esa-sceva/satcom-chunk-collection huggingface dataset:
# ABPCaps: A Novel Capsule Network-Based Method for the Prediction of Antibacterial Peptides
###### Abstract  
The emergence of drug resistance among pathogens has become a major challenge to human health on a global scale. Among them, antibiotic resistance is already a critical issue, and finding new therapeutic agents to address this problem is therefore urgent. One of the most promising alternatives to antibiotics are antibacterial peptides (ABPs), i.e., short peptides with antibacterial activity. In this study, we propose a novel ABP recognition method, called ABPCaps. It integrates a convolutional neural network (CNN), a long short-term memory (LSTM), and a new type of neural network named the capsule network. The capsule network can extract critical features automatically from both positive and negative samples, leading to superior performance of ABPCaps over all baseline models built on hand-crafted peptide descriptors. Evaluated on independent test sets, ABPCaps achieves an accuracy of 93.33% and an F1-score of 91.34%, and consistently outperforms the baseline models in other extensive experiments as well. Our study demonstrates that the proposed ABPCaps, built on the capsule network method, is a valuable addition to the current state-of-the-art in the field of ABP recognition and has significant potential for further development.  
antibacterial peptide; capsule network; feature extraction; deep learning; antibiotic resistance +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Effect of Biosurfactant on Contact Angle of Grapes  
Contact angle (\(\theta\)) varies depending on the relationship between the different adhesion and cohesion forces between the drop of water and grapes. If the adhesion forces are high, the contact angle that forms between the drop and the surface of the grapes should be less than 90\({}^{\circ}\), as the liquid is attracted by the solid, promoting the liquid expansion on the grape. However, if the attraction forces between the grape surface and the liquid are low, the cohesive hardness promotes liquid contraction on the grape surface. In this case, grapes coated with the biosurfactant extract solution, after one day of storage, gave an average contact angle for a drop of water of about 89.6\({}^{\circ}\), while grapes without treatment gave an average contact angle of 81.1\({}^{\circ}\). The contact angle of the grape surface is similar to the contact angle of apple (81.0\({}^{\circ}\)) and lower than the contact angle of tomato (95.0\({}^{\circ}\)) and persimmon (88.0\({}^{\circ}\)), as reported by [PERSON] et al. [24]. When grapes were coated with the biosurfactant extract solution, an increase in the hydrophobicity of the grape surface was observed, which decreases its wettability. The biosurfactant extract evaluated in this work is composed of C16-C18 fatty acids and amino acids with hydrophobic side chains, including alanine and leucine/isoleucine, which could explain the increase in the hydrophobicity of the grape surface with the addition of the biosurfactant extract.  
Figure 5: 2D optical real images and topographical maps of non-coated (**a**) and coated (**b**) grapes after 11 days of storage, with a field of view of 400 \(\times\) 400 μm2.  
Figure 6 shows the values of contact angle for a drop of water when it is added on the surface of grapes coated with biosurfactant after different storage times.  
Furthermore, Figure 7 shows the images of the drop of water on the grape surface under different conditions and storage times, which show the increase in the roughness with the storage time. After applying the T-test to pairs of data, no significant differences (\(p=0.05\)) were observed between 3-5 days and also between 5-11 days.  
Figure 6: Contact angle (\(\theta\)) of coated grapes at different storage times. Different Latin letters indicate significant differences among days (\(T\)-test; \(p=0.05\)).  
Figure 7: _Cont_.  
It was observed that grapes with a longer storage time had a higher contact angle because the contact angle can be affected by the surface roughness. Hence, [PERSON] et al. [25] found that adding roughness to a hydrophilic surface increases its wettability and reduces the contact angle. However, if a surface is chemically hydrophobic, it would increase its hydrophobic character by increasing its roughness. These results are in agreement with the increase in the contact angle observed in those coated grapes with more storage time, which possess a higher roughness. For instance, coated grapes after 11 days of storage had an average contact angle of 106.6\({}^{\circ}\).
