esa-sceva/satcom-chunk-collection huggingface dataset:
# Reflective Fiber Surface Plasmon Resonance Sensor for High-Sensitive Mercury Ion Detection
## 2 Simulation and Experiment
### Preparation and Coating of Mercury Ion Sensitive Film  
Chitosan (CS)/polyacrylic acid (PAA) was selected as the sensitive film for Hg\({}^{2+}\) sensing. During the sensitive film coating process, the surface of the Au film was subjected to activation-COOH treatment so that the CS/PAA sensitive film was stably fixed on the Au film. As shown in Figure 5, the Au film surface activation-COOH treatment step and the CS/PAA sensitive film coating were as follows: (1) The sensor probe was thoroughly cleaned by using anhydrous ethanol and distilled water in turn; (2) the sensor probe was inserted into mercapto undecanoic acid (MUA) solution with a concentration of 50 mmol/L for one day at room temperature conditions, so the -HS in the MUA molecule combined with the Au film to form a stable Au-S bond, and the -HS and -COOH were modified on the surface of the Au film; (3) the sensor probe was immersed in a mixed solution, which consisted of 0.4 mol/L EDC (1-ethyl-(3-dimethylaminopropyl) carbodiimide hydrochloride) and 0.1 mol/L NHS (N-hydroxysuccinimide) at a ratio of 1:1 for one hour at room temperature, which activated the -COOH; (4) the sensor probe was attached to a dip coater, and then immersed in a 2% CS solution at a speed of 100 mm/min, then allowed to stand for 7 min, and finally pulled out from the CS  
Figure 4: Optical fiber surface micrographs before and after Au film coating.  
Figure 3: Experimental SPR spectra when the Au film is coated with different coating currents (**a**) and coating times (**b**).  
solution at a speed of 100 mm/min, during which the -COOH on the surface of the Au film coupled with -NH\({}_{2}\) in the CS to adsorb the CS molecule onto the Au; (5) the sensor probe was immersed in an 8% PAA solution at a speed of 100 mm/min and stand for 7 min, and then pulled out at a rate of 100 mm/min, during which the PAA solution was absorbed; (6) steps (4) and (5) were repeated until an appropriate number of layers of CS/PAA sensitive film were coated on the surface of the Au film; (7) the sensor probe coated with the sensitive film was placed in a 60 \({}^{\circ}\)C incubator for 12 h to completely dry the sensitive film and fix it on the probe surface.
## 3 Flow Behavior Characteristics of Superalloy Nimonic 80A  
The true compressive stress-strain curves for nimonic 80A, heat-resisting alloy, are illustrated in Figure 1a-d, which show that both deformation temperatures and strain rates have considerable influence on the flow stress of nimonic 80A heat-resisting alloy.  
As shown in Figure 1, the strain rate and temperature have a significant effect on the flow curves. Apparently, the flow stress decreases markedly as the temperature increases at a specific strain rate. In contrast, the flow stress increases with the increasing of the strain rate while for a fixed temperature, which is owing to an increase of the dislocation multiplication rate and dislocation density [4]. All the true strain-stress can be summarized in three distinct stages of the stress evolution with strain [4; 6; 7]. At the first stage of the forming process, the flow stress rapidly increases to a critical value, where  
Figure 1: The true stress-strain curves of superalloy nimonic 80A under the different temperatures with strain rates (**a**) 0.01 s\({}^{-1}\); (**b**) 0.1 s\({}^{-1}\); (**c**) 1 s\({}^{-1}\); (**d**) 10 s\({}^{-1}\).  
