esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimal Design Parameters for Hybrid DC Circuit Breakers Using a Multi-Objective Genetic Algorithm
## 3 Parameters Optimization Using Multi-Objective Genetic Algorithm  
### Multi-Objective Formulation  
The optimal values of the HCB parameters are found using a multi-objective formulation that includes the objective functions (1) to (3). Weighting factors are used to combine the objective functions into a single objective function [29]. Furthermore, normalization is required in order to consistently combine objective functions (1) to (3). The objective function is formulated in (4).  
Minimize  
\[\text{f}^{\prime}(\text{x})=\sum_{\text{i}=1}^{3}\text{w}_{\text{i}}\text{f} _{\text{i}}^{\prime}(\text{x})=\text{w}_{1}\text{f}_{\text{i}}^{\prime}(\text {x})+\text{w}_{2}\text{f}_{2}^{\prime}(\text{x})+\text{w}_{3}\text{f}_{3}^{ \prime}(\text{x}) \tag{4}\]  
\[\text{f}_{\text{i}}^{\prime}(\text{x})=\frac{\text{f}_{\text{i}}(\text{x})- \text{f}_{\text{i,min}}}{\text{f}_{\text{i,max}}-\text{f}_{\text{i,min}}}\text { and }0\leq\text{f}_{\text{i}}^{\prime}(\text{x})\leq 1\]  
Subject to  
\[\text{x}=(\text{x}_{1},\text{x}_{2},\ldots,\text{x}_{n})\in\Omega\]  
\[\text{\sum_{i=1}^{3}\text{w}_{\text{i}}=1\text{ and }0\leq\text{w}_{\text{i}} \leq 1}\]  
where \(\text{f}_{\text{i}}^{\prime}(\text{x})\) is the normalized objective function \(\text{f}_{\text{i}}(\text{x})\), \(\text{f}_{\text{i,min}}\) and \(\text{f}_{\text{i,max}}\) are the minimum and maximum values for the \(\text{i}^{\text{th}}\) objective function \(\text{f}_{\text{i}}(\text{x})\), and x is a sample of individuals containing n design parameters of the HCB. The \(\Omega\) represents the constraint sets for x. The weight coefficient w\({}_{\text{i}}\) is a non-negative value corresponding to the \(\text{i}^{\text{th}}\) objective function, and it determines the contribution of each objective function to the global objective. Accordingly, in order to prevent damages to the system, the operation of the HCB should minimize the magnitude of the fault current and the interruption time. In contrast, up to 50 ms is allowed for the recovery time of the HCB after the fault clearance [30]. Therefore, the weight coefficients of \(\text{I}_{\text{pk}}\) and \(\text{T}_{\text{int}}\) should be larger than that of \(\text{T}_{\text{rec}}\), and the weight coefficients are selected as follows (w\({}_{1},\text{w}_{2},\text{w}_{3}=0.45;0.45;0.1\)).
## 1 Introduction
Hence, iron chelators are an attractive therapeutic option for restoring normal iron homeostasis and consequently inhibiting cell growth and intracellular ROS formation by removing iron from the labile iron pool and DNA enzymes [9]. Most cancer cells have a much higher basal level of ROS due to the increased labile iron and an increased level of antioxidants, which often counterbalance this increase in ROS [10]. This counterbalance can thus result in constantly increased levels of ROS not being toxic to the cells. However, cancerous cells can be killed by compounds that increase ROS; these can tip the balance, resulting in lethal levels of DNA damage. Iron chelators can be designed whereby their complexes participate in redox cycling, thus increasing ROS levels to a toxic level in cancer cells. These iron chelators can cause DNA damage and kill cancer cells and have recently been used in combination therapy for cancer [11; 12]. This review will seek to elucidate iron's role in metabolism and how this is altered in cancer. Therefore, the review will touch on iron chelators that can limit the labile iron available for incorporation into vital Fe-S clusters used in DNA replication and repair. Lastly, the review will focus on the vital role of iron in DNA replication and repair pathways and the design considerations that need to be considered so that iron chelators can be designed to take advantage of cancer cells' susceptibility to toxic levels of ROS. This review will not cover iron chelators and  
Figure 1: The reduction of Fe (III) by the superoxide radical (**A**), the Fenton reaction (**B**) and the complete oxidation-reduction process are known as the Haber–Weiss reaction (**C**). cancer stem cells; we refer the reader to additional reviews for the complete picture of iron chelators, metabolism and cancer stem cells [13; 14].
