esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### An Elucidation of the Molecular Mechanisms Regulating AsA Accumulation of A. latifolia  
To date, much progress has been made toward understanding AsA biosynthesis and recycling in higher plants [26]. At least four distinct metabolic pathways, including the L-galactose, L-glucose, galacturonic, and myo-inositol pathways, form a complex network for AsA biosynthesis [1]. AsA biosynthesis and metabolism are complex reactions that depend on the co-expression and coordination of a cluster of genes [1; 4; 12]. All the genes encoding enzymes in the L-galactose pathway, and some of the genes encoding enzymes in the other three pathways, were identified in _A. latifolia_ (Table 3), supporting that the predominant pathway of AsA biosynthesis in higher plants is the L-galactose pathway, which cooperates well with the other three alternative biosynthesis pathways [1; 26].  
In the present study, we quantified the global gene expression accompanying fruit development and ripening of _A. latifolia_ and identified candidate genes associated with AsA biosynthesis and recycling (Figures 4 and 5). Previous studies have demonstrated the correlation between _GMP_ transcript level and AsA accumulation rate to some extent [23; 27]. In this study, the _GMP_ transcript level tended to be higher in the young fruits, and had a similar trend with AsA accumulation in early fruit developmental stages (Figure 6). _GMP_ plays an important role in cell wall biosynthesis and protein glycosylation. Therefore, the two processes may also be involved in high _GMP_ transcript levels in young fruits [27]. Transformation of plants with over-expression of the _GGP_ gene in _Arabidopsis_ resulted in a more than four-fold increase in AsA content [28]. As an early committed step of the L- galactose biosynthesis pathway, _GGP_ contributes greatly to the rapid increase in AsA accumulation in several fruit species [23; 29; 30]. _GGP_ has been considered the best regulatory control point for AsA biosynthesis [31]. In the present study, _GGP_ showed relatively high expression levels in the early stages of fruit development (Figure 6), supporting the contribution of \(GGP\) to the AsA biosynthesis. In addition, the gene expression level of \(GME\) was also correlated with AsA content (Figure 6), emphasizing the central role and positive regulation of \(GME\) in AsA accumulation in immature fruit [23]. In addition to the three upstream enzymes, we observed a positive correlation between \(GalDH\), \(GPP\), \(PGI\), \(MIOX\), and \(APX\) and the cumulative AsA content (Figure 6), suggesting that the high expression of these enzymes resulted in high AsA accumulation in \(A\). _taitglia_. Overall, the current study provided insights into the molecular mechanisms regulating AsA accumulation of \(A\). _taitglia_ fruit, which are expected to be useful for breeding cultivars with super-high AsA content in the future.
# Effect of MXene Nanosheet Sticking on Supercapacitor Device Performance
## 3 Factors Influencing Sticking in MXene Electrodes
### Electrode Processing Techniques
#### 3.3.5 Drying Techniques  
The drying process during or after electrode fabrication is another critical step that can influence sticking behavior. Improper drying conditions, such as excessive heat or prolonged drying times, can result in the formation of strong inter-particle bonds, causing MXene flakes to stick together. Optimizing the drying process to maintain suitable temperature and time conditions is crucial for preventing sticking and preserving the desired electrode structure. For example, as illustrated in Figure 14, when a droplet of a well-wetting ink is placed on a substrate, the irregular evaporation of the solvent from the sides, due to the higher surface area/volume ratio, leads to an outward flow from the center of the droplet to the edges, particularly in low viscosity inks. This flow carries and accumulates the particles on the edge of the droplet, resulting in the formation of a ring-like structure known as a \"coffee-ring\" [69]. Addressing this issue can be achieved  
Figure 12: Electrochemical characterization of Ti\({}_{3}\)C\({}_{2}\)@3 DnCE for electrochemical capacitors in 2 M H\({}_{2}\)SO\({}_{4}\) using a three-electrode cell. (**a**) CVs at different scan rates and (**b**) analysis of the capacitive current contribution of Ti\({}_{3}\)C\({}_{2}\)@3 DnCE. (**c**) Galvanostatic charge/discharge cycles at different currents and (**d**) the derived rate capability. (**e**) Nyquist plot showing the measured data (indicated by dots) and the fitted data (continuous line) and (**f**) the derived Bode plot of the Ti\({}_{3}\)C\({}_{2}\)@3 DnCE [66]. Reprinted from [PERSON] et al. Electrochem. Commun., copyright (2021), with permission from Elsevier.  
using multicomponent carrier solvents for ink formulation. The differential evaporation rates of various components in the inks give rise to surface tension and compositional gradients within the droplet, thus creating inward Marangoni flows, which facilitate a more regular distribution of the particles.