work hardening (WH) predominates. At the second stage, where the thermal softening owing to dynamic recrystallization (DRX) and dynamic recovery (DRV) gets more and more predominant, flow stress slowly increases to the peak value even exceeds work hardening. At the third stage, the curves can be divided into two types based on the variation tendency. Evidence of DRX softening, the flow stress decreases continuously, which corresponds to the conditions of 0.01 s\({}^{-1}\) and 1050-1250 \({}^{\circ}\)C, 0.1 s\({}^{-1}\) and 1050-1200 \({}^{\circ}\)C and 1-10 s\({}^{-1}\) and 1050-1200 \({}^{\circ}\)C. However, in the parameter domains of 0.1-10 s\({}^{-1}\) and 1250 \({}^{\circ}\)C, the stress approximately keeps a steady state with significant DRV softening. From the previous descriptions, the typical form of flow curve with DRX softening involved a single peak followed by a flow of steady state. The reason lies in the fact that the higher rate of work hardening slows down the DRX softening rate with lower temperatures and higher strain rates, therefore, the onset of steady state flow is shifted to higher levels [4].
## 3 Optical Sensing with NDs
### Electric Field Sensing  
The electric field sensing capability of the NV-NDs is related to electric-field-dependent shifts in energy levels [10]. The Stark shift in the excited triplet states of NV centers was detected at low temperature [100], and the ground state Stark shift of NV ensembles was proven at low temperature [101]. A voltage [10] was applied to a gold layer deposited on a bulk diamond sample containing NV centers, to generate the electric field (see Figure 15b). To generate and orientate the applied magnetic field, two Helmholtz coil pairs (x-y axes) and a single coil (z-axis) were used. A set of coils, fabricated as part of the gold layer, generated the resonant microwave field. To measure the shifts of spin sub-levels induced by the electric field, the electron spin resonance transition between the \(\mathrm{m_{s}}\) = 0 and \(\mathrm{m_{s}}\) = \(\pm\)1 sub-levels in the triplet ground state of the NV center was optically detected. Upon application of an electric field of ca. 1000 V/cm, resonance line shifts of 28.4 kHz were observed. The final estimated electric field sensitivity was 202.6 V cm\({}^{-1}\) Hz\({}^{-0.5}\).
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 7 Future Directions
### Integration of Remote Sensing with Field Observation and Numeric Modeling  
Despite their advantages, remote sensing techniques for detecting SUHI wall-to-wall at multiple scales have clear shortcomings. Satellite-derived SUHI are not only different from air UHI, but also strongly influenced by the spatial-temporal resolutions of the data, satellite overpass time, and weather conditions [29; 30; 269]. Comparatively, direct observations can provide time-continuous and all-sky measurements of air UHI [8], while numerical modeling can help predict and attribute surface and air UHs at a temporally and spatially seamless manner [24]. A combined use of these three techniques can certainly provide a more thorough picture of an urban thermal environment [30; 125]. For example, we can use remote sensing data and field observations to parameterize, improve, and/or evaluate numeric models. An improved model can then be used to simulate and predict UHI based on various urban growth scenarios. These UHI projections are crucial for decision-makers to formulate the urban growth policies that aim to minimize the possible negative impacts of the UHI effect. However, such studies are very limited [270; 271; 272] because (1) there are large spatial (vertical and horizontal) and temporal heterogeneities in the energy budgets in an urban environment. (2) There is high inconsistency in measurement altitudes and resolutions among these three techniques. (3) It is still a challenge for remote sensing to quantitatively characterize the 3-D urban structure (e.g., urban canyon) and its spatiotemporal dynamics and (4) numerical models still need to be improved to be able to simulate the energy exchange processes associated with the complex urban structure. Accordingly, future studies should focus on promoting the comprehensive utilization of multiple methods to better understand the UHI phenomenon, and designing a decision-making framework to assess the influence of various urban planning policies on UHI.
## 4 Result Analysis
### Proposed Model vs. Literature Studies  
In this section, we present a comparison between our suggested model and models proposed in the literature in terms of accuracy, AUROC score, and the number of features. It should be noted that different MIMIC-III datasets were used in the several studies described here. However, all of the studies analyzed here were conducted for the purpose of mortality prediction in the ICU. The AUROC scores of our model and those proposed in other studies are presented in Table 8. The RF model utilized in this study with the FPA feature selection algorithm achieved an 83.0% AUROC score with only 21 features. Our proposed model is  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Rank** & **Feature Selection with the Classifiers** & **Rank Sum** \\ \hline
1 & FPA & 16 \\ \hline
2 & No feature selection & 11 \\ \hline
3 & GA & 7 \\ \hline
4 & PSO & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Additional information from Friedman test results.  
