esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Co-Administration of Two Different Antimicrobial Polymers
### N-Halamines Based Polymers and Polyacations Working in Tandem
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Schematic Representation of the Polymeric Agent**} & \multicolumn{1}{c}{**Surface**} & \multicolumn{1}{c}{**Micro-Organism**} & \multicolumn{1}{c}{**Percentage Reduction of Bacteria 1**} & \multicolumn{1}{c}{**Contact Time**} & \multicolumn{1}{c}{**Ref.**} \\ \hline \hline \multirow{4}{*}{HQ\({}_{3}\)} & \multirow{4}{*}{Macroporous crosslinked chloromethylylated polystyrene (CMPS) resin} & _E. coli_: \(1.8\times 10^{7}\) CFU/mL & \multirow{4}{*}{5 min} & \multirow{4}{*}{[58]} \\  & & & & Unmodified-CMPS = 32.39\% & \\ \cline{1-1} \cline{5-5}  & & & & HQ\({}_{3}\)-gCMPS = 63.69\% & \\ \cline{1-1} \cline{5-5}  & & & Chlorinated HQ\({}_{3}\)-gCMPS = 99.99\% & & \\ \hline \hline \multirow{4}{*}{HQ\({}_{4}\)} & \multirow{4}{*}{Polypropylene (PP)} & \multirow{4}{*}{_L. mono-cytogenes_} & \multicolumn{1}{c}{_L. monocytogenes_: \(1.0\times 10^{6}\) CFU/mL } & \multirow{4}{*}{120 min} & \multirow{4}{*}{[59]} \\  & & & & \({}^{\text{a}}\)Unmodified-PP \(\approx\) 0\% & \\ \cline{1-1} \cline{5-5}  & & & Chlorinated-PP \(\approx\) 0\% & & \\ \cline{1-1} \cline{5-5}  & & & Chlorinated-HQ\({}_{4}\)-gP \(>\) 99.99\% & & \\ \cline{1-1} \cline{5-5}  & & & & \({}^{\text{E. coli.}}\) 2.51 \(\times\) 10\({}^{6}\) CFU/mL & \multirow{4}{*}{30 min} & \multirow{4}{*}{[60]} \\ \cline{1-1} \cline{5-5}  & & & C + H\({}_{2}\) = 2.27\% & & \\ \cline{1-1} \cline{5-5}  & & & Chlorinated-C + H\({}_{2}\) = 100\% & & \\ \cline{1-1} \cline{5-5}  & & & Chlorinated C + H\({}_{2}\) + Q\({}_{2}\) = 100\% & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Separate investigations of antibacterial systems that integrate \(N\)-chloramines and QAS attached to different surfaces are summarized in Table 1. In general, the reported results suggest that the presence of both functionalities provides a potent antibacterial capacity. However, it seems that the antimicrobial activity of these materials is more pronounced due to the presence of \(N\)-chloramines, rather than the QAS moieties, suggesting that the immobilization of the QAS moieties onto surfaces hinders their ability to diffuse into cell walls limiting their biocide capability (in particular in Gram-negative bacteria) [62]. Nevertheless, a part from promoting electrostatic interactions with bacteria, cationic charges may also cause cellular damage through an ion exchange process [63]. Generally, dual systems based on \(N\)-halamines and QAS have excellent antimicrobial properties achieved through two different mechanisms of action.
## 5 Conclusions  
In this paper, a unilateral contact mechanics model of the composite interface of an ancient timber column was proposed and the formula of the stress distribution of that composite interface was deduced. Furthermore, based on the hypothesis, the stress distribution of the interface under the effect of iron hoops was obtained and the correctness of the theoretical model was verified by comparing it with the shear sliding test results of interfaces. According to these test results, the expressions and curves between the shear strength of the interface, the number of iron hoops and the inclination angle of the interface are able to be fitted, respectively. On this basis, the influences of the number and the spacing of the iron hoops and the inclination angle of the interface on the shear sliding performance of the interface were studied. The main conclusions are as follows:  
1. Based on the analysis of the slip characteristics of the interface, the limit inclination angle of the point slip and the limit inclination angle of the surface slip of the interface were obtained. According to this, a three-stage slip failure mode of the interface has been proposed: the fully sticky stage, the partially sticky stage and the sliding stage;
2. Using the elastic mechanics equation, the sliding displacement in the sliding stage of the interface can be calculated by Equation (22) and the shear stiffness of the interface can be expressed by Equation (23);
3. The number and the spacing of the iron hoops and the inclination angle of the interface were found to be the important factors affecting the shear strength of the interface. When the number of iron hoops \(n=2\), \(3\) and \(4\), the interfacial shear strength was \(1.72\) times, \(2.52\) times and \(3.10\) times that which was found when the number of iron hoops \(n=1\), respectively, indicating that increasing the number of iron hoops can improve the shear strength of the interface significantly. There is an optimal value of hoop spacing. If the spacing of the iron hoops is too large or too small, the shear strength of the interface cannot be improved effectively. Within a certain range of the inclination angle, the shear strength is positively correlated with the inclination angle of the interface.  
