esa-sceva/satcom-chunk-collection huggingface dataset:
# Phytochemical Analysis and Amoebicidal Evaluation of Different Agave Species
## 4 Conclusions  
The results obtained in this study demonstrate that the ethanolic leaf extracts of _Agave tequilana_, _Agave angustifolia_, _Agave modacanttha_, and _Agave maximiliana_ present different efficacies of amoebicidal activity. This is the first study focused on identifying structural damage in _E. histolytica_ due to Agave extracts. The result of this study supports the traditional claim that the plants are antiparasitic alternatives, and efforts are currently under way to identify the bioactive component(s) of the Agave species and their cytotoxicity and selectivity.  
Conceptualization, R.R.-O. and A.C.-R.; data curation, A.L.R.-Z, L.B.-R. and M.N.-V.; formal analysis, A.C.-R.; funding acquisition, A.C.-R.; investigation, A.L.R.-Z, J.I.M.-F., M.A.B.-E., A.P.-C. and A.C.-R.; methodology, A.L.R.-Z., J.I.M.-F., M.A.B.-E., A.P.-C., L.B.-R. and A.C.-R.; project administration, A.C.-R.; resources, L.B.-R. and A.C.-R.; software, A.L.R.-Z and A.P.-C.; supervision, A.C.-R.; validation, A.P.-C., L.B.-R., R.R.-O., M.N.-V. and A.C.-R.; visualization, A.L.R.-Z.; writing--original draft, A.L.R.-Z., L.B.-R. and A.C.-R.; writing--review and editing, A.P.-C., L.B.-R., R.R.-O., M.N.-V. and A.C.-R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Consejo Estatal de Ciencia y Tecnologia de Jalisco (Coecytjal, Fodecijal 2019-8129).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
[PERSON] thanks CONACYT for the undergraduate scholarship (761449) received. [PERSON] thanks COECYTJAL for the scholarship received.  
The authors declare no conflicts of interest.  
Figure 5: Inverted optical microscope (Nikon Eclipse Ts2) pictures of trophozoites treated with ethanolic extracts of Agave. Untreated trophozoites (**A**) and those treated with 0.4% DMSO (**B**). _A. Tequilana_ (**C**), _A. angustifolia_ (**D**,**E**), _A. rhodachanta_ (**F**), and _A. maximiliana_ (**G**,**H**). White arrows indicate alterations in nuclei. Black arrows indicate filopodial protrusions. Bar = 50 \(\upmu\)m.
## 2 Basic Principles
### PCA-WOA-XGBoost Model  
XGBoost is a powerful machine learning algorithm but carries a risk of overfitting. Overfitting is primarily caused by the choice of hyperparameters. The main hyperparameters of XGBoost include decision tree depth max_depth, number of iterations (n_estimators), and learning rate (learning_rate). An excessively large decision tree depth can lead to a complex model that memorizes noise and outliers rather than capturing general patterns. Too many iterations may result in overfitting to the training data. An inappropriate learning rate can cause the model to quickly adapt to noise. Therefore, appropriately setting these hyperparameters is key to avoiding overfitting. The predetermined search ranges for the hyperparameters of the XGBoost algorithm are shown in Table 1.  
As shown in Table 1, the hyperparameter ranges are relatively wide, leading to numerous possible combinations. Using a traditional grid search to find the optimal hyperparameter combination is inefficient. Therefore, the Whale Optimization Algorithm (WOA) is introduced for hyperparameter optimization. Additionally, since the parameters collected for earthquake fatalities are not completely independent, this can result in overly complex and redundant information in the model's input features. To achieve fast and effective prediction of earthquake fatalities, Principal Component Analysis (PCA) is used to perform dimensionality reduction on the initial features.  
The construction approach for the PCA-WOA-XGBoost model is as follows: PCA is applied to reduce the dimensionality of the initial features, simplifying feature correlations. The Whale Optimization Algorithm is then used to optimize the hyperparameters of the XGBoost algorithm, including decision tree depth, learning rate, and number of iterations, to further improve model accuracy. The specific process is illustrated in Figure 4.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Description** & **Search Range** \\ \hline Max\_depth & Decision tree depth & 1–20 \\ N\_estimators & Number of iterations & 1\(\sim\)100 \\ Learning rate & Learning rate & 0.01–1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Predetermined search ranges for XGBoost algorithm hyperparameters.  
Figure 4: The detailed process of the PCA-WOA-XGBoost model.
