esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Properties of Nanocellulose, Its Synthesis, and Potential in Biomedical Applications
## 4 Nanocellulose-Based Composites
### Polyvinyl Alcohol/Nanocellulose  
The combination of nanocellulose and polyvinyl alcohol (PVA) is crucial for developing green bio-nanomaterials. In order to create CNC-PVA film by solution casting, [PERSON] et al. isolated nanocrystalline cellulose (NCC) from _Acacia manguum_ and incorporated it into the PVA film. Upon adding 2%, 5%, 7%, and 10% of NCC, the film's tensile strength was enhanced by 30%, 35%, 38%, and 50% compared with pure PVA film [85]. By employing different PVA/CNC composites membrane ratios, [PERSON] and co-workers investigated the composite's thermodynamic, mechanical, and swelling properties. They reported that the composite membrane's elastic modulus and tensile strength were proportional to the amount of CNC at an increased relative humidity. However, incorporating CNC can marginally lessen the thermal stability of the membrane [86]. [PERSON] et al. developed CNC/PVA nanocomposite by combining poly (D,L-lactide-co-glycolide) nanoparticles. The inclusion of CNC improved the elongation characteristics and Young's modulus of the membrane, these properties making this nanocomposite a potential tool for drug delivery [87]. [PERSON] et al. addressed the function of nano-hydroxyapatite and CNF as fillers in electrospun PVA nanofibers-based composite scaffold for bone tissue regeneration. The presence of fillers increased the cellular activity of the scaffold and affected the in vitro degradation in phosphate-buffered saline. The outcomes supported the fibrous scaffold for tissue engineering [88].
## 3 Results and Discussion
### Geophysical Model  
The 3D geological/hydrogeological model was refined and strengthened by integrating the geophysical evidence [22]. Geophysical investigations were carried out by performing three ERT profiles covering the area of the pilot test. The three resulting ERT sections are presented in Figure 4. A resistivity model in 3D (Figure 5) was produced by interpolation across the three ERT lines.  
Figure 3: (**a**) Boreholes, stratigraphic contacts, and (**b**) geological model of the pilot test area in 3D.  
Figure 2: Representation of piezometric surface (m a.s.l.) for (**a**) shallow and (**b**) intermediate aquifer, dashed white rectangle represents excavation hosting station structure.  
The spatial continuity of the geoelectrical data correlates high-resistivity zones with sandy deposits of the shallow aquifer, which are characterized by good hydraulic permeability. These represent preferential flow paths and transport routes, while the layers with lower resistivity correspond to clayey horizons, which feature low permeability. The use of closely spaced 2D ERT data facilitates the spatialization of data [22]. The combination of noninvasive methods with traditional and nonreplaceable punctual prospecting techniques captures the variability of geological heterogeneity and the complexity of the transport process [23]. High-resolution characterization and integration of geophysical models represent key elements for remediation design and intervention optimization [15].  
Figure 4: (**a**) Arrangement of electrodes in the soil in the pilot test area and (**b**) resulting in electrical resistivity tomography (ERT) profiles.  
Figure 5: 3D resistivity model covering the pilot test area.
## 7 Conclusions  
This study draws inspiration from the seismic design concept of split columns in high-rise building structures, and sets up split columns in a two-story, two-span subway station to form a new structural system. Through quasi-static pushover analysis of the middle column and soil-structure system, the main conclusions obtained are as follows:  
1. When the cross-sectional area and the longitudinal reinforcement ratio is similar, the horizontal bearing capacity of split columns is reduced compared to traditional columns, and the stiffness of split columns is basically lower than that of traditional columns under various loading displacements.
2. Compared to traditional columns, split column components have better displacement capacity, and the ultimate inter-story drift ratio of split columns under high axial compression ratio is 1.5-fold higher than that of traditional columns.
3. Although the ability of split columns to share shear and bending moments is reduced compared to traditional columns, they still have higher vertical bearing capacity under larger horizontal deformations, and the damage to split columns is much smaller than that of traditional columns under the same inter-story displacement angle.
