esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Structural Model of Interaction of the GERT Model, Kalman Filter and Intelligent Module
To mimic real-world disruptions, 15% of the measurements were randomly removed to simulate signal losses; the noise intensity was varied to emulate interference dynamics. These simulation parameters were chosen to closely reflect the conditions under which radar tracking systems typically operate during the initial detection phase. The simulated scenarios represent typical radar tracking conditions under varying levels of interference, measurement loss, and signal uncertainty during the initial detection phase. These data allowed us to evaluate the system's adaptability in a controlled environment. The dataset used to train the LSTM-based reliability estimation module consisted of over 10,000 synthetic measurement sequences generated based on the statistical characteristics of Kalman filter residuals. These sequences included diverse tracking conditions such as stable measurements, noise-induced deviations, and periods of partial or complete data loss.
# Heart Failure and Diabetes Mellitus: Biomarkers in Risk Stratification and Prognostication
## 3 Biomarkers in Diabetics with Known HF
### Biomarkers of Inflammation and Fibrosis
#### 3.2.5 Interleukin-6 Family Cytokines  
The interleukin (IL)-6 family is a heterogenic group of cytokines, consisting of IL-6, IL-11, cardiotrophin 1 (CT-1), cardiotrophin-like cytokine (CLC), ciliary neurotrophic factor (CNTF), leukemia inhibitory factor (LIF), oncostatin M (OSM), and IL-27 [139]. These cytokines are engaged in metabolic and immune regulation, cardiac repair, and tissue protection [140; 141]. The development of both T2 DM and HF is associated with high variability in the oxidative stress-dependent activation of eNOS, which is regulated by IL-6 family cytokines. While these findings have hypothetical value, recently, SGLT2 inhibitors have been shown to have a favorable effect on CV outcomes in HFrEF/HFpEF, regardless of T2 DM presence in connection with the reduction in inflammatory and oxidative stress and improvement of the NO-sGC-cGMP cascade and protein kinase G type I\(\alpha\) (PKGI\(\alpha\)) activity [142; 143; 144]. Whether CT-1 and other IL-6- family cytokines can be used as future biomarkers to monitor prospective effects of SGLT2 inhibitors on clinical course of HF and T2 DM at higher risk for HF is not fully clear and needs further thorough evaluation in large clinical trials.
## 3 Results and Discussion
### Composition Analysis
Lignin has diverse application potentials, including uses in energy production, construction (e.g., adhesives, binders, cement), bioremediation, polymer synthesis (e.g., polyurethane, polylactic acid, polyethylene, polypropylene), biomedicine (e.g., drug carriers, hydrogels, delivery systems), and the food industry (e.g., antioxidants, antimicrobials), among others [58]. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Composition \%**} \\ \hline  & **Protein** & **Cellulose** & **Hemicellulose** & **Lignin** & **Ash** & **Extractives** & **Reference** \\ \hline DSG & 12.38–26.32 & 11.75–32.75 & 6.97–19.47 & 8.44–15.71 & 2.01–4.11 & 17.33–31.77 & This work \\ \hline BSG & 15.25 & 16.8 & 28.4 & 27.8 & 4.6 & 5.82 & [54] \\ \hline BSG & 20.3 & 11.1 & 22.7 & 13.8 & 4.7 & 35.5 & [43] \\ \hline BSG & NR & 19.21 & 26.94 & 30.48 & NR & NR & [50] \\ \hline BSG & 14.2 & 12 & 40 & 11.5 & NR & NR & [49] \\ \hline BSG & 28 & 13.2 & 16.75 & NR & 4.5 & 34.8 & [43] \\ \hline BSG & NR & 21.84 & 30.43 & 25.26 & 4.41 & NR & [51] \\ \hline BSG & 22.54–30.24 & NR & NR & NR & 3.30–4.29 & NR & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of spent grain composition from various studies. DSG is also a rich source of phenolic compounds, which are known for their antioxidant, antiradical, anticarcinogenic, and anti-apoptotic properties [6]. These phenolics are predominantly located in the barley husk, where they accumulate in the cell walls. They are often bound to sugar residues, which reduces their antioxidant efficacy as free hydroxyl groups are required to stabilize free radicals [59]. The primary phenolic compounds in DSG are hydroxycinnamic acids, with ferulic acid being the most prevalent. Ferulic acid is strongly correlated with the antioxidant capacity of cereals [60]. In this study, the total phenolic content (TPC) of DSG ranged from 1.42 to 3.91 mg GAE/g DSG. The phenolic content did not vary significantly across the samples. These results align with findings by [PERSON] and [PERSON] [33], who reported TPC values ranging from 0.62 to 1.76 mg GAE/g SG DW for Ultra-Turrax extraction and 0.57 to 2.11 mg GAE/g SG DW for ultrasound-assisted extraction of spent grain from malt whiskey. Similarly, [PERSON] et al. [61] used 72% (\(\upsilon/\upsilon\)) aqueous ethanol at 80 \({}^{\circ}\)C to extract phenolic compounds from brewer's spent grain derived from pale barley malt and reported TPC values of 4.11, 3.91, and 3.62 mg GAE/g for ultrasound extraction, microwave-assisted extraction, and batch extraction, respectively. Conclusively, DSG represents a cost-effective source of valuable phenolic compounds.
