esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Secondary Structure of bR Protein  
The secondary structure was analyzed using the timeline plugin for both the bR-graphene (Figure S3) and control bR (Figure S4). The timeline keeps track of secondary structure (turns, \(\beta\)-sheet, \(\alpha\)-helix, Pi-helix, isolated bridge, etc.) during all time steps and represents these different secondary structure types with different colors (Figure S5). Surprisingly, the bR secondary structure in the control group lost most of its \(\beta\) turns as well as the two \(\beta\)-sheets at the end of the simulation. bR appears to have slowly lost its secondary structure (Figure S4). Some changes were also observed in bR-graphene, including the loss of one or two turns, which was influenced by the graphene sheet during interaction and adsorption (Figure S3). However, unlike the control, it was observed that the overall stability of the secondary structure was maintained after adsorption. Moreover, the secondary structure at the active site amino acids has been also examined and compared. The \(\alpha\) helices where active site amino acids Arg82, Asp85, Asp96, Asp212, Lys216 and Glu204 are present remained intact for the entire simulation in both control and bR-graphene. However, the \(\beta\) turn where Glu194 is present near the cytoplasm was highly unstable for the control, while it remains stable for bR-graphene. Furthermore, in the secondary structure of a known 'O' state from the bR protein (3 VIO) shown in Figure S6, it was also found that the residues 191 to 199 that make up the turns are highly unstable and at times conformed to coils in the absence of graphene. This indicates that the presence of graphene stabilizes the secondary structure at the active site in bR.
# Polymeric and Crystalline Materials for Effective and Sustainable CO\({}_{2}\) Capture
## 4 Biobased Polymers
Activated PET adsorbents, analyzed for elemental composition, show increased oxygen content and enhanced CO2 uptake capacity with a higher KOH-to-carbon mass ratio [207]. Nitrogen-containing groups, incorporated through urea during the KOH activation process, have been shown to further enhance CO2 adsorption and selectivity in porous carbons derived from PET. These functionalized materials exhibit promising CO2 adsorption capacities of 1.31 mmol g\({}^{-1}\) at 303 K and 1 bar, with easy regenerability [207]. Literature data also point to poly(L-lactide) as a candidate for CO2 capture as reported by [PERSON] et al. [208]. Three PLA samples were prepared, differing in morphology commercial pellet (cPLA), powder (pPLA), and flakes (fPLA). The fPLA showed a CO2 uptake of 3.59 mmol g\({}^{-1}\) at 333 K and 15 bars. The enhanced CO2 adsorption performance of the fPLA sample can be linked to its more disordered crystalline structure (pseudo-orthorhombic \(\alpha^{\prime}\) structure) and increased porosity. [PERSON] and coworkers reported the use of a high molecular weight PLA-b-PEO-b-PLA triblock copolymer for CO2 capture [187] (Figure 11). The material served as the sole template for generating large mesoporous carbons, utilizing a resol-type phenolic resin as the carbon source. By subjecting the material to thermal curing and carbonization processes, large mesoporous carbons (>50 nm) were produced as well as a substantial surface area (>600 m\({}^{2}\)g\({}^{-1}\)). This outcome was attributed to the preference of the hydroxyl (OH) units in phenolics to interact with the poly(ethylene oxide) (PEO) block rather than the poly(lactic acid) (PLA) block. The polymer exhibited a CO\({}_{2}\) uptake of 5.22 mmol g\({}^{-1}\) at 298 K and 1 bar. Polybenzoxazines prepared from amino acids have been shown to possess CO\({}_{2}\) adsorption capacity [188]. The selected amino acids (\(\gamma\)-aminobutanoic acid, 6-aminohexanoic acid, L-lysine, phenylalanine) were readily available and inexpensive. The first step was the preparation of the diphenolic acid methyl ester (MPD) by reacting the diphenolic acid with trimethoxymethane in the presence of \(p\)-toluenesulfonic acid as a catalyst. The amino acid methyl esters were prepared by reacting the chosen amino acid with TMSCL. The targeted polymers were obtained by reacting MDP with the amino acid intermediate in the presence of Et\({}_{3}\)N and paraformaldehyde affording the monomers. These were then thermally polymerized at 120-240 \({}^{\circ}\)C. One of the polymers prepared this way, poly(E-lyme), showed a CO\({}_{2}\) adsorption of 5.8 mmol g\({}^{-1}\) at 273 K and 1.06 bars (Figure 11).
