esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper proposes a novel unified bus carbon intensity incentive mechanism for renewable energy and energy storage based on the carbon emission flow theory. A two-stage Stackelberg game WT-ES planning model with the proposed incentive mechanism is also built. Based on the modified IEEE 24-bus power system, the validity and superiority of the model were tested in two different wind resource scenarios. In brief, the following conclusions can be drawn:  
1. The proposed model can effectively facilitate the system wind power and energy storage for planning and reduce bus carbon intensities in the system, thus effectively reducing the system carbon emissions by more than 11.32%;
2. The proposed bus carbon intensity incentive mechanism can effectively guide investors to invest in planning wind turbines at the buses with higher carbon intensity in the system, which is more conducive to promoting the decarbonization of the system;
3. The proposed bus carbon intensity incentive mechanism can effectively guide investors to invest in planning energy storage at the buses in the system where carbon intensities fluctuate more dramatically. This makes it easier for low-carbon electricity to be accommodated in the system, thus reducing system carbon emissions;
4. Compared with the existing incentive mechanisms applied in practical projects, the proposed bus carbon intensity incentive mechanism has significant advantages in promoting the system to reduce carbon emissions. Moreover, it still has visible carbon reduction advantages with similar total fixed investment costs. The bus carbon intensity incentive mechanism proposed in this paper is based on the carbon emission flow theory. With the development of computer network technology and the urgent need for carbon reduction in power systems, the carbon emission flow theory has a good application prospect in practical engineering. Additionally, the proposed bus carbon intensity incentive mechanism adopts a fixed carbon price for the incentive. In future research, a stepped carbon price or a real-time carbon price can be used, and the impact of carbon pricing mechanism on the incentive effect can be further studied. Conceptualization, J.N., J.F. and H.Z. ; methodology, J.N. and J.F. ; software, J.N. and J.F. ; validation, L.G., K.S. and X.D. ; formal analysis, J.N., J.F. and H.Z. ; investigation, L.G. and X.D. ; resources, K.S. and H.Z. ; data curation, J.N. and J.F. ; writing--original draft preparation, J.N., J.F. and H.Z. ; writing--review and editing, L.G., X.D. and K.S. ; visualization, J.N. and J.F. ; supervision, H.Z. ; project administration, K.S. and H.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available in this article. The authors declare no conflict of interest.
## 4 Results and Discussion
### Analysis of Time and Frequency Domain  
To figure out the effect of inlet distortion of the engine in essence clearly, it is necessary to analyze the signal in the time and frequency domain. Figure 7(a) shows the AE signal measured during the normal status of the engine (\(J=0\%\)), and the corresponding frequency-domain waveform is shown in Figure 7(b). Figure 7(a) shows the typical AE signal measured during the inlet distorted status of the engine (\(J=12\%\)), and the corresponding frequency-domain waveform is shown in Figure 7(b).  
Figure 8: Time-domain and frequency-domain waveform of the normal status of the engine.  
Figure 7: Variation of the parameter of vibration monitoring technique change with \(J\).  
Figure 9: Time-domain and frequency-domain waveform of the inlet distorted status of the engine.  
By comparing the time-domain waveform between the normal and distorted signals, it can be seen that the signals are both continuous-type signals and the amplitude of the distorted signal exceeds largely over the normal signal. By comparing the frequency-domain waveform between the normal and distorted signals, it can be seen that the energy lies mainly in the frequency band in the range of 50-250 kHz, and the most obvious frequency component is 150 kHz, followed by 100 kHz. Meanwhile, the proportion of the 100 kHz frequency component in the distorted signal is significantly higher than that in the normal signal. From the time-domain waveform and frequency-domain waveform, it can be seen comprehensively that there are certain differences between the normal signal and the distorted signal. However, there are also some overlaps in the main frequency, which will affect the signal identification.
## 4 Discussion
This integration could be achieved, ideally using UAV vehicles equipped with various sensors for high-resolution data collection [61]. The map of VOC abundance can be automatically overlaid with aerial maps of the area, changes in reflectance, dedicated vegetation indices [6], or temperature fluctuations [62].