Figure 5: AUROC curves for ML models (**a**) without feature selection, (**b**) with feature selection via FPA, (**c**) with feature selection via PSO, and (**d**) with feature selection via GA.  
compared with those proposed in other studies in literature in term of accuracy in Table 9. The RF algorithm with FPA feature selection achieved the highest accuracy of 92.80% with only 21 features.
# Passive Microwave Imagers, Their Applications, and Benefits: A Review
## 2 PMWI Applications and Impact Areas  
PMWI data are essential for a broad spectrum of applications, ranging from forecasting TCs and hurricanes to analyzing extreme precipitation, floods, and fire severity, as well as estimating carbon emissions. They also support NWP models and play an important role in monitoring the cryosphere, sea surface conditions, soil moisture, and droughts. NWS is a primary user of PMWI data, utilizing them to enhance weather forecasts, improve precipitation estimates, and refine flood modeling. These applications underscore the significance and multifaceted utility of PMWI data. The following subsections delve into these applications and their far-reaching impacts, further highlighting the adaptability and value of PMWI data in addressing critical environmental and societal challenges.
## 3 Ambient Parameter Monitoring and Sensor Technology
### Relative Humidity  
During storage and distribution, it is essential to find the right level of RH that minimizes moisture loss but at the same time prevents microbial growth [5]. For example, [PERSON] et al. [47] observed increasing weight loss in fresh mushrooms with decreasing RH. They state that the RH has the largest effect on lowering the transpiration rate, but that a high RH at the same time can promote growth of certain microorganisms [47]. Therefore, real-time monitoring and control of RH is especially important for fresh fruit and vegetables that are either prone to water loss or very susceptible to microbial decay, such as berries and citrus fruit. Apart from moisture loss, RH can adversely influence further quality characteristics. [PERSON] et al. [35] describe that broccoli heads lose visual quality and glucosinolates' content faster at low RH than at high RH when stored at ambient temperature. For carrots, studies indicate that high RH is beneficial for maintaining levels of \(\beta\)-carotene, ascorbic acid, glucose and fructose [48]. This shows that monitoring of RH with IoT technology can be beneficial for several fruit and vegetable supply chains. Different technologies are possible, including optical, gravimetric, capacitive, resistive, piezo-resistive and magnetoelastic sensors [49]. For application in IoT devices, mostly capacitive and resistive sensors are used, because of their robustness, small size and low power consumption [6]. IoT solutions for measuring RH are widely available and often combined with temperature sensors [6].
## 4 Experimental Results
### The Planetary Gear Train Optimization
Additionally, the values for the reference AGMA gearbox are included in Table 4 for comparison. To achieve an in-depth understanding of the data shown in Table 5, a visual overview of the Pareto-optimal curves for the multi-objective optimization problem under consideration, which were derived using both the proposed and the NSGA-II algorithm, were analyzed. The Pareto optimum curves offer a graphical depiction of the trade-offs among several objectives, enabling decision-makers to choose the most appropriate solution according to their preferences, not just the compromise solution. Since the gearbox efficiency and dimensions are of greatest importance, it is vital to examine the impact of variations in the contact ratio (representing the gearbox dimensions) of the planetary gearbox on the overall efficiency of the gearbox, where the appropriate Pareto curves are presented in Figure 6.  
