esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 4 Silver Nanoparticles and Their Antibacterial Role
### Results of Studies  
The most studied bacteria is represented by _Staphylococcus aureus_ (Gram positive) and _Escherichia coli_ (Gram negative). Other strains include Multiresistant _Staphylococcus aureus_ (MRSA), _Pseudomonas aeruginosa_, _Klebsiella pneumoniae_, _Staphylococcus epidermidis_, _Listeria monocytogenes_, _Vibrio cholerae_, _Proteus mirabilis_, _Enterobacter and Enterococcus faecalis_. Due to the irrational use of antibiotics, bacteria have developed a resistance to classical antibiotics over time, thus leading to the appearance of a new generation of antibiotics.  
By combining a small quantity of antibiotics with metallic nanoparticles (Ag, Au, Pt), the antibacterial activity is greatly enhanced, especially when fighting multi strain resistant bacteria (see Figure 4).  
The authors describe (1) The biogenic synthesis of bimetallic silver- gold nanoparticles and their functionalization with standard antibiotics (viz., bactracin, chloramphenicol,erythromycin, gentamicin, kanamycin, streptomycin) and (2) The antibacterial effect of the resulting compound. Synergistic action of (a) biogenic metabolites formed along the synthesis process,(b) physical interaction of silver nanoparticles with cell structures, (c) cationic silver release, and (d) synthetic antibiotic specific mechanism result in enhanced antibacterial effect by attacking different structural and functional regions of the cell (flagelia, pili, membrane, cell wall, cytoplasm, DNA, plasmid, ribosomes) in various bacterial strains (_E.coli_, _B subtilis_, _K. pneumoniae_) [55].  
Silver nanoparticles have attracted a lot of attention due to their important antibacterial activity against several bacteria types. They do not possess a toxic side effect for humans for doses up to 350 \(\upmu\)g/day [44].
# A Blockchain-Based Cooperative Authentication Mechanism for Smart Grid
## 4 Our Proposed Mechanism
### Cross-Domain Key Cooperative Algorithm  
After the devices \(D_{i}\) in domain A and devices \(D_{j}\) in domain B complete mutual authentication, they can negotiate a key together, after which they can communicate securely through that key. We will use the ECHDE key exchange algorithm to exchange keys, as shown in Figure 4; its specific algorithm is as follows.  
Step 1: Share the same elliptic curve parameters \(\{p,a,b,G\}\) by initializing the system as described above.  
Step 2: Device \(D_{i}\) picks a random number \(r_{i}\longleftarrow Z_{p}\) in \(Z_{p}\) as its private key \(sk_{i}\), then calculates its public key as \(pk_{i}=sk_{i}\).\(G\) and sends its public key \(pk_{i}\) to device \(D_{j}\) afterwards.  
Step 3: After the device \(D_{j}\) receives \(pk_{i}\) it also picks a random number \(r_{j}\longleftarrow Z_{p}\) in \(Z_{p}\) as its private key \(sk_{j}\), then calculates its public key as \(pk_{j}=sk_{j}\).\(G\) and sends \(pk_{j}\) to the device \(D_{i}\).  
Step 4: After that \(D_{i}\) and \(D_{j}\) can compute the shared key separately, both shared key \(SK=pk_{i}\).\(r_{j}=pk_{j}\).\(r_{i}=r_{i}\).\(r_{j}\).  
After that, both parties have a common key and can use the common key for encrypted communication, which ensures the efficiency and the security of the information of both parties.
# Performance of Satellite-Based Evapotranspiration
## 2 Study Area and Datasets
### Satellite and Ancillary Data Used to Drive the Models  
A total of 125 images of the Surface Reflectance product (Level 2-A) and the digital numbers (ND) of thermal bands were processed from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) [39; 40] for the period 2014-2017 (available at [[https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov)]([https://earthexplorer.usgs.gov](https://earthexplorer.usgs.gov))). All images were filtered by cloud cover using the C Function of Mask (CFMask) algorithm [41] and then resampled to the spatial resolution of the Landsat 8 thermal band (\(100\times 100\) m).  
