esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Evaluation Results
\begin{table}
\begin{tabular}{l l r r r r r r r} \hline \hline  & & \multicolumn{2}{c|}{**Non-Text Classes**} & \multicolumn{4}{c|}{**Text Classes**} & \multicolumn{2}{c}{**Macro Avg**} \\ \hline  & & **DropCapital** & **Footer** & **Header** & **Marginalia** & **Text** & **Column** & \\ \hline CamStd & GT lines & & 4 & 80 & 78 & 0 & 2279 & 0 \\  & Prec & (100\%) & 100\% & 97.5\% & & 100\% & & \\  & Rec & (100\%) & 100\% & 100\% & & 99.9\% & & \\  & F1 & (100\%) & 100\% & 98.7\% & & 100\% & \\  & line ending accuracy 1\% & (100\%) & 100\% & 100.0\% & & 100\% & & \\ \hline CamMarg & GT lines & 2 & 145 & 194 & 437 & 6037 & 0 \\  & Prec & (40\%) & 98.6\% & 98.5\% & 97.7\% & 99.9\% & & \\  & Rec & (100\%) & 98.6\% & 99.0\% & 97.5\% & 99.9\% & & \\  & F1 & (57\%) & 98.6\% & 98.7\% & 97.6\% & 99.9\% & & \\  & line ending accuracy 1\% & (100\%) & 99.3\% & 99.0\% & 97.5\% & 99.9\% & & \\ \hline CamMan & GT lines & 102 & 102 & 116 & 421 & 4467 & 0 \\  & Prec & 83.5\% & 98.0\% & 93.2\% & 95.8\% & 99.4\% & & \\  & Rec & 94.1\% & 95.1\% & 94.0\% & 91.5\% & 99.5\% & & \\  & F1 & 88.5\% & 96.5\% & 93.6\% & 93.6\% & 99.5\% & & \\  & line ending accuracy 1\% & 99.0\% & 99.0\% & 94.0\% & 91.9\% & 99.7\% & & \\ \hline GW5051 & GT lines & 0 & 38 & 67 & 0 & 142 & 7095 & \\  & Prec & 85.4\% & 100\% & & 95.1\% & 99.8\% & \\  & Rec & & 92.1\% & 94.0\% & & 96.5\% & 99.9\% & \\  & F1 & & 88.6\% & 96.9\% & 95.8\% & 99.8\% & \\  & line ending accuracy 1\% & & 94.7\% & 94.0\% & & 96.5\% & 94.7\% & \\ \hline GW5056 & GT lines & 0 & 28 & 145 & 1872 & 2903 & 0 \\  & Prec & & 100\% & 96.0\% & 98.5\% & 99.1\% & & \\  & Rec & & 92.9\% & 99.3\% & 98.7\% & 99.6\% & & \\  & F1 & & 96.3\% & 97.6\% & 98.6\% & 99.3\% & & \\  & line ending accuracy 1\% & & 100\% & 100\% & 98.7\% & 99.7\% & & \\ \hline Macro Avg & F1 (Text classes only) & & 96.0\% & 97.1\% & 96.6\% & 98.9\% & 99.8\% & 97.7\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Evaluation results of our method on our evaluation datasets. CamStd, CamMarg, and CamManual were evaluated with the default configuration. The results for GW5051 were obtained using the following settings: “no dropped capitals, no marginalia, two-column layout, split top line in headline and header”. The results for GW5056 were obtained using the following settings: “no dropped capitals”. The precision, recall, and F1 are given for each class following our evaluation scheme. In addition, the macro average F1-score was calculated for each text class across all datasets and then macro averaged to achieve a macro average F1-score for all text classes. Results shown in brackets are not representative, as the sample size was too small. In total, our method achieved a macro-averaged F1-score of 97.7% for all text line instances averaged across the datasets and different classes. For the dropped capitals, which belong to the non-text class, an F1-score of 88.5% could be achieved on the CamManual dataset.
# Kernel-Density-Based Particle Defect Management
## 3 The Proposed Method
### Particle Map Generation Based on the Kernel Density  
As the particle map indicates only the distribution of the particles using the cumulative data, the density of the particles should be considered. Therefore, this method utilizes a function for the estimation of kernel density. Kernel density estimation is a non-parametric method for estimating the probability density of a dataset. As the particle map consists of two dimensions, the \(X\)-axis and \(Y\)-axis of the wafer, this map is constructed using multivariate kernel density estimation. Considering  
Figure 7: Illustration of the conventional monitoring method. PD, particle defect.  
