esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Processing Overview  
The full flowchart of the gap-filling procedure is shown in Figure 2. The continuous input data (\(z\)) form the training subset of ERA or PCI datasets used for generating the continuous time series of modelled data (\(y\)). The modelled data are compared against the target data (\(x\)) and optimal model parameters are seeking to minimize the RMSE between the target and modelled data. The model parameter optimization in the training phase is performed by the Regression Learner App from MATLAB software. During the validation phase the model is run using the optimal model parameters determined in the training phase and the input data from the validation data set is used for the generation of the continuous modelled data. The model performance metrics (_CC_, _NRMSE_, and _KGE_) are calculated for the validation dataset of the modelled and target data. The model performance metrics are compared for various regression and NN models to reveal the best model for gap filling. The model training and validation procedures were repeated for five models and eight target datasets. The application phase includes the running of the best model using the full input dataset and optimal model parameters determined during the training phase. The modelled data are used for filling gaps in the training data where the observations are missed.  
Figure 2: The flowchart of the data processing. TRN—training dataset, VAL—validation dataset, FUL—full dataset.
## 3 Results and Discussion
### Effects of the Grafting Degrees on the Physicochemical Properties of EWP
#### 3.2.2 The Mechanical Strength of EWP Aerogels Changed with Grafting Degrees  
Generally, aerogels with higher mechanical strength reflect better mechanical stability and are therefore recommended for practical applications [56]. The typical stress-strain curves and compressive strength of the aerogels are shown in Figure 3a. In Figure 3a, it can be seen that the stress-strain curves during compression were nearly linear and fractured at about a 25% strain, indicating that these samples formed a rigid structure. The slope  
Figure 2: Scanning electron micrographs of the aerogels. Graphs **(a-f)** show the microscopic morphology of the EWP, EC\({}_{0}\), EC\({}_{12}\), EC\({}_{24}\), EC\({}_{36}\), and EC\({}_{48}\) at \(\times\)500 magnification, respectively.  
of the compression curve can be used to evaluate the stiffness of the aerogels: the higher the slope, the stiffer the aerogel. Therefore, the grafted aerogel samples possessed higher stiffness compared with the EWP aerogel. At the same time, with the increasing degree of grafting, a stress \"plateau\" appeared in the curve, indicating a stable state, which may have been due to the elimination of heterogeneous pores and cavities in the internal structure of the aerogels during the compression process [37]. The compressive moduli of aerogels with different grafting degrees are shown in Figure 3. Clearly, the compressive moduli of the aerogel samples were significantly increased by adding CMC-Na to the EWP with or without the grafting process, which suggested that the introduction of CMC-Na had a positive and significant effect on the mechanical strength of EWP aerogels. Moreover, the compressive moduli of all aerogel samples showed a trend of increasing and then decreasing, and peaked with the EC\({}_{12}\) aerogel, reaching 2.05 MPa, which was more than three times the compressive modulus of the EWP aerogel and significantly higher than the EC\({}_{0}\) aerogel (1.78 MPa). It has been shown in the literature that appropriate cross-linking reactions contribute to the enhancement of covalent interactions in the network, which, in turn, reinforces the mechanical strength of the entire aerogel network [35]. Meanwhile, the higher the degree of grafting, the more flexible the network structure, which result in the weaker mechanical properties. Based on that, we believe that the grafting of EWP with CMC-Na could significantly improve its mechanical properties and obtain more mechanically stable composite aerogels.  
Figure 3: Mechanical properties of the aerogels in dry conditions: (a) stress\(-\)strain curves and (b) compressive strength.
## 3 Probabilistic Topology Control Algorithm
### Communication Network Architecture of SWARMs Project
#### 3.1.2 Hardware Parameters  
In an underwater cooperating AUVs network, communication modules are equipped on the underwater AUVs to allow them to communicate with each other. Three different kinds of AUVs are used in SWARMs project, and the parameters of these AUVs can be found in Table 1.  
