esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Materials and Methods
### Secondary Metabolites  
Relative abundance of secondary metabolites of the _Helichrysum_ spp. extracts were quantified by pipetting \(\approx\)5 mL of each extract into 20 mL solid-phase micro-extraction (SPME) vials. Each vial was allowed to equilibrate for 5 min at 50 \({}^{\circ}\)C in the CTC auto-sampler incubator at 250 rpm. Volatile compounds trapped in the headspace of the vials were extracted using the static headspace (SPME) method [41]. Subsequently, SPME fiber (50/30 \(\mu\)m) coated with divinylbenzene/-carboxen/-polydimethylsiloxane (DVB/CAR/PDMS) was exposed to the sample headspace for 10 min at 50 \({}^{\circ}\)C. After volatile extraction from SHS of the vials, desorption of the adsorbed compounds from the fiber coating was carried out in the injection port of the gas chromatography-mass spectrometry (GC-MS) for 10 min. The injector temperature was maintained at 250 \({}^{\circ}\)C.  
Separation and quantification of the volatile compounds were performed on a gas chromatograph using Agilent 6890 N (Agilent, Palo Alto, CA, USA), coupled with an Agilent mass spectrometer detector Agilent 5975 MS (Agilent, Palo Alto, CA, USA). The system was equipped with a polar ZB-FFAP column (Model No.: ZB 7 KM-G009-17), with a nominal length of 60 m, 0.25 \(\upmu\)m internal diameter, and 0.5 \(\upmu\)m film thickness. Helium gas was used as the carrier gas for these analyses at a flow of 2 mL min\({}^{-1}\) with a nominal initial pressure of 196.0 kPa and an average velocity of 36 cm s\({}^{-1}\). The oven temperature program was as follows: 35 \({}^{\circ}\)C for 5 min; and then ramped up to 50 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\) and held for 3 min, and again ramped up to 120 \({}^{\circ}\)C at 3 \({}^{\circ}\)C min\({}^{-1}\) with a 5 min holding time. Finally, the temperature was ramped up to 240 at 8 \({}^{\circ}\)C min\({}^{-1}\) and held for 5 min. The MSD was operated in full scan mode and the ion source and quadrupole temperatures were maintained at 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The transfer line temperature was maintained at 250 \({}^{\circ}\)C. Compounds were tentatively identified by comparison of retention times (RT) and retention index (RI) with those registered in the National Institute of Standards and Technology (NIST v. 05, Gaithersbug, MD, USA) and the WHILEY 275 mass spectral libraries. Only compounds that occur across the triplicates and with correlation coefficient \(\geq\) 80% from the NIST MS library were considered for further analysis. For quantification, the calculated relative abundances were used. This experiment was independently replicated in triplicate (\(n\) = 3).
## 2 Results and Discussion  
### Isolation and Identification of the Isolate  
The isolation and selection of plant-growth-promoting, hydrocarbon-degrading, and heavy-metal-resistant bacteria from the rhizosphere of plants seems to be particularly important for exploiting the bioremediation potential of plant-bacteria systems [11]. In this study, bacterial strain ZCR6 was isolated as one of 26 strains from the rhizosphere of _Zea mays_ growing in soil co-contaminated with hydrocarbons and heavy metals in the vicinity of the Coke Plant \"Jadwiga\" in Zabrze (Upper Silesia, Poland). Among the isolates, the ZCR6 strain showed the highest ACC deaminase activity, very high surface-active and emulsification activity, demonstrated an ability to produce IAA, siderophores, ammonia, and cellulase, and solubilized Ca\({}_{3}\)(PO\({}_{4}\))\({}_{2}\) (Table 1). Based on these results, the ZCR6 strain, as the most promising strain, was chosen for further characterization. Phylogenetic analyses showed that the ZCR6 strain is closely related to _Pseudomonas qingdaonensis_ JJ\({}^{\rm T}\), an aflatoxin-degrading bacterium isolated from peanut rhizosphere soil, the taxonomic name of which is not validated yet (Figure 1) [20]. According to the EZBioCloud database, the closest relatives of strain ZCR6 (OK597188), on the basis of the 16S rRNA gene se quence, were _Pseudomonas qingdaonensis_ JJ\({}^{\text{T}}\) (100%, MG589917), _Pseudomonas brasicae_ MAFF 212427\({}^{\text{T}}\) (99.86%, LC514379), and _Pseudomonas defluvii_ WCHP16\({}^{\text{T}}\) (99.29%, KY979145) (Supplementary Figure S1). In the MLSA, the sequence length of three concatenated genes after alignment was 2563 bp. Through the analysis of concatenated 16S rRNA and two housekeeping genes, ZCR6 was placed into the _P. putida_ subgroup [21]. For the ZCR6 strain, the highest average nucleotide identity was observed for _P. qingdaonensis_ (99.95%), _P. brasica_ (97.30%), and _P. laurentiana_ (95.95%) (Figure 1). The genomic comparison using the TrueBac ID tool showed 99.18% similarity between the genomes of the ZCR6 and _P. qingdaonensis_ JJ\({}^{\text{T}}\) strains.  
