esa-sceva/satcom-chunk-collection huggingface dataset:
# Detection and Characterization of Physical Activity and Psychological Stress from Wristband Data
## 3 Results
### Energy Expenditure Estimation  
Estimation error was evaluated using mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), which are computed as follows:  
\[MAE(MET)=\frac{1}{n}*\sum_{i=1}^{n}\left|y_{i}-\hat{y}_{i}\right|, \tag{8}\]  
\[RMSE(MET)=\sqrt{\frac{1}{n}*\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}, \tag{9}\]  
\[MAPE(\%)=\frac{100\%}{n}*\sum_{i=1}^{n}\left|\frac{(y_{i}-\hat{y}_{i})}{y_{i }},\right| \tag{10}\]  
where \(y_{i}\) and \(\hat{y}_{i}\) for \(i\coloneqq\{1,\ldots,n\}\) denote the actual and predicted values of the EE, respectively.  
EE estimation performance was evaluated with 144 min of testing data (15% of the whole data). The estimated values were compared with the indirect calorimeter data. A very low estimation error was achieved with the k-NN algorithm: 0.59% MAPE, 0.02 metabolic equivalent of task (MET) MAE, and 0.06 MET RMSE. Figure 9 illustrates testing data set estimation versus indirect calorimeter measurements. The different algorithms performed significantly differently. The Gaussian process algorithm performed significantly worse with the following estimation error: 29.3% MAPE, 1.66 MET MAE, and 2.09 MET RMSE.
# Adsorption of Arsenic and Heavy Metals from Solutions by Unmodified Iron-Ore Sludge
## 3 Results and Discussion  
### Characteristics of Materials  
The results of the XRD analysis of the SBC show high contents of quartz (39%), goethite (20%), kaolinite (11%), magnetite (11%), and muscovite (10%) and low contents of illite (7%), hematite (3%), and pyrite (1%). The SEM images show that the surface of SBC is porous and it has a large surface area with many fibers that facilitate ion adsorption processes on the surface of the material (Figure 1).  
The adsorbent was also characterized by FTIR spectroscopy, as shown in Figure 2. The obtained spectra has peaks at 3696 cm\({}^{-1}\) and 3619 cm\({}^{-1}\) that are probably from hydroxyl groups and O-H bending (Figure 2). The peaks at 1030 cm\({}^{-1}\) and 798 cm\({}^{-1}\) indicate the presence of Si-O-Si and Si-OH groups (Figure 2). The presence of large amounts of silica and hydroxyl groups could enhance the adsorption capacity of the adsorbent [31].  
Figure 1: SEM images of iron-ore sludge at 3880\(\times\) (**a**) and 6055\(\times\) (**b**) magnifications.  
Figure 2: FTIR pattern of iron-ore sludge.  
The specific surface area (\(\rm S_{BET}\)) and cation exchange capacity (CEC) of an adsorbent can be used as indicators of adsorption capacity. An adsorbent with high \(\rm S_{BET}\) and CEC values is expected to have a high binding capacity for potentially toxic metals in aqueous solutions [32]. The \(\rm S_{BET}\) and CEC values of SBC were 47.4 m\({}^{2}\)/g and 75 mmol\({}_{\rm c(-)}\).Kg\({}^{-1}\), respectively. The \(\rm S_{BET}\) and CEC values of this adsorbent are higher than those reported for other materials such as laterite [33; 34], clay [35; 36], and red mud [19] (Table 1).  
The point of zero charge (PZC) is defined as the solution conditions under which the surface charge density equals zero. The pH\({}_{\rm PZC}\) of the material is 5.0 (Figure 3). Therefore, the adsorbent surface is positively charged at pH! 5.0 and becomes negatively charged at pH? 5.0. For pH values of!5.0, adsorption is hindered by repulsive electrostatic interactions between the metal ions and positively charged functional groups [37].
