esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Energy Consumption
### Average Energy Consumed by RN1 during Awake Cycles  
When RN1 is in _awake_ mode, it wakes up at the beginning of _awake_ cycles and remains awake until the cycle ends. During _awake_ cycles, RN1 listens to the channel for the possible transmission of _SYNC_ packets from neighboring SNs. For energy conservation, when RN1 hears a \(RTS\) packet, indicating the beginning of a PF transmission, it calculates the time the channel will remain occupied from the information contained in the \(RTS\) packet, and switches to the _sleep_ mode. When the PF transmission ends, it returns to the _awake_ state.  
We now determine the average energy consumed by RN1 per _awake_ cycle. It is assumed that SNs activate the _awake_ mode at the beginning of the _data_ period, i.e., once the _sync_ period ends. For the average energy consumed by RN1 per _awake_ cycle, we consider two terms: (i) the energy consumed due to the channel listening activity; (ii) the energy consumed due to being in the _sleep_ mode while other SNs occupy the channel.  
We consider the following cycle types:  
_Cycle 1A_: RN1 is active, (\(i_{1}>0,k_{1}\geq 0\)).  
Zero or more C1 SNs might also be active. When other SNs are active, in addition to RN1, the following contention outcomes are possible: (i) RN1 completes a successful PF transmission; (ii) the PF from RN1 collides; (iii) another node completes a successful PF transmission, and (iv) other nodes different from RN1 collide.  
_Cycle 1B_: RN1 is inactive, (\(i_{1}=0,k_{1}>0\)).  
C1 SNs access the channel while [PERSON] is _awake_. RN1 listens to the channel to decode the _RTS_ packet and determine the duration of the PF transmission. The following contention outcomes are possible: (i) a SN completes a successful PF transmission; (ii) multiple PFs collide.  
_Cycle 1C_: All C1 SNs remain inactive, including RN1, (\(i_{1}=0,k_{1}=0\)).  
There are active C2 SNs that access the channel while RN1 is _awake_, (\(i_{2}>0,k_{2}>0\)). RN1 listens to the channel, decodes the _RTS_ packets in collision-free cycles and determines the duration of the PF transmissions. The following contention outcomes for C2 SNs might be possible: (i) a C2 SN completes a successful PF transmission; (ii) multiple PFs from different C2 SNs collide.  
_Cycle 1D_: All C1 and C2 SNs remain inactive, \(i_{1}=k_{1}=i_{2}=k_{2}=0\), while RN1 is _awake_.
## 4 Case Study of Crash Scenarios
### Concept
#### 4.1.2 Passive Mechanism  
In _Passive Mechanism_, VRU devices participate in V2P communication _passively_ i.e., they transmit the safety messages only when they detect the possibility of a crash. In our scenario, when the VRU device receives the first message from the vehicle that is crashing with the VRU, it waits for two seconds before it transmits the safety message. All subsequent safety messages are then transmitted every two seconds.
## 3 Research Design
### Dynamic Retraining Module  
Another problem encountered when analyzing OTT users in real-world situations is the that where the data characteristics change continuously. Studies on conventional ML-based classifications tend to continuously utilize data that were initially collected after annotation. The critical disadvantage of this method is that newly changed characteristics are not reflected. To overcome this, [PERSON] et al. proposed a method for updating training data at fixed intervals [55]. Based on their studies, we propose a method suitable for OTT user analysis.  
