the performance of the proposed medium baseline RTK, we selected six baselines at latitude 30\({}^{\circ}\), namely HZDQ-HZLA, HZDQ-HZXS, HZXS-HZLA, WHJA-EZEC, EZEC-WHJX, and WHJX-WHJA, with the first three baselines located in Hangzhou and the remaining three baselines located in Wuhan. The distances of these baselines range from 45 km to 66 km, with data collection taking place on 23 May 2022, which corresponds to the DOY (day of year) 143. The distribution of these stations can be seen in Figure 1. These stations are equipped with a Sepentrito PolaRx4 receiver, receiving BDS/Galileo/GPS multi-frequency signals at a data sampling rate of 5 s. Three data processing schemes were set up: Scheme 1 used dual-frequency data, including BDS B1I/B2a, Galileo E1/E5a, and GPS L1/L2; Scheme 2 used triple-frequency data, including BDS B1I/B2a/B3I, Galileo E1/E5a/E5b, and GPS L1/L2/L5; Scheme 3 simultaneously used both dual-frequency and triple-frequency data. When performed RTK data processing, the software Net_Diff developed at the GNSS Analysis Center of Shanghai Astronomical Observatory was used [17].  
For the combined BDS/Galileo/GPS data, a loosely coupled approach is employed, where each constellation utilizes its own base satellite for its respective DD observations. In addition, during parameter estimation, a kinematic forward Kalman filter is applied. For each satellite, the stochastic model is calculated using a weighting strategy that depends on the elevation angle.  
When fixing the WL ambiguity and the raw ambiguity, the LAMBDA method is adopted. Furthermore, in order to improve the fixing rate, a partial ambiguity resolution (PAR) method based on the elevation angle strategy is utilized [22]. Additionally, to verify the reliability of AR, the ratio test is adopted with a threshold set to 3. The main data processing strategies are listed in Table 1.
# Bioactive Compounds and Nanodelivery Perspectives for Treatment of Cardiovascular Diseases
## 5 Nano-Delivery Systems of Bioactive Compounds
When assessing the beneficial effects of polyphenols, it must be considered that only a small proportion of the ingested polyphenols are absorbed in the intestine, and therefore, a high quantity of polyphenols is required to achieve the expected improvement in terms of blood pressure lowering. The poor bioavailability is due not only to the low water solubility of polyphenols but also to the instability in alkaline conditions of biological fluids [159]. In order to overcome these drawbacks, nanoencapsulation technologies have emerged as a novel trend in drug carrier development, being nontoxic in nature, with the ability to escape from the host immune system, along with other advantages such as biodegradability, biocompatibility, and drug-targeting properties. The encapsulation of active ingredients is nowadays a routine fabrication process and can be realized through different techniques: spray or freeze drying, coacervation, ionic gelation, extrusion, emulsion, electrospinning, electro-spraying, and liposomes formulation [160]. Encapsulation is also designed to protect the bioactive compounds during processing, storage, and transport from different undesirable factors such as temperature, light, and environmental oxidation. In this respect, biological materials such as polysaccharides, proteins, lipids, or low molecular surfactants are used [161], being highly biocompatible and nontoxic for human consumption. The release mechanisms might be related to diffusion, swelling, erosion, fragmentation, dissolution, or stimuli-controlled  
Figure 2: Types of nano-delivery systems. release. \(\beta\)-carotene, curcumin, quercetin, resveratrol, and epigallocatechin-3-gallate are only a few examples of nano-encapsulated bioactive compounds with improved bioavailability and metabolism when compared to non-encapsulated ones [26]. Based on literature research, Table 11 summarizes the most recent nanocarrier formulations developed for bioactive compounds with therapeutical indications in CVDs, along with experimental models (in vitro or in vivo) and the main outputs.