esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### The Shape of Nanoparticles and Their Applications on Polyurethanes  
The morphology of AgNPs has been reported as a relevant factor for evaluating antimicrobial activity, and the most common shapes include plates, rods, and nanoparticles [32]. The effects of these morphologies have been tested on _S. aureus_ and _E. coli_[33]. [PERSON] et al. [34] studied the antibacterial activity of spherical, rod-shaped, and truncated triangle AgNPs against _E. coli_; their results showed that nanotriangles had the highest biocide activity, followed by nanospheres and rod-shaped nanoparticles. As noted, the evaluation of morphology is highly important for nanometric systems. The cellular toxicity is also related to the nanoparticle size, which is crucial to consider while designing AgNPs for biomedical applications [35].  
In materials research, the application of AgNPs in polymers and subsequent visualization via SEM techniques has allowed us to corroborate the efficiency of their antibacterial and antifungal activity. Studies on thermoplastic polyurethane (TPU) with incorporated AgNPs have successfully evaluated the antiviral activity of the composite material against the spring viraemia of carp (SVC) virus and SARS-CoV-2 [36]; similarly, powdered coatings made of PUR with AgNPs as a biocide agent have been developed, demonstrating efficiency against the SARS-CoV-2 virus [37].  
The concept of active PUR with AgNPs and copper nanoparticles (CuNPs) has been reported in the literature. The preparation of the composite material is described as the mixture of liquid polyether (the monomer for polyurethane synthesis) with the silver and copper components, followed by the PUR synthesis; this allows us to consider these materials as medical supplies with biocidal activity, given that the samples did not exhibit cytotoxicity against the tested cells in tissue culture (rat hypodermis) and biodegradation products were not observed in the culture medium [38]. The cationic surfactants of PUR have been evaluated, specifically, their effect on the stability of AgNPs and surface and biological activity, showing biocide activity against bacteria and fungi [39].  
Other applications of AgNPs have been developed for footwear manufacturing, mainly leather and synthetic elements, aiming to reduce the growth of skin disease-causing fungi [40; 41]. Given the increasing number of products that incorporate AgNPs, it is crucial to analyze the potential risks of their use on humans; data of interest can be found in [PERSON] et al. [40]
# A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands
## 3 Optimization of Wind-Energy Bands
### Experimental Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\lambda\) & \% Anomalous & \(\overline{BW}\) & \%\(\overline{BW}\) & \(\mathbf{t(s)}\) & \% ANOMALOUS & \(\overline{BW}\) & \%\(\overline{BW}\) & \(\mathbf{t(s)}\) & \% Anomalous & \(\overline{BW}\) & \%\(\overline{BW}\) & \(\mathbf{t(s)}\) \\ \hline
**1.00** & 6.6\% & 21.3 & 29.6\% & \textless{1} & 5.6\% & 34.3 & 47.6\% & \textless{1} & 5.4\% & 62.3 & 86.5\% & \textless{1} \\ \hline
**0.95** & 14.8\% & 16.0 & 22.2\% & 117 & 12.2\% & 26.8 & 37.2\% & 129 & 8.0\% & 56.6 & 78.6\% & 77 \\ \hline
**0.90** & 20.4\% & 13.8 & 19.2\% & 138 & 16.3\% & 24.8 & 34.4\% & 428 & 11.2\% & 51.6 & 71.7\% & 66 \\ \hline
**0.85** & 27.0\% & 12.5 & 17.4\% & 265 & 22.5\% & 22.1 & 30.7\% & 272 & 13.4\% & 48.4 & 67.2\% & 292 \\ \hline
**0.80** & 32.1\% & 11.7 & 16.3\% & 664 & 25.5\% & 19.7 & 27.4\% & 1518 & 16.3\% & 45.6 & 63.3\% & 421 \\ \hline
**0.75** & 36.7\% & 10.8 & 15.0\% & 1296 & 37.2\% & 17.4 & 24.2\% & 1097 & 20.7\% & 41.8 & 58.1\% & 657 \\ \hline
**0.70** & 45.4\% & 9.9 & 13.7\% & 2020 & 45.4\% & 15.6 & 21.7\% & 1950 & 28.3\% & 38.5 & 53.5\% & 842 \\ \hline
**0.65** & 49.0\% & 8.9 & 12.3\% & 1225 & 49.0\% & 13.1 & 18.2\% & 1480 & 28.6\% & 35.9 & 49.9\% & 1468 \\ \hline
**0.60** & 54.6\% & 8.3 & 11.5\% & 1390 & 52.6\% & 11.9 & 16.5\% & 1560 & 37.0\% & 