\(\{x_{1},y_{1}\},\{x_{2},y_{2}\},\ldots,\{x_{n},y_{n}\}\) as the sample data of two-dimensional vectors and \(n\) as the number of particles, the kernel density estimation function is expressed as [10], where \(H\) is the scale coefficient of the kernel function and \(K\) is the Gaussian kernel function, which is a symmetric multivariate density.  
\[\hat{f}_{H}(x,y)=\frac{1}{n}\sum_{i=1}^{n}K_{H}\{(\overline{x},\overline{y})-( x_{i},y_{i})\}. \tag{5}\]  
The scale coefficient \(H\) of Gaussian kernel function \(K\) is estimated using a well-supported rule [11] and is dependent on the performance of particle inspection equipment. \(H\) means the estimated sigma. In this study, we selected 99,370 as the value of \(H\). However, this value can vary depending on the resolution of image data from equipment. Consequently, this function calculates the density \(\hat{f}_{H}(x,y)\) of the wafer. We build a three-dimensional kernel-density-based particle map using \(\hat{f}_{H}(x,y)\) in the \(X\)-axis and \(Y\)-axis. Figure 8a,b illustrate the particle map and the kernel-density-based particle map, respectively. In Figure 8a, the dotted area indicates dense particles, and Figure 8b shows a high density at the corresponding area in Figure 8a. Hence, Figure 8b shows the probability density of the corresponding particles of the map in Figure 8a. This particle map contains dense particles regardless of the total count of the particles.  
As these dense particles can cause an abnormal state in the facility, this study devises new monitoring statistics that consider the density of the particles using the kernel-density-based particle map.
# The Travel Time of Floatable Litter of Different Densities Influenced by River Flow Velocity
## 3 Results and Discussion
### Analysis of Floatable Litter Characteristics
Among all FL, cloth (FL9) has the highest density, \(\rho\) = 1154.44 kg/m\({}^{3}\), while the FL with the lowest density is food polystyrene (FL5), \(\rho\) = 4.76 kg/m\({}^{3}\). Explaining the concept of FL density, the density of an object is the mass of an object divided by its volume. Density is proportional to mass but inversely proportional to volume. In other words, density increases with mass if the volume is constant. An object has a higher density when it is heavier and more compact. Comparing the mass and volume of a 1.5 L mineral bottle (FL1) and 0.5 L mineral bottle (FL2), the mass and volume of FL1 are higher (m = 0.028 kg, V = 0.0015 m\({}^{3}\)) than FL2 (m = 0.015 kg, V = 0.0005 m\({}^{3}\)). Meanwhile, the density of FL1 is lower, \(\rho\) = 18.96 kg/m\({}^{3}\), than FL2, \(\rho\) = 30.94 kg/m\({}^{3}\). Taking the ratio of mass-to-volume, the ratio of FL1 is smaller than FL2, being 1:53 and 1:33, respectively. Thus, FL1 density is lower than FL2. Another example is the wood board (FL15) (m = 0.277 kg, V = 0.000388 m\({}^{3}\)), which has the biggest mass but the second highest density. Instead, cloth (FL9) (m = 0.104 kg, V = 0.00009 m\({}^{3}\)) has the highest density with its fourth rank position in mass value. This is due to the smaller mass-to-volume ratio of FL15, which is 1:0.0014 compared to FL9 which is 1:0.0008. From Figure 6, the density of FL is classified into a range to see the distribution of FL density. A total of 13 out of 20 (65%) FL have a density in the range of 4.76-173.33 kg/m\({}^{3}\) which is under 200 kg/m\({}^{3}\). Meanwhile, 9 out of 13 (69%) of the FL with a density under 200 kg/m\({}^{3}\) are plastic. Only sanitizer bottles (FL19) and parcel bags (FL18) are of a plastic type that has a density of more than 200 kg/m\({}^{3}\), alongside other types of materials. However, most FL is classified as low-density which is \(<\)1000 kg/m\({}^{3}\). Except for cloth (FL9) which is classified as high-density. The density classification is referred to [18], where density is classified into three classes based on average water density: (i) low density (\(<\)1000 kg/m\({}^{3}\)); (ii) intermediate density (1000-1100 kg/m\({}^{3}\)); and high density (\(>\)1000 kg/m\({}^{3}\)). From Figure 6, the majority of FL selected in this study is plastic based. The most plastic type has a low-density class. They are less dense than metal, but they can have a similar density to wood. Plastic molecules are in a polymer structure which can be exceedingly lengthy and branching, making them difficult to 'fit' together. As a result, they are not as dense as metals, since they are not as tightly packed. Hence, the selected FL which is plastic is commonly found in rivers, as suggested by previous studies. In addition, this floating litter is commonly found in the ocean, where plastic is the major constituent of floatable marine litter composition. Figure 6: The distribution of FL density according to the given class.