Land surface emissivity (\(\varepsilon_{\lambda}\)) was obtained from the ASTER Global Emissivity Dataset (ASTER GED). This product has global coverage and includes the five thermal bands from the ASTER sensor between 2000 and 2008 [42; 43]. The topography of the study area was derived from the SRTM (Shuttle Radar Topography Mission) Digital Elevation Model product [44]. Surface land cover types for the region were extracted from a land cover product based on Landsat 8 imagery at a spatial resolution of 30 m [7].  
The Atmospheric Correction Parameter Calculator (ACPC) [45] was used in order to obtain atmospheric downward radiance (\(\mathrm{L_{d}}\)), ascending radiance (\(\mathrm{L_{u}}\)) and transmittance (\(\mathrm{\tau}\)) for land surface temperature (\(\mathrm{T_{s}}\)) retrievals over the study area. These parameters were obtained for the center latitude and longitude of each Path and Row of Landsat during the study period from July 2014 to December 2017. Earlier work demonstrated the capability of ACPC for \(\mathrm{T_{s}}\) retrievals over the study region in relation to MOD07 and AIRS datasets [46].
## 3 Results and Discussion
### Effect of AC on CO  
The effect of activated carbon on CO at different powers is shown in Figure 5. It can be seen that the running condition of the diesel engine was unstable at low load (0-6 kW), and the CO concentration in the exhaust before entering the reactor was relatively high. The diesel engine reached the best working condition at medium and high load (9 kW and 12 kW), and the CO concentrations also decreased. However, the CO concentration rose sharply at full load (15 kW), which was due to the increase of fuel injection at the full load. The lack of air for natural intake diesel engines results in incomplete fuel combustion. This phenomenon can also be confirmed from the PM change process (Figure 4). CO may act as a reducing agent to participate in denitration reaction to form CO\({}_{2}\) under the catalysis of activated carbon. The removal efficiency of CO by activated carbon decreased with the increase of power, and even turned negative when the power was 15 kW. This is because the temperature of the exhaust was low (less than 200 \({}^{\circ}\)C) at low power and the adsorption performance of activated carbon is relatively strong. As the power increased, the temperature of the exhaust gas gradually increased, and the adsorption performance of activated carbon gradually decreased. However, the CO removal efficiency turned into -7.5% when the temperature was higher than 300 \({}^{\circ}\)C (15 kW). The activated carbon contains some oxygen-containing functional groups such as carboxyl, lactone, and phenolic hydroxyl. The carboxyl and lactone groups are decomposed in the temperature range of 200-300 \({}^{\circ}\)C [24; 25], resulting in AC degradation and the increase of CO concentration. In the future, it will be necessary to improve the material and production process to increase the thermal stability of activated carbon.  
Figure 4: Effect on particulate matter (PM) concentration by AC reactor.
## 4 Strengths, Limitations, and Practical Implications of the Study  
To the best of our knowledge, no reviews are currently available about the use of phenolic compounds for the control of the MR in bakery products. The main strength of this study relies also on its methodological approach. A systematic examination of primary studies was performed. The search criteria and keywords used in sourcing the studies were specific and predefined before starting the review, as well as the inclusion and exclusion criteria for selecting the primary studies. This approach enabled the limiting of bias that can occur in narrative reviews that may lack thoroughness and rigor.  
Thanks to its rigorous approach, a systematic review is also reproducible, and its replicability helps establish a greater degree of confidence in the obtained results. Hence, this study offers a trusty overview of the use and efficacy of phenolic compounds in controlling the Maillard reaction. It also helps uncover areas in which more research is needed.  
In particular, the study findings can have practical implications. The investigation of the inhibitory effect of pure phenolic standards on MRPs suggests, for instance, that more studies should be designed to investigate mechanisms beyond the inhibitory effect of adding phenolics. The review also pointed out that the intrinsic properties of the food matrix, such as the pH, were not taken into consideration in the evaluation of the inhibitory action of phenolics. This finding should be considered in future research.  
Possible limitations of this review include the analysis of the role of phenolic compounds for the control of the MR only in bakery products. It could be interesting to also investigate the effect of phenolic compounds on the pathways of the MR and product formation in other food matrices, such as in UHT-processed bovine milk during thermal processing and storage.