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 1 Introduction
Recently, various studies have used graph neural networks and graph embedding to perform learning based on graph modeling of papers and author information to discern individuals with the same name [11; 12; 13; 14; 15]. However, these existing schemes only use structured datasets. In actual academic search sites, the available metadata vary across sites; thus, research that considers this should be conducted. For instance, in academic search sites where specific metadata do not exist, weight learning for such metadata cannot be performed, necessitating research to address this gap. In addition, since users may be seeking research materials published on different sites, searching and collecting data from all these sites are essential. Lastly, a method that analyzes name disambiguation based on the information collected from different academic search sites is needed. Information collected from two or more sites may contain overlapping papers along with those that exist on only one site. Name disambiguation is imperative in such an environment. In this paper, we propose a name disambiguation system that enables name disambiguation analysis across different academic search sites by collecting papers from currently active academic search services. The proposed scheme conducts rule-based name disambiguation analysis that can operate dynamically based on metadata from different academic search sites. In addition, we propose a multi-classifier according to the characteristics of metadata. The proposed classifier selects a more accurate scheme between the existing name disambiguation schemes and the proposed scheme based on metadata characteristics. The proposed multi-classifier provides a feature to flexibly perform name disambiguation based on the input metadata. The excellence and validity of the proposed method are demonstrated through various performance evaluations. This paper is organized as follows. The term \"Name Disambiguation\" is defined, and the characteristics and problems of existing name disambiguation schemes are described in Section 2. The proposed name disambiguation method is detailed in Section 3. The better fitting of the proposed method is demonstrated through a comparative analysis with existing schemes in Section 4, and the study is concluded, along with future research directions, in Section 5.
## 2 Model Description
### Criteria of Station Locations  
A station is regarded as an object whose location must assure meeting the transportation demands of inhabitants. It was assumed that the transportation demands are proportional to the number of people living in an area. Therefore, the main input here is a map of population density covering the considered region. In the rough approach, which involves neglecting the street layout and existing possibilities of inhabitants' mobility, the main criterion of passenger satisfaction is the Euclidean distance to the nearest metro station--e.g., circular shape of the catchment areas was assumed [21]. The present work expresses the number of inhabitants being satisfied with the location of \(i\)-th metro station as [22; 23]  
\[n_{i}=\int\limits_{A}e^{-\frac{r_{i}^{2}}{\sigma^{2}}}\rho dA \tag{1}\]  
where \(r_{i}\)--distance to \(i\)-th station; \(\rho\)--local population density; \(\sigma\)--standard deviation, determining the distance regarded as satisfactory; and \(A\)--considered area.  
The model also takes into account the existence of point generators of transportation demands like stadiums, multi-arenas, railway stations, airports, or big factories. In this case, the number of people related to \(j\)-th generator whose transportation demands are satisfied by \(i\)-th station can be expressed as  
\[n_{ij}^{g}=e^{-\frac{r_{ij}^{2}}{\sigma^{2}}}g_{j} \tag{2}\]  
where \(r_{ij}\)--distance between \(j\)-th generator and \(i\)-th station, and \(g_{j}\)--number of people related to \(j\)-th generator.  
The important issue is to establish the proper value of \(\sigma\) parameter. This depends on the way the passengers reach the station. [PERSON] and [PERSON] considered walking accessibility and adopted the values in range 400-800 m [24]. [PERSON] used questionnaire research to determine the acceptable distance to the nearest metro station: about a half of respondents claimed it to be less than a 1500 m, and one fifth of respondents claimed more than 3000 m [25]. The distance up to 1000 is commonly accepted as comfortable [26]. The accessibility of a metro station can be significantly enhanced by the introduction of Park & Ride systems [27]; it concerns especially areas of a low population density.  
In the optimization process, the locations of all stations are varied in such a way as to maximize the objective function, which is simply the number of people whose transportation demands are satisfied, as follows:  
\[f_{obj}^{1}=\sum_{i}n_{i}+\sum_{i}\sum_{j}n_{ij}^{g}\overset{opt}{\to}\text{ max}. \tag{3}\]  
After optimization is complete, the best locations of all stations are known and the number of potentially serviced people is known for each station as well. Let us denote this value for \(i\)-th station as \(s_{i}\) and then it can be expressed as  
\[s_{i}=n_{i}+\sum_{j}n_{ij}^{g}. \tag{4}\]
## 3 Simulations and Results
Combined with the above analyses, we conclude that the wavelength of the Fano resonance mainly relies on the relevant parameters of the elliptical ring resonator, such as \(R_{1}\) and \(r_{2}\). Furthermore, it is found that when we change the parameters related to the MIM waveguide or the coupling distance, such as \(d\), \(h\), and \(R_{2}\), the transmittance and FOM of the Fano resonance will be altered at the same time, which could be explained by the phenomenon that the SPPs are almost confined within the elliptical ring resonator and few SPPs are in the MIM waveguide.
