esa-sceva/satcom-chunk-collection huggingface dataset:
# Chaotic Features of Decomposed Time Series from Tidal River Water Level
## 4 Chaotic Features Analysis of Decomposed Time Series
### Estimation of Delay Time and Attractor
#### 4.1.2 Attractors for the Decomposed Time Series  
The estimated delay times are used to reconstruct the attractors in the two-dimensional phase space, as shown in Figure 7. At the low water level with no rainfall, the attractor is located within the spatial boundary. However, when the rainfall occurs, the increasing water level leads the trajectory out of the periodic boundary, causing a spark (Figure 7a). On the other hand, the tide component slightly deviates from the periodicity as it moves with its own boundary (Figure 7b). In fact, since tides fluctuate with a cycle of 24 h and 25 min, drawing an attractor using daily data has a limitation in that it partially deviates from the circular orbit. Meanwhile, the wave component also shows periodicity, but it deviates from the trajectory more than the tidal component. Noise component shows a  
Figure 6: Average mutual information of each component: (**a**) water level, (**b**) tide component, (**c**) wave component, (**d**) noise component, (**e**) rainfall-runoff component.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Component**} & \multicolumn{2}{c}{**Delay Time (Day)**} \\ \cline{2-3}  & **ACF** & **Average Mutual Information** \\ \hline Water level & 9 (first minimum) & 9 (first minimum) \\ Tide & 7 (zero crossing) & 6 (first minimum) \\ Wave & 8 (zero crossing) & 7 (first minimum) \\ Noise & 6 (first minimum) & 4 (first minimum) \\ Rainfall-runoff & 5 (zero crossing) & 9 (first minimum) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Estimated delay times by the autocorrelation function(ACF)and average mutual information.  
more sensitive response to rainfall compared to the tide component (Figure 7d). Because the rainfall-runoff component only increases with rainfall (see Figure 4d), it is not possible to draw an attractor with a certain boundary.
## 2 Materials and Methods
### Description of the LUE-NDWI Model  
The LUE-NDWI model can be expressed as follows:  
\[GPP~{}=~{}PAR\times FPAR\times F_{CI}\times F_{CO2}\times LUE_{(MAX)}\times \min(T_{S},W_{S\_NDWI}). \tag{7}\]  
It can be seen that the only difference between LUE-EF and LUE-NDWI is the expression for water stress. The NDWI, strongly related to vegetation water content [35; 36], can be a very good proxy for vegetation water stress. In addition, NDWI data can be obtained through satellite observation directly and the EF obtained by calculating sensible heat and latent heat, cannot be directly observed. After examining measurements from many flux towers, we found that the following nonlinear function can be used to represent \(W_{S}\), the regulation scalar of water stress on GPP, using NDWI:  
\[W_{S\_NDWI}=a*(-NDWI+0.5)^{b}+\text{c}. \tag{8}\]  
Using the 'nls' method of parameter optimization adjustment, the coefficients were determined to be a (= 0.35), b (= 2.14), and c (= 0.086). The \(W_{S\_NDWI}\) values vary between 0 and 1, with values beyond the bounds set to 0 or 1, respectively. The use of \(W_{S\_NDWI}\) can be very convenient for applications from local to global scales as the NDWI fields can be directly derived from satellite observations.
# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 1 Introduction  
It is a common practice to estimate traffic loads in specific locations. However, doing the same with pedestrians is rather rare. The detection of pedestrians (along with the deployment of clustering techniques) may provide a means to limit the spread of COVID-19. Due to the COVID-19 pandemic, many object detection systems have been developed to monitor the situation and provide pandemic mitigation measures. There are systems that feature drug delivery [1], disinfection spraying [2], face mask detection, or body temperature detection; however, there is the potential for more general-purpose systems that could be used even after the COVID pandemic.  
Our objective was to estimate where people form groups using input from unmanned aerial vehicles (UAVs). To do this, it is necessary to know their positions and numbers. To achieve this, a detection process is required. There are many approaches to the detection process, e.g., neural networks (NN), support vector machine (SVM), or clustering techniques [3, 4]. Once detected, we can estimate locations following our proposed algorithm; the output is then used by the clustering algorithm.  