## 6 Ablation Experiments
### Analysis of Ablation Experiment Results  
The evaluation indicators of the benchmark SE-TransUNet model and each ablated model in the test set are shown in Table 8.  
As shown in Table 8, the removal of the shallow SE-Block1 led to a decrease in IoU to 0.8159, accompanied by slight declines in Recall and other related metrics, indicating its supportive role in low-level feature extraction. When the intermediate SE-Block2 and SE-Block3 were removed, the IoU dropped further to 0.7974 and 0.7948, respectively, suggesting a substantial reduction in segmentation performance and highlighting the importance of mid-level feature enhancement and global context integration. The removal of the deepest SE-Block4 resulted in a marginal decrease in IoU to 0.7864, while the Dice coefficient increased to 0.8471. Upon removing the ViT module, IoU further declined to 0.7614, while Dice increased to 0.8631, indicating that ViT primarily contributes to global region segmentation, whereas its removal may lead the model to focus more on boundary precision.  
Figure 10: Key module ablation steps.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Step Number** & **Ablation Module** & **IoU** & **Dice** & **Recall** & **Precision** & **F1-Score** \\ \hline
0 & None & 0.8318 & 0.8304 & 0.9394 & 0.8480 & 0.9218 \\
1 & SE-Block1 & 0.8159 & 0.8385 & 0.9210 & 0.8668 & 0.8847 \\
2 & SE-Block2 & 0.7974 & 0.8242 & 0.9117 & 0.8575 & 0.8817 \\
3 & SE-Block3 & 0.7948 & 0.8221 & 0.9100 & 0.8423 & 0.8742 \\
4 & SE-Block4 & 0.7864 & 0.8471 & 0.9142 & 0.8481 & 0.8714 \\
5 & ViT & 0.7614 & 0.8631 & 0.8985 & 0.8213 & 0.8911 \\ \hline \hline \end{tabular}
\end{table}
Table 8: The evaluation indicators of the water leakage detection model in the ablation experiment.  
Overall, the integration of SE and ViT modules into the baseline model contributes positively to the accuracy of leakage detection in tunnel linings. The baseline model achieves an IoU that is 4.50% higher than the variant without SE modules, and 7.04% higher than the variant with both SE and ViT modules removed.
## 5 Standards of Islanding Detection  
Various international organizations, such as IEEE and IEC, have defined the standards for the integration, control, and operation of DGs with the main utility grid. The main purpose of those standards is to provide specific requirements, including the performance, security and safety, monitoring, and maintenance of the networks of integrated power systems. Furthermore, the standards can provide a comprehensive guide to experts during the designing of islanding detection methods by considering some factors, such as load quality factor, detection time, voltage operating ranges, and frequency operation ranges. The most common standards for islanding detection which are used to evaluate the performance of islanding detection are presented in Table 1.The majority of islanding detection methods that were discussed previously can fulfill the IEEE and IEC standards requirement which require detecting the islanding condition within less than 2 s, except some signal processing-based methods, such as time-time transformer methods, which take 25 s to detect the islanding condition. However, according to other standards which require detecting the islanding condition in a time less than 0.5 s, such as the Korean standards, many islanding detection methods fail to fulfill the requirements, such as the impedance measurement method, active frequency drift method, phasor measurement method, and continuous wavelet transformer method.
# Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks
## 3 Materials and Methods
### Problem Formulation and Objective Function  
In the network model considered in this research, the tasks generated by the IoT devices are evaluated and processed based on this multi-objective optimization problem, such as optimizing the cost, fault, energy, etc. The issues encountered during task-offloading are addressed by two objective functions, as follows:  
1. Fault minimization--the prior identification of faulty nodes and immediate task-offloading to the alternate candidate Fog access nodes, either in the same Fog server or a neighbouring Fog server, without disconnecting the task-processing activities. 2. Residual energy optimization--the continuous monitoring of residual energy levels after the completion of each task by candidate fog access nodes. The optimization can be achieved by selecting the maximum residual energy for high-priority tasks and minimum residual energy for low-priority tasks. \[\min(\mathrm{N}(\mathrm{F}))=\min\left\{\int_{0}^{t}\mu_{f}(t)\ \mathrm{dt};\mathrm{Candidate}\ \mathrm{F}_{na}\right\}\] (18) \[\mathrm{Opt}(\mathrm{E}_{r})=\max\{\mathrm{E}_{r};\mathrm{high}\ \mathrm{priority}(\mathrm{X}_{i})\}\] (19) \[\mathrm{Opt}(\mathrm{E}_{r})=\min\{\mathrm{E}_{r};\mathrm{low}\ \mathrm{priority}(\mathrm{X}_{i})\}\] (20)  
Fog access nodes. For example, the initial cost of Fog control node N7 is calculated as follows:  
\[\text{C}_{7}=\sigma_{\text{c}}\text{r}_{\text{i}}+\sum\limits_{\text{j}\in \text{c}}\sigma_{\text{j}}\text{d}_{\text{ij}}=0+\sum\left(1\right).\text{d}_{ \text{ij}}=0+\text{d}_{\mathcal{J}\mathcal{A}}+\text{d}_{\mathcal{J}\mathcal{, 3}}\]  
\[\text{C}_{7}=0+2+3=5\]  
where \(\sigma_{\text{c}}=1\) and \(\sigma_{\text{j}}=1\) for all cases. For Fog control nodes, the initial source cost is 0 (i.e., \(\text{r}_{\text{i}}=0\)), and the minimum number of hops between the next two Fog control nodes is added (i.e., the minimum number of hops between N7 and N4 is 2, and that between N7 and N3 is 3). The initial cost of Fog access node N9 is calculated as follows:  
\[\text{C}_{9}=\sum\left(1\right).\text{d}_{\text{ij}}=\text{d}_{9\mathcal{,7}} +\text{d}_{9\mathcal{,11}}+\text{d}_{9\mathcal{,A}}\]  
\[\text{C}_{9}=1+2+3=6\]  
here, for Fog access nodes, the term \(\sigma_{\text{c}}\text{r}_{\text{i}}\) is not considered because the selectivity of the Fog control node is zero and the minimum number of hops between three Fog control nodes was added (i.e., the minimum number of hops between N9 and N7 is 1, that between N9 and N11 is 2, and that between N9 and N4 is 3).
# Research on Penetration Loss of D-Band Millimeter Wave for Typical Materials
###### Abstract  
The millimeter-wave frequency band provides abundant frequency resources for the development of beyond 5 th generation mobile network (B5G) mobile communication, and its relative bandwidth of 1% can provide a gigabit-level communication bandwidth. In particular, the D-band (110-170 GHz) has received much attention, due to its large available bandwidth. However, certain bands in the D-band are easily blocked by obstacles and lack penetration. In this paper, D-band millimeter-wave penetration losses of typical materials, such as vegetation, planks, glass, and slate, are investigated theoretically and experimentally. The comparative analysis between our experimental results and theoretical predictions shows that D-band waves find it difficult to penetrate thick materials, making it difficult for 5G millimeter waves to cover indoors from outdoor macro stations. The future B5G mobile communication also requires significant measurement work on different frequencies and different scenarios.  
millieter wave; penetration loss; attenuation characteristics; channel model +
Footnote †: journal: 2022, 22, 7666. [[https://doi.org/10.3390/s22197666](https://doi.org/10.3390/s22197666)]([https://doi.org/10.3390/s22197666](https://doi.org/10.3390/s22197666))
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Characterization of Biosynthesisized Zinc Oxide Nanoparticles
#### 2.4.3 X-ray Diffraction  
An X-ray diffraction analysis of powder was performed, the spectra were recorded, and the region of 500 to 4000 cm\({}^{-1}\) was included. The size and crystalline shape of the ZnO-NPs were determined based on the X-ray diffraction (XRD) dimensions (Philips-X'Pert MPD X-ray diffractometer, Naperville, IL, USA). A glass substrate was coated with the dried NP powder while being scanned in the vicinity of 2 0, from 4 to 90\({}^{\circ}\) at 0.5\({}^{\circ}\)/min, and with a time constant of 2 s. Based on the XRD pattern and the line width of the maximum intensity reflection peak, [PERSON]'s equation was used to determine the mean particle diameter of ZnO-NPs as follows: The values \"\({}^{\circ}\)2\" denotes the Bragg's angle, is the corrected full width at half maximum (FWHM) in radians, \"sample\" and \"ref.\" are the FWHM of the sample and reference peaks, respectively, \"D\" represents the average crystal size, \"K\" is the Scherrer coefficient (0.89), and is the X-ray wavelength (1.5406).