To our knowledge, little is known about the _P. qingdaonensis_ strains associated with plants in the context of their use in environmental biotechnology. Therefore, the presented research will help to expand our knowledge about this species, recently isolated for the first time by [PERSON] et al. [20].
# Prediction of Dry Mouth Condition Using Radiomics Features from Tongue Diagnosis Image
## 4 Discussion
Image feature extraction methods were employed for the analysis of tongue images, focusing on aspects such as the tongue body, color, moisture, size, shape, and coating. The gray-level co-occurrence matrix (GLCM) has been widely used for textural feature extraction [26]. However, the number of features extracted by GLCM is limited, and a limited number of features may not fully represent the condition of the entire tongue [45; 46; 47]. In this paper, we introduced various textural feature extraction methods, which provide more comprehensive information about tongue condition. We hope this will enhance the accuracy of disease diagnosis and prediction. Also, it could be useful in the future to conduct new research involving smartphone applications [48] and specific AI-based computer software [49]. Currently, computed tomography (CT) and ultrasound are used in accurately diagnosing salivary gland function. These diagnostic tools provide the precise identification of anatomical issues but come with several drawbacks, including high costs, radiation exposure (in the case of CT), and potential adverse reaction from skin contact (with ultrasound). In contrast, our proposed method offers a simpler and potentially more accessible approach to diagnosing salivary gland function. While it may not match the accuracy of CT or ultrasound, it provides significant advantages in terms of ease of use and the potential for patient self-assessment.
## 4 Discussion  
This study set out to evaluate and to predict the springback behavior in sheet metal forming with a focus on industrially relevant geometries. The primary objective was to bridge the gap between theoretical modeling and real-world application by simulating a complex, commercially manufactured gas cooktop part made of austenitic steel 1.4301 and explore the limitations regarding validation with experimental measurements. The study has successfully demonstrated that despite inherent modeling limitations, the general springback behavior can be captured using the Barlat YLD2000 anisotropic criterion, thus enabling early design iterations in industrial environments.  
Unlike much of the existing literature, which typically employs simple geometries with limited industrial value, this work provides a practical case study using a real component. This advances the understanding of springback by illustrating how established modeling approaches perform when applied to geometrically complex parts and real manufacturing conditions. The study confirms that the blank-holder force (BHF) has a measurable impact on springback primarily through its influence on bending moments in the sidewall regions. As demonstrated, increasing BHF reduces the bending moments, which correlates with decreased springback magnitudes. These findings are consistent with theoretical expectations and have been validated through experimental comparison.  
However, the results also highlight several modeling limitations. Discrepancies between the simulation and experiment--especially in the 50 t and 200 t cases--are primarily attributed to the inability of the current model to represent work-hardening stagnation in high-strength steels. Incorporating more advanced material models, such as the Yoshida-Uemori model, could improve accuracy by better capturing the Bauschinger effect and transient hardening behavior. Additional uncertainties stem from assumptions regarding boundary conditions, material variability, and simplifications in friction modeling.  
