esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dataset and Experimental Setup
### Feature Extraction  
Statistical features are extracted over the four raw signals from the time and frequency domains. Feature values are padded to match the maximum available sampling rate of 2048 Hz. In total, 73 statistical features are computed over the four raw physiological measurements: 49 features related to the BVP signal and 24 features coming from the rest three modalities.  
* _BVP features_: Time domain statistical features such as mean, minimum, maximum and standard deviation are computed describing both the overall behavior of the signal but also the relation between consecutive inter-beat interval (IBI). NN related features describe the interval between two normal heartbeats. pNN features refer to the total number of pairs of consecutive normalized IBI values that differ more than 50 ms [43]. Additional features are computed to describe the spectral power statistics of different frequency bands by grouping the frequencies into three frequency bands, very-low  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Recording Segment**} \\ \hline \multirow{3}{*}{Free-driving} & \multirow{3}{*}{\begin{tabular}{c} Texting \\ Physical \\ \end{tabular} } & \begin{tabular}{c} NBack \\ Cognitive \\ Neutral \\ \end{tabular} & \
\begin{tabular}{c} Radio \\ Cognitive \\ Emotional \\ \end{tabular} &
\begin{tabular}{c} GPS \\ Cognitive \\ Frustration \\ \end{tabular} \\ \hline \#Data (hours) & \(\sim\)7.4 & \(\sim\)3.1 & \(\sim\)2.2 & \(\sim\)3.4 & \(\sim\)2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Total duration of available data under each recording segment. For each segment, approximately half of the data come from the morning recordings and half from the afternoon.  
(<0.04 Hz), low (0.04-0.15 Hz) and high frequencies (0.15-0.4 Hz). For each frequency band, power related statistics are calculated.
* _Respiration features_: Amplitude, period and respiration rate are calculated along with the standard statistics from the raw respiration signal.
* _Respiration+BVB features_: Four features are computed that combine BVP and respiration measurements towards describing the peak to through difference in heart rate that occurs during a full breath cycle (_HR Max-Min_ features as seen in Figure A1 in the Appendix A).
* _Skin conductance and skin temperature features_: Six features are extracted from each signal describing standard temporal statistics over short and long term windows on top of the raw the measurements. Features include the measurement as a percentage of change, the long and short term window means, the standard deviation of the short term window, the direction/gradient of the signals and the measurement as a percentage of the mean in the short term window.  
Feature estimation and hyperparameter tuning (i.e., window strides and sizes) were automatically selected by the BioGraph Infinity software.
###### Abstract
While there has been initial exploration of the mechanisms and methods related to compound floods, further research is required to understand how climate change affects the interactions between multiple drivers and how new approaches, such as coupled models and machine learning, can effectively simulate these complex interactions. In-depth exploration of these areas will help elucidate the dynamic mechanisms of compound floods under climate change and enhance future risk assessments and disaster mitigation strategies. The primary question addressed by this research is whether the factors and mechanisms causing compound floods in coastal areas under climate change can be effectively studied through an extensive literature review, and whether further insights into the simulation and evaluation of compound flood hazards can be gained in this way. To meet the development needs of the country and regions, it is crucial to formulate disaster reduction strategies for compound floods and implement sustainable development plans for coastal zones. Specifically, the research objectives of this article are (1) to examine the main driving factors and their quantitative impacts on compound floods in coastal cities under climate change, and (2) to introduce common methods and applications used in compound flood research under climate change. The structure of this review is organized as follows: First, the methodological approach adopted in this study is detailed. Second, the quantitative impacts of flood-driving factors, such as sea level rise, storm surges, extreme rainfall, and urbanization factors including land subsidence, impervious surface, and drainage systems, on compound floods risk are analyzed. Third, the application of statistical models, numerical simulations, machine learning, and coupled models in the assessment of compound flood risk under climate  
Figure 1: Conceptual model of compound flood events under climate change. change is discussed. Finally, the review concludes with a summary and recommendations for future research.
