esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Label Classification for Predicting Antimicrobial Resistance on _E. coli_
## 2 Predicting Antimicrobial Resistance
### Prediction Models  
A total of eleven ML models were used in this research. These included Decision Tree (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Adaptive Boosting classifier (AdaBoost), Extra Trees Classifier (ETC), Gradient Boosted Classifier (GBC), k-Nearest Neighbors (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multinomial Naive Bayes (MNB).
## 4 Common Synthesis Methods of ZnFe\({}_{2}\)O\({}_{4}\)
### Electrospinning  
The electrospinning technique is an excellent candidate for fabricating nanomaterials possessing outstanding features such as low cost, flexibility, and simplicity [69; 70]. The obtained nanostructured materials offer high surface area, which not only could shorten Li\({}^{+}\) insertion/extraction pathways but also restrain the volume variations and particle aggregation during cycling, as well as providing good electrical conduction. Peculiar drawbacks include poor reproducibility and inhomogeneity of the mats. The electrospinning technique involves uniaxial stretching of a viscoelastic solution by the application of a strong electric field resulting in non-woven nanofiber layers or mats. Different fibrous arrays with tuneable length and diameter at nano- or meso-levels, high surface area, and controllable textures can be produced by simply varying the precursor viscosity, flow rate, applied electric field, and distance between the syringe and collector. Many examples of the application of this technique to produce 1D ZnFe\({}_{2}\)O\({}_{4}\) can be found in the literature [69; 70; 71; 72]. The surface area and the prevalence of macro, meso, or micro porosity, fundamental to hypothesize the electrolyte permeation and the cell functioning, can be determined by using N\({}_{2}\) adsorption measurements, on the base of BET theory [34; 73].  
In addition, the characterization of carbon, the main component of the resulting fibers, can be performed by Raman spectroscopy [74; 75; 76; 77]. In fact, two characteristic signals in the Raman spectrum at about 1350 and 1580 cm\({}^{-1}\) are ascribable to D and G bands of carbon. The D band (A\({}_{\text{1g}}\) mode) results from a break in the symmetry at the edges or defects in graphene sheets, while the G band, assigned to the E\({}_{\text{2g}}\) mode, is related to the C-C stretching within the hexagonal lattice. The intensity ratio of the D and G peaks is sensitive to the type and order of the carbon material forming composites with ZFO [75].  
In conclusion, we can compare the cited synthesis methods with special attention to the costs and the environmental impact of the entire processes. Equally inexpensive can be considered the wet chemistry and solid-state methods, starting from cheap and easily disposable reagents. In addition, the solid-state synthesis combined with the ball milling can produce pure spinels at low temperature, further reducing the costs of the synthesis process. In general, no toxic wastes are produced and, particularly the solid-state synthesis, can be easily scalable at an industrial level. Therefore, with these premises, they can be fully defined as green methods and, as we will see later, they represent the preferred choice for the ZFO production.
## 5 Conclusions  
In this study, 67 VOCs were detected in _Citrus_\(\times\)_microcapra_ flowers at the BS, HS, FS, and SS stages using HS-SPME-GC-MS. These included forty-eight terpenids, six esters, five aromatics, four aldehydes, one olefin, one alcohol, and two others. The number of VOCs varied significantly across the stages, with 33 VOCs shared among all stages. Additionally, BS, HS, FS, and SS had three, three, four, and nine unique VOCs, respectively. The total VOC content showed an initial increase followed by a decrease across the four stages, with terpenio being the dominant compounds (90.81-95.10%). PCA and HCA analyses indicated significant differences in VOC emission characteristics at different stages, and K-means analysis classified the 67 VOCs into five patterns. Finally, PLS-DA analysis identified five VOCs with VIP values greater than one, including limonene, linalool, \(\beta\)-pinene, germacrene D, and \(\beta\)-ocimene, indicating significant differences in their emissions across the stages. These findings provide valuable data for understanding the VOC emission mechanisms in _Citrus_\(\times\)_microcapra_ flowers and offer new insights for further development and utilization.  
**Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/separations11120359/s1](https://www.mdpi.com/article/10.3390/separations11120359/s1)]([https://www.mdpi.com/article/10.3390/separations11120359/s1](https://www.mdpi.com/article/10.3390/separations11120359/s1)), Table S1: Comparative analysis of the relative emission levels of 67 VOCs at four developmental stages of calamondin flowers; Table S2: Count of VOCs of different categories at four developmental stages; Table S3: Proportion of relative emissions of different VOC categories at four developmental stages; Table S4: Sixty-seven VOCs loading coefficients based on PCA analysis; Table S5: K-means clustering of 67 VOCs; Table S6: VIP values of 67 VOCs based on PLS-DA analysis.  
**Author Contributions:** Conceptualization, Y.C., T.G. and Y.Z.; methodology, Z.L., S.C. and F.Y.; software, Z.L., S.C. and J.H.; validation, Z.L., S.C. and J.H.; formal analysis, F.Y. and J.H.; investigation, Z.L. and S.C.; resources, Z.L. and Y.Z.; data curation, Y.C., F.Y. and T.G.; writing--original draft preparation, Y.C., T.G. and Y.Z.; writing--review and editing, T.G. and Y.Z.; visualization, F.Y., J.H. and Y.Z.; supervision, T.G. and Y.Z.; project administration, Y.Z.; funding acquisition, Y.C. and Y.Z. 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 51908228, the Ministry of education of Humanities and Social Science project, grant number 19 YJCZH013, and the Guangzhou Philosophy and Social Science \"14 th Five-Year Plan\" Project, grant number 2023 GZDD05.
## 2 Materials and Methods
### Landslide Conditioning Factors
#### 2.3.2. Topographical Factors  
The spatial configurations of the topographic features, elevation, slope, aspect, and curvature, were derived from the DEM data, as shown in Figure 6a-d. According to past research, although no direct correlation exists between elevation and landslide frequency, an observable increase in landslide occurrences is found at higher elevations of the study area, where elevation fluctuates from 196 m to 7933 m above mean sea level. Likewise, the slope of a terrain signifies the steepness at each pixel on the surface, which is computed as the first derivative of the DEM data. Within the study area, the slope extends from 0\({}^{\circ}\) to 82.77\({}^{\circ}\). Similarly, the aspect refers to the direction in which the slope faces and is a vital factor in landslide susceptibility modeling because it influences solar radiation exposure and weathering processes. Finally, curvature, a measure of directional change of slope in a specific direction, can be used to differentiate between ridges and valleys, based upon which slope is divided into concave, straight, and convex slopes. This information facilitates an understanding of the morphological characteristics of the landscape and can provide insights into potential landslide-prone regions.
It enables precise detection of ionized peptides and proteins represented as peaks of mass-to-charge ratio \((m/z)\). This technology has been considered to have high potential for clinical diagnostics and is already used in clinical microbiology [10; 13]. One of the main reasons for this is the reduced cost per test in comparison with genetic-based testing and most currently used microbiological techniques for clinical identification of infection [11; 12; 14]. Such 10-fold reductions in cost can enable screening programs to be rolled out, even in poorer countries. In the last years, MALDI-ToF MS has further evolved to become an ultra-fast high-throughput technology suitable for diagnostic screening purposes, with the capacity of each machine to generate approximately seven mass spectra per-patient samples per minute [15]. Thus, this technology would be ideal for mass screening at-risk ovarian cancer populations within a sensible and reasonable processing time [11; 12; 14]. Applying MALDI-ToF technology to ovarian cancer detection has previously been attempted using blood serum samples with an optimal sensitivity of 71% and specificity of 68% [16]. However, although promising, this is still far from an ideal solution for population screening purposes. More recently, MALDI-ToF application for human disease diagnostics has been enhanced by cutting-edge automated bioinformatics pipelines for spectral analysis [17; 18; 19; 20]. This approach relies on the detection of mass spectral pattern recognition rather than specific biomarkers, which is the equivalent of simultaneously detecting multiple relevant biomarkers. Using mathematical models for disease scoring and machine learning approaches, these mass spectral patterns have been successfully applied to the detection of aneuploidies in spent IVF-embryo/blastocyst media and fetal Down's syndrome from the urine of pregnant women, with high sensitivity and reasonable specificities [20; 21; 22]. Further, these pipelines have been developed enabling automated and ultra-fast mass spectral data processing within seconds, creating software tools that enable screening for diseases in general populations by clinical laboratories [18; 19]. Thus, it is highly feasible that this MALDI-ToF-based technology can be deployed for early-stage detection of ovarian cancer. This study set out to test the hypothesis that this technological approach can provide a more suitable solution for general population screening programs. To address this question,we conducted a retrospective cohort study using blood serum samples from ovarian cancer patients in the development of MALDI-ToF MS-based diagnostic tools, for early- and late-stage cancer detection. Here, we report the diagnostic power and performance of these tools using distinct spectral profiles of MALDI-ToF MS generated from two experimental approaches, optimized using advanced bioinformatics and machine learning.