# Statistical Modeling of NaCl and FeSO4 Pretreatment Effect on Refractory Copper Ore Leaching
## 4 Conclusions  
The present investigation focused on the dynamics of a black copper reductive leaching process, examining how time, AAC, NaCl, and FeSO\({}_{4}\) influence sulfation. The key findings can be summarized as follows:  
* The study successfully modeled complex sulfation dynamics associated with the pretreatment of copper ones with black oxides, utilizing multivariate nonlinear regression models;
* The experimental design and response surface methodology proved valuable tools for analyzing sulfation behavior in regards to variations in independent variables;
* An exploratory analysis, validated by main effects analysis, revealed that the relationships between the independent variables--analytical acid consumption (AAC), NaCl, and FeSO\({}_{4}\)--are directly proportional to sulfation across the entire sampled domain;
* Time was found to have a statistically significant effect only within the 24-48 h variation, with no significance observed in other comparisons;
* Analytical acid consumption exhibited a consistent positive gradient for sulfation throughout the domain, maintaining statistical significance at all levels sampled;
* While the presence of NaCl was statistically significant, the substantial effect was noticed when comparing the tests without chloride and with the maximum addition of NaCl;
* No statistically significant differences were detected among the levels of FeSO\({}_{4}\) sampled;
* The regression model developed to represent sulfation demonstrated a beneficial statistical fit, with a marginal difference between the fitted and predicted coefficients of determination, indicating its reliability as a predictive tool;
* The model optimization identified optimum sulfation values around 60 h, with an analytical acid consumption of 60%, 90 g of NaCl, and 42 g of FeSO\({}_{4}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Optimal Value** \\ \hline \(x_{1}\) & 60.14 \\ \(x_{2}\) & 60 \\ \(x_{3}\) & 90 \\ \(x_{4}\) & 42 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Values of the independent variables at the optimal level.  
**Author Contributions:** Conceptualization, M.S., E.G., and J.C.; methodology, R.S. and E.G.; software, M.S., M.M., and I.C.; validation, R.S., N.T., and A.G.; formal analysis, M.M. and I.C.; investigation, R.S. and J.C.; resources, R.S. and E.G.; data curation, R.S.; writing--original draft preparation, M.S. and J.C.; writing--review and editing, A.G., E.G., and N.T.; visualization, N.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available upon request from the corresponding author. The data are not publicly available due to privacy constraints.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
## 3 Results
### Statistical Patterns of Bedform Features
#### 3.1.2 Dune Height Variation Characteristics  
The probability density distribution of dune height, as fitted by the program, is shown in Figure 7. Panels (a) and (b) of Figure 7 depict the distributions of dune heights (\(\Delta s\) and \(\Delta l\)) for the 2D bedforms, and panels (c) and (d) illustrate the distributions for the 3D bedforms. The results reveal that the dune heights (\(\Delta s\) and \(\Delta l\)) for the 2D bedforms follow a Gamma distribution, whereas those for the 3D bedforms follow a lognormal distribution. The distribution parameters for the dimensionless dune heights of the 2D and 3D bedforms are detailed in Table 3. For the 2D bedforms, the Gamma distributions of \(\Delta s\) and \(\Delta l\) exhibit shape parameters of 15.185 and 15.610, respectively, and scale parameters of 0.066 and 0.064, respectively. The shape and scale parameters for \(\Delta s\) are similar to those for \(\Delta l\). The mean and standard deviations corresponding to the lognormal distributions of \(\Delta s\) for the 3D bedforms exhibit characteristics similar to those of \(\Delta l\).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**2D Bedforms**} & \multicolumn{3}{c}{**3D Bedforms**} \\ \cline{2-7}
**Statistic** & **Distribution** & \multicolumn{2}{c}{**Distribution Parameters**} & \multicolumn{2}{c}{**Distribution Parameters**} \\ \cline{3-7}  & **Form** & **Log Mean (\(\mu\))** & \
\begin{tabular}{c} **Log Standard** \\ **Deviation (\(\sigma\))** \\ \end{tabular} & **Distribution** & \multicolumn{2}{c}{**Form**} & **Log Mean (\(\mu\))** &
\begin{tabular}{c} **Log Standard** \\ **Deviation (\(\sigma\))** \\ \end{tabular} \\ \hline _Lc_ & Lognormal & \(-\)0.205 & 0.595 & Lognormal & \(-\)0.091 & 0.432 \\ _Lt_ & Lognormal & \(-\)0.150 & 0.524 & Lognormal & \(-\)0.097 & 0.446 \\ _Lupcr_ & Lognormal & \(-\)0.190 & 0.580 & Lognormal & \(-\)0.115 & 0.488 \\ _Ldowncr_ & Lognormal & \(-\)0.247 & 0.622 & Lognormal & \(-\)0.096 & 0.445 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of dune length distributions for different bedforms.  
