esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### SERS Applications
#### 3.2.3 Detection of Melamine Using CD-Capped AgNPs  
Another example of the favorable effect of using CDs-capped NPs is the detection of melamine. Recent reports claimed that using CD-capped AgNPs significantly enhances the signal of melamine and reduces the detection limit [46]. In Figure 11, we present SERS spectra obtained on citrate-capped AuNPs and CD-capped AgNPs synthesized at room temperature. It can be seen that the CD capping of AgNPs significantly enhances the signal of melamine, which is comparable to that of the analyte detected by citrate-capped AuNPs. Obtaining the detection limit of melamine is beyond the scope of this paper, but from the intensity of the SERS signal, it seems that, for CD-capped AgNPs, the detection limit is in the tens of nanomolar range.  
These examples show, in general, the potential of CD-capped NPs to detect, in SERS, hydrophobic molecules containing aromatic groups and lacking thiols, amines, or other groups with low affinity for metallic surfaces.  
Figure 11: SERS spectra of 0.1 \(\upmu\)M melamine on CD-capped AgNPs and 1 \(\upmu\)M on citrate-reduced gold NPs. The characteristic ring breathing is obtained at 704 cm\({}^{-1}\) in the case of silver colloid and at 711 cm\({}^{-1}\) in the case of gold.  
Figure 10: (**a**) Raman and SERS spectrum of naphthalene 2 \(\upmu\)M obtained with citrate-reduced gold colloids; (**b**) SERS spectra of different concentrations of naphthalene on silver colloids reduced in ultrasound field with \(\beta\)-CDs.
# Efficient MIMO Signal Predistortion for Secrecy-Enhancing
## 4 Simulation Results
We note that the AN method achieves better secrecy capacity than the AI method. This is because the difference in capacities between Bob and Eve is smaller in the AI method compared to that in the AN method. Figure 6: Capacity comparison of various PLS schemes at Bob and Eve.
# A Deep Learning Approach to Teeth Segmentation and Orientation from Panoramic X-Rays
## 3 Materials and Methods
### Model Architecture
#### 3.2.1 Encoder  
To avoid manual scale adjustments, the study employs an EfficientNet architecture as the core encoder. Convolutional neural networks often require tweaking depth, width, and resolution, a traditionally cumbersome and random process. EfficientNet's authors [19] introduce a novel approach, using fixed coefficients (\(\alpha\), \(\beta\), and \(\gamma\)) and a compound coefficient \(\phi\) for uniform scaling. Depth, width, and resolution are scaled as \(\alpha^{\phi}\), \(\beta^{\phi}\), and \(\gamma^{\phi}\), respectively. The authors used platform-aware neural architecture search via MnasNet [20] to discover the baseline EfficientNet-B0 architecture. Based on this, they conducted a small grid search to determine the compound scaling coefficients \(\alpha=1.2\), \(\beta=1.1\), and \(\gamma=1.15\), which were used to uniformly scale the model to EfficientNet-B1 through B7. While EfficientNet-B0 is 224 \(\times\) 224, EfficientNet-B7 has \(\phi=6\), yielding \(224\times\gamma^{\phi}=224\times\ 1.15^{6}\approx 518\) resolution. So, we use an EfficientNet-B7 model trained on ImageNet for patches of size \(512\times 512\).
###### Abstract  
With the growing problem of global water pollution, nitrogen pollution has become a key factor affecting aquatic ecosystems and human health. The biological sponge iron system (BSIS) has gained attention as a research hotspot due to its efficient denitrification capability. This study focused on the changes in microbial community structure and the relative abundance and interrelationships of nitrogen cycle-related functional bacteria at different operational stages of the BSIS with a sponge iron (SFe) dosage of 90 g/L. The results showed that as the operation time of the reactor extended, the relative abundance of denitrifying genera such as _Saccharimonadales_, _Arenimonas_, and Acinetobacter significantly increased, while the relative abundance of Proteobacteria showed a trend of initial increase followed by a decrease. The relative abundance of nitrifying bacteria exhibited a more complex variation, whereas the abundance of denitrifying bacteria showed a continuous upward trend. In addition, there were complex interrelationships among different denitrifying bacteria, such as a positive correlation between _Saccharimonadales_ and _Aectobacteraceae_, and a negative correlation between _Rlodothermus_ and _Pseudoxanthomonas_. This study not only revealed the changes in the relative abundance and interrelationships of microbial communities and nitrogen cycle-related functional bacteria over time with an SFe dosage of 90 g/L, but also provided a new perspective for understanding the intrinsic mechanism of enhanced biological denitrification by sponge iron. These findings are of great significance for optimizing the operating parameters of the BSIS, improving denitrification efficiency, and promoting the practical application of this technology in the field of environmental engineering.  
