esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Load Transfer Mechanisms and Performance Dynamic in Hybrid Composite Pile Foundation Systems  
Figure 8a-d show the mechanical behavior and load transfer mechanisms of hybrid composite piles across different soil densification states. For the hybrid steel-concrete composite pile (Case Study I), the ultimate load capacity (Q\({}_{\text{ult}}\)) demonstrates pronounced dependence on soil density and the computational method employed. The skin friction (Q\({}_{\text{s}}\)) and base resistance (Q\({}_{\text{b}}\)) exhibit systematic variations across different relative density (Dr) conditions, ranging from very loose (10%) to very dense (95%) soil environments. Notably, the method of [PERSON] shows a progressive increase in skin friction contribution from 23.3% at very loose conditions to 46.5% at very dense conditions. This trend suggests the existence of an enhanced mechanical interaction between the pile and surrounding soil as density increases. Conversely, the base resistance proportion correspondingly decreases from 76.7% to 53.5%, indicating a shifting load transfer mechanism with increasing soil compactness. Comparative analysis across different computational methods reveals significant methodological variations. The [PERSON] method, for instance, demonstrates the most extreme load distribution, with base resistance accounting for over 93% of the ultimateload across all density ranges. In contrast, the [PERSON] method shows a more balanced load distribution, with skin friction contributing between 32 and 39% of the total load capacity.  
The hybrid timber-concrete composite pile exhibits distinct load transfer characteristics, with skin friction contributing 15% in very loose conditions up to 39% in very dense conditions according to [PERSON]'s method. The Vesic method reveals more dramatic variations, with skin friction accounting for 43.7% of ultimate load in very loose conditions, a number that decreases significantly to 9.9% in very dense conditions. This variability highlights soil density's critical influence on load transfer mechanisms. The performance differences across analytical approaches emphasize the necessity for comprehensive geotechnical investigation and the use of multiple analytical methods when evaluating hybrid composite piles. The observed mechanical behavior complexity stems from material interfaces, soil conditions, and computational methodologies. Results indicate that hybrid composite piles provide sophisticated foundation solutions with adaptable load-bearing characteristics--steel-concrete composites demonstrate superior capacity in dense to very dense soils, while timber-concrete variants offer environmentally conscious alternatives with comparable performances. These findings validate the hybrid composite pile approach, demonstrating potential for optimized foundation design through strategic material composition and sophisticated load transfer mechanisms, while underscoring the importance of site-specific considerations, material interface dynamics, and comprehensive geotechnical analysis.
# The Development of a Spaceborne SAR Based on a Reflector Antenna
## 3 Reflector Antenna Configurations in Spaceborne SAR Systems
### Feed Configurations for Reflector Antenna
#### 3.2.2 Multi-Beam Feeds  
Compared to single-beam feeds, multi-beam feeds can generate multiple independent beams simultaneously, providing benefits such as expanded spatial coverage, increased data transmission capacity, and support for multi-functional radar systems. The key advantages of multi-beam antennas include the following:  
(1) Increased System Capacity--By utilizing spatial and polarization isolation, multi-beam antennas enable frequency reuse, significantly enhancing bandwidth utilization and communication capacity.  
(2) Enhanced Antenna Gain.  
(3) Greater Flexibility--Multi-beam antennas offer beam scanning and beam reconfiguration capabilities.  
Multi-beam reflector antennas often employ an array of offset feeds, typically consisting of multiple horn antennas, to illuminate the reflector and generate multiple beams. This configuration is also known as a reflector-fed feed array multi-beam antenna. As shown in Figure 10(a), the multi-beam feed of RISAT-2 [57] uses an array of multi-beam feeds. In Figure 10(b), we can see that the multi-beam feeds of HJ-1-C [30] can generate nine different beams.  
Since multi-beam reflector antennas can achieve high-gain, multi-beam operation with relatively small feed arrays, they reduce the complexity of the system. Consequently, multi-beam feeds have been widely adopted in SAR applications. Examples include TecSAR (Israel, 2008) [14; 56], RISAT-2 series (India, 2009) and HJ-1-C (China, 2012) [18].  