Figure 7: Probability density distribution of dune height. (**a,b**) probability density distributions of dune height of 2D bedforms, (**c,d**) probability density distributions of dune height of 3D bedforms.
## 3 Results and Discussion
Relative to the chain extension with St, the apparent rate constant in the case had increased by over an order of magnitude (\(k_{\rm app}=1.42\times 10^{-4}\) s\({}^{-1}\)), with a gradual evolution of the MW in the GPC traces (Figure 10b). The initiation efficiency was 0.98 based on the molecular weight at 7 h. These results illustrate our ability to perform a controlled/living polymerization to form an hbPSt-\(g\)-P\(t\)BA hyperbranched core star polymer (\(M_{\rm n}\): 27,000; \(D\): 1.98). Figure 10: **(a)** Kinetic plot of Ln(\(M_{0}\)/\(M\)) _vs._ time for the chain extension of hbPSt MI with \(t\)BA in A at 100 \({}^{\circ}\)C and **(b)** GPC traces (eluent: THF; \(t\)BA/hbPSt/CuCl/CuCl\({}_{2}\)/PMDETA = 200/1/1/0.1/1.1; [\(t\)BA]\({}_{0}\) = 3.5 M). Figure 9: **(a)** Kinetic plot of Ln(\(M_{0}\)/\(M\)) _vs._ time for the chain extension of hbPSt MI with St in A at 100 \({}^{\circ}\)C and **(b)** GPC traces (eluent: THF; St/hbPSt/CuCl/CuCl\({}_{2}\)/PMDETA = 200/1/0.9/0.1/1; [St]\({}_{0}\) = 4.3 M). We performed the hydrolysis of hbPSt-g-Pr/BA under basic conditions to obtain an hbPSt-g-PAA amphiphilic star polymer, transforming the outer segment from hydrophobic to hydrophilic (Scheme S2). Figure 11 presents the \({}^{1}\)H NMR spectra of the hbPSt-g-PtBA (in CDCl\({}_{3}\)) and the hbPSt-g-PAA (in D\({}_{2}\)O). Most significantly, we observe the disappearance of the signal for the _tert_-butyl groups (_i.e._, peak c). FT-IR spectra revealed (Figure S4) that the signals for the ester linkage (_ca._ 1725 cm\({}^{-1}\)) and ether linkage (_ca._ 1250 cm\({}^{-1}\)) had vanished, with the appearance of a broad signal (_ca._ 3380 cm\({}^{-1}\)) for carboxylic acid moieties. Together, these spectra confirmed the hydrolysis of hbPSt-g-Pr/BA to form hbPSt-g-PAA. The hydrolyzed star polymer hbPSt-g-PAA, with hydrophobic inner and hydrophilic outer regions, readily dissolved in MeOH with high transparency. Thus, performing polymerizations at a suitable concentration can provide amphiphilic star polymers, with nanoscale dimensions, that can be dispersed well in highly polar solvents--an attractive property for the applications of such polymers as nanocarriers and nanoreactors and for drug delivery. Hence, our facile catalyst-phase-switching mechanism, from AT-FRP to AT-SCVP, in one-pot can be performed successfully to obtain branched polymer topologies. Indeed, we have demonstrated the synthesis of hyperbranched core star polymers, as well as the ability to transform the chemical structure of the outer compartment.
# Research on Temperature Field Analysis Method of High-Speed Bearing Chamber-Bearing System
## 2 Model and Method  
### Geometric Model of Bearing Chamber-Bearing System  
The geometric model of the bearing chamber-bearing system employed in this article consists of four ball bearings, sequentially numbered 1, 2, 3, and 4 from left to right along the axial direction, as depicted in Figure 1. The structural parameters for these four ball bearings are identical, as specified in detail in Table 1. The system is equipped with three distinct oil supply paths. Lubricating oil is delivered to the left and right annular cavities via two supply routes located at the bottom of the system. Each annular cavity is fitted with two oil injection nozzles, which provide jet lubrication to Bearings 1 and 4, respectively. The oil supply path on the side of the system diverts the lubricating oil into an oil separator, which divides the oil into two streams. One of these streams individually lubricates Bearings 1 and 2 through two injection nozzles that face opposite directions. Similarly, the other stream is directed to lubricate Bearings 3 and 4.  
