esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description of Selected Classifiers Implemented in the Python Scikit-Learn Library
### Decision Tree (DT) Classifier  
A decision tree [43] is a suite of non-parametric multi-stage learning methods that break down a complex decision-making process into a collection of simpler decisions. At each stage (a tree node), the algorithm partitions an input training dataset (a tree root), by means of simple thresholding tests, into smaller data subsets, forming consecutive branches of a decision tree. These tests are constructed statistically in a variety of ways to increase the homogeneity of the resulting subgroups. At the last stage within a branch (a tree leaf), the residual data subset is assigned to one of the final classification labels. Usually, the tree grows until all of the training observations are correctly classified (i.e., classification accuracy = 100%). This leads to overfitting of the decision tree and results in a poor classification of samples unseen in the training dataset. Thus, the ultimate step of a DT classifier should involve tree pruning to remove sections of a tree with limited classification significance and to improve its predictive skills. Approaches towards the construction of a DT can be categorized into three groups: (1) univariate (decisions at a single stage made using a single feature), (2) multivariate (decisions at a single stage made using multiple features), (3) hybrid (fusion of univariate and multivariate trees within a single tree). The process of tree building can be optimized within the DecisionTreeClassifier routine by means of several hyper-parameters that control the number of leaves, tree depth, minimum number of points to split a node or a criterion of splitting. The advantages of a DT classifier are related to: (1) direct interpretation of a classification result depicted as a graph, and (2) handling of numeric and categorical variables featuring non-linear relationships with the classes. On the downside, DT have a tendency to overfit training data and do not perform well for high-dimensional data [86].
## 5 Case Study
### Jeju Island Power System  
#### 5.1.1 Architecture and Description of the Electrical System  
The architecture and description of the electrical system of Jeju Island are shown in Figure 13[7]. Two criteria, including the generator type and the generator total capacity, are used to represent the mainland network system. For the generator model's features, the largest percentage of generation in the Korean power system, i.e., coal-fired power generation, was used [1]. From the viewpoint of a power system researcher, since thermal power plants have a similar operating concept, thermal power plants might be represented as a GENROU, which is an abbreviation for the 2030 Eastern Interconnection Grid Report [33]. The capacity value of an equivalent generator on the mainland is shown in Appendix A, specifically in Table A1, in accordance with the general strategy of generating capabilities in the mainland power system. In addition, to ensure the correctness of the simulation findings, as mentioned in [33], the original capacities were replaced by an equivalent quantity that is 1.1 times the generator's apparent output. The Jeju Island power system consists of four generators equally divided across 15 bus operating systems, and three HVDCs of two types, i.e., LCC and VSC. The first and second interconnections, which were constructed in 1998 and 2013, respectively, both employ the LCC type. Due to this, two LCC HVDCs may be installed at 180 kV/300 MW and 250 kV/400 MW, respectively. Furthermore, in this compressed model of the Korean energy system, the total demand on Jeju Island is 750 MW.  
The capacity is 500 MW for typical HVDC operation and 250 MW for generation. According to several studies that were previously covered in the introduction, the VSC HVDC with a capacity of 200 kV/200 MW is a planned installation that can be addressed in the network system model of the Korean power system. Therefore, the first HVDC interconnection, established in 1998 with a capacity of 180 kV/300 MW, was utilized for contingency simulation studies. In consideration of [34], the highest limit of the HVDC operating reserve might be established using the rated power capacity. Additionally, the limit reserve operation of the second HVDC interconnection and VSC type was established to be completely accessible (250 MW to 400 MW, then 100 MW to 200 MW, respectively) in terms of frequency stability operations.
# Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
###### Abstract
Finally, the method of determining the analysis model can achieve better analysis results. In the analysis of slope stability and the establishment of an intelligent prediction system, [PERSON] et al. [12] fully considered the five parameters that affect the stability of the slope, comprehensively analyzed the five parameters, and combined the correlation between the five parameters. On this basis, the identification model was established and achieved good recognition results. It is proved that in the face of complex situations, comprehensive analysis of multiple parameters can achieve better results to a certain extent. Therefore, this paper uses the analysis method of comprehensive analysis of multiple parameters to analyze and establish the microseismic signal recognition model and uses the Hilbert-Huang transform, which has a good effect on nonlinear and nonstationary signals, to process microseismic signals. Specifically, through time-frequency [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28], energy [29; 30; 31; 32; 33; 34], and fractal features [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47], the characteristic values of rock fractures and rock blasting signals are extracted from three perspectives, and the differences between the two types of eigenvalues are compared, which provides a reference range for the manual identification and the necessary reference range for computerized automatic signal recognition.