Figure 10: Single-beam feed: (**a**) Qilu-1; (**b**) Capella Acadia SAR satellite [19].
## 2 CDS Color System
### CDS Geometry  
All the CDS samples were arranged and denoted with an angular scale as shown in Figure 3(a) and a trilinear axes space as shown in Figure 3(b).  
The angular scale is also known as the CDS hue circle. As can be seen in Figure 3(a), the eight elementary colors named Red, Orange, Yellow, Kelly, Green, Turquoise, Blue, and Purple are placed equally around the circle. Then, other colors between the two neighboring elementary colors were produced via visual experiment, and placed on the circle proportionately according to their relative resemblances to the two neighboring elementary colors.  
Figure 4: Illustration of the CDS color space.  
Figure 3: Illustration of the CDS atlas and 10 CDS elementary colors.  
In the current version of the CDS atlas, there are nine colors between every two neighboring elementary colors, respectively, and hence, there are 80 colors total in the hue circle. Meanwhile, 40 colors were evenly selected from the 80 colors of the hue circle to define 40 hue pages of the CDS atlas, respectively. Note that there are 66 samples on each hue-page, and hence, there are a total of 2640 samples on the 40 pages. As a result, in the current version of the CDS atlas, there are 2740 samples in total, including 2640 samples on the 40 hue pages, the 80 samples on the hue circle, and another 20 neutral samples.  
The trilinear axes' space is also known as the CDS color triangle. As can be seen in Figure 4b, the trilinear axes include the b-w axis, the b-c axis, and the c-w axis. Note that the CDS color triangle is a two-dimensional space, because each color sample on the triangle can be denoted with a pair of coordinates such as (b, w), or (b, c), or (c, w). It should be noted that the w-b axis of the CDS color triangle is also known as the CDS neutral axis.  
As can be seen in Figure 4b, the values ranges of b, w, and c are [0,100], respectively, and the sum of b, w, and c is 100. Therefore, there are a total of 5051 grids on each CDS hue page, respectively, and there are over 400 thousand color samples on the 80 CDS hue pages in total.  
Note that, on the space of the CDS color triangle, the same lightness samples are found on a straight line perpendicular to the b-w axis, and the same chromaticess samples are found on a straight line parallel to the b-w axis.
# Towards a Predictive Analytics-Based Intelligent Malaria Outbreak Warning System+
## 3 Intelligent Malaria Outbreak Warning System
### Discussion  
The current prototype of the intelligent malaria outbreak warning system relies on a batch machine learning process. That is, the learning algorithm is trained and tested offline using the available dataset, and the prediction model is embedded within the tool. Hence, the prediction process relies on the prediction model trained offline at once.  
A more effective approach is to make the learning process online. That is, whenever new data is available, the data is automatically updated, and the learning process is run again to encapsulate the new data. This will not only allow an automatic and dynamic learning process, but also increase the accuracy of the prediction by adapting to new patterns in the data.  
The online learning approach requires a mechanistic data collection mechanism, which is very challenging to perform as hospitals and health service providers do not make the relevant data available online. Even acquiring permission to have access to the available data is a long and bureaucratic process. On the other hand, as discussed in Section 2.2, most available data cannot be directly used in this system as they are incomplete and/or not processed.  
To alleviate these issues and to support the online learning process, the Malaria outbreak warning application can be extended to collect online data from its users. Namely, the users, e.g., hospitals, healthcare providers, individuals etc., report a Malaria case to the system. Using the geographical location of the incident, the application will acquire all the necessary information for the ecological factors. In this way, new data will be collected at run time, and the learning process will be instantiated each time new data is available. We are currently working on the development of this approach.