## 3 Experiment
### Application of Aging Model  
#### 3.4.1 Basic Model  
The general exponential decay aging model for materials over time is given by Equation (1). In this equation, P(t) represents the material performance at time t, P\({}_{0}\) denotes the initial performance at t = 0, \(\alpha\) is the aging coefficient, and t corresponds to the elapsed time or operating duration. The exponential function has been applied as an aging model [17].  
\[P(t)=P_{0}\cdot exp^{-\alpha t} \tag{1}\]
## 1 Introduction
It is listed under the European Waste Code (EWC) 190809 and can be easily separated from incoming municipal sewage by flotation in the pretreatment stage. Nowadays, sewage sludge is disposed of as wet material in landfills (total solids content 20-30%wt at approx. 200-300 \(\ell\) ton\({}^{-1}\)). Several physical, chemical and biological processes have been developed to minimize sludge production [35; 36; 37]. The most common methods for disposing sewage sludge are incineration, landfilling [38; 39] or reuse in agriculture [40; 41]. Recently, new technologies have been developed for their utilization, with particular attention paid to the lipid component, which can produce biodiesel. Biodiesel is a mixture of fatty acid methyl esters (FAMEs) produced mainly by the transesterification of refined vegetable oils in the presence of alkaline catalysts [42; 43]. To reduce the use of renewable energy sources and the cost of biodiesel production, alternative, less expensive feedstocks have been used, such as waste oils [44; 45], animal fats [46; 47] and oils from macro- and microalgae [48; 49]. In this context, sewage sludge represents a possible alternative source of lipids that meets sustainability criteria [50; 51]. In this work, a detailed analysis of the lipid component in primary sludge and sewage scum samples up-taken from several wastewater treatment plants (WWTPs) in southern Italy was carried out to achieve an optimal synthetic process for extracting raw lipids and producing biodiesel following European standards (EN14214 [52]). Lipids in the primary sludge accounted for 200-250 mg/g of the total solids (TS), with calcium soaps being the main component (70-82%). In contrast, the total lipids in the sewage sludge amounted to about 350-500 mg/g\({}_{\text{TS}}\) and consisted mainly of FFAs (45-60%) and calcium soaps (27-35%). In addition, estolides and 10-hydroxysteratic acid were found in considerable amounts, with (R)-10-hydroxysteraric as the most abundant enantiomer. Subsequently, a novel strategy for the extraction and chemical upgrading of primary sludge and sewage scum was proposed and experimentally tested in order to obtain biodiesel, methyl estolides and methyl 10-hydroxysterate efficiently. Finally, a feasibility study of the proposed processes was carried out to evaluate the possible integration into a wastewater treatment plant, taking a critical look at the positive aspects and the associated limitations.
###### Abstract  
This study investigates the impact of subglottic stenosis (SGS) on voice production using a subject-specific laryngeal and airway model. Direct numerical simulations of fluid-structure-acoustic interaction were employed to analyze glottal flow dynamics, vocal fold vibration, and acoustics under realistic conditions. The model accurately captured key physiological parameters, including the glottal flow rate, vocal fold vibration patterns, and the first four formant frequencies. Simulations of varying SGS severity revealed that up to 75% stenosis, vocal function remains largely unaffected. However, at 90% severity, significant changes in glottal flow and acoustics were observed, with vocal fold vibration remaining stable. At 96%, severe reductions in glottal flow and acoustics, along with marked changes in vocal fold dynamics, were detected. Flow resistance, the ratio of glottal to stenosis area, and pressure drop across the vocal folds were identified as critical factors influencing these changes. The use of anatomically realistic airway and vocal fold geometries revealed that while anatomical variations minimally affect voice production at lower stenosis grades, they become critical at severe stenosis levels (>90%), particularly in capturing distinct anterior-posterior opening patterns and focused jet effects that alter glottal dynamics. These findings suggest that while simplified models suffice for analyzing mild to moderate stenosis, patient-specific geometric details are essential for accurate prediction of vocal fold dynamics in severe cases.  
subglottic stenosis; voice production; subject-specific simulation; fluid-structure interaction +
Footnote †: journal: Applied sciences  
Article
# Functionalized Carbon-Based Electrochemical Sensors for Food and Alcoholic Beverages Safety
## 3 Carbon Nanomaterial-Based Electrochemical Sensors
### CnTs
Copyright Elsevier, 2020.  