## 3 Materials and Methods
### Products
#### 3.2.1 VMF-ZTD  
TU Wien provides ZTD products as a part of the proposed Vienna Mapping Functions (VMF) which can be found at the webpage: [[https://vmf.geo.tuwien.ac.at/](https://vmf.geo.tuwien.ac.at/)]([https://vmf.geo.tuwien.ac.at/](https://vmf.geo.tuwien.ac.at/)) accessed on 9 August 2020.  
The discrete VMF are based on direct raytracing through NWM by using meteorological data from the ECMWF [34]. Currently, two versions are available for the user, namely VMF1 and VMF3.  
The VMF1 [35] is a model providing discrete values for ZHD, ZWD and the hydrostatic and wet mapping functions \"mfh\" and \"mfw\". It relies on empirical equations for the \"b\" and \"c\" coefficients of the continued fraction form, whereas the \"a\" coefficients are determined from rigorously ray-traced mapping functions at 3\({}^{\circ}\) elevation based on the information of NWMs by the ECMWF. VMF1 are provided on a global grid (2.5\({}^{\circ}\)\(\times\) 2.0\({}^{\circ}\)) as well as at IGS sites, and the time resolution is 6 h.  
VMF3 is the successor of VMF1, with refined model accuracy. Ray-traced delays for 10 years of ECMWF ERA-Interim re-analysis data on a global grid was used to re-determine empirical mapping function coefficients \"b\" and \"c\" through spherical harmonics expansions up to degree and order 12, by which spatial as well as temporal components are equipped [12]. The grid-wise VMF3 data is available in a horizontal resolution of 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) as well as 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\), and in a time resolution of 6 h.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **EGNOS** & **UNB3** & **UNB3m** \\ \hline five meteorological parameters & \(P\), \(T\), \(e\), \(\beta\), \(\lambda\) & \(P\), \(T\), \(e\), \(\beta\), \(\lambda\) & \(P\), \(T\), \(RH\), \(\beta\), \(\lambda\) \\ Input parameters & & height (\(H\)), latitude (\(\varphi\)) and day of year (DOY) & \\ Output parameters & & ZTD & \\ Remarks & Simplification of UNB3 & / & Improved wet delay calculation accuracy based on UNB3 \\ \hline \end{tabular}
\end{table}
Table 2: Information of three empirical zenith tropospheric delay (ZTD) models. (\(P\)—pressure, \(T\)—temperature, \(e\)—water vapor pressure, \(\beta\)—temperature lapse rate, \(\lambda\)—water vapor pressure height factor, \(RH\)—relative humidity, UNB—University of New Brunswick).  
\begin{table}
\begin{tabular}{c c c} \hline  & **VMF-ZTD** & **IGS-ZTD** \\ \hline Data Sources & ECMWF ERA-Interim re-analysis data & GNSS observation data \\ Time resolution & 6 h & 5 min \\ Calculation method & Ray-tracing & PPP (software: Bernese GNSS Software 5.2; mapping function: GMF) \\ \hline \end{tabular}
\end{table}
Table 3: Information about Vienna Mapping Functions (VMF)-ZTD and International Global Navigation Satellite Systems (GNSS) Service (IGS)-ZTD products.
## 4 Conclusions  
We explored whether the addition of a zwitterionic surfactant to a colloidal dispersion affected oil recovery from porous rock. NPs were co-stabilized by surfactants; we hybridized an anionic polymer (PSS-_co_-MA) to a zwitterionic surfactant (TPS) on the surfaces of silica NPs. The adsorption of PSS-_co_-MA on silica NPs was possibly generated via van der Waals attraction. In addition, adsorption of zwitterionic surfactant onto NPs is mediated by dipole-charge interaction between the trimethylammonium groups of zwitterionic surfactant and negatively charged particles with PSS-_co_-MA under brine.  
Core flooding experiments showed that colloidal dispersions with the zwitterionic surfactant prevented NP aggregation due to repulsive particle-to-particle interaction, unlike colloidal dispersions with a cationic surfactant. Thus, the dispersion readily entered the pore throat and adhered to the oil-water interface, lowering the interfacial tension and improving oil recovery. Regarding residual oil saturation as a function of capillary number, colloidal dispersion containing a zwitterionic surfactant reduced the residual oil level to half that noted in the absence of the surfactant; interfacial tension was significantly reduced. Therefore, our nanoscale colloidal dispersion led to EOR.  