34.2 & 47.5\% & 2425 \\ \hline \multicolumn{10}{c}{**Meteologica**} & \multicolumn{4}{c}{**Garrad Hassan**} & \multicolumn{4}{c}{**PSF44**} \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimentally estimated attributes for confidence \(\theta=0.035\) bands as \(\lambda\) decreases. is equivalent to expect lower power variations. According to official sources, the total electricity produced by Uruguay during 2017 (to meet internal demand plus energy exports to Argentina and Brazil) was of 12,600 GWh. The equivalent hourly average power is 1438 MW. The total wind-power plant by late 2017 was of 1437 MW (the fact these final figures match is just a coincidence). Hence, aiming on having energy bands whose relative width is below 20% is equivalent to expect average power fluctuations (either upwards or downwards the centroid) below 10% of the installed wind-power plant, which matches the average power consumption. In summary, our targets are: \(\theta\leq 0.035\), %\(\overline{BW}\leq 20\%\) and %\(anomalous\leq 10\%\). Observe that no record in Table 2 fulfills all these goals simultaneously. Along Section 4 we see how to deal with that issue.
# Radiation-Induced Structural Changes of _Miscanthus_ Biomass
## 3 Results and Discussion
### Influence of Irradiation Treatment on Structural Properties of Miscanthus Biomass
#### 3.3.2 TGA Measurement  
The TGA thermal stability of biomass usually has two obvious narrow peaks: one is the degradation peak of hemicellulose, the other is the peak of cellulose. However, the TGA peak of lignin is very broad and partly overlaps hemicellulose and cellulose [25]. The TGA profiles of the irradiated _Miscanthus_ biomass are depicted in Figure 2. There are two peaks (T\({}_{\mathrm{max\ L}}\) and T\({}_{\mathrm{max\ H}}\)) observed from TGA curves at doses of 400, 600, and 800 kGy, but only a single peak (T\({}_{\mathrm{max\ L}}\)) at 1000 and 1200 kGy. The disappearance of the high-temperature peak (T\({}_{\mathrm{max\ H}}\)) was caused by the side chains cleavage of biomass under the higher absorbed doses. In addition, a small shift in T\({}_{\mathrm{max\ L}}\) value to a lower temperature (Table 3) occurred when absorbed dose increased up to 1200 kGy, indicating the backbone of the biomass was degraded by the irradiation treatment. The phenomenon does not align with the _CrI_ values of cellulose in Table 3.  
Figure 1: Effect of absorbed dose on reducing sugars yield and methylene blue adsorption.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dose/kGy** & **0** & **400** & **600** & **800** & **1000** & **1200** \\ \hline Mw (Da) & 542,342 & 228,791 & 168,982 & 160,859 & 151,213 & 150,821 \\ Mn (Da) & 45,544 & 37,981 & 36,273 & 34,793 & 33,565 & 30,237 \\ Mw/Mn & 14.952 & 5.024 & 4.449 & 4.623 & 4.505 & 4.988 \\ _DP_ & 366,225 & 44,230 & 30,947 & 20,569 & 18,200 & 11,354 \\ _CrI_ (\%) & 37.86 & 33.26 & 28.92 & 29.03 & 30.02 & 28.43 \\ \hline \hline \end{tabular} Note: Mw, the weight average molecular mass; Mn, the number average molecular mass; Mw/Mn, the polydispersity index; _DP_, the degree of polymerization; _CrI_, the crystallinity index.  
\end{table}
Table 3: Influence of absorbed doses on molecular weight distribution, _DP_, and _CrI_ of _Miscanthus_ biomass.
## 5 Implementation  
Based on the aforementioned scenarios (Section 3), we constructed a network topology consisting of three simple SFs and two underlying pairs of encrypted channels. Figure 6 shows the components that are involved in the data plane forwarding of the NSH packets.  
This network topology setup implements the use case scenario (Figure 2) and also reflects the tiered network topology (Figure 3). We implemented this topology by running one Compute Node per ISP where each Compute Node had one inner and one outer SFF. We also configured an Edge gateway per network domain that was responsible for interconnecting the data centre domains. We used one Compute Node per NFV domain. Correspondingly, there is one Edge gateway per Compute Node.  