4. The working mechanism is to avoid the central column bearing excessive shear and bending forces, while fully utilizing the vertical support capacity and horizontal deformation capacity of split columns. Compared to traditional columns, the construction steps of split columns are more complex. To promote the application of split columns in underground structures, it is necessary to strengthen both experimental research and construction technology research.  
Conceptualization, Z.X. ([PERSON]) and Z.X. (Zongyao Xia); methodology, Z.X. (Zigang Xu) and R.H.; software, Z.X. ([PERSON]); validation, Z.X. (Zigang Xu) and X.B.; formal analysis, [PERSON] ([PERSON]); data curation, [PERSON] ([PERSON]); writing--original draft preparation, [PERSON] ([PERSON]); writing--review and editing, [PERSON]; visualization, [PERSON] ([PERSON]); project administration, [PERSON] ([PERSON]); funding acquisition, [PERSON] ([PERSON]) and R.H. All authors have read and agreed to the published version of the manuscript.  
This research is jointly funded by the National Natural Science Foundation of China (No. 52108453), the Natural Science Foundation of Jiangxi Province in China (No. 2021 BAB214014 and No. 2023 BAB204084), the China Postdoctoral Science Foundation Project (No. 2023M741159), and the Natural Science Foundation of Inner Mongolia Autonomous Region in China (No. 2023 QN05012).  
Not applicable.  
Data Availability Statement: Data are available in a publicly accessible repository.  
The authors declare no conflicts of interest.
## 4 Discussion
### Influencing Factors of Spatial Prediction of SOM  
SOM is a very sensitive to time and space. This paper mainly studies the relationship between the number of soil samples and the spatial-prediction accuracy of SOM. However, factors such as topography [6; 21], land-use patterns [34; 35], vegetation types [36] and human management [2] can all affect the spatial variability of SOC or SOM. Therefore, in the future research, we should further explore the factors that affect the spatial variation of SOM in Kenli County from the perspective of different influencing factors.
# Characterization of CYGNSS Ocean Surface Wind Speed Products
## 3 Results
### Sensitivity to Wind Speed Persistence
NOAA consistently overestimates the wind speed for winds above 15 m/s, with a bias of \(\sim\)3-5 m/s at winds speeds of 15-20 m/s and a bias of \(\sim\)10 m/s at wind speeds \(>\) 20 m/s. Also note the very large standard deviation of individual bias values at wind speeds \(>\) 20 m/s. Both of these characteristics (high mean bias and large variations in individual bias) are believed to be due to the fact that the high wind portion of the NOAA GMF has been tuned to tropical cyclone conditions, which tend to have very different surface roughness spectra than do Fully Developed Seas. Figure 18: Bias in the CYGNSS FDS wind speed product as a function wind speed persistence for wind speed ranges of (**a**) 0-5 m/s, (**b**) 5-10 m/s, (**c**) 10-15 m/s, (**d**) 15-20 m/s, (**e**) >20 m/s. Persistence < 0 implies prior winds were higher. Bias is the average (FDS-ERA5) difference for all of CY2022. Error bars are the standard deviation of all samples averaged in each wind speed and persistence bin.
# Current Promising Strategies against Antibiotic-Resistant Bacterial Infections
## 2 Discovery of New Antibiotics
### Modifying Old Antibiotics  
Modifying the basic chemical structure of an already existing antibiotic may circumvent the resistance mechanisms developed against it. Omadacycline, a semisynthetic tetracycline derivative, has modifications at the C-7 and C-9 positions of the tetracycline D-ring (Figure 2), enabling it to overcome common tetracycline resistance mechanisms, including tetracycline-specific efflux pumps and ribosomal protection [8]. In 2018, the United States approved omadacycline use for treating community-acquired bacterial pneumonia and acute bacterial skin infections. Cefiderocol is a Food and Drug Administration (FDA)-approved cephalospor-derived antibiotic used to treat carbapenem-resistant _Enterobacterates_ and drug-resistant non-fermenting gram-positive bacterial pathogens (GPBPs) [9]. This new antibiotic was synthesized by conjugating a catechol-type siderophore to the typical cephalospor-core (Figure 2), thereby enhancing antibiotic stability against the  
Figure 1: Strategies against antibiotic-resistant bacterial infection.  
