esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This study aimed to determine the bactericidal efficacy of sequential use of NaOCl pH 12 followed by acidified NaOCl pH 6.5, and compare it to that of either of these NaOCl solutions alone. _E. faecalis_ biofilm was grown on standardized dentate specimens for four weeks. The specimens were randomly divided into four groups: (A) 4 min exposure to 0.9% saline solution (control); (B) 4 min exposure to 4% NaCl pH 12; (C) 4 min exposure to 4% NaCl pH 6.5; and (D) 2 min exposure to 4% NaOCl pH 12 followed by 2 min exposure to 4% NaOCl pH 6.5. The bactericidal activity was evaluated after the 4 min of contact time using confocal laser scanning microscopy. The volume ratio of red fluorescence to green and red fluorescence indicated the proportion of dead cells in the biofilm. The percent of dead cells in the saline solution group was significantly lower than those in the other groups. There was no significant difference between NaOCl pH 12 compared to NaOCl pH 6.5. The sequential use of NaOCl pH 12 followed by pH 6.5 significantly increased the percent of dead cells compared to both the samples exposed to either NaOCl pH 12 or pH 6.5. These results show that sequential irrigation protocol had a stronger bactericidal effect than the commonly used NaOCl pH 12.  
bactericidal efficacy; biofilm; confocal laser scanning microscopy; _Enterococcus faecalis_; NaOCl; pH 12
# Erasure Codes for Cold Data in Distributed Storage Systems
## 6 Conclusions  
In order to improve the encoding and decoding speed of distributed storage systems, especially for \"cold\" data, we proposed a new erasure code process, the NewLib code, which can strip the encoding data to data alignment. The NewLib code improved the performance of reading and writing in the distributed storage systems. For uniform data distribution and task scheduling, we developed the N-Schedule strategy. The data nodes were divided into multiple virtual nodes, and the virtual nodes were dispersed to a hash ring through consistent hashing to construct a fully symmetric and decentered hash ring.  
In the future, we need to address the correct application of the generalized delete code in big data storage systems to improve the decoding speed. By increasing the decoding speed or adjusting the decoding time, the corrective censoring codes can be applied to \"hotter\" data. In this way, the strategy of copying data can be completely discarded so as to improve the efficiency of the storage system.  
Figure 14: The throughput in the sequential writing.  
Figure 13: The IOPS in the sequential writing.  
**Author Contributions:** Conceptualization, C.Y.; Data curation, S.Y.; Formal analysis, W.L. and T.L.; Funding acquisition, C.Y.; Investigation, S.Y.; Methodology, C.Y.; Resources, C.Y. and Y.L.; Software, W.L. and T.L.; Supervision, Y.L.; Validation, C.Y. and Z.X.; Visualization, S.Y.; Writing--Original draft, Z.X.; Writing--Review and editing, C.Y. and Z.X. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China, grant number 61662038, and in part by the fund of the Science and Technology Project of Jiangxi Provincial Department of Education, grant number GJJ211816.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 4 Signal Processing from RSL to Rainfall Map  
The main steps from obtaining the RSL data to the generation of rainfall map include the identification of the starting and ending time of rainfall (wet/dry classification), the baseline determination, the compensation of WAA, the calculation of path-average rain rate, and the reconstruction of rainfall map. As shown in Figure 3, each step can be implemented in different ways. The function of each step and the related methods will be described in detail below, and the sources of uncertainty and the reasons to cause errors are also analyzed.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 3 Evaluation of Different Knowledge-Based Approaches
### Assessment Results
#### 3.3.1 Bayesian/Causal Networks  
In terms of declarative aspects, Bayesian/Causal Networks excel in providing robust frameworks for representing knowledge via probabilistic relationships, offering precise and formal representations aligned with real-world uncertainties and complexities. They accurately model complex probabilistic dependencies and causal relationships within the domain, but might face challenges in handling missing or incomplete knowledge beyond encoded probabilities or causality. While these models generalize within encoded probabilistic relationships, they might struggle with entirely new scenarios.  