# SLoPCloud: An Efficient Solution for Locality Problem in Peer-to-Peer Cloud Systems
## 6 Conclusions  
P2P cloud systems have become an attractive area of research in recent years. Such systems, similar to any other P2P network, consist of two layers, overlay and physical layer. Locality problem, the mismatch between overlay and physical layer of P2P cloud systems, may lead to bandwidth waste and network delay. In this paper, a solution for locality problem is proposed to put the physically closer nodes beside each other, in overlay network. The proposed solution is an extension of our previously published works, extended to be a general solution applicable on P2P cloud systems. While the proposed solution considers RTT as the metric of distance, it is generally proposed to be able to be applied on the existing P2P systems. For this reason, GHCC is suggested to be the topology of the overlay. The proposed solution is then supported with comprehensive simulation to verify its applicability. The main concern about the proposed solution is the delay and control traffic caused by the proposed solution. Simulation results show that the imposed traffic is negligible, while the delay is very low. The wide range of simulated network sizes is a further validation of the scalability of the proposed solution. Since GHCC could be reshaped to many other well known topologies, the formal analysis of its parameters, such as traffic overhead and delay, could be considered as a general formal analysis of many topologies. We propose the mentioned interesting analysis as a future work of this paper. Furthermore, experimental analysis on the data of real existed P2P cloud systems could represent interesting results, which could be considered as another future work.  
Conceptualization, M.G.; Methodology, M.G.; Project administration, M.G.; Supervision, A.M.; Validation, M.G.; Visualization, M.M.; Writing--original draft, M.G.; and Writing--review and editing, M.G. and M.M..  
The authors would like to thank [PERSON] [PERSON], [PERSON], [PERSON], and [PERSON] [PERSON] for their insightful comments.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CCC & Cube Connected Cycle \\ DHT & Distributed Hash Table \\ DOLR & Decentralized Object Location and Routing \\ GHCC & Generalized Hypercube Connected Cycle \\ KBR & Key Based Routing \\ OBAP & Optimal Bandwidth Allocation Problem \\ P2P & Peer to Peer \\ RTT & Round-Trip Time \\ TTL & Time to Live \\ VoD & Video on Demand \\ \end{tabular}
## 3 Feature Selection and Decision
### RNCA  
The performance of the RNCA algorithm is affected by the normalization parameter \(\lambda\). Therefore, the RNCA algorithm must be defined to set the parameters effectively. The regularization parameter was adapted using the mean squared error and 5-fold cross-validation, as shown in (Algorithm: RNCA). A user-defined robust loss function was applied, given as \(\zeta=1-\exp(-|y_{i}-y_{j}|)\). Thus, the value of \(\lambda\) was determined to represent the minimum average loss value. Finally, the weighted feature vectors were detected using RNCA without selecting other features, as shown in Table 4. Therefore, the feature selection reduced the dimensionality of the algorithm training.  
AIC Based on GP Algorithm  
The AIC can be used to compare and rank multiple feature sets and determine the most appropriate feature set [40]. After computing several other groups, they were compared using this criterion. A feature set was considered as a group. According to [PERSON]'s theory, the best feature subset has the lowest AIC value.  
\[\mathcal{A}=-2\ln(\mathcal{M})+2\eta \tag{13}\]  
where \(\mathcal{M}\) denotes the maximum likelihood obtained from the previous result of the GP algorithm, and \(\eta\) represents the number of feature dimensions selected. The weight of the features can change according to the target variables SBP and DBP, as shown in Table 4. The weight of the features also changes according to repeated experiments. Thus, an effective feature subset that could most accurately estimate SBP and DBP was determined using the proposed GFSDP algorithm.
## 4 Experimental Results and Analysis
### Robustness Experiments  
In this section, we verify the robustness of our KBDSP. As shown in Figure 2, pixels of a certain percentage in each image of the Yale dataset were corrupted Gaussian noise with a mean 0 and a variance 0.1. The percentage of corrupted pixels in each image varies randomly from 10% to 90% with a step of 20%. To ensure the reliability of the results, we repeat experiments for each method 10 times and report the average results. The ACC metric is used for evaluation. The experiments' results are shown in Figure 3. As can be seen from Figure 3, the ACC of all methods decreases significantly when more pixels are corrupted by noise. In addition, it can be seen that our KBDSP method obtains the best results. This is mainly because KBDSP uses the similarity-preserving strategy, which can effectively suppress noise.  