# Research on Networking Protocols for Large-Scale Mobile Ultraviolet Communication Networks
## 4 Simulations  
### Mobility Model  
In practical engineering applications, the motion states of UAV nodes exhibit significant dynamic characteristics due to the coupling effects of multiple factors such as mission planning, time scheduling, and meteorological conditions. Particularly in extremely harsh weather environments, the trajectories of UAVs can be approximated using a Brownian motion model.  
For theoretical analysis and simulation verification, this paper abstracts the relative displacement of UAV nodes into a two-dimensional planar random motion model. Within a square area of side length \(L\), starting from the initial time \(t_{0}\), each node randomly selects a direction of motion within the range [0, 2\(\pi\)] and moves in a straight line at a constant velocity \(v\). Every \(t\) seconds, the node reselects its direction while maintaining the same speed. If the node predicts that its trajectory will exceed the boundary of the predefined area after \(t\) seconds, a direction reselection mechanism is immediately triggered.  
This random mobility model introduces high unpredictability and disorder into node trajectories. As the velocity \(\
u\) increases, the dynamic variation of the network topology grows nonlinearly. LSM-UVCN relies heavily on the neighbor table constructed by the neighbor discovery algorithm. Under the aforementioned random mobility model, frequent node movements cause the connection matrix to quickly become outdated, making it difficult to accurately reflect the real-time network topology. When nodes plan transmission paths based on outdated neighbor tables, the success rate of data transmission decreases significantly, leading to a severe degradation of overall network performance. Therefore, to mitigate the negative impact of node mobility on network performance, efficient dynamic updates of the connection matrix are essential to maintain the stability and reliability of the protocol.
# A Pseudo-3D Model for Electromagnetic Acoustic Transducers (EMATs)
## 4 Conclusions  
A pseudo-3D model for simulating meander-line-coil EMATs was proposed. A method combining the analytical method for the EM model and FDTD for the US model was utilised to simulate the Rayleigh waves' propagation. On the other hand, a wholly analytical method was utilised to simulate the radiation pattern of the Rayleigh waves. For both cases, analytical solutions to the EM model were adapted from the classic Dodd and Deeds solution in order to calculate eddy currents under straight wires. By comparing with the FEM, the analytical solutions are more accurate. Experiments were conducted in order to validate the proposed method, and these showed a good consistency. The beam directivity of Rayleigh waves at various depths were investigated, and results confirmed that Rayleigh waves mainly distribute within a depth of one wavelength of Rayleigh waves. Overall, this pseudo-3D model combines both the vertical plane and surface plane of EMAT models, and provides the beam directivity of Rayleigh waves at various depths, which have not been reported previously. Therefore, this work can be a starting point to build an advanced EMAT 3D model in the future. There are some limitations of the proposed 3D EMAT model. Firstly, it can only be applied in a homogeneous medium. It is worth investigating the 3D EMAT model for a multiple-layer medium, since multiple-layer samples are widely used in applicable industries. Secondly, the EMAT-US model on the vertical plane of the sample uses an approximated model with only point sources (Lorentz force density) to generate Rayleigh waves. A more detailed model with volume sources within the skin depth is worth considering in the future. In addition, it is worth investigating the scattering behaviours of defects in other orientations, as it is a variable in practical application.  