# Data Augmentation for Audio-Visual Emotion Recognition with an Efficient Multimodal Conditional GAN
## 2 Related Works
### Gan  
The concept of GAN was first proposed in [40] to generate new visual data via adversarial learning. It represents a two-player zero-sum game by adversarially training a generator and a discriminator. The compelling generation capability of GANs has attracted widespread attention from the research community [69; 70; 71]. Different variants of GANs are proposed. For example, in [72], CycleGAN is proposed for unpaired image-to-image translation by introducing a cycle consistency loss function. In [73], [PERSON] et al. propose SRGAN for image super-resolution by designing a perceptual loss function. In [74], [PERSON] et al. propose StyleGAN for style transfer by introducing the separation of high-level attributes from stochastic variation in the generated data.  
By supplementing the given dataset with the generated data, different forms of GANs are designed for data augmentation in many domains, such as medical image analysis [29; 41; 75; 76], brain-computer interface [42; 77], agriculture [43; 78; 79], and civil engineering [30; 80]. In particular, researchers increasingly utilize GANs for data augmentation in the field of emotion recognition, including speech emotion recognition [27; 28; 81], facial expression recognition [31; 82; 83], EEG emotion recognition [44; 84], and so on. These works have significantly improved the performance of unimodal emotion recognition.  
However, as far as we know, there are few works using GANs for data augmentation to improve the performance of multimodal emotion recognition, especially audio-visual emotion recognition. This may be due to two facts: (1) Both audio modality and visual modality in multimodal data are high-dimensional. It is difficult to design GANs to effectively extract information from them and generate them simultaneously; (2) In addition, these two modalities are highly correlated in terms of their emotional information [17; 85]. Elucidating how to measure their relationship in GANs is also not easy. In our framework, the proposed multimodal condition GAN can effectively tackle the above two issues.  
In addition, GANs can be used to deal with class imbalance [86; 87; 88; 89]. For example, in [86], [PERSON] et al. use a conditional GAN as an oversampling approach to generate synthetic data for the minority class. In [87], [PERSON] et al. use multiple fake classes to ensure a fine-grained generation and classification of the minority class instances. In [88], multiple generation modules are adopted for data augmentation of minority classes. In [89], a balanced semisupervised GAN and a balanced batch-sampling strategy are introduced to resolve class imbalance.
###### Abstract  
LED displays can be used to realize the dual functions of display and communication simultaneously. However, existing LED display-based visible light communication (VLC) systems suffer due to their short transmission distance and are not practical. A long-distance, real-time display-camera communication (DCC) system is proposed in this paper. First, a LED-DCC point clustering scheme is proposed to increase the transmission distance by clustering multiple adjacent LED display points for improving the quality of the VLC signal captured by an image sensor. Then, a lightweight, back-forth, fast image processing algorithm is proposed to reduce the introduced additional computational complexity caused by point clustering and enhance the reliability of information extraction from the real-time captured images/video frames. The experimental system was implemented with a 2.2-inch \(16\times 16\) point LED display and the CMOS camera on the smartphone. Experimental results show that the proposed system can achieve a maximum data transmission distance of 7 m under a bit error rate (BER) of 0.5, which is about 9 times that of the previous LED-DCC system, and can achieve a data transmission distance of 175 cm under the 7% forward error correction (FEC) limit, which is about 12 times that of the previous LED-DCC system. Additionally, the decoding latency for extracting information from each video frame is only 13.26 ms, which guarantees real-time data reception.  