To classify OTT users, data are collected first, then they are labeled by an expert. If the collected dataset is \(T_{init}\), the classification algorithm, \(L\), is used to perform training using \(T_{init}\). The classifier, \(C_{init}\), is composed as follows:  
\[C_{init}=L(T_{init}). \tag{4}\]  
Our proposed framework also uses \(C_{init}\), which is based on labeled data that have already been collected. However, although most studies continue to use only \(C_{init}\), we update the training data continuously. If the pre-set time interval, \(\tau\), elapses, the classification result of the data that were already collected is obtained. If an additionally collected dataset of high consumption users is \(H_{t}\), that of medium consumption users is \(M_{t}\), and that of low consumption users is \(L_{t}\). Here, \(t\) is a time unit that increases by one whenever the pre-set time interval, \(\tau\), elapses. The newly added dataset, \(T_{new}\), can be summarized as Equation (5), and the classifier \(C_{new}\) that reflects it is Equation (6):  
\[T_{new}=\sum_{t}(H_{t}\;\cup\;M_{t}\cup L_{t}), \tag{5}\]  
\[C_{new}=L(T_{init}\;\cup\;T_{new}). \tag{6}\]  
Periodically, at a given time interval, the training dataset will incorporate the newly changed characteristics of each user group. \(C_{new}\), is updated based on retraining to facilitate accurate classification while accounting for changes. This enables flexible responses and updated learning.
## 2 Materials and Methods
### Normalized Difference Vegetation Index (NDVI)  
The MODIS 16-day composite NDVI at a resolution of 250 \(\times\) 250 m at 11 sites was downloaded (MODIS 13Q1, [[https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)]([https://modis.ornl.gov/data.html](https://modis.ornl.gov/data.html)) accessed on 1 November 2019). Data with poor quality caused by the interference of clouds and precipitation were also removed. The spikes presented in the raw NDVI data were smoothed by removing unrealistic abrupt short-term changes in the NDVI. The gaps were filled by linearly interpolating the closest available data. Then, the 16-day NDVI was interpolated to a daily scale using a spline function for daily scale analysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Crop Type** & **Site ID** & **Country** & **Latitude** & **Longitude** & **MAT** & **MAP** & **Period** & **References** \\  & & & **(*)** & **(*)** & **(\^{\circ}\)C** & **(mm)** & & **(34)** \\ \hline \multirow{4}{*}{Paddy rice} & US-Twt & USA & 38.11 & \(-\)121.6 & 15.6 & 421 & 2011–2013 & [12] \\  & JAN-MSE & Japan & 36.05 & 140.02 & 13.7 & 1200 & 2004–2006 & [33] \\  & IT-Cas & Italy & 45.06 & 8.66 & 13.2 & 576 & 2007–2009 & [34] \\ \hline \multirow{4}{*}{Soybean} & US-Ne2 & USA & 41.16 & \(-\)96.47 & 10.1 & 789 & 2002/2004/2006 & [35] \\  & US-CRT & USA & 41.62 & \(-\)83.34 & 10.1 & 849 & 2011–2012 & [36] \\  & US-Ne3 & USA & 41.17 & \(-\)96.43 & 10.1 & 784 & 2002/2004/2006 & [35] \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Summer \\ maize \\ \end{tabular} } & FR-Gri & France & 48.84 & 1.95 & 12.0 & 650 & 2005/2008/2011 & [34] \\  & IT-BCi & Italy & 40.52 & 14.95 & 18.0 & 600 & 2005–2007 & [34] \\  & US-Ne1 & USA & 41.16 & \(-\)96.47 & 10.1 & 790 & 2005–2007 & [37] \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Winter \\ wheat \\ \end{tabular} } & CH-Oe2 & Switzerland & 47.28 & 7.73 & 9.8 & 1155 & 2006–2007/2008–2009/2010–2011 & [38] \\  & E-Lon & Belgium & 50.55 & 4.64 & 10.0 & 800 & 2004–2005/2006–2007/2008–2009 & [39] \\ \cline{1-1}  & FR-Gri & France & 48.84 & 1.95 & 12.0 & 650 & 2005–2006/2009–2010/2011–2012 & [34] \\ \hline \hline \end{tabular} Note: MAT means annual temperature and MAP means annual precipitation in Table 1.  
\end{table}
Table 1: Descriptions of the eddy covariance (EC) flux sites of the croplands studied.