This work was financially supported by Engineering and Physical Sciences Research Council (Grant No. EP/M020835/1).  
[PERSON] conceived and reviewed the manuscript, did some simulations and designed the experiments. [PERSON] performed the experiments and wrote this manuscript. [PERSON] contributed some simulation results. [PERSON] provided related instruments for carrying out experiments.  
The authors declare no conflict of interest.
# Quality Characteristics and Flavor Analysis of Five Mulberry Varieties
## 2 Materials and Methods
### Flavor Component Analysis
#### 2.4.3 Determination of Amino Acid  
The amino acid content was assessed through HPLC by using a C18 column (250 mm \(\times\) 4.6 mm, 5 um, Agilent, USA) with UV detection equipped [26]. The 20 g sample was dried at 80 \({}^{\circ}\)C, crushed, dissolved with 0.1 mol/L hydrochloric acid, ultrasonicated for 40 min, and then filled with 0.1 mol/L hydrochloric acid in a 25 mL volumetric bottle. After filtration, 2 mL of supernatant were extracted and 2.5 mL of amino acid mixed standard solution were obtained, and 0.625 mL of derivative A (phenyl isothi cyanate 2 mL, acetonitrile 8 mL) and 2.5 mL of derivative B (triethylamine 2 mL, acetonitrile 4 mL) were added, respectively. After reaction at room temperature and in a dark place for 1 h, 5 mL of n-hexane were added for extraction twice, for 10 min each time. The lower solution was absorbed and mixed with 100 \(\mu\)L of acetic acid solution and filtered by a 0.22 um filter membrane. The chromatographic conditions used were mobile phase A consisting of 0.1 mol/L sodium acetate solution:acetonitrile (97:3, _v/v_) (acetic acid adjusted pH was 6.5) and mobile phase B consisting of acetonitrile:water (4:1, _v/v_), with a flow rate of 1 mL/min and a gradient elution of 0-11 min, 0-1.5% A, 100-98.5% B; 11-21.7 min, 15-7.6% A, 98.5-92.4% B; 21.7-23.9 min, 7.6-11% A, 92.4-89% B; 23.9-39 min, 11-30% A, 89-70% B; 39-42 min, 30-70% A, 70-30% B; 42-45 min, 70-100% A, 30-0% B; 45-52 min, 100% A, 0% B; 52-55 min, 100-0% A, 0-100% B; and 55-70 min, 0% A, 100% B. The detection wavelength was 254 nm, the column temperature was 36 \({}^{\circ}\)C, and the sample size was 20 \(\mu\)L. The standard solution of 21 kinds of amino acids (purity \(\geq\) 98%, Shanghai Amp Golden Standard Technical Service Co., Ltd., Shanghai, China) was taken as the standard curve. The amino acid concentration of the sample was calculated by aligning the measured value with the standard curve. Each treatment was repeated 3 times.
# On-Edge Deployment of Vision Transformers for Medical Diagnostics Using the Kvasir-Capsule Dataset
## 4 Results and Discussion
### Performance on Other Datasets  
As further proof of concept, we have adapted a similar paper to compare the performance of our ViT-based lightweight models to that of conventional CNNs. In particular, we will look at [25] and their use of the X-ray Lung Diseases dataset [26]. The dataset contains nine classes (similar to the work presented in this paper) and contains X-ray scans of patients for classifications of nine types of lung diseases.  
Table 13 compares the best-performing model from [25] and our approach, showing that with the same quantization approach (QAT) on the same dataset, we are able to outperform what already exists in the literature simply with the introduction of lightweight ViT-based models. Although this paper is entirely based on the Kvasir-Capsule dataset, this works as a testament to the generalization of ViT-based models as lightweight classifiers for on-edge deployment.