## 4 Results
### Analysis Results
#### 4.3.1 Analysis of Rail Transit Station Area Based on Crste  
According to the above results, the comprehensive development efficiency composition statistics of the seventeen DMU station areas are explained as follows. * Analysis of Crste calculation results of the full-factor index  
In the measurement results based on the BCC model, 13 station domains have a Crste of 1, while 4 station domains have a composite efficiency lower than 1. In the SBM super-efficiency model, the results show that 15 station domains have a composite efficiency greater than 1, generally ranging from 1.1 to 1.6, all of which are considered high-efficiency station domains (Table 3). This finding is consistent with the notion that the sites selected are located in commercially developed areas. Because the Crste values of each station area did not vary significantly, this set of data does not have a strong analytical reference value. Thus, it is necessary to analyze the Crste results of the indicators after factor dimensionality reduction. * Analysis of Crste calculation results of indicators after factor dimensionality reduction (Table 4), the analysis using the BCC model shows that the composite efficiency for 10 station areas is 1, while 7 station areas have a composite efficiency lower than 1. Based on the SBM super-efficiency model analysis, 11 station areas have a composite efficiency greater than 1, classifying them as high-efficiency stations. Four station areas have composite efficiency values between 0.8 and 1, namely Xiaozhai, Kangfulu, Weiyijie, and Bampo, which are classified as medium-efficiency stations. Two station areas, Tonghuamen and Woloukou, have composite efficiency values between 0.6 and 0.8, classifying them as low-efficiency stations. Combining the Crste results obtained based on the two sets of input-output indicators described above, minimal differences are noted between the DMUs for the results calculated using the original indicators, which makes it impossible to effectively divide the spatial efficiency level of the rail transit station area. The index calculation results obtained after dimensionality reduction better reflect the efficiency differences and relationships of each DMU. Therefore, this study uses the efficiency results calculated based on dimensionality reduction indicators to determine the level of the sample station area. The spatial efficiency gradient map of each station is shown in Figure 9.
###### Abstract  
Diabetes requires effective monitoring of the blood glucose level (BGL), traditionally achieved through invasive methods. This study addresses the non-invasive estimation of BGL by utilizing heart rate variability (HRV) features extracted from photoplethysmography (PPG) signals. A systematic feature selection methodology was developed employing advanced metaheuristic algorithms, specifically the Improved Dragonfly Algorithm (IDA), Binary Grey Wolf Optimizer (bGWO), Binary Harris Hawks Optimizer (BHHO), and Genetic Algorithm (GA). These algorithms were integrated with machine learning (ML) models, including Random Forest (RF), Extra Trees Regressor (ETR), and Light Gradient Boosting Machine (LightGBM), to enhance predictive accuracy and optimize feature selection. The IDA-LightGBM combination exhibited superior performance, achieving a mean absolute error (MAE) of 13.17 mg/dL, a root mean square error (RMSE) of 15.36 mg/dL, and 94.74% of predictions falling within the clinically acceptable Clarke error grid (CEG) zone A, with none in dangerous zones. This research underscores the efficiency of utilizing HRV and PPG for non-invasive glucose monitoring, demonstrating the effectiveness of integrating metaheuristic and ML approaches for enhanced diabetes monitoring.  
non-invasive; blood glucose level (BGL); photoplethysmography (PPG); heart rate variability (HRV); feature selection (FS); metaheuristic algorithms; machine learning (ML); improved dragonfly algorithm (IDA); Clarke error grid (CEG); diabetes monitoring +
Footnote †: journal: _Algorithms_
## 5 Discussion  
After tracing DL history for CMR segmentation (Section 2), we have highlighted the shortcomings that currently prevent this technology from meeting some of the requirements to be safely deployed and used in clinical routine and cardiac analysis pipelines [109]. In this work, we focus on two main factors: a lack of reliability and robustness of many state-of-the-art methods. After providing formal definitions for the two terms, we have identified and discuss the elements that lead to poor reliability and/or robustness and we presented a wide range of works that have recently been published tackling both problems in CMR segmentation.  