Conceptualization, methodology, resources, formal analysis and funding acquisition, P.C.; experiment, validation and writing--original draft preparation, Q.F.; experiment and data curation, N.W.; investigation and methodology, N.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Fundamental Research Funds for the Central Universities, grant number 2022 JBZY008, the National Natural Science Foundation of China, grant numbers 51878034 and 51778045, and 111 Project of China from Ministry of Education and the Bureau of Foreign Expert of China, grant number B13002.  
Not applicable.  
The authors would like to thank the financial support from the Fundamental Research Funds for the Central Universities, National Natural Science Foundation of China and 111 project.  
The authors declare no conflict of interest.
## 3 Validation and Results
### Large Systems  
As can be seen in Figure 2, the number of possible **P** matrices increases factorially with the number of intermediates. For systems with four and five intermediates, very large numbers of **P** matrices must thus be tested. Fortunately, our new approach allows us to test all these combinations efficiently first, to then determine which combinations of **P** and **K** require further non-linear least-squares optimization. We therefore used the same approach as described for the three intermediate case above to generate 100 sets of progress curves for four and five intermediate systems. We then applied Equation 9 to determine the subset of **P** matrices that produce only positive state curves. We report the average number of **P** matrices for each \(I\) that generate only positive state curves in Figure 2 (black line).  
For all \(I\), it is clear that our approach offers a significant reduction in the number of models that need to be tested by non-linear least-squares optimization, making this approach computationally tractable for systems with large numbers of intermediates like the Ribosome. Interestingly, the number of models that need to be tested is highly dependent on the curves, as evidenced by the large standard deviation over our 100 models (and the even larger spread in the min and max values, gray shadow Figure 2). It is therefore difficult to _a priori_ predict the total number of **P** matrices that will produce only positive curves for a given data set. Our ability to identify a single kinetic model that best fits the experimental data will ultimately depend on the experimental data. Our approach offers a computationally simple solution to determining any potential degeneracy or lack thereof.
# ATOSE: Audio Tagging with One-Sided Joint Embedding
## 3 Architecture: Audio Auto-Tagging with One-Sided Joint Embedding  
In this section, we present our audio auto-tagging model with a one-sided joint embedding technique. Our model consists of four modules: a _tag autoencoder_ that extracts tag domain features, a _feature extractor_ that extracts acoustic features of the audio domain from raw waveforms, a _projector_ that maps audio domain features to tag domain features, and a _classifier_ for predicting whether tags should be included. The overall architecture of our model is shown in Figure 1. The remainder of this section is devoted to the detail of the modules and how to train the model with two-phase steps.
# A New Route for Low Pressure and Temperature
## Materials and Methods
### Characterization Methods  
Bright-field transmission electron microscopy (TEM) analysis was performed on the PtRu/MoS2 NPs and the nanocomposite precursor using a FEI Tecnai G12 Spirit Twin (LaB6 source, Eindhoven, The Netherlands). at 120 kV acceleration voltage. Before analysis, the PtRu/MoS2 nanoparticles were dispersed in toluene by sonication and collected on holey carbon-coated copper grids, and ultrathin sections of the nanocomposite precursor were prepared with a Leica UC7 ultramicrotome (nominal thickness 100 nm) and deposited on TEM copper grids. TEM images were acquired by a FEI Eagle 4k CCD camera (Eindhoven, The Netherlands).  
