esa-sceva/satcom-chunk-collection huggingface dataset:
# Review on Silver Nanoparticles as a Novel Class of Antibacterial Solutions
## 2 Metal Nanoparticle Synthesis
### Synthesis Methods for Silver Nanoparticles for Antibacterial Applications  
Several methods of obtaining metallic nanoparticles have described, such as chemical, physical and green methods, each yielding different chemical and physical properties. By using these methods, nanoparticles have been obtained with sizes ranging from 3 to 100 nm, with different shapes: spherical, cubical, triangular, pentagonal, hexagonal and nano-wire. All referenced studies show that the size of nanoparticles directly influences the antibacterial activity (Table 1).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Synthesis Method** & **Chemicals** & **Size (nm)** & **Shape** & **UV-VIS** & **Ref.** \\ \hline Chemical & Silver nitrate, potassium sodium tartrate & 12.16 \(\pm\) 4.13 & Spherical & 422 & [15] \\ Chemical & Silver nitrate, sodium citrate, sodium borohydride, cotton cellulose whiskers & 15 \(\pm\) 5 & Spherical & 425 & [16] \\ Chemical & Silver nitrate, N-Hydroxy succinimide, Thioglycolic acid, 1-3 diethylamino propyl-e-ethyl carbodiimide hydrochloride, 3 N-Morpholinopropanesulfonic acid, sodium borohydride & 16–25 & Spherical & 398 & [2] \\ Green & Silver nitrate, ethanol, fenugreek leaf extract & 20–30 & Spherical & 410 & [17] \\ Chemical & Silver nitrate, organosolv lignin, chloroform, ethanol, polylactic PLA & \textless{}100 & Spherical & 450 & [18] \\ Chemical & Silver nitrate, sodium citrate, tannic acid & 10.2 \(\pm\) 2.3 & Spherical & 392 & [19] \\ Green & Silver nitrate, aqueous leaf extract of & 20.52 & Spherical & 435 & [20] \\ Green & Corchorus capsularis, & 50 \(\pm\) 25 & Spherical & 421 & [21] \\ Green & Silver nitrate, streptomycinsely NH28 strain & 29.8 \(\pm\) 6.4 & Spherical & 392 & [22] \\ Green & Silver nitrate, sodium borohydride & 26 & Spherical & 410 & [23] \\ Chemical & Silver nitrate, chondro sulfate scaffolds & 20–40 & Spherical & 445 & [24] \\ Chemical & Silver nitrate, trisodium citrate & 30–80 & Spherical & 426 & [10] \\ Chemical & Silver nitrate, polyvinyl pyrrolone, trisodium citrate, hydrogen peroxide, sodium borohydride & 150 & Triangular & 392 & [10] \\ Chemical & Silver nitrate, sodium borohydride & 25–70 & Spherical & 403 & [10] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Silver nanoparticles characterized using UV-VIS obtained with various methods.
# Research on Multi-Objective Evolutionary Algorithms Based on Large-Scale Decision Variable Analysis
## 2 Related Work
### Grouping Method Based on Contribution Targets of Decision Variables  
When analyzing the contribution targets of convergence decision variables in different objective spaces, it is necessary to group the convergence decision variables according to their contribution targets. Specifically, by examining the contribution targets of convergence decision variables in different spaces, the decision variables with the same contribution targets are grouped into \(desSet(m)\). If \(desSet(m)\) is empty, indicating that there is no convergence decision variable with the contribution target \(m\), a convergence decision variable is randomly selected and added to the target group. Algorithm 4 provides specific steps for grouping convergence decision variables based on their contribution targets.  
Figure 1: Three subpopulations generated by perturbing in different spaces.  