The communication modules used in the SWARMs project include medium frequency (MF) modules and high frequency (HF) modules. The MF communication modules are used for data exchanges between different AUVs; the HF communication modules are used for point to point (P2P) communication between different AUVs and ROVs. In this paper, the topology control algorithm is mainly designed for MF communication networks. The MF communication modules used in the SWARMs project are the S2 CR communication module. The module operates in the frequency band 18-34 kHz around a central frequency of 25 kHz with an efficient frequency bandwidth of 16 kHz. Sweep-spread carrier is used for data encoding in the S2 CR module. The details of this communication module can be found in Table 2. These parameters will be used in our simulation.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**AUV** & **Length (m)** & **Width (m) (Circular)** & **Height (m) (Circular)** & **Weight (kg)** \\ \hline ALISTER 9 & 2 & 0.22 & 0.22 & 70 \\ IXN & 1.9 & 0.5 & 0.3 & 150 \\ Naiad & 0.84 & 0.6 & 0.25 & 30 \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of the AUVs used in the SWARMs project.  
Figure 6: Average salinity with different water depths.  
Figure 7: Average sound speed with different water depths.
# An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering +
###### Abstract
The cluster-based approach in solution selection have become increasingly popular in multiobjective optimization (MOO). The cluster-based selection approach applied to more than one objective function optimization was first proposed by [11]. The approach generated a reduced Pareto set in many-objective optimization (MaOO) problems to solve its scaling problem. Furthermore, cluster-based approaches are also applied to improve good Pareto set in multi- and many-objective optimization problems, such as applying clustering with a flexible similarity metric [12] and applying cluster-based solution selection [13]. [PERSON] et al. [14] applied density-based fuzzy C-means clustering in a dynamic nondominated sorting multiobjective genetic algorithm to improve traffic congestion. While these cluster-based approaches improve the MOO algorithms, to the best of our knowledge, such approaches are not commonly applied to data clustering problems. In this paper, we aim to incorporate the cluster-based MOEA selection strategy into a BBPSO clustering framework and investigate its clustering performance. By applying clustering on the Pareto set, we hope to benefit from the combined strength of solutions based on found cluster representatives to guide the solution selection. As part of our ongoing studies in PSO clustering, we also investigate and compare with the MOEA strategies in a PSO and fuzzy clustering framework. Our objectives are two-fold: first, to compare and study the clustering performance of CM-BBPSO and MOPSO clustering when compared to single objective BBPSO clustering algorithms to determine whether cluster-based MOEA/D strategy is effective for clustering; secondly, we would like to study the clustering behavior of these algorithms by applying them on datasets of different characteristics, such as size, cluster number, number of dimensions, density and overlap. We test on 13 UCI datasets [15] and compare with BBPSO and MOEA clustering algorithms from our previous work. The outline of the paper is as follows. We discuss a few latest works that applied cluster-based selection in MOEA. Next, we present the materials and methods we used in our proposed technique, starting with a brief introduction of MOEA/D, followed by an explanation of our methodology of cluster-based MOEA/D BBPSO (CM-BBPSO). Next, we present the details of our experiments and data used. The results and discussion are presented next, and the paper ends with conclusion.
# Functional and Antimicrobial Properties of Propolis from Different Areas of Romania
## 2 Materials and Methods
### Antimicrobial Activity  
The antimicrobial efficacy of 10% propolis extracts was assessed against the following indicator bacteria: _Enterococcus faecalis_ ATCC 29212, _Staphylococcus aureus_ ATCC 25923,Staphylococcus aureus MRSAATCC 43300, Staphylococcus epidermidis ATCC 12228, Listeria monocytogenes ATCC 35152, Klebsiella pneumoniae ATCC 13883, Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853, Salmonella enterica ATCC 25928, Acinetobacter baumanii ATCC 19606, and Candida albicans ATCC 10231 (ATCC, American Type Culture Collection, Manassas, VA, USA). The above pathogenic strains were aerobically grown in 10 mL BHI broth at 37 \({}^{\circ}\)C for 16 h; bacterial suspensions were adjusted to a concentration of 0.5 McFarland to give a cell density of \(\sim\)10\({}^{8}\) CFU/mL. The antimicrobial activity of propolisols samples was performed according to [PERSON] et al. [32] with some modifications. Briefly, 15 mL of BHI agar was inoculated with a 0.5 McFarland of pathogens into Petri dishes (\(\O\) 90 mm). Wells of 0.6 mm diameter were cut in the plates, and 50 \(\upmu\)L of propolisols extract was added to the wells. As a negative control, 50 \(\upmu\)L of 80% ethanol was used, while as a positive control, vancomycin (30 \(\upmu\)g) was used for Gram-positive bacteria, gentamicin (10 \(\upmu\)g) for Gram-negative bacteria, and funconazole (25 \(\upmu\)g) for yeasts. The plates were observed, and antimicrobial activity was recorded as the diameter (mm) of the zone of clear inhibition (ZOI) around the inoculated wells after incubation at 37 \({}^{\circ}\)C for 24-48 h. All the tests were performed in triplicate.