Original draft manuscript, H.A.S.; supervision and review writing, T.A. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Pukyong National University Research Fund in CD20180563. This study was supported by the Ministry of Trade, Industry and Energy (MOTIE), Korea, the Gas Hydrate R&D Organization (GHDO), and the Korea Institute of Geoscience and Mineral Resources (KIGAM) (GP2020-004). This work was also supported by a Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Mistry of Trade, Industry tand Energy (No. 20182510102400).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.  
Figure 10: Residual oil saturation as a function of capillary number in the presence or absence of a zwitterionic surfactant (red: no surfactant; black: with surfactant).
## 3 SSC and Equivalent White Noise Level
### Data Bits Misaligned by a Fraction of a Code Chip  
Section 3.2 \"_Data Bits Misaligned by an Integer Number of Code Chips_\" summarizes SSC expressions that is valid when the differential time delay between undesired and desired signals, \(\Delta\) is multiples of a single code chip \(T_{c}\). With this constraint, \(R_{11}(s-\Delta,t-\Delta)R_{22}(s,t)\) is a pulse train of width \(T_{c}\), given by Equation (17).  
Now we take a step further to examine what the SSC should look like when \(\Delta\) is only a fraction of a code chip. In this case, \(R_{11}(s-\Delta,t-\Delta)R_{22}(s,t)\) is still a pulse train of uniformed width, but the pulse width is no longer equal to \(T_{c}\). As illustrated in Figures 6 and 7, for fixed \(s\), \(R_{11}(s-\Delta,t-\Delta)R_{22}(s,t)\) is either a train of pulses of width \(\Delta\) or \(T_{c}-\Delta\).  
When the differential time delay takes on arbitrary value, we proceed by using \(\tau=\text{mod}(\Delta,T_{c})\), \(0\leq\tau<T_{c}\). Therefore, \(R_{11}(s-\Delta,t-\Delta)R_{22}(s,t)\) will be a train of pulses of width \(\tau\) for \(\tau/T_{c}\) percent of \(T_{c}\), or a train of pulses of width \(T_{c}-\tau\) for \((T_{c}-\tau)/T_{c}\) percent of \(T_{c}\). The total effect of such arbitrary time delay, for the differential Doppler, is that a factor of \(T_{c}\) in the \(SSC_{B1-I}\) expressions in the Section 3.2 \"_Data Bits Misaligned by an Integer Number of Code Chips_\" is replaced by [12]:  
\[\frac{\tau}{T_{c}}\cdot\tau+\frac{(T_{c}-\tau)}{T_{c}}\cdot(T_{c}-\tau) \tag{21}\]  
which implies the expectation of receiving such a time difference. It can be seen from Equation (21) that spreading code self-interference is maximized when \(\tau=0\), i.e., when the misalignment between the desired and undesired signals happens to be multiples of a code chip; it can be seen also that self-interference is minimized then \(\tau=T_{c}/2\). Moreover, the average of Equation (21) can be computed as:  
\[\frac{1}{T_{c}}\int_{0}^{T_{c}}\left[\frac{\tau^{2}}{T_{c}}+\frac{(T_{c}-\tau) ^{2}}{T_{c}}\right]d\tau=\frac{2}{3}T_{c} \tag{22}\]which, when substituted for \(T_{c}\) in Equation (19) yields:  
\[SSC_{B1-I}\simeq\frac{2}{3}\frac{TT_{c}}{T_{b}}\left[\frac{\sin^{2}(\pi fKT)+\sin ^{2}(\pi f(20-K)T)}{\sin^{2}(\pi fT)}\right] \tag{23}\]  
which is used to compute SSC in the following sections.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 3 Proposed Method
### Feature Importance Calculation Method Based on Category Contribution  
Calculating the importance of each feature accurately and selecting features with higher importance to construct the feature space directly determine the performance of classification. In this paper, the contribution of the feature to classification is considered from two aspects of inter-category and intra-category, and a method of feature importance based on CC is proposed.  