bio-sponge iron system; microbial community structure; denitrifying functional microorganisms; evolution +
Footnote †: journal: Applied Sciences
## 2 Materials and Test Program
### California Bearing Ratio (CBR) Testing
#### 2.3.2 CBR Testing for Soils under Different Freeze-Thaw Cycles  
Roads in the seasonally frozen area will be subject to damage from the freeze-thaw cycles. The effect of freeze-thaw cycles on the CBR value was studied. Samples with a compaction degree of 90% (the dry density was 1656 kg\(\cdot\)m\({}^{\text{-3}}\)) and 100% (the dry density was 1840 kg\(\cdot\)m\({}^{\text{-3}}\)) were prepared. The moisture contents were 19% and 16.4% for samples with K = 90% and K = 100%, respectively. Note that the degree of compaction K is calculated as the quotient of the dry density of the sample to the maximum dry density obtained by the compaction test at a compaction energy of N = 98; the maximum dry density in this study was 1840 kg\(\cdot\)m\({}^{\text{-3}}\). First, the samples were prepared according to the compaction test procedure. The samples were then sealed with plastic wrap, so that there was no increase in or loss of moisture. The samples underwent 0, 1, 2, 4, 6, 8, 10 and 12 freeze-thaw cycles (freezing at -15 \({}^{\circ}\)C for 12 h and thawing at 25 \({}^{\circ}\)C for 12 h) in a temperature control box operating from -40 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. Lastly, the samples, after completing the corresponding freeze-thaw cycles, were placed in a water container for soaking, and then CBR tests (as described in Section 2.3.1) were carried out. The expansions produced during the immersion process were also measured. The samples prepared are listed in Table 5.
## 2 Methods
### SE Attention Module  
The main components of the SE attention module include global pooling layer, fully connected layer, and Sigmoid activation function. Firstly, the global pooling layer performs global average pooling on the input feature map, resulting in a \(1\times 1\times C\) vector, where \(C\) represents the number of channels. Then, the fully connected layer maps this vector to a vector with the same number of channels. Finally, the Sigmoid activation function limits the output of the fully connected layer to a value between 0 and 1, resulting in a weight matrix with the same shape as the input feature map. This weight matrix can be seen as a recalibration of the input feature map, guiding the model to focus on features with important information.  
Figure 1 shows the SE attention module [9], which mainly consists of squeeze and excitation parts. The main steps of the SE attention module are as follows: first, the squeeze operation is performed. The \(W\times H\times C\) feature map is first passed through a dual-channel pooling layer to obtain the global information descriptor of \(1\times 1\times C\). After excitation, two fully connected layers are used to predict the importance between channels.  
First, the first fully connected layer is scaled through, with the input and output being \(1\times 1\times C\) and \(1\times 1\times C/r\), respectively, where \(r\) is the scaling parameter. Then, the second fully connected layer is cycled through, with the input and output being \(1\times 1\times C/r\) and \(1\times 1\times C\), respectively.  
Afterwards, Sigmaid is used to output vectors of weight values for each layer in the feature map. Finally, there is the scale operation, which multiplies the output weight vector with the feature map to obtain a feature map with weight information. This step focuses on effective features, avoids invalid features, and can better extract target features, all of which can improve the accuracy of the model.
## 5 Damage Detection in Wavefield Images Based on NCA-RPCA Algorithm
### Wavefield Image Damage Identification of Single Defects Based on NCA-RPCA Algorithm
#### 5.2.4 Comparison of Algorithms
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm Name** & **Intermediate Damage** & **Edge Damage** & **Two Damages** & **Three Damages** \\ \hline IALM & 434.39 s & 263.21 s & 308.38 s & 225.96 s \\ APG & 21.10 s & 21.93 s & 24.30 s & 25.38 s \\ RPCA-GD & 9.85 s & 12.56 s & 16.78 s & 16.71 s \\ NCA-RPCA & 0.80 s & 0.86 s & 0.85 s & 0.85 s \\ (ARCIAN) & 0.82 s & 0.82 s & 0.84 s & 0.84 s \\ NCA-RPCA (Log) & 1.44 s & 1.10 s & 0.87 s & 0.85 s \\ NCA-RPCA (GCAD) & 0.85 s & 0.85 s & 0.87 s & 0.89 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The processing time of different algorithms. improvement in the overall performance of the algorithm proposed in this paper, providing a more reliable and efficient solution for practical applications in related fields. Figure 29: The decomposition results of the improved algorithm regarding different non-convex penalty functions. (**a**) NCA-RPCA (ARCTAN) decomposition result. (**b**) NCA-RPCA (ETP) decomposition result. (**c**) NCA-RPCA (SCAD) decomposition result. (**d**) NCA-RPCA (Log) decomposition result.
