esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiment
### Metrics  
To evaluate our proposed network's ICH segmentation capabilities, we employed commonly utilized metrics, including the Dice similarity coefficient, Jaccard index, precision, and sensitivity. Due to the dominance of background categories, TN values may be excessive in the forecast. Consequently, accuracy and specificity metrics are unsuitable for handling unbalanced data. The small lesion area renders these metrics inapplicable as evaluation indicators, yielding near 0.99 values with no practical reference value. The Dice coefficient, prevalently used in medical segmentation, is sensitive to both false negatives (FNs) and false positives (FPs). Both Dice and Jaccard similarity coefficients range from 0 to 1, with values closer to 1 indicating superior segmentation outcomes.  
\[\text{Dice}=\frac{2 TP}{2 TP+FN+FP} \tag{11}\]  
\[\text{Precision}=\frac{TP}{TP+FP} \tag{12}\]  
\[\text{Sensitivity}=\frac{TP}{TP+FN} \tag{13}\]  
\[\text{Jaccard}=\frac{TP}{TP+FP+FN} \tag{14}\]  
Among them, TP, TN, FP, and FN represent the true positive rate, true negative rate, false positive rate, and false negative rate of classified pixels, respectively.
# DKsA Modulates Antimicrobial Susceptibility of _Acinetobacter baumanii_
## 4 Materials and Methods
### Construction of the \(\Delta\)dksA Mutant of 1656-2 Strain  
The \(\Delta\)_dksA_0298_ gene of clinical _A. baumaniii_ 1656-2 strain, corresponding to the _A1S_0248_ gene of _A. baumaniii_ ATCC 17978, was deleted by a markerless gene deletion method [27]. Genomic DNAs purified from _A. baumaniii_ 1656-2 and pFL02 were used as polymerase chain reaction templates for the amplification of _dksA_ and erythromycincin resistance cassettes, respectively. The upstream and downstream regions of _dksA_ were combined with an erythromycincin resistance cassette through overlap extension PCR using specific primers with a ProFlex PCR system (Applied Biosystems, Foster City, CA, USA) (Supplementary Table S1). This mutated DNA fragment was ligated into _Apal_-digested pDM4. The pDM4 carrying the mutated DNA fragment was inserted into the chromosome of _A. baumaniii_ 1656-2 strain by transformation using Gene Pulser Xcell (Bio-Rad, Hercules, CA, USA) and homologous recombination (Supplementary Figure S1A). The \(\Delta\)_dksA_ mutant of _A. baumaniii_ 1656-2 was named NY0298D (Table 2).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Bacteria/Plasmids** & **Relevant Characteristics** & **Reference of Source** \\ \hline _A. baumaniii_ & & \\ ATCC 17978 & Wild-type strain & ATCC \\ KM0248D & \(\Delta\)_A1S\_0248_ of _A. baumaniii_ ATCC 17978 & [25] \\ KM0248C & _A1S\_0248_ with T1 terminator in & [25] \\ KM0248D & & \\
1656-2 & Clinical isolate & [21] \\ NY0298D & \(\Delta\)_ABK1\_0298_ of _A. baumaniii_ 1656-2 & This study \\ Plasmids & & \\ pDM4 & Suicide vector, _ori_ R6K; Cm\({}^{x}\); sacB & GenBank accession no. \\  & pWH1266 with _amTA_ coding region and & KC795686 \\ pFL02 & its promoter less _nptl_, and origin of & [26] \\  & replication with _ermAM_; Km\({}^{x}\), Ery\({}^{x}\) & \\ \hline \hline \end{tabular} Abbreviations: Cm\({}^{x}\), chloramphenicol-resistant; Km\({}^{x}\), Kanamycin-resistant; Ery\({}^{x}\), erythromycincin-resistant.  
\end{table}
Table 2: Bacterial strains and plasmids used in this study.
# Text Classification Model Based on Graph Attention Networks and Adversarial Training
## 4 Conclusions  
This paper combines the advantages of graph attention networks (GATs) and adversarial training to propose a text classification model that employs TextCNN for extracting sequential text information and GATs for capturing the semantic information of textual context, leading to a richer integration of information. The model incorporates an attention mechanism to allocate varying weights to the global sequence representation. Experimental results demonstrate that the proposed text classification model achieves commendable results on both multi-class and binary classification datasets. With the inclusion of additional auxiliary features, the model can extract more features beneficial for classification, thereby enhancing its representational power. This study can help businesses understand consumer emotions and market trends, support decision-making, and optimize market strategies by categorizing social media comments, customer feedback, and product reviews.  