In this survey, we proposed to categorize the existing literature into two families: quality control and model improvement techniques. Quality control techniques can be seen as simpler strategies that only aim at flagging situations where a model may be incurring poor reliability or robustness, without aiming to fix the problem. Their main advantage is that these methods are typically external modules that can be promptly attached to an existing segmentation pipeline. However, they leave the problem to the expert, who needs to decide how to address the identified situation. Therefore, QC tools contribute to reducing the analysis time for the expert and providing some safety guarantees, through the generation of alerts, but do not contribute to improving CMR segmentation performance.  
Model improvement techniques, instead, bring specific improvements in several aspects of the segmentation model development process, with the final goal of addressing the limitations of DL models that lead to poor reliability or robustness. As such, these type of methods are not only capable of identifying a potential problem, as QC tools do, but they can also act on it and aim to fix it. This being a more complex problem to tackle, it may explain why the number of existing QC methods is larger than MI techniques. A second possible explanation to this may be that the development of QC techniques has been strongly driven by the need to fully automate the processing pipelines of large databases, such as the UK Biobank.  
A current limiting factor to further research on new QC and MI techniques addressing robustness and reliability is the lack of a common and well-established framework for their evaluation. QC techniques use different types of outputs, such as quantitative scores or a wide range of qualitative labels, with no clear mapping among them. MI techniques, as discussed in Section 4.2.5, rely on different backbone architectures and configurations that cannot be directly compared. The heterogeneity of existing solutions for both categories of methods challenges an objective and consistent evaluation. Moreover, as demonstrated by [PERSON] et al. [11], current performance measures, such as the DSC or HD, are not well-suited to identify errors which are associated with poor reliability and robustness. Progress in the field should therefore be accompanied with the investigation of better evaluation strategies.
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)
### Survey and Interview Methods  
A mix of quantitative and qualitative methods was applied during the final, public, KCAP open house in 2009 [48]. First, pre-/post questionnaires were distributed before and after the open house to capture changes in individual awareness and understanding. In the questionnaires, participants were asked to rate the benefits of the visualizations in general and then, to rank (a) slide presentation; (b) 2D maps; (c) posters; and (d) the mediated presentation in Google Earth. The ranking provided self-assessed data showing which percentage of participants favored which media and how the virtual globe was ranked in comparison to non-interactive presentation media. Second, 17 in-depth guided interviews were conducted with stakeholders and community members using an interactive 3D Google Earth model of regional climate change impacts and possible adaptation and mitigation scenarios directly at the public open house. Participants were recorded on video while using Google Earth and the analysis of the interviews was related to the actual video recording (cf. data triangulation [52]). A year later, eight KCAP participants were interviewed again to investigate long-term policy and implementation outcomes. The evaluation of the overall planning process and the long-term outcomes will be subject of future publications.
## 2 Materials and Methods
### Data Collection and Preprocessing
#### 2.3.1 Data Preprocessing
\begin{table}
\begin{tabular}{l c l} \hline \hline
**Variable** & **Typical Range** & **Remarks** \\ \hline PM\({}_{1}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) & 0–25 & Low concentration; urban sources. \\ PM\({}_{2.5}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) & 0–35 & WHO daily limit: 25; frequently exceeded. \\ PM\({}_{4}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) & 0–50 & Mid-size particles; often industrial origin. \\ PM\({}_{10}\) (\(\mathrm{\SIUnitSymbolMicro g}\)/m\({}^{3}\)) & 0–100 & WHO daily limit: 45; exceeds near roads. \\ Humidity (\%) & 30–90 & Low under 30\%; variable near water bodies. \\ Temperature (\(\mathrm{\SIUnitSymbolC}\)) & –5–40 & Season-dependent; typically 10–30\(\mathrm{\SIUnitSymbolC}\). \\ Wind speed (\(\mathrm{m}\)/s) & 0.5–10 & Calm \textless{} 1 m/s; strong winds \textgreater{} 6 m/s. \\ Wind direction (\(\mathrm{\SIUnitSymbolC}\)) & 0–360 & Circular; 0\(\mathrm{\SIUnitSymbolC}\)/360\(\mathrm{\SIUnitSymbolC}\) = North. \\ AQI & 0–500 & \textless{}50: good, \textgreater{}150: unhealthy. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Typical value ranges for the input and output variables used in this study.