## 1 Introduction
However, studies on multiband-enabled in-building small cells used to exploit the vertical spatial reuse of spectra are not obvious. We first studied this case and proposed techniques to show the potential to achieve high spectral and energy efficiencies in both fourth generation (4G) [9] and 5G [10; 11; 12] networks by exploiting both microwave and mmWave spectrum sharing with small cells of the same or a different system. This paper extends the contribution of these previous studies, where small cells are enabled only with high-frequency bands, by reusing these spectrum bands as much as possible with each small cell to address the expected requirements for 6G. Unlike previous works, multi-millimeter-wave (multi-mmWave) channel models are exploited to define a more accurate 3D cluster of small cells to reuse the whole spectrum with each cluster of small cells. Since the microwave spectrum is not reused by small cells and because 28 and 60 GHz mmWave spectra of the mobile network operator (MNO) are reused only by in-building small cells, no co-channel interference management technique is needed, unlike in previous studies. Transceiver 1 for each small cell operates in the 28 GHz licensed spectrum band, whereas transceiver 2 operates in the 60 GHz unlicensed spectrum band. Based on the service requirements, both intra-floor- and inter-floor-level co-channel interference constraints are set to define a minimum distance between co-channel interferers at both intra-floor and inter-floor levels, which in turns gives the size of a 3D cluster of small cells. The entire 28 and 60 GHz spectra are reused by each 3D cluster in each building for \(L\) number of buildings where \(L\) denotes the number of buildings of small cells per macrocell. The average system-level capacity, SE, and EE performance metrics are derived for numerous values of 3D spatial reuse factors. Finally,the performance of the proposed technique is then compared with that of 6G mobile networks to show that the 3D spatial reuse of multi-mmWave spectra can satisfy the average SE and EE requirements for 6G mobile networks. The paper is organized as follows. The system model, including the system architecture and modeling of 3D clusters of in-building small cells operating in multi-mmWave spectrum bands, is detailed in Section 2. In Section 3, system-level average capacity, SE, and EE performance metrics are derived and an algorithm for the proposed technique for 3D in-building spatial reuse of multi-mmWave spectra in ultra-dense small cells are developed. Performance evaluation, as well as performance comparison of the proposed technique with the expected requirements for 6G in terms of average SE and EE, is carried out in Section 4. We conclude the paper in Section 5.
## 6 Results  
In the case of multi-class classifiers, the full probability density of both classes should be known for estimating the actual error during training. The calculation of the classification performance is calculated by constructing a Confusion Matrix (CM), where the numbers of misclassifications are related to the False Positive (FP), False Negative (FN), True Positive (TP), and True Negative (TN). The Total Accuracy (TA) is defined by using the equation [33]:  
\[\text{TA}=\frac{\text{TP}+\text{TN}}{\text{N}} \tag{11}\]  
Equations (12), (13), and (14) below, define the Precision (PRE), the Recall (REC), and the F-Score indices, respectively:  
\[\text{PRE}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{12}\]  
\[\text{REC}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{13}\]  
\[\text{F}-\text{Score}=2.\frac{\text{PRE}\cdot\text{REC}}{\text{PRE}\ +\text{REC}} \tag{14}\]  
The Root Mean Square Error (RMSE) is defined by using the equation:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{j=1}^{n}\bigl{(}P_{(ij)}-T_{j}\bigr{)}^{2}} \tag{15}\]  
Also, a Receiver Operating Characteristic (ROC) area is a performance metric of all classification thresholds.  