Figure 7 shows the categorisation, the enumerations and the virtual bridge connections of the VMs running on each Compute Node. The SFs, the EFs and the classifiers are instantiated as multiple instances of VMs. These VMs are instantiated per SFC during service provisioning. The SFFs are statically deployed VMs that are pinned to the hypervisor. The control plane components are also categorised as a special group of VMs. This is because they are only instantiated at one of the Compute Nodes and because they are not connected to the data plane.  
Figure 6: Service plane topology.  
Figure 7: Virtual Machines and networks per Compute Node.  
From a networking perspective, Figure 7 also shows that each VM is connected to different Linux-bridge domains. We used virtual Linux interfaces (veth) to interconnect VMs to virtual bridges. Furthermore, these virtual bridges are also connected to the physical network interfaces. This network construction follows the principle of virtual network infrastructures in Linux that is also used in, for example, OPNFV [34] and OpenStack [35].  
For local Virtual Infrastructure Management (VIM), we ran the VM provisioning tools and the local network/bridge management as non-virtual function alongside the Kernel-based Virtual Machine (KVM) environment. We used Linux scripts and Vagrant to control the instantiation of VMs and to control the mapping the virtual network interfaces to the underlying VXLAN-GPE infrastructure. The local VIM is orchestrated by a simple top-level RESTconf based orchestrator. Figure 8 shows the hierarchy of both this orchestrator and the other control plane components. For proof-of-concept purposes, we only used one Compute Node per domain controller.  
The NFV implementation is developed by the use of Vagrant, Linux bash scripting and the switch programming language P4. The source code, the demonstrations and the test results are available at [[https://github.com/gunleifsen/encNSFinP4](https://github.com/gunleifsen/encNSFinP4)]([https://github.com/gunleifsen/encNSFinP4](https://github.com/gunleifsen/encNSFinP4)).
# End-to-End Performance Evaluation of MEC Deployments in 5G Scenarios
## 3 Co-Simulation Based E2E Evaluation Framework
### Simu5G  
Simu5G [21, 22, 23, 24] is a system-level simulator for the data plane of the 5G NR technology, based on OMNeT++ [25]. It evolves from the well-known SimuLTE, which is a library for the simulation of 4G networks [26, 27]. By integrating the INET framework [28], Simu5G allows the evaluation of end-to-end applications, incorporating the whole TCP/IP protocol stack and standard network devices, e.g., routers. It is backward-compatible with SimuLTE, and thus it allows one to simulate also 4G and mixed 4G/5G scenarios. The main components of Simu5G are the User Equipment (UE) and the gNodeB (gNB). Both elements include a Network Interface Card (NIC) module that implements all the layers of the NR protocol stack, as shown in Figure 3. The gNB can connect either directly to the Core Network (CN), i.e., in a 5G SA deployment (Option 2), or act as a secondary node (SN) and connected to a master node (MN) implemented by an LTE eNB through the X2 interface. The latter represents the 5G NSA deployment (Option 3) and enables E-UTRA/NR dual connectivity (ENDC) [29]. In an ENDC deployment, the UE can attach to either the gNB or the eNB, or both. Within Simu5G, this is modeled by endowing the UE with two protocol stacks, one for LTE and one for NR, sharing the packet data convergence protocol (PDCP) layer.  
Simu5G supports carrier aggregation, where each component carrier can be configured to use either frequency- or time-division duplexing (FDD, TDD) and different numerologies, thus allowing maximum flexibility in building simulation scenarios. Simu5G models the effects of path loss,  
Figure 3: Main components of Simu5G.  
fading and inter-cell interference on message transmission, without modeling the transmission of individual symbols, which is instead the focus of link simulators such as ref. [30]. This makes it more scalable, hence apt to evaluate both network- and applications-related aspects in large-scale systems.  
Simu5G can also run as a network emulator. In this last setting, it can be connected to real applications and carry packets between them in real time, with the delay and bandwidth constraint of a 5G cellular network. This is useful for rapid prototyping of MEC applications. For instance, Simu5G can be connected to the Intel OpenNESS framework for MEC hosting [31]. Work [22] evaluates Simu5G emulation capabilities.