```
0:\(P\) (Current population), \(CV\) (Convergence-related decision variables), \(FO\) (Contribution goals of decision variables)
0:\(desSet\) (Decision variable group results)
1: Initialize \(desSet=\emptyset\)
2:for\(i=1\) to \(|CV|\)do
3:for\(k=1\) to \(|W|\)do
4:\(desSet_{k}(m)\leftarrow\) add the decision variables in \(FO(i)\) whose contribution objective is the \(m\)-th objective \(f_{m}\)
5:\(CV/desSet_{k}(m)\)
6:endfor
7:endfor
8:for\(m=1\) to \(M\)do
9:if\(desSet(m)\)then
10:\(desSet(m)\leftarrow\) a convergence related decision variable is randomly selected from \(CV\)
11:endif
12:endfor
```  
**Algorithm 4** Convergence Grouping-Based Contribution Target (\(P\), \(CV\), \(FO\))
## 3 Proposed Method
### Weighted Bidirectional Feature Pyramid Network (BiFPN)  
The Feature Pyramid is a widely used technique in computer vision, particularly for tasks such as object detection, image segmentation, and target tracking. Its main idea is to extract features from different image scales, capturing information about objects of varying sizes and resolutions. YOLOv8 follows the PAN concept introduced in YOLOv5, adopting PAN-FPN as its feature extraction network. PAN-FPN incorporates both top- down and bottom-up paths, preserving more spatial information and thereby enhancing the accuracy and robustness of model training.  
However, when addressing the problem of small target defects in the packaging of bags on industrial assembly systems, the PAN-FPN network exhibits high computational complexity and insufficient spatial feature fusion capabilities. This reduces the efficiency of utilizing shallow features and fails to meet the requirements for detecting small target defects. Therefore, this paper introduces a weighted bidirectional feature pyramid network (BiFPN) to replace the original feature pyramid network. BiFPN enhances the network's ability to extract shallow features while reducing computational complexity, thereby improving the overall performance of the model in detecting small target defects.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 5 Conclusions
Since, when comparing aerospace and wind turbines systems with bridges and civil engineering structures in general, the characteristics of detected and monitored events are less sudden and dynamic, savings can be achieved by optimizing the sensor network, data transmission frequency, and accepting the reduction of stability and reliability of signal transmission. On the other hand, the challenge is the area's extent to be covered by the sensor network, data transmission distance to the processing, inference, and management center. LTE and 4G are used presently, and the 5G technology is under consideration and testing. This is the main challenge for the near future for wireless monitoring in wind energy production. The next development direction seems to be the LoRa technology, which is excellent from an energy consumption perspective. There are, however, significant challenges, especially the transmission capacity. Maybe, in the future, one should consider hybrid systems in which \"Big data\" transmission happens only from time to time when interesting wind conditions appear. In such a case, the most effective transmission technologies would be used. However, most of the time, the wind conditions are relatively stable, and no important data behavior can be expected. In such a case, less detailed information on turbine monitoring is sufficient and could be secured by systems with a lower transmission rate and lower power consumption. Conceptualization, R.E.S., P.D. ([PERSON]), M.M., B.K., M.J., T.R., D.K., P.F., P.D. ([PERSON]), K.D., S.G.; methodology, R.E.S., P.D. ([PERSON]), M.M., K.D., T.R., D.K., P.F., P.D. ([PERSON]), K.D., S.G.; writing--original draft preparation, R.E.S., P.D. ([PERSON]), M.M., B.K., M.J., T.R., D.K., P.F., P.D. ([PERSON]), K.D., S.G., R.U. ; writing--review and editing, R.E.S., P.D. ([PERSON]), M.M., B.K., M.J., T.R., D.K., P.F., P.D. ([PERSON]), K.D., S.G., R.U. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. This publication is based upon work from COST Action CA18203-Optimizing Design for Inspection (ODIN-www.odin-cost.com accessed on 15 January 2022) supported by COST (European Cooperation in Science and Technology-www.cost.eu accessed on 15 January 2022).
## 6 EEG Signal Pre-Processing and Feature Extraction
### EEG Signals Classification
#### 6.3.1 Methodologies in EEG Signal Classification  
The classification of EEG signals typically involves advanced algorithms and methods. Techniques such as support vector machines (SVMs), neural networks, and k-nearest neighbor (k-NN) are commonly employed. These classifiers are selected for their proficiency in handling the high-dimensional nature of EEG data and their effectiveness in addressing non-linear relationships within these data [108].
## 2 Materials and Methods
### Finite Element Models
(Note that the FE model of the prototype open-face helmet is shown in Figure 5d-f.) The model used to simulate the impact test for the full-face helmet consisted of a total of 8950 elements, including 1864 quadrilateral type S4R elements for the ABS shell, 4775 hexahedral type C3D8R elements for the EOS liner, 1332 rigid hexahedral type C3D8R elements for the headform, and 979 hexahedral type C3D8R elements for the flat steel anvil. For the impact tests performed with the remaining helmets, the headform and anvil models were unchanged, and the open-face and half-face helmet models consisted of 2532/4875 and 2189/8784 (54R/C3D8R) elements, respectively. For each helmet, the headform acceleration during impact was computed in the \(Z\)-axis direction, over an interval of 20 ms. The simulations were performed using ABAQUS/Explicit software with the large deformation option. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Item** & **Elastic Modulus (GPa)** & **Poisson’s Ratio** & **Density (kg/m\({}^{3}\))** \\ \hline Headform & 45 & 0.3 & 1750 \\ ABS shell & 1.867 & 0.35 & 1390 \\ \hline \hline \end{tabular} \({}^{*}\)Data from [PERSON] et al. [10; 15].  