## 1 Introduction
The difficulty of recognising changes in intensity has been suggested [24] as an explanation for this, but to date, no isolated intermittent effort protocols have been proposed to test their accuracy. In addition, a two-week multi-HR device following intervention concluded that the OH1 armband provides reliable HR data under normal daily conditions, but some inter-individual differences were found with no particular pattern [25]. Moreover, new devices have to be considered, as new brands and models constantly enter the market. Therefore, there is a need to compare new HR monitoring devices with those that have already proven their validity. Despite the progress in validating HR devices, there is a notable lack of studies comparing recently introduced armbands with well-established chest straps in intermittent exercise protocols. Addressing this gap, our study contributes to the literature by evaluating the accuracy of two chest straps (Garmin HRM-Dual and Coospo H6) and one armband (Coospo HW807) under controlled laboratory conditions that simulate typical intermittent exercise intensities. By examining these devices under both effort and recovery phases, this study aims to better understand the strengths and limitations of armband HR monitoring compared to chest straps. It was hypothesised that (i) the armband would present differences compared to the chest straps and that (ii) these differences would be obtained during the effort stages of the athletes.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 2 Materials and Methods
### Data  
FY-4A satellite China region data are provided by the National Meteorological Administration with 4 km spatial resolution and 15 min time interval, including FY-4A AGRI L1-level products, Cloud Detection Products (CLM), and Land Surface Temperature products (LST). FY-4B satellite full-disk products are the same as FY-4A, in 4 km spatial resolution and 15 min time resolution ([[http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx)]([http://satellite.nsmc.org.cn/portalsite/default.aspx](http://satellite.nsmc.org.cn/portalsite/default.aspx)), accessed on 1 July 2022). The data of SRHI (depth 0-10 cm) are provided by the Real-time Product Datasets of the China Meteorological Administration Land Data Assimilation System (CLDAS-V2.0) with a spatial resolution of 0.0625\({}^{\circ}\)\(\times\) 0.0625\({}^{\circ}\) ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 6 July 2022). The station data of MCI are obtained from the National Meteorological Administration. MODIS data used the 16-day synthetic MOD13A2 Version 6 NDVI products and 8-day synthetic MOD11A2 Version 6 LST products, both at a spatial resolution of 1 km (km) and obtained from the NASA website ([[https://www.nasa.gov/](https://www.nasa.gov/)]([https://www.nasa.gov/](https://www.nasa.gov/)), accessed on 22 July 2022). The Landsat-8 data were obtained from the Earth Explorer official website, covering the period from 16 to 23 May 2022 with a spatial resolution of 30 m ([[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)), accessed on 15 July 2023).
## 2 Materials and Methods
### Search Strategy  
The present meta-analysis is divided into three distinct phases. In the first phase, to determine the eligibility of the studies, we inspected the results of the electronic search. A comprehensive search of published studies, with no time limit, was performed in PubMed, Scopus, Web of Science, and Google Scholar using the keywords \"fMRI\" AND \"Psychogenic Erectile Dysfunction\" OR \"Psychogenic ED\" OR \" Neuroimaging\" AND \"MRI\" AND \" premature ejaculation\" and the Boolean search operators (AND) and (OR) (Appendix A).  
After duplicate removal, we obtained 2558 PED studies. The titles and abstracts were evaluated according to the inclusion/exclusion criteria (Table 1).  
Hence, 13 studies were considered eligible. Similarly, after duplicate removal, we obtained 749 PE studies. In addition, the titles and abstracts were examined in accordance with eligibility criteria and 11 PE studies were eligible.  
Two researchers carried out this process independently. Nineteen studies were included, of which nine were PED and ten PE (Figure 1). In addition, the studies were read, assessed for the potential risk of bias, and the brain clusters were extracted. We assessed the overall quality of the studies, and the risk of bias, using the Newcastle-Ottawa quality assessment Scale case-control studies (NOS) [34]. NOS consists of three domains: selection, comparability, and exposure. Therefore, NOS assigns a maximum of nine stars to the studies with a low risk of bias [34] Table 2.  
