esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Advantages of DITS  
Previous studies on crop extraction from time series data have indeed affirmed that superior outcomes can be achieved by identifying crops using such data [52; 53; 54; 55]. The diverse vegetation within the study area, coupled with the presence of winter wheat sharing spectral and phenological similarities with garlic, presented a formidable challenge for garlic extraction. As depicted in Figure 4, garlic and winter wheat exhibit considerable similarity in their NDVI during the phenological period, rendering it arduous to discern the two solely through remote sensing imagery on a given day during the growing season. Field surveys have indicated that the foliar index of garlic during the same period is lower than that of winter wheat, theoretically suggesting that the DITS of garlic should also be smaller than that of winter wheat. However, as evident from Figure 4, the NDVI of garlic and winter wheat closely align before January, with both exhibiting lower foliar indexes in the early growth stage, thereby complicating differentiation between the two using NDVI time series data at the onset to achieve the desired effect.  
From the classification results, it is apparent that the inclusion of additional classification features enhances the accuracy of classification outcomes. This is evident from Tables 4 and 6, where SWIR bands play a pivotal role in improving accuracy, likely attributed to the substantial difference in leaf water content between garlic and winter wheat from February to June [56; 57]. Both the SAVI and MSAVI mitigate the influence of the vegetation-soil environment on the vegetation spectral characteristics in the early stages of crop growth [58; 59], with the MSAVI exhibiting dynamic adaptability to changes in vegetation density and soil effects. Hence, employing the MSAVI as the first half of DITS during the early stages of crop growth could prove more beneficial in discriminating garlic from winter wheat, a hypothesis validated by the results in Tables 5 and 6. The near-simultaneous maturity period of garlic and winter wheat, when crop density is higher and soil background reflection exerts less influence, renders NDVI values of both crops convergent, posing challenges to differentiation using the NDVI. Conversely, the EVI offers a distinct advantage in observing denser vegetation and is less susceptible to noise [60], thus utilizing the EVI as the latter half of DITS may facilitate better discrimination between garlic and winter wheat. Notably, as depicted in Table 5, utilizing time series data comprising the EVI combined with microwave and several single-band time series data yields improved results in several regions, underscoring the significance of the EVI in classification contributions, as highlighted in Table 6.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 4 Analysis
### Line of Sight Scenario  
High data rates can be achieved up to 420 m, and the maximum length of the radio link with low data rates can reach 700 m with a high PDR. There is not useful radio link at 1000 m.
# Blood Pressure Monitoring Based on Flexible Encapsulated Sensors
## 2 Analysis and Research of Blood Pressure Measurement Methods
### The Pulse Wave Parameter Method  
The pulse wave parameter method is a continuous and noninvasive blood pressure measurement technique that involves extracting characteristic parameters from the pulse waveform and establishing a regression analysis between blood pressure and the pulse wave. Some characteristic parameters of the pulse signal are shown in Figure 2, including the pulse onset; the percussion peak; the preceding tidal wave trough; the tidal wave; the dicrotic notch; the dicrotic wave peak; systolic time (\(St\)); diastolic time (\(Dt\)); time required to reach 10%, 25%, and 50% of the pulse peak height during systole (\(St10\), \(St25\), and \(St50\), respectively); and the time required to reach 10%, 25%, and 50% of the pulse amplitude during diastole (\(Dt10\), \(Dt25\), and \(Dt50\), respectively), among others [41]. Currently, most blood pressure monitoring devices based on the pulse wave parameter method use photoelectric sensors to collect pulse signals. However, photoelectric sensors are susceptible  
Figure 1: Schematic diagram of the arterial tonometry method.  
to changes in light intensity, skin color, sweat, and other factors that can lead to low measurement accuracy.
## 3 Results and Discussion
### Infrared Spectroscopy  
The major peak assignments in olive pomace fractions measured by FTIR include lipids, polysaccharides and pectin and cellulose components (Figure 2). Unsaturated fatty acids (like linoleic and oleic acid) and triacylglycereeds were identified at 3008 cm\({}^{-1}\) (C-H stretching of olefinic double bonds) and 1744 cm\({}^{-1}\) (ester peak because of C=O stretching vibration of carbonyl groups of the triacylglycereeds), respectively [49]. In agreement with the more abundant lipid content of the pulp fraction, larger peak areas were detected at 2922 and 2850 cm\({}^{-1}\) in the pulp fraction (methylene absorbance peaks associated with antisymmetric and symmetric stretching vibrations of aliphatic C-H in H2 and terminal CH3 groups, respectively) [49].  