## 3 Analyzing HCAHPS Data
### Data Preparation  
The first problem that should be handled in data preparation is the nature of some questions, called skip questions. A skip question by itself does not provide any information about a patient but it determines whether some other questions, called dependent questions, are applicable to the patient. For instance, a skip question inquires if the patient has used the bathroom or not. If not, the patient skips all of the dependent questions related to the bathroom cleanliness. Since skip questions, by themselves, do not provide any data about a patient, it would be reasonable to omit them from the dataset and treat their empty dependent questions as missing values. For example, if a patient has not used the bathroom, all of the bathroom-related questions have missing value for that patient.  
There are two basic approaches for handling missing values: (1) the complete case analysis which ignores the records with missing data, or (2) the imputation of missing values. The imputation method can be further divided into single imputation where each missing value is replaced by a single value, and multiple imputations where each missing value is replaced by multiple values to reflect the uncertainty. The complete case analysis could introduce a selection bias and may lead to loss of information. The added bias makes the data related to those questions more homogenous. If all of the patients have similar opinion on the related questions the added bias will make those questions insignificant. However, if in fact there are two opposite opinions on this matter that are separated based on the demographic features of patients, the separation will be more statistically significant. Therefore, a single imputation method is applied in this study for handling missing values in HCAHPS dataset. This approach may reduce the variance and add bias to range of the imputed variables but will not result in loss of information. The reduction in the variance of an imputed variable may decrease the chance of that variable being selected as a significant feature when compared to other variables, but there is still a chance of being selected if the data is extensively divided on that variable.  
For imputing the missing values, the \(K\)-nearest neighbor imputation method (KNNI) [13] is used. This method is chosen because it is applicable to both continuous and categorical variables and it can be applied to the data set automatically and without any supervision. This method imputes the missing value based on the \(K\) nearest neighbors of the record with missing value. For categorical features, the missing value is replaced with the category which has the majority in the \(K\) nearest neighbors. For continuous features, the missing value is replaced with the weighted average of that feature in the nearest neighbors. To have minimum computational complexity, \(K\) = 3 has been chosen for KNNI as it is the least number of neighbors that could produce reasonable results for finding majority of classes among categorical variables.
## 3 Results and Discussion
### Characterization of the Catalysts  
#### 3.2.1 Molecular POMs with Organic Cations  
* _IR characterization_  
The IR spectra of the organic salts of POMs (Figure 3), **(Org)\({}_{3}\)PMo\({}_{12}\)O\({}_{40}\)** and **(Org)\({}_{4}\)PMo\({}_{11}\)VO\({}_{40}\)** (Org = BuPyr or BmIm), present characteristic peaks confirming the presence of polyoxometalate (POM) structures and organic functional groups. The Mo=O stretching vibrations appear around 950-970 cm\({}^{-1}\), while the Mo-O-Mo bridging vibrations are observed in the range of 750-880 cm\({}^{-1}\). The presence of BuPyr and BmIm cations is indicated by C-H stretching bands in the 2800-3100 cm\({}^{-1}\) region, along with C=N or aromatic ring vibrations near 1600 cm\({}^{-1}\)[51].  
For better comparison, the range of the IR spectrum in Figure 3 is restricted to the region of interest. Full spectra are in the SI.
## 2 Materials and Methods
### The Mechanism and Framework of DA  
In recent years, the deep integration of RS technology and CGMs has brought significant innovation power to the DA method in the agricultural field and has shown great potential for application is undoubtedly worthy of recognition [44; 108; 109; 110]. The core mechanism of DA is to use mathematical theory and advanced model algorithms to combine the multi-dimensional and high spatio-temporal resolution observation data from RS platform with the internal biophysical process simulation of CGMs so as to bridge the gap between model simulation and actual observation and enhance the simulation accuracy and predictive behavior of the model to complex agricultural systems [52; 111]. To some extent, based on proficiency in RS data analysis, CGMs, and other professional skills, and a deep understanding of the internal mechanism of DA, building an efficient and adaptable assimilation framework is the top priority of DA. A simple assimilation framework is shown in Figure 4.  