# Cluster-Based Control Plane Messages Management in Software-Defined Flying Ad-Hoc Network
## 3 Cluster-Based Control Message Management for FANET
### Network and System Model
Specifically, controller \(CN\) must have full knowledge about each UAV \(v_{i}\) contextual information, such as, remaining energy ratio \(E_{ratio_{v_{i}}}(t)\), location information \(L_{i,t}\), mobility trajectory \(traj_{i}\), speed \(s_{i}\), and others, for efficient decision-making [12]. Each UAV \(u_{i}\) must send control message via SDN southbound API with contextual information \(msg_{CI}\) to the Global Controller at controller \(CN\). All the control messages are transmitted considering delivery guarantee to increase the reliability. Global Controller plays the role of an interface between the controller \(CN\) and FANET, synchronizing data exchange in both directions, requesting UAV contextual information, sending instructions to UAVs, and others [12]. Global Manager module performs all control functions, such as routing, topology management, path planning, relay manager, event monitor, and others, based on the inputs provided by the Global Controller module. For instance, the Global Manager module uses the UAV contextual information and the event location. It can establish a reliable routing path (by the routing manager module), place UAVs at the optimal locations to forward the packets (by the relay manager module), and how each UAV \(v_{i}\) should move by considering the global topology information (by the path planning module). Then, the Global controller sends all the output decisions to the UAVs. For large-scale environments, it might be required multi-hop communication to disseminate control message [6]. Some UAVs can be responsible only to forward the control message to/from the Controller \(CN\), enabling to transmit control messages with lower transmission power, which leads to reduced interference and energy consumption, as well as better control message reliability [12]. In this context, hierarchical network architecture enhances the overall network performance of control messages management, where UAVs are organized into groups, and a set of UAVs are considered as CH to work as a local controller to perform more complex tasks, such as the controlling of each group members, collecting data from non-CHs for data aggregation, take local decisions, and sending aggregated data to the controller [21]. The controller node \(CN\) is responsible for creating the cluster, electing the CH, assign the cluster members, and disseminating this information. In the cluster operation, each cluster member sends contextual information to its CH, which performs data aggregation to avoid unnecessary data transmission. Afterward, each CH sends the aggregate data packets to the controller \(CN\). Based on the received UAV context information, Controller \(CN\) considers mobility and energy prediction algorithms to reduce the contextual information dissemination. In the following section, we introduce the control message dissemination operations.
## 2 Remote Sensing Surface Albedo Products
### NASA MCD43 C6 Based on MODIS Data  
The MODIS BRDF/Albedo (MCD43A3) Collection 6 (C6) dataset, which is available from [47], provides both directional albedo at local solar noon and bi-hemispherical albedo for MODIS bands 1-7 and for three broadbands (visible [0.3-0.7 \(\upmu\)m], NIR [0.7-5.0 \(\upmu\)m], and total shortwave [0.3-5.0 \(\upmu\)m]). The MCD43A3 albedo quantities are delivered at a resolution of 500 m in a sinusoidal projection. They have been produced every day since 2000 with a synthesis period of 16 days. Data from both Terra and Aqua satellites are used in the generation of this product.  
The MODIS albedo algorithm uses atmospherically corrected reflectance data (MOD09 product) to establish the best fit to a linear kernel-driven BRDF model, with the exception of the observations flagged as \"cloud\", \"cirrus high\" or \"aerosol high\". Like for C3S SPOT/VGT SA v1.0, the parametric BRDF model uses the Ross_Thick kernel for volumetric scattering and the Li_Sparse_Reciprocal kernel for geometrical scattering [45; 48]. A full retrieval of the model is attempted if there are seven or more high-quality observations well distributed over the viewing hemisphere during the 16 day synthesis period. When the number of observations is strictly less than seven and strictly greater than two, or if observations are not well sampled or do not well fit the BRDF model, a back-up algorithm with prior information is used. Then, a fill value is stored if the number of observations is strictly less than three, and the separated snow-free gap-filled products are also accessible [49]. Then, the BRDF model parameters are used for estimating spectral albedos from angular integration. The broadband albedosare computed using the spectral to broadband conversion approach [50]. The MCD43 C6 products use an improved back-up database, which is pixel-based updated from the latest full inversion as opposed to the land cover-based database used in the previous Collection 5.  
