esa-sceva/satcom-chunk-collection huggingface dataset:
# Reducing Defense Vulnerabilities in Federated Learning:
## 3 Methods
### Dynamic Participation and Byzantine Fault Tolerance  
Federated learning environments frequently experience dynamic participation, where clients may join or leave the training process across rounds. NC-FLD naturally accommodates this variability since our detection mechanism operates independently at each federated round, requiring no persistent client history or cross-round tracking. When new clients join, their updates are evaluated using the same neuron selection and classification process applied to existing participants. Client dropouts are inherently handled by our round-based evaluation approach, as detection decisions are made solely based on currently available updates.  
Regarding Byzantine fault tolerance (BFT), NC-FLD effectively implements a BFT-compliant aggregation mechanism by identifying and excluding malicious updates before the aggregation phase. Unlike traditional BFT protocols that typically require consensus across distributed nodes, our server-centric approach establishes a classification boundary that filters out malicious contributions with high accuracy.  
The server's detection mechanism also addresses adaptive adversaries through its dynamic neuron selection strategy. Since the specific neurons examined may vary between rounds based on gradient impact patterns, adversaries cannot easily determine which parameters to manipulate to avoid detection. Additionally, our dimensionality reduction phase creates a transformed feature space that conceals the decision boundary from the perspective of individual clients, further enhancing resistance against adaptive Byzantine attacks.
## 3 Data Processing and Analyses
### Satellite Image Matching and 3D Reconstruction  
Image matching algorithms were used to identify homologous objects or pixels within the oriented images. These algorithms can be divided into area-based (e.g., [33]), feature-based (e.g., [34]), and pixel-based matching (e.g., [35]). Alternatively, image-matching approaches are often classified into sparse and dense matching or into local and global matching methods. The automatic image matching and DSM reconstruction processes were individually performed for each tri-stereo-scene (Pleiades and WorldView-3) by using the specialized module of the Inpho software, called Match-T DSM. The DSM derivation was based on three matching strategies [30]: (a) least squares matching (LSM); (b) feature-based matching (FBM) [36]; and (c) cost-based matching (CBM). Like in most image matching procedures, where image pyramids were used to reduce computation time [37], in our case, the iterative processing chain contains ten pyramid levels. On each pyramid level, three processes were performed: the matching of homologous image points, 3D intersection in object space and DEM modelling. For the first seven pyramids, FBM was used, and the last three image pyramid levels were processed with CBM. CBM is a pixel-by-pixel matching technique similar to the semi-global matching algorithm [35; 38]. The CBM strategy within the Match-T DSM module uses a search-path in a so-called 3D-cost-cube for finding the corresponding pixels in images. The cost functions (e.g., correlation coefficient) are used to find the minimum cost path, and each direction represents an \(X\)-\(Y\) movement in the image to match. Finding the pixel with the lowest cost generates a lowest-cost 3D model-surface model [30]. For each image pixel, the 3D object point coordinates were calculated by applying forward intersections, finally resulting in dense photogrammetric point clouds for the entire study area.  
In the last step, high resolution DSMs were generated by using the robust moving planes interpolation method. For each grid node all points within a circular neighborhood are used to robustly estimate a best fitting tilted plane (minimizing the vertical distances); points classified as outliers are not considered in the plane fitting procedure. This step was performed with the scientific software OPALS (Orientation and Processing of Airborne Laser Scanning data) [39].
## 4 The Proposed NSGA-II-MOEA/D-CIS Algorithm
### Design of the Lower-Lever MOEA/D Algorithm  
#### 4.3.1 Encoding and Decoding  
Considering the characteristics of the lower-level order reorganization problem and the properties of the MOEA/D algorithm, this section adopts an integer vector encoding method. In this encoding scheme, the entire chromosome is divided into multiple segments, with each segment representing a newly reorganized order. Each chromosome segment is structured as a variable-length gene block, which includes the first gene (new order number), one or more middle genes (original order number from which the new order  
Figure 3: NSGA-II algorithm encoding example. (Each cell is encoded in the form of _batch index/splitting level/splitting flag_. For example, the value “\(1/4/1\)” means the specification is assigned to batch 1, split at level 4, and needs to be split (flag = 1); “\(0/1/1\)” indicates no splitting is required.)  
is derived), and the last gene (the assigned number of production batches for this new order). To illustrate the encoding and decoding process of this integer vector representation more clearly, this paper selects three order reorganization instances as examples. In these instances, Orders 1, 2, and 3 are reorganized from 2, 1, and 3 original orders, respectively. Figure 6 presents the encoding result of one feasible solution.  
Figure 4: Example of block crossover operation. (The red boxes highlight the crossover regions selected from the two parent individuals (Parent 1 and Parent 2). The yellow arrows indicate the exchange of these segments between the two parents to generate two offspring).  