Such information may have a dramatic impact on where to establish stores, which paths to keep, or even build; other use cases may even be related to the military (to track the formations of groups of people).  
Our contributions to this paper are as follows:  
* We propose a comprehensive system for the detection of pedestrians, estimations of their locations based on UAV data, and evaluations of their 'groupings'; based on this, we could obtain a visual representation in the form of a heat map.
* We designed and tested our approach for estimating the geolocations of pedestrians.
* To provide more variability, the solution was separated into three individual parts: UAV as the source of the visual data and their locations; the application for detection and estimation of pedestrian geolocations; and the web interface for the end user, which implements clustering algorithms. The solution was designed to be universal and utilizable on various UAV devices.
## 1 Introduction
In the past several years, many opportunistic routing algorithms and non-deterministic solutions have been proposed [13; 14], but due to the very unique characteristics of this kind of networks, the efficiency is limited, and the search for the best algorithm that can provide seamless and reliable communication is still an ongoing primary quest. Some algorithms, like the epidemic routing [15] and spray and wait [16], make use solely of a flooding-based principle, in which copies of the message to deliver are spread in a controlled way. Others, like PRoPHET [17], make use of probabilistic encounters maximizing the chances of two nodes meeting in the future. One third group, like SeeR [18], seeks to make use of heuristic approaches to approximate a global optimization in a larger search space. Nowadays and for the past few years, artificial intelligence (AI) has become an increasingly important field of study to assist in the evolution of science, engineering, business, medicine, and more [19]. Industry, finance, healthcare, education, and transportation have seen important advances with the aid of image and speech recognition, natural language processing, and intelligent control and predictions [19; 20; 21], which are possible thanks to different AI techniques. In this paper, we propose DLR+, a deep learning-based router that is capable of learning to make intelligent decisions based on local and global conditions from a dual perspective. The simulation results show that this router outperforms some well-known routers in network overhead and hop count, while maintaining an acceptable delivery rate. The rest of the paper is organized as follows: In Section 2, we dive into relevant related work, and in Section 3, we frame the routing problem in a more formal way. In Section 4, an overview of the proposed router is given, presenting the router architecture and its modules, and in Section 5, we describe the routing algorithm in more detail. Section 6 is dedicated to the experiment and in Section 7, we discuss the obtained results. Finally, in Section 8, we conclude this paper, providing a quick summary of this work and the findings, and provide some recommendations to further advance on this topic taking the proposed router as a starting point.
# IEEE 802.11 ah: A Technology to Face the IoT Challenge
## 3 Comparative Analysis of IEEE 802.11 ah with Previous IEEE 802.11 Amendments
### Comparison of IEEE 802.11 Amendments Based on MAC Features
#### 3.1.23. Low Power Mode of Operations  
IEEE 802.11 ah enables a station to inform the AP about the duration of time it intends to remain in sleep mode. During the sleep mode, the station is not intended to listen to Beacons and then it is able to reduce its power consumption.
## 4 Results
### Uncertainty-Based Explanation Methods
#### 4.2.1 How Do Explanations Contribute to Understanding, Confidence, and Justification?  
We assessed the radiologists' comprehension and trust in the explanation techniques provided alongside the MRI images. Each radiologist was shown a specific MRI image with its corresponding explanation method and asked to rate their perceived usefulness for the model's given classification. Radiologists were asked to provide their rating on a scale of 1 to 5, with 5 representing high understanding, confidence, and justification, and 1 representing low understanding, confidence, and justification.  
For understanding, they were asked to rate _This visualization helped me understand why the model's classification was made_. For confidence, they rated _This visualization makes me feel confident in the model's classification_. For justification, they assessed _This visualization adequately justifies the classification_. The results are presented in Table 5. Table 3 highlights the top-performing methods in terms of reported scores. BayLIME stood out with the highest score of 4.0 for understanding. In the confidence category, both BayLIME and BLRPwere leading, each scoring 3.71. For justification, BayLIME, again, was the top scorer with 3.71. The radiologists emphasized that the clarity of the explanation significantly influenced their ratings for understanding, marking down methods that produced noisy explanations.  