The bands at 3300-3400 (vibration of hydrogens in bound OH groups of the type alcohol or phenol) and 2922 cm\({}^{-1}\) (C-H stretching vibrations of asymmetric aliphatic structures) could be attributed to high levels of less condensed structures, such as phenolic compounds and acids [49]. These bands were more evident in liquid than in pulp fraction.  
The vibration peaks of cellulose and lignin were more evident in pulp fraction, at wavelengths ranging between 1315 and 1460 cm\({}^{-1}\) (1370 cm\({}^{-1}\), CH\({}_{2}\) bending mode (xyloglucan, cellulose), 1338 cm\({}^{-1}\), CH deformation from ring vibration (polysaccharides, pectin and cellulose)). At 1080 and 1024 cm\({}^{-1}\), two significant peaks in the pulp fraction were identified, which are typical from xyloglucans (C-O stretching and C-C stretching) and C-O stretching and C-C stretching (pectin, cellulose). Pectin was noticed mainly in peaks between 1100 and 1600 cm\({}^{-1}\) (namely 1630 cm\({}^{-1}\), COO\({}^{-}\) asymmetric stretching (pectin ester group), 1240 cm\({}^{-1}\), C-O stretching (pectin), 1160 cm\({}^{-1}\) O-C-O symmetric stretching (glycosidic link of cellulose, pectin)). The presence of soluble sugars was identified between 1045 and 1160 cm\({}^{-1}\) in the liquid fraction.  
The differences in peak intensity and some shifts in peak position between the liquid and pulp fractions were related to the content, type and structure of its components. The principal spectral variations among the liquid and pulp fraction were principally due to higher fat content and fibre richness (lignin and xylans composition) of the pulp fraction versus the higher phenolic and sugar content of the liquid fraction obtained from olive pomace. The FTIR results allowed to deduce that the pulp fraction could not only be explored as a source of fibre but also of unsaturated fatty acids in food formulation for its beneficial effects on the reduction of cholesterol and triglycerides [50].  
Assignments were based on studies from previous work on the FTIR analysis of olive pomace [1], olive mill wastewater [51], olive pomace fractions [17] and olive oil [49].
## 3 Circuit Architecture for Multi-MFC PMS
### Power Ranking from Multiple MFCs  
Due to the bioelectrochemical nature of the MFC (parasitic reactive elements), its open circuit output voltage does not hold a direct relationship to the total amount of energy. Measuring the MFC open circuit output voltage it is not necessarily indicative of the amount of energy available from a specific device in the array because its internal resistance (\(R_{MFC}\)) is not considered. Therefore, the followed approach was chosen to properly estimate the power available per MFC:  
The circuit shown in Figure 4 uses a 100 \(\upmu\)F power measurement capacitor (\(C_{PM}\)) and a charging switch (Cap Charge) in a series to an MFC under testing. A second switch (Cap Discharge) is in parallel with \(C_{PM}\) to reset its voltage conditions at the end of a power measurement by setting its initial voltage to zero. By neglecting parasitic elements, the power in the circuit shown in Figure 4 can be calculated using Equation (2) below:  
\[P=V\cdot I=V(t)\cdot C_{PM}\;\frac{dV}{dt}. \tag{2}\]  
It can be noticed from Equation (2) that the voltage change in time \(\frac{dV}{dt}\) of \(C_{PM}\) is directly proportional to the total available power of the MFC under test, and most importantly, such as charging time, as estimated in Equation (3), intrinsically considers resistances \(R_{MFC}\) and \(R_{Switch}\), where the latter resistance is constant. Using this principle, the power ranking algorithm registers an accurate power measurement every 16.52 ms.  
\[\tau_{Discharge}=C_{MFC}\cdot R_{out}=C_{MFC}\cdot(R_{MFC}+\ R_{Switch}). \tag{3}\]  
Figure 6: Flow diagram for MFC power extraction: (**a**) Main Program and (**b**) Power Ranking Algorithm subroutine.  