Specific surface area (SSA) and pore size distribution analysis were performed on the hyper-crosslinked resin and nanocomposite by nitrogen adsorption measurements at 77 K, using a Micromerities ASAP 2020 analyzer (Norcross, GA, USA). Before the analysis, all the samples were degassed at 120 \({}^{\circ}\)C under vacuum (\(p<10^{-5}\) mbar) and the measurements were performed using high purity gases (>99.999%). Brenauer-Emmett-Teller (BET) SSA was determined by the linear part of the BET equation. Nonlocal density functional theory (NLDFT) was applied to the nitrogen adsorption isotherms to evaluate the pore size distribution of the materials. Phenol concentrations were measured by the colorimetric method by using a Thermo-Scientific UV-vis Evolution Q60 spectrophotometer. Aliquots (800 \(\mu\)L) of the diluted sample were placed in a spectrophotometer cuvette (1 mL) with 100 \(\mu\)L AAP solution (20.8 mM in 0.25 M sodium bicarbonate solution) and 100 \(\mu\)L potassium ferricyanide solution (83.4 mM in 0.25 M sodium bicarbonate solution). Absorbance was measured at 510 nm against a blank. Concentrations were measured using a previously recorded calibration curve (mg/mL of phenol = 0.0195 x Abs + 7 \(\times\) 10\({}^{-5}\), \(R^{2}\) = 0.9999). Thermogravimetric analysis (TG-DTG) (SDTQ 600 Analyser (TA Instruments, New Castle, DE, USA) was performed in flowing air at a 10 K/min heating rate. FT-IR spectra (Vertex 70 apparatus, Bruker Corporation, Billerica, MA, USA), by applying KBr technique, X-ray diffraction measurements (Bruker D8 X-ray diffractometer using CuK\(\alpha\) radiation) were also performed.
## 2 Materials and Methods
### Identification of Endophytic and Rhizosphere Bacterial Strains  
Endophytic and rhizosphere bacterial strains were identified based on 16S rRNA gene sequencing. Genomic DNA was isolated using the Higher PurityTM Bacterial Genomic DNA isolation kit (Canvax). The 16S ribosomal RNA (rRNA) gene was amplified by PCR using 27F (5\({}^{\prime}\)-AGA GTT TGA TCM TGG CTC AG-3\({}^{\prime}\)) and 1512R (5\({}^{\prime}\)-CGG CTA CCT TGT TAC GAC TT-3\({}^{\prime}\)) [37]. The PCR was performed with the Taq PCR Master Mix (Thermo Scientific, Bioportugal, Portugal). The PCR thermal cycling was conducted with an initial denaturation at 94 \({}^{\circ}\)C for 5 min, followed by 30 cycles of denaturation (94 \({}^{\circ}\)C for 45 s), annealing (56 \({}^{\circ}\)C for 45 s) and extension (72 \({}^{\circ}\)C for 1 min 30 s). The final extension phase was conducted at 72 \({}^{\circ}\)C for 10 min. Amplification products were sequenced using Primer 27F in a Sanger sequencing process at Eurofins (Germany). The obtained sequences were subjected to chimera removal, alignment and clustering into operational taxonomic units (OTUs), with a distance limit of 0.03 (97% similarity), following the tutorial for16S rRNA data of the unsupervised method, available from the Ribosomal Database Project (RDP) [38]. An OTU table was built with abundances for the five sampled sites. The taxonomic classification of all the isolates as well as for a representative of each OTU was determined based on the best matching 16S rRNA gene sequences of reference sequences from the National Centre of Biotechnology Information website (NCBI), using blast against the Reference RNA sequences Database (refseq_rna) [39] and the EzBioCloud server [40]. The nucleotide sequence of the representative sequence of each OTU is available in the Genebank Database under the accession numbers MT981726-MT981766.
# Evaluating the Efficacy of a Passive Exoskeleton for Enhancing Ergonomics in Manufacturing
## 3 Results and Discussion
### BExo Impact on Body Parts  
The comparison for the average sum of PPE with and without the use of an exoskeleton across different body parts is shown in Figure 4. The BExo significantly reduced the perceived physical exertion for several body parts. For the lower back, the reduction was substantial, with a decrease of 22.67 points, indicating a major alleviation of exertion. The knee showed a notable reduction of 9.33 points, and the shoulder a significant drop of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Source** & **DF** & **Sum of Square** & **Mean Square** & **F-Ratio \({}^{*}\)** \\ \hline Support & 1 & 889.3500 & 889.350 & 8.0678 \\ Error & 58 & 6393.6333 & 110.235 & \\ C. Total & 59 & 7282.9833 & & \\ \hline \hline \end{tabular} \({}^{*}\)Corresponding \(p\)-value is 0.0062 at \(\alpha\) = 0.05 level.  
\end{table}
Table 2: ANOVA of PPE for exoskeleton support.  
Figure 3: Participants’ average PPE per support type.  
10 points. The upper back experienced a reduction of 9 points, while the ankle and wrist showed decreases of 6.66 and 5.33 points, respectively. The feet and elbow also benefited from the exoskeleton, with reductions of 4 and 3.67 points, respectively.  