## 2 Cloud-Native Network Architecture in Future Networks  
### Network Function Virtualization and Software-Defined Networking  
ETSI has been a leading force for realizing network virtualization since it defined the NFV architecture in 2012. The ETSI NFV Industry Specification Group (ISG) community has evolved through several phases from defining the initial architectural framework to developing detailed standard specifications. NFV enables network functions to be realized as software instances and hosted by virtual machines or containers running on commodity network/compute equipment such as standard servers and switches. The ETSI NFV architecture comprises a set of Virtual Network Functions (VNFs) deployed upon a virtualized infrastructure. The Management and Orchestration (MANO) system of the architecture contains a Virtual Infrastructure Manager (VIM) for unified management of network-compute resources in the virtualized infrastructure, a VNF Manager (VNFM) for managing VNFs, and an NFV Orchestrator (NFVO) for life-cycle management of network services [12].  
Recent developments of the NFV specifications in Releases 3 and 4 have focused on enriching the NFV architecture for global deployment and operations. The set of new features in NFV mainly includes support for the latest network technologies such as edge computing and network slicing, new operational aspects such as multiple administrative domains and policy frameworks, advances in acceleration technologies and lightweight (e.g., container-based) virtualization platforms, and enhancement of NFV automation capabilities for improving life-cycle management and orchestration of VNFs and network services.  
Multiple network SDOs, including ONF, ITU-T, and IETF, have developed their own versions of SDN specifications, but they all share the same basic architecture--an architecture that comprises three planes (the data plane, control plane, and application plane) and two standard interfaces (the southbound interface between the data and control planes and the northbound interface between the control and application planes). The key principles behind both SDN and NFV are decoupling with abstraction, but focus respectively on the layer dimension (for NFV) and plane dimension (for SDN) [13]. Therefore, NFV and the SDN are expected to be integrated in future networks in order to fully exploit the advantages of both paradigms. For example, multiple SDN controller instances may be realized upon a network hypervisor in order to control different virtual networks (network slices) implemented by NFV [14].
## Appendix B Inspecting the Robustness of 36-Month Averages  
The results in the previous appendix have been derived using average values in windows of 36 months, i.e., three years. While the number of points is small, it covers two annual cycles. As it is known--and as shown in Appendix C--malaria spreading shows periodic behavior following annual seasonality.  
To evaluate the robustness of the estimated values for malaria incidence, the Hurst exponent and entropy, shown in the previous appendix and discussed in the main text, we present, next, the results for estimates done from two and four annual cycles, i.e., 24 and 48 months, respectively. The results are shown in Figures 16-17.  
Figure A6: Cont.  
Figure 6: (Cont. Figure A5): [PERSON] et al. (Karuzi in Burundi, 2007) [24], [PERSON] et al. (Uganda, 2017) [26], [PERSON] et al. (Est Mono district in Togo, 2012) [25], and [PERSON] et al. (Kenya, 2008) [27].  
We choose to use the standard method to estimate the Hurst exponent, based on the quotient \(R/S\)[34, 37, 40, 41, 42, 43]. An alternative method could be the so-called generalized Hurst exponent (GHE), defined from the function  
\[K_{q}(\tau)=\frac{\langle|I(t+\tau)-I(t)|^{q}\rangle}{\langle I(t)^{q}\rangle}\,, \tag{10}\]  
where \(\langle\cdot\rangle\) is the average over time \(t\). The GHE \(H_{G}\) depends in general on \(q\) as [44]  
\[K_{q}(\tau)\sim\tau^{qH_{G}(q)}\,. \tag{11}\]  
A general Hurst exponent varies with the moment \(q\) flags the presence of multifractality in the series.  
GHE is a modern approach to Hurst analysis with some specific advantages applied in the analysis of complex and imhomogeneous time series in electrocardiography (ECG) signals, and it has been shown to be a promising tool for the study of atrial fibrillation (AF) organization from surface ECG [45]. It is usually recommended in the presence of short time series, where it has been shown to be slightly more efficient. It has been used mainly in the assessment of the stability of financial firms as applied to the stock market. However, GHE does not solve the problem of small time series, such as the ones analysed in this paper. Moreover, deriving the exponent from the scaling relation in Equation (11) imposes a linear relation between \(\log K_{q}\) and \(\log\tau\) that usually occurs only for the smallest values of \(\tau\). Consequently, it may overestimate more recent events, reducing the significance of past events. This is indeed the case for the GHE derived for Okech (see Figure 1), where, as malaria is eradicated towards the end of the data record, it can wrongly show an anti-persistence value (\(H_{g}<0.5\)). See Table 11. For this reason we choose the \(R/S\) analysis to estimate the Hurst exponent.