Thus, the final value of \(C_{PM}\), holds a direct relationship to the MFC's individual power. Both, the capacitor value and the constant charging time were selected to provide meaningful information regarding the electrical characteristics of all the MFCs connected to the system. The previously described process is repeated for each MFCs located in the MFC array. After all the MFCs are measured, the data is sorted in a look-up table that mandates the order in which each MFC is harvested.  
The description of the power ranking sub-routine shown in Figure 6b is presented below:  
1. The power ranking measurement capacitor is initialized to 0 V by Cap Discharge switch
2. The MFC under testing is connected to the ranking circuit using Cap Charge switch
3. ADC values of MFC measured power are stored in the look-up table
4. The ADC values are sorted, assigning the highest rank to the MFC with maximum voltage, and lowest rank to the MFC with minimum voltage. MFCs that cannot provide significant power are ranked zero and neglected in the subsequent EH process.
# Nano-/Microcapsules, Liposomes, and Micelles in
## 2 Spherical Nanoparticles
### Inverted Micelles  
In an apolar environment, amphiphiles spontaneously form inverted micelles, which are polar at the core and hydrophobic on the outer surface. While the literature uses \"inverted microemulsion\" and \"inverted micelle\" interchangeably, they represent two distinct colloidal systems that differ in thermodynamic stability. Reverse micelles exhibit an organized structure, as opposed to reverse microemulsions, because of the petite water droplets that make up the surfactant monolayer [94].  
Inverted micelles are surfactant aggregates with a water molecule at the core in a non-polar solution. The size and structure of these micelles depend on various factors such as the quantity of water added, expressed as mole [water]/mol [surfactant], the type and amount of apolar solvent used, the structure of the encapsulated polar solvent, the temperature at which the process of inverted micelle formation occurs, and even the quantity of water employed [94]. Water molecules enclosed within inverted micelles are immobilised due to local water interactions with the counter ions and dipoles of the surfactant core groups. Inverted micelles possess cores that resemble the physiological environment (water), which allows them to protect encapsulated biomolecules from denaturation [94].  
Due to the ability of the aqueous cores of inverted micelles to encapsulate biomolecules and then release the trapped compounds, they have become useful in the food industry. In particular, they are utilised for extracting, separating, and purifying food compounds such as enzymes, peptides, proteins, and edible oils. They are also used for protecting food enzymes and antioxidants, developing analytical and detection media, and creating multifunctional nanomaterials [94; 95]. Given that most inverted micelles are made of undesirable components (e.g., iso-octane, sodium bis-[2-ethylhexyl] sulfosuccinate), the development of secure inverted micelles employing polysaccharides in non-polar media, such as ethanol, glycerol, or vegetable oils, presents a promising solution for the use of inverted micelles in food applications. Nonetheless, the utilisation of inverted micelles presents several benefits, such as the ability to retrieve surfactants and non-polar solvents, which consequently reduces cost [94]. Additionally, inverted polysaccharide micelles have practical implications for the food industry in serving as nanocarriers for the encapsulation, targeted delivery, and controlled release of hydrophilic food compounds within fat-based food products such as spreads, cocoa butter, and vegetable oils [70]. In the context of food applications, including the detection of food and the inhibition of edible oil oxidation in non-aqueous foods, the synthesising of inverted micelles with biodegradable polysaccharides is an area of great interest that necessitates further research [70].
# Investigating Feature Selection and Random Forests for Inter-Patient Heartbeat Classification
###### Abstract  
Finding an optimal combination of features and classifier is still an open problem in the development of automatic heartbeat classification systems, especially when applications that involve resource-constrained devices are considered. In this paper, a novel study of the selection of informative features and the use of a random forest classifier while following the recommendations of the Association for the Advancement of Medical Instrumentation (AAMI) and an inter-patient division of datasets is presented. Features were selected using a filter method based on the mutual information ranking criterion on the training set. Results showed that normalized beat-to-beat (R-R) intervals and features relative to the width of the ventricular depolarization waves (QRS complex) are the most discriminative among those considered. The best results achieved on the MIT-BIH Arrhythmia Database were an overall accuracy of 96.14% and F1-scores of 97.97%, 73.06%, and 90.85% in the classification of normal beats, supraventricular ectopic beats, and ventricular ectopic beats, respectively. In comparison with other state-of-the-art approaches tested under similar constraints, this work represents one of the highest performances reported to date while relying on a very small feature vector.  