The DA framework encompasses four core steps: RS observation acquisition and processing, model simulation and parameter setting, assimilation algorithm application, and cyclic iterative updating [112]. RS observation focuses on acquiring high-resolution RS images, involving the integration of multi-source RS data, prepro  
Figure 4: Sketch of crop model framework for remote sensing data assimilation. Note: The figure on the left is composed of remote sensing observations, crop models, and assimilation systems, and the figure on the right is the assimilation comparison between the regeneration assimilation process and the whole growth period. The green line indicates the addition of remote sensing observations, and the blue line indicates the addition of no remote sensing observations.  
metric calibration, atmospheric correction, geometric correction, and error elimination), and data fusion techniques (such as super-resolution, multi-scale fusion, and spatio-temporal filtering) to inversely derive vegetation bio-chemical parameters for assimilation input. In the model simulation stage, suitable numerical models and observation operators are selected based on input parameters, generating an integration of bio-chemical parameters, establishing model and parameter errors, and employing diverse assimilation strategies to assess the feasibility of observational data, which, when available, are incorporated into the CGMs. The assimilation algorithm stage employs statistical methodologies (e.g., Kalman filtering, variational methods) or physically-based approaches (like 4D-Var, Ensemble Kalman Filtering) to identify the optimal state estimate via the model's internal residuals, constructing the framework of the assimilation process system. Lastly, during the iterative updating cycle of assimilation, key state variables are selected and updated by the assimilation algorithm, driving the CGMs to update according to the new state until all updates conclude, yielding the final predictive outcome [111; 113].
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 2 Earthquake Database  
Since the National Research Institute for Earth Science and Disaster Prevention (NIED) in Japan has applied seismometers on boreholes and surfaces in each station to record earthquakes throughout the nation, the strong-motion seismograph network Kiban Kyoshin Network (KiK-net) presents the acceleration history measurements, information of the sites, and earthquake events required to perform the seismic ground response analysis [15; 16; 17].
## 4 Influence of the Cathode's Geometry  
The conversion of electric to mechanical energy is made primarily by the electric field, making the potential distribution a key factor in the design of the thruster. In turn, the electric potential morphology relies on the electrode geometry. Therefore, the optimized performance of the EHD thrust, which includes the flow pattern, depends greatly on the thruster geometry.  
In a first approach, we investigated the influence of the intrinsic cathode geometry on the thruster performance for two noble propellant gases, argon (Ar) and xenon (Xe). The first gas, because it was used in our previous studies [24; 25; 26] and so it serves well for comparison, between the results then obtained and the new ones; the second gas is here due to its widespread use in space applications. For example, xenon propellant is the most used in ion engines and Hall thrusters, despite its costs and availability, making other noble gases, such as argon and krypton (Kr), potential substitutes. However, the last gases, Ar and Kr, present more issues in terms of thruster performance specifications, as having a lesser molecular mass than xenon and need higher ionization energy. In general, a denser propellant will improve the output thrust and its efficiency as it has a higher energy per particle for the same exhaust velocity.  
For the study of the influence of the cathode geometry on the thruster, the electric circuit developed to control and maintain the self-sustain plasma discharge (for both gases) were designed with 20 kV-applied DC voltage, a ballast resistor, and a blocking capacitor of 500 M\(\Omega\) and 1 pF, respectively. As for the plasma fluid, the initial pressure was set at 10 Torr with an ambient temperature of 300 K.