## 3 Polyunsaturated Fatty Acids (PUFA) Exploitation from Autotrophic Microalgae
### PUFA Role in Human Health  
From microalgal lipids, PUFA are the most studied for their pharmacological potential. In human health, C20-C22 PUFA play important roles in many physiological and pathological processes [57]. Moreover, most of PUFA health-benefits are due to their key roles as lipid mediators in inflammatory processes and as important compounds for growth and development. EPA and DHA are parent compounds of specialized pro-resolving lipid mediators (protectins, resolvins, and maresins) that act as inflammatory brakes and promote the return of the affected site to homeostasis (Figure 6) [62; 63]. As with \(\omega\)3-PUFA, AA-derived lipoxins and their carbon-\(15\) position epimers have beneficial effects on inflammation and resolution [62].  
Figure 5: Biosynthesis of long-chain polyunsaturated fatty acids in microalgae [55; 58; 60]. AxD— “front-end” desaturase, adds a double bond at position x from carboxyl end; \(\omega\)yD—“methyl-end” desaturase, adds a double bond at position y from methyl end; E—elongase, which catalyzes carbon chain extension; PKS—polyketide synthase.  
EPA competitively inhibits the utilization of AA by cyclooxygenasegenasse/lipoxygenases to less pro-inflammatory mediators [65]. In contrast with eicosanoid products from AA (prostaglandin E2, thromboxane A2, and leukotriene B4), EPA-derived eicosanoids (thromboxane A3, and leukotriene B5) are weak inducers of inflammation and have attenuated platelet-aggregating and vasoconstriction abilities [65]. In mammalian cells, \(\omega\)6- and \(\omega\)3-fatty acids are not interconvertible because they lack \(\omega\)3-desaturase; therefore, their balance in the diet is important [65]. In the Western diet, the greatest amounts of \(\omega\)6-fatty acids (\(\omega\)6\(\omega\)3 of 20:1) drive higher levels of AA eicosanoids products (e.g., prostaglandandins, thromboxanes, leukotrienes, and hydroxy fatty acids), causing an imbalance between pro- and anti-inflammatory molecules and shifting the physiological state to one that is proinflammatory, prothrombotic, and proaggregatory [65]. Thus, a balanced intake of \(\omega\)6- and \(\omega\)3-fatty acids is crucial.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Problem Formulation  
Due to the asynchronous nature of the aggregation process on edge and cloud servers, it is hard to find the optimal solution of the whole training process. Instead, we aim to achieve a balance between model performance and latency by optimizing the client scheduling and bandwidth allocation, taking into account the delay (including the delay of transmission and local training) and data quality.  
After each round of cloud aggregation, the cloud server selects \(n\) training clients. These clients are then allocated to the nearest edge server \(j\) in order to optimize their training delay (\(\tau_{i}\)) and data quality (\(Q_{i}\)). Accordingly, the optimization problem is formulated as  
\[\begin{split}&\min_{\beta_{i}\beta_{j}}\sum_{j=1}^{n}\left( \alpha\tau_{i}+\frac{(1-\alpha)}{Q_{i}}\right),\\ \text{s.t.}& S_{j}\in\mathcal{I},\\ & S_{j}\cap S_{j^{\prime}}=\emptyset,\forall j,j^{\prime}\in \mathcal{J},j\
eq j^{\prime},\\ &\sum_{i\in S_{j}}\beta_{ij}\leq 1,\end{split} \tag{7}\]  
where \(\alpha\) is the weight coefficient and \(\tau_{i}=\tau_{i}^{\text{comp}}+\tau_{ij}^{comm}\). Note that one client can associate only with one edge server during the training process.  
However, solving this optimization problem is challenging due to the vast combinatorial search space of the association decisions and the interdependence with resource allocation. Instead of finding the optimal solution, we aim to find suboptimal solutions by employing a divide-and-conquer approach to split the critical problem into two subproblems, i.e., reducing client training delay and enhancing client data quality. Our proposed approach includes the following steps:  
1. We develop a load balance algorithm that calculates the number of clients associated with each edge server based on the remaining available bandwidth and the state transition probability of client mobility.
2. Then, we propose a client scheduling algorithm which selects the clients with low latency and high data quality.
3. Furthermore, we also develop an algorithm that accelerates model parameter uploading by making full use of the available bandwidth of the edge servers.