In contrast, the chest experienced a slight increase in PPE with the exoskeleton, with a rise of 3 points, suggesting that the exoskeleton might add some load to this area. The neck showed a minimal reduction in PPE, with a decrease of only 1.34 points, indicating that the exoskeleton's impact on the neck was not as pronounced. Overall, the exoskeleton appeared to be effective in reducing physical exertion in most body parts, particularly in the lower back, knee, and shoulder, while having less significant or slightly adverse effects on others like the chest and neck.
## 2 Materials and Methods
### Data Preprocessing  
The analysis software was developed in Matlab programming language (The MathWorks, Inc., Natick, MA, USA). The structure of the analysis framework is depicted in the block diagram of Figure 1. The input data of the software consisted in picrosirius red stained tissue section images acquired in PLM, using a LP-S-LP configuration. For a batch of samples composed by N sections, bright-field images, \(A_{p}^{i}\ i=1\ldots N\), and the dark-field images, \(A_{c}^{i}\ i=1\ldots N\), were obtained setting the polarizers in parallel and crossed configurations, respectively. For each batch, two white images in parallel (\(A_{p}^{0}\)) and crossed (\(A_{c}^{0}\)) configurations were also acquired with a white plate inserted (i.e., without any sample). The images were saved as uncompressed 16 bit/channel TIFF files, in RGB photometric representation (i.e., each image corresponded to a _mxmx3_ matrix). The light intensity, or radiant flux, associated with the image files was obtained by converting the original RGB images into double precision, linearizing the pixel intensity by gamma correction, and normalizing for the exposure times, according to:  
\[I_{p}^{i}=\frac{A_{p}^{i\ \gamma}}{t_{p}},\ I_{c}^{i}=\frac{A_{c}^{i\ \gamma}}{t_{c}}\qquad i=0\ldots N \tag{7}\]  
where \(t_{p}\) and \(t_{c}\) are the image exposures in the parallel and crossed configurations, respectively, and a camera gamma encoding of \(\frac{1}{\tau}\) was assumed. The use of the radiant flux, instead of the original images, allowed us to control for the variability related to variations in exposure time among different acquisition sessions (see Section 2.6). The linearized light intensity images \(I_{p}^{i}\) and \(I_{c}^{i}\) were subsequently processed to obtain a signal representative of collagen density in the tissue, independent from experimental confounding variables, such as spatial nonuniformity of illumination in each plate and variations of light source intensity, sample section thickness, and tissue area between different plates.  
Figure 1: Block diagram of the model-based collagen segmentation framework. The framework input data are bright-field and dark-field tissue images. Both the bright- and dark-field images concur to compute the tissue mask (_mask\({}_{tis}\)_), the transmittance signal (\(T_{s}^{i}\)), the average optical thickness (\(\mu_{(r)}L^{i}\)), and the grayscale collagen signal (\(S_{col}^{i}\)), which is thresholded to obtain the collagen mask (_mask\({}_{col}\)_). The tissue and collagen masks are combined to obtain the collagen fraction (\(c^{i}\)) as output. See text for intermediate step details.
# Allelopathic Activity of Leaf Wastes of _Lriodendron tulipifera_ for Sustainable Management
## 2 Materials and Methods  
### Plant Materials  
The fallen and fresh leaves of _L. tulipifera_ were collected on the campus of Kagawa University, Faculty of Agriculture, on November 2020 and July 2021, respectively. The fresh leaves are substitutes for the waste from logging operations of _L. tulipifera_ trees. The fallen and fresh leaves were dried under shade conditions. Weed species--_Vulpia myuros_ (L.) [PERSON], _Echinochla crus-galli_ (L.) [PERSON], and _Lolium multiformum_ Lam.--were used as bioassay test plant species for the determination of the allelobathic potential of _L. tulipifera_ fallen and fresh leaves. A dicotyledonous plant species, _Lepidum sativum_ L., was used as a bioassay plant through a bioassay-guided separation process of allelochemicals, because the species is a crop plant and shows a high as well as stable germination rate.  
Determination of the Allelobathic Activity of L. tulipifera Fresh and Fallen Leaves under Laboratory Conditions  
The fresh leaves of _L. tulipifera_ were extracted, and the allelobathic activity of the extracts was determined by the procedure described in the published papers [48]. Test plant seeds were germinated in the dark at 25 \({}^{\circ}\)C for 48 h, and 10 germinated seeds of _V. myuros, E. crus-galli_, and _L. multiformum_ were incubated in Petri dishes with the extracts in darkness at 25 \({}^{\circ}\)C. After 48 h incubation, the length of the coleoptiles and roots of these weed species was determined. Petri dishes contained the extracts obtained from 1, 3, 10, 30, or 100 mg _L. tulipifera_ leaves per 1 mL of the assay solution. The experiment was independently repeated four times with 10 test plants for each determination. The fallen leaves of _L. tulipifera_ were extracted, and the allelobathic activity of the extracts was determined with the same procedure as for the fresh leaves.  