Let \(E_{j}\)={\(e_{1j}\),\(e_{2j}\),... \(e_{qj}\)} denote the inter-category contribution set of the feature \(F_{j}\) in \(q\) categories, and \(R_{j}\)={\(r_{1j}\),\(r_{2j}\),... \(r_{qj}\)} denote the intra-category contribution set of the feature \(F_{j}\) in \(q\) categories. When the difference between the data in \(E_{j}\) is great, the category discrimination ability of \(F_{j}\) is strong and when the difference between the data in \(R_{j}\) is great, the category discrimination ability of \(F_{j}\) is strong too. In this paper, the variance is used to represent the difference between the data in the set. The variance of the data in \(E_{j}\) and \(R_{j}\) are called the inter-category contribution variance and intra-category contribution variance respectively. The formulas are as follows.  
\[V_{E}(F_{j})=\frac{\sum{(e_{jk}-\overline{e_{j}})^{2}}}{q} \tag{14}\]  
\[V_{R}(F_{j})=\frac{\sum{(r_{jk}-\overline{r_{j}})^{2}}}{q} \tag{15}\]  
where \(V_{E}(F_{j})\) is the inter-category contribution variance of \(F_{j}\), \(V_{R}(F_{j})\) is the intra-category contribution variance of \(F_{j}\), \(e_{jk}\) is the inter-category contribution of \(F_{j}\) to category \(k\), \(r_{jk}\) is the intra-category contribution of \(F_{j}\) to category \(k\), \(\overline{e_{j}}\) is the mean inter-category contribution of \(F_{j}\), \(\overline{r}_{j}\) is the mean intra-category contribution of \(F_{j}\) and \(q\) is the number of categories.  
The greater the inter-category contribution variance and intra-category contribution variance are, the stronger the category discrimination ability of the feature is. Therefore, it is necessary to consider from the perspective of making the inter-category contribution variance and intra-category contribution variance both greater when defining the formula of feature importance calculation. In the field of information retrieval, F-measure is the harmonic mean of precision and recall, which ensures that both precision and recall get a greater value [38]. Based on this idea, the formula for feature importance calculation is defined as follows.  
\[f(F_{j})=\frac{2*V_{E}(F_{j})*V_{E}(F_{j})}{V_{E}(F_{j})+V_{E}(F_{j})} \tag{16}\]  
where \(f\left(F_{j}\right)\) is the feature importance of \(F_{j}\).  
After the importance of each feature is calculated, the features with higher importance are selected based on the predefined dimension, to construct the feature space.
## 3 Results  
### Spatio-Temporal Variation Characteristics of \(ET_{0}\)  
Figure 3a shows the tendency of annual \(ET_{0}\) on the TP. The annual average \(ET_{0}\) was 1037.17 mm, ranging from 983.53 to 1091.94 mm from 1979 to 2018. The annual \(ET_{0}\) experienced a non-significant (\(\alpha\) = 0.05) decrease by 2.1 mm/decade (Table 1). According to the [PERSON]'s test, there are two abrupt change points in \(ET_{0}\) series of TP, which are 1997 and 2000, respectively (Figure 3b). We found the decline rate of \(ET_{0}\) was \(-\)25.9 mm/ decade before 1997 and the change is significant, but the annual \(ET_{0}\) increased significantly (\(\alpha\) = 0.05) at a rate of 31.1 mm/decade after 1997. Furthermore, the [PERSON]'s test was also performed on all 73 stations to verify the representativeness of 1997, and the results showed that 41 of them could detect the mutation point in 1997. Therefore, it can represent the main mutation characteristics of \(ET_{0}\) in the region. Since the variation of \(ET_{0}\) is the result of the comprehensive effects of climate variables, it is necessary to analyze each climate variable during the two periods separately. Using the inflection point of \(ET_{0}\) as the basis for dividing the period, all the time series of the 73 stations were divided in these two periods for the subsequent analysis.  
Figure 4 shows spatial distribution of \(ET_{0}\) trends on the TP during 1970-1996 and 1997-2018. There were significant spatial differences in the trends of \(ET_{0}\) before and after 1997. During 1970-1996, there are 52 stations showing a downward trend, and these downward trends were remarkable (\(\alpha\) = 0.05) at 34 meteorological stations; while 21 stations increased in the southeast TP from 1997 to 2018, there were annual \(ET_{0}\) increasing trends at 49 meteorological stations, of which 36 meteorological stations increased significantly (\(\alpha\) = 0.05). Only 24 stations in the eastern TP showed a decreasing trend.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Period** & \(RS\) & \(RH\) & \(T_{mean}\) & \(U_{2}\) & \(ET_{0}\) \\ \hline
1970–1996 & 0.08 & 3.13 \({}^{*}\) & 2.54 \({}^{*}\) & \(-5.50\) \({}^{*}\) & \(-4.84\) \({}^{*}\) \\
1997–2018 & \(-3.63\) \({}^{*}\) & \(-3.63\) \({}^{*}\) & 2.48 \({}^{*}\) & 4.32 \({}^{*}\) & 3.80 \({}^{*}\) \\
1970–2018 & \(-3.59\) \({}^{*}\) & \(-1.08\) & 6.54 \({}^{*}\) & \(-5.42\) \({}^{*}\) & \(-1.20\) \\ \hline \end{tabular} \({}^{*}\)Statistically significant trends at the 5\(\%\) significance level.  