Finally, in comparison with prior studies cited in the introduction, our findings support earlier observations regarding BHF effects but uniquely contribute by applying them in a realistic industrial context, studying its limitations. This reinforces the value of this study as a practical reference for simulation-based design in production environments.  
Figure 16: Springback results: experimental vs. simulation.
# Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer
## 3 Dynamic Heterogeneous Search-Mutation Structure-Based Equilibrium Optimizer (DHSMEO)
### Computational Complexity of DHSMEO  
Computational complexity serves as a crucial metric for evaluating an algorithm's execution efficiency. Non-polynomial order and polynomial order are two main categories of computational complexity. Polynomial-order algorithms are regarded as efficient, and non-polynomial-order algorithms are comparatively considered inefficient. The reason is that non-polynomial-order algorithms' computational complexity grows exponentially with the optimization problem scale. Based on the detailed explanation of DHSMEO, along with its overall structure in Section 3, this part analyzes the computational complexity of DHSMEO using a commonly adopted big-O notation. Meanwhile, a comparative analysis was conducted against the computational complexity of EO.  
The overall computational complexity of EO primarily consists of four components: population concentration initialization, equilibrium pool generation, concentration updating, and concentration evaluation. The complexity of the population concentration initialization is \(O(ND)\). Due to the use of the quicksort algorithm in equilibrium pool generation, its worst-case computational cost reaches \(O(l_{\max}N^{2})\). The complexity of the concentration updating is \(O(l_{\max}ND)\). The complexity of concentration evaluation is \(O(l_{\max}N)\). Therefore, the overall computational complexity of the standard EO is \(O(l_{\max}(N^{2}+ND))\). The computational complexity of the proposed DHSMEO must have an additional consideration of the impact from the dynamic dual-subpopulation adaptive grouping strategy, the heterogeneous hybrid search-based concentration-updating strategy, and the dynamic Levy mutation-based optimal equilibrium candidate-refining strategy. The dynamic dual-subpopulation adaptive grouping strategy also involves the quicksort algorithm, leading to a worst-case complexity with \(O(l_{\max}N^{2})\). The complexity of the heterogeneous hybrid search-based concentration-updating strategy is \(O(l_{\max}ND)\). The complexity of the dynamic Levy mutation-based optimal equilibrium candidate-refining strategy is \(O(l_{\max}D)\). Based on the aforementioned results, the complexity of DHSMEO is determined to be \(O(l_{\max}(N^{2}+ND))\).  
In summary, the complexity of DHSMEO remains polynomial order and is identical to EO. This result shows that, although DHSMEO incorporates new strategies to enhance performance compared to EO, these improvement strategies do not increase the computational complexity. Meanwhile, DHSMEO exhibits a polynomial level of computational complexity, which sustains its algorithm efficiency.  
Figure 1: Flowchart of DHSMEO.
## 4 Experiment
### Module Validity Experiments
#### 4.4.1 Effects of the SGD  
There are common optimizers such as Stochastic Gradient Descent (SGD), Adaptive Moment Estimation (Adam), and Adam with Weight Decay Fix (AdamW). Their role is to update the network parameters based on the gradient information of the network Back-Propagation as a way to reduce the computed value of the loss function. However, unsuitable optimizers may lead to problems such as local optima or gradient exploding during training.  
Therefore, it is necessary to analyse the sensitivity of the optimizer and thus assess fits applicability in network models. The performance of the same model under different optimizers is judged by the metric of mean average precision. In this paper, the applicability of the above optimizers was compared in 150 epochs of training, and Figure 14 shows the experimental results.  
In this study, SGD, Adam1, and AdamW were experimented with their respective default learning rates, and Adam2 was experimented with a learning rate of 0.01. The importance of appropriate learning rates can be seen in Figure 14. It can be seen that the training process oscillates and diverges because of the inappropriate learning rate of Adam2, as its convergence results are far worse than the SGD and Adam1 optimisers with suitable learning rates. In addition, the SGD optimizer shows faster convergence and higher accuracy in the experiments, which is better for optimizing the model training. Consequently, the SGD algorithm was selected for subsequent research in this paper.
