esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification
## 3 Materials and Methods
### Adaptive Boosting (AdaBoost) Algorithm  
AdaBoost, which stands for \"adaptive boosting\", AdaBoost, or \"adaptive boosting\" is a well-known machine learning algorithm used for binary classification. It combines many weak classifiers to form a stronger classifier. Its core principle is to award larger weights to misclassified samples. This technique seeks to correct misclassifications in succeeding rounds, eventually resulting in the right classifications. The Pseudo Code of AdaBoost Algorithm is presented in Algorithm 2.  
The working principles of Adaboost are stated below:  
1. Weight initialization: At the start, each sample in the dataset is given an equal weight. Weights for each sample: \(\frac{1}{N}\) Number of training instances: \(N\)
2. Training weak classifiers: Training samples are trained on classifiers, and the overall error of the model is determined as \(\Sigma\) (weights of erroneously categorized samples)
3. Calculating classifier weight: The weight of each weak classifier is determined by its performance. A classifier that performs better receives more weight. The basic classifier's performance is computed using the formula: \[\text{Performance}=\frac{1}{2}\times\log\frac{1-\text{Total Error}}{\text{Total Error}}\] (2)
4. Weight updating: This provides for a reduction in the weight of correctly identified samples while raising the weight of wrongly categorized ones. New weights for misclassified samples: \[\text{New Weight}=\text{Old Weight}\,\times\,\text{e}^{\text{performance}}\] (3) To calculate new weights for successfully identified samples, multiply the old weight by \[\text{New Weight}=\text{Old Weight}\,\times\,\text{e}^{-\text{performance}}\] (4)
5. Combining weak classifiers: The weak classifiers are combined by assigning weights to them based on their performance. These weights determine the contribution of each classifier to the final prediction.
6. Making predictions: The final strong classifier is constructed by integrating the predictions of all weak classifiers. The forecast is produced based on the weighted majority vote of the weak classifiers.
# SEM Studies Assessing the Efficacy of Laser Treatment for Primary Teetis: A Systematic Review
## 2 Materials and Methods
### Quality Assessment (QA)  
Two independent reviewers ([PERSON], P.J.P.) systematically assessed the methodological quality of each study included in the article. The evaluation criteria were centered on the presence of relevant data regarding the efficacy of laser treatment for primary teeth, as assessed using SEM. Criteria encompassing the design, conduct, and analysis of the studies were considered, including a stipulated minimum group size of 10 subjects, the inclusion of a control group, the application of sample size calculations, a comprehensive description of laser parameters (power, frequency, wavelength, spot size, etc.), the irradiation protocol (distance, time, steps, etc.), and a detailed account of the treatment efficacy. Studies received scores on a scale ranging from 0 to 6, with higher scores indicative of superior study quality. The assessment of bias risk followed a categorization where 0-2 points signaled a high risk, 3-4 points suggested a moderate risk, and 5-6 points indicated a low risk. Any discrepancies in scoring were meticulously resolved through discussion until a consensus was achieved.
# An Improved Mixed AC/DC Power Flow Algorithm in Hybrid AC/DC Grids with MT-HVDC Systems
## 1 Introduction
### Solving the Power Flow Problem for Hybrid AC/DC Grids with MT-HVDC Systems  
The unified method and sequential method are the two well-known methods to solve the PF problem for hybrid AC/DC grids with MT-HVDC systems.
## 2 CO Device Implementation
### Electronic Circuit  
For the signal adaptation of our chemical sensor, an economic electronic circuit was proposed. The electronic circuit consists of a Wheatstone bridge, a differential amplifier and an operational amplifier (Op-amp) comparator have been applied. The electronic circuit has been supplied by Vcc1 and Vcc sources. The Wheatstone bridge consists of four resistors: \(R_{1}=R_{2}\) are precision resistors, \(R_{s}\) is the resistance of the chemical sensor and \(R_{x}\) is a variable resistance for calibration. The differential amplifier consists of an operational amplifier and four precision resistors, \(R_{3},\ R_{4},\ R_{5},\text{ and }R_{6}\). Finally, the Op-am comparator will be an operational amplifier. Basically, the signal adaptation circuit requires two stages: calibration and detection.
