esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Equilibrium Surface Tension  
The \(\gamma\)-logC curve of betaine surfactants Figure 2 shows that the critical micelle concentration (CMC) value of DCB is 0.1852 mmol/L and that of \(\gamma_{\text{CMC}}\) is 27.6 mN/m. The CMC of DSB is 0.0750 mmol/L and that of \(\gamma_{\text{CMC}}\) is 30.0 mN/m. When the concentration of the surfactant solution is low, a large number of surfactant molecules adsorb on the surface of the solution, reducing the contact area between water and air, which leads to a sharp drop in surface tension. When the concentration reaches a certain value, the molecules reach saturation adsorption on the surface of the solution, micelles begin to form inside the solution, and the surface tension basically does not change. The concentration corresponding to this point is CMC, and the corresponding surface tension is \(\gamma_{\text{CMC}}\).  
Figure 1: TGA curves of DCB and DSB.  
Both betaine surfactants have the same hydrophobic chain and differ only in the hydrophilic group. On the one hand, the volume of carboxyl group is smaller than that of the hydroxypropyl sulfonic group [24]. With the increase in surfactant concentration, DCB molecules are arranged more closely after saturation and adsorption on the liquid surface. Therefore, the surface tension of DCB is lower than that of DSB. On the other hand, CMC of sulfobetaine is lower than carboxybetaine due to solvation effects [25]. This is because, during micelle formation, the hydroxypropyl sulfonic group and the carboxyl group may have different solvation/desolvation characteristics.
## 4 Discussion  
### Verification of Electrophysiological Models of Cardiomyocytes and Ventricles  
For this study, on the basis of constructing a rabbit ventricular model with trans-mural heterogeneity, a ventricular model of endocardial ischemia caused by left anterior descending branch occlusion was established. The effects of different extracellular potassium concentrations on arrhythmia vulnerability were studied by giving cardiomyocytes different extracellular potassium concentrations and inducing arrhythmias. By simulating the action potentials of various cardiomyocytes and calculating virtual electrocardiograms under normal and ischemic conditions, the correctness of cardiomyocyte models and ventricular models were verified. This manuscript performed sensitivity analyses of some important input parameters when the extracellular potassium concentration was 12 mM. When the conductivity was increased by 1.5 times, the vulnerable window was reduced by 1 ms and the sensitivity was 8.33%. When the time step was reduced to 45 ms, the size of the vulnerable window did not change.
## 2 Experimental Preparation for Fuzzy Logic Application
### Battery and Supercapacitor State-of-Charge  
For the batteries to charge/discharge healthily, they must be operated within a certain SoC range. When the studies in the literature are examined, it is seen that the most efficient operating SoC range of Li-ion batteries is 20-90% [49]. In this context, charge/discharge operations at low and high SoC values increase the stress on the battery [50]. SCs can charge and discharge very quickly [51]. Moreover, because of their high power density, SCs can cater to the sudden acceleration demands in EVs. In addition, the cycle life of SCs is  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenarios** & **Driver** & **Drive + 1** & **Drive + 2** & **Full Load** \\ \hline Scenario 1 & 1.558 C & 1.618 C & 1.678 C & 2.275 C \\ \hline Scenario 2 & 0.710 C & 0.738 C & 0.765 C & 1.037 C \\ \hline Scenario 3 & 1.277 C & 1.326 C & 1.376 C & 1.866 C \\ \hline \hline \end{tabular}
\end{table}
Table 2: Energy gained from regenerative braking under different loads.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**SC Parameters** & **Parameter Values** \\ \hline SC cell voltage & 2.7 V \\ SC pack voltage & 400 V \\ Continuous cell current & 40 A (for 40\({}^{\circ}\)C) \\ Maximum cell current & 170 A \\ Internal resistance & 0.0032 C \\ Total weight & 27 kg \\ Total capacity & 159.3 Wh \\ Total cost & \$3000 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the Maxwell SC model BCAP0350.  
more than 1 million [52]. Because of their high cycle life, they can be operated in a wider SoC range than batteries. Similarly, their operating temperatures are also wider than those of Li-ion batteries. In this way, SCs are preferred in applications that require a continuous charge/discharge, such as regenerative braking. SCs can be operated in the 5-95% SoC range to achieve high efficiency from their energy density [53; 54].