LED display; visible light communication (VLC); display-camera communication (DCC); clustered LED points; lightweight image processing; CMOS image sensor +
Footnote †: journal: _Photonics_
# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 8 Conclusions  
In this study, we addressed the complex problem of plant classification using the Cope Leaf Dataset, which comprises 1584 images of leaves from 99 different species, each characterized by 64 attributes related to margin, shape, and texture. We aimed to build robust classification models to accurately identify plant species based on these leaf characteristics despite challenges such as class imbalance, variations in image quality, and intra-class variability due to environmental factors and leaf aging.  
For the largest classification task involving 99 classes, we observed that preprocessing steps, including feature selection and data transformation, significantly enhanced model performance. After these steps, the Naive Bayes Classifier achieved an accuracy of 89.63%,Convolutional Neural Networks has 88.72%, while the Hoeffding Tree algorithm reached 89.92%, underscoring the effectiveness of these enhancements.  
In conclusion, our study demonstrates the potential of machine learning algorithms, particularly the Hoeffding Tree algorithm, for classifying plant species using leaf images. The results highlight the importance of appropriate feature selection, preprocessing methods, and the choice of algorithm in tackling the complexities of large-scale plant classification tasks. Future work could further optimize these models, explore deep learning approaches, and expand the dataset to include more diverse plant species for broader applicability.  
E.E., A.E.T., E.C., A.I.Z., C.Z., A.S. and W.A. were involved in the whole process of producing this paper, including conceptualization, methodology, modeling, validation, visualization, and manuscript preparation. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are openly available in the Kaggle Dataset weblink provided at reference number [6].  
The authors declare no conflicts of interest.
## 4 Results & Discussion
### Influence of Feature Randomness on Classification Accuracy  
As described in Section 3.4.4, we repeated the RFC experiments with the same architecture used in the baseline tests, except we replaced each coefficient with a random number between zero and one with a feature vector dimensionality as those calculated using DARTH-VAT. The results of these trials are listed in Table 14 as well as depicted graphically in Figure 9. Results suggest that, even when the random coefficients do not include any information related to the speakers, it is possible to obtain accuracies over 72% in ItalianPVS, and over 67% in Neurovoz with the proposed baseline experiments. This effect is less clear in mPower. The reason is that ItalianPVS contains many fewer audio recordings than mPower, and this number is much smaller than the feature vector dimensionality. The larger the difference between the number of files and feature vector dimension, the higher chances to find a feature that randomly correlates to the class labels.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Corpus** & **Vowel** & **DARTH-VAT** & **Random** \\ \hline ItalianPVS & /a:/ & 99.0 & 62.6 \\  & /ei:/ & 98.0 & 66.7 \\  & /i:/ & 99.0 & 69.7 \\  & /oi:/ & 98.9 & 67.7 \\  & /ui:/ & 98.0 & 72.7 \\ \hline Neurovoz & /a:/ & 71.6 & 63.6 \\  & /ei:/ & 74.5 & 67.6 \\  & /i:/ & 79.4 & 61.8 \\  & /oi:/ & 74.6 & 63.9 \\  & /ui:/ & 83.6 & 66.4 \\ \hline mPower & /a:/ & 60.2 & 56.6 \\ \hline \end{tabular}
\end{table}
Table 14: Classification accuracy (%) per vowel and corpus for the baseline experiments employing features obtained with DARTH-VAT (which provides the longest feature vectors of the study) and random feature vectors of the same length. This scenario is described in Section 3.4.4.  
Figure 8: Maximum cross-validation accuracy results in mPower employing \(S_{R0}\) (age difference = 0.3 years) and 4 new subsets with mean age difference between classes ranging between 4 and 16 years (in steps of approximately 4 years). This scenario is described in Section 3.4.3. The dashed lines represent a break between the 16-year gap (\(S_{A16}\)) and the larger 26.6-year gap (S).