# Stability of Halloysite, Imogolite, and Boron Nitride Nanotubes in Solvent Media
## 2 Halloysite Nanotubes
### Colloidal Stability in Water
The choice of the surfactant in terms of the headgroup charge is a key factor, and it has an important effect on the colloidal stability of halloysite because of its differently charged surfaces. For instance, it has been demonstrated that the adsorption of surfactants that are negatively charged (e.g., sodium alkanoates) onto the internal surface of the nanotubes increases their overall negative charge, and thus enhances both electrostatic repulsions and colloidal stability (Figure 2) [56; 59]. The change of the \(\zeta\) potential, which becomes more negative, predicts a better dispersibility of the hybrid materials in comparison with the neat clay. The sedimentation process is strongly slowed down by the surfactants [59]. According to the DLVO (Derjaguin-Landau-Verwey-Overbeek) theory, colloidal stability is influenced by the balance between attractive and repulsive van der Waals forces coming from the double layer that surrounds each particle [60], meaning that experimental results are consistent with the theory. Moreover, the structure of neat HNTs and negatively charged surfactant-functionalized nanotubes were studied by small angle neutron scattering [52]. The importance of both the structural organization of the surfactants and their headgroup was shown. In particular, it was noted that sodium dodecanoate is organized in a densely packed, multilayer structure within the halloysite cavity, and is promoted by the carboxylate groups, as seen from a correlation peak in SANS (Small Angle Neutron Scattering) [52]. Finally, these inorganic, micelles-like hybrid materials can be used to solubilize and deliver species of a different nature in water, thus exploiting their sustainable and biocompatible properties [52; 56; 59]. It was also found that perfluoroalkylylated surfactants, when adsorbed at the internal surface, created stable suspensions, and these systems can be exploited as nanocontainers of non-foaming oxygen in aqueous media for gas delivery by external stimuli [61]. More interestingly, it was shown that the concentration effect was negligible and that the hybrid materials did not associate; however, nanotubes diffuse as single particles [61]. [PERSON] et al. [62] reported a method where sodium dodecyl sulfate (SDS) was used to prepare uniform and stable halloysite nanotubes dispersions. The \(\zeta\)-potential values of an HNTs/SDS system became more negative than those of the neat clay, indicating that SDS is adsorbed on the inner surface, enhancing the dispersibility by electrostatic effect. It was observed that the dispersibility is not effected by the content of the dispersant, thus confirming the saturation effect.
# Two-Stage Robust Lossless DWI Watermarking Based on Transformer Networks in the Wavelet Domain
## 4 Experiments
### Robustness Experiment
#### 4.2.2 Common Attacks  
The common types of image attacks mainly contain median filtering and Gaussian filtering with convolution kernel size \(K\) of \(3\times 3\), \(5\times 5\), \(7\times 7\), SPN with a noise density \(p\) of 0.01, 0.02, and 0.03, Gaussian noise with a variance \(v\) of 0.001, 002, and 003, and JPEG compression with a quality factor \(Q\) of 90, 70, 30, and 10. The above attacks were applied to the slice images in each gradient direction of the DWI, and the watermark extraction BER values are shown in Table 3. It can be seen that almost all values of BER are equal to 0 after the images are subjected to conventional attacks, indicating that our proposed algorithm has excellent robustness against common attacks.