With regard to their procedural qualities, Bayesian/causal reasoning approaches effectively encode uncertainty and probabilities, ensuring high consistency and reliability in the reasoning process. However, due to their probabilistic nature, their explainability might be less intuitive, impacting the explainability of the reasoning process.  
In summary, probability-based formalisms excel in representing uncertainty and offer high accuracy, consistency, and confidence. They provide precise and comprehensive descriptions but might encounter challenges in scalability and intuitive explainability due to their probabilistic nature. Additionally, while expandable, generalizing beyond encoded relationships might be a limitation.
# Temporal Saliency-Based Suspicious Behavior Pattern Detection
## 3 Results and Discussion
### Overall Performance Evaluation Results of 10 Different Types of Video Sequences  
The actual results of the proposed method are compared with those of the actual suspicious behavior region, which is regarded as a ground truth. Frames that successfully detected a region containing suspicious behaviors are used as a component of a true positive (_tp_), and frames that detected a suspicious behavior region even if there were not any suspicious behaviors in the frame were used as a component of a false positive (_fp_). Frames that did not detect any regions, even if there were suspicious behavior in the frame, were used as a component of false negative (_fn_). The proposed method achieved a 100% true negative (_tn_) rate, because nothing was detected as a suspicious behavior  
Figure 17: Examples of detection results for abnormality from the No. 10 data.  
region in experiments with No. 5 data where no suspicious behavior is included. The accuracy, precision, recall, and FNR (False Negative Rate) is calculated as follows.  
\[\begin{array}{c}accuracy=\frac{tp+tn}{tp+fp+fn+tn},\text{ }precision=\frac{tp}{tp+fp}\\ recall(True\text{ }Positive\text{ }Rate)=\frac{tp}{tp+fn},\text{ }FNR(\text{ } False\text{ }Negative\text{ }Rate)=\frac{fp}{fp+tn}\end{array} \tag{5}\]  
Table 4 summarizes the overall performance evaluation result of the proposed method.  
The reason for the low performance of the No. 9 data was analyzed as follows. In the No. 9 data, all the motion vectors were not detected and only a part of them was detected because the position of the walking man is too close to the photographing camera. It was a difficult environment to measure the motion vector properly. For this reason, false positives have been increased, resulting in lower performance compared with that of the other data.  
As a summary, the proposed system detects various suspicious behaviors captured in various environments with high performance, and it is also robust to differences in brightness depending on the weather and time. However, given the results of the No. 9 data, it is necessary to secure a suitable shooting distance to accurately run the proposed method.
## 2 The Planar Code  
The planar code is the planar variant of the surface codes introduced by [PERSON] [7; 14]. It is defined on a spin lattice, as in Figure 1, with a spin-\(1/2\) particle on each vertex. There are two types of plaquette, labelled \(s\) and \(p\), for which we define Hermitian operators as follows,  
\[A_{s}=\prod_{i\in s}\sigma_{i}^{x},\;\;B_{p}=\prod_{i\in p}\sigma_{i}^{z}. \tag{1}\]  
These operators mutually commute, and are the stabilizer operators of the code. Their eigenvalues can be interpreted in terms of anyonic occupations, with no anyon present for an eigenvalue of \(+1\). Eigenvalues of \(-1\) are interpreted as so-called flux anyons on \(p\)-plaquettes and charge anyons on \(s\)-plaquettes. The stabilizer space of the code therefore corresponds to the anyonic vacuum, and the anyon configuration is the syndrome of the code.  
Edge operators may also be defined which commute with the stabilizers. These can be interpreted in terms of the anyon occupations of the edges, with flux anyons corresponding to a \(-1\) eigenvalue of the  
Figure 1: The spin lattice of an \(L\times L\) planar code. The \(s\)-plaquettes are shown in blue and the \(p\)-plaquettes are shown in white. A spin-\(1/2\) particle is situated at each vertex. In this example the linear size is \(L=5\). The linear size \(L\) is also the distance of the code.  
left or right edge and charge anyons corresponding to a \(-1\) eigenvalue of the top or bottom. The edge occupations define the state of the logical qubit stored in the code. The \(X\) (\(Z\)) basis of the logical qubit can be defined such that the \(\left|\,+\right\rangle\) (\(\left|\,0\right\rangle\)) state corresponds to vacuum on the top (left) edge and the \(\left|\,-\right\rangle\) (\(\left|\,1\right\rangle\)) state corresponds to a flux (charge) anyon. Errors acting on the spins of the code will create and move anyons, and so disturb the stored information when they cause anyons to move off the edges. The job of decoding is to remove the anyons in a way that yields no net anyon moved off any edge. A logical error results when the decoding does this incorrectly.  