MCD43A3 SA products have reached CEOS LPV validation stage 3 [51]. [PERSON] et al. [46] reported an overall uncertainty (RMSD) of 0.053 and a low negative bias (-11.9%) of MCD43A3 C6 compared with the ground data from 15 FLUXNET homogeneous sites for the 2000-2005 period (653 samples), and a slight overall uncertainty (RMSD = 0.032) was reported compared with European FLUXNET measurements over snow-free conditions [52]. Existing studies of the previous collection 5 indicate that the accuracy of the MODIS shortwave broadband albedo meets the requirements (<5%) for both snow-free and snow-covered surfaces [31; 32; 33; 34].
## 3 Results
### AMR Awareness among Health Professionals  
Knowledge about AMR was low among the HCPs. Less than 50% of the respondents demonstrated good AMR knowledge in some of the questions, and this improved considerably after training (Table 4).  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multirow{2}{*}{**Terminology (\(u=50\))**} & \multicolumn{2}{c}{**Proportion of Participants**} \\ \cline{2-3}  & **Before Training (\%)** & **After Training (\%)** \\ \hline Antibiotic resistance & 100 & 100 \\ Superbugs & 35 & 76 \\ Antimicrobial resistance & 94 & 100 \\ Antimicrobial stewardship & 54 & 100 \\ Drug resistance & 98 & 100 \\ _Antimicrobial capacity building_ & 2 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Familiarity with AMR/AMS terminology.  
\begin{table}
\begin{tabular}{c l c c c c c c} \hline \hline \multirow{2}{*}{**Sr No**} & \multirow{2}{*}{**Indicator (\(u=50\))**} & \multicolumn{4}{c}{**Strongly Agree**} & **Appe** & **Not Sure** & **Disagree** & **Strongly Disagree** \\ \hline \multirow{2}{*}{1} & Antibiotics are used in the management of all infections & Before & 22 & 18 & 0 & 26 & 34 \\  & After & 5 & 7 & 0 & 25 & 63 \\ \hline \multirow{2}{*}{2} & Antibiotic use should be strictly controlled & Before & 84 & 12 & 0 & 0 & 2 \\  & After & 83 & 12 & 0 & 0 & 5 \\ \hline \multirow{3}{*}{3} & Treatment with antibiotics should be stopped once you feel better, especially the expensive ones & Before & 2 & 0 & 2 & 24 & 72 \\  & & After & 13 & 0 & 0 & 18 & 70 \\ \hline \multirow{4}{*}{4} & It is okay to use antibiotics that were given to a friend or family member, as long as they were used to treat the same illness & Before & 0 & 0 & 0 & 10 & 90 \\ \cline{2-3}  & It is okay to buy the same antibiotics, or request these from a doctor, if you are sick and they helped you get better when you had the same symptoms before & After & 5 & 0 & 13 & 83 \\ \hline \multirow{2}{*}{6} & Frequent use of antibiotics may decrease the efficacy of treatment & Before & 0 & 0 & 4 & 21 & 74 \\  & After & 58 & 23 & 3 & 3 & 15 \\ \hline \multirow{2}{*}{7} & Poor counselling of patients can lead to antibiotic misuse & Before & 80 & 20 & 0 & 0 & 0 \\  & After & 90 & 10 & 0 & 0 & 0 \\ \hline \multirow{2}{*}{8} & Poor skills and knowledge of prescribers can cause irrational antibiotic & Before & 72 & 26 & 0 & 0 & 2 \\  & prescribing & After & 90 & 10 & 0 & 0 & 0 \\ \hline \multirow{2}{*}{9} & Patient self-medication can increase AMR & Before & 74 & 24 & 2 & 0 & 0 \\  & After & 85 & 15 & 0 & 0 & 0 \\ \hline \multirow{2}{*}{10} & Indequate supervision of the administration of medicine & Before & 60 & 36 & 4 & 0 & 0 \\  & After & 77 & 23 & 0 & 0 & 0 \\ \hline \multirow{2}{*}{11} & It is possible for the antibiotics we are using today to stop working properly in the future & Before & 58 & 30 & 2 & 4 & 6 \\  & After & 75 & 23 & 3 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: General knowledge about antibiotics.