In assessing confidence, radiologists noted that for confidence, it was vital for the explanation method to accurately identify and emphasize the lesion when present. If the method only partially highlighted the lesion, it received lower confidence ratings than the method that accurately highlighted the entire lesion. In other words, radiologists rated explanation methods higher for confidence when they could accurately segment the lesion. This suggests that the ability of an explanation method to precisely localize and emphasize lesions plays a crucial role in radiologists' confidence in the model's classification.  
While justification ratings were closely aligned with those for confidence, the key distinction was the radiologists' emphasis on the overall detection of the lesion by the explanation method, rather than its precise segmentation. In other words, even if the explanation method highlighted only half of the lesion, it was still deemed as justified as highlighting the entire lesion. Radiologists stressed the significance of examining the explanation method to verify the presence of a lesion. These findings imply that the perceived quality of the explanation is related to the lesion detection performance, and BayLIME delivered the highest lesion detection accuracy and perceived quality in terms of understanding, justification, and confidence.
## 3 Results and Discussion
### Particle Transport and Deposition  
Suspended particles in porous media can be subjected to either surface-cake filtration or deep filtration. Permeability in surface-cake filtration decreases as larger suspended particles become trapped at the column inlet [27]. Deep filtration occurs when suspended particles enter into porous media and are retained within [28]. The type of filtration that occurs depends on the particle diameter ratio between the suspension and the porous medium (\(d_{\text{p}}/d_{\text{g}}\)). [PERSON] et al. reported that surface-cake filtration occurs when \(d_{\text{p}}/d_{\text{g}}\) is greater than 0.150 [28], while [PERSON] and [PERSON] found the appropriate conditions to be when \(d_{\text{p}}/d_{\text{g}}\) is above 1/6 [29]. The average particle size of our porous media was 0.944 mm, indicating that surface-cake filtration should occur when the diameter of the suspended particles is higher than 141 \(\upmu\)m. However, over 90.0% of the particles in this study were around 96.6 \(\upmu\)m, suggesting that deep filtration was the main form of particle removal. During internal clogging, collision and adhesion interact to remove suspended particles from porous media. During internal clogging, particles are transported from the pores to the vicinity of the collector. Thereafter, the suspended particles of the solution are removed by collision and attachment with porous media [19]. To investigate clogging mechanisms, we combined the transport model with the DLVO theory to calculate \(\eta_{0}\) and the attachment efficiency between the suspended particles and the porous media.
## 3 Results and Discussion
### Subsystem Evaluation Findings and Analysis
#### 3.2.2 Findings and Analysis of the Economic Climate Assessment  
The economic climate evaluation result is shown in Figure 5 and Table 7. The Baltic countries are on par with the Visegrad Group and Slovenia on the whole. However, in 2018, the mean value of the five countries' economic resources and climate in the Visegrad Group and Slovenian segment was 0.1674, slightly lower than the Baltic Three mean value of 0.1913. In terms of country performance, in 2018, Slovenia had a score of 0.2815. The Czech Republic and Poland topped the list of CEE countries with a score of 0.1755 and 0.1648, respectively, and were ranked fourth and fifth among the 16 countries. The Czech Republic takes the top spot in CEE in terms of the 10-year average score. The three Baltic countries also performed well. They had the highest average regional scores in 2018, and all three countries were ranked among the top six countries in CEE countries. In 2018, the Balkan countries' average score was only 0.0881, ranking last in the three sectors. Even in the highest-scoring Albania, the economic resources and climate index was only 0.1269, ranked seventh in CEE countries. Bosnia and Herzegovina had a single-year score of 0.0265 and a 10-year average score of 0.0348 at the bottom of 16 countries, with certain problems in the domestic investment and business climate. Although most countries have improved their scores in this indicator in recent years, there are still six countries  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Country** & **Ave.** & **Rank** & \
\begin{tabular}{c} **Relative Change** \\ **(2009/2018)** \\ \end{tabular} & **Country** & **Ave.** & **Rank** &
\begin{tabular}{c} **Relative Change** \\ **(2009/2018)** \\ \end{tabular} \\ \hline Albania & 0.0855 & 12 & 61.2\% & Lithuania & 0.1634 & 4 & 10.3\% \\ Bosnia & 0.0262 & 16 & \(-\)25.2\% & Montenegro & 0.0957 & 10 & 6.2\% \\ Bulgaria & 0.0902 & 11 & \(-\)5.4\% & Macedonia & 0.0791 & 13 & \(-\)13.3\% \\ Croatia & 0.1037 & 9 & \(-\)13.9\% & Poland & 0.1531 & 6 & \(-\)8.4\% \\ Czech & 0.1652 & 3 & \(-\)2.7\% & Romania & 0.0772 & 14 & \(-\)19.3\% \\ Estonia & 0.2278 & 1 & \(-\)4.9\% & Serbia & 0.0449 & 15 & 93.0\% \\ Hungary & 0.1278 & 8 & \(-\)12.5\% & Slovakia & 0.1445 & 7 & \(-\)20.5\% \\ Latvia & 0.1570 & 5 & 6.8\% & Slovenia & 0.1705 & 2 & \(-\)25.3\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Average evaluation results of political climate in CEE countries.  
