esa-sceva/satcom-chunk-collection huggingface dataset:
# The Early Detection of Cardiac Fatigue: Could the HRV Be Used as a Physiological Biomarker by AI?
## Appendix A  
_Scopus AI Results for the Query \"Questionnaires to Assess the Subjective Experience of Fatigue\"_  
_Visually Induced Symptoms Questionnaire_ (VISQ): A 15-item questionnaire used to evaluate visually induced motion sickness (VIMS) and visual fatigue caused by stereoscopic 3D images. It was developed through factor analysis and is validated for assessing visual fatigue [63].  
_Short Fatigue Questionnaire_ (pSFQ): Specifically designed for _children_, this four-item questionnaire assesses subjective fatigue. It has been validated for use in healthy children, children with chronic fatigue syndrome and children with chronic diseases, showing good psychometric properties [64].  
_Subjective Fatigue Scale_: Used in a study to assess fatigue-related complaints and symptoms in _young healthy_ males during head-down bed rest. This multidimensional questionnaire includes sections on drowsiness, difficulty in concentration and physical disintegration [65].  
_Fatigue Severity Scale_ (FSS): Commonly used _in sleep disorder_ studies, this scale measures the severity of fatigue and its impact on daily functioning. It has been used alongside other scales to differentiate between fatigue and sleepiness [66].  
_Modified Fatigue Impact Scale_ (MFIS): This scale assesses the impact of fatigue on physical, cognitive and psychosocial functioning. It has been used in studies involving _traumatic brain injury_ to compare self-reported fatigue with informant reports [67].  
_Chalder Fatigue Scale_ (CH): A unidimensional scale used to measure the severity of fatigue. It has been compared with the Fatigue Impact Scale (FI) in studies involving _chronic fatigue syndrome_ and other conditions, showing strong correlations [66].  
_Multidimensional Fatigue Symptom Inventory--Short Form_ (MFSI-SF): This 30-item inventory assesses the physical, _mental and emotional_ dimensions of fatigue. It has been widely used and evaluated for its psychometric properties [68].  
_Cancer-Related Fatigue_ (CRF) Questionnaires: Various questionnaires exist to measure fatigue in _cancer patients_, including both one-dimensional and multidimensional scales. These have been reviewed for their psychometric properties and user-friendliness [69].  
_Rating of perceived exertion_ (RPE) [49]. RPE is a tool used to measure how physically demanding an activity is perceived by an individual. The \"Borg scale\", a psychophysical, category scale with rating ranges from 6 (no exertion at all) to 20 (maximal exertion), is the most popular RPE tool (ACSM 2010). While the OMNI-RPE has demonstrated higher reliability and validity with pediatric groups, the Borg and CR10 scales have demonstrated reliability and validity in healthy, clinical and athletic adult populations [49; 70; 71].
# Performance and Mechanism of Asphalt Modified by Butadiene-Rubber
## 5 Conclusions  
In this study, two types of SBR, powder SBR and latex SBR, were utilized to achieve the improved properties of asphalt modified with BRA, respectively. The anti-crack capacity at low temperatures, anti-deformation capacity at high temperatures, and anti-aging capacity of compound modified asphalt were compared and evaluated. The modification mechanism of two types of SBR on the BRA modified asphalt was also revealed. Some conclusions are drawn from test results.  
1. Both the powder SBR and latex SBR could significantly enhance the anti-crack capacity of BRA modified asphalt at low temperatures. With the increase in SBR content, the positive effect was more obvious. However, compared to the powder SBR, the addition of latex SBR could increase the low temperatures cracking performance to a greater extent.
2. The good high-temperature deformation resistance and anti-aging capacity of BRA modified asphalt were increased by the addition of powder SBR. The enhanced effect was more obvious with the increasing powder SBR content. However, the high-temperature anti-deformation capacity and anti-aging capacity of BRA modified asphalt declined slightly due to the addition of latex SBR.
3. After the addition of two types of SBR, there was no new functional group generated in the infrared spectra of BRA modified asphalt. The BRA, powder SBR or latex SBR, and base asphalt composed a physical blending system.