## 1 Introduction  
The COVID-19 pandemic emerged in 2019 and, since then, more than 775 million cases have been confirmed worldwide, according to the World Health Organization [1]. It has been observed that around a third of patients may present some type of pathology two years after passing the acute phase of the disease [2]. Therefore, although the state of emergency was suspended on 5 May 2023, the disease continues to affect a large portion of the population. The set of symptoms that remain after the disease has been referred to as \"long-COVID\" or \"post-COVID-19\" [3]. Several of these symptoms are directly related to metabolism, but the underlying mechanisms have not yet been elucidated.  
Among the strategies used to study metabolism, metabolomic studies stand out due to the large amount of information they provide. These studies can be conducted on different biological matrices, such as urine, blood, feces or breath. Urine is particularly useful because it is an easily accessible biofluid and has the advantage of being a concentrated biological sample.  
Metabolomic studies have already been used in the analysis of COVID-19 patients using nuclear magnetic resonance (NMR) [4; 5; 6; 7; 8], high-performance liquid chromatography coupled with mass spectrometry (HPLC-MS) [9; 10; 11; 12], or gas chromatography coupled with mass spectrometry (GC-MS) [13; 14; 15; 16; 17; 18].  
Various studies aim to distinguish COVID-19 patients from healthy individuals based on the analysis of volatile organic compounds (VOCs) [13; 15; 16; 19; 20; 21; 22; 23; 24]. However, the selected biomarkers vary greatly across investigations. To reduce errors in the subsequent chemometric analysis of VOCs, different techniques are often used to increase the signal of the peaks of interest and reduce interference from particles released by the fiber used in the GC-MS equipment. These techniques include adding a strong acid to promote protonation and the volatility of the compounds or lyophilizing the sample before analysis [25; 26].  
Our objective is to provide a more robust selection of biomarkers in the analysis of VOCs present in urine samples from COVID-19 patients, including results from different sample pretreatments. This is not a comparison of pretreatment methods but rather an integrated analysis of the results. To this end, VOCs present in urine samples from COVID-19 patients will be analyzed, and biomarkers that could aid in identifying the infection and assessing the possible metabolic alterations caused by the SARS-CoV-2 virus in the body will be sought. The samples will be analyzed by GC-MS using three different preparations: untreated samples, lyophilized samples, and samples treated with H\({}_{2}\)SO\({}_{4}\).
## 1 Introduction
Nonetheless, the evidence for the relation between the chemical composition and prebiotic properties of these bioactive oligosaccharides is still not well established and requires further investigations to evaluate their potential [11; 15; 20]. _Plantago major_ L. is a perennial herb from _Plantaginaceae_ family. It is well known as a functional food source and medicinal plant related to the diverse content of biological active substances as polysaccharides, flavonoids, phenolic acids, iridoids and vitamins [21; 22; 23]. Cell wall pectic type polysaccharides have been previously isolated from _P. major_ leaves and identified as rhamnogalacturonan and arabinogalactan type II [24; 25]. Immunological activity of the isolated polysaccharides was proven [26; 27]. To best of our knowledge, no study has been performed to investigate the prebiotic potential of _P. major_ polysaccharides and lower molecular fractions (including oligosaccharides). The aim of the present study was to evaluate the relation structure-prebiotic activity of water-extractable polysaccharides from Bulgarian _P. major_ leaves (PWPs) and the enzymatically obtained _P. major_ low molecular weight fraction (PLM). The prebiotic potential of PWPs and PLMwas estimated by evaluating the growth, the synthesis of metabolites (lactate, acetate, ethanol) and the production of specific glycohydrolases of four different strains lactic acid bacteria: _Lactobacillus acidophilus_ N, _L. plantarum_ S30, _L. sakei_ S16 and _L. brevis_ S27.