## 2 Materials and Methods
### Study Design, Patient Population, and Clinical Data  
A retrospective observational study was approved by the Research Ethics Committee of the Principality of Asturias, Spain (reference: CElmPA; No. 2023.089) and was conducted among patients of both sexes aged 18 to 77 who were admitted to the ICU between 1 January 2022 and 31 January 2023.  
The study focused on patients who received continuous administration of an EN formula as their sole source of nutrition. The inclusion criteria for the study were a minimum of a seven-day stay in the ICU and the same EN formula administered for at least five days, excluding COVID-19 patients. Eight patients met this criterion. The reasons for admission to the ICU was the need for exhaustive and constant monitoring of the patient's clinical situation. The primary pathologies were abdominal-origin sepsis, liver transplant, sepsis, ketoacidosis, haemodynamic instability, cardiorespiratory arrest, cerebral haemorrhage, and rhomencephalitis. Patients admitted to the ICU during the study period who did not meet the inclusion criteria were excluded.  
The daily intake of polyamines in the patients included in the study was estimated based on the volume and polyamine content of the EN formula administered. Clinical information was obtained for each patient, including age, sex, body mass index (BMI), EN formula prescribed, volume of EN and water administered, GRV (considered for the entire day), vomiting, and number and volume of stools.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Formula Type** & **Name** & **Kcal Per 100 mL** & **Manufacturer*** \\ \hline Complete, polymeric & & & \\ \hline Hyperprotetic and hypercaloric & Fresubin Thickened & 150 & Fresenius Kabi \\  & Isosource Protein Fibre & 133 & Nesté Health Science \\  & Novosource CI Protein & 117.4 & Nesté Health Science \\  & Osmolite Plus & 121 & Abbott \\ \hline Normoprotetic and hypercaloric & Nutrison Energy & 150 & Nuticia \\  & Nutrition Energy Multi Fibre & 153 & Nuticia \\ \hline Normoprotetic and normocaloric & Fresubin Original Fibre & 100 & Fresenius Kabi \\  & Nutrition & 100 & Nuticia \\  & Osmolite & 101 & Abbott \\ \hline Hyperprotetic and normocaloric & Impact Enteral & 144 & Nesté Health Science \\ \hline Complete, special & & & \\ \hline Hyperprotetic and hypercaloric & Impact & 144 & Nesté Health Science \\  & Neprop HP & 180 & Abbott \\  & Nutrition Advanced Diason & 150 & Nuticia \\  & Energy HP & & \\ \hline Normoprotetic and hypercaloric & Nutricomp Hepa & 132 & B Braun Sharing Expertise \\  & Ovepa & 152 & Abbott \\ \hline Hypoprotetic and hypercaloric & Neprop LP & 180 & Abbott \\ \hline \hline \end{tabular}  
* Abbott: Zwolle, The Netherlands; B Braun Sharing Expertise: Melsungen, Germany; Fresenius Kabi: Bad Homburg, Germany; Nesté Health Science: Vitaflo, Germany; Nütricia: Zoetermeer, The Netherlands.  
\end{table}
Table 1: Enteral nutrition formulas analysed.