The creation of pairs of plaquette anyons is achieved by bit flip errors. Pairs of vertex anyons are created by phase flips. The plaquette and vertex syndromes of the code can therefore be corrected independently. This will not give the best decoding in general, but it does represent a significant simplification. It is this approach that our decoder will take. Since the two syndrome types are equivalent, we restrict to plaquettes without loss of generality.
## 1 Introduction  
Toxins play an important role in pathogenicity in the host-pathogen interaction, and especially during the process of infection. These virulence determinants can inhibit protein synthesis, help the pathogen evade host immune responses, and cause damage to the target cell membrane in multiple ways [1, 2]. Depending on the nature, structure, and mode of action, bacterial toxins can be classified into several functional groups [3, 4]. The largest group of protein toxins is the pore-forming toxins (PFIs), which form pores on a target cell membrane, increasing membrane permeability and creating an ion imbalance [5, 6, 7]. Based on the secondary structures of the domains that form the transmembrane pores, PFIs can be broadly classified into two structural families [8, 9]: those containing helices (\(\alpha\)-PFT), such as the colicins produced by _Escherichia coli_[10], and those containing sheets (\(\beta\)-PFT), such as alpha-hemolysin produced by _Staphylococcus aureus_[11].  
Cytolysin A (ClyA, also known as HlyE or SheA) is a 34-kDa \(\alpha\)-PFT that is produced by some bacteria from the _Enterobacteriaceae_ family, including _E. coli_ and _Salmonella enterica_. This toxin was first reported at around the same time by both the [PERSON] and [PERSON] groups, who observed that overexpression of the _Salmonella enterica_ subsp. _enterica_ serovar Typhimurium regulatory protein SlyA in the _E. coli_ strain K-12 resulted in the synthesis of a novel hemolysin/cytolysin that conferred a hemolytic phenotype on the recombinant _E. coli_strain [12; 13]. Since its initial discovery, the structure and function of the ClyA toxin, as well as its role as a virulence factor, have been explored in numerous studies. Structural studies using X-ray crystallography and electron microscopy have shown that ClyA is one of only a few \(\alpha\)-PFTs that can exist as both a soluble monomer and as a transmembrane pore (undergoing large-scale conformational changes) [14; 15; 16]. These findings provided detailed structural information that has accelerated the subsequent structural analysis of other \(\alpha\)-PFTs [17; 18; 19]. Another remarkable finding is that ClyA is secreted from bacterial cells via a vesicle-mediated pathway [20]. Currently, one of the most intriguing research areas regarding this toxin is its potential utility in applied research, such as in the clinical setting. A recent report demonstrating that recombinant ClyA can be displayed on the surface of bacterial cells or released by membrane vesicles suggests that ClyA could be used in applications such as vaccine development and tumor therapy.  
This review first presents a brief summary of the current state of knowledge regarding ClyA (including the prevalence of the encoding gene and its transcriptional regulation, the secretion pathway used by the protein, and the mechanism of protein assembly into the final form). The unique functional features of ClyA are highlighted, and recent research exploring its potential use in biotechnology applications (vaccine development, tumor therapy, and nanopore formation) are discussed.
## 2 Data and Methods
### Sensitivity Analysis Methods  
The sensitivity analysis methods (Morris, Sobol, FAST, and EFAST) used in this study were included in the SWAT-AS platform. A brief introduction to these sensitivity analysis methods is provided in this section.