## 5 Results for AOI-1
### Anthropic Instabilities in AOI-1
#### 5.1.2 Motorway and Railway Infrastructure  
In this study, we analyzed in detail an 18 km-long section of the M18 motorway running north-south near the town of Gort, and a 25.6 km-long section of the M6 and N6 roads running east-west from Atenny to Galway (Figures 4 and 7). The M17 motorway section connecting Gort to Tuam, which opened in September 2017, was not detected by the PS-InSAR analysis due to its very recent construction. A total of 2,779 PSs were extracted over the roads with the 10 m buffer, among which only 253 were outside the \(\pm\)1.5 mm/year range and hence considered unstable. Most of these points are in Galway city in the area already shown in Figure 9. Other subsiding points with velocities of up to \(-\)4.9 mm/year and marked as locality 'd' in Figure 7 were detected over one localized section of the M18 motorway (Figure 12a). The field reconnaissance revealed several slightly depressed, uneven and recently re-surfaced areas of the motorway in correspondence with several of the detected subsidence signals.  
AOI-1 contains 72 km of the Irish national railway system. The Galway-Limerick route runs north-south for 48 km; the Dublin-Galway route runs east-west for 24 km. Using the 10 m buffer, we extracted a total of 2913 PSs along these railway sections, 701 of which are regarded as unstable. A maximum velocity of \(-\)16.4 mm/year has been detected on the Galway-Limerick line at the 38 th km mark, while a maximum velocity of \(-\)8 mm/year was detected for the Dublin-Galway line at the 23.5 th km mark. The respective maximum cumulative displacements produced in the 2.8 years period covered by this analysis are \(-\)46 mm and \(-\)20 mm. One example of unstable area, marked as locality 'e' in Figure 7, is presented in Figure 12c. The railway appears to be built on an embankment immediately over the ground at the base of a slope. The nearby motorway, which runs parallel to the railway for around 3 km, is located up slope. Boggy and marshy areas with high vegetation (reeds) are present close this location.  
Figure 11: Subsidence of a coastal defense wall in Galway city (denoted locality ‘c’ in Figure 7): (**a**) coastal road embankment showing signs of instability. (**b**) Bedrock geology map; the colors correspond to the legend in Figure 4a. (**c**) Displacement time series of the selected point (white circle).
# A Cross-Chain Solution to Connect Multiple DNS Blockchains in Consensus Roots System
## 7 Discussion  
### Comparative Benchmarks Against Existing DNS Solutions  
Prior studies have addressed DNS security and availability from various perspectives. DNSSEC enhances data authenticity via cryptographic signing [27], while HARD-DNS improves fault tolerance through redundant distribution [28]. DepenDNS focuses on reliability by modeling server dependencies [29], and an adaptive scheme has been proposed to defend against cache poisoning attacks [34]. While these approaches improve specific aspects, they often face trade-offs in scalability, efficiency, or coordination--issues our cross-chain synchronization scheme is designed to overcome. The benchmark reference from TI-DNS [30] was used to approximate performance ranges under similar conditions. As shown in Table 2, our proposed solution achieves consistently lower DNS resolution latency across all tested query sending rates when compared with existing approaches, such as ODD, DNSSEC, HARD-DNS, and DepenDNS. At 40 TPS, our solution demonstrated a latency of only 460 ms, which was 13.1% lower than the best baseline (DNSSEC, 530 ms). As the load increased, our approach maintained a steady performance advantage: at 160 TPS, it recorded 628 ms compared to the 950 ms for DepenDNS and 790 ms for HARD-DNS--improvements of 33.9% and 20.4%, respectively.  