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 3 The MASU Framework
### Control Messages  
In order to support the interaction among units, the MASU framework defines the following six types of messages of the Control Tier. The _Unit Detection_ messages that are used to detect the units  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Dynamic Centralized** & **Distributed** \\ \hline Control Tier & By leader election algorithm & All active units \\ Sensing Tier & By the manager & By consensus algorithm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Role selection and activation mechanisms for the different role settings.  
that are present in the collaborative sensing activity. Therefore, these messages are used to monitor changes in the composition of the activity, regarding the units and roles that are present in such activity. The _Device Information_ messages that are used by the units to share information about their hardware capabilities, such as battery level, processor load or available memory. The _Election_ messages that are sent by the distributed leader election algorithm, used to select the manager and monitor roles in a dynamic centralized role architecture of the Control Tier. The _Consensus_ messages that are used in a distributed role architecture of the Control Tier, so that all the managers could agree on the selection of roles in the Sensing Tier. The _Role Activation_ messages are only used in case of a Dynamic Centralized role architecture of the Control Tier. They are sent by the manager to activate roles in the Sensing Tier or to activate the Monitor if it fails. Finally, the _Network Monitoring_ messages represents a special type of message that is necessary for the network monitoring functions performed by the monitor.
## 2 Materials and Methods  
### Studied Regions  
Four municipalities in China were selected as research objects, namely Beijing, Tianjin, Shanghai, and Chongqing. They are on a large scale and among the most developed cities in China. They have dense populations and a higher demand for urban Infrastructure. As the leaders of three economic circles (Bohai Economic Rim, the Yangtze River Delta Economic Circle and Upper Yangtze River Economic Circle), these municipalities play an increasingly important role in radiating to the surrounding areas and their infrastructure resilience level directly affects the regional development. Therefore, it is necessary to explore the UI resilience level of China by taking four municipalities as examples. Nevertheless, it was worth noting that these four cities may differ in their resilience due to their different development processes and strategic arrangements. Therefore, it was assumed that the resilience levels of different cities are various. This paper attempted to evaluate the pressure, state, response, and infrastructure resilience levels of the four cities based on the PSR framework. The data were from the China Urban Statistical Yearbook, China City Construction Statistical Yearbook, China Environment Statistical Yearbook, and Environmental Quality Bulletin in this study. Mean values of adjacent indicators replace missing data.
## 3 Results and Discussion
### Sensory Evaluation  
Table 4 shows the results of sensory evaluation of OM and DM during storage at different temperatures. The average value of the putrid odor exceeded the two points (\"weak putrid odor\") on day 6 for refrigerated, day 7 for ice storage, and day 17 for SC for both muscle parts. The odor intensity increased significantly during storage in both muscle groups. SC storage of OM suppressed the increase in odor intensity compared with other storage temperatures for OM, whereas lower storage temperatures for DM had a similar effect. These results suggest that SC storage can suppress odor changes in yellowtail muscles during storage, including spoilage, compared with refrigeration or ice storage. Storage causes greater odor deterioration in DM than in OM. Our previous study found that ice storage of yellowtail meat until it reached spoilage levels increased the odor intensity of DM, whereas it decreased that of OM [10]. This inconsistency with the results of this study on OM is thought to result from an increase in the number of components involved in the odor, owing to an increase in microorganisms.