The Pareto optimum curves depicted in Figure 6 illustrate the presence of conflicting objectives. The ideal values for the contact ratio and efficiency of the gearbox are \(\varepsilon_{\alpha}=1.25\) and \(\eta_{gb}^{H}=0.9949\), correspondingly, while the compromise solution for NSGA-II is \(\varepsilon_{\alpha}=1.4\) and \(\eta_{gb}^{H}=0.9932\) and \(\varepsilon_{\alpha}=1.35\) and \(\eta_{gb}^{H}=0.9949\) for the HMOBPSO algorithm. It has been observed that the NSGA-II algorithm fails to achieve the entirety of the Pareto curve, hence omitting crucial information that is essential for the designer. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(z_{1}\) & 20 \\ \(z_{2}\) & 37 \\ \(z_{3}\) & 94 \\ \(m\) (mm) & 9 \\ \(a\) (mm) & 295.6 mm \\ \(\eta_{gb}^{H}\) & 0.97 \\ \(V\) (mm\({}^{3}\)) & 3.66 \(\times\) 10\({}^{10}\) \\ \(\varepsilon_{\alpha}\) & 1.36 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of reference gearbox outlined in [36]. Another important aspect for a gearbox is its safety and dimensions, which is analysed with the following combinations of objectives \(\varepsilon_{\alpha}\) and \(\sigma_{F}\), as well as \(\varepsilon_{\alpha}\) and \(\sigma_{H}\), where the appropriate Pareto-optimal curves are presented in Figure 7.
## 5 Discussion
While FLAC3D numerical simulations require approximately 90 s to compute energy data for each case, machine learning models achieve comparable predictions with a training time of only 1.5 s and a testing time of 0.01 s. This substantial reduction in computational time enables real-time predictions and rapid decision-making, facilitating timely prevention and mitigation of geological hazards. Despite the significant theoretical and practical contributions of this study, several limitations must be acknowledged. First, the lack of validation with field data remains a major constraint. The machine learning models developed in this study rely entirely on simulation data generated by FLAC3D, which have not been validated through field monitoring. Future research should prioritize the collection of field data, such as elastic strain energy and dissipation energy, to validate and refine the simulation results, thereby enhancing the practical applicability of the models. Second, the study assumes that the rock mass is homogeneous and isotropic, which may not adequately reflect the complexities of real geological conditions. Incorporating anisotropic and heterogeneous properties into future models could improve the accuracy and reliability of the simulations. Additionally, this study focuses primarily on static loading conditions and does not account for dynamic factors, such as seismic activities, or time-dependent effects, such as creep, which could significantly influence the energy evolution process. Furthermore, the use of default parameters in the machine learning models has resulted in suboptimal performance for certain models, such as the BP model. Future work should focus on hyperparameter tuning, ensemble learning, and model integration to enhance predictive accuracy and robustness. Lastly, the study does not consider dynamic loading conditions, such as seismic events, or time-dependent effects, such as creep. Integrating these dynamic factors into the modeling framework could provide a more comprehensive understanding of energy evolution in complex geological systems. In conclusion, this study combines numerical simulation and machine learning techniques to establish a novel theoretical framework and practical guidance for optimizing support parameter design and understanding the energy evolution characteristics of surrounding rock. The demonstrated efficiency and reliability of machine learning models in energy prediction highlight their potential to complement traditional computational methods, particularly in improving predictive efficiency and supporting rapid decision-making. To further enhance the robustness of the results, future research should incorporate field data and address the complexities of dynamic geological environments and conditions.
## 3 Results and Discussion
### Effects of Bond-Disrupting Agents on the Binding Capacity  
The interactions between MPs and fermentation-stinky compounds could be attributed to the actions of various forces. Figure 2B displays the actions of different bond-disrupting agents. Na\({}_{2}\)SO\({}_{4}\) is regarded as a salt that stabilizes proteins, which can enhance their hydrophobicity through intra- and intermolecular interaction [23]. Figure 2C observed that the binding of indole, isovaleric acid, dimethyl disulfide, and dibutyl phthalate to MPs was significantly reduced (\(p<0.05\)). Contradictory results were reported for aldehydes and furans [24; 25]. The increased surface hydrophobicity of MPs limited their binding to fermentation-stinky compounds. This may be related to the hydrophobicity of fermentation-stinky compounds themselves. The partition coefficient-P (log P) value is used to assess the hydrophobicity of the compound, and a high log P indicates that the compound has a strong hydrophobicity [26]. As shown in Figure 1, all the log \(p\) values of these fermentation-stinky compounds were greater than one with strong hydrophobicity, especially dibutyl  
Figure 2: Effects of (**A**) different concentrations and (**C**) different bond-disrupting agents on the free fermentation-stinky compounds. (**B**) The actions of different bond-disrupting agents. Different letters indicate significant differences between groups (\(p<0.05\)).  
phthalate, which reached 4.82. Therefore, this explained the highest presence of the free percentage of dibutyl phthalate (Figure 2C).  