There are still some limitations to the model in this article: the model is specifically developed for Chinese and may not be applicable to other languages, especially those with different syntactic structures and word orders, such as Japanese, Korean, Arabic, etc. The model needs to consider complex character combinations and contextual relationships when processing Chinese text, resulting in high computational complexity and resource consumption. There are many polysemous and ambiguous words in Chinese, which poses additional challenges to text classification. The model may require more contextual information to accurately classify.  
One of the future research directions is to focus on multimodal analysis, which combines multiple data sources, such as images and videos, for comprehensive analysis. The combination of text, images, and videos can provide richer information sources and improve the accuracy of classification models. For example, in news classification, textual content can be analyzed together with relevant images and videos to obtain a more comprehensive understanding. Multimodal analysis faces challenges such as data alignment, feature extraction, and computational complexity. Future research needs to develop more efficient algorithms and models to address these challenges and fully leverage the advantages of multimodal data.  
Conceptualization, J.L.; Software, J.L.; Data curation, J.L.; Writing--original draft, J.L. and Y.J.; Writing--review and editing, J.L and Y.X. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# Strategies for Dielectric Contrast Enhancement in 1D Planar Polymeric Photonic Crystals
## 2 Background
As discussed in the next paragraph, these numbers determine the intensity and spectral width of the peaks assigned to the PBG which, in accordance with the theory [37], increase as the dielectric contrast grows.
# Sedimentation of microparticles in Highly Concentrated Non-Newtonian Emulsions
## 3 Results
### Effect of Emulsion Parameters on the Sedimentation Process of Solids
Figure 11 shows plots of the change in sediment height (a) and change in sedimentation velocity (b) over time with the descent of 0.3 g/mL microspheres with a diameter range of 300-400 \(\upmu\)m for 70% emulsions with different oil droplet sizes. As can be observed from the graphs presented in Figure 11, the speed of the sedimentation process in emulsions is directly related to the size of the oil droplets. For the smallest  
Figure 11: Plot of (**a**) sediment layer height and (**b**) sedimentation velocity versus process time for sedimentation of 0.3 g/mL microspheres in the size range 300–400 \(\upmu\)m in a 70% emulsion for different oil droplet diameters. Figure 10: Scheme of transport of solid particles through the emulsion structure at different emulsion concentrations: (**a**) low emulsion concentration, (**b**) medium emulsion concentration, and (**c**) high emulsion concentration. droplets d\({}_{\rm e1}\) (8.27 um), the process rate was high, and the final sediment layer height was the largest. In contrast, for the largest droplets d\({}_{\rm e3}\) (15.72 um), the process was slowest, while the h\({}_{\rm sk}\) layer thickness was about four times smaller. That is, nearly doubling the average diameter of the emulsion droplets results in a fourfold increase in the sediment layer. The effect of emulsion oil droplet size on the rate of solid-particle fall is shown schematically in Figure 12.  
Assuming the same oil phase concentration, for smaller droplets, their number in the system is globally larger than for large droplets. However, smaller droplets have a greater ability to move relative to each other. For even not very high stresses induced by squeezing solid particles, the small droplets will be able to move and create free space for the particle to enter (Figure 12a). For larger droplets (Figure 12b), the stresses induced by the particles are not sufficient to move the droplets sideways and create a track for the particle to move through. In the case of very large droplets (Figure 12c), the system forms a compact structure, and the solid particles become trapped between the droplets; hence, their ability to fall is impeded. Therefore, the largest sediment layers were observed for small droplets, while small layers were observed for emulsions characterized by large droplets.
# A Real-Time Water Level and Discharge Monitoring Station: A Case Study of the Sakarya River
## 2 Materials and Methods  
The real-time river monitoring station is installed on the Orencik Bridge, located at coordinates 40\({}^{\circ}\)35\({}^{\prime}\)2.33\({}^{\prime\prime}\) N 30\({}^{\circ}\)19\({}^{\prime}\)56.21\({}^{\prime\prime}\) E on the Lower Sakarya River. As shown in Figure 1, the Orencik Bridge, located downstream of the Dogancay HPP I (hydroelectric power plant), is strategically positioned upstream of central residential areas, industrial establishments, and factories. This location is crucial for gaining lead time in flood warnings and implementing preventive measures.