# Response Prediction for Linear and Nonlinear Structures Based on Data-Driven Deep Learning
## 3 Construction of Dataset and Training of Models  
### Linear and Nonlinear Structural Models  
A six-story reinforced concrete frame structure was taken as the research object, which was simplified as a linear structure system with multiple degrees of freedom, as shown in Figure 5. A nonlinear structural system is established by adding a nonlinear component to the linear structural system.
## 4 Conclusions  
A theoretical comprehensive model of the TLS technique is proposed. The model quantitatively explains the experimental results in TLS technology.  
Firstly, it shows that after the laser irradiation, the temperature variation has a positive correlation with the laser power and a negative correlation with the scanning velocity. Since the current variation is proportional to the temperature variation, it also meets these correlations.  
Secondly, the response curve of the current variation is similar to the temperature response curve. A high coefficient \(k=\frac{\kappa}{\rho c}\) at the abnormal spot of the DUT leads to a faster response rate than that at the normal spot. Besides, the response times in the OBIRCH and SEI techniques are almost equal. Therefore, these two techniques can be switched simply by adjusting the voltage bias.  
Finally, the resistivity variation and the Seebeck voltage are generated together with the temperature field. At constant voltage bias, these two effects have opposite contributions to the current variation. As voltage varies, the current variation \(\Delta I\) can be positive or negative under the laser irradiation. To get more precise results in the testing process, a high voltage bias should be used in the OBIRCH technique. Similarly, a high current bias should be used in the TIVA technique. Conversely, the SEI technique requires a low bias condition.  
**Author Contributions:** Conceptualization, Y.L. and H.W.; Formal analysis, H.Y.; Investigation, H.Y.; Methodology, H.Y.; Project administration, R.C.; Resources, Y.M.; Supervision, J.H.; Validation, Y.L.; Writing--original draft, H.Y.; Writing--review & editing, R.C. and J.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This project was supported by Beijing Municipal Commission of Science and Technology (Grant No. E039360101).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Analysis of Experimental Results
### Simulation Experiment
#### 4.1.3 Statistical Significance Testing
**} & \multirow{2}{*}{**Items**} & \multirow{2}{*}{**Min**} & \multirow{2}{*}{**Max**} & \multirow{2}{*}{**Std**} & \multirow{2}{*}{**Mean**} \\ \hline  & CMA-ES-RES & \(3.68\times 10^{-1}\) & \(9.72\times 10^{3}\) & \(2.27\times 10^{3}\) & \(1.34\times 10^{3}\) & & CMA-ES- & \(2.17\times 10^{2}\) & \(1.17\times 10^{3}\) & \(2.66\times 10^{2}\) & \(7.09\times 10^{2}\) \\  & RMPA & \(9.72\times 10^{3}\) & \(5.73\times 10^{3}\) & \(1.20\times 10^{4}\) & \(1.35\times 10^{1}\) & & RMPA & \(7.01\times 10^{3}\) & \(6.89\times 10^{2}\) & \(1 When \(p\) is less than 5%, the original hypothesis cannot be ruled out at a significance level of 100 \(\times\) 5%, indicating that the enhanced algorithm produces better results than the comparison algorithm does, and vice versa. Table 8 displays the rank-sum test \(p\) values for each algorithm. Among these, \"NAN\" denotes that the numerical comparison is irrelevant and that the ideal value of the two procedures is 0. \"+\", \"-\", and \"=\" signify that IGEO's performance is better than, worse than, or equal to that of the comparison algorithm, respectively. Table 8 shows that when compared to the original algorithms GEO and SSA, IGEO achieves a 100% optimization rate; when compared to BOA, PSO, and SCA, IGEO achieves a 91.2% optimization rate; and when compared to GWO, WOA, and GEO DLS, IGEO achieves an 83.3% optimization rate. These results illustrate IGEO's thorough performance across the 1  
After further