The allelobathic activity of the fresh and fallen leaf extracts of _L. tulipifera_ was also evaluated using _L. sativum_. The _L. sativum_ bioassay was carried out with the same procedure described for the weed species except the pre-germination process. _L. sativum_ seeds (not germinated) were incubated with the leaf extracts of _L. tulipifera_.
# Stochastic Modeling of Smartphones GNSS Observations Using LS-VCE and Application to Samsung S20
## 2 Stochastic Modeling of Smartphone Observations Using Least-Square Variance Component Estimation  
### Least-Squares Variance Component Estimation (LS-VCE)  
Let us assume the linear(ized) model of observation equations as  
\[E(y)=Ax;\ \ \ D(y)=Q_{y}=\sum_{k=1}^{p}\sigma_{k}Q_{k} \tag{1}\]  
where \(E\) and \(D\) are the expectation and dispersion operators, respectively, \(y\) is the \(m\)-vector of observations, \(x\) is the \(n\)-vector of the unknown parameters, \(A\) is the \(m\times n\) design matrix, and \(Q_{y}\) is the \(m\times m\) covariance matrix of the observables. The structure of the covariance matrix \(Q_{y}\) is expressed as an unknown linear combination of some known cofactor matrices. The estimation of these unknown (co)variance parameters \(\sigma_{k};k=1,2,\ldots,p\) is referred to as variance component estimation (VCE). There exist many VCE methods and among them we will make use of the least-square variance component estimation (LS-VCE). The unknown (co)variance components are estimated as \(\sigma=\begin{bmatrix}\sigma_{1}&\ldots&\sigma_{p}\end{bmatrix}^{T}=N^{-1}l\), in which the component of the matrix \(N_{p\times p}\) and vector \(l_{p\times 1}\) are as follows [24]:  
\[n_{ij}=\frac{1}{2}trace\Big{(}Q_{i}Q_{y}^{-1}P_{A}^{\perp}Q_{j}Q_{y}^{-1}P_{A }^{\perp}\Big{)} \tag{2}\]  
and  
\[l_{i}=\frac{1}{2}trace\Big{(}\hat{e}^{T}Q_{y}^{-1}Q_{i}Q_{y}^{-1}\hat{e}\Big{)} \tag{3}\]  
where \(trace\) is the sum of the diagonal elements of a matrix, \(P_{A}^{\perp}=I_{m}-A(A^{T}Q_{y}^{-1}A)^{-1}A^{T}Q_{y}^{-1}\) is an orthogonal projector, \(I_{m}\) is an \(m\)-by-\(m\) identity matrix, and \(\hat{e}=P_{A}^{\perp}y\) denotes the \(m\)-vector of residuals. For more details about the LS-VCE method, the reader may refer to [21; 22; 23; 24].
## 6 Conclusions  
In order to realize dynamic spectrum resource allocation based on service types, this paper proposes a link priority-based spectrum resource allocation scheme based on reinforcement learning to maximize the total capacity of V2V and V2I links and minimize the received interference of priority links to achieve optimal spectrum allocation. Simulation results show that this method effectively solves the complex spectrum of on-board network resource allocation problems, maximizes V2V and V2I link capacity--the V2V link rate is 2.54 times that of the traditional random spectrum allocation scheme, and the V2I link rate is 13.5% higher than that of the traditional random spectrum allocation scheme--reduces the priority link interference by 14.2 dB relative to the common link and not only provides the high priority link the transmission quality of service guarantee, but there is no harm to ordinary link performance; in addition, the scheme of communication has good noise robustness and better universality in the actual scene. In the future, the proposed scheme will also be improved, and further in-depth research will be carried out in terms of diversified business types and distributed decision control so as to explore resource scheduling and access control technologies that are more suitable for future high-dynamic complex vehicle-mounted networks.  
Conceptualization, Z.G. and M.H.; Formal analysis, M.H.; Funding acquisition, Z.G.; Investigation, Y.W.; Methodology, Z.G. and M.H.; Project administration, Z.G.; Resources, Y.W. and M.H.; Visualization, Y.W.; Writing--original draft, Y.W.; Writing--review & editing, [PERSON] and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China (NSFC), grant number 61761045.  
The authors declare no conflict of interest. The funder had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.