4. SBR could attribute the compatibility between BRA and base asphalt, and the facilitation of latex SBR was better than powder SBR.  
Based on the above test results, BRA and powder SBR can be considered a suitable combination for improving the characteristics of base asphalt. The BRA-powder SBR compound modified asphalt could possess better high-temperature, low-temperature, and anti-aging performance than that of BRA modified asphalt. Additionally, the modification was a physical process.  
[PERSON] and [PERSON] came up with the frame and designed the experiments; [PERSON] and [PERSON] carried out the experiment; [PERSON] completed the original draft preparation; [PERSON], [PERSON] and [PERSON] reviewed and edited the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by National Natural Science Foundation of China (No.51578081, 51608058). The Ministry of Transport Construction Projects of Science and Technology (2015318825120), The Projects of Transportation Science and Technology of Hunan (201701), Key Projects of Hunan Province-Technological Innovation Project in Industry (2016 GK2096).  
The authors declare no conflict of interest.  
Figure 10: Scanning electron microscope (SEM) images of BRA modified asphalt before and after modifying by SBR B.
# All-in-Focused Image Combination in the Frequency Domain Using Light Field Images
## 3 Experimental Results  
Experiments are conducted on five image sets: 'Bag', 'Cup', 'Bike', 'Mouse', and 'Flower' that are captured by a Lytro camera [10]. The experimental results are evaluated in terms of the focus measurement and the fused process for the all-in-focused image. We conduct the experiments on \(360\times 360\) pixel test images. The experimental parameters are assigned as a DCT window size of 8, a UML window size of 3, a UV window size of 3, and \(T_{\text{SUML}}\) of 10.
## 1 Introduction
Sometimes this distilled biomass, called \"distilled straws\", is partly recovered to be used as animal fodder, as a bedding substrate for domestic animals, manure after composting or as a base for the growth of mushrooms [1]. Nowadays, the eco-friendly extraction of bio-active compounds from agro-industrial wastes is of great interest to answer the serious economic and environmental problem of significant waste amounts generated by the processing industries [16]. Ultrasound is an innovative eco-compatible food processing technique endowed with an important role in the promotion of the sustainable food industry [17]. This technique has recently emerged among green technologies due to their advantages shown in several processing steps such as degassing, filtration, preservation (i.e., inactivation of microorganism and enzymes), and extraction of natural products [18]. According to the six principles of green extraction [19], ultrasound-assisted extraction (UAE) allows one to extract, in very short time, with high reproducibility, and with increased extraction efficiency (extraction yield) compared to the conventional extraction methods, several classes of bioactive compounds from different plants and foodstuffs [20; 21; 22]. In pulsed ultrasound-assisted extraction (PUAE), ultrasounds are applied in pulsed mode, i.e., the ultrasound processor is switched on and off intermittently during the entire extraction process. This extraction modality, compared to the continuous sonication, leads to less heat generation allowing a better control of the temperature rise. For this reason, it is the most suitable method for the extraction of thermolabile biomolecules (e.g., polyphenols) [23]. Moreover, as recently reported by [PERSON] et al. [24], ultrasounds are a very useful method for the treatment of biomass. In this research, a strategy to recycle and valorize the solid waste remaining after LEO distillation, both the total waste (TW, distilled floral spikes) and the only-flower waste (FW, flower material detached from the distilled spikes), has been proposed. Particularly, PUAE was employed for the first time, according to our knowledge, to extract polyphenols and flavonoids from this natural matrix. The obtained extracts, using a food-grade solvent (a mixture of ethanol and water) named respectively total waste extract (TWE) and flower waste extract (FWE), were spectrophotometrically and chromatographically analyzed and compared to the corresponding liquid aqueous by-products (hydrolat and leachate) of the distillation process. The goal of this study has been the development and the characterization of a new natural extract starting from an agro-industrial waste according to eco-sustainable and circular economy criteria. Figure 2 shows the global scheme of the present work. Figure 1: Lavender steam distillation scheme.