hydrolytic action of most \(\beta\)-lactamases. Vancomycin and its close chemical relatives are glycopeptide antibiotics that inhibit the growth of gram-positive bacteria by binding to the \(L\)-Lys-\(D\)-Ala-\(D\)-Ala-CO\({}_{2}\)H terminals that need to be cross-linked with pentaglycine to form peptidoglycan, a mesh-like structure surrounding the cytoplasmic membrane that maintains cell shape and protects cells from bursting due to turgor pressure [2; 10; 11]. Vancomycin is widely used to treat infections caused by _E. facecium_ or methicillin-resistant _S. aureus_ (MRSA) but has been rendered ineffective as vancomycin-resistant strains are now quite commonly found in hospitals and communities. To overcome vancomycin resistance, the Boger laboratory at the Scripps Research Institute has spent over ten years modifying the basic structure of vancomycin and finally generated a series of vancomycin synthetic analog molecules (also called maxamycins group) (Figure 2) that have notably bactericidal activities against vancomycin-resistant _E. facecium_ (VRE) and _S. aureus_ (VRSA) isolates [12; 13; 14]. To produce quantities necessary for preclinical evaluation, this research group has developed a scalable atroposelective total synthesis process of vancomycin analogs that noticeably reduces the number of steps required and enhances the overall yield [15]. This work has, thus, been considered a breakthrough toward the development of next-generation glycopeptide antibiotics with improved activity against vancomycin-resistant bacteria [16].
## 3 Dataset and Benchmark
### Benchmark  
This section primarily focuses on the acquisition of cost-effective datasets with the ground truth serving as the benchmark. In essence, our task is to devise a methodology for obtaining ground truth data and crafting scenarios that align with our specific evaluation requirements for assessing the performance of various algorithms. In order to manage costs, it becomes crucial to obtain datasets that are economically viable while still serving as a valid benchmark for algorithmic effectiveness. As a result, we need to derive ground truth values based on the available sensors and data sources. Fortunately, the wheeled robots are typically designed to travel on flat ground and often have fixed sensors. Thus, the 2D SLAM algorithm aids in deriving the ground truth, encompassing both the robot's poses and their corresponding LiDAR timestamps. Additionally, we utilize interpolation to enhance the data content and synchronize LiDAR and image timestamps, contributing to the accurate calculation of error between camera poses obtained from ORB-SLAM3 and ground truth. Finally, leveraging the Cartographer as our foundation, we have proposed a technique for calculating ground truth. At the same time, we select scenes with engineering drawings or tiles as references while recording dataset scenes.  
In this study, we recorded real-world datasets from two scenes on the campus of National Taiwan University (NTU) in Taipei, Taiwan. These scenes include (1) the corridor on the sixth floor of the College of Engineering Building as an indoor environment and (2) the second floor outdoor venue of the College of Engineering Building as an outdoor environment. Combined with comparing the engineering drawings of the building, we believe that the accuracy of this dataset can be guaranteed. In addition, the paper [23] shows that the relative error of the Cartographer is less than 1%, which is within our error margin. At this point, we have obtained benchmarks for comparing localization effects.