Figure 5: Example of the hierarchical random mutation operation (The red boxes indicate positions where mutation occurs, i.e., the original values are altered during the mutation process. The red circle labeled “Correlated mutation” highlights a case where the mutation in one gene triggers a related update in another).  
Additionally, the decoded result of this chromosome is as follows: New Order 1 is composed of Original Orders 2 and 5, reorganized into a single batch. New Order 2 is composed of Original Order 4, reorganized into a single batch. New Order 3 is composed of Original Orders 1, 3, and 6, reorganized into two batches.
## 6 MIMO Antenna Performance Enhancement Techniques
### Decoupling Network  
Cross admittance becomes transformed to purely imaginary value in decoupling network by adding a discrete component or transmission lines. To reduce mutual coupling, this technique employs a plane decoupling network which acts like a resonator. The pattern diversity for multielements could also be achieved through decoupling by implementation of dummy load and coupled resonators technique. To improve isolation, it is a cost effective way to be implemented [88].
# A Secure Link-Layer Connectivity Platform for Multi-Site NFV Services
## 4 Implementation and Validation
### Functional Validation
#### 4.3.4 Deployment of L2S and IPTV Services  
After creating the NFV descriptors for all the components shown in Figure 6, we took the last steps to realize our validation scenario. In a first phase, we used the STONIC MANO platform to deploy the L2S platform, resulting in the creation and configuration of an L2S VNF on each NFVI. These VNFs provided the abstraction of a layer-2 switch with an access port on the head end and on each of the video-serving offices (conforming the VLAN identified as 102 in our example).  
In a second phase, we instructed the MANO platform to deploy the IPTV service, following the VNF placement policies indicated in Figure 6. This way, the head end was created on site \(A\), whereas sites \(B\) and \(C\) were used to host the functions of the video service offices. Every multicast router VNF was attached to an access port of the L2S platform, through the virtual links pre-created at the NFVIs (VL\({}_{1}\), VL\({}_{2}\) and VL\({}_{3}\)). Finally, all the VNFs of the IPTV service have been configured through _Ansible playbooks_, which can be provided to the OSM software stack within their corresponding NFV descriptors. Configuration operations include setting up IP unicast addresses and network routes on every VNF, as well as the configuration and activation of the _pimd_ routing daemon in the multicast routing functions.
# Enhanced Ant Colony Optimization for Vehicular Ad Hoc Networks Using Fittest Node Clustering
## 4 Results and Discussion
### Cluster Head Lifetime (CHL) Analysis  
In our study, we analyzed cluster head (CH) epochs using the TR-PED technique, enabling us to investigate how rapid changes in vehicle positions influence the topology of the vehicular system. These positional changes have the potential to impact the lifetimes of both cluster heads (CHs) and cluster members (CMs). As observed in Table 7 and Figure 5a, when the network has a wider coverage area, it extends the CHs' lifetimes, thus ensuring continuous network connectivity for the CMs. Furthermore, this prolonged CH lifetime results in a decreased frequency of vehicle entries and exits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No. of Vehicles** & **Distance (m)** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 0.5 & 50.12 & 203.1 \\ \hline
40 & 1.0 & 65.14 & 254.12 \\ \hline
60 & 1.5 & 53.12 & 312.44 \\ \hline
80 & 2.0 & 75.12 & 270.14 \\ \hline
100 & 2.5 & 68.12 & 211.11 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative results of end-to-end delay analysis for network performance evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Vehicles** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 120 & 185 \\ \hline
40 & 147 & 180 \\ \hline
60 & 125 & 177 \\ \hline
80 & 155 & 173 \\ \hline
100 & 151 & 156 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparative results of cluster head lifetime (CHL) analysis for network performance evaluation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No. of Vehicles** & **Distance (m)** & **DA-TRPED** & **Conventional ACO** \\ \hline
20 & 0.5 & 7.82 & 18.23 \\ \hline
40 & 1.0 & 7.23 & 18.74 \\ \hline
60 & 1.5 & 7.12 & 25.34 \\ \hline
80 & 2.0 & 6.23 & 35.78 \\ \hline
100 & 2.5 & 6.88 & 38.47 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative results of dropping ratio analysis for network performance evaluation.