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 4 The Beta RED Scheme  
The Beta RED algorithm that we introduce is inspired by the classical RED, where the packet drop probability function is dependent on two new parameters. The idea is to adapt the parameters in order to improve the performance and stability of the system according to the characteristics of the congestion scenarios, such as traffic load, available bandwidth and desired delay, etc. At first it might be thought that introducing two new parameters complicates the already intricate parameter tuning that the RED algorithm undergoes since its inception, but we will show that it is actually an advantage, since it is possible to easily and intuitively adapt these parameters to stabilize the average queue length as close as possible to a preset reference value. We will now briefly describe the biparametric family of probability beta functions and the properties on which we will rely in the design of the new algorithm.
## 2 Theoretical Background  
### Artificial Neural Networks  
Artificial neural networks (ANNs) provide a powerful solution for handling complex, non-linear relationships in various industries where conventional methods struggle. They mimic the information processing structure of the human brain and are widely used in fields such as control engineering and robotics. In ANNs, artificial neurons process data by multiplying inputs with weights and applying an activation function, resulting in output generation. This process is represented by the simplest ANN, the perceptron in Figure 1, the data operations of which are shown in Equation (1). In Equation (1), \(x\) represents the input variables, while \(w\) and \(b\) denote the weights and biases required for model updates. \(F\) represents the activation function in Figure 1.  
\[y=f\left(\sum_{i=1}^{n}(x_{i}w_{i}+b)\right) \tag{1}\]  
An ANN is a computational processing system, as shown in Figure 2, that consists of multiple interconnected perceptron structures [9]. Unlike perceptrons, the structure in Figure 2 contains several intermediate computational layers called hidden layers. These hidden layers are typically so called because the processes taking place within the computational layers are not readily observable by users. Numerous nodes (neurons) are distributed within these layers, and this configuration is known as an ANN. Hidden layers can consist of multiple layers, and the term \"deep learning\" implies the depth of these hidden layers. Equation (2) represents the process of calculating the output value, \(y_{i}{}^{(l)}\), of the \(i\)th neuron in layer \(l\)th. It depicts the formula for computing the output of neurons in a multi-layer ANN, extending from the output form of the perceptron shown in Equation (1).  
\[y_{i}{}^{(l)}=f\left(\sum_{j=1}^{n(l-1)}\left(y_{j}{}^{(l-1)}w_{ij}{}^{(l)}+b _{i}{}^{(l)}\right)\right) \tag{2}\]  
Figure 1: Structure of perceptron with bias [8].
## 2 Proposed EPP-Based Beam Scanning Approaches  
This section introduces two array beam scanning approaches that employ element position perturbation (EPP) in the azimuth plane. In these techniques, the array elements are distributed using well-known waveform patterns such as sine, cosine, and triangle waves. In the first approach, the original excitations of the elements are kept constant. While the amplitude and repetition rate of the chosen waveform pattern along the array line define the element position and the main beam's steering angle. So, it can be denoted as the EPP-based scanning approach (EPP-BSA). However, the EPP-BSA results in a wide scanning range with a significant increase in the side lobe level (SLL).  
To mitigate the SLL problem of the EPP-BSA, a second array beam scanning approach is introduced. In this approach, SLL reduction of the array pattern is firstly performed using the single convolution/genetic algorithm (SC/GA) technique introduced in [17] and then performance-optimized EPP in the azimuth plane. This combination between SLL reduction and EPP (SLL/EPP) results in a limited scanning range but with a relatively constant HPBW and minimal variations in the synthesized SLL. The proposed SLL/EPP-based beam scanning approach can be denoted as the SLL/EPP-BSA.