## 3 Materials and Methods
### Top Intrusion Features List  
We provide a detailed list of the most significant network intrusion features along with their explanations for the three datasets under investigation. Specifically, explanations for each feature in the RoEduNet-SIMARGL2021 dataset are detailed in Table 1, descriptions of the features for the CICIDS-2017 dataset are included in Table 2, and the features for the NSL-KDD dataset are itemized in Table 3. It is essential to note that our XAI framework will be employed to analyze various metrics regarding the importance of these features in our evaluation (Section 4). We stress that these tables contain explanations for all top intrusion features extracted in our evaluation. This encompasses features selected by baseline methods utilizing information gain or K-best (as per [35; 36]), model-specific features derived from SHAP global summary plots, overall top features common across AI models (illustrated in Table 4 based on frequency analysis from SHAP plots), and attack-specific features. Attack-specific features are obtained through manual rearrangement and frequency analysis, focusing on individual attacks rather than a holistic view. Details regarding overall feature importance and attack-specific features are thoroughly discussed in our evaluation in Section 4.3.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**RoEduNet-SIMARGL2021 Features** & **Explanation** \\ \hline FLOW\_DURATION\_MILLISECONDS & Flow duration in milliseconds \\ PROTOCOL\_MAP & IP protocol name (tcp, ipv6, udp, icmp) \\ TCP\_FLAGS & Cumulation of all flow TCP flags \\ TCP\_WIN\_MAX\_IN & Max TCP Window (src\_>dst) \\ TCP\_WIN\_MAX\_OUT & Max TCP Window (dst\_>src) \\ TCP\_WIN\_MIN\_IN & Min TCP Window (src\_>dst) \\ TCP\_WIN\_MIN\_OUT & Min TCP Window (dst\_>src) \\ TCP\_WIN\_SCALE\_IN & TCP Window Scale (src\_>dst) \\ TCP\_WIN\_MS\_IN & TCP Max Segment Size (src\_>dst) \\ TCP\_WIN\_SCALE\_OUT & TCP Window Scale (dst\_>src) \\ SRC\_TOS & TOS/DSCP (src\_>dst) \\ DST\_TOS & TOS/DSCP (dst\_>src) \\ FIRST\_SWITCHED & SysUptime of First Flow Packet \\ LAST\_SWITCHED & SysUpdate of Last Flow Packet \\ TOTAL\_FLOWS\_EXP & Total number of exported flows \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of main features for RoEduNet-SIMARGL2021 dataset [63].  
Figure 3: An example of a local explanation for correctly predicting a DoS sample from the RoEduNet-SIMARGL2021 dataset for the RF model using SHAP.
# Review of Power Electronics Technologies in the Integration of Renewable Energy Systems
## 3 Literature Survey
### PV Power Optimizer  
Power systems connected with PV power optimizers have revolutionized the way we harness solar energy. PV power optimizers as shown in Figure 11 are the devices that are installed on each individual solar panel, with which the reliability of the system can be enhanced. PV power optimizers work by maximizing the power output of each solar panel. They perform a crucial role in the electrical power system network by optimizing the voltage and current levels of each panel, ensuring that they operate at their maximum potential [74; 79; 83; 84; 85]. This is especially beneficial in scenarios where panels may be partially shaded or experiencing different levels of sunlight. The connections between solar panels and PV power optimizers are vital for the optimal functioning of the system. Each solar panel is equipped with a PV power optimizer, which constantly monitors and adjusts the electrical characteristics of the panel.  