After being jetted into the bearings, the lubricating oil is ejected from the inner and outer rings and the cage of the bearings due to the centrifugal force generated by their high-speed rotation. Within the bearing chamber, the lubricating oil forms droplets of varying diameters as it encounters resistance from the air-phase medium within the chamber and its own surface tension. These oil droplets are propelled toward the walls of the bearing chamber at high speeds, where they interact with the air in the chamber to create a complex oil-air two-phase flow. Ultimately, the oil droplets are discharged from the chamber and out of the system through an oil scavenging channel located at the bottom of the system.
# Toward Generative AI-Based Intrusion Detection Systems for the Internet of Vehicles (IoV)
## 2 Background
### IoV Communication Architecture
The Control and Management Layer coordinates services within the IoV environment, applying traffic policies and inspection mechanisms to optimize information flow. In the Processing Layer, large volumes of data are analyzed either locally or via cloud computing to enhance services and inform infrastructure planning. Finally, the Security Layer acts transversally across all other layers, implementing functions such as authentication, access control, integrity checks, and cyber threat mitigation to ensure system-wide trust and resilience.
# Research Progress and Applications of 2D Antimonene
## 2 Theoretical Research and Experimental Methods
### Mechanical Exfoliation  
In order to obtain high quality single or multilayer nanocrystals from natural multilayer structures or substrates, mechanical exfoliation and transfer techniques have been developed, which involve interfacial exfoliation and intraformational tearing processes [38]. In two-dimensional material exfoliation, this method has also received much attention.  
[PERSON] et al. [39] prepared single-layer and few-layer antimonene sheets by an improved mechanical exfoliation method in 2016. The antimonene was first transferred by tape to a thin layer of an elastic polymer. The softness of the viscoelastic polymer resulted in a higher yield of thin sheets on the polymer surface. The process and appearance of the stripped antimonene by pushing the polymer onto the silicon oxide substrate are shown in Figure 2. The average thickness of the antimonene is in nanoscale and the antimonene has  
Figure 1: Reflectance of a layer of antimonene and antimonite material: (**a**) reflectance spectra; (**b**) absorption spectra (upper right illustration is the energy band value of single–layer antimonene estimated by the “Tauc” method). Reprinted with permission from Ref. [18]. Copyright 2021, Science China Press.  
stepped properties. It is also proved that the antimonene is of the \(\beta\)-phase structure with good stability.  
Since few-layer antimonene cannot be measured by Raman spectroscopy, [PERSON] et al. [40] proposed to measure few-layer antimonene using optical microscopy and atomic force microscopy, to measure the thin slices using wavelengths in the visible spectrum, and then quantitatively interpret the optical contrast using a model based on Fresnel's law to obtain the refractive index and absorption coefficient of these thin crystals in the visible spectrum. Thus, the optical microscope data can be quantitatively analyzed to determine the thickness of the sheet in a rapid and nondestructive manner.  
[PERSON] et al. [41] prepared antimonene nanosheets with a thickness of 14 nm by mechanical exfoliation method in 2020. First, the high-viscosity film is used to gently press the new cutting surface of antimony crystal, then the mucous membrane is pressed on the same mucous membrane, the process is repeated, and then the adhesive film containing thin antimony microcrystals is pressed on the SiO\({}_{2}\)/Si substrate.
## 4 Numerical Optimization Experiments
### Experimental Settings  
All simulation experiments in this paper were performed on a PC independently, which had the following configuration: operating system, 64-bit Windows 10; CPU, Intel Core i5-8300 2.30 GHz, and simulation software, MATLAB R2012a. The specific information and parameter settings of the examined method are presented in Table 2. In this paper, 30 simulation experiments were carried out for each benchmark function with different dimensions, as listed in Table 1, and the average values (Mean), as well as the standard deviations (S.D.), are shown in Table 3. Meanwhile, all algorithms were sorted in accordance with its mean, and the rank results are presented in Table 3. Moreover, the population size and the maximum iteration numbers of all numerical optimization experiments were set to 40 and 500, respectively.
# A Clustering WSN Routing Protocol Based on k-d Tree Algorithm
## 2 Related Work
### Cluster Formation  
In WSNs, cluster formation is a technique that allows the classification of nodes in groups or clusters so that every node in a cluster shares a certain degree of homogeneity regarding the techniques, rules or heuristics on which the selection is based.  
In the majority of distance-based cluster formation algorithms for WSNs, it is assumed that the nodes of the network know their a priori location in the plane. However, the literature includes proposals where nodes transmit their distance to a Base Station node (BS) via either Received Signal Strength Indicator (RSSI) [3] or geo-positioning by using the Global Positioning System (GPS) [4].  