# Microwave Planar Resonant Solutions for Glucose Concentration Sensing: A Systematic Review
## 5 Quality Factor-Based Sensors
With this approach, non-planar techniques have been proposed with this \(Q_{\text{u}}\)-based configuration for aqueous glucose characterization, such as whispering-gallery-mode resonators [147; 148], although requiring bulky setups. As for planar devices for glucose concentration measurement with this approach, the use of SRR has been commonly considered. An SRR loaded with a coplanar waveguide line was used in [143] for measurements in aqueous solutions. An asymmetric SRR for aqueous glucose solutions was proposed in [149]. The use of a single SRR with a dielectric sample holder was studied for water-glucose [94] (Figure 5a) and blood plasma-glucose [31] solutions, pointing the challenges when multicomponent solutions are involved. A significant sensitivity raise was reported for a novel technique based on two mutually coupled SRR [105] (Figure 5b). Another work proposed an open-loop line resonator for water-glucose solutions [81]. A more compact solution was shown with a coplanar quarter-wavelength resonator [150], which included a study regarding the effect of the temperature upon the finally obtained sensitivity. Table 3 shows a summary for the review of this sensing approach, with all the works in order considering the operating resonant frequency and type of sample. Although no references have hitherto shown measurements of physiological concentrations using this approach, the corresponding column is kept in the table (with no data) to maintain consistency and ease the global comparison. Data for some references had to be taken from the published plots and must be consequently considered as approximate. Following the general analysis, the \(RS(G)\) value was computed from the available data for all the references in the table, considering the lowest \(\rho_{\text{g}}\) for each one as reference. The reference [105] was separated since it does not involve a strictly \(Q_{\text{u}}\)-based design technique, but an inter-resonator coupling one. However, the computation of a virtual quality factor was proposed and used for glucose concentration tracking with interesting results, and it also deserves careful attention. Figure 5: Examples of \(Q_{\text{u}}\)-based sensors: (**a**) Single SRR sensor with dielectric sample holder. © (2019) IEEE. Reprinted, with permission, from [94]. (**b**) Pair of mutually coupled SRR exploiting inter-resonators coupling. © (2019) IEEE. Reprinted, with permission, from [105].
## 4 Combined Stress Analysis of Composite Lining  
### Model Establishment and Parameter Determination  
The numerical tunnel model was established by ABAQUS 6.14 software. The specific dimensions of the model are shown in Figure 11. The lining is double layered. The pipe piece sheet is made of C55 concrete. The reinforced concrete lining is made of C35 concrete and HRB400 reinforcement. All these materials use the linear elastic model, and the rock mass uses the elastic-plastic model based on the Mohr-Columba (M-C) principle. The C3D8P linear hexahedral unit represents the geotechnical layer, the shield tube sheet, and the lining concrete. The T3D2 3D truss unit is used to simulate the reinforcement. The total number of units is 25,730, and the number of nodes is 31,565. The load takes into account the gravitational field, and introducing the radial load inside the tunnel, simulates the internal water pressure. The top surface of the model is free. The left and right sides apply a head pressure that changes along the gravity gradient. The bottom surface is entirely fixed. The sides are restrained from normal deformation while vertical deformation is permitted. The physical and mechanical properties of the model are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Strata Age**} & \multirow{2}{*}{**Lithology**} & \multirow{2}{*}{**Weathering Degree**} & \multicolumn{2}{c}{**P-Wave Velocity (m/s)**} & \multicolumn{3}{c}{**Deformation Modulus (GPa)**} \\ \cline{3-8}  & & & **Range** & **Mean** & **Lower Limit** & **Upper Limit** & **Mean** \\ \hline Tertiary lower system & & Total & 2008–3145 & 2368 & 2.56 & 4.93 & 3.25 \\ Xinzhuang village group & & Strog & 2433–3745 & 3288 & 3.38 & 6.36 & 5.26 \\  & & Weak & 3175–4274 & 3886 & 4.99 & 7.71 & 6.71 \\ \hline Lower Cretaceous & & & & & & & \\ Baizushan Formation & & & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: Estimation of deformation modulus of peilitic siltstone in different strata.  
Figure 11: Overview of numerical simulation.