## 3 Results and Discussion
The intensity of the Si-H bond decreased and that of the Si-C bond increased after curing due to the catalytic effect of the curing agent (Figure 8b,c), leading to the cross-linking addition reaction of C = C with Si-H [38; 39; 40], resulting in an increase in the degree of cross-linking and forming a three-dimensional network structure with Si-C-Si bonds as the skeleton [41; 42] (the absorption bands appearing at 496 cm\({}^{-1}\) in Figure 8a could be Si-O, and the acetone added may have absorbed water in the air slightly, which further react with PCS to form a small number of Si-O bonds). The amorphous phase was achieved with different acetone contents, indicating that the curing process was not related to the acetone content (Figure 8d), which was consistent with the results of FT-IR and Raman analysis. Figure 9 presents the XRD patterns of all samples pyrolyzed at 1300 \({}^{\circ}\)C in the Ar gas environment. There were obvious SiC diffraction peaks detected in the pyrolyzed PCS samples prepared with different acetone contents. The XRD peaks of crystalline SiC were broad, indicating that the introduction of acetone did not damage L-PCS properties during the pyrolyzing process. Figure 2:1 Raman spectra, FT-IR spectra, and X-ray diffraction (XRD) patterns. (**a**) FT-IR spectra after curing, (**b**) Raman spectra after curing, (**c**) Raman patterns before and after curing, and (**d**) XRD patterns with different acetone contents after curing. Finally, the distribution of PCS molar mass was measured by gel permeation chromatography (GPC), which was shown in Figure 2:2 The molecular weight of PCS fluctuated within a certain range (Mn = 2100 \(\pm\) 50) before and after acetone introduction, indicating that acetone introduction did not change the length of PCS chains. The proportion of micro-molecules (<1500) decreased from 27% to 23% after 30% acetone volatilization, resulting in an increase in the number and weight average molecular of L-PCS solution. In addition, the average molecular weight could be the key parameter in dissolving, as seen in Table 1, it was those molecules whose weight were below 1500 that dropped the most. In addition, the curing yield of PCS with 30 wt.% acetone was measured at 86.6%, lower than that of pure PCS (90.6%), which was due to the decrease of the micro-molecules ratio triggered by acetone volatilization [43; 44; 45]. Therefore, the decrease of the micro-molecules ratio triggered by acetone volatilization was beneficial for forming a more compact embryo body. According to those tests mentioned above, acetone just dissolved micro-molecules of PCS to lower its viscosity, rather than produce chemical reactions with PCS. Additionally, micro-molecules volatilized with acetone during curing, leading to a higher compactness after curing and pyrolyzing, which was consistent with the results of SEM.
## 2 Results and Discussion
### Purification of Antioxidant Peptides from Hydrolysate of STB-G  
#### 2.2.1 Preparation and Fractionation of Hydrolysate from STB-G  
The ideal in vitro digestion method can provide a useful alternative to animal and human models and serve as a tool for rapid screening foods or delivery systems with different compositions and  
Figure 2: Fourier transform infrared (FTIR) spectra of STB-G and STB-C from the bones of skipjack tuna (_K. pelamis_).  
structures [33]. Therefore, in vitro gastrointestinal (GI) digestion models are usually applied to prepare protein hydrolyses due to their rapid, inexpensive, and safe properties [34; 35]. STB-G was hydrolyzed under in vitro GI digestion (pepsin-trypsin system), and the resulting hydrolysate (referred to as STB-GH) with a degree of hydrolysis of 23.78 \(\pm\) 1.24% could strongly scavenge DPPH radical with a half elimination ratio (EC\({}_{50}\)) value of 3.28 mg protein/mL, which is lower than those of gelatin (collagen) hydrolyses from tilapia skin (EC\({}_{50}\) value of 3.66 mg/mL) [36], skate (_Raja porosa_) cartilage (EC\({}_{50}\) value of 13.13 mg protein/mL) [37], and bluefin leatherjacket skin (EC\({}_{50}\) value of 5.23 mg protein/mL) [38] and heads (15.98% at 10 mg/mL) [39], but higher than those of gelatin (collagen) hydrolyses from salmon pectoral fin (EC\({}_{50}\) value of 1.63 mg /mL) [34] and thomback ray (EC\({}_{50}\) value of 1.98 mg/mL) [40].  
Gelatin hydrolyses contain multiple peptides with different chain lengths and amino acid compositions. Hydrolysate fractions with smaller MW showed stronger antioxidant activity than those of larger MW hydrolyses because peptides with a short chain length are more accessible to free radicals and allow them to more easily trap the free radical [41; 42]. For enrichment of functional peptides, STB-GH was further divided into three fractions, including STB-GH-I (\(<\)3 kDa), STB-GH-II (3-5 kDa), and STB-GH-III (\(>\)5 kDa), by ultrafiltration with MW Cut Off (MWCO) membranes of 3 and 5 kDa. As shown in Table 2, the EC\({}_{50}\) value of STB-GH-I on the DPPH radical was 1.84 mg protein/mL, which was significantly stronger than those of STB-GH (3.28 mg protein/mL), STB-GH-II (4.36 mg protein/mL), and STB-GH-III (\(>\)10 mg protein/mL) (\(p<0.05\)). This data was in line with previous reports that the antioxidant abilities of protein hydrolyses were negatively correlated with their average MW [43]. Therefore, STB-GH-I was selected for the subsequent chromatographic separation.