## 4 Experimental Results and Analysis
### Quantitative Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**SSIM Values**} \\ \hline
**CT Images** & **Median** [25] & **Mean [28]** & **Wiener** [42] & **Gaussian** [24] & **NLM [23]** & **DWT [27]** & **DrCNN** [56] & **Proposed Approach** \\ \hline Image R1 & 0.9796 & 0.9775 & 0.9863 & 0.9774 & 0.9834 & 0.9952 & 0.9987 & 1.000 \\ \hline Image R2 & 0.9570 & 0.9647 & 0.9589 & 0.9408 & 0.9694 & 0.9876 & 0.9899 & 1.000 \\ \hline Image R3 & 0.9418 & 0.9291 & 0.9335 & 0.9071 & 0.9712 & 0.9854 & 0.9887 & 1.0000 \\ \hline Image R4 & 0.9051 & 0.8934 & 0.9045 & 0.8703 & 0.9391 & 0.9591 & 0.9786 & 0.9967 \\ \hline Image R5 & 0.9042 & 0.8963 & 0.9123 & 0.9278 & 0.9545 & 0.9589 & 0.9785 & 0.9796 \\ \hline Image R6 & 0.8964 & 0.9121 & 0.9345 & 0.9221 & 0.9486 & 0.9675 & 0.9897 & 0.9986 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of SSIM values of images R1, R2, R3, R4, R5, and R6 using the proposed approach and standard filtering schemes (wiener, mean, median, DWT, Gaussian, NLM, and DnCNN). Figure 5: (a) SNR for different denoising schemes at various SPN noise intensities (5–30% noise intensity. (b) SNR for different denoising schemes at various SPN noise intensities (35% to 60%). Figure 6: Graph comparison of MSE values of different filtering schemes and the proposed approach for CT images R1, R2, R3, R4, R5, and R6. Figure 8: Linear graph depicting SSIM values for CT images R1, R2, R3, R4, R5, and R6 at various AGBN intensities. Figure 7: Linear comparisons of MSE values of different filtering schemes and the proposed approach for CT images R1, R2, R3, R4, R5, and R6.
## 4 Materials and Methods
### GC-MS Analysis and Metabolite Identification  
A 436-GC model coupled to a SCION single quadrupole (SQ) mass spectrometer (Bruker Daltonics, Bremen, Germany) was used for VOCs analysis. The GC system was equipped with a fused silica capillary column (Rxi-5 Sil MS, 30 m \(\times\) 0.25 mm internal diameter \(\times\) 0.25 \(\upmu\)m; Restek Corporation, U.S., Bellefonte, PA, USA) and high purity helium C-60 (Gasin, Porto, Portugal) was used as carrier gas at a constant flow rate of 1.0 mL/min. The oven temperature was programmed at 40 \({}^{\circ}\)C for 1 min, increased at 5.0 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C where it was held for 5 min, and then increased at 5.0 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C. The injector, transfer line, ion source and manifold temperatures were maintained at 250, 250, 260 and 41 \({}^{\circ}\)C, respectively. The SQ-MS was operated in electron impact (EI) mode (70 eV). Data acquisition was performed in full scan mode from _m/z_ 40 to 400 with a scan time of 500 ms. For VCCs analysis, a 436-GC model coupled to an EVOQ triple quadrupole (TQ) mass spectrometer (Bruker Daltonics, Bremen, Germany) was used. The column and the carrier gas were the same described above for VOCs analysis. The oven temperature was programmed at 40 \({}^{\circ}\)C for 1 min, increased at 5.0 \({}^{\circ}\)C/min to 250 \({}^{\circ}\)C where it was held for 5 min, and then increased at 20.0 \({}^{\circ}\)C/min to 300 \({}^{\circ}\)C. The injector, transfer line, ion source and manifold temperatures were maintained at 250, 260, 270 and 41 \({}^{\circ}\)C, respectively. The TQ-MS was operated in EI mode (70 eV) and data acquisition was performed in full scan mode from _m/z_ 35 to 600 with a scan time of 250 ms. The software used in the two equipment was a Bruker Daltonics MS workstation (version 8.2.1, Bruker Daltonics, Bremen, Germany). Urine samples were randomly injected, and QCs were repeatedly analyzed on every five samples.  