# A Sigfox Energy Consumption Model
## 1 Introduction  
Low-Power Wide Area Networks (LPWANs) have recently emerged as a category of wireless technologies suitable for enabling Internet of Things (IoT) applications in a diversity of domains [1]. LPWAN technologies have been designed to support low energy consumption, since IoT devices (e.g., sensors and actuators) often rely on a limited energy source, such as a battery. However, in contrast with the rather short range of many established IoT technologies [2], LPWAN technologies provide an extended link range of up to several kilometers. Furthermore, a single radio gateway can offer network connectivity to hundreds of thousands of IoT devices. In consequence, LPWAN requires a low amount of infrastructure to be deployed and maintained, which has fueled its momentum in recent years.  
One of the most popular LPWAN technologies is called Sigfox [3]. This technology was developed in 2009 by the Sigfox company (Labege, France). As of the writing, Sigfox offers network coverage allowing bidirectional communication for IoT devices in more than 50 countries, covering a population of 1 billion inhabitants. Currently, the IETF LPWAN working group is developing functionality to support IPv6 over Sigfox, thus enabling Internet connectivity for Sigfox devices [4; 5].  
Many academic studies focus on evaluating the performance of Sigfox [6; 7; 8; 9; 10; 11; 12; 13; 14; 15]. Since Sigfox devices, such as sensors or actuators, are expected to run on constrained energy sources, investigating the energy consumption of Sigfox is crucial. However, as of the writing, this topic has received limited attention, and existing models in the literature [9; 10; 11; 12; 13; 14; 15] are too simple (see Section 2).  
This paper analytically models device current consumption, device lifetime and energy efficiency of data delivery with Sigfox. In order to capture a realistic behavior, our model is based onmeasurements conducted on a real hardware platform. The model presented allows to determine the influence of crucial Sigfox parameters and mechanisms, such as the uplink physical layer data rate, payload size, unidirectional or bidirectional communication, and message losses, on Sigfox energy performance. Our evaluation results illustrate the energy performance and trade-offs of Sigfox. Among others, we have found that a Sigfox device running on a 2400 mAh battery can achieve a lifetime of 1.5 years (at 100 bit/s) or 2.5 years (at 600 bit/s) while transmitting one uplink message every 10 min, and an asymptotic theoretical lifetime of 14.6 years as the message sending rate decreases.  
The remainder of the paper is organized as follows: Section 2 reviews related work. Section 3 overviews Sigfox, describing its network architecture and its main communication mechanisms. Section 4 models Sigfox end-device current consumption, end-device lifetime, and energy cost of data delivery. Section 5 evaluates and discusses the results obtained by using the model presented, assesssing also the sensitivity of the model to relevant parameters, and the use of energy harvesting sources. Finally, Section 6 concludes the paper.