## 2 Materials and Methods
### Characterization of Group-Velocity Dispersion  
The group-velocity dispersion (GVD) of different modes was experimentally measured using a Mach-Zehnder interferometer with a controllable delay line to investigate the time delay and find the equalization wavelength for different modes (Figure 1) [12; 15]. A camera at the free output of the second polarization-independent beam splitter allowed control over the near field pattern of the analyzed guided modes. An aperture in the image plane enabled the selection and suppression of individual modes. For the setup, a fiber-coupled SC light source, developed and manufactured in cooperation with fiberware GmbH, was used. A monochrome camera (acA2040-90 umNIR, Basler, Ahrensburg, Germany), used during alignment, had an enhanced sensitivity in the near infrared spectral range up to 900 nm and a dynamic range of 59 dB. The test fiber was placed in a straight configuration with a length of 0.46 m \(\pm\) 0.001 m in the interferometer.
## 3 Results and Discussion
### Shrinkage  
Figure 7 shows the effect of pressure and temperature on shrinkage of wood after 1 h SuCO\({}_{2}\) dewatering. Theoretically, wood shrinks as MC decreases to fiber-saturated point (FSP); however, shrinkages of wood were observed after SuCO\({}_{2}\) dewatering in each test when their MCs were over FSP (Figure 6). Collapse, an abnormal shrinkage, occurs when MC of wood is higher than FSP, which generally results in abnormal deformation. Severe collapses cause enormous loss of timber and high costs for industries. Therefore, the shrinkages in Figure 7 that occurred in each SuCO\({}_{2}\) dewatering indicate collapse of wood. However, the collapses of wood are only between 0.8% and 0.25%, which are much smaller than that of 2.8% in CKD and 3.6% in oven drying [35]. These results suggest that SuCO\({}_{2}\) dewatering may mitigate wood collapse and is in agreement with previous study [28]. Thus, SuCO\({}_{2}\) dewatering is an effective technology for improving timber drying quality. The less severe collapse is mainly attributed to the mechanism of SuCO\({}_{2}\) dewatering, which decreases capillary tension due to the fact free water in wood is expelled by CO\({}_{2}\) bubbles during decompression [32]. Additionally, collapse increases significantly with temperature at higher pressure dewatering.
## 3 System Model  
The cluster-based UAV communication system is composed of UAVs, the ground base station (gBS) and the communication link between them. As shown in Figure 1, consider a network composed of \(N\) UAVs which are randomly distributed in a finite area with a constant height \(H\). Each UAV is set to fly around the gBS following the circumference trace. During the movement process, all UAV nodes will be clustered according to their locations, mobility, channel conditions, and other factors. It is assumed that all UAV nodes obey the free space propagation model and the signal strength is only related to the propagation distance [22; 23]. A pCH and a bCH are selected in each cluster. The cluster head is regarded as a service node, which constitutes the aerial backbone routing network. It is responsible for collecting information within the cluster, maintaining communication between clusters, and transmitting the collected information back to the gBS. In our model, the pCH collects the information in the cluster and backs up the information to the bCH. When the energy of the pCH is lower than the threshold or the pCH fails, the bCH can immediately replace the pCH to play the role of the cluster head. When both the pCH and the bCH are in a low power state (i.e., selfish state, see Section 5 for details), the bCH will enter the failure state as soon as possible and fly to the charging pile to charge, so as to restore the system to an available state as soon as possible.  
In this paper, we assume that all UAV nodes are divided into \(K\) clusters, and the set of all clusters in the UAV network is denoted by \(\mathcal{C}=\{\mathcal{C}_{i},i=1,2,\ldots,K\}\), \(\mathcal{C}_{i}=\{\mathcal{C}_{ij},j=1,2,\ldots,m_{i}\}\) denotes the cluster members of cluster \(i\), where \(m_{i}\) is the size of cluster \(i\), \(\mathcal{C}_{ij}\) represents the \(j\)th member in cluster \(i\). The set of pCH and bCH are respectively denoted by \(\mathcal{P}=\{\mathcal{P}_{i},i=1,2,\ldots,K\}\) and \(\mathcal{B}=\{\mathcal{B}_{i},i=1,2,\ldots,K\}\), in which \(\mathcal{P}_{i}\) and \(\mathcal{B}_{i}\) are the pCH and bCH of cluster \(i\), respectively.