# Interferometry of Gas-Phase Flows during Selective Laser Melting
## Appendix A
### Numerical Method  
The partial differential Equation (A4) written in the cylindrical coordinate system \((r,z)\) are solved numerically by the finite-volume method according to the Kurganov--Tadmore scheme [34] of the second order of approximation. The cylindrical computational domain is divided evenly along the \(r\) and \(z\) coordinates into cells. On the \(z=0\) plane, physical boundary conditions are set by equation (A4) within the evaporation spot and the adhesion condition outside the evaporation spot. On the axis, \(r=0\), the symmetry condition is set. In order to the outer plane and cylindrical boundaries influenced the central region of the flow as little as possible, two layers of ghost cells are determined outside them, in which the velocity and pressure fields are extrapolated in accordance with the asymptotics \(\mathrm{u}\approx 1/\mathrm{R}^{2}\) and \(p\approx 1/\mathrm{R}^{3}\)[35], where \(R\) is the distance from the origin. The stationary solution is found by the method of establishment. Artificial compressibility of the medium is introduced, so that the pressure \(p\) depends on the density as (A5):  
\[p=B\left(\frac{\rho}{\rho_{0}}-1\right).\] (A5)  
Here, the comprehensive compression modulus \(B\) is a parameter of the numerical method, which is chosen so that the speed of sound is much greater than the flow velocity, thereby ensuring a small deviation of the density \(\rho\) from \(\rho_{0}\).  
The calculations were made for an argon atmosphere at atmospheric pressure and a temperature of 298 K, the parameters of which are given in Table 11. Kinematic viscosity is calculated by dynamic as \(v=\eta/\rho_{0}\). The diameter of the evaporation spot is taken equal to \(D=50~{}\mathrm{\upmu m}\). The jet intensity is determined by the Reynolds number by the evaporation spot (A6):  
\[\mathrm{Re}=\frac{DU}{v}.\] (A6)  
The mass flow \(M\) and the dynamic force \(F_{0}\) are found by integration over the evaporation spot, taking into account the velocity distribution (A4):  
\[M=2\pi\int\limits_{0}^{D/2}\rho u_{z}rdr=\frac{\pi}{4}\mathrm{Re}\eta D.\] (A7)  
\[F_{0}=2\pi\int_{0}^{D/2}\rho u_{z}^{2}rdr=\frac{\pi}{4}\mathrm{Re}\eta v.\] (A8)  
Numerical calculations are made for the three values of the Reynolds number given in Table 12 together with the values of \(M\) and \(F_{0}\) calculated from them.  
The Landau analytical similarity solution is represented in spherical coordinates (\(R\), \(\theta\)) as (A9):  
\[u_{R}=\frac{2v}{R}\Bigg{\{}\frac{A^{2}-1}{\left(A-\cos\theta\right)^{2}}-2\Bigg{\}};\] (A9)  
\[u_{\theta}=-\frac{2v}{R}\frac{\sin\theta}{A-\cos\theta};\] (A10)  
\[p=-\frac{4\eta v}{R^{2}}\frac{A\cos\theta-1}{\left(A-\cos\theta\right)^{2}},\] (A11)  
where the parameter \(A\) is found from (A4):  
\[\frac{F}{\eta v}=16\pi A\Bigg{\{}1+\frac{4}{3\left(A^{2}-1\right)}-\frac{A}{2} \ln\frac{A+1}{A-1}\Bigg{\}},\] (A12)  
where the right hand side of this equation is found according to data from Table A2. Its solution is shown in the last line of this table.