# Identification and Evaluation of Composition in Food Powder Using Point-Scan Raman Spectral Imaging
## 3 Results and Discussion
### Estimation of Component Concentrations  
The image processing method discussed above was used to color-code pixels of each component for images of samples at all 10 concentration levels. A total of 10 color-coded images were created; for each image, the total numbers of identified pixels of each component were calculated. Figure 5a shows the color-coded images for 4 of the 10 sample concentrations. The pixels of vanillin, melamine, and sugar are scattered throughout the sample surface. The concentrations of colored pixels are low in the 1% concentration image, and progressively increase in the 5%, 8%, and 10% concentration images. This shows that the detected numbers of component pixels are correlated to sample concentration. The red and black combination pixels, indicating the presence of multiple overlapping components, can also be observed to increase progressively from low frequency occurrences at 1% concentration to higher frequency occurrences at 10% concentration. This progressive increase was observable because the volume and presented area of the sample holder was held constant (100 mm \(\times\) 50 mm \(\times\) 3 mm) for all sample concentrations while the component concentrations increased, thus resulting in overlapping of components at different layers within the sample depth. Figure 5b shows Raman spectra of pixels for the 10% concentration sample. The fingerprint peaks of the three components and the non-dairy creamer are readily visible in the spectra.  
Figure 5: (**a**) Raman chemical images generated from contribution images for 1%, 5%, 8%, and 10% concentration samples; (**b**) Raman spectra of pixels for the 10% concentration sample.  
The numbers of detected pixels increased considerably with increasing actual concentration. The increasing trend was observed to be linear. This shows that component concentrations can be estimated by calculating the number of detected pixels of any component as a percentage of the total number of pixels in the detection image. To develop an estimation model, the numbers of detected pixels were correlated with actual sample concentration. The results showed a very strong positive correlation between numbers of detected pixels and the actual component concentrations, which can be used to estimate the concentrations of individual components. Figure 6 shows the correlation between component concentrations and the numbers of detected pixels, with a correlation coefficient of 0.99 for each of the three components.  
Pixels representing non-dairy creamer at each concentration were calculated simply by subtracting the sum of detected pixels of the other three components from the total number of all image pixels. It was observed that the numbers of detected pixels of non-dairy creamer were also strongly correlated with the actual concentration of non-dairy creamer (a correlation coefficient of 0.99, figure not shown).
## 4 Discussion
### The Overview of Legal Regulations and Standards
This matter was left to the states, and some of them introduced their own regulations and limits for EMF exposure--some states chose not to establish any limit value and focused on precautions to avoid excessive exposure, e.g., by establishing minimal distances between installations and human dwellings; in other states, there are limits established, and they range between 20% and 240% of ICNIRP depending on the state [25]. Japan has a standard regulated by the Ordinance of the Ministry of Economy, Trade and Industry, in which the limit of \(E=3\) kV and of \(B=200\)\(\mu\)T are established [36]. China has its own reference set of values, which are regulated mostly by Chinese norms GB 8702-88 [37] and GB 9175-88 [38]. They are lower than EU/ICNIRP standards [18,39] with percentage differences differing depending on \(f\)[40]--up to 800 Hz, they are mostly concordant with the EU standard; at higher \(f\), they are lower than ICNIRP. Australia also does not have any national standard, but the country's authorities decided to use the guidelines from ICNIRP. The reference limit values for 50 Hz EMFs defined by different standards and local regulations are summarized in Table 6.
# AI-Driven Safety and Security for UAVs: From Machine Learning to Large Language Models
## 5 Challenges and Future Directions
### Future Directions
#### 5.2.3 6G Native Intelligent Communication Network  
The 6G native intelligent communication network can help LLMs in UAV safety and security with its smart architecture. For safety, the high speed and low latency of this network enable LLMs to process real-time data for adaptive obstacle avoidance and path planning in complex settings. Its smart edge computing ensures instant decisions, whereas predictive analytics enhances fault prevention. In terms of security, the intelligent protocols and dynamic encryption of 6G protect LLM-UAV communications, adapting to threats instantly. Smart resource allocation and cross-domain coordination ensure reliable, secure connectivity across diverse missions. This intelligent 6G framework empowers LLMs to improve UAV safety and security with exceptional adaptability and efficiency. In 2023, [PERSON] et al. provided an overview of the synergistic integration of AI methods, UAVs, and 6G networks, with a focus on predictive network analysis, autonomous fault detection and self-healing, and dynamic resource allocation [230]. [PERSON] et al. proposed an efficient 6G federated learning energy-efficient scheme for UAV deployments, which improves the data transfer rate while protecting data sensitivity and achieving very low latency [231].