Table 3 presents the results of the method proposed in this study, as well as the results of the corresponding methods tested in the dataset under consideration.  
As can be seen, the use of ELM creates a robust learning system that effectively solves a realistic, multifactorial, and highly complex problem. The choice of the proposed model is significantly superior to the algorithms compared, both in categorization metrics and in training time, where ELM is almost 15 times faster than the other models. This feature is a clear demonstration of the potential of the proposed method, taking into account the difficulties of the research environment.  
In conclusion, it should be emphasized that ELM implements, in the most realistic way, the process of identifying conflicting rules that can lead to profiling, as the training of the algorithm was based on a highly specialized initial set of training, but in which the proposed system managed to generalize and respond optimally to unspecified situations.  
It is also important to highlight that the stability of the ELM and the clear identification it offers in the modeling of complex systems offered by FCMs advocate the adoption of a method that delivers high-precision results, capable of responding to the problem, as well as in cases of similar complex situations.
## 4 Experimental Setup and Condition
### Combustion Experimental Setup  
After the preliminary experiment, the combustion experiment was performed with a metal fiber burner to analyze the characteristics of temperature and H\({}_{2}\)O concentration generated from the premixed CH\({}_{4}\)/air flame. Figure 4 shows the combustion setup part of the metal fiber burner for generating the premixed CH\({}_{4}\)/air flame and the laser setup. The heat load of the metal fiber applied to the experiment was approximately 9000 kcal/h with an equivalence ratio of 1.00. The fuel was supplied as CH\({}_{4}\) gas to the center below the metal fiber burner. After general compressed air was converted to dry air via a drying system, the oxidizer was supplied to the sides of the metal fiber burner. This dry air was mixed with CH\({}_{4}\) gas at the center of the metal fiber burner. The premixed CH\({}_{4}\) gas and dry air were formed into a flame from the porous surface of the metal fiber burner. The flow rates of the fuel and oxidizer for controlling the equivalence ratio (\(\Phi\)) were controlled by a mass flow controller (MFC), and N\({}_{2}\) gas was injected on the side of the burner to eliminate the inflow of external air into the flame.  
Figure 3: Schematic diagram of the measurement system applied to an electric furnace.  
The laser setup part based on TDLAS was applied with the same setup as in the preliminary experiment. The light source was transmitted directly into the premixed CH\({}_{4}\)/air flame to measure the temperature and H\({}_{2}\)O concentration. A gold mirror with high reflectance was used to ensure a high SNR of the absorption signal because the integrated absorbance increased as the optical path length increased (94 cm). The light source was also transmitted to a height of 5 mm above the surface of the metal fiber burner.  
Table 2 shows the conditions of the combustion experiment. The fuel was fixed at 8.46 L/min for a heat load of 9000 kcal/h, and the equivalence ratio was determined by adjusting the oxidizer. The equivalence ratio was set at 0.15 intervals from the fuel lean condition of 0.70 to the fuel-rich condition of 1.30.
# A REM Update Methodology Based on Clustering and Random Forest
## 4 Evaluation  
### Historical Dataset and Comparative Models  
Initial dataset \(D_{H}\) is composed of measurements collected inside Room 302, illustrated in Figure 1, with a total of \(N=1160\) samples covering the whole room. We considered three different error metrics to evaluate the performance of the proposed scheme: mean absolute percentage error (MAPE), root mean square error (RMSE), and R2 score. As comparative schemes, we considered the support vector regression (SVR) algorithm [21], multilayer perceptron (MLP) [22], and the AdaBoost regressor [23]. Moreover, we obtained error results by considering different numbers of clusters. The RF model and the AdaBoost regressor were trained with 200 decision tree regressors; the comparative SVR algorithm considered the amount of regularization, \(C=1000\), and the radial basis function (RBF) kernel; and MLP had three hidden layers with 100 hidden units per layer from using a rectified linear unit (ReLU) activation function. The computer used for the simulations had an AMD Ryzen 9 5900X 12-Core processor and 48 GB of RAM.  
Table 1 presents the aforementioned error metrics of the proposed scheme and several comparative approaches by using 5-fold cross-validation with historical dataset \(D_{H}\), where the presented results are averaged over several independent simulations. We can see that the RF-based scheme achieved the fewest errors among the comparative methods, and the AdaBoost algorithm was the second-best scheme. In addition, the impact from the number of clusters in the RF algorithm was very small since the same error rate can be obtained by using one cluster or four clusters. Note that Table 1 analyzes the error results on dataset \(D_{H}\), where measurements for the whole area of interest are available. However,in Section 4.2, we analyze the performance from different numbers of clusters in a realistic case when only partial data from the area of interest are available.  