## 4 Discussion
It is thought that increased or maintained LPA offset the effects of iMetS on HGS, while decreased LPA reduces HGS, increasing iMetS. As the pace of aging increases, interest in dementia is also increasing. A recent meta-analysis has shown that MetS groups have 2.95-fold higher progression from MCI to dementia than in normal groups [47]. Therefore, the prevention of MetS is considered an important preventive factor for chronic diseases as well as the dementia of aging. In addition, HGS is used for the diagnosis of sarcopenia and frailty [48]. Sarcopenia is emerging as a large problem in old age. Only persistent LPA can maintain or increase muscles, which is essential for improving the quality of health, life, and living in aging [18; 42]. MetS is not a disease. It is clustering of risk factors. MetS is a public problem because it leads to cardiovascular disease morbidity, T2 DM, serious disabilities, and mortality [10]. Interpretation is also very complex and not fully elucidated because the development of MetS is a combination of five components [49]. Recent GWAS results have given us a partial understanding of their varying pathophysiology [50]. The relevant genes included genes related to T2 DM (APOC1, FADS2, NEU2, SLC18A1, CMIP, PABPC4, etc. ); HTN (blood pressure; NEU2, CMIP, CELF1, JMJD1C, ARID1A, CEP68, etc. ); lipid regulation (ZPR1, CETP, LPL, CD300 LG, TRIB1, ANGPTL4, etc. ), and obesity (APOC1, FTO, BAZ1B, CELF1, PABPC4, PCCB, etc.). The analysis contained most genes traditionally thought to be the cause of metabolic syndrome [49; 51]. Moreover, results of pathway analysis using these significant genes showed involvement of immunity pathway [51], inflammation [52], endosomal/ vacuolar and ER-phagosome [53], and regulation of lipids and lipoprotein [54]. Recently, gender difference was confirmed in the longitudinal study [26]. However, our results did not show the relative HGS of genders and baseline and the interaction of BMS. Interestingly, in the delta change of relative HGS, women were not significant in DM development, but men showed significance with iMetS. As a result, it is thought that in-depth research between genders will be needed. This study had some limitations. First, this study was limited to Koreans and, due to ethnic differences, it is difficult to generalize the results of our research. Second, a more accurate measurement of exercise is needed because the amount of exercise was calculated by the questionnaires. Third, muscle mass, muscle strength, and function may be different in various outcomes, so more research on this is needed.
# Dynamic Response Analysis of Mega-Sub Isolated Structures under Multiaxial Earthquakes
## 4 Analysis of the Influence of the Vertical Parameters of the Mega-Sub Isolated Structure
### Analysis of the Influence of Vertical Frequency Ratio
#### 4.2.2 Influence of Vertical Frequency on the Response of the Seismically Isolated Subframe  
Under the vertical ground motion, the isolation sub-frame acceleration and the isolation layer's vertical displacement response vary with the frequency ratio, as shown in Figure 9.  
From the above curve, the vertical acceleration of the sub\(-\)frame first decreases and then increases with the frequency ratio \(\eta\) and reaches the minimum value at 1.2; for the vertical displacement of the isolation layer, the vertical relative displacement response value increases with the frequency of the increase of the ratio have always shown a decreasing trend. When the frequency ratio \(\eta\) is greater than about 1.1, the decrease in the response value tends to be gentle.
## 2 Hybrid Edge-Cloud Approach
### Motivation for the Edge-Cloud Approach
#### 2.1.4 IEC 61850-90-5 Evaluation--Conclusions  
Based on the previous Subsection, it is safe to conclude that in DNs with LTE connectivity, the TCP protocol should not be used for any time-critical scheme/application as it substantially surpasses the latency threshold. In contrast, the UDP connection fulfils the latency requirement, however, it introduces another potential issue--missing data. Naturally, the reliability of applications that are heavily reliant on the corresponding PMU data becomes a great concern in such EPSs. Furthermore, the potential loss of packets contradicts the very purpose of PMUs, which is providing a higher granularity of measurements, capable of providing enhanced insight, in case of events.  
Figure 2: One-way delay for the user datagram protocol (UDP).  
We can conclude that a strictly centralized approach of data analysis and storage is not the optimal solution in DNs with LTE connectivity as we cannot simultaneously ensure both the low latency and the reliability of data transmission. An intuitive solution that comes to mind is to keep the monitoring and non-time-critical applications centralized, whilst decentralizing the analysis and decision-making processes of time-critical applications. The approach that decentralizes part of the computational power and relocates it closer to the measuring devices is not new as it was already used in the past to address a different issue, i.e., a huge amount of data that need to be transferred from devices to the control center, and was also adopted by 5G networks currently in the roll-out phase in most countries. The basics of the approach, known as edge-cloud computing, are presented in the next subsection, whereas the specifics of the proposed hybrid edge-cloud framework tailored for the purposes of automated fault localization are presented in Section 5.