## 4 Methods
### Cross-Calibration of FASat-C NAOMI-1  
The average FASat-C DN per sample were adjusted by multiplying the \(Ai\) factor Equation (4). For both sensors, the offset values are periodically estimated on a pixel basis during the relative radiometric calibration process, performed over dark oceanic areas, and are subtracted at the Level 1A/1B product generation [10; 74]. The RapidEye radiances were obtained applying the radiometric scale factor given in the metadata file (9.99999776483 \(\times\) 10\({}^{-3}\) W\(\cdot\) m\({}^{-2}\cdot\) sr\({}^{-1}\cdot\)\(\mu\)m\({}^{-1}\)) to all bands, as follows Equation (5):  
\[L_{R}=G_{R}\;DN_{R} \tag{5}\]  
The absolute radiometric calibration coefficients of NAOMI-1 bands were obtained through a linear fit, considering Equations (6)-(8):  
\[L_{F}=DN_{F}\;G_{F}^{-1} \tag{6}\]  
\[\frac{DN_{R}}{DN_{F}}=\frac{\rho_{R}\;(E_{0}cos\theta)_{R}}{\rho_{F}\;G_{R}\;G _{F}\;(E_{0}cos\theta)_{F}} \tag{7}\]  
\[DN_{F}^{*}=Ai\;DN_{F}=\;L_{R}\;G_{F} \tag{8}\]  
where L\({}_{R}\) and L\({}_{F}\) are the at-sensor radiance of the \(F\) and \(R\) sensors, expressed in W\(\cdot\) m\({}^{-2}\cdot\) sr\({}^{-1}\cdot\)\(\mu\)m\({}^{-1}\); \(G_{R}\) and \(G_{F}\) are the gains of \(R\) and \(F\), expressed in W\(\cdot\) m\({}^{-2}\cdot\)sr\({}^{-1}\cdot\)\(\mu\)m\({}^{-1}\); \(DN_{R}\) and \(DN_{F}\) are the digital numbers of \(R\) and \(F\); \(\rho_{R}\) and \(\rho_{F}\) are TOA reflectances of \(F\) and \(R\); \(DN_{F}{}^{*}\) is the adjusted \(DN\) of the sensor to be calibrated against the reference sensor. In Equation (7), the term \(\rho_{R}/\rho_{F}\) is replaced by the SBAF and, in conjunction with (\(E_{0}\;cos\theta)_{R}/(E_{0}\;cos\theta)_{F}\), constitutes the final compensation factor \(Ai\), previously obtained using Equation (4).
# Semi-Empirical Satellite-to-Ground Quantum Key Distribution Model for Realistic Receivers
## Appendix B Atmospheric Extinction Coefficient  
In this section, we provide the experimental findings of the atmospheric extinction coefficient obtained from the measurements of star fluxes in the I wavelength band. To measure star fluxes, we point our 600-mm ground station at the chosen star and measure the photon count rate throughout a 20-s period (Table 1).  
Using Equation (5), the difference in magnitude (\(m\)) for two stars at the Earth's surface in the same weather conditions (i.e., with the same atmospheric extinction coefficient \(\varkappa\)) can be written as:  
\[m^{i}-m_{0}^{j}-m^{j}+m_{0}^{j}=\varkappa(f(\theta_{EI}^{i})-f(\theta_{EI}^{j} )), \tag{11}\]  
where superscripts (\(i\) and \(j\)) denote the different stars.  
According to Equation (4), it can be expressed as follows:  
\[-2.5\log_{10}F_{i}-m_{0}^{i}=\varkappa f(\theta_{EI}^{i})-2.5\log_{10}F_{j}-m_ {0}^{j}-\varkappa f(\theta_{EI}^{j}) \tag{12}\]  
Then, for the stars observed under identical weather conditions, Equation (12) turns into:  
\[-2.5\log_{10}F_{i}-m_{0}^{i}=\varkappa f(\theta_{EI}^{i})+const, \tag{13}\]  
Therefore, using Equations (13) and (6), we found that the atmospheric extinction coefficient \(\varkappa\) ranges from 0.23 on a clear night to 0.41 on a foggy, hazy night. Note that the spectral range for both ground stations is the same.