\end{table}
Table 1: Z value of reference evapotranspiration (\(ET_{0}\)), solar radiation (\(RS\)), relative humidity (\(RH\)), air temperature (\(T_{mean}\)) and wind speed (\(U_{2}\)) in different periods.  
Figure 4: Spatial distribution of \(ET_{0}\) trends on the TP during 1970–1996 (**a**) and 1997–2018 (**b**) (mm/10a).  
Figure 3: Variation of annual \(ET_{0}\) (**a**) and [PERSON]’s test results for detecting change in annual \(ET_{0}\) (**b**) on the TP from 1970 to 2018. (The red points represent the years in which the mutation occurred in the \(ET_{0}\) series.)
# A Method of Detections' Fusion for GNSS Anti-Spoofing
## Appendix A  
In the case that only two detections are fused, \(m(C)>0.5\) is equivalent to \(m_{1}(C)+m_{2}(C)>1\), and \(m(A)\geq 0.5\) is equivalent to \(m_{1}(A)+m_{2}(A)\geq 1\). The derivation is given as follows:  
\[\begin{split} m(C)=\frac{m_{1}(C)m_{2}(C)}{m_{1}(A)m_{2}(A)+m_{1 }(C)m_{2}(C)}>0.5\\ \Updownarrow\\ m_{1}(C)m_{2}(C)>m_{1}(A)m_{2}(A)\\ \Updownarrow\\ m_{1}(C)m_{2}(C)>\Big{(}1-m_{1}(C)\Big{)}\Big{(}1-m_{2}(C)\Big{)}\\ \Updownarrow\\ m_{1}(C)+m_{2}(C)>1\end{split} \tag{10}\]  
where \"\(\Updownarrow\)\" means \"be equivalent to\". Similarly, there is:  
\[m(A)=\frac{m_{1}(A)m_{2}(A)}{m_{1}(A)m_{2}(A)+m_{1}(C)m_{2}(C)}\geq 0.5\quad \Longleftrightarrow\quad m_{1}(A)+m_{2}(A)\geq 1 \tag{11}\]
# Feedback Characterization for Enhanced Heat Recovery from Composing Processes: A Review
## 2 Materials and Methods
### Selection Criteria  
The process of selecting the studies was carefully organized to make sure that only the research papers most relevant to the scope of this study were included. At first, all the articles that were collected were screened for duplicates, with 51 duplicates found, which were removed directly. The remaining articles underwent a multi-tier screening process in the following screening stages, as shown in Figure 1:  
1. Abstract screening: During this phase, the articles were carefully assessed based on their abstracts to determine how relevant they were to the goal of this review, i.e., if they contained reference to CHRS feedstocks. The screening criteria focused on studies that explored how different feedstocks affect CHRSs. The results of this evaluation were documented, noting reasons for including or excluding each article to ensure a systematic review process.
2. Full-text screening: Articles that passed the abstract screening were subjected to a detailed full-text examination. The selection process at this stage focused on the completeness of the data, the soundness of the studies, and their alignment with the goals of the review. Like the abstract screening phase, every decision regarding whether to include or exclude an article was carefully documented. Papers were excluded for various reasons. For example, studies lacking quantitative data on feedstock characteristics--such as heating values (kJ/kg), moisture content, carbon, and nitrogen contents--were excluded. Additionally, articles focused on economic or environmental analysis without relevant thermal data or those centered around unrelated processes like anaerobic digestion or combustion were removed. Broad discussions on waste management or circular bioeconomy principles without specific details on composting feedstocks were also excluded. These examples illustrate the rigorous screening process, ensuring only the most relevant studies were included.  
Figure 1: Flow diagram showing the stepwise procedure followed for the performance of the systematic critical review.
# Performance of Numerically Optimized Tuned Mass Damper with Inerter (TMDI)
## 3 Simulation Procedure
### Excitation  
Harmonic and random excitations were adopted. For harmonic excitation, steady state responses of the primary structure displacement and damper relative motion were computed for an excitation frequency range between \(0.85\times f_{1}\) and \(1.15\times f_{1}\). For random excitation, a zero-mean white noise signal at sampling frequency 200 Hz was generated that was high pass-filtered (second-order filter) at 0.005 Hz to remove any offset and low pass-filtered (second-order filter) at 99 Hz to remove unnecessary higher frequencies, considering \(f_{1}=0.14\) Hz (Figure 3).