# Multivariate Time Series Deep Spatiotemporal Forecasting with Graph Neural Network
## 4 Experiment Analysis
### Metrics  
To evaluate the performance of our proposed MDST-GNN model, the same evaluation metrics are used for the comparison methods in this paper. The pros and cons of different methods can be clearly displayed by the same evaluation metrics. The evaluation metrics are the relative root mean square error (RRMSE) and the empirical correlation coefficient (CORR):  
\[\text{RRMSE}=\frac{\sqrt{\sum\limits_{t}\frac{1}{n}\sum\limits_{i=0}^{n}\left( Y_{ti}-\hat{Y}_{ti}\right)^{2}}}{\sqrt{\sum\limits_{t}\frac{1}{n-1}\sum\limits_{i=0}^{n} \left(Y_{ti}-\left(\overline{Y}_{i}\right)\right)^{2}}},t\in\Omega_{Test} \tag{19}\]  
\[\text{CORR}=\frac{1}{n}\sum\limits_{i=1}^{n}\frac{\frac{\sum\limits_{t}\left(Y _{ti}-\left(\overline{Y}_{i}\right)\right)\left(\hat{Y}_{ti}-\left(\overline{ \hat{Y}}_{i}\right)\right)}}{\sqrt{\sum\limits_{t}\left(Y_{ti}-\left( \overline{Y}_{i}\right)\right)^{2}\left(\hat{Y}_{ti}-\left(\overline{\hat{Y}}_ {i}\right)\right)^{2}}},t\in\Omega_{Test} \tag{20}\]  
In Formulae (19) and (20), \(Y\) and \(\hat{Y}\) are the truth value and the predicted value, respectively. \(\overline{Y}\) and \(\overline{\hat{Y}}\) represent the mean of the truth and predicted values. \(\Omega_{Test}\) represents numerical computation using the test set. For RRMSE, lower values are better, and for CORR, higher values are better.
# Radiometric Comparison within the Sentinel-1 SAR Constellation over a Wide Backscatter Range
## 3 Results
### Transition to Lower Backscatterer (Greenland Region)

Table 3: Mean radar brightness and standard deviations (\(\mu\pm\sigma\)) of five selected areas within the Greenland ice region using HV polarization channel. Figure 6: Observed radar brightness \(\beta^{0}\) as a function of ten month acquisition time for five defined distributed target areas (symbols) of the Greenland ice region each with different incidence angle (A...E, compare to Figure 5) using S-1A (blue) and S-1B (red) for the HV-polarization channel.
## 3 Obtaining Waste-Based Bioadsorbents
### Raw Wastes as Bioadsorbents  
Many of the biosorbents that have been studied for the removal of pollutants were employed in raw form with or without chemical, physical, or even magnetic pretreatment (Table 2). Residues such as exhausted coffee can be used without any chemical modification to adsorb hazardous contaminants such as Hg(II) found in industrial effluents [55]. Raw tamarind seeds have been presented as a remarkable alternative for the removal of Pb(II) ions because of their high content of lignocellulosic along with surface groups such as phosphorus, magnesium, vitamin C, potassium, calcium, and proteins, which can be chemically modified by H\({}_{2}\)SO\({}_{4}\) (1:2 weight ratio) and ultrasonic waves (24 kHz) to increase their surface area [32]. [PERSON] et al. [56] and [PERSON] et al. [57] used _Moringa oleifera_ seed powder (75-500 \(\upmu\)m particle size) without pretreatment to adsorb silver and reactive red 120 dye. Because the presence of surface functional groups is crucial to synthesize materials with higher adsorption capacity, [PERSON] et al. [28] developed a magnetic spent coffee nanocomposite with Fe\({}_{3}\)O\({}_{4}\) nanoparticles to increase its affinity to Pb. Coconut is one of the most abundant agro-industrial products, and is able to remove several heavy metals by taking advantage of the fact that approximately 62-65% of the whole fruit (coir pith, coconut bunch, and husk) is considered waste [10; 58]. For instance, coir pit was employed without chemical pre-treatment for the removal of Co(II), Cr(III) and Ni(II) after being air-dried, ground, and sieved using a 300-600 \(\upmu\)m mesh [59]. Banana peels were used by [PERSON] et al. [60; 61] for Cd(II) and Cr(VI) adsorption. Dried slices of banana peel were crushed and passed through a mesh of 125 \(\upmu\)m, re-dried in an air oven at 100 \({}^{\circ}\)C, and finally esterified with acidic methanol. Watermelon rinds have characteristics suitable for the removal of metal elements (Zn, Pb and Cr) in their native form and after employing calcium hydroxide and citric acid treatment (Cu), followed by a drying and crushing process a particle diameter between 150 and 300 \(\upmu\)m can be achieved [62; 63; 64; 65; 66]. Certain functional groups such as carboxyl and hydroxyl are responsible for the sorption of pollutants [3].