developed a simple, low-cost electrochemical sensor (Figure 4e) for detecting very low concentrations of BPA in water using a WCNTs-\(\beta\)CD decorated screen-printed carbon electrode (SPCE). The electrochemical response of the sensor for detecting BPA showed a completely irreversible process including two electrons and two protons. The sensor showed a two-step linear response from 125 nM to 2 mM and 2 to 30 mM, with a detection limit of 13.76 nM. Moreover, a mesoporous MWCNT/zirconia composite was synthesized by the sol-gel method to detect methyl parathion (MP) in food samples. In this work, the electrochemical responses for MP were obtained by DPV with a detection limit of 9 nM and in a linear interval of 19.9-176.8 mM. Specifically, the developed sensor was applied in ethanolic soybean extract and showed satisfactory recovery and repeatability [93]. In addition to modifying CNTs with metal or metal oxide nanoparticles, directly installing active molecules on CNTs was also a very effective method for the rapid detection of contaminants in food. Recently, as shown in Figure 4f, [PERSON] et al. [94] reported that molecularly imprinted polymer (MIP)/carboxylated single-walled carbon-nanotubes/chitosan (MIP/SWNTs-COOH/CS) was prepared as an electrochemical sensor using MIP as the recognition element. Three different electrochemical techniques, including CV, DPV, and ESI, were applied for the detection of semicarbazide (SEM) in four different real samples. The linear range of 0.04-0.6 ng mL\({}^{-1}\) and the low detection limit of 0.025 ng mL\({}^{-1}\) were obtained at an optimized experimental condition. Based on the good conductivity and strong recyclability of carbon nanotubes, we first reviewed the detection and monitoring of chemicals in foods and alcoholic beverages using CNT-based materials as sensor electrodes. Furthermore, a variety of electrochemical analysis methods used for qualitatively and quantitatively detecting chemical substances in foods were also introduced. In addition, compared with CDs, CNTs can be directly used to detect the antioxidant and bioactive substances in alcoholic beverages due to the multiple active sites exposed on CNTs surface. Although a large number of advances have been achieved, more strategies aiming to construct a specific and effective sensor electrode remain to be further studied.
# Effect of Incorporating Waste Limestone Powder into Solid Waste Cemented Paste Backfill Material
## Materials and Methods  
### Raw Materials  
#### 2.1.1 Composition Analysis  
Tailing samples used in the present study were obtained from an underground lead-zinc mine located in the south of China. The tailings (tailing A and tailing B) from two different mineral processing areas were selected as the combined SWCPB. The binder used in this study was a complex binder made of limestone powder (0-20%) and ordinary Portland cement (OPC) (80-100%). The OPC produced by Changsha Xinxing Cement Plant with C30 grade strength was selected as the major cementing material. Abandoned limestone powder from the mine quarry was selected to replace part of the cement. The proportion of main elements in raw materials was analyzed by using a X-ray fluorescence (XRF, PANalytical B.V., Almelo, The Netherlands) spectrometer, as shown in Table 1.  
Siliceous limestone was used as raw material for limestone powder. Table 1 shows that SiO\({}_{2}\) accounted for about 36.87% of content, and SiO\({}_{2}\) reacted with Ca(OH)\({}_{2}\) to form C  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{2}{c}{**Tailing A**} & \multicolumn{2}{c}{**Tailing B**} & \multicolumn{2}{c}{**Limestone Powder**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Content/\%**} & \multicolumn{2}{c}{**Compound**} & \multicolumn{2}{c}{**Concentration/\%**} \\ \hline O & 34.70 & 44.38 & CaO & 57.60 \\ Fe & 23.10 & 12.66 & SiO\({}_{2}\) & 36.87 \\ S & 15.86 & 8.88 & Al\({}_{2}\)O\({}_{3}\) & 1.58 \\ Ca & 14.03 & 9.09 & MgO & 1.17 \\ Si & 4.48 & 16.24 & Fe\({}_{2}\)O\({}_{3}\) & 1.17 \\ Mg & 2.12 & 0.40 & SO\({}_{3}\) & 0.74 \\ Zn & 1.23 & 1.14 & K\({}_{2}\)O & 0.27 \\ Al & 1.11 & 3.06 & BaO & 0.12 \\ As & 0.99 & 1.48 & P\({}_{2}\)O\({}_{5}\) & 0.085 \\ K & 0.50 & 0.67 & Cr\({}_{2}\)O\({}_{3}\) & 0.085 \\ Sn & 0.37 & 0.24 & TiO\({}_{2}\) & 0.082 \\ Pb & 0.34 & 0.52 & MnO & 0.081 \\ Sb & 0.32 & 0.35 & Na\({}_{2}\)O & 0.039 \\ Mn & 0.14 & 0.41 & SrO & 0.039 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main mineral composition of tailings A and B.  
the gelation of filling. Formation of C-H-S bonds served as the crucial part of the hydration reaction, which resulted in the limestone powder used in replacing the cement.