Figure 1: The figure depicts on the **left** side the flow chart of the selection process for PED studies and on the **right** side, the process for the PE studies.
## 3 Results
### Stability Analysis of Canal Slope under Different Working Conditions
#### 3.3.2 Stability Analysis of Canal Slopes during the Shutdown Period  
Simulations of the safety and stability of the canal slopes during the shutdown period continued to use the same numerical conditions as during the operation period, and the hydrostatic pressure was not taken into account. These simulations were performed to investigate whether the canal slope without the action of water loading can continue to be safe and stable. To simplify the model, the change in soil shear strength due to the volume expansion and deformation caused by water freezing in the canal was ignored.  
The variations in the \(F_{s}\) of the canal slopes under the coupling effect of the WD-FT cycle and the water level behind the impermeable membrane are plotted in Figure 10. Meanwhile, to further investigate the impact of the change in the water level behind the impermeable membrane and the strength deterioration of the loess at the canal base on the stability of the canal slope, the displacements of the canal slope under different WD-FT cycles during the shutdown period of the canal were selected for analysis, as shown in Figure 11.  
Figure 11: The displacement programs of the canal slope during the shutdown period.  
Figure 10: The \(F_{s}\) of the canal slope during the shutdown period.  
As can be seen from Figures 10 and 11, the \(F_{s}\) of the canal slope during the shutdown period undergoes a significant reduction compared to that during the operating period after nine WD-FT cycles. The slope with no water level behind the impermeable membrane always remains above the limit equilibrium state (\(F_{s}>1.05\)) after nine cycles. The slope with a water level of 4.5 m behind the membrane will be in an unstable state after three cycles. Under the coupling effect of the WD-FT cycle and the water level behind the impermeable membrane, the stability decay pattern of canal slopes during the shutdown period is more pronounced than that during the operation period.
# Study of the Migrating Mine Gas Piston Effect during Reactivation of Tectonic Faults
## 2 Theoretical Background
### The Morphology of Fault Planes  
The surfaces of the fault planes have heterogeneity of various orders, from microscopic roughness to large asperities, whereby the fault walls contact only parts of their surfaces. The specific area of fault walls' contact patch is estimated as a few percent of the total area of the fault contact walls by using special tools in natural conditions [42], in laboratory tests, and theoretically [43; 44]. In the study [45], the amplitude and wavelength of bumps, or asperities, of a number of faults were empirically studied. The asperity amplitude ranges between 3.5 mm and 6.5 mm for 1 m profiles parallel to slip (along the axis of striations) to 2 m on a 35 m long profile. In the k study [43], a brief review of the main recent results obtained by studying geometry of sliding surfaces in rocks was presented. As it follows from the study, for cracks in the length range from 1 to 1500 m, the roughness measurements are described adequately by the equation:  
\[A_{aV}=7.5\times 10^{-3}\times L^{0.8} \tag{1}\]  
where \(A_{aV}\)--average amplitude of profile deviation from a straight line (m); \(L\)--block length (m).  
According to Formula (1) for a 10-m-long fault, \(A_{aV}\) = 4.72 cm, whereas, for a 100 m long fault, \(A_{aV}\) = 29 cm.  
It has also been observed that there is a certain critical angle \(\theta_{cr}\) (7-20\({}^{\circ}\) for strong rocks) of the local slant of roughness, so that protrusions with \(\theta<\theta_{cr}\) do not collapse during the shear but deform elastically [43; 46].  
Two extreme states of displacer inhomogeneities' contacts are distinguished in simple models: ridge-ridge state and ridge-sag pattern (Figure 1) [47; 48]. The \"ridge-ridge\" type contact ensures maximum permeability of the fault plane and maximum volume of the fracture space adjacent to the fault plane, while the \"ridge-sag\" type of contact provides the opposite (Figure 2). We assume that, in the first phase of reactivation, the fracture space expands, resulting from displacer opening and the fact that fault plane asperities are engaged in \"ridge-ridge\" type of contact. With subsequent relative displacement of the fault walls, i.e., in the second phase of reactivation, the contact changes to \"ridge-sag\" type and a sharp reduction in the fracture space volume occurs. Thus, when the fault is reactivated and the type of contacts is changed, the volume of the void space also changes and the effect of piston extrusion of gas occurs in the direction of the pressure gradient, i.e., towards the earth's surface. A computer-generated model and the results of such modeling are considered below.