Metabolite identification was performed by comparing the mass spectra and retention indices (RI), determined using a commercial hydrocarbon mixture (C6-C20), obtained for urine samples with the National Institute of Standards and Technology (NIST) standard reference database (version 14, Gaithersburg, MD, USA). A tolerance of \(\pm\) 20 was accepted between the experimental and NIST RI and the minimum reverse match considered was 770. The identification was further confirmed by analysis of commercial standard compounds under the same conditions. This procedure enabled the definition of levels of confidence in metabolite identification as recommended for metabolomic studies [20; 21].
## 4 Numerical Results
### Error Estimation  
For the simulation of induction thermography NDT, current and power estimation is important. According to our model, the potential has been solved, the current can be calculated according to Equation (8), and the power is defined as the power generated by the eddy current that can be calculated according to the current and resistance. In order to verify the accuracy, one should compare the potential and current obtained by the new method and those obtained directly by the global system (11).  
The relative error is defined as  
\[\mathrm{err}_{S}=\frac{\|S_{\mathrm{new}}-S\|}{\|S\|}, \tag{23}\]  
where \(S_{\mathrm{new}}\) is the solution being the potential \(V\), the current \(I\) and the power \(M\) are solved by the new method (DD or DD-POD), and \(S\) is the reference solution directly solved by the global system.  
Firstly, the comparison of convergence is performed between DD and DD-POD. The imposed magnetic source is the magnetic field \(\mathbf{B}\) along the \(x\), \(y\), and \(z\) directions, noted as \(\mathbf{B}_{x}\), \(\mathbf{B}_{y}\), and \(\mathbf{B}_{z}\). In order to compare the convergence without penalising DD with a bad initialisation, one uses the first solution of DD-POD from the Equation (20) as the initialisation of DD.  
Figure 7 shows that DD-POD converges faster than DD. As in Figure 8, DD converges slowly--it even takes more than 600 iterations to converge.  
For DD-POD, when the number of iteration is 100, the relative errors of potential, current, and power are shown in Table 1. The errors are less than 1%, which means DD-POD converges well. In fact, DD-POD converges so rapidly that it needs only 20 iterations to let the relative error of current achieve the 1%.  
Figure 7: _Cont._  
Figure 8: The convergence of DD.  
Figure 7: The comparison of convergence between DD and DD-POD.
###### Abstract  
Plant leaf temperature and its environmental parameters provide valuable information on plant growth. This paper presents the development of a plant monitoring system using an IoT-based SCADA (Supervisory Control and Data Acquisition). The developed SCADA system monitors the leaf temperature and the air parameters of temperature and humidity, as well as the soil parameters of temperature, moisture, pH, electrical conductivity, nitrogen, phosphorous, and potassium. A novel method is proposed for measuring the leaf temperature using a low-cost 8 \(\times\) 8 array thermal camera. The sensor systems in the field are developed to wirelessly communicate with the Hawell IoT Cloud HMI via a Modbus TCP protocol. To visualize the thermal image on the HMI dashboard, a novel approach is proposed wherein the data are transferred using the Modbus TCP protocol. The HMI is connected to a cloud server and can be accessed by the users using the web browser or mobile application on a smartphone. The experimental results show that the proposed hardware, software, and communication protocol are reliable for real-time and continuous plant monitoring. Further, the evaluation of sensor data shows that the data from the thermal camera and air parameters sensor can be independently interpreted. However, the data from the soil sensor should be interpreted in consideration of the other parameters.  
Internet of Things; SCADA; leaf temperature; plant monitoring +
Footnote †: journal: Applied sciences
# Mini-Review: Modeling and Performance Analysis of Nanocarbon Interconnects
## 5 Cu-Nanocarbon Interconnect
### Cu-CNT Composite Interconnect
Graphene was recently proposed as the ultimate diffusion barrier layer for Cu interconnect technology. By coating graphene onto Cu wires, the surface scattering of electrons in Cu can be reduced, and the grain size can be increased. To maximize the effective conduction area, large grain single-layer graphene is desired, and more efforts should be devoted to the development of low temperature and transfer free graphene growth techniques on dielectric and Cu. The scheme of Cu-CNT composite interconnect was realized by co-depositing Cu with CNTs. Such interconnect can achieve a balance between performance and reliability. However, with the scaling of IC feature size, both Cu-graphene and Cu-CNT composite interconnects face the challenge of performance degradation due to the increased scattering probability. Therefore, Cu-nanocarbon interconnects are a temporary but practical solution to meet short-term interconnect challenges. Yet in long-term, pure nanocarbon interconnects are still the most promising schemes, and they can be utilized together with Cu-nanocarbon interconnects. Figure 19: (**a**) Effective resistivity and (**b**) time delay of Cu, SWCNT, and Cu-SWCNT composite interconnect [114].