## 2 Materials and Methods
### Tactical Variables  
The positional data during the SSG were recorded by a 10 Hz GPS device (Polar, Team Pro, Kempele, Finland). Each player wore a chest strap with the device connected to it. The validity and reliability of the device have been reported in a previous study [51]. The tactical behaviour of players and teams was analysed by processing the positional data provided by the GPS device, as previously adopted in the literature [52; 53], using the software MATLAB R2010a (The MathWorks Inc., Natick, MA, USA). Each player's latitude and longitude data were synchronised and converted into meters using the Universal Transverse Mercator (UTM) coordinate system and a MATLAB routine [54]. The corners of the field were manually obtained by the researchers in every field and were adopted as the referential system for the positional analysis. The data were smoothed using a second-order 5 Hz Butterworth low-pass filter. After converting the positional data into meters, a rotation matrix was calculated for each game with the positions of the field vertices, aligning the length of the playing field along the x-axis and the width along the y-axis. Then, the rotation matrix was applied to players' positional data for alignment with the playing field referential [55].  
After the data collection, the players' analysis during the SSG was performed by identifying parameters: (a) SEI (m)--defined as the standard deviation of the players' positioning in relation to their centroid position at each moment of the game, indicating how exploratory the  
Figure 1: Procedures for carrying out the games.  
player's behaviour was, with higher values of the spatial exploration index indicating more exploratory behaviour, and lower values indicating less exploratory behaviour; (b) length (m) and width (m)--determined by the distance between the farthest players in length and other players on the right and left in width; (c) stretching index (m)--defined as the dispersion of the players from the geometric centre, with higher values indicating greater dispersion; (d) length per width ratio--LpWratio--(ua)--indicates the preferred displacement axis of a team on the field, with values above 1 au indicating a more lengthwise positioning, and values below 1 ua indicating a more widthwise positioning [46,56].
# A Matrix Effect Correction Method for Portable X-ray Fluorescence Data
## 2 Materials and Methods  
### Samples and Analyses  
A total of 16 CRMs were selected, including 10 rock samples and 6 soil samples (Table 1), for pXRF analysis, and 34 elements were detected, including Mg, Al, Si, P, S, K, Ca, Ti, Mn, Fe V, Cr, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Y, Zr, Nb, Mo, Cd, Sn, Sb, Ba, Ta, W, Tl, Pb, Bi and U. The pressed powder pellet technique was used for sample preparation. The sample powder was pressed into a pellet with a diameter of 5 cm and a thickness of about 2 cm under 30 kPa for 30 s. The experimental instrument was a portable energy dispersive X-ray fluorescence spectrometer (X-MET7000) from the Oxford Instruments Group, founded in Oxford, England in 1959. The instrument had a rhodium (Rh) anode target and a fourth-generation silicon drift detector (SDD). Elements between Mg (atomic number of 12) and U (atomic number of 92) on the periodic table of elements could be analyzed [8]. The samples were determined under the conditions of a voltage of 40 kV and current of 60 mA, and each test lasted 60 s. In order to reduce the influence of sample heterogeneity, the analysis was repeated 5 times in different positions homogeneously distributed in the sample, and the average value was calculated as the final result [20].
## 3 Results
### Google Trends Relative Search Volumes for \"Elderly\" and \"Teledentistry\"
Periodontitis (r = 0.508; \(p\) value < 0.001), Dentures (r = 0.234; \(p\) value < 0.001), Caries (r = 0.580; \(p\) value < 0.001), and Stomatitis (r = 0.505; \(p\) value < 0.001), among the oral and dental needs common in the elderly; and Diagnosis (r = 0.637; \(p\) value < 0.001), Monitoring (r = 0.470; \(p\) value < 0.001), Screening (r = 0.496; \(p\) value < 0.001), Assistance (r = 0.333; \(p\) value < 0.001), and Education (r = 0.376; \(p\) value < 0.001), among the potential teledentistry applications, were strongly correlated with the Elderly search throughout Google. Figure 4: Worldwide interest by region for “Teledentistry” search term on Google search engine. Tooth loss (r = 0.017; \(p\) value = 0.795) was the only Google Trends RSV not correlated with the Elderly one. Correlation between Worldwide Google Trends RSV for \"Elderly\" and \"Teledentistry\" Combined with Oral and Dental Needs, or Teledentistry Applications are detailed in Table 2.  