\end{table} 
Table 2: Finite element model details \({}^{*}\). Figure 4: Experimental stress vs. strain curves for Expanded Poly-Polystyrene (EPS) foam (reproduced with permission from [15], Elsevier, 2003).
# Broadband Plasmonic Metamaterial Optical Absorber for the Visible to Near-Infrared Region
## 3 Results Analysis  
### Unit Cell Absorption Characteristics  
Impedance matching plays a critical factor in MA absorption characteristics. The equation for relative impedance for the MA is given in Equation (4). [28] The relative impedance, permeability, and permittivity of the MA are represented by \(Z\), \(\mu\) (\(\mu=\mu_{r}\mu_{0}\)), and \(\varepsilon\) (\(\varepsilon=\varepsilon_{r}\varepsilon_{0}\)) respectively. The relative impedance of the MA is presented in Figure 5. The mechanism for wideband operation can be explained by the absorber's normalized impedance. When the absorber's normalized impedance (\(Z\)) matches with the free-space impedance (\(Z=Z_{0}=1\)), the perfect absorption has been attained. From the Equation (5), we can say, the closer the imaginary part is to zero, and the closer the real part is to one, the higher the absorption of the MMA. The Figure 5 shows that the impedance of the MA matches with the free space impedance for the wavelength 400 nm to 2400 nm.  
Here, \(\mu_{r}\) represents the relative permeability and permittivity is represented by \(\varepsilon_{r}\) in Equation (4). For free space, those are \(\mu_{0}\) and \(\varepsilon_{0}\). Electromagnetic waves cannot transmit through the Tu back layer, so transmission is considered zero. Absorption, which is denoted as A, can be derived from A = 1 \(-\) R. Here R is reflectance. The reflectance, absorption and transmittance characteristics of the MA are given in Figure 6. The proposed MA's symmetrical design gives a unique absorption curve. An absorption above 99% appeared from 565 nm to 635 nm with a 99.6% peak absorption at 600 nm wavelength. The MA obtained a 92% average absorption bandwidth of 2000 nm for the visible and near-infrared spectrum. The near-unity absorption feature of the suggested MA is advantageous for the visible optical wavelengths of solar energy harvesting, sensors, micro-imaging technologies and stealth technology [29].  
\[Z=\sqrt{\left(1+S_{11}\right)^{2}-\frac{S_{21}^{2}}{\left(1-S_{11}\right)^{2} }-S_{21}^{2}}=\frac{\sqrt{\mu/\varepsilon}}{Z_{0}}=\sqrt{\mu_{r}/\varepsilon _{r}} \tag{4}\]\[A_{TM,TE}=1-\left|\frac{Z-Z_{0}}{Z+Z_{0}}\right|^{2}=1-\left|\frac{\sqrt{\mu_{r}}- \sqrt{\varepsilon_{r}}}{\sqrt{\mu_{r}}+\sqrt{\varepsilon_{r}}}\right|^{2} \tag{5}\]
## 2 Theoretical Basis
During pure rolling motion, the reaction force \(R_{x}\) given by Equation (17) is needed to guarantee equilibrium. \(|R_{x}|\) has to be smaller than the maximum static friction force (\(\mu_{s}R_{y}\)). Hence, the ratio \(|R_{x}|/R_{y}\) has the meaning of static friction coefficient needed to guarantee equilibrium during pure rolling. Figure 4 shows the ratio \(|R_{x}|/R_{y}\) against non-dimensional acceleration (\(a/g\)) for \(\theta=3^{\circ}\), \(f_{v}=0\) and parametric values of \(\psi\). \[ma_{x}=-mgsin(\theta)+\ R_{x}+R_{n} \tag{3}\]6 Friction coefficient needed to guarantee the pure rolling motion, \(\theta=3^{\circ}\),\(f_{v}=0\), and parametric values of \(\psi\). \[ma_{x}=-mgsin(\theta)+\ R_{x}+R_{n} \tag{3}\]7 Average non-dimensional conveying velocity (\(\overline{v_{r}}/x_{0}\omega\)) versus period number, \(\theta=3^{\circ}\), \(a/g=1\) and parametric values of \(\psi\) (a); \(\psi=0\) and parametric values of \(a/g\) (b). With \(\psi=0\), the friction coefficient needed to guarantee the pure rolling motion is only slightly influenced by the versus of acceleration. With \(\psi>0\), the friction coefficient needed to guarantee the pure rolling motion is significantly smaller when the conveyor acceleration is positive than when acceleration