Urea can denature proteins by decreasing their hydrophobicity and destroying hydrogen bonds [11]. The importance of hydrophobic interactions and hydrogen bonds for MPs-fermentation-stinky compound systems was thus highlighted by observing the changes in binding capacities in the presence of urea (Figure 2C). In contrast to urea, GH is a protein denaturant that destroys ionic bonds in addition to hydrophobic and hydrogen bonds [27]. Significant reductions in the binding of MPs to dimethyl disulfide were observed in the GH sample compared to the urea treatment. This indicated that hydrogen bond interactions provided higher contributions to the MPs-dimethyl disulfide system than hydrophobic interactions. Yet hydrogen bond interactions did not significantly affect the binding of indole or dibutyl phthalate to MPs. Moreover, PG inhibits hydrophobic interactions while promoting hydrogen and ionic bonds [28]. For dimethyl disulfide, the presence of PG in the binding capacity was between urea and GH, in which ionic bone interaction occurred. Compared to the GH samples, the PG samples in indole, isovaleric acid, and dibutyl phthalate presented lower interactions in the MPs-fermentation-stinky compound systems. In brief, hydrogen and ionic bonds could compete with electrostatic or other forces for the binding sites of MPs.
## 6 Conclusions  
In this paper, based on the monthly runoff data for the Chengbi River basin, spanning a total period of 492 months from 1979-2019, we aimed to investigate the predictive performance of standard AI models, AI models coupled with pre-processing techniques, and AI models coupled with both pre-processing techniques and quantile mapping post-processing methods. The primary conclusions obtained from this study are as follows.  
The monthly runoff of the watershed was predicted based on standard artificial intelligence models, namely, the BP Elman models. The results showed that the BP model predicted better than the Elman model, but that the overall prediction accuracy of both models was poor.  
Four models based on Empirical Modal Decomposition and Ensemble Empirical Modal Decomposition, each coupled with the two standard artificial intelligence models, were constructed to predict monthly runoff in the watershed. The results showed that pre-processing techniques can improve the accuracy of runoff prediction. On the two pre-processing techniques used in this study, EMD produced better predictions and higher NSE values than EEMD, and the EMD-Elman model was the best double-coupled model of the four that were tested.  
A QM-EMD-Elman triple-coupled model based on the quantile mapping (QM) method, coupled with the EMD-Elman double-coupled model, was constructed to predict monthly runoff in the watershed. The results showed that the QM-EMD-Elman model had improved prediction results, and that the post-processing method improved the accuracy of runoff prediction. The triple-coupled approach of pre-processing techniques, standard artificial intelligence models, and post-processing methods is an effective method for monthly runoff prediction in karst areas.  
Conceptualization, C.J. and X.L.; Data curation, C.J. and X.L.; Formal analysis, C.J. and Y.D.; Funding acquisition, C.M.; Investigation, C.J. and W.C.; Methodology, C.M. and C.J.; Project administration, C.M. and S.L.; Resources, C.J. and W.C.; Software, C.J. and Y.D.; Supervision, Z.X.; Validation, C.J. and X.L.; Visualization, C.J. and Y.D.; Writing--original draft, C.J. and Y.D.; Writing--review and editing, G.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant Nos. 52269002, 51969004), the Interdisciplinary Scientific Research Foundation of Guangxi University (Grant No. 2022 JCC028), and the science and technology award incubation project of Guangxi University (Grant No. 2022 BZJL023).  
Not applicable. This study did not involve humans or animals.  
Not applicable. The study did not involve humans.  
Some or all of the data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare no conflict of interest.