Comparison between Google Trends Relative Search Volumes for \"Elderly\" and \"Teledentistry\" Combined with Oral and Dental Needs, or Teledentistry Applications  
Worldwide RSVs concerning teledentistry, elderly, oral and dental concerns, and potential teledentistry (Table 3) applications generally increased in the 36 months following \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Worldwide Relative** & **Mean** & **Lower** & **Upper** & **Median** & **SD \({}^{1}\)** & **IQR \({}^{2}\)** & **Minimum** & **Maximum** \\ \hline Teledentistry & 0.303 & 0.270 & 0.336 & 0.500 & 0.254 & 0.500 & 0.000 & 1.00 \\ \hline Elderly & 45.579 & 44.462 & 46.696 & 44 & 8.559 & 11.000 & 34 & 100 \\ \hline Oral and dental needs & & & & & & & & \\ \hline Tooth loss & 0.919 & 0.895 & 0.943 & 1 & 0.185 & 0.000 & 0.500 & 1.00 \\ \hline Periodontitis & 2.566 & 2.482 & 2.650 & 2 & 0.644 & 1.000 & 2 & 5 \\ \hline Tooth extraction & 8.461 & 7.830 & 9.091 & 7 & 4.835 & 9.000 & 2 & 19 \\ \hline Dentures & 10.105 & 9.596 & 10.614 & 9 & 3.900 & 7.000 & 5 & 19 \\ \hline Caries & 7.193 & 7.031 & 7.355 & 7 & 1.244 & 2.000 & 5 & 11 \\ \hline Stomatitis & 2.250 & 2.188 & 2.312 & 2 & 0.473 & 0.000 & 2 & 4 \\ \hline Teledentistry applications & & & & & & & & \\ \hline Consult & 15.886 & 15.057 & 16.716 & 13 & 6.373 & 8.000 & 8 & 42 \\ \hline Diagnosis & 41.013 & 40.121 & 41.906 & 39 & 6.855 & 7.000 & 30 & 62 \\ \hline Monitoring & 4.153 & 3.998 & 4.308 & 4 & 1.191 & 1.000 & 3 & 8 \\ \hline Screening & 3.000 & 2.922 & 3.078 & 3 & 0.600 & 0.000 & 2 & 5 \\ \hline Assistance & 70.751 & 69.490 & 72.012 & 70 & 9.684 & 14.000 & 55 & 100 \\ \hline Education & 45.218 & 43.137 & 47.299 & 39 & 15.981 & 15.000 & 28 & 100 \\ \hline \hline \end{tabular} Abbreviations: \({}^{1}\) SD: standard deviation; \({}^{2}\) IQR: interquartile range.  
\end{table} 
Table 1: Descriptive statistics of worldwide relative search volumes for “Teledentistry”, “Elderly”, oral and dental needs, and Teledentistry applications from January 2004 to December 2022.
# 3 MSF: A Multi-Modal Adaptation of the 6 TiSCH Minimal Scheduling Function for the Industrial IoT
## 5 Simulations and Results
### Scenario 3: Four Nodes
The data presented in Figure 20 show that the second configuration, wherein each node has only interface-modes (2, 1) and (1, 1), performed the worst. Regardless of the ranks advertised by #2 and #3, #4 always switched preferred parent from #2 to #3 eventually. That is, even if #4 first switched the frozen interface-mode towards #2, the normalized metric #4 calculated for #2 eventually became bad enough to warrant a preferred parent change, either because the inferred metric of the new frozen interface-mode (i.e., (1, 1)) increased significantly shortly after it became frozen or because of a penalty incurred due to (2, 1) becoming unavailable. This is not to say that having two interfaces with one mode each is always a bad solution. In fact, if both interface-modes had a similar range (which is, however, unlikely if both interfaces operate in different frequency ranges), it would likely perform on par with the other two configurations. Indeed, it might even outperform them in other areas, as both interface-modes are used for every simultaneous transmission (which was not the case for the first and third configurations). At first glance, the first and third configurations performed similarly. In most cases where #2 ended up in #3's parent set (meaning #3 advertised a higher rank than #2), and in both configurations, #4 could simply switch the frozen interface-mode towards #2 and did not need to switch preferred parent (from #2 to #3). Conversely, in many cases where #3 ended up in #2's parent set (meaning #2 advertised a higher rank than #3), and in both configurations, #4 ultimately switched preferred parent from #2 to #3. Most often, this was due to the inferred metric of #4's frozen interface-mode towards #2 increasing by a large enough amount such that the resulting increase in normalized metric/path cost would trigger a parent switch before #4 was allowed to switch the frozen interface-mode to #2. The slight advantage in favor of the third configuration is due to this happening somewhat less frequently with the third configuration. In any case, the first configuration would be vulnerable to wide-band interference/jamming anyway, as this would impact all communication modes operating in the same frequency range, whereas the third configuration guarantees continuity.