is negative. This phenomenon happens because when acceleration changes sign, the decrease in reaction force \(R_{y}\) (Equation (7)) is not compensated by the decrease in \(R_{x}\) (Equation (17)), and this effect becomes even more important when \(\psi\) is large. Since in most industrial material \(\mu_{s}\) is in the range of \(0.6\div 1\)[1], the transition from a pure rolling motion to a rolling with sliding can take place only in the presence of large negative accelerations of the vibration conveyor. When the cylindrical part rolls backwards, it may impact the stop plate; in this case, the equation of motion in the x direction becomes:  
\[ma_{x}=-mgsin(\theta)+\ R_{x}+R_{n} \tag{3}\]8  
in which \(F_{imp}\) is the impact force, which has the same direction of \(R_{n}\) in \[ma_{x}=-mgsin(\theta)+\ R_{x}+R_{n} \tag{3}\]9 \(F_{imp}\) strongly increases \(R_{x}\); hence, the ratio \(|R_{x}|/R_{y}\) may reach very large values, and the sliding motion can begin.
## 7 Simulation Results and Discussions
### Discussion  
The transient simulation analyses in Sections 7.1.1 and 7.1.2 reveal that the necessary power rating of the DFIG to stabilize the SCIG in the WF, as well as to prevent conventional SGs from becoming out of step during a 3 LG fault, is much lower in the case of the proposed rotor-side controller than in the case of the conventional rotor-side controller, where the total capacity of the DFIG and the SCIG is 100 MW. Table 6 summarizes the results, which reveal that, for stable operation of the WF and SGs, the lowest power rating of the DFIG is 28 MW for the proposed method and 59 MW for the conventional method.  
The dynamic simulation analysis confirmed that the proposed FLC-controlled DFIG can effectively inject reactive power and thus maintain the terminal voltage constant under a randomly varying wind speed.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Controller for RSC** & **DFIG (MW)** & **SCIG (MW)** & \begin{tabular}{c} **Total Capacity** \\ **of WF (MW)** \\ \end{tabular} & \
\begin{tabular}{c} **WF Condition** \\ **(After the Fault)** \\ \end{tabular} &
\begin{tabular}{c} **SGs Condition** \\ **(After the Fault)** \\ \end{tabular} \\ \hline Proposed FLC & 28 & 72 & 100 & stable & stable \\ Conventional PI controller & 59 & 41 & 100 & stable & stable \\ Proposed FLC & 27 & 73 & 100 & unstable & out of step \\ Conventional PI controller & 58 & 42 & 100 & unstable & out of step \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performances of the proposed and conventional rotor-side controllers of DFIG.  
Figure 40: Power system frequency response.  
Figure 39: Active power output of SGs.
## 4 Experiment and Results
### Results and Discussions
#### 4.2.2 Performance Comparison of Delay Prediction Methods  
The initial experiment compares the predictive performance of the MTWSI-based CNN model with several machine learning methods that rely on numerical features. These machine learning methods used for comparison include Gaussian Naive Bayes (GNB), k-Nearest Neighbors (KNN), logistic Linear Regression (LLR), Support Vector Machines (SVM), Multi-Layer Perception (MLP), and integrated learning algorithms such as Random Forests (RF) and Adaptive Boost (AdaBoost) [31; 32].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Combination Name** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline Chanel\_combi1 & 88.72\% & 0.86 & 0.89 & 0.86 \\ Chanel\_combi2 & 88.85\% & 0.86 & 0.89 & 0.86 \\ Chanel\_combi3 & 90.03\% & 0.88 & 0.90 & 0.89 \\ Chanel\_combi4 & 90.49\% & 0.89 & 0.90 & 0.88 \\ Chanel\_combi5 & 89.84\% & 0.89 & 0.90 & 0.89 \\ Chanel\_combi6 & 90.56\% & 0.89 & 0.91 & 0.89 \\ Chanel\_combi7 & 90.82\% & 0.90 & 0.91 & 0.90 \\ Chanel\_combi8 & 89.70\% & 0.89 & 0.90 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Impact of different channel combinations on cumulative delay level prediction.  