## 3 Results
### The Torque Measurement  
Figure 8 shows the relationship between the torque (M) and the corresponding compressed angle of the robotic arm. The angle change (\(\theta\)) and corresponding auxiliary force (F) of the robotic arm were measured from \(0^{\circ}\) to \(61^{\circ}\) using the MPU6050 and loadcell sensor. The results indicate that the parameters exhibited a linear regression relation. Equation (3) shows the relationship between the torque of the robotic arm and \(\theta\). The R-squared value was 96.56%.  
\[M=0.2214\theta-0.665 \tag{3}\]
## 4 Discussion
It is also evident that these studies achieve better performance metrics than the current work. The nature of the dataset deployed in the current study makes it impossible to achieve top-of-class accuracy for CAD classification. More specifically, without the use of data from medical scans or the medical expert's input, this model cannot compete with others that contain such information. Nevertheless, not only is this singularity what enables the proposed model to serve as a preliminary computer-aided diagnosis system for cardiovascular diseases in primary healthcare, but also it does achieve results on parwith medical experts for this particular dataset (i.e., model's accuracy: 78.82% versus doctor's accuracy: 78.81%). By accurately identifying unhealthy patients at an early stage, it could assist in alleviating the strain on healthcare systems like the NHS. Other proposed models, though more accurate, cannot be used in such a manner, mainly because general practitioners and family doctors that comprise the primary healthcare system do not have access to tomographs nor to cardiologists and nuclear doctors like hospitals do to collect all the data needed for the rest of the prediction models to function properly. In summary, incorporating explainability mechanisms like Cohen effect sizes and SHAP values in machine learning models enhances our comprehension regarding the prediction making process and factors influencing decisions. This fosters trust and makes these prediction tools more appealing to the medical community. Furthermore, the insights gained from explainability can serve as a suggestion tool, aiding in the identification of outlier cases and potential errors in input data. This, in turn, leads to more precise predictions and better diagnosis of CAD. Ultimately, the integration of explainability mechanisms contributes to advancing the field of medical diagnosis. Figure 4: Cohen effect sizes for each feature of the dataset. Magnitude of effect sorting is used. Nevertheless, this study has certain limitations. Specifically, the data used as input in the current work lack any image data. Image data derived from SPECT/PET examinations hold crucial information that is widely used by medical experts to assess the health condition of a patient. Thus, we consider it highly likely that incorporating such images into the input dataset would further improve the prediction accuracy of a decision model for CAD/NO-CAD classification. Furthermore, the patient population in this dataset are all people from roughly the same country/region. This low geographic diversity/dispersion means that these people share a common lifestyle and nutritional habits. Therefore, it is possible that certain risk factors were more prevalent than others due to this fact.
# Learning Translation-Based Knowledge Graph Embeddings by \(N\)-Pair Translation Loss
## 5 Experiments
### Experimental Settings
#### 5.1.3 Implementation  
Two well-known translation-based knowledge graph embeddings are adopted to compare the proposed loss with the margin-based ranking loss. The adopted embeddings are TransE [9] and TransD [10]. To minimize both the margin-based ranking risk (Equation (3)) and the \(N\)-pair translation risk (Equation (4)), we use Adam optimizer [35] with learning rate 0.001 and two momentum parameters \(\beta_{1}=0.9\) and \(\beta_{2}=0.999\). Minibatch size is set to the number of the training triples divided by 100, and the epoch limit is set to 1000. The Xavier uniform initializer [36] is used to initialize the vector representations of entities and relations. In order to find other hyper-parameters, we performed an exhaustive grid search over the following settings: a dimension of vector representations \(k\in\{25,50,100\}\), and a norm for the score function \(L\in\{\ell_{1},\ell_{2}\}\). The best hyper-parameters are tuned by the Hits@10 metric on a validation set. As a result, the dimension size is set to 50 for WN18 RR, while it is 100 for FB15K-237. \(\ell_{1}\) is used as the norm for a score function in all data sets.
# Regulated Assembly of LPS, Its Structural Alterations and Cellular Response to LPS Defects
###### Abstract  
Distinguishing feature of the outer membrane (OM) of Gram-negative bacteria is its asymmetry due to the presence of lipopolysaccharide-ascharide (LPS) in the outer leaflet of the OM and phospholipids in the inner leaflet. Recent studies have revealed the existence of regulatory controls that ensure a balanced biosynthesis of LPS and phospholipids, both of which are essential for bacterial viability. LPS provides the essential permeability barrier function and act as a major virulence determinant. In _Escherichia coli_, more than 100 genes are required for LPS synthesis, its assembly at inner leaflet of the inner membrane (IM), extraction from the IM, translocation to the OM, and in its structural alterations in response to various environmental and stress signals. Although LPS are highly heterogeneous, they share common structural elements defining their most conserved hydrophobic lipid A part to which a core polysaccharide is attached, which is further extended in smooth bacteria by \(O\)-antigen. Defects or any imbalance in LPS biosynthesis cause major cellular defects, which elicit envelope responsive signal transduction controlled by RpoE sigma factor and two-component systems (TCS). RpoE regulon members and specific TCSs, including their non-coding arm, regulate incorporation of non-stoichiometric modifications of LPS, contributing to LPS heterogeneity and impacting antibiotic resistance.  