verification using Holm, the average chi square value after testing is 34.296, the critical chi square value of the system is 15.507, the degree of freedom gained through data testing is 9, and the \(p\)-value is significantly below 0.05. The original presumption is disproved, i.e., the presumption that all optimization results follow a uniform distribution. Table 11 demonstrates that the WOA, GEO_DLS, PSO, and GWO models reject the null hypothesis and significantly deviate from the enhanced method. SCA, BOA, GEO, and SSA all support the initial theory and differ little from the initial method. The final order of preference for the algorithms is IGEO \(>\) WOA \(>\) GEO_DLS \(>\) PSO \(>\) GWO \(>\) SCA \(>\) BOA \(>\) GEO \(>\) SSA. Similarly, in the experiment using CEC2021 as the test object, the critical chi square value of the system is 19.943, which therefore also rejects the original hypothesis. Table 12 demonstrates that the L-SHADE, GEO_DLS, CMA-ES-RIS and FMMPA models reject the null hypothesis and significantly deviate from the enhanced method. Only EIW_PSO rejects the hypothesis that there is a significant difference from IGEO. The final order of preference for the algorithms is FMMPA \(>\) IGEO \(>\) L-SHADE \(>\) GEO_DLS \(>\) CMA-ES-RIS \(>\) EIW_PSO.
# Nanomaterials-Based Ion-Imprinted Electrochemical Sensors for Heavy Metal Ions Detection: A Review
## 3 Application of Nanomaterials in HMIIECSs
### Gold Nanomaterials
The Pb\({}^{2+}\)-IIP sensor displayed an advanced performance owing to the synergistic effects from hierarchically nanostructure, including the inner layer of Nafion/MWCNTs which enhanced the electronic conductivity and facilitated the charge transfer to the electrode, the imprinted CS/AuNPs/GR outer layer which provided more recognition sites and pathways for Pb\({}^{2+}\), and the NPs which greatly improved the electroactive surface and layed nano-micro porous structure. The proposed sensor exhibited sensitive determination of Pb\({}^{2+}\) with high repeatability, good reproducibility and stability. It showed a linear range of  
Figure 11: Schematic diagram of the preparation process of Cd\({}^{2+}\) imprinted polymers. Reprinted with permission from Ref. [123]. Copyright 2021, the Springer Nature. Figure 12: (**A**) DPV of IIP electrode after elution in different concentration of Cd\({}^{2+}\) and (**B**) calibration curve. Reprinted with permission from Ref. [123]. Copyright 2021, the Springer Nature. \(1.0\times 10^{-9}\)-\(5.0\times 10^{-5}\) M with a LOD of 2.83 \(\times\) 10\({}^{-10}\) M under optimized conditions, and has been successfully applied to the determination of Pb\({}^{2+}\) in water and milk samples. Nanoporous gold (NPG) has attracted many researchers because of its large specific surface area, high conductivity, good catalytic activity and biocompatibility [124]. NPG can be combined with MIP to construct a simple electrochemical sensor, which has great potential to improve the sensitivity of electrochemical sensor by combining their advantages [125]. [PERSON] et al. developed an electrochemical sensor based on IIP and NPG modified gold electrode (IIP/NPG/GE) for the determination of As\({}^{3+}\) in different types of water [71]. NPG had the characteristics of high conductivity, large specific surface area, and high biocompatibility and was electrochemically deposited on the electrode surface of GE by the Tominaka method. The layer of IIP was obtained on the surface of NPG/GE by employing As\({}^{3+}\) as the template and OPD as the functional monomer via in situ electropolymerization. CV and EIS were utilized to study the electrochemical performance of IIP/NPG/GE, which had good stability and selectivity, and showed a linear range for As\({}^{3+}\) of 2.0 \(\times\) 10\({}^{-11}\)-9.0 \(\times\) 10\({}^{-9}\) M and the LOD of 7.1 \(\times\) 10\({}^{-12}\) M (N = 3). It has been successfully applied to the determination of As\({}^{3+}\) in four kinds of water.