## 2 UWB Signal Processing Steps
### Data Cleansing and Clutter Rejection
#### 2.1.2 Principal Component Analysis  
Computing PCA of a data set \(X\) involves subtracting the mean of each measurement type and computing the eigenvectors of \(XX^{T}\)[17]. Given the radar-scan data represented by a rectangular matrix \(X_{ij}\), whose dimension is \(M\times N\) (\(i\) = 1, 2, \(\ldots\), \(M\); \(j\) = 1, 2, \(\ldots\), \(N\)), the N principal components of data matrix \(X\) can be given by:  
\[Y\ =Z^{T}X, \tag{3}\]  
where \(X\) = \([x_{1}\), \(x_{2}\), \(x_{3}\), \(\ldots\), \(x_{\rm n}]^{T}\) is the zero-mean input vector, \(Y\) = \([y_{1}\), \(y_{2}\), \(y_{3}\), \(\ldots\), \(y_{n}]^{T}\) is the output vector called the vector of principal components, \(Z\) is an \(M\times N\) matrix that transforms \(X\) into \(Y\).-Z can be computed using the covariance matrix. PCA assumes that \(Z\) is an orthonormal matrix (\(Z_{i}^{T}\cdot Z_{j}\) = \(\delta ij\)) such that the covariance matrix of \(Y\); (\(C_{y}\)) is diagonalized. Then, the covariance matrix \(C_{x}\) of \(X\) is given by:  
\[C_{x}=\frac{1}{N}XX^{T}. \tag{4}\]  
The eigenvector and eigenvalue matrices of \(C_{x}\), denoted \(\Phi\) and \(\Lambda\) respectively, are computed by:  
\[C_{x}\Phi\ =\ \Phi\Lambda, \tag{5}\]  
Figure 3: Raw signal frame: (**a**) before bandpass-filtering; (**b**) after bandpass-filtering.  
where \(\Lambda=\text{diag}(\lambda_{1},\,\lambda_{2},\,\lambda_{3},\,\ldots,\,\lambda_{N})\) and \(\lambda_{1},\,\lambda_{2},\,\lambda_{3},\,\ldots,\,\lambda_{N}\) are the eigenvalues. If one assumes that the eigenvalues are sorted in an ascending order, \(\lambda_{1}\geq\lambda_{2}\geq\lambda_{3}\geq\ldots\geq\lambda_{N}\), then the N-leading eigenvector-matrix \(Z\) is given by:  
\[Z=\left[\Phi_{1}\,,\,\Phi_{2}\,,\,\Phi_{3}\,,\,\ldots,\,\Phi_{N}\,\right]\,. \tag{6}\]  
The clutter-free signal space can be extracted from the original entire signal space according to  
\[E\,=\,Z_{n}X\,. \tag{7}\]  
We select the matrix \(Z_{n}\) to be a matrix whose rows \(z_{\text{n}}\) are the eigenvectors of \(XX^{T}\) (the principal components of \(X\)).
## 4 Discussion  
Previous research results have revealed the following: in the surface soil layer (0-20 cm), the concentration of cadmium is up to 0.031 mg kg\({}^{-1}\), while lead is below the MPC (maximum permissible concentration) [56] for most of the soil samples. The average zinc content slightly exceeds the MPC, with 1.35 under the rice fields of Bakbakty, but exceeds the MPC for all of the soil samples [34]. The largest amount of lead and copper was registered in the soils adjacent to the tailings dumps of the Balkhash Mining and Metallurgical Combine (BMMC): lead values ranged from 3.5 to 6.0 MPC, and copper values ranged from 3.5 to 9.4 MPC.  
In general, the content of heavy metals in soils is relatively low compared to other investigations [9; 19]. A significant exception is cadmium, wherein cadmium exceeds the MPC standard by 29 times. This indicates that different amounts of heavy metals may accumulate in soils under the same site conditions in terms of pollution. As a rule, cadmium pollution is polymetallic in nature. The distribution over the study area is mosaic-like, and centers of contamination form.  