# An Improved MB-LBP Defect Recognition Approach for the Surface of Steel Plates
## 4 Experiment and Analysis of Surface Defects Detection Based on MB-LBP  
### Experimental Design  
#### 4.1.1 Architecture of Surface Defect Detection System  
A complete online surface defect detection system for steel plates should include image capturing, image preprocessing, defect feature extraction, and defect classification [22], which is shown in Figure 9. In pre-processing, the image segmentation algorithm is used to mark suspected defect areas. It can not only make preparations for identifying the sizes and locations of defects, but can also greatly reduce the amount of calculation for defect feature extraction and defect classification and identification. After the defect areas are determined, the defect features are extracted by the feature extraction algorithm. Then, the obtained feature vectors will be input into the classifier for classification and identification. Finally, a series of information such as class, size, location, and severity of the defect can be obtained.  
Figure 8: Obtain the MB-LBP pattern value of the entire image.  
Figure 7: Binarization process of the MB-LBP.  
Figure 9: The flow chart of the surface defect detection system for steel plates.
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### General Melody Extraction  
General melody extraction is the sub-task of frame-level transcription that deals with identifying the pitch information of the dominant melody present in the polyphonic audio. The research work in [36] proposed a new encoder/decoder network for extracting melodies from polyphonic musical audio. The authors offer two key contributions: first, they use the semantic pixel-wise segmentation method to improve melody localization in frequencies with fewer convolutional layers. Second, they propose utilizing the network's bottleneck layer to estimate the presence of a melody line for each time frame in the audio. In general melody extraction, each frame is analyzed to estimate the fundamental frequency or pitch of the most prominent musical note. The early work presented in [37] extracts the melody played by a solo instrument by generating multiple fundamental frequency (F0) hypotheses at each time frame and employing various knowledge sources, including instrument recognition and temporal information, to select the most likely F0 sequences. Additionally, the work in [38] introduced a fully convolutional neural network trained on a large, semi-automatically generated dataset for fundamental frequency estimation. The model proves the effectiveness in both multi-fundamental frequency (multi-F0) and melody tracking in polyphonic audio, achieving state-of-the-art results across various datasets. The application of general melody extraction is often used in instrumental music analysis and can help in tasks such as automatic transcription, cover song generation, and music recommendation [39; 40].
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 2 Methods  
Neural networks commonly used in this field, such as the U-Net [18] with standard parameters, can only store patches of size \(160^{3}\) on a 32 GB VRAM GPU. This relatively small field of view causes significant issues as we will show in the experiments section.  
We use an end-to-end multi-scale CNN to enlarge the field of view while maintaining high-resolution features. The network takes input patches from whole-body MR images at three resolution levels to synthesise high resolution, realistic CT patches.  
We further incorporate uncertainty in our network. It is evident that in our setting the aleatoric uncertainty should be modelled as heteroscedastic, as task performance is expected to vary spatially due to the presence of artefacts, tissue boundaries, and small structures. Thus, the network incorporates explicit heteroscedastic uncertainty modelling by casting our task likelihood probabilistically. Epistemic uncertainty estimation is performed via traditional Monte Carlo dropout. More specifically, the network is trained with channel dropout enabling us to stochastically sample from the approximate posterior over the network weights to obtain epistemic uncertainty measures [19]. The network is encouraged to assign high levels of uncertainty to regions with large residual errors, providing a means of understanding what aspects of the data pose the greatest challenges.  
We employ a patch-based training approach whereby at each resolution level of the framework a combination of downsampling and cropping operations results in patches of similar size but at different resolutions, spanning varied fields of view. Three independent instances of HighRes3 DNet are trained simultaneously with multi-channel input, not sharing weights, taking patches of each resolution as input each resulting in a feature map with different resolution. Lower level feature maps are concatenated to those at the next level of resolution until the full resolution level, where these concatenated feature maps are passed through two branches of \(1\times 1\times N\) convolutional layers resulting in a synthesised CT patch and the corresponding voxel-wise heteroscedastic uncertainty. Figure 1 illustrates the MultiRes framework.  
We posit, similarly to [10], that the multi-resolution framework allows the network to benefit from the fine details afforded by the highest resolution patch while simultaneously increasing the spatial context provided by the larger field of view of the low-resolution patches. However, compared to [10] we incorporate an additional level of deep supervision into our MultiRes network. Compared to the MultiRes network in our preliminary work, we extend the network architecture allowing us to train with multi-channel input data (here \(T_{1}\) and \(T_{2}\)-weighted MR images).