## 4 Non-Ideality and Sensitivity Analysis
### Non-Ideal Parasitic Analysis  
The non-ideal model of the VD-EXCCII is presented in Figure 5. As can be deduced, the various parasitic resistance and capacitance appear in parallel with the input and output nodes of the device. The low impedance \(X_{P}\) and \(X_{N}\) nodes have a parasitic resistance and inductance in series with them. The associated parasitics at the X nodes can be quantified as \(Z_{XP}=Z_{XN}=R_{X(N,P)}+sL_{X(N,P)}\). However, for the frequency of interest, the inductive effect can be ignored. The parasitic resistance and capacitance associated with the \(P\), \(N\), \(R_{ZP+}\), \(R_{ZP-}\), \(R_{ZN+}\), \(R_{ZN-}\), \(W\), \(W_{C+},W_{C-}\) and \(Z\) nodes are \(R_{N}/C_{N}\), \(R_{P}//C_{P}\), \(R_{ZP+}/C_{ZP+}\), \(R_{ZP-}/C_{ZP-}\), \(R_{ZN+}//C_{ZN+},R_{ZN-}/C_{ZN-},R_{WC+}//C_{WC+}\), \(R_{WC-}//C_{WC-}\) and \(R_{W}//C_{W}\), their ideal values being equal to zero.  
Figure 5: Non-ideal model of VD-EXCCII with parasitics.  
Including the VD-EXCCII parasitics, the denominator of the filter transfer function will be modified as presented in Equation (35):  
\[D(s)=s^{2}+s\bigg{[}\frac{C_{1}^{\prime}R_{ZA}R_{2}^{\prime}+C_{1}^{\prime}R_{ZA }R_{W}^{\prime}+C_{2}^{\prime}R_{W}^{\prime}R_{2}^{\prime}}{C_{1}^{\prime}C_{2}^ {\prime}R_{W}^{\prime}R_{ZA}R_{2}^{\prime}}\bigg{]}+\frac{R_{1}^{\prime}R_{2}^{ \prime}+R_{1}^{\prime}R_{W}^{\prime}+g_{m1}R_{ZA}R_{2}^{\prime}R_{W}^{\prime}}{ C_{1}^{\prime}C_{2}^{\prime}R_{W}^{\prime}R_{ZA}R_{2}^{\prime}R_{1}^{\prime}} \tag{35}\]  
where \(C_{1}^{\prime}=(C_{1}+C_{ZN-}+C_{P})\), \(C_{2}^{\prime}=(C_{2}+C_{ZP-}+C_{W})\), \(R_{2}^{\prime}=(R_{2}+R_{XP})\) and \(R_{ZA}=R_{ZN-}/R_{P}\), \(R_{W}^{\prime}=R_{ZP-}/R_{W}\). The modified expressions of the frequency and quality factor including the various parasitic effects are presented in Equations (36) and (37):  
\[f_{0}^{\prime}=\frac{1}{2\pi}\sqrt{\frac{R_{1}^{\prime}R_{2}^{\prime}+R_{1}^{ \prime}R_{W}^{\prime}+g_{m1}R_{ZA}R_{2}^{\prime}R_{W}^{\prime}}{C_{1}^{\prime }C_{2}^{\prime}R_{W}^{\prime}R_{ZA}R_{2}^{\prime}R_{1}^{\prime}}} \tag{36}\]  
\[Q^{\prime}=\frac{1}{R_{2}^{\prime}C_{1}^{\prime}R_{ZA}+C_{1}^{\prime}R_{W}^{ \prime}R_{ZA}+C_{2}^{\prime}R_{W}^{\prime}R_{Z}^{\prime}}\sqrt{\frac{C_{1}^{ \prime}C_{2}^{\prime}R_{W}^{\prime}R_{ZA}R_{2}^{\prime}(R_{1}^{\prime}R_{2}^{ \prime}+R_{1}^{\prime}R_{W}^{\prime}+g_{m1}R_{ZA}R_{2}^{\prime}R_{W}^{\prime}) }{R_{1}^{\prime}}} \tag{37}\]  
To minimize the parasite effects the values of the passive components should be selected such that \(C_{1}\gg(C_{ZN-}\) and \(C_{P})\), \(C_{2}\gg(C_{ZP-}\) and \(C_{W})\). In addition, note that the resistors \(R_{1}\) and \(R_{2}\) are connected to the low impedance X terminals, so they will absorb the parasitic resistance present at XP and XN terminals since (\(R_{2}\gg R_{XP}\) and \(R_{1}\gg R_{XN}\)).