## 4 Results and Discussions
### Parametric Effects Discussions
#### 4.2.1 Nonlinear H-B Criterion Parametric Analysis  
As shown in Figure 7, the Geological Strength Index \(GSI\) and the parameter for the class of rock substance \(m_{i}\) are proportional to FS, i.e., the larger that \(GSI\) and \(m_{i}\) are, the higher the stability of the slope, where the FS rise is more noticeable in Figure 7a. In the \(GSI\) rating system, the value of \(GSI\) is determined by the structure of the rock mass and the quality condition of the structural surface,  
Under the rating system, the quality conditions of the structural surface influence the value of \(GSI\), from which it can be observed that the overall quality of the rock mass has more of an impact on the stability of rock slopes than the quality characteristics of the rock in the stability analysis of the rock slopes. Thus, special attention should be paid to both the structure of the rock mass and the quality condition of the structural face in the engineering design.  
Furthermore, the fall in FS with an increase in B/H occurs when nonlinear H-B criteria factors are maintained. This indicates that, given the same other factors, 3D slopes are more stable than 2D slopes. This shows that it is overly cautious to simply handle the 3D slope in actual engineering as if it were just a 2D case. Additionally, Figure 7c shows the trend of FS with \(m_{i}\) for different values of \(GSI\). And it can be noticed that the FS growth amplitude is also small when the \(GSI\) is small, implying that the rock type and quality do not significantly enhance the stability of the rock slopes when the joint fractures in the rock mass are excessively developed.  
Figure 7: Impacts of nonlinear H-B criterion parameters on the safety of 3D two-stage rock slopes: (**a**) the Geological Strength Index \(GSI\); (**b**) the parameter for the class of rock substance \(m_{i}\); (**c**) the interactions between \(GSI\) and \(m_{i}\).
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 3 Design of _uw_-Cellular Network
### Protocol Design
#### 3.3.4 Transport Layer Protocol Design  
In the transport layer, we provide two mechanisms to guarantee the reliable data transmission. In some cases, retransmission mechanism is a basic approach to ensure the reliability of _uw_-Cellular. In other cases where higher reliability is required, reliable coding mechanisms can be used as a supplement to ensure data reliability.  
We designed a protocol named Reliable Segmentation Transport Protocol (RSTP) for _uw_-Cellular network. When the transport layer gets a jumbo message from application layer that beyond the current ability to transmit, the RSTP breaks the message into a group of smaller segments. The receiving end must wait for all segments which can be reassembled into the original jumbo messages and pass them to the application layer with no error. Each segment has several additional fields to make it possible to provide selective retransmission service when packet loss occurs. Reliable network coding is performed for each data segment to increase the probability of successful data parsing at the receiving end when the communication channel quality is poor.  
```
0: The pre-defined minimum Estimated Score \(\gamma_{min}\). The destination address of the packet \(ID_{dst}\). Send Queue \(q_{s}\).
0: The highest Estimated Score \(\gamma_{max}\). Send out the packet or put the packet in the \(q_{s}\).
1: Get a packet from upper layer.
2: Obtain the \(ID_{dst}\) from the packet.
3: Calculate the \(\gamma\) of the possible path, and record \(\gamma_{max}\).
4:if\(\gamma_{max}>\gamma_{min}\)then
5: Update data header information.
6: Sending packet to lower layer.
7:else
8:if The packet type is Send Anyway then
9: Select the path represented by \(\gamma_{max}\), and mark \(\gamma_{max}\) need to be recalculated.
10:else
11: Putting packet into the \(q_{s}\).
12: Send a request to send packet in a flood manner.
13: Waiting for feedback packet.
14:if Timeout then
15: Perform step 9.
16:else
17: Perform step 3.
18:endif
19:endif
```  
**Algorithm 1** Sending side processing flow.  
```
0: The highest Estimated Score \(\gamma_{max}\). The source address of the packet \(ID_{src}\). The destination address of the packet \(ID_{dst}\). The next hop address of the packet \(ID_{next}\). The local address \(ID_{loc}\).