## 3 Materials
### Feature Extraction
#### 3.2.1 Pre-Emphasis  
In audio communication systems, pre-emphasis is often applied to audio signals before they are transmitted or recorded, and pre-emphasis signals are then de-emphasized on the receiving end to restore the original frequency. This technique enhances the clarity of speech signals and makes them easier to understand. Equation (1) demonstrates how to apply the pre-emphasis filter to a signal \(x(t)\).  
\[y(t)=x(t)-ax(t-1) \tag{1}\]  
where \(\alpha\) is set to 0.97.
## 4 Results and Discussions
### Effect of Falling Height under Different Submergence Conditions
#### 4.1.1 Cases of Subaerial Landslides  
The falling heights (h) refer to the vertical distance between the initial lowest point of the slider and the still water surface, which are set as 0.05, 0.1, 0.15, 0.2, 0.25 and 0.3, respectively, and the submergence condition of landslide is a subaerial landslide. When the falling heights (h) are 0.05, 0.15 and 0.25 m, the temporal evolution of the water elevation in the G\({}_{1}\) and G\({}_{2}\) point are shown in Figure 6. The first crest in the wave list of the offshore component of the landslide-induced tsunami is attenuated when propagating from G\({}_{1}\) to G\({}_{2}\) (from the solid lines to the dotted lines in Figure 6), but the second crest in the wave train is not attenuated or even increased (Figure 6). Because, on the one hand, the first crest of the wave train is caused by squeezing and pushing the shallow water as the slider enters the water. After the slider completely enters the water, the extrusion effect moves from the shallow water to the deep water, which leads to the fact that after the first crest is generated, there is no new energy input and only energy dissipation. On the other hand, the second crest of the wave train is caused by water pouring into the depression. When the slider completely enters the water, there will still be a pressure gradient above the trailing edge of the moving slider, causing the water to move downward and rebound upward, which means that the second crest will be affected by the water moving above the trailing edge of the slider in the process of propagation. With energy input during propagation, the second crest is larger at G\({}_{2}\) than at G\({}_{1}\). Similarly, the first trough of the wave train is also affected by the change of flow field caused by the slider, so the attenuation is not obvious when propagating from G\({}_{1}\) to G\({}_{2}\) (from the solid lines to dotted lines in Figure 6).  
Figure 5: Setting diagram of the cases of falling height under different submergence conditions.  
Figure 6: Cont.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV DT Technologies
#### 3.3.5 ML-AI Integration and Algorithms  
Digital twins enable the extraction and examination of an immense amount of data produced by PV systems. Utilizing ML/AI algorithms allows for predicting, forecasting, optimizing, and modeling data from a physical PV counterpart. DT frameworks for monitoring the operational condition of solar panels, thereby improving life cycle management and defect detection capacities, have been described by [4; 5]. They explore the capacity of DTs to transform the process of identifying faults in PV panels, therefore enhancing maintenance techniques and minimizing periods of inactivity. The capacity to replicate different operational situations enables intelligent decision-making, ultimately improving the effectiveness of solar energy systems.  
As demonstrated in Figure 8, reliable solar forecasting models depend on input data, including solar irradiance, cloud cover, and temperature, typically obtained from satellite photos, sky imagers, and numerical weather prediction (NWP) algorithms. Numerous forecasting methodologies are available, encompassing physical techniques that rely on irradiance data and meteorological measures, as well as advanced statistical and artificial intelligence algorithms. Artificial intelligence (AI) methods such as artificial neural networks (ANNs) and support vector machines (SVMs) improve forecast accuracy by effectively using past data and adapting to real-time fluctuations. Also, some hybrid models enhance prediction accuracy by integrating two or more forecasting methodologies, to realize more dependable forecasts for energy trading, unit commitment, and power grid management. The dependence of the current models on ML/AI is due to their higher accuracy and their dynamic inferencing capability.  
In Table 4, we present a review of ML algorithms that have been implemented specifically for DT in PV systems. This list is not exhaustive as several ML and AI algorithms could be applied in a PV DT.  
Figure 7: PT-DT communication architecture.