## 3 Results and Discussion
### Multivariate Statistical Analysis
#### 3.4.1 Analysis of Differential Volatile Components in Black Tea from Different Seasons
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Volatile Components** & **VIP Value** & _p-Value_ \\ \hline Geraniol & 4.70 & 0.00 \\ Linalool & 3.07 & 0.00 \\ Hotriend & 2.48 & 0.00 \\ Methyl salicylate & 2.31 & 0.01 \\ \(\alpha\)-Farnesene & 2.19 & 0.00 \\ Nonanal & 2.05 & 0.00 \\ Quinuclidine & 2.04 & 0.00 \\ (_E_)-Geranic acid & 1.95 & 0.00 \\ Caffeine & 1.74 & 0.02 \\ Phenylacetaldehyde & 1.71 & 0.00 \\ Benzaldehyde & 1.64 & 0.00 \\ Methyl geraoate & 1.60 & 0.00 \\ Phenethyl alcohol & 1.56 & 0.00 \\ Tetradecane & 1.52 & 0.00 \\
2-Heptanol & 1.49 & 0.00 \\ Benzyl alcohol & 1.49 & 0.00 \\ (_Z_)-6-Methyl-2-undecene & 1.46 & 0.00 \\ (_E_)-Linalool oxide (furanoid) & 1.39 & 0.01 \\ 4.6,8-Trimethyl-1-nonene & 1.37 & 0.00 \\ (_E_)-Linalool oxide (pyranoid) & 1.16 & 0.00 \\
3,4,5-Trimethyloxazole & 1.16 & 0.00 \\ Jasmine lactone & 1.15 & 0.00 \\
5-Methyl-2-hexanol & 1.15 & 0.00 \\
2,6,11-Trimethyldodecane & 1.14 & 0.00 \\
2,6-Dimethyl-3,7-ocdadien-2,6-diol & 1.13 & 0.00 \\
2-Hexenal & 1.11 & 0.00 \\ Benzyl nitrile & 1.08 & 0.00 \\
2,6,10-Trimethyldodecane & 1.06 & 0.03 \\ (_Z_)-3-Hexenoate & 1.05 & 0.00 \\
2,6-Di-tert-butyl-4-methylphenol & 1.05 & 0.01 \\ _B_-ionone & 1.05 & 0.00 \\ _Trans_-neroidoid & 1.04 & 0.00 \\ (_E_)-Citral & 1.02 & 0.00 \\ Hepadecane & 1.00 & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Differential volatile components of Rucheng Baimao black tea in different seasons.
# Novel Approaches to Combat Medical Device-Associated BioFilms
## 3 Novel Approaches to Combat Medical Device-Associated Biofilms
### Physical Therapy
Water jets can wash away loosely attached plaques, eliminate bacterial cells, and interfere with biofilm maturation [131] (Figure 1). A previous study has shown that under different pressure conditions, the ability of dental water jets to remove biofilms differs. The research results showed that, under pressures of 707, 350, and 102 kPa, the rate of reduction in biofilm thickness was 85.5%, 85.1%, and 63.4%, respectively [132]. A recent study has shown that the combination of PDT and water jets has good application prospects for removing scale from the surface of traditional implants. Compared with an untreated biofilm, the biofilms of _Streptococcus mutans_ grown on a titanium surface treated with PDT were more easily removed by the impact force of a water jet. This showed that PDT can weaken biofilms in different stages, making them easier to separate with a simple washing force [133]. Another study demonstrated that the cavitating jet can also effectively clean biofilms formed on the rough surface of implant screws, especially on the root sector [134]. In addition, mechanical methods, such as the use of a plastic curette, an air-abrasive device (Perio-Flow), a titanium brush (TiBrush), and implantoplasty, and electrochemical treatment can effectively clean dental biofilms and improve hygiene around the implant [135; 136]. However, it has been found that the mechanical treatment strategies used to remove implant biofilms may cause titanium dissolution and increase the cytotoxicity. Therefore, the dissolution of implant material should be an important consideration in the clinical choice of treatment of peri-implant inflammation [137].