Figure 9: Class distribution of the training set after applying various sampling techniques.  
Table 4 summarizes the results produced by the prediction methods using various sampling techniques. An in-depth analysis of the table shows that the ADLCNN method proposed in this paper outperforms other traditional machine learning methods overall, regardless of the sampling technique used. In particular, the prediction performance of the ADLCNN is more balanced when using the ROS sampling technique, where the recall is 0.83 for average delays and 0.78 for severe delays, with an overall accuracy of up to 0.831.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Sampling** \\ **Methods** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Prediction** \\ **Methods** \\ \end{tabular} } & \multirow{2}{*}{**Accuracy**} & \multirow{2}{*}{**Label**} & \multicolumn{2}{c}{**Classification Report**} & \multicolumn{2}{c}{**Confusion Matrix**} \\ \cline{5-8}  & & & & & **Precision** & **Recall** & **F1** & **Low** & **High** \\ \hline \multirow{8}{*}{RUS} & GNB & 0.873 & General delay & 0.94 & 0.92 & 0.93 & 1942 & 167 \\  & GNB & 0.873 & serious delay & 0.24 & 0.30 & 0.26 & 123 & 52 \\  & KNN & 0.673 & General delay & 0.96 & 0.68 & 0.79 & 1428 & 681 \\  & LLR & 0.720 & serious delay & 0.14 & 0.63 & 0.23 & 65 & 110 \\  & SVM & 0.720 & General delay & 0.97 & 0.72 & 0.83 & 1514 & 595 \\  & \multirow{2}{*}{\
\begin{tabular}{c} \\ \end{tabular} } & \multirow{2}{*}{0.720} & \multirow{2}{*}{
\begin{tabular}{c} \\ \end{tab
## 4 Experimental Design and Results  
### Dataset  
The research utilized two primary datasets: the ImageNet-22k dataset and the oilfield paragraph card dataset. The ImageNet-22k dataset is renowned in the machine learning domain for its broad applicability and has been extensively employed in previous studies [33]. Conversely, the oilfield paragraph card dataset was gathered from real-time oilfield operations, lending practicality and authenticity to the research.  
The ImageNet-22k dataset consists of 22,000 categories and an extended dataset with over 100 million image samples. Compared to ImageNet-1k, which has 1000 categories, ImageNet-22k covers a wider range of objects, scenes, and concepts. The image data are collected through web crawling, crowdsourced annotation, and filtering, encompassing various scenes, perspectives, and qualities. This dataset is primarily used for training and evaluating computer vision models, including tasks such as image classification, object detection, and image generation. With more categories and samples, ImageNet-22k presents a higher level of challenge for models, requiring better generalization to recognize and understand a broader range of objects and scenes.  
The paragraph card dataset is derived from real pumping units and contains paragraph card curve data. The acquisition process of these data involves manual measurement and recording using instruments, which inevitably introduces some errors. These errors can be attributed to measurement inaccuracies of the instruments, sensor noise, environmental factors, and human factors. In actual oil pumping processes, there are various pumping conditions, but the main types include normal operation, fluid bound, gas influence, gas lock, delayed closing of the traveling valve, pump barrel slippage, and the combination of fluid pump and delayed closing of the traveling valve. Therefore, the original paragraph card dataset was filtered, resulting in a total of 11,928 datasets, with 1704 data points per category. The splitting of a dataset into training, testing, and validation sets is implemented to effectively evaluate the model's performance and enhance its generalization capabilities. The criterion for partitioning is randomly chosen to ensure that each subset is a representative sample of the data. Typically, the split ratios involve allocating a substantial portion of the data to the training set, a smaller proportion to the testing set, and including a validation set size that is often comparable to the testing set. In the article, the training set constitutes 60% of the data, while both the testing and validation sets each account for 20%. These datasets were divided into a training set (7000 data points, 1000 per category), a test set (2464 data points, 352 per category), and a validation set (2464 data points, 352 per category). Refer to Figure 7 for an illustration.  
Figure 7: Divided dataset.