Figure 1: The results of ANOVA for Purity of compared methods (\(F=2.65\), \(p=0.0132\)).
## 3 Proposed Design Scheme
### PCB Design  
The circuit design proposed in this study was implemented on a 10-layer PCB to optimize the circuit in a limited area. The parameters that determine high-speed signal loss are the dielectric constant (Dk) and the dissipation factor (Df) for frequency. Although PCB material with less Dk and Df has the advantage of less high-frequency loss, its price is higher. Therefore, an appropriate trade-off design is required considering cost and performance. In this study, we used FR4 substrates, which are primarily used for industrial purposes, and Panasonic MEGTRON6 with relatively lower Dks and lower dielectric losses compared to FR4 substrates. The 25 Gbps NRZ high-speed signal and PAM4 signal transmission lines were placed in the top and bottom planes using the MEGTRON6 substrate, and the remainder was designed with FR4 substrates. Table 3 shows the PCB layer structures designed by the proposed method and their circuit functionality implemented on each layer.  
The design of the transmission line for transmitting 26 giga-baud (GBd) PAM4 signals per channel over the PCB requires a more sophisticated transmission line design than conventional NRZ signals. The PAM4 signals are more susceptible to noise and attenuation than NRZ signals. A signal passing through a transmission line on a PCB designed with differential 100 Ohms results in reflection or radiation of the signal at the point of the characteristic impedance mismatch, thus impacting the signal characteristics. This causes overshoot and undershoot on the signal eye pattern and degrades jitter properties. The  
Figure 5: QSFP28 hardware design block diagram.  
main areas where impedance nonconformities occur are IC pads and the flexible PCB assembly. The proposed scheme is used to apply a coplanar waveguide type transmission line on the PCB. The impedance of the area where the transmission line width thickens increases capacitance, and the dielectric substrate thickness below the transmission line decreases capacitance [18]. The proposed scheme minimizes impedance nonconformity by subtly calculating the impedance characteristics according to the line width, the dielectric thickness, and the gap between the track and the ground (GND) and increases inductance to solve the impedance degradation problem occurring on IC pads on the path through which the signal passes, thereby improving the overall impedance convergence.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 5 Conclusions and Future Work  
### Conclusions  
Utilizing energy collaboration between RSUs and the transfer of energy across RSUs and downstream vehicles, this research maximizes the network lifespan in vehicular ad hoc networks. Every RSU and vehicle in the vehicle-mounted ad hoc network must address the flow of data and energy usage at almost the same instance. In a VANET simulation, the propagation model utilized has a significant impact on the outcomes. It has an influence on which nodes are able to interact and the likelihood of receiving data correctly. As a result, it can affect the rate at which messages travel over the network, affecting the end-to-end delay in multi-hop scenarios. The overhead in terms of collisions and medium utilization is also influenced by the probability distribution of accurate reception. Since real-world implementation may differ from the simulation, caution must be exercised when mapping the model and parameters to the target environment. Because deterministic site-specific propagation modeling, such as in wireless systems site design, is not feasible in VANET simulations due to the variable nature of the environment and the mobility associated, the propagation environment is often treated stochastically. This study provides a performance analysis of VANET propagation models in terms of both vehicle count and packet size. For simulation, SUMO and NS-3.29 are utilized in this research work. The findings are presented and examined in depth. According to the outputs of the Friis, Nakagami, Rayleigh, Rician, and range propagation models, the AODV has good throughput, average delay, packet delivery ratio, hop count, and energy usage values. In practically every scenario, the mRSU is the best. The average delay and packet delivery ratio of the mRSU in the three-log distance and Friis models are demonstrated to be superior to those of others with a varied number of nodes (vehicles). When integrating the mobile  
Figure 12: Computational modeling outcomes pertaining to the deployment of mRNAs and sRSUs in the context of propagation models, in relation to the variation in the number of vehicles. (**a**) Average delay analysis of mRSU and sRSU with regard to number of vehicles; (**b**) hop count analysis of mRSU and sRSU with regard to number of vehicles; (**c**) PDR analysis of mRSU and sRSU with regard to number of vehicles; (**d**) energy consumption analysis of mRSU and sRSU with regard to number of vehicles; (**e**) throughput analysis of mRSU and sRSU with regard to number of vehicles.  