## 2 Methodology
Figure 2: Overview of the Tampere Image Database from 2013 (TID2013) images employed in the Augmented Reality (AR) scenario. The simultaneously-captured EEG data for TP9, FP1, FP2 and TP10 positions (denoted as positions \(p\), \(p\in\{1,2,3,4\}\), respectively) were at 10 Hz and provided several EEG band data points:  
* Low \(\iota_{p}\) at 2.5-6.1 Hz,
* Delta \(\delta_{p}\) at 1-4 Hz,
* Theta \(\theta_{p}\) at 4-8 Hz,
* Alpha \(\alpha_{p}\) at 7.5-13 Hz,
* Beta \(\beta_{p}\) at 13-30 Hz, and
* Gamma \(\gamma_{p}\) at 30-44 Hz. We employ the Interaxon MUSE EEG head band, obtaining the EEG data directly through the device's software development kit (SDK). For each of the subjects, we captured the data during the entire experimentation session time \(t\), \(T_{u}^{s}\leq t\leq T_{u}^{e}\), which included time before and after the actual media presentation. The EEG headband was connected via Bluetooth to a laptop where the data were stored. Similarly, the viewer device was connected to the laptop, as well, and communicated using a dedicated wireless network to send images and commands to the device and obtain subject ratings. The introduced communications delay is minimal and provides a more realistic environment for real-world implementation considerations. We make our gathered data publicly available as described in the following section.
# Plant Extracts for the Control of _Listeria monocytogenes_ in Meat Products
## 3 Results
### Growth Control of L. monocytogenes by Plant Extracts in Artificially Contaminated Sausages  
The results of the study conducted on sausages, untreated (LMC) and treated (PELM) with plant extracts and contaminated with _L. monocytogenes_, are shown in Figure 1.  
During the sausage production in both types of samples (treated and untreated), a progressive decrease in _L. monocytogenes_ viable count was observed. In untreated samples (LMC) from an initial viable count of 5.46 log cfu/g, a reduction up to values of 4.73 log cfu/g was detected after the first 3 days (fermentation phase). In the following sampling, the decrease was constant until reaching 2.60 log cfu/g at the end of the seasoning (decimal reductions of 2.86 log cfu/g).  
In sausages treated with plant extracts (PELM) analogous results were recorded: From an initial _Listeria_ viable count of 5.43 log cfu/g, we observed a value of 2.88 log cfu/g (decimal reduction equal to 2.55 log cfu/g) at end seasoning. The only difference was found on the 17 th day, with a difference between LMC and PELM of 0.26 log.
# A Method of Barkhausen Noise Feature Extraction Based on an Adaptive Threshold
## 3 Results
### Comparison with Traditional MBN Features  
Traditional MBN features such as root mean square (RMS) and MBN energy are often used for stress detection [9; 10].  
Energy feature describes the strength of the signal, and the calculation formula is the sum of the square of the amplitude of each point signal. The calculation formula is as follows:  
\[Energy_{MBN}=({x_{1}}^{2}+{x_{2}}^{2}+\dots+{x_{n}}^{2}) \tag{3}\]  
where \(x_{1}\)\(x_{2}\)... \(x_{n}\) is the amplitude of MBN discrete signal, and \(n\) is the number of MBN signal points.  
Normalized RMS, energy and the adaptive pulse count feature are compared in Figure 13. The sum of standard deviations of different features is shown in Table 3. It can be found that the adaptive pulse count feature proposed in this paper has a lower standard deviation. Adaptive pulse count has low dispersion and better stability.  
Figure 12: Diagram of multi-threshold pulse count feature as a function of stress (threshold = 0.034 for tensile stress, threshold = 0.025 for compressive stress).  
Figure 13: The figure compares normalized root mean square (RMS), energy and the adaptive pulse count feature under different stress conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Sum of Standard Deviation** & **Sum of Overlap** \\ \hline RMS & 0.1720 & 0.0121 \\ Energy & 0.1987 & 0.0139 \\ Adaptive Pulse Count & 0.1633 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The sum of standard deviation and sum of ‘overlap’ of different features.
## 4 Simulation Verification and Discussion
### Influence of Model Mismatches and Sensor Noises
The results are summarized in Table 5, and the influence of the model mismatches on the state response and control output is shown in the grey shaded area in Figure 11.  