0: Receive the packet or forward the packet.
1: Get a packet from lower layer.
2: Obtain the \(ID_{src}\), \(ID_{next}\), \(ID_{dst}\) from the packet.
3: Obtain the information from physical layer and packet header.
4:if\(ID_{dst}==ID_{loc}\)then
5: Update routing table information.
6: Sending packet to upper layer.
7:else
8:if\(ID_{next}==ID_{loc}\)then
9: Update routing table information.
10:if The packet is marked. then
11: Enter the step 2 of Sending side processing flow
12:else
13: No need to recalculate the \(\gamma_{max}\) and send the packet directly to the lower layer
14:endif
15:else
16: Update routing table information only.
17: Send a request to send packet in a flood manner.
18:endif
19:endif
```  
**Algorithm 2** Receiving side processing flow.
## 2 Materials and Methods
### DFT Simulations  
All the studied saccharides are modeled as crystals and their geometry is optimized by using a DFT plane-wave code with ultrasoft pseudopotentials that are included in the Quantum Espresso package. The convergence criterion of geometry optimization for total energy and force were both set at 1.0d-5 Ry. The theoretical THz spectra were obtained by exploiting DFT ab initio techniques. The Quantum Espresso package was used with the plane-waves basis set, within the finite displacements framework (FD code) [46; 47]. To account for van der Waals interactions, a non-local van der Waals density functional known as vdW-DF2 was utilized. This functional builds upon the precise semilocal exchange functional, PW86 [48], and was selected to enhance the portrayal of H-bonds [49; 50].  
Ultrasoft pseudopotentials [51], suitable for the non-local van der Waals functionals, were generated [52]. When using such functionals, the convergence on both wavefunctions and the charge cutoff is a delicate issue. In this study, Kohn-Sham wavefunctions were truncated using a cutoff of 40 Ry, while the charge density employed a cutoff of 800 Ry. The Irreducible Brillouin Zone was discretized using 14 k-points. The vibrational properties were evaluated through finite displacements within the DFT framework under the harmonic approximation. The calculations encompassed phonon eigenvalues and eigenfunctions at the \(\Gamma\) point, alongside the determination of Born effective charges. The simulated crystal structures and parameter information can be found in the Supplementary Material.
## 3 Results
### Random Forest Model Construction  
The samples are randomly divided into a training subset and a test subset by a simple cross-validation method, where the training set has a sample capacity of 80% and is used for model construction. The test set sample capacity is 20%, which is used for the evaluation of the model. Parameter optimization was performed using grid search in the Random Forest algorithm so as to construct the prediction model and further analyze the importance of the influence of \(p\) < 0.05 on the eigenvectors in the binary logistic regression analysis.  
* Optimal parameter The Random Forest algorithm is run to perform a grid search, in which the number of classifiers selected is 10 to 70, the step size is 5, and the maximum depth ranges from 5 to 30; the step size selected is 1, the maximum number of features ranges from 1-14, the minimum number of samples required for internal node delineation ranges from 1-30, and the minimum number of samples for leaf nodes ranges from 1-30. The results show that the model is most accurate and achieves the local optimum when the number of classifiers is 45, the maximum depth is 9, the maximum number of features is 8, the minimum number of samples required for internal node division is 19, and the minimum number of samples for leaf nodes is 3.
* Characteristic importance results The eigenvalues characterizing the importance of each eigenvariable were obtained from the Random Forest algorithm as shown in Figure 7, where the larger the parameter value of the eigenvariable, the stronger the correlation between the characteristic and whether or not the person has chronic atrophic gastritis in the model of this study.  
Figure 5: Flowchart of Random Forest implementation.  
Figure 6: Model flowchart.  
Max_features is an important parameter of the Random Forest model and is the maximum number of features allowed in the forest for a single decision tree. According to the results of the grid search shown in Figure 8, the performance of the model is optimized when _max_features_ = 8. The top eight ranked indicators of feature importance in the model are taken as strong correlation factors, and the strong correlation factors derived in this paper using the random forest model are PDW, Hct, Age, P-LCR%, ALB, Fbg, Neut#, and MCH.