ECG feature selection; heartbeat classification; arrhythmia detection; random forest classifier +
Footnote †: journal:  
Article
# Nonparametric Probability Density Function Estimation Using the Pade Approximation
## 2 Mathematical Background
### Implementation Considerations of the Pade Transform  
The first question that we must resolve is that of determining how many poles and zeros (i.e., the values of \(L\) and \(D\)) are adequate to yield an accurate approximation. For tackling this issue, we follow the arguments proposed by [29]. For uniqueness and convergence, we limit ourselves to Pade approximations where the degree of the numerator is less than the degree of the denominator. Thereafter, we invoke the residue theorem to find the inverse Laplace transformation as:  
\[P_{D}^{L}(u)=\frac{\sum_{n=0}^{L}a_{n}u^{n}}{1+\sum_{n=1}^{D}b_{n}u^{n}}=\sum_ {i=1}^{D}\frac{\lambda_{i}}{u+p_{i}}. \tag{15}\]  
\[\mathcal{L}^{-1}(P_{D}^{L}(u))=f(x)=\sum_{i=1}^{M}\lambda_{i}e^{-p_{i}x},\quad p _{i}>0. \tag{16}\]  
In Equation (15), \(\lambda_{i}\) represents the residue associated with each term in the decomposition and \(p_{i}\) denotes the poles of the rational function, where each \(p_{i}\) corresponds to a specific root of the denominator. One major issue with this method is the distortion incurred in estimating the PDF, near zero. As previously discussed, the accuracy of the one-point Pade approximation method is compromised in the right tail; hence, the left tail of the approximation is more reliable and accurate. To illustrate this issue, consider the example of a Gaussian PDF:  
\[f(x)=\frac{1}{\sqrt{2\pi\sigma^{2}}}\,e^{-\frac{(x-p_{i})^{2}}{2\sigma^{2}}}, \tag{17}\]  
where \(\sigma\) and \(\mu\) are the standard deviation and mean, respectively, as illustrated in Figure 1, where \(\sigma=50\) and \(\mu=15\). More specifically, the figure was drawn using 300 samples generated from this distribution. Indeed, we have estimated the PDF and compared it to the ideal (true) one. In Figure 1, we compare the ideal PDF with the estimated one.  
Figure 1: Comparison between the Gaussian function and its estimation using the Pade approximation. Here, the degree of the numerator and denominator are \(L=8\) and \(D=10\), respectively. The blue series represents the ideal PDF, while the red series represents the estimated PDF.  
As seen in Figure 1, the left part of the estimated PDF does not resemble the shape of the original PDF. Therefore, we require a method to estimate the left part of the PDF accurately. In the next section, we will propose a solution to address this issue. Subsequently, we will compare the proposed method with other methods to assess its effectiveness.
## 5 EEG-Based Multidimensional Emotion Recognition Model
### Recognition Criteria of Multidimensional Emotions  
In this section, the criteria rules of recognition on each EEG-based emotion are trained based on association rule learning, the frequent pattern growth (FP-growth) algorithm is conducted to discover the criteria rules of emotions among partial fluctuation patterns. Firstly, each partial fluctuation pattern is calculated with its frequency that appeared in dataset, and for those patterns which have a greater support value than the threshold are reserved. Secondly, reserved patterns are formed as multiple serial sequences, and the frequency of each sequence is calculated, for those which have a greater confidence value than the threshold are reserved as criteria rules, and the confidence is regarded as the intensity value of the rule. Thirdly, the relevance value between each reserved pattern is calculated which refers to the possibility that a former pattern and a latter pattern occurred at the same time. Therefore, for the possibility value is greater than the threshold, and the former one is already existed in the criteria rule set, then those two pattern are combined as a potential rule to the criteria rule set, and the multiplication of the confidence of former one and the possibility value is regarded as the intensity value of the potential rule.  