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### Forest AGB Maps from Field Inventory Data and Optical and/or Radar Remotely Sensed Data
#### 3.2.10. China AGB Maps from 2001 to 2013  
[PERSON] et al. [72] integrated field measured biomass with MODIS reflectance data and a forest type map using the Model Tree Ensembles approach, and produced an aboveground biomass map of China at a spatial resolution of 1 km from 2001 to 2013. The R\({}^{2}\) and RMSE between the predicted forest biomass data and independent field data in the validation dataset were 0.46 and 22.7 Mg C/ha, respectively. Compared with other previous studies, estimated biomass in this study was higher, probably because [PERSON] et al. [72] used a new AGB measurement dataset rather than previous provincial forest inventory plot data. Uncertainty was ascribed to the mismatch of field plot size and remote sensing pixel data, as well as forest type map quality and MODIS data used.
## 7 Experimental Results
### Performance Comparison of EAR2 and EAR2M
#### 7.3.1 Impact of Sink Speed  
To simulate the impact of sink speed, we fix the end-to-end distance between the source and the sink at 300 m. Figure 7a shows the effect of sink speed on DSR with the different number of flooding points. \(EA\) is exponentially proportional to the sink speed. This means the average time that the sink receives data is increased, because the number of the average data forwarding hops for flooding is increased. Because these two values can decrease using data flooding in \(EA\) through multiple flooding points, the higher number of flooding points in EAR2M leads to the higher DSR.  
Figure 7b shows the EC according to the sink speed with the different number of flooding points. Because the number of nodes that participate in data forwarding is proportional to the number of flooding points, more data flooding points in EAR2M leads to more energy consumption, as shown by the difference in energy consumption at the same sink speed on the graph. As the sink speed increases, energy consumption for restricted flooding increases due to the extension of \(EA\). However, the cost for flooding is not different according to the number of flooding points at the same sink speed. Accordingly, each graph has a similar increasing pattern.
## 4 Hypothesis Test and Detection Method
### Hypothesis Test  
From IAM, it is known that the edge node and mobile users estimate their CSI and extract the channel vector after receiving the training signals in the channel estimation stage. Therefore, the transmitted training signals received by the receiver is known as the channel vector and is denoted by \(\beta_{a}^{b}\). Furthermore, each channel vector received by the receiver is recorded as a channel record donated by \(\gamma_{a}^{b}\). In addition, the \(W\left(\beta_{a}^{b}\right)\) is the channel gain of the nel vector of the training signal sent by the legal nodes, and \(H_{1}\) represents the channel vector of the training signal sent by the illegitimate nodes. Thus, the hypothesis test is established as follows.  