The measured soil pH averages 8.5, indicating an alkaline environment where the sedimentation of cadmium compounds has been taking place [46; 52; 57; 58]. The concentration of other elements is significantly lower than their Clarke values, which may indicate their low migration activity [54]. However, considering the Clarke values for comparison is problematic because our metal analyses are the first investigations carried out in this area. All of the heavy metals were arranged in decreasing order of their concentration as follows: Zn > Ni > Co > Cu > Pb > Cd. According to this, the total content of heavy metals and chemical elements in the soils of the Balkhash region in terms of the average concentration of the studied elements in descending order are as follows: Mn > Cr > As > Ni > Pb > Cu > Cd [31]. The average concentration of metals in all of the studied soil samples exceeds the background by 1.6-22.5 times. The maximum excess of the background was typical for cadmium (5.4 times).  
The soils of the Iii River Delta and the Iii-Balkhash State Nature Reserve are being studied for the first time. The measured metal concentrations are cumulative enrichments over a period of time [19]. Land degradation, as a consequence of long-term and/or intensive metal accumulation in the delta soils of this vast territory (about 8 thousand km\({}^{2}\)), cannot be excluded in the future.  
Heavy metal soil contamination is common on a global scale [51; 59]. The metals contained in soils can migrate into plant tissues, which, in turn, migrate along the food chain, leading to an increased concentration of heavy metals in grazing plants, which can be dangerous for wild animals, grazing animals and people who consume the meat of grazing animals [60; 61; 62; 63].
# Robust Text-to-Cypher Using Combination of BERT, GraphSAGE, and Transformer (CoBGT) Model
## 3 Proposed Model
### Cypher Query Prediction  
The value extracted from the Key Information Extraction module along with the relevant relations and properties identified by the Question-Schema Relationship Modeling module are fed into the Cypher query prediction module to generate the corresponding Cypher query. In our proposed system, we employ a small version of the T5 Transformer  
Figure 7: Building the connection between token nodes (V\({}_{1}\)) based on similarity matching score.  
model [32] to generate the final Cypher query from the extracted key information and the schema relationships. The key features of the Transformer model in our context are:  
Input sequence: The contextual information from the Key Information Extraction and Question-Schema Relationship Modeling modules are fed into the Transformer model.  
Output Generation: The Transformer predicts the Cypher query by learning to map the input representations to the correct sequence of query tokens.  
The equation used to construct the input sequence is represented as follows:  
\[X=[CLS],D_{1},R,[SEP],D_{2},K,[SEP],D_{3},Q,<EOS>\]  
where:  
\[\begin{split} X:Input\ sequence\\ D_{1}:direct\ sequence\ 1\\ D_{2}:direct\ sequence\ 2\\ D_{3}:direct\ sequence\ 3\\ R:relation-properties\ feature\\ K:key\ value\ feature\\ Q:question\end{split} \tag{3}\]  
To guide the Cypher prediction module in identifying the information and question components, we defined three sequence guidelines: \(D_{1}\), \(D_{2}\), and \(D_{3}\). The guideline is a defined sequence, which helps the model better understand which information is addressed after. Specifically, \(D_{1}\), \(D_{2}\), and \(D_{3}\) guideline was used for relation-properties features, key-value features, and the question, respectively. Therefore, the relation-properties features follow the \(D_{1}\) guideline, the key-value feature follows \(D_{2}\), and the question follows \(D_{3}\). To make the input sequence for the Cypher prediction module, firstly, a special token [CLS] is inserted at the beginning to signal the start of the information. Next, the \(D_{1}\) guideline is added, followed by the relation-properties information. After that, the pairs key value guideline (\(D_{2}\), \(K\)) and question guideline (\(D_{3}\), \(Q\)) are also included in the sequence. We separated those pairs by adding [SEP] token between them. At the end of the sequence, an \(<\)EOS\(>\) token is added to indicate the end of the information. In our experiment, we defined \(D_{1}\) = \"With this information\", \(D_{2}\) = \"Key value:\", and \(D_{3}\) = \"Convert this to Cypher query:\". The detail input is illustrated in Figure 8.  
Figure 8: Flow process in Cypher query prediction module.  
By leveraging BERT, GraphSAGE, and Transformer models, our system can generate syntactically and semantically accurate Cypher queries, ensuring precise retrieval of information from the Neo4j graph database.