# Retrieval of Similar Evolution Patterns from Satellite Image Time Series +
## 4 Discussion
### Final Remarks  
Finally, the running time for performing a query-by-example retrieval over the short Landsat SITS is 1 min 18 s/query, whereas over the long Landsat SITS, the running time is 10 min/query. The running times are considerably smaller than those required to perform unsupervised clustering of the spatio-temporal evolutions using the DTW-based K-means algorithm with \(K~{}=~{}15\) classes, as presented in [7] (i.e., 38 min 17 s for the short SITS and approximately 16 h for the long SITS). A shorter running time was registered for the LDA-based method--almost 25 min for short SITS and approximately 2 h for the long SITS.
## Materials and Methods
### Metasurface Fabrication  
The metasurfaces were fabricated by etching the 1.5 mm thick FR-4 laminates with one side of 35 \(\upmu\)m copper cladding. On the 200 mm \(\times\) 150 mm laminates, the following matrixes of the unit elements were produced:  
* \(8\times 6\) of cross-unit elements,
* \(12\times 9\) of Jerusalem cross-unit elements,
* \(15\times 11\) of split ring resonator unit elements.  
The differences in the number of elements resulted from their size--a fixed value of the distance between the elements was adopted. The size of the metasurfaces was chosen in order to obtain an acceptable level of the reflected signal (the smaller the active size of the MS, the weaker the response).
## 5 Conclusions and Future Perspectives  
According to this review, coriander, known for its medicinal and dietary benefits, was subjected to several treatments prior to the extraction of its bioactive compounds, and multiple techniques were employed for the attainment of higher yields. Thus, some general conclusions can be drawn from existing research and publications:  
1. The studies relating to the therapeutic effects and the biological activity of the different parts of coriander have revealed its bioactivity whether _in vitro_ or _in vivo_, particularly that assigned to the oils extracted from its seeds (Table 2). 2. The key treatments and pretreatments affecting the quality and quantity of coriander bioactive substances, especially phenolic and volatile compounds, are those applied to coriander either before or during their harvest, during storage, or during extraction processes preparation (Table 3), which have either positive or negative effects on its phytochemical content. 3. Given the involvement of coriander in different fields (food, medical, cosmetic), and with the aim of replacing synthetic antioxidants and overcoming the disadvantages of conventional techniques that consume time and solvents to produce a lower yield, non-conventional extraction technologies (MAE, UAE, and SFE) have been used. So, several authors have optimized and identified the optimal technique necessary to maximize the extraction of phenolic and volatile compounds from coriander. 4. Considering their respective advantages and disadvantages, the extraction processes presented in this article can be classified in the following order: MAE/MAHD, UAE, SWE/SFE, Soxhlet extraction, hydrodistillation, and finally maceration (Table 5). In the coming years, mastering the right conditions, as well as the involvement of green extraction methods, including MAE, UAE, and SFE, could provide an innovative approach to increasing the production of specific compounds extracted from coriander for various uses. Nevertheless, it is interesting to note that other techniques which are not cited in this review have been exploited for the extraction of the bioactive compounds of coriander. Overall, the application of novel technologies to scale-up processes is still largely constrained from an industrial perspective. The recovery of target chemicals from the extracted samples and the reuse of the used solvents present the biggest challenges.