Figure 4: Evaluation results of political climate in CEE countries from 2009 to 2018.  
with declines in their scores. Macedonia has become the most obvious country with a 38.4% drop in this indicator, which indicates its economic climate has to be strengthened.
## 4 Results and Analysis
### Error Analysis
#### 4.4.2 Errors from H
Hence, the sensitivity of the LST is unstable and depends on the strength of the turbulence. The strength of the turbulence determines the mass and energy transport and the resistance to heat transfer, which influences the sensitivity to the LST. A weaker turbulence corresponds to a weaker LST sensitivity and vice versa. (2) Errors in canopy height. The canopy height was obtained from a phenophase and classification map. Thus, the accuracy of the canopy height depends mainly on the plant growth state and the classification accuracy. Even within the same region, the canopy height of a crop can differ due to differences in seeding times and soil attributes, such as soil moisture and fertilization. For example, the orchard land use type was present at EC17; however, on our land use type classification map, the land use at EC17 was other crops, which includes orchards and vegetables. Thus, it was difficult to set the canopy height. In our study area, the canopy heights of orchards were approximately 4 m, most of the other crops were vegetables, which had a canopy height of 0.2 m. Hence, a value of 0.2 m would lead to an overestimate in LE. The LE estimates with incorrect canopy heights and the correct orchard canopy heights at EC17 are shown in Table 10. The LE-I relative error indicates the relative error between the LE obtained using the incorrect canopy height and the observed LE and is expressed as ((LE obtained using the incorrect canopy height)-(EC-LE))/(EC-LE) \(\times\) 100%. Moreover, the LE-C relative error is the relative error between the LE from correct canopy height and the observed LE and is expressed as ((LE obtained using the correct canopy height)-(EC-LE))/(EC-LE) \(\times\) 100%. Days with large LST biases were removed, and the bias between the model results and the ground-based observations decreased. Figure 12: Sensitivity analysis of the surface variables for the energy balance components.
## 3 Results and Discussion
### Effect of Temperature  
Figure 4 displays the scattering functions obtained from the same solution at two different temperatures 25 and 65 \({}^{\circ}\)C. They show definite absence of any temperature effect in this range. We find the same lack of hydrophobicity as checked in completely sulfonated linear PSS [44]. On the one hand, the lack of T-dependence here is strong support for the high degree of sulfonation of our stars, since for lower sulfonation rates a temperature effect has indeed been observed in the same range [7; 45]. On the other hand, it can be considered as another test of hydrophilicity of highly sulfonated PSS chains, since if the unit concentration increased when closer to the center of the star, this could make it more sensitive to hydrophobicity, which remains unnoticeable.  