## 3 Results and Discussion
70%) is present as TiCl\({}_{3}\) associates, which are observed in the ESR spectrum only after treatment of the catalyst with pyridine [18]. According to our data obtained in the study of the above-listed catalysts in the homopolymerization of ethylene [18], differences in the oxidation state of titanium ions and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Exp. No.**} & \multirow{2}{*}{**Catalyst**} & \multirow{2}{*}{**[Ti] wt.\%**} & \multirow{2}{*}{**Catalyst Composition**} & \multirow{2}{*}{**Ti(IV)**, & \multicolumn{2}{c}{**Ti (III)**, wt.\%**} & \multicolumn{2}{c}{**Ti(III)**, wt.\%**} \\  & & & & **wt.\%** & **Isool.** & **Ass.** & **Isool.** & **Ass.** \\ \hline
1 & TMC-1 & 1.5 & [\(\eta^{6}\)-BenzeneTiCl\({}_{2}\)-2 AlCl\({}_{3}\)]/MgCl\({}_{2}\) & - & - & - & 100 & - \\ \hline
2 & \multirow{2}{*}{TMC-1-2} & \multirow{2}{*}{1.5} & [\(\eta^{6}\)-BenzeneTiCl\({}_{2}\)-2 AlCl\({}_{3}\)]/MgCl\({}_{2}\)/C\({}_{6}\)F\({}_{5}\)Cl & - & 100 & - & - & - \\
2-1 & & & [\(\eta^{6}\)-BenzeneTiCl\({}_{2}\)-2 AlCl\({}_{3}\)]/MgCl\({}_{2}\)/C\({}_{6}\)F\({}_{5}\)Cl/TIBA & - & 100 & - & - & - \\ \hline
3 & \multirow{2}{*}{TMC-2} & \multirow{2}{*}{1.5} & TiCl\({}_{3}\)/MgCl\({}_{2}\) & - & 35 & 65 & - & - \\
3-1 & & & TiCl\({}_{3}\)/MgCl\({}_{2}\)/TIBA & - & 35 & 55–60 & - & 5–10 \\ \hline
4 & \multirow{2}{*}{TMC-3} & \multirow{2}{*}{1.2} & TiCl\({}_{4}\)/MgCl\({}_{2}\) & 100 & - & - & - & - \\
4-1 & & & TiCl\({}_{4}\)/MgCl\({}_{2}\) & 15 & 7 & 63 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Content of titanium in different oxidation states and different environments in TMC synthesized using titanium compounds of different compositions. their coordination state (isolated or associated Ti(II) and Ti(III) ions) in the active component of catalysts exert virtually no effect on the molecular weight and polydispersity (M\({}_{\rm w}\)/M\({}_{\rm n}\)) of the produced homopolyethylenes. Copolymerization of Ethylene with \(\alpha\)-Olefins over Ti-Mg Catalysts Containing Titanium Compounds in Different Oxidation States and Different Coordination Environments
## 3 Results
### Impact of Factors on the LUEmax
#### 3.2.3 Impact of Factors on the LUEmax  
The results show the explanatory power of each factor on the spatial differentiation of the LUEmax and assess the explanatory power of factor interactions on the LUEmax (Figure 9). The geographical detector's factor detection and interaction detection results show that the impacts of the different factors on the LUEmax vary significantly. Regarding the influence of single factors, LAI and VPD were the main driving factors affecting the LUEmax, indicating that vegetation conditions and atmospheric humidity greatly impact the LUEmax. Variables such as SWC, CO2_F, and TA have moderate influences, reflecting that soil moisture, carbon dioxide concentration, and air temperature also affect the LUEmax. The influences of P and NETRAD were relatively low, indicating that these factors have a weak direct effect on the LUEmax.  
Further analysis of the interaction among factors reveals that the combined effect of two factors is significantly greater than that of a single factor effect, suggesting that LUEmax is jointly driven by multiple environmental factors. In particular, the interaction effects of combinations such as TA and EVI and TA and LSWI are the most significant, reflecting a strong correlation between vegetation indices and climate factors. These results indicate that, in addition to the direct influence of individual factors, the change in the LUEmax is also significantly affected by the interaction of multiple factors, especially the coupling effect between vegetation indices and climate factors.