# Proximate Composition and Antioxidant Activity of Selected Morphological Parts of Herbs
## 2 Materials and Methods
### Methods  
#### 2.2.1 Proximate Composition  
Before conducting analyses by the weight-dryer method, the dry matter content was determined and afterwards components in the air-dry mass were analysed. The proximate composition of the samples was determined according to the Association of Official Analytical Chemists (AOAC) methods [23]. To determine dry matter, samples were dried at 105 \({}^{\circ}\)C to constant weight (method 945.15). Crude fat (as ether extract EE); method 2003.06) was determined using the Soxhlet extraction method with diethyl ether as solvent; crude ash (CA; method 920.153 by incineration in a muffle furnace at 580 \({}^{\circ}\)C for 8 h; crude protein (CP; method 945.18) (N \(\times\) 6.25) by Kjeldahl method using a Buchi B-324 distillation unit (Buchi Labortechnik AG, Switzerland). Crude fiber (CF) was determined as the residue after sequential treatment with 1.25% H\({}_{2}\)SO\({}_{4}\) and with 1.25% NaOH using an ANKOM\({}_{220}\) Fibre Analyser (ANKOM Technology, New York, NY, USA). Total carbohydrates were calculated as: nitrogen free extract (NFE) (%) = 100 \(-\) % (moisture + crude protein + crude fat + crude ash + crude fiber).
## 1 Introduction
The proposed approach was evaluated in virtual driving environments, and the results demonstrated that reliable environment perception and collision prediction can be achieved by introducing V2X communications, even in scenarios where it is difficult to avoid a collision with existing safety systems based only on on-board sensors. In this paper, we present the design and implementation of cooperative environment perception based on the fusion of V2X communications and automotive remote sensors. In continuation of our previous work [23], the Kalman-filter-based approach is employed for the high-level fusion of radar, camera, and V2X communication data, and the proposed cooperative approach is tested and evaluated in test-track environments. The experiments are carried out with two test vehicles, where each vehicle is equipped with a 5.9 GHz dedicated short-range communications (DSRC) transceiver along with a global navigation satellite system (GNSS) receiver for the exchange of vehicle state information. The host vehicle is additionally equipped with automotive radar and camera systems for remote sensing of the surrounding objects. The performance of the proposed approach for driving environment perception is evaluated at varying relative distances between the two test vehicles. The positioning accuracies at different longitudinal and lateral target vehicle positions are computed for each remote sensing system as well as for the proposed fusion method. The performance of the trajectory prediction and risk assessment is evaluated in different cut-in driving scenarios where the remote vehicle performs a lane-change maneuver in front of the host vehicle. The rest of the paper is organized as follows. In Section 2, the overall architecture of the proposed driving environment perception system is described and background information about automotive remote sensors and V2X communications is provided. In Section 3, the proposed method for object state estimation and risk assessment is presented. The experimental results are reported and discussed in Section 4, and conclusions and directions for future research are presented in Section 5.
## 1 Introduction  
### Background  
Precipitation, as one of the most significant links in the Earth's water cycle, is intimately associated with people's lives. The spatial and temporal heterogeneity of precipitation development is the main cause of natural disasters such as droughts, floods and mudslides [1]. Accurate monitoring of precipitation has significant implications in fields such as agriculture, hydrometeorological research and water resource management.  
Currently, meteorological operations mainly rely on rain gauges, weather radar and meteorological satellites to measure precipitation. However, due to the inherent limitations of these dedicated rain sensors, quantitative precipitation products still cannot satisfy the requirements of comprehensive meteorological observations, especially as global warming may lead to more extreme precipitation events [2]. Rain gauges, which are believed to provide point measurements of surface rainfall, are unsuitable for constructing a monitoring network due to their low spatial representativeness. In addition, wind and splash effects introduce uncertainties in its measurement [3]. Contrary to rain gauges, based on the statistical power-law relationship between the radar reflectivity \(Z\) and the rain rate \(R\), weather radar allows for quantitative precipitation estimation (QPE) over large areas. However, the parameters of the \(Z\)-\(R\) relationship are highly sensitive to the raindrop size distribution (DSD) associated with precipitation types, geographical locations and climatic characteristics, which may cause large systematic biases [4]. Other uncertainty sources include ground clutter, beam blockage, bright bands and the vertical variability of the precipitation system [5]. As meteorological satellite technology matures, satellites can measure precipitation almost globally, which is especially important in areas where radar and rain gauges are not available [6]. However, subject to low spatial and temporal resolution, satellite products are not available for small-scale hydrological applications, such as catchment modeling in urban and mountainous areas. Moreover, although several satellite-based rainfall inversion algorithms have been developed [7; 8], the accuracy of their estimates still needs to be improved [9].  
Consequently, the concept of opportunistic rainfall measurement methods emerged, and the potential of devices such as commercial microwave links (CMLs) [10], personal weather stations [11], vehicle windscreen wipers [12] and street cameras [13] has been successively explored. These methods can provide additional precipitation information and promise to improve dedicated precipitation products, with CMLs being one that has the most prospective application. Based on existing CML networks built and maintained by mobile network operators, it can provide alternative precipitation observations in a cost-effective manner in areas lacking rain sensors [14] or additional precipitation information in areas with existing rainfall observations to enhance the spatial and temporal resolution as well as the accuracy of precipitation products [15; 16].