\[H_{0}:W\left(\beta_{a}^{b}\right)\geq Z_{(E,F)}, \tag{3}\]  
\[H_{1}:W\left(\beta_{a}^{b}\right)\geq Z_{(E|F,I)}, \tag{4}\]  
where \(E,F\), and \(I\) represent the edge node, end-user, and impersonation attacker, respectively. \(Z_{(E,F)}\) denotes the estimated channel gain of the legitimate mobile user, and \(Z_{(E|F,I)}\) is the estimated channel gain of the potential impersonation attacker. CSI is unique and represents the channel's state. Therefore, \(\beta_{a}^{b}\) and \(\gamma_{a}^{b}\) are also uniquely determined. On this basis, the statistic of the hypothesis test is  
\[D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)=\frac{ \parallel\left(\beta_{a}^{b}\right)-\left(\gamma_{a}^{b}\right)\parallel^{2}} {\parallel\left(\gamma_{a}^{b}\right)\parallel^{2}}. \tag{5}\]  
where \(\parallel\cdot\parallel\) is the Frobenius norm and \(D\) is the normalized Euclidean distance between \(\beta_{a}^{b}\) and \(\gamma_{a}^{b}\). We compare \(D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)\) with the test threshold, denoted by \(\lambda\). If \(D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)<\lambda\), it is considered as a legitimate user; otherwise, it is considered to be an attacker. The hypothesis test from Equations (3) and (4) can be expressed by  
\[D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)<\lambda \Rightarrow H_{0}, \tag{6}\]  
and  
\[D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)>\lambda \Rightarrow H_{1}. \tag{7}\]  
By establishing the above hypothesis test to detect impersonation attack and comparing \(D\left(\left(\beta_{a}^{b}\right),\left(\gamma_{a}^{b}\right)\right)\) with the test threshold \(\lambda\), we can judge whether each training signal received by the receiver is legitimate. It is assumed that the accuracy of the IAD is related to the threshold value. The larger the threshold value, the higher the MDR, whereas the smaller the threshold value, the higher the FAR. Therefore, the impersonation detection method is introduced in the following subsection.
# Fretting Fatigue Behavior under Tension-Bending Mixed-Mode Loading
## 4 Results and Discussion
### Damage Initiation Prediction
#### 4.3.2 The Extent of Damage Initiation  
During the fretting damage initiation process, we care not only about the damage initiation location but also the extent of the damage initiation. The fretting damage initiation extent of the specimen can be represented by the maximum damage parameter, and its effectiveness has been verified in many experiments [9; 37; 46; 47].  
In order to clearly analyze the relationship between the control variable and the extent of fretting damage initiation, the normalized maximum damage parameters were separately extracted and plotted, as shown in Figure 14. Despite the two damage parameters being based on different mechanical parameters, their prediction trends given by different dimensionless variables for the extent of fretting damage initiation are generally consistent. For the relationship between the inclined load and the maximum damage parameter, the result of \(\lambda=1.594\times 10^{-4}\) (Exp. 9) was used for normalization, as shown in Figure 14a. The positive correlation between the oblique load \(\lambda\) and the damage initiation extent is shown in the figure, which reveals that it is necessary to properly control the range of cyclic load to improve the fretting fatigue damage initiation life of the component. Figure 14b illustrates that the maximum damage parameters increase with increasing stiffness ratio, and the relationship between them is almost centrally symmetric. This shows that the stiffness ratio is a very effective parameter for adjusting the tension-bending mixed-mode fretting fatigue initiation life of the component, and a reasonable design of the stiffness ratio between two contact bodies will improve the fretting fatigue resistance of the structure. Figure 14c shows that with the increase of the mode angle, \(\theta\), the damage initiation extent of the specimen increases, but the damage extent for pure bending mode (\(\theta=90^{\circ}\)) is lower than that of \(\theta=75^{\circ}\). This phenomenon demonstrates that the fretting damage extent of the specimen under tension-bending mixed-mode loading is more serious than that under single-tension or single-bending mode loading. It can also be seen from Figure 14c that the extent of fretting damage is more sensitive at smaller mode angles (tension dominant mode), which is consistent with the bridge-type pad fretting experimental results [44].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \(\theta=0^{\circ}\) & \(\theta=15^{\circ}\) & \(\theta=30^{\circ}\) & \(\theta=45^{\circ}\) & \(\theta=60^{\circ}\) & \(\theta=75^{\circ}\) & \(\theta=90^{\circ}\) \\ \hline MD\({}_{\text{max}}\) location (\(x/t\)) & 0.127 & 0.121 & 0.114 & 0.109 & 0.105 & 0.102 & 0.102 \\ SWT\({}_{\text{max}}\) location (\(x/t\)) & 0.113 & 0.123 & 0.118 & 0.113 & 0.110 & 0.107 & 0.107 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Damage initiation locations for different mode angles (\(\lambda=1.063\times 10^{-4}\), \(\gamma=1\)).