This efficiency stems from our cross-chain architecture, which optimizes trust synchronization between regional root servers via a master-subchain model, reducing inter-domain communication delays. The staged atomic swap mechanism enables efficient and secure transaction coordination without introducing the heavy cryptographic overhead observed in DNSSEC or the resource duplication in HARD-DNS.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 4 Conclusions  
The HF communications allow transmitting signals beyond line-of-sight propagating them through the ionosphere. The ionospheric effects lead to the degradation of the signal suffering attenuation, time dispersion and frequency dispersion (Doppler spread and Doppler shift). HF sounding of the ionosphere has been of great interest for research community to obtain knowledge about the behavior of the plasma that comprises the different reflected layer of the channel and its influence in the signal impairments.  
HF ionospheric communications systems present mechanisms that enable communication in a large area without the need of a network infrastructure, satellites or repeaters. This possibility of independence of local infrastructure is absolutely crucial for disaster relief communications, when the infrastructure is usually absolutely destroyed by a large scale natural disaster, or in remote regions where this infrastructure is lacking, as the Antarctica.  
This research work has conducted a review of an ITU-R on recommendation on HF field-strength measurement campaigns. Afterwards, vertical sounding is addressed with a world wide network of ionosondes (GIRO), and finally, three oblique sounding projects have been reviewed for high latitude (DAMSON), mid latitude (WHISPER), and trans-equatorial of 12,760 km latitude (Antarctica).  
The review shows that during the last decades a huge progress has been made in the design of ionospheric sounding systems to adjust the construction of HF inospheric communication systems. Even so, in the design of any new communication system, the previously detailed techniques should be applied to check the reaction of the channel under study to the different modes of propagation and communication.  
[PERSON] and [PERSON] wrote the descriptions of the projects focused on sounding, and both reviewed the paper. [PERSON] structured the paper, wrote the historical introduction and the state of the art, and reviewed the paper. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Thanks to [PERSON], director of Observatori de l'Ebre from which we obtained the data of Lowell Digisonde 4D Ionospheric (Lowell Digisonde International, www.digisonde.com, 2015.).  
The authors declare no conflict of interest.  
Figure 13: (**a**) High robustness data frame definition and (**b**) high throughput data frame definition.
# Remediation of Metal/Metalloid-Polluted Soils: A Short Review
## 4 Removal Techniques
### Phytostabilization  
In phytostabilization, plants prevent the mobility and the bioaccumulation of heavy metals and metalloids. The mechanism entails the complexation of metal ions with the roots, or with the cell walls, binding with molecules present in the roots, such as phytochelatins and metallothioneeins, and finally sequestering them to root vacuoles [139]. [PERSON] et al. [111] studied _Helianthus petiolaris_ to treat soil contaminated with cadmium and lead, and showed that this plant does not interact with the activity of microorganisms already present in the root zone, and is efficient if the soil Cd concentration is lower than 50 mg\(\cdot\)kg\({}^{-1}\) and the Pb concentration is lower than 1000 mg\(\cdot\)kg\({}^{-1}\).
# A Forwarding Latency Optimization Method for Software Data Plane Based on Spin-Polling
## 2 Related Work
### Software Data Plane  
In software-defined networking (SDN) and network functions virtualization (NFV), software data planes play one of the crucial roles. Compared with traditional high-coupling network data plane facilities, software data planes can provide higher flexibility and programmability to meet more application requirements [25; 26; 27]. Software data planes in the SDN field can provide more packet operations, such as modification, insertion, and deletion of packet headers. In addition, unlike traditional devices, software data planes use multiple layers of headers, including but not limited to Layer 2 to Layer 5.  
Software data planes based on kernel IO stacks have been stretched to the limit in terms of performance. Many modern software data planes improve performance by bypassing the kernel IO stack, enabling cores to poll on user-level queues, and applying batch processingmechanisms. To reduce system calls or interrupts, using user-level IO to bypass the kernel IO stack is a classic approach. There is a lot of previous work on user-level IO capabilities, such as user-level networking stacks (mTCP [9], Sandstorm [8], MICA [7], and eRPC [10]), user-level storage frameworks (NVMeDirect [28], SPDK Vhost-NVMe [29]), and direct access to virtualized IO (Arrakis [11]), to name a few.  