## 4 Functional Model of the Framework
### Technologies Used at Each Level
#### 4.5.3 Third Level--Cloud Level  
The data from the first and second levels are collected for a period of time, and then analyzed to discover the behavior of traffic on roads at the city level, in order to support the decision-maker in taking special actions for city planning using Microsoft Azure.
## 3 Results
### Comparison of Sampling Techniques and Models
#### 3.3.1 SMOTE Oversampling  
In terms of precision, Random Forest outperforms other models with a value of 0.766, followed by XGBoost with a value of 0.761. In terms of accuracy, recall, and F1, XGBoost outperforms other models with values of 0.762, 0.769, and 0.763, followed by Random Forest with values of 0.755, 0.755, and 0.754, respectively. Combining the four evaluation metrics, the XGBoost model performs best among the data category imbalance processing methods for SMOTE oversampling, with better generalization ability. The following figure shows the confusion matrix for the XGBoost model. As shown in Figure 3 and Table 3.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature Selection Methods** & **Screening Features** \\ \hline Univariate and Multivariate Cox & Age, sex, histological type, tumor metastasis, radiotherapy, AJCC staging, surgery, sequence of surgery and radiotherapy, “B” symptoms, degree of tumor invasion, marital status, number of malignancies \\ \hline Recursive Feature Elimination & Age, sex, histological type, radiotherapy, chemotherapy, “B” symptoms, marital status, region, tumor metastasis, AJCC stage, surgery, degree of tumor invasion, number of malignancies, income \\ \hline Boruta Feature Selection & Age, histological type, tumor metastasis, AJCC staging, radiotherapy, chemotherapy, “B” symptoms, degree of tumor invasion, number of malignancies \\ \hline \end{tabular}
\end{table}
Table 2: Important features screened by different feature selection methods.  
Figure 3: XGBoost confusion matrix after oversampling.
## 4 SWellEx-96 Experimental Data Analysis
### Data Processing Results and Analysis
#### 4.2.2 The Broadband Case  
In the experiment, the sound source emits signals of multiple frequencies, so the broadband range localization method can also be used to estimate the source range. Three frequencies of 109, 127, and 130 Hz are selected to estimate the source range, and the remaining parameter settings are the same as the narrowband case. According to the calculation of Equation (9), the radial velocity estimation result is obtained and shown in Figure 14. It can be seen from the figure that the interference in the 10 th minute has no effect on the radial velocity estimation result, showing the advantage of broadband processing. After obtaining the radial velocity using the broadband method, the source range estimation can be accomplished following the same steps as the narrowband case, which will not be described herein again.  
Figure 12: Estimated radial velocity from Figure 11 and best fit to the model given in Equation (1).  
Figure 13: Comparison of moving source range from GPS data and from estimated radial velocity.
# Reasoning about Confidence in Goal Satisfaction
## 6 Case Study
### Application of Data Quality Tagging and Propagation Mechanism
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{3}{c}{**By Nurse**} & \multicolumn{2}{c}{**Automated RTTS**} & \multicolumn{2}{c}{**Current Method**} \\
**Goals** & **Sat** & **Conf** & **Sat** & **Conf** & **Sat** & **Conf** \\ \hline Increase process efficiency & 70 & 66 & 76 & 66 & 23 & 30 \\ Reduce turn around time & 84 & 94 & 84 & 94 & 59 & 20 \\ Identify process break points & 56 & 56 & 56 & 56 & 7 & 8 \\ Reduce risk & 100 & 57 & 100 & 57 & 7 & 8 \\ Monitor collecting samples till delivering results & 75 & 75 & 75 & 75 & 10 & 10 \\ Have low cost & 52 & 20 & 46 & 20 & 100 & 20 \\ Get the lab results within the allowed timeframe & 30 & 30 & 30 & 30 & 0 & 0 \\ Reduce number of interactions with patients & 12 & 25 & 12 & 25 & 0 & 25 \\ Reduce number of duplicated tasks & 30 & 75 & 75 & 75 & 22 & 75 \\ Stay updated about the sample status in real time & 75 & 75 & 75 & 75 & 0 & 100 \\ Stay informed/updated about the arrival of samples in real time & 100 & 100 & 100 & 100 & 0 & 100 \\ Track sample position in real time & 100 & 100 & 100 & 100 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Propagated confidence levels of top-level goals in the critical context. **Sat** is the satisfaction value and **Conf** is the propagated confidence value.