# A Review on Wind Turbine Deterministic Power Curve Models +
## 4 Sigmoid Models  
Sigmoid models are described by means of the general expression given by Equation (17) [50; 51; 52; 32; 53],  
\[P=b_{5}+(b_{2}-b_{5})\frac{(1+b_{6}f(v,b_{0},b_{1}))}{(b_{3}+f(v,b_{0},b_{1})) ^{1/b_{4}}} \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(j\) & \(n_{j}\) & \(a_{0j}\) & \(a_{1j}\) & \(a_{2j}\) & \(a_{3j}\) & \(v_{j}\) \\ \hline
1 & 0 & 0 & 0 & 0 & 0 & 0 \\
2 & 3 & \(a_{02}\) & \(a_{12}\) & \(a_{22}\) & \(a_{32}\) & \(v_{ci}\) \\
3 & 3 & \(a_{03}\) & \(a_{13}\) & \(a_{23}\) & \(a_{33}\) & \(v_{ci}+0.5\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) \\ \(k-1\) & 3 & \(a_{0k-1}\) & \(a_{1k-1}\) & \(a_{2k-1}\) & \(a_{3k-1}\) & \(v_{k}-0.5\) \\ \(k\) & 3 & \(a_{0k}\) & \(a_{1k}\) & \(a_{2k}\) & \(a_{3k}\) & \(v_{k}\) \\ \(k+1\) & 3 & \(a_{0k+1}\) & \(a_{1k+1}\) & \(a_{2k+1}\) & \(a_{3k+1}\) & \(v_{k}+0.5\) \\ \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \(\ldots\) & \\ \(m-1\) & 3 & \(a_{0m-1}\) & \(a_{1m-1}\) & \(a_{2m-1}\) & \(a_{3m-1}\) & \(v_{r}-0.5\) \\ \(m\) & 0 & \(P_{r}\) & 0 & 0 & 0 & \(v_{r}\) \\ \((m+1)\) & - & - & - & - & - & \(v_{co}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Relationship among parameters of the CM and the parameters of the WT.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(j\) & \(n_{j}\) & \(a_{0j}\) & \(a_{kj}\) & \(a_{Nj}\) & \(v_{j}\) \\ \hline
1 & 0 & 0 & \(\ldots\) & 0 & \(\ldots\) & 0 & 0 \\
2 & \(N\) & \(-v_{ci}^{N}\cdot P_{r}\big{/}\big{(}v_{r}^{N}-v_{ci}^{N}\big{)}\) & \(\ldots\) & 0 & \(\ldots\) & \(P_{r}\big{/}\big{(}v_{r}^{N}-v_{ci}^{N}\big{)}\) & \(v_{ci}\) \\
3 & 0 & \(P_{r}\) & \(\ldots\) & 0 & \(\ldots\) & 0 & \(v_{r}\) \\
4 & - & - & - & - & - & - & \(v_{co}\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Relationship among parameters of the CM and the parameters of the WT.  
where all the values \(b_{i}\) are parameters, and \(f(v,b_{0},\ b_{1})\) is a function that can be expressed in two different ways, each of them including the parameters \(b_{0}\) and \(b_{1}\). The notation used in Equation (17) allows for the simplification of the implementation of several sigmoid models.  
The model expressed in Equation (17) includes five parameters plus those included in \(f(v,b_{0},\ b_{1})\). However, there is not a seven-parameter model. As long as the model needs to be simplified, the values of the parameters that have traditionally been used are as follows, in this order: \(b_{5}=0\), \(b_{4}=1\), \(b_{3}=1\), and \(b_{2}=P_{r}\). Moreover, it must be noted that any number of parameters can be used. More than seven involves more accuracy while a lower number means the model is easier to deal with. Applied to WTPCs, just two types of models are currently used, i.e., exponential models (EM) and algebraic models (AM).