Direct Estimate of the Star Size from the \(c\) Dependence of the First Maximum and Comparison with Theories
## 3 Meshless Approach Overview
### Methodology  
The addition of polynomials to the radial basis functions will ensure consistency and guarantees the approximation properties of the scheme. However, it complicates the system of equations making them difficult to enforce and contribute extra to computational cost and time. In the proposed method, \(2\times 2\) symmetric strain tensor field \(\varepsilon(\mathbf{x})\) is composed of two parts: radial basis functions \(R_{i}(\mathbf{x})\) and polynomial basis functions \(P_{j}(\mathbf{x})\)[25; 31]. Hence, the local approximation is given by  
\[\varepsilon_{lk}(\mathbf{x})=\sum_{i=1}^{n}a_{i}\ R_{i}(\mathbf{x})+\sum_{j=1} ^{m}b_{j}\ P_{j}(\mathbf{x}),\ \ l,k=\{1,2\}, \tag{4}\]  
where \(m\) is the number of polynomial terms and \(n\) is the number of nodes in the local support domain \(\Omega\). Unknown vectors to be calculated are \(a\) and \(b\). The addition of polynomial terms is an additional prerequisite. The following conditions are generally imposed to ensure a unique approximation [32]  
\[\sum_{i=1}^{n}\ P_{j}(\mathbf{x}_{i})\ b_{i}=0,\ \ \ j=1,2,...,m, \tag{5}\]  
Figure 2: Radial basis function model network architecture.  
where \(m\) is a number of augmented multivariate polynomial basis functions, see Table A1. We assume \(P_{j}(\mathbf{x})=1+x+y+xy+x^{2}+y^{2}\) and \(R_{i}(\mathbf{x})=exp(-\alpha^{2}||\mathbf{x}-\mathbf{c}_{i}||^{2})\) where \(\alpha\) is the shaping parameter. Hence, above Equation (4) is expressed in the matrix form  
\[U=R\mathbf{a}+P\mathbf{b}, \tag{6}\]  
where \(U\) is the vector of function values: \(U=[\epsilon_{lk}^{1},\ \epsilon_{lk}^{2},\ \cdots,\epsilon_{lk}^{n}]^{T}\), \(R\) is the matrix of RBFs, \(P\) is the matrix of polynomial basis function and \(\mathbf{a},\mathbf{b}\) are the values of unknown coefficients (Radial and polynomial respectively). We note that to obtain the unique solution of Equation (4), the constraint condition (5) should be combined with Equation (6) as follows  
\[U_{1}=\begin{bmatrix}U\\ 0\end{bmatrix}=\begin{bmatrix}R&P\\ P^{T}&0\end{bmatrix}\begin{bmatrix}\mathbf{a}\\ \mathbf{b}\end{bmatrix}=F\mathbf{a_{0}}, \tag{7}\]  
The unknown vector in Equation (7) is obtained by the inversion of the matrix \(F=\begin{bmatrix}R&P\\ P^{T}&0\end{bmatrix}\).
## 3 Materials and Methods
### DLCTC Model
#### 3.2.6 BiLSTM  
LSTM networks extend traditional RNNs to address issues of vanishing gradients, achieving this by incorporating the input, forget, and output gates and a cell state that allows the network to retain information over long sequences. Consequently, bidirectional LSTM (BiLSTM) is applied to capture information from both past and future contexts in a sequence, containing two separate LSTM layers: one processes the input sequence in the forward direction, and the other processes it backward [39].  
Although the forward and backward LSTMs operate independently and have separate parameters, they share the same input word embeddings \(\{w_{1},\ w_{2},...w_{n}\}\) where \(\mathbf{n}\) is the length of the input sentence. This structure enables the model to effectively summarize information from both directions, enhancing its ability to understand contextual nuances in the text.  
Contextual information is important for short text classification because it captures the semantic relationships between words and characters. Existing methods use the sequence model based on CNN, RNN [40], LSTM [41], and BERT [42] to capture the context information, which has already gotten a lot of attention due to its effectiveness. However, in the handling of short texts, these RNN-based methods can only process one word in the short text at a time and process the next word after the previous one has been processed and do not consider the high-dimensional and sparse issues due to the limited text length. Consequently, we introduce TextCNN and BiLSTM [38] instead of the RNN and CNN models to capture the contexts of short texts at the character, word, and concept levels. This is because TextCNN effectively captures local dependencies through n-grams, while BiLSTM understands context from both directions in a sequence, which is crucial in short texts [39].  
After TextCNN and BiLSTM are applied, and the context of each character, word, and concept is established, an attention layer is proposed to measure the weight of context-relevant characters, words, and concepts, where the weight is assigned dynamically by the attention mechanism. The larger the weight, the greater the importance of the word or the concept; that is, these words and concepts are more important to the classification of the short text. Multi-stage concept feature representations can be obtained by combining all representations of different stages at the concept level. Finally, these features are merged to produce the text representation.