During the 1000 Monte Carlo simulations, the controller exhibits robustness, as it does not oscillate or diverge. This confirms the effectiveness of the ESO-based INDI controller in mitigating the effect of model parameter perturbations. Figure 11a,c show that there is minimal impact on the roll control channel, and the grey shaded areas are almost non-existent. As a result, accurate roll maneuvering can be achieved despite the model mismatches listed in Table 5. The root-mean-square (RMS) error is used to evaluate the tracking performance of the angular rate control loop, which can be computed as follows:  
\[RMS(x)=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\left|x_{c}(t)-x(t)\right|^{2}} \tag{43}\]  
where \(N\) is the sampling times; \(x_{c}(t)\) and \(x(t)\) are the reference value and state response, respectively. Figure 12 illustrates the dispersion of the RMS error for roll and yaw angular rate tracking under various model mismatches. The median values of the RMS error for roll and yaw are approximately 2.5 deg/s and 0.22 deg/s, respectively. The ESO-based INDI controller demonstrates a high-precision tracking performance in the inner angular rate control loop despite the uncertainties in the system model. To assess the effectiveness of the proposed control law, the performance is compared with the NDI control law introduced in Reference [41]. The control parameters for the roll and yaw rate channels are selected from Table 4, while the model parameters undergo perturbation within the range specified in Table 5. To evaluate the control performance, a Monte Carlo simulation consisting of 1000 runs is conducted. The RMS of the angular rates tracking error is calculated and is depicted in Figure 13, providing a quantitative measure for comparing the proposed control law with the NDI control law. Figure 13 demonstrates that the INDI control law outperforms the NDI control law in regulating both the roll and yaw angular rates under parameter perturbation conditions. Specifically, during supersonic roll maneuvers, the INDI control law reduces the roll angular rate tracking error by 50%. This significant improvement contributes to enhancing the quality of outer-loop roll angle control, leading to improved performance and stability in the control system. Figure 12: Dispersion analysis of RMS errors with Gaussian distribution fitting: (**a**) Roll angular rate tracking error RMS and (**b**) Yaw angular rate tracking error RMS.
# Microbial and Enzymatic Biodegradation of Plastic Waste for a Circular Economy
## 2 Microbial and Enzyme-Based Degradation of Plastic
### Microbial Degradation of Plastics
#### 2.1.1 Bacterial Biodegradation  
Bacteria produce enzymes which are involved in plastic biodegradation [49]. Many bacterial communities isolated from landfills and oceans in recent years have demonstrated capabilities in plastic decomposition and utilisation [50]. The capabilities of certain microorganisms, particularly _Pseudomonas_ sp., in accelerating the degradation and metabolism of synthetic plastics, including polystyrenes and polyethylene, are of special interest [51; 52]. For example, a _Pseudomonas_ strain isolated from a landfill site showed 8.7% PE degradation following 60 days of incubation [53]. In another _Pseudomonas_ study, _Pseudoxanthomonas_ sp. NyZ600, isolated from activated sludge, was found to depolymerise polycaprolactone (PC) films into two degradation products--bisphenol A and 4-cumylphenol--which are monomers of PC [54]. A PET-degrading enzyme from _Ideonella sakaiensis_ PETase is capable of completely breaking down PET in six weeks. This plastic was previously considered non  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Types** & **Structure** & **Biodegradation in Soil** & **Biodegradation in Aqueous Environment** & **Uses** & **References** \\ \hline Polycaprolactone (PCL) & 99\% after (PCL) & 78\% after (PCL) & 78\% after (117\) days & Agriculture (PCL), (PCL) & [35; 37; 38] \\ \hline Polyplactic acid (PLA) & 16\% after (PCL) & 2\% after 117 days & Food containers and shopping bags & [35; 39; 40] \\ \hline Polyhydroxy butyrate (PHB) & 16\% after (PCL) & 99\% after (PCL) & 83\% after (PCL) & Bottles, wrapping material, shopping bags & [35; 37; 40] \\ \hline Poly(butylene adipate-co- (PBAT) & 0\% after (PCL) & 21\% after (PCL) & 2\% after 117 days & Bugs, multiching film & [35; 39; 41] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._biodegradable. _Ideonella sakaiensis_ secretes PETase enzymes, which causes the breakdown of PET at 30 \({}^{\circ}\)C. This enzyme produces an intermediate, mono (hydroxyethyl) terephthalate (MHET), which is absorbed by the cell and processed by the enzyme MHET hydrolase. The end-product of these two enzymatic processes is terephthalatic acid and ethylene glycol, both of which are monomers that the organism uses for its growth [55].