# Source Microphone Identification Using Swin Transformer
## 4 Experiments and Discussion
### Intra-Model Classification  
Intra-model source microphone classification is the task of identifying which specific microphone was used to record a given audio signal when multiple recordings were made using the same type of microphone. This problem is more challenging than inter-model source microphone classification because the differences between microphones of the same model are often subtle and may not be easily discernible. We performed experiments to classify the microphone model for all models in the database, with each model undergoing a separate experiment. The accuracy comparison between our Swin-T method and two other methods (AST and KNN-Gabor) is shown in Figure 3. The data reveals that the Swin_T model outperforms the other two methods in improving the accuracy of the classification of seven microphone models. In contrast, the Swin_T model accuracy is slightly lower for AKG_0451 model than the other methods. Figure 3 also illustrates the varying degrees of improvement in accuracy achieved by the Swin_T model compared with the other AST and KNN-Gabor across the seven microphones, where the microphone SHU-0058 exhibits the highest accuracy improvement, followed by the microphone ELE-0020, while the microphone AKG-3000 shows the least difference in accuracy improvement. These improvements in accuracy can be attributed to the fact that the Swin Transformer can capture multi-scale contextual information with remarkable effectiveness.
## 3 Results and Discussion
### Numerical Results
#### 3.2.2 Sample Deformations  
Resulting from the assumed values for the elastic parameters of the rock material, sample deformations, where an example is shown in Figure 14 for the \"basis\" parameter set acc. to Table 2, occur due to the uniaxial compression loading of 10 MPa. Under non-restrained conditions with \(\varphi=0^{\circ}\), the sample theoretically undergoes uniform vertical displacements. The restraining effect of any interface friction at the sample-planet contact results in the non-uniformity of the vertical sample displacements.  
From a theoretical point of view, axisymmetric conditions with regard to deformations are only reached for samples with horizontal plane inclinations (\(\beta=0^{\circ}\)). For any other value of \(\beta\), axisymmetry is not retained. However, planar symmetry in the deformations is still acquired around the sample's symmetry plane, which corresponds to the plane \(xz\) as shown in Figure 1.
## 4 Discussion
### Response Mechanism of GPP to Climate Change  
Vegetation responses to changes in hydrothermal conditions often exhibit temporal asynchrony, demonstrating distinct lag effects and cumulative impacts over time. Notably, monthly-scale analyses have been shown to provide a more precise characterization of vegetation-climate interactions compared to annual or seasonal scales [50; 51]. Consequently, this study adopts a monthly temporal resolution to investigate vegetation responses to both normal and extreme climatic conditions. While precipitation primarily influences vegetation dynamics through soil moisture mediation [52; 53], extreme climatic events exert relatively limited negative effects on GPP. The observed GPP variations are predominantly driven by normal climatic factors, with desert steppes exhibiting a positive feedback relationship between mean precipitation and GPP, contrasted by a negative feedback relationship with mean temperature [43; 54]. Recent research by [PERSON] et al. [10] has revealed that concurrent anomalies in temperature and precipitation can exert more substantial impacts on terrestrial ecosystem carbon fluxes than isolated extreme events, highlighting the critical need for further investigation into multifactorial interactions.  
The increasing frequency of extreme high-temperature events enhances surface evaporation and transpiration rates, potentially inducing vegetation stress through the inhibition of root physiological functions [55; 56]. This mechanistic understanding explains the more immediate vegetation response to extreme temperature events compared to extreme precipitation events. The NFYS desert steppes are characterized by three dominant soil types: Kastanozem, Calcisol, and Arenosol [57], all exhibiting limited water retention capacity. Analysis of the 2001-2023 period reveals a declining trend in the intensity, frequency, and duration of extreme precipitation events, suggesting that vegetation growth requires the cumulative effect of multiple extreme precipitation occurrences. Furthermore, this study identifies that the lagged response of GPP to climatic conditions diminishes the positive influence of normal climate conditions while amplifying the negative impacts of extreme conditions. This finding underscores the long-term cumulative effects of extreme climatic conditions on vegetation, resulting in increasingly pronounced ecological consequences over time.