Figure 4 illustrates the REMs obtained for the historical dataset by using 4 clusters with the RF model, SVR, and AdaBoost. We can see that the REMs obtained with the RF algorithm and AdaBoost have similar patterns since both algorithms use the decision tree regressor as the base estimator. However, the REM obtained with RF is considered the most realistic because it has the lowest error, as we can observe in Table 1.
## 3 Results and Discussion  
### Investigation of Phloroglucinol Inclusion into \(\gamma\)-CD in Aqueous Solution  
The interaction of phloroglucinol with \(\gamma\)-CD was first investigated in aqueous solution. For this, we resorted to NOESY (Nuclear Overhauser Effect Spectroscopy), a 2D NMR technique that allows observing molecular interactions through space. The spectrum for a D\({}_{2}\)O equimolar solution of phloroglucinol and \(\gamma\)-CD mixture, depicted in Figure 2, shows evidence of inclusion. The H5 and H6 protons of \(\gamma\)-CD appear as a single resonance with a maximum at 3.75 ppm, slightly shifted in comparison with the values reported for pure \(\gamma\)-CD (3.56-3.65 ppm) [33; 34]. Note that the H5 protons are located inside the cavity of \(\gamma\)-CD, and therefore a shift in their signal is associated with the formation of inclusion complexes in solution. The shift in H6 protons suggests that phloroglucinol may be interacting with the primary hydroxyl side of \(\gamma\)-CD [34]. Furthermore, NOESY correlations were found between the H5 protons of the host and the benzene protons of phloroglucinol, which corroborates inclusion of the benzene ring into the cavity of \(\gamma\)-CD.
# Connecting the Brain with Augmented Reality: A Systematic Review of BCI-AR Systems
## 6 Conclusions  
This systematic review presents an overview of BCI-AR systems, including studies from 2012-2024. The 41 studies were grouped into three main categories: reactive BCI, passive BCI, and active BCI. The review provides a summary of the conducted experiments, the obtained results, and the signal processing and classification techniques utilized. It also reveals several important contributions from the existing research on BCI-AR systems. The most significant finding is the consistent use of reactive BCIs, particularly SSVEP, which demonstrates high accuracy and ease of use, making it a valuable paradigm for controlling AR environments. Furthermore, the integration of BCI with AR through HMDs shows considerable potential for creating immersive, hands-free interaction systems. Despite these advances, there are key limitations in the current state of research. A major challenge is the discomfort associated with EEG caps, which not only limits user participation but also affects the long-term feasibility of BCI-AR systems. Using a wearable BCI-AR system that involves two separate devices placed on the head--such as an EEG cap and an HMD--further adds to the discomfort, making these systems less practical for extended use. Looking forward, several promising directions for future research have emerged. One key area involves developing more comfortable, user-friendly BCI devices that can seamlessly integrate with HMDs. The adoption of advanced machine learning and deep learning techniques is also expected to significantly enhance classification accuracy and improve system performance. Additionally, hybrid BCI systems, which combine multiple paradigms, can help increase functionality and expand applications. Addressing these challenges will be essential for advancing the field and unlocking the full potential of BCI-AR technologies.  
Conceptualization, M.G.T.; methodology, G.P., P.A., P.S., S.B. and M.G.T.; software, G.P. and P.S.; validation, G.P. and P.A.; data curation, G.P. and M.G.T.; writing--original draft preparation, G.P.; writing--review and editing, G.P., P.A., P.S., S.B. and M.G.T.; supervision, M.G.T.; project administration, M.G.T. All authors have read and agreed to the published version of the manuscript.  
This work is financed in part by the project \"AGROTOUR-New Technologies and Innovative Approaches to Agri-Food and Tourism to Boost Regional Excellence in Western Macedonia\" (MIS 5047196), which is implemented under the action \"Reinforcement of the Research and Innovation Infrastructure\", supported by the operational program \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020), and co-financed by Greece and the European Union (European Regional Development Fund).  
The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflicts of interest.