## 3 Design Challenges of MAC Protocol for IBSN
### Requirements of MAC Protocol Design for IBSN
#### 3.1.5 Latency  
Latency is the delay between sending and receiving data across any two nodes. A heterogeneous IBSN has different medical applications that have a wide range of latency requirements. For example, in the case of DBS applications, 100 ms of latency is strictly required, whereas in long-term monitoring applications, such as cardiac monitoring, latency up to 1 s is tolerated. The latency is to be increased when the data is hopped through a relay node between transmitter and receiver. However, IEEE 802.15.6 suggests that the relay node should account for achieving the overall latency of the communication. A MAC protocol is required to accommodate the relay node and also should be able to achieve the latency required by the medical application. For life-critical medical nodes, achieving both energy efficiency maximization and latency minimization is difficult. A complementary solution needs to be developed instead of resorting only to the regular MAC frame structure.
# Optimal Energy Consumption Tasks Scheduling Strategy for Multi-Radio WSNs
## 5 Performance Evaluation and Analysis  
In order to carry out a detailed evaluation of the algorithm's behavior, some experiments and simulations are deployed. Both the experiments and simulation results are compared with the FIFO algorithm and EMRSA [39] concerning items of average energy consumption, the first dead node, the network lifetime and the average latency. The proposed optimization energy-consumption task scheduling strategy is abbreviated to OETS for convenience. The experiment, the simulations and the proposed strategy based on the PSO algorithm are applied in the remote multi-radio nodes. In detail, the position and velocity correspond to the energy consumption of nodes and the scheduling task of the RF transceiver, respectively. The velocity (scheduling tasks) iteratively updates according to the scheduling results and then updates the position (energy consumption).
## 4 Results and Discussion
### Effect of Energy Density of Water Jet-Guided Laser
Figure 7 shows the effect of the energy density of the water jet-guided laser on the surface roughness of the sample. To ensure the accuracy of the measurement results, each sample is tested three times and then averaged. It can be seen from the diagram that with the increase in energy density of the water jet-guided laser, the roughness of the cleaning surface is the \"N\" type change trend. Combining Figures 7 and 8, the variation process of sample surface roughness with energy density can be divided into five stages. When the energy density of the water jet-guided laser is small, as shown in Figure 8a, the temperature rise effect on the coating surface is not significant, and the crater diameter \(d\) and depth \(h\) produced by the laser on the coating surface are small. The adjacent craters do not overlap, so the coating integrity is maintained better, and the Ra parameter of the roughness is only 1.736 \(\upmu\)m. As the laser energy increases, as shown in Figure 8b, the diameter and depth of the craters produced by the water jet-guided laser on the coating surface increase accordingly. The adjacent craters lap each other; most of the coating surface is covered by craters, and part of the metal matrix is exposed. At this time, the Ra parameter increases to 3.251 \(\upmu\)m. When the energy density of the water jet-guided laser is 15 \(\upmu\)m\({}^{2}\) to 20 J/cm\({}^{2}\), as shown in Figure 8c, the resin-based coating continuously absorbs energy and is removed. As the energy density continues to increase, the cleaning depth increases, the remaining coating thickness decreases, and the smooth surface of the metal substrate is gradually revealed. The surface flatness of the sample also increases, and the Ra parameter of roughness reaches the minimum value (0.762 \(\upmu\)m). When the energy density of the water jet-guided laser reaches 25 J/cm\({}^{2}\), as shown in Figure 8d, the Ra parameter of the roughness increases substantially to 3.825 \(\upmu\)m. Due to the increase in energy density and the heat accumulation effect of spot overlap, the thermal ablation depth of the water jet-guided laser continues to increase. After the surface of the metal substrate is cleaned by a water jet-guided laser, a layer of honeycomb-like craters is formed, and the damaged substrate improves the surface roughness of the sample. When the energy density of the water jet-guided laser exceeds 30 J/cm\({}^{2}\), as shown in Figure 8e, the Ra parameter of the sample exceeds 4.689 \(\upmu\)m. At this time, the metal substrate absorbs a lot of heat and forms a deep, molten layer. This leads to further accumulation of melt and further increases the surface roughness of the sample. Figure 8: Variation of sample surface roughness with energy density of the water jet-guided laser. (**a**) Stage 1; (**b**) Stage 2; (**c**) Stage 3; (**d**) Stage 4; (**e**) Stage 5. Figure 7: Effect of water jet-guided laser energy density on the surface roughness of samples.