## 4 Discussion
With excessive temperatures at the precooler heat sink outlet, the co-current precooler design seems like the best choice for the IC, but to achieve a comparable level of technology to the counter-current design, the precooler has to be very large in size and the design has to be of a fin ribbed type arrangement with turbulators and intricate geometry, which will be challenging to manufacture, thus impacting the capital costs of the NPP. To understand the size implications, Figure 13 illustrates a simplified level of heat exchange for both designs for the same size and technology level. It is clear that for a 'close to' 100% heat exchange in a counter-current configuration, the co-current only produced close to 50% heat exchange for the same size [18]. The counter-current design is very efficient and transfers more heat per unit mass, but it may not be suited in extreme hot conditions without an efficient condensing and return medium. On this basis, it would be prudent to utilise the hot gas of the IC for use in an adjoining processing facility prior to returning to the precooler. The SCR would be suitable in cooler and mild climates, but not in a hot climate as it would be challenging to justify its potential size due to the recuperator, in addition to a large co-current precooler as the counter-current precooler would yield unfavourable heat sink return temperatures for cycle inlet temperatures above 30 \({}^{\circ}\)C. Furthermore, an adjoining processing plant may improve the operational business case but the size of the plant would need to be significantly up scaled, which would prove to be a disadvantage because of the increase in capital costs. The ICR offers the best performance figures in cold and hot climates in terms of minimising the potential for high inlet temperatures. This is due to the modest heat sink exit conditions with a counter-current precooler design, in addition to the best efficiency figures. However the size of the NPP due to the inclusion of a recuperator and an intercooler adds complexities to the construction and requires significant capital investment.
## 4 Design of Proposed Scheme: FAPR
### Proactive Part  
In the proactive part, the pro-VLAN method based on the protection mechanism is adopted. As introduced in Section 2.1, the fast failover group table is proposed in the OpenFlow protocol version 1.1, providing the function of fault tolerance, and multiple methods based on the proactive scheme basically use this type of group table for fast link failure recovery. Traditional proactive schemes calculate backup paths for specific flows. It can be inferred that, when the number of flows increases, the switches' TCAMs will also be quickly consumed.  
Unlike these methods, the pro-VLAN method cleverly disassociates the flow table and group table entries from flows. Specifically, for each link of the topology, it calculates a backup path and assigns a globally unique VLAN ID, so the backup path for each link  
Figure 4: FAPR overview.  
is prepared before a flow is generated, and the corresponding flow table and group table entries can be installed in the respective switches in advance. When a link failure occurs, the switches will automatically apply the actions in the group table and tag the affected flow with the corresponding VLAN ID. These packets with specific VLAN IDs will be automatically switched to the backup path, and execute the actions of flow table entries whose match fields are the link's VLAN IDs. Before leaving the backup path, the VLAN ID tag information is erased, thus returning to the normal forwarding afterwards. The pro-VLAN method can recover from link failures at a high speed and does not require modification of any hardware or protocols. Moreover, it can significantly reduce the consumption of TCAMs in switches, as forwarding table entries are independent of flows. Regardless of the scale and the diversity of the network, it can keep the number of backup flow table entries in switches stable, as this number is only related to the number of links on each switch.  
As shown in Figure 4, this part mainly consists of the data collection module and the backup path generation module. The pro-VLAN method obtains relevant information from the network and installs corresponding rules into the switches by assigning a VLAN ID to each link after generating the backup path. More detailed information about this method can be found in our previous work [19], and is not described here any more.
# Research Needs for Realization of Zero-Carbon Power Grids with Selected Case Studies
## 1 Introduction
### Paper Objective, Scope, and Structure  
Zero-carbon power grids can be realized by solving many research challenges that differ with the time and resources required, although the technological pathways remain somewhat uncertain because multiple paradigm shifts will impact grid planning, operational scheduling, and real-time control. This paper describes the main research challenges to be solved when moving to 100% renewable electricity and presents relevant case studies. There is an urgent need for research on supply-and-demand balancing and incorporation of large numbers of VRE inverters into grids. The case studies offer new solutions for improved grid operation, estimation, and protection. They use state-of-the-art power and control technologies. They are discussed with numerical results from the perspectives of originality, feasibility, and effectiveness. We expect that this will attract the attention of universities, research institutes, and funders, and encourage the establishment of roadmaps or plans that prioritize effort and investment. This paper focuses on the technical and economic aspects of the challenges and feasible, implementable solutions; we do not address policy. Although this paper focuses on transmission and distribution networks in the United States and Europe, most of the research challenges and solutions apply more generally. The trends in zero-carbon power-grid establishment, and the difficulties encountered, are similar in other countries.  