This allows for the maximum power to be extracted from each panel, even under less-than-ideal conditions. PV power optimizers have the ability to mitigate the impact of partial shading. In a traditional solar panel system, the overall efficiency of the system is minimized when any one panel is shaded. However, with PV power optimizers, the shaded panel's performance is isolated; therefore, the impact on the rest of the system can be curtailed [86; 87; 88; 89; 90]. This ensures that the system operates efficiently, even in challenging shading conditions. Another benefit of power systems connected with PV power optimizers is their ability for enhanced system monitoring and maintenance. Each PV power optimizer provides real-time data on the performance of individual solar panels. This allows for easy identification of any issues or malfunctions, making troubleshooting and maintenance tasks more straightforward and efficient. Furthermore, these power systems contribute to increased energy production and overall system efficiency. By optimizing the performance of each solar panel, PV power optimizers maximize the amount of electricity generated. This results in higher energy yields and a more efficient use of available sunlight. Power systems connected with PV power optimizers also offer scalability and flexibility.
# Using Geospatial Information to Map Yield Gain from the Use of _Azospirillum brasilense_ in Furrow
## 1 Introduction
These models showed higher accuracy with the use of images taken in the phenological stage between R2 and R3, representing the period from 12 to 18 days after fertilization, where the crop presents a significant biomass gain. On the other hand, neural networks (multilayer perceptron, MLP) are used more frequently in studies related to yield estimation in corn crops, both through images from remotely piloted aircraft [10] and from orbital sensors [11]. The latter study used MLP based on a back-propagation algorithm to estimate yield through MODIS and Global Land Surface Satellite images. The authors estimated yield with mean absolute percentage error (MAPE) at \(<\)10% and root mean square error (RMSE) at 700 kg/ha in most cases. As observed in these experiments, work with prediction models based on machine learning was highly accurate in predicting corn yields [7; 12; 13]. In one of these studies, the possibility of predicting maize yield with RMSE deviation (RMSE%) was 84% from Bayesian neural networks and Landsat multispectral images [7]. In another study, maize yield was estimated with an R\({}^{2}\) of 0.64 from algorithms based on random forests and aerially surveyed multispectral images [13]. It is possible to map the spatial distribution of maize using interpolators, such as Kriging and spectral prediction models, based on machine-learning algorithms in different treatment and management conditions. Furthermore, it is difficult to demonstrate the actual yield of maize crops after the application of biological products through statistical analysis. Thus, it remains unanswered whether it is possible to map areas of yield gain and loss after the application of biostimulants based on _A. brasilense_ to maize crops using geospatial data manipulation techniques. This study aims to evaluate the potential of techniques based on geospatial information data, such as Kriging and spectral models, to estimate the yield gain of maize under field conditions with the application of _A. brasilense_. It is worth noting that our method was applied to a commercial area in a tropical region, but it can be replicated in areas of different sizes and climatic and management conditions, providing an alternative way to demonstrate the actual yield gain of maize yield after the application of a biostimulant (_A. brasilense_).
# Effect of Aging on Class G High Sulfate-Resistant Oil Well Cement Under High Relative Air Humidity
###### Abstract  
Previous research on cement aging mainly focuses on construction cement, exploring the mechanisms through which aging conditions affect cement properties. However, the impact of aging on oil well cement remains understudied. Aging of cement under high-humidity conditions leads to significant alterations in its properties, indicating that the cement formulation needs to be adjusted to reduce the negative effects during cementing operations. The effect of aging on particle size, mineral composition, and early hydration behavior of oil well cement after 0, 7, 14, and 28 d at 90% relative humidity (\(\pm\)3%RH) and 25 \({}^{\circ}\)C (\(\pm\)2 \({}^{\circ}\)C) was investigated. The results showed that, during the aging process, the uptake of H\({}_{2}\)O and CO\({}_{2}\) from the surrounding atmosphere by cement leads to slight hydration. This process was associated with a reduction in the specific surface area and surface energy. The contents of hydration products ettrinige (AFt) and calcium hydroxide (CH) increased, whereas the amounts of C\({}_{3}\)S and C\({}_{3}\)A decreased. Consequently, the early hydration rate of cement decreased along with a reduction in the cumulative heat release. As the aging time increased, the compressive strength and thickening time of the cement pastes decreased, and the rheological properties deteriorated. Under the experimental temperature and humidity conditions, the permissible aging time without significant deterioration should not exceed 7 d, with a maximum permissible aging time of 14 d.  
oil well cement; hydration; aging; cement mineral compositions; specific surface area +
Footnote †: journal: Applied Sciences
## 4 Results and Analysis
### Performance Analysis of the Proposed Model for Dataset 1  
Figure 5 illustrates the confusion matrix and the ROC AUC for the ensemble model. The ensemble model misclassified the most instances for class 2, with eight instances misclassified. Conversely, classes 1, 3, 5, and 6 were the least misclassified. The model was capable of accurately classifying 627 instances from class 1, 2617 instances from class 2, 1802 instances from class 3, 1141 instances from class 4, 288 instances from class 5, and 410 instances from class 6.  