## 2 The Proposed Image Fusion Method
### SML and SMLF Energy  
#### 2.2.1 SML Energy  
The low frequency sub-band image retains most of the energy information from the source image, so the selection of its fusion criterion is crucial for the reconstructed fusion image. The work of [14] showed that SML can provide better performance than other focus functions for guiding the selection of low frequency sub-bands, such as variance, energy of image gradient (EOG) [27], energy of Laplacian  
Figure 1: Flowchart of the proposed fusion method. \(C_{A\beta\bar{F}}\) is represented as the low frequency sub-band image. \(d_{ij}^{A\beta\bar{F}}\) is denoted as the high frequency sub-band image. The characters in the matrix are representative values of key parameters in the proposed fusion criteria. NSCT, nonsubsampled contourlet transform; NSML, novel sum-modified Laplacian; SMLF, SML energy filtering; DPCNN, dual-channel pulse coupled neural network; RLV, relating sum-modified Laplacian energy with visual contrast value; MCF, multi-channel filters.  
(EOL), SF, and so on. SML represents the Laplacian energy in the horizontal and vertical directions of the image [28], and its parameter is defined as follows:  
\[\begin{split} SML(x,y)&=\sum\limits_{p=-1}^{1}\sum \limits_{q=-1}^{1}EOL(x+p,y+q)^{2}\\ EOL(x,y)&=\begin{bmatrix}2c(x,y)-c(x-1,y)-c(x+1,y) \big{|}+\\ 2c(x,y)-c(x,y-1)-c(x,y+1)\big{|}\end{bmatrix}\end{split} \tag{1}\]  
where \(EOL\) (\(x,y\)) is the energy of Laplacian. \(c\) (\(x,y\)) denotes as the low frequency sub-band coefficient.  
For a pair of the low frequency sub-band images obtained by NSCT decomposition, the difference between the clear object and the fuzzy object in the image is relatively large, which reflects the feature information in the local neighborhood by calculating its Laplace energy. Therefore, SML energy is utilized to judge the low frequency sub-band coefficient, and the coefficient with larger SML energy is taken as the low frequency coefficient after fusion.
## 3 Results
### Analysis of Random Influence between Reference Stations and Model Construction  
The random influence among the reference stations refers to the effect of the motion trend of each reference station in the horizontal and vertical directions on the motion trend of the baseline in the horizontal and vertical directions. The magnitude of the random influence of the reference stations in adjacent areas is mainly determined by finding the standard deviation of each direction of the residual sequence after excluding the baseline CME.  
The statistics of the random influence of each direction of the baseline is calculated. The results show that average random influence of the baseline is larger in the north-south and east-west components, 0.39 mm and 0.54 mm, respectively, and smaller in the vertical component and the long component of the baseline, 0.02 mm and 0.22 mm, respectively. The random influence between the reference stations is mainly distributed in the north-south and east-west directions.  
The random influence model treats the random influence as a random variable and fits its covariance function. The covariance function is fitted to visualize the spatial distribution characteristics of the random influence. The random influence of each directional component of the baseline was fitted by baseline length statistics using four different functional models (Figure 8).  
The Hirvonen function model fits poorly, especially in the L direction, and the Gaussian, polynomial and exponential function models fit more closely to the random influence values with smooth fitting curves. Table 4 demonstrates the fitted point errors. The polynomial fit has the smallest error in fitting all baseline components, and the fitting effect of each component is improved by 5%, 50%, 23.8% and 46%, respectively, compared with  
Figure 8: Function Model fitting of Residual Baseline Series in (**a**) N Component; (**b**) E Component; (**c**) U Component; (**d**) L Component. RI on the vertical axis represents random influence.  
the exponential function fit. The improvement is most obvious in the E direction, and it is better than the exponential function fit and the Hirvonen function fit. Therefore, using a polynomial function as a random influence covariance function fitting method can effectively improve the fitting accuracy.
# Phased Antenna-Array Synthesis Using Taylor-Series Expansion and Neural Networks
## 3 The Taylor Method
### Synthesizing Antenna Arrays Using Taylor One-Parameter Distribution  
Synthesizing antenna arrays using the Taylor one-parameter distribution involves defining requirements such as radiation pattern and beamwidth, selecting array geometry and element patterns, applying the Taylor distribution to determine element amplitude tapering, calculating the array factor to describe pattern formation, optimizing parameters through iterative refinement, and validating the design via simulation or testing. This process enables the creation of antenna arrays tailored to specific needs, whether for beamforming, direction finding, or other applications [41; 42; 43; 44].