Once all the criteria rules are obtained, clustering is conducted due to some rules have attached with multiple emotion labels. Firstly, for rules only attached with single emotion, are labeled as the \"Representative\". Secondly, for rules contained with all emotions, the standard deviation of rule intensity is calculated, if the standard deviation is smaller than the threshold, those rules are labeled as \"Common\", which refers to that the rule is commonly occurred among all emotions. Thirdly, for rest of the rules, the emotion distance and standard deviation of those distance values are calculated, if the average distance and standard deviation are smaller than the thresholds, those rules are labeled as \"Similar\". And to furtherly refine the \"Similar\" label, 9 subcategories are designed according to the valence-arousal domains, the definitions of those 9 subcategories are listed at Table 3.
# Semantic Similarity Based on Taxonomies
## 5 The Parametric Semantic Similarity Method \(\text{SemSim}^{p}\)  
Given two annotation vectors, the \(\text{SemSim}^{p}\) method [18] allows the evaluation of their semantic similarity degree by relying on the method \(\text{SemSim}\)[27]. \(\text{SemSim}^{p}\) is based on two parametric functions, \(sim_{lin,h}\) and \(\text{semsim}_{h,\mu}\), the former used to compute the similarity of pairs of concepts, whereas the latter was conceived to evaluate the similarity of pairs of annotation vectors, as formally defined below.  
Let \(sim_{lin,h}\) be the parametric Lin similarity defined by Equation (3) (see Equation (21))  
\[sim_{lin,h}(c_{1},c_{2})=\frac{2*ic_{h}(lcs(c_{1},c_{2}))}{ic_{h}(c_{1})+ic_{h} (c_{2})} \tag{21}\]  
where \(ic_{h}\) is the IC computed according to one among the approaches of _Resnik_, _Annotation Frequency_, _Top Down_, and _Seco_, as recalled in Section 2, which are formally defined in Appendix A (in particular \(h=\{\text{resnik},\,\text{af},\,\text{td},\,\text{seco}\}\), Formulas (A1), (A9), (A14) and (A2), respectively).  
Consider now two annotation vectors, say \(av_{1}=(c_{11},\dots,c_{1n})\) and \(av_{2}=(c_{21},\dots,c_{2m})\) and the Cartesian product of \(av_{1}\), and \(av_{2}\), say \(\text{S}=av_{1}\times av_{2}\). We borrow the matching approach from the graph theory in line with the _maximum weighted matching_ problem in bipartite graphs [56]. Accordingly, \(\mathcal{P}(av_{1},av_{2})\) is the set of sets of pairs, defined as follows:  
\[\mathcal{P}(av_{1},av_{2})=\{P\subset S|\forall(c_{1i},c_{2j}),(c_{1q},c_{2k}) \in P,c_{1i}\
eq c_{1q},c_{2j}\
eq c_{2k},|P|=min\{n,m\}\}. \tag{22}\]  
Formally, the \(\text{semsim}_{h,\mu}\) function identifies the set of pairs of concepts of \(av_{1}\) and \(av_{2}\) that maximizes the sum of the \(\text{consim}_{h}\) values, as follows:  
\[\text{semsim}_{h,\mu}(av_{1},av_{2})=\frac{\max_{P\subset\mathcal{P}(av_{1}, av_{2})}\left\{\sum\limits_{(c_{1i},c_{2j})\in P}sim_{lin,h}(c_{1i},c_{2j}) \right\}}{\mu(n,m)} \tag{23}\]  
where \(\mu\), named the _similarity normalization factor_, is defined below:  
\[\mu(n,m)=\begin{cases}max(n,m)\\ min(n,m)\\ ave(n,m)=\frac{n+m}{2}&(\text{arithmetic\,\  
annotation) as a redundancy. Hence, the choice of maximum or minimum emphasizes the differences and commonalities between the compared annotation vectors, respectively.  
The choice of the normalization factor as the arithmetic mean implies a compromise between the two previous cases. This case takes missing and redundant information into account to some extent when comparing resources in the same way. Finally, the geometric mean behaves essentially like the arithmetic mean but is more sensitive to small values.  
In accordance with the computational complexity of the Hungarian algorithm, the \(SemSim^{p}\) method is polynomial in the size of \(n\), specifically \(O(n^{3})\), where \(n\) is the cardinality of the larger of \(av_{1}\) and \(av_{2}\).