The model performs exceptionally well, as evidenced by the ROC AUC, which indicates that all the class contours are exceedingly near to 1. Additionally, the AUC score for each class is 1.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Method** & **Model** & **Precision** & **Recall** & **F1 Score** \\ \hline \multirow{8}{*}{[PERSON] et al. [14]} & LR & 0.63 & 0.65 & 0.59 \\  & KNN & 0.92 & 0.929 & 0.924 \\  & RF & 0.99 & 0.99 & 0.99 \\  & GNB & 0.74 & 0.73 & 0.74 \\  & SVM & 0.94 & 0.94 & 0.94 \\  & DT & 0.98 & 0.99 & 0.99 \\  & XGB & 0.98 & 0.983 & 0.987 \\ \hline \multirow{8}{*}{This study} & LR & 0.70 & 0.69 & 0.701 \\  & KNN & 0.92 & 0.927 & 0.93 \\  & RF & 0.99 & 0.99 & 0.99 \\  & GNB & 0.74 & 0.73 & 0.74 \\  & SVM & 0.97 & 0.967 & 0.96 \\  & DT & 0.99 & 0.99 & 0.998 \\  & XGB & 0.99 & 0.99 & 0.99 \\  & HGB & 0.99 & 0.99 & 0.99 \\  & Ensemble Model & 1.00 & 0.99 & 0.99 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Classification report for dataset 1 on different algorithms.  
Figure 5: \(Cont\).
# Quantum-Based Analytical Techniques on the Tackling of Well Placement Optimization
## 4 Results and Discussion
### Performance Evaluation and Statistical Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **GSA** & **PSO** & **CSA** & **GA** & **DE** & **BA** & **QPSO** & **QBA** \\ \hline Max & \(3.84\times 10^{9}\) & \(5.14\times 10^{9}\) & \(3.72\times 10^{9}\) & \(5.09\times 10^{9}\) & \(5.13\times 10^{9}\) & \(5.30\times 10^{9}\) & \(5.03\times 10^{9}\) & \(5.33\times 10^{9}\) \\ Min & \(2.83\times 10^{9}\) & \(3.43\times 10^{9}\) & \(2.43\times 10^{9}\) & \(3.01\times 10^{9}\) & \(3.38\times 10^{9}\) & \(2.10\times 10^{9}\) & \(4.38\times 10^{9}\) & \(4.29\times 10^{9}\) \\ Average & \(3.33\times 10^{9}\) & \(4.07\times 10^{9}\) & \(3.24\times 10^{9}\) & \(3.67\times 10^{9}\) & \(4.26\times 10^{9}\) & \(3.28\times 10^{9}\) & \(4.77\times 10^{9}\) & \(4.67\times 10^{9}\) \\ Standard deviation & \(2.62\times 10^{8}\) & \(5.72\times 10^{8}\) & \(3.73\times 10^{8}\) & \(5.11\times 10^{8}\) & \(4.59\times 10^{8}\) & \(8.35\times 10^{8}\) & \(1.60\times 10^{8}\) & \(2.74\times 10^{8}\) \\ Effectiveness & \(6.24\times 10^{-1}\) & \(7.63\times 10^{-1}\) & \(6.08\times 10^{-1}\) & \(6.88\times 10^{-1}\) & \(8.00\times 10^{-1}\) & \(6.16\times 10^{-1}\) & \(8.94\times 10^{-1}\) & \(8.76\times 10^{-1}\) \\ Efficiency & \(1.39\times 10^{-1}\) & \(5.53\times 10^{-1}\) & \(5.09\times 10^{-1}\) & \(4.78\times 10^{-1}\) & \(6.46\times 10^{-1}\) & \(8.25\times 10^{-1}\) & \(4.28\times 10^{-1}\) & \(5.38\times 10^{-1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistical data of applied metaheuristic algorithms on well placement optimization for case study 2. Again, the result of case study 2 in Table 5 shows that the QPSO and QBA algorithm is superior on three and five criteria, respectively. However, PSO and DE also achieved the same maximum value, their average value is lower than QBA and QPSO. The box plot results in Figure 7b illustrates that QBA has the lowest standard deviation compared to other algorithms, and QPSO has the second-lowest standard deviation. The main results of the study of case 2 are somewhat tantamount to the previous study as it shows quantum-based algorithms are a better choice considering all 6 criteria.