# Robust Human Pose Estimation Method for Body-to-Body Occlusion Using RGB-D Fusion Neural Network
## 3 Human Pose Estimation Method by Progressive Feature Fusion
### Generation of Body-to-Body Occlusion Samples  
To evaluate the pose estimation performance of the proposed network, we generate a body-to-body occlusion test set by selecting images with inter-body occlusion from the existing multi-person pose estimation test set. RGB-D images in the dataset are identified as body-to-body occlusion if two or more people exist and their bounding boxes are overlapped. This occlusion test set enables the evaluation of pose estimation performance under controlled body-to-body occlusion scenes.  
The presence of body-to-body occlusion in a sample is determined by the intersection area \(\beta_{z}\) of the bounding box pair \(z\), where occlusion is identified if \(0<\sum_{z}\beta_{z}\). The intersection area \(\beta_{z}\) is calculated as \(\beta_{z}=w_{inter}\).\(h_{inter}\), where \(w_{inter}\) and \(h_{inter}\) represent the width and height of the intersection area, respectively. These values are derived from the bounding box coordinates of individuals \(i\) and \(j\), where each bounding box is defined by its top-left coordinates (\(x_{min}\), \(y_{min}\)) and bottom-right coordinates (\(x_{max}\), \(y_{max}\)). Figure 6 providesan example of bounding boxes, intersection areas, and the corresponding \(w_{inter}\) and \(h_{inter}\) values for multi-person samples.  
The values \(w_{inter}\) and \(h_{inter}\), which are used to compute the intersection area, may be negative when the bounding boxes do not overlap. To prevent negative values from contributing to the occlusion determination, \(w_{inter}\) and \(h_{inter}\) are computed only when they are positive; otherwise, they are assigned a value of zero to exclude them from the calculation. Therefore, \(w_{inter}\) and \(h_{inter}\) are computed as follows:  
\[w_{inter}=\left\{\begin{array}{ll}w_{i,j}&(w_{i,j}>0)\\ 0&(w_{i,j}\leq 0)\end{array}\right.,\;\;h_{inter}=\left\{\begin{array}{ll}h_{i,j}& (h_{i,j}>0)\\ 0&(h_{i,j}\leq 0)\end{array}\right., \tag{12}\]  
where \(w_{i,j}\) and \(h_{i,j}\) represent the width and height of the intersection area between the bounding boxes of individuals \(i\) and \(j\), respectively. They are computed as follows:  
\[\begin{array}{ll}w_{i,j}=min\big{(}x_{max,i},x_{max,j}\big{)}-max\big{(}x_{ min,i},x_{min,j}\big{)}\\ h_{i,j}=min\big{(}y_{max,i},y_{max,j}\big{)}-max\big{(}y_{min,i},y_{min,j}\big{)}.\end{array} \tag{13}\]  
If there is no overlap between the two bounding boxes, \(w_{inter}\) or \(h_{inter}\) is computed as a negative value. In such cases, (12) assigns a value of zero to exclude them from the final area calculation. In some cases, bounding boxes overlap even though actual occlusion does not occur. To address this, we manually verified these cases and selected the occluded samples accordingly.
## 4 Experiments and Result Analysis  
To verify the validity and the generalization of the model, we conduct experiments on two datasets. The main experiments rely on the self-built birdsong dataset BirdSong30, while the public dataset BirdSdata is used for generalization experiments. The dataset is divided into a training set and a test set by stratified sampling with a ratio of 8:2.
## 4 Experiment Setup and Results
### Charging 1 F Super Capacitor  
To view how the process of charging is happening and if it is possible to actually use it to charge a battery, first, we have carried out the testing using super capacitors. Note, that, the super capacitors are used when the regular electrolytic capacitors are not enough for  
Figure 11: First charging time: (**a**) for the c5 built in capacitor (50 mF); (**b**) Charging 1F capacitor using patch antenna.  
Figure 10: The theoretical versus practical power results for different distances.  
the application under consideration. Furthermore, super capacitors have been proposed in the literature, for use in wireless sensor nodes [37]. In the conducted experiment, 1F capacitor was used as a reference value. In the context of 1F super capacitor, it took 55 min at a distance of 0.6 m to fully charge when the dipole antenna was used. However, as can be seen from Figure 11b it took 34 min when the patch antenna was utilized. Furthermore, 15 h were needed to fully charge a 1F super capacitor when we employed the patch antenna at a 6 m distance.