The main objective of partition clustering algorithms in WSNs is to divide the nodes into k partitions, according to their position. Each partition is considered a cluster. The partitioning technique will depend on an objective function. The most representative clustering mechanisms employed in WSNs are k-means [5; 6; 7], fuzzy c-means [8], k-medoids [4; 9].  
The hierarchical grouping method in a WSN tries to build a tree-based network topology mainly derived from the position of each node. This grouping process is represented by topologies with two or more jumps to a BS node. Some of the most representative algorithms are LEACH [10], LEACH variations [11; 12; 13].  
Density-based cluster formation methods assume that nodes in each group are extracted from a probability distribution in relation to the total number of nodes in the network. Some works in this area are: to maximize the network lifetime [14; 15], adaptive clustering [16], and density-based fuzzy imperialist competitive clustering algorithm [17]. The approaches of cluster formation based on cooperation have provided solutions to the problem of energy management using the information of the energy spectrum detected [18].
# Prism-Based Spatial Heterodyne Spectrometer with a Fixed Fringe Localization Plane
## 2 Principles and Designs
### Performance Parameters
In the simulation in this paper, six tuning steps are required to achieve detection in the range of 0.39 \(\upmu\)m to 1 \(\upmu\)m: the angles of the mirror tilt with respect to its outgoing optical axis are 20.99\({}^{\circ}\), 20.51\({}^{\circ}\), 20.01\({}^{\circ}\), 19.44\({}^{\circ}\), 18.78\({}^{\circ}\), and 18.19\({}^{\circ}\), respectively corresponding to Littrow wavelengths of 0.398 \(\upmu\)m, 0.420 \(\upmu\)m, 0.450 \(\upmu\)m, 0.500 \(\upmu\)m, 0.600 \(\upmu\)m, and 0.800 \(\upmu\)m. The bimirror is tilted at twice the angle of the mirror to ensure that its outgoing optical axis is aligned with the incoming optical axis of the mirror. Finally, consider the usable FOV. In SHS, the limiting off-axis angles are reached when the OPD at the edge of the fringe pattern changes by 1/2 wavelength. Since the usable FOV is negatively correlated with the spectral resolving power, the usable FOV when the incident light is at the wavelength with maximum spectral resolving power (0.39 \(\upmu\)m)  
\[OPD_{\rm max}=N\lambda_{1}=2d(\sigma_{0})\sin\beta_{1} \tag{6}\]0 Theoretical spectral resolving power curve of the TSHS with a prism–bimirror–mirror structure for the given parameters. determines the usable FOV of the instrument. By setting the conditions to get a series of solutions and fitting the relationship between \(\phi\) and \(\varphi\),  
\[OPD_{\rm max}=N\lambda_{1}=2d(\sigma_{0})\sin\beta_{1} \tag{6}\]1  
where \(\phi\) denotes the angle between the on-axis rays and the component of the off-axis rays in the dispersion plane and \(\varphi\) denotes the angle between the off-axis rays and the dispersion plane. The FOV of this system is comparable to that of the typical prism-based SHS shown in Figure 1 in cases of equal spectral resolving power.
## 4 The Evaluation Results and Discussions
### The Experimental Setup
#### 4.1.1 Data Corpus  
We used the well-known TIMIT corpus [37] to experiment. TIMIT contains phonemically and lexically transcribed speech utterances from American English speakers of different genders and dialects. These utterances correspond to the broadband recordings of 630 speakers of eight major dialects of American English, each speaker reading ten phonetically rich sentences. Each utterance is a 16-bit, 16 kHz sampling rate waveform file accompanied by time-aligned orthographic, phonetic, and word transcriptions. The utterances in TIMIT were used as the training and test sets in our evaluation experiments. As for the multi-condition training set, 1000 utterances were randomly selected and corrupted with any of three noise types (babble, car, and street) at five **signal-to-noise ratio (SNR)** levels (\(-\)5 dB, 0 dB, 5 dB, 10 dB, and 15 dB). For the test set, 400 utterances different from the training set were selected and corrupted with any of three noise types (white, engine, and jackhammer) at six SNR levels (\(-\)6 dB, \(-\)3 dB, 0 dB, 3 dB, 6 dB, and 12 dB). In the experiments, we tested the utterances with respect to each noise type and each SNR level to see the detailed effects of the evaluated methods.