# Protein Dynamics in F-like Bacterial Conjugation
## 7 Conclusions  
In analyzing the known structural data of F-like plasmid proteins from the _tra_ operon, some trends have emerged. Cytoplasmic proteins have been well-studied and have high-resolution structures, while little structural insight is known for proteins that are part of the T4 SS complex; this is expected based on the difficulty to purify and crystallize proteins, which are part of large complexes and contain hydrophobic regions. The solved cytoplasmic proteins have either a T4 SS transcription regulatory role or a role in conjugative DNA transfer, and are confirmed to have a moderate degree of disorder in their structure; however, this degree of flexibility is common in bacterial DNA-binding proteins [158; 159]. The periplasmic proteins involved in complexes have a diminutive level of structural data and only distant homologs with solved structures in the PDB, despite a large body of biochemical information. The identity of the highest confidence models as determined through Phyre2 are below 20% for many of these proteins, and are all models of partial sequences less than half the size of the protein (Table 1; Table S1) [20]. As many of these proteins are vital for conjugation, elucidating their structures would greatly aid in determining their precise role and interacting partners, and provide novel structures to shared databases. Of the IM proteins involved in pilus assembly and extension, TraE, -P, -L, and the NTD of TraG have unconfirmed function. The only other Tra protein discussed with unconfirmed function is TraU; all described proteins would have novel folds based on their low homology to solved structures. Structure-based drug discovery is considered to be the most efficient method proposed for the development of novel conjugation inhibitors [2]. As mentioned Section 5.2, the relaxase TraI was determined to be a druggable protein; however, small molecule ligand inhibitors belonging to the bisphosphonate family of compounds were determined to function as non-specific chelating agents [1, 160]. Specific single-chain \(F_{\text{V}}\) antibody inhibitors have been produced to target TraI homologs, however their expression requires a recipient transgenic population and each inhibitor would only function on the cognate relaxase [161]. Development of small molecule ligand inhibitors that successfully inhibit bacterial relaxases from plasmid families or subfamilies would be optimal; if a full structure of TraI were solved, structural-based drug discovery would be facilitated for the IncF plasmid family. The TraG homolog VirB8 has a proposed druggable site at a PPI interface (Section 4.2), however small molecule ligands proven to be optimal for disrupting conjugation in Ti plasmid donors show poor inhibitory action in the related TraE from pKM101 [2, 117, 162]. Exploiting the Fin-TraI expression system through the use of activators or inhibitors would be highly effective (discussed in Section 2), however drugs targeting these biomolecules are likely to suffer from pronounced plasmid specificity [163]. The
# Solution for Interference in Hotspot Scenarios Applying Q-Learning on FFR-Based ICIC Techniques
## 3 Fractional Frequency Reuse
### ICIC Techniques
#### 3.1.2 Soft Frequency Reuse  
The Soft Frequency Reuse (SFR) also splits the bandwidth into two sub-bands. However, the sub-band allocated to cell-edge UEs is not private, as illustrated in Figure 3. However, cell-edge users only share bandwidth with cell-center users from adjacent cells. Consequently, the cell-edge sub-bands are disjoint. Thus, cell-edge UEs do not use the same frequency band as neighboring cells, and each occupies 1/3 of the available spectrum.  
The SFR can lead to better spectral efficiency when compared to the Strict FR, given that all cells can use the entire bandwidth. However, the SFR also increases the interference suffered by all users.  
A higher power level is used for the edge sub-band, and a UE is considered in the cell-center if the reported RSRQ is greater than the threshold. Otherwise, it is allocated in the cell-edge sub-band.
# Name-Based Security for Information-Centric Networking Architectures
## 2 Background and Related Work
### Identity-Based Proxy Re-Encryption  
An Identity-Based Proxy Re-Encryption (IB-PRE) scheme is an extension to IBE that allows a third party to alter a ciphertext, encrypted using an arbitrary string ID1, in a way that another user that owns a secret key \(SK_{ID2}\) can decrypt it. Such a scheme specifies two new algorithms [4], RKGen and Reencrypt, in addition to the IBE algorithms already discussed:  
* RKGen: it is executed by a user that owns a secret key \(SK_{ID1}\). It takes as input \(PP\), the secret key \(SK_{ID1}\) and an arbitrary string ID2 and generates a (public) re-encryption key \(RK_{ID1\to ID2}\).
* ReEncrypt: it is executed by a third party. It takes as input \(PP\), a re-encryption key \(RK_{ID1\to ID2}\) and a ciphertext \(C_{ID1}\) and outputs a new ciphertext \(C_{ID2}\).  
The ciphertext generated by the ReEncrypt algorithm can be decrypted only using \(SK_{ID2}\). The entity that performs the re-encryption learns nothing about the encrypted plaintext or about the secret keys that correspond to ID1 and ID2.  
Figure 1 illustrates an IB-PRE example. There are three users each owning a name root, namely \"User A\", \"User B\", and \"User C\". \"User A\" and \"User B\" have received the secret key that corresponds to their roots from a SKG (Step 2). \"User C\" encrypts a message by executing the IBE Encrypt algorithm using \"User A\" as a key and stores the generated ciphertext in a storage entity. Supposedly, \"User A\" wishes to enable \"User B\" to access the ciphertext generated by \"User C\" in the previous step: She generates an appropriate re-encryption key and sends it to the storage entity. The storage entity is now able to re-encrypt the ciphertext in a way that can be decrypted using the private key of \"User A\".