A few surveys on zero-carbon power grids were recently conducted, for example, in [1; 2; 4; 23]. It is the opinion of the authors that this paper complements the existing surveys in that we (1) comprehensively searched for the recent literature, most of which were published from 2016 to 2022 (apart from a few original works); (2) selected the literature mainly in four areas that significantly affect the realization of zero-carbon power grids (i.e., supply-and-demand balancing, inverters as VRE interfaces, energy and capacity adequacy, and electricity market design); (3) further classified the four main areas into 15 sub-areas; (4) outlined challenges and future research directions for each sub-area; and (5) presented the results of comparative case studies selected from the literature that are relevant with more than one challenge simultaneously.  
The remainder of this paper is organized as follows: Section 2 presents the critical research agenda and the specific challenges, Section 3 discusses relevant case studies and their numerical results, Section 4 describes the further research required, and Section 5 concludes the paper.
## 4 Conclusions  
1. When the cross-section area of the pipe changes abruptly from small to large, the shock wave overpressure decreases. In contrast, the shock wave overpressure increases when the cross-section of the pipe changes abruptly from large to small. When the diameter of the larger section of pipe is less than 0.24 m, the peak overpressure decreases, then sharply increases, and finally decreases. However, when the diameter is greater than 0.24 m, the peak overpressure exhibits a more complex sequence of decreasing, increasing, decreasing, increasing, and finally decreasing;
2. The overpressure changes in pipes along abrupt cross-section changes can be characterized by the attenuation coefficient, increase coefficient, and reflection coefficient. For a fixed ratio of cross-section areas, the attenuation coefficient increases with an increase in initial peak overpressure before the abrupt cross-section change, whereas the increasing coefficient and reflection coefficient both exhibit a decreasing trend. The coupling relationships between attenuation coefficients, increasing coefficients, reflection coefficients, and initial peak pressure and the ratio of cross-section areas were obtained;
3. The peak temperature decreases as the cross-section area changes. Moreover, a larger fuel volume creates a greater decrease in peak temperature. This demonstrates that the counter-current effect of the gas explosion pressure wave and reflected wave can inhibit the propagation of the explosion flame under the influence of an abrupt change in cross-section structure;  
Figure 10: Maximum kinetic energy versus propagation distance from ignition source.  
Figure 9: Maximum vorticity versus propagation distance from ignition source.  
* The vorticity in pipes with abrupt cross-section area changes presents a clear peak, which indicates that gas explosion propagation is affected by the action of turbulence induced by an abrupt cross-section change. Moreover, a larger fuel volume or ratio of cross-section areas has a more obvious effect on turbulence action. In the propagation process of a gas explosion, when the cross-section area changes abruptly, kinetic energy is converted to static pressure energy and vice versa. This indicates that the large eddy motion formed by strong restraint enhances kinetic energy.  
J.Q., conceptualization, methodology, and writing; B.J., writing--original draft preparation and data curation; M.T., formal analysis and investigation; L.Z., writing--reviewing and editing; Y.Y., visualization. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 52074012, 51774014).  
Not applicable.  
The data used to support the findings of the study are included in the article.  
The authors declare no conflict of interest.
# Molecular Mechanisms of Biofilm Formation in Helicobacter pylori
## 4 Mechanism of Biofilm Formation
### Initiation of Adherence
#### 4.1.2 Role of Mucin Binding Protein  
The human gastrointestinal tissue is coated with several mucins. NapA is a protein that specifically binds to sulfated oligosaccharides in \(H\). _pylori_, and its primary function is to attach to mucin [29; 30]. A transcriptomic study reported that NapA expression was upregulated in biofilm cells [31], and another study reported that strains lacking HopZ produce more biofilm [32].