L pxxC; LapB; RpoE sigma factor; Rcs two-component system; lipid IVA; lipid A modifications; Lpt transport system; noncoding small regulatory RNA +
Footnote †: journal: Molecular Sciences
# A Drone-Enabled Approach for Gas Leak Detection Using Optical Flow Analysis +
## 6 Conclusions  
In recent years, the interest in the employment of thermal imaging systems for gas leak detection and visually-enhancement of gas emanation has been raised significantly. However, most of these studies focused more on the visually-enhancement of the gas emanations and also they are not equipped to be used in an aerial platform. In this study, a drone-enabled gas leak detection technique is proposed involving two steps: (a) a video stabilization method and (b) a gas leak detection method based on the optical flow analysis. In order to find the most suitable video stabilization technique for the defined application, a comparative analysis is conducted to investigate the candidate methods from different aspects. Finally, the proposed method and the whole system are evaluated using three experiments: (a) an experiment in an isolated environment, (b) an experiment in a real environment, and (c) an experiment using a drone. The results of applying the proposed algorithm on the data collected in Experiments A and B showed that the method successfully enhanced the visualization and detected the gas flow and the area with the highest flow intensity. Moreover, the algorithm demonstrated a notable result while processing the footage collected by a drone. In addition, the comparative analysis of candidate stabilization techniques provided detailed insight into their abilities for the targeted application.  
For future work, we will work on using deep learning methods to detect and visualize the gas flow. Moreover, we will conduct more experiments using drones for Methane gas emission and gas leak detection in the oil and gas industry. In addition, we will investigate the use of the stabilization method based on deep learning techniques to improve the performance of the stabilization. Finally, we will work on developing a method to locate the origin of the gas leak more effectively.  
Conceptualization, Methodology, and validation P.N., F.L. and X.M.; Experimental Data Acquisition P.N. and F.L.; Formal analysis P.N. and X.M.; writing--review, editing, and data visualization P.N.; supervision, resources, funding acquisition X.M. All authors have read and agreed to the published version of the manuscript.  
Figure 11: The results of applying the proposed method to the data collected in Experiment C Scenario 2.  
**Funding:** This research is supported by the Canada Research Chair in Multi-polar Infrared Vision (MiViM), the Natural Sciences, and Engineering Research Council (NSERC) of Canada through a Discovery Grant and by the \"oN Duty!\" NSERC Collaborative Research and Training Experience (CREATE) Program.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data sharing not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders 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.
## 3 Results and Discussion
### The Entrapped Air in Fresh Mixture  
The test results of air content for all 64 UHPC mixtures are shown in Figure 7. It can be seen that most of the UHPC mixtures had 4%-6% air content, remarkably higher than that of normal concrete (1%-2%), when no air entraining agent was used. The higher amount of entrapped air has a detrimental effect on the mechanical properties of UHPC, and special mixing technology to lower the air content has been developed [23]. Obviously, air content of a UHPC mixture decreased with the increase of steel fiber content, and the addition of 3% steel fiber led to about 0.9%-2.4% reduction of entrapped air content for different water-to-binder ratios. Such decreasing effects of steel fiber on air content were also found in the experiments of normal strength concretes [24]. The increasing air content in normal concrete was also reported by other researchers, being attributed to the difficulties of orientation and distribution of long fibers [25]. Therefore, the reduction effect of entrapped air content in UHPC mixtures can be explained by the good dispersion of short fibers when a high dosage of superplasticizer was used. Other possible reasons should be studied in future.  
The influence of superplasticizer dosage on air content was related to water-to-binder ratio and the addition of steel fiber. The fluctuation range of air content decreased with lowering of the water-to-binder ratio. The influence of the water-binder ratio on the air content is not significant when a similar fluidity was obtained.  
Figure 7: Effects of steel fiber content, water-binder ratio and superplasticizer dosage on air content of