Software data planes running on an x86 server can access a set of logical CPU cores and a set of physical or logical ports. Modern network interface controllers (NICs) support hardware multiple Rx/Txqueues. Some NIC technologies, such as Receive Side Scaling (RSS), can distribute packets from the same link to multiple different hardware Rx queues by hashing the packet's header. The data planes can bind different logical CPU cores to hardware Rx queues and Tx queues, respectively. Therefore, several logical CPU cores can efficiently process the packets from the same link in parallel [30]. Figure 1 shows the basic architecture of the software data plane with M NIC ports and N CPU cores for packet processing. In the architecture shown in Figure 1, each NIC port of the data plane is configured with N Rx queues and Tx queues, corresponding to N logical CPU cores for packet processing. Therefore, each logical CPU core needs to process packets from M Rx queues, and it can forward packets to M Tx queues. In addition, each logical CPU core is independent of the other and does not need to compete for packets, so there are no synchronization and locking issues in packet processing. Therefore, we can focus only on a single logical CPU core and its bound Rx/Tx queues.
## 2 Materials and Methods
### Preparation of the Alginate/Polypyrrole Hydrogels Disks and Beads  
The alginate-polypyrrole hydrogels were prepared according to two different protocols. Protocol 1 was used to form polypyrrole particles dispersed throughout the alginate matrix, and then used to form hydrogel beads. Pyrrole (0.014 mol L\({}^{-1}\)) was dispersed in a 2% (m/V) alginate solution, and 8 aliquots of 100 \(\mu\)L of APS 0.4 mol L\({}^{-1}\) at intervals of 10 min were added to chemically polymerize the monomer. The polymerization reaction was accompanied by UV-Vis spectroscopy (Supplementary Material, Figure S2). The resulting dispersion was washed three times (30 min, 14,500 rpm). The hydrogel beads were prepared by dispensing 1 mL of polypyrrole-alginate dispersion into 5 mL of a 3% (m/V) CaCl\({}_{2}\) solution, with a syringe, at a rate of 0.8 mL min\({}^{-1}\), under gentle stirring, using a home-made dispensing pump apparatus. This hydrogel contains polypyrrole particles dispersed throughout the alginate matrix and was named PPy-ALG.  
Protocol 2 began with the preparation of a neat alginate hydrogel (ALG) by direct contact with CaCl\({}_{2}\). The alginate hydrogel beads were formed by dispensing 1.0 mL of a 2% (m/V) alginate solution into a CaCl\({}_{2}\) solution (3% m/V) according to the same procedure previously described. The beads were washed with ultrapure water and subsequently dipped in a pyrrole solution (0.014 mol L\({}^{-1}\)), and the chemical polymerization was induced by the addition of APS (8 aliquots of 100 \(\mu\)L, 0.4 mol L\({}^{-1}\)) at 20 \({}^{\circ}\)C. After 60 min, the beads were filtrated and washed with ultrapure water. This hydrogel contains polypyrrole only surrounding the alginate bead and was named ALG@PPy.  
For each protocol, the hydrogels were prepared in two morphologies: beads and disks. Disks were employed to characterize the properties of the hydrogels, and the beads were used for the extraction experiments. Pure alginate hydrogels were also prepared for comparison purposes. Both procedures are depicted in Figure 1a. More details about the two different morphologies are available in the Supplementary Material.
### Modulation on the Complex Amplitude in Real Space  
In order to generate the beam whose polarization varies with propagation distance in free space, one approach is to modulate the initial phase distribution. The phase modulation determines the transverse component of the wave vector and will further influence the longitudinal component \(k_{z}\). If two collinear orthogonally polarized optical beams have different \(k_{z}\), a dynamic phase difference as a function of propagation distance will appear, which drives the variation of polarization during propagation. It is found that the initial phase modulation of two collinear polarized beams can be implemented based on the phase masks and the holographic grating.