RSU, the Nakagami, Rician, and Rayleigh models deliver superior outcomes in terms of throughput. The experimental computational findings suggest that collaborating for energy usage in VANET technology may significantly increase the network lifespan.
## 2 Related Work
[PERSON] [10] proposed constructing logical topologies over a physical network using different objective functions designed to meet different QoS requirements of IoT applications, while adhering to the features of Directed Acyclic Graph (DODAG). [PERSON] [11] constructed logical topologies for Audio Video Bridging (AVB) streams to reduce interference with the transmission of Time-Triggered (TT) streams while satisfying their delay requirements. Similarly, [PERSON] [12] aimed to determine the routing of both TT and AVB streams, as well as the scheduling of TT flows, ensuring that all frames are schedulable and the worst-case end-to-end delay of AVB is minimized. These methods provide varying transmission quality for different types of services, but they do not address the overall bandwidth maximization between node pairs, nor do they limit the number of paths a node can be traversed. Regarding the studies on multi-topology construction mentioned above, none of them consider maximizing the overall bandwidth between every node pair while limiting the number of paths that each node can be traversed. In other words, they do not account for the flow entry capacity limitations of switches when constructing multi-topologies in SDN networks. Therefore, it is necessary to explore a new method for constructing a multi-topology that addresses these challenges.
## 2 Materials and Methods
### Quantification and Statistical Analysis  
All statistical analyses were performed using non-parametric methods appropriate for the small sample size and non-normal data distributions. The sample size was determined based on feasibility for an exploratory study; no a priori power analysis was conducted. Effect sizes were calculated to support interpretation and inform future sample size estimation. Continuous outcome variables (strength, YBT, FMS scores) were compared across the four expert-defined functional profiles (\(n\) = 37 soccer athletes) using Kruskal-Wallis one-way ANOVA on ranks. When the global test indicated statistical significance (\(p\) < 0.05), post hoc pairwise comparisons were performed using [PERSON]'s test with Bonferroni adjustment for multiple comparisons. Effect sizes for Kruskal-Wallis tests were reported as eta-squared (\(\eta^{2}\)). Median and standard deviation (SD) or interquartile ranges were used to describe distributions, depending on skew. Two-group comparisons between the soccer and handball cohorts (\(n\) = 37 vs. \(n\) = 9) were conducted using Mann-Whitney U tests (two-sided) for continuous variables and ordinal FMS scores. Differences in categorical distributions (profile frequencies, flag categories) were tested using chi-square (\(\chi^{2}\)) tests of independence. Confidence intervals (95%) and [PERSON]'s delta were reported for group effect sizes where applicable. Model performance metrics--including accuracy, balanced accuracy, macro-averaged F1-score, [PERSON]'s kappa (\(\kappa\)), and Matthews correlation coefficient (MCC)--were computed using 5-fold stratified cross-validation. To assess the stability of the random forest (RF) model, bootstrapping with 1000 iterations was used to generate 95% confidence intervals for performance metrics. Confusion matrices were aggregated across folds to assess misclassification patterns. All modeling and statistical analyses were conducted using Python 3.10 with the following packages: scikit-learn (v1.2), scipy (v1.10), numpy (v1.24), pandas (v1.5), shap (v0.42), and matplotlib (v3.7). Significance was defined as \(\alpha\) = 0.05 for all tests.