## 1 Introduction
In this case, the distances between vehicles are very small even with a high velocity, which is unrealistic [9]. The ID model has been incorporated in MovSim to evaluate longitudinal vehicle movement [25] and also in PTV VISSIM and Simulation of Urban Mobility (SUMO). However, driver behavior is not considered in this model [30]. One application of the ID model is with Connected and Autonomous Vehicles (CAVs) to improve traffic safety and passenger comfort [31; 32]. [PERSON] et al. [33] employed this model to characterize the car-following behavior of CAVs, while [PERSON] et al. [34] proposed an improved ID model to explore CAV traffic stability. In this case, there is an abrupt decrease in velocity when the density reaches half its maximum (critical density), which is unrealistic as the velocity should be smooth. A modified ID model was proposed in [35] to better characterize real traffic conditions. However, CAV behavior with this model is not realistic because under actual traffic conditions, the drivers take longer to achieve a smooth car-following behavior and the variations in headway are greater [36]. The ID model has also been incorporated into cooperative and Adaptive Cruise Control (ACC) systems [37; 38]. Unfortunately, the safe distance between vehicles with this model is too small when the velocity is high, which can result in accidents when employed in ACC and related systems. A model is introduced here to realistically characterize traffic flow based on velocity differences and driver sensitivity. This improves the ID model which can produce unrealistic traffic behavior because of the constant acceleration exponent. Driver response to changes in velocity is known as sensitivity. The interval for a vehicle to adapt to variations in traffic conditions is referred to as the distance headway, while the time required to traverse this interval is known as the time headway. Both the proposed and ID models are evaluated for a platoon of 52 vehicles on a circular road of length 2000 m with periodic boundary conditions. The results obtained demonstrate that the proposed model provides better traffic characterization than the ID model.
# Dynamic Energy Consumption Modeling for HVAC Systems in Electric Vehicles
###### Abstract  
Motivated by the strong transition to electric mobility we are witnessing currently, in this paper, we present a novel methodology to predict the dynamic behavior of heat, ventilation and air conditioning (HVAC) systems for electric vehicles. The approach is based on a lumped parameter energy balance between the vehicle cabin, the external loads (such as solar radiation, ventilation and metabolic load) and the HVAC system. Detailed models are used to obtain the time evolution of the heat transfer coefficients of each subsystem in the HVAC (i.e., evaporator and condenser) on the basis of correlations available in the literature. The model is validated on a real HVAC system, built ad hoc for a retrofitted electric vehicle, by comparing the results obtained from the model with experimental measurements performed in a climatic chamber. Then, some scenarios that represent interesting cases in electric automotive applications, such as vehicle cabin precooling during battery charging and a regulated driving cycle which simulates urban mobility, are considered. The energy consumption of the HVAC system is evaluated from the model in these scenarios and compared. The methodology herein presented is general and easily extendable to other systems, proving to be a powerful method to compare the energy consumption of HVAC systems under unsteady conditions with a more standard approach based on steady considerations. By this approach, it is shown that significant improvement can be obtained with a nonsteady approach.  
Keywords:air conditioning system; electric vehicle; dynamic modeling; MATLAB/Simulink; HVAC energy consumption +
Footnote †: journal: Applied sciences
## 5 Experimental Results
### Quantitative Results
#### 5.3.3 Performance of DA System and Model Forecasting
ACC provides a more insightful measure of the predictive skillof numerical models, particularly in capturing large-scale atmospheric structures. Given the essential role of ACC in long-term forecast validation, we assess the impact of different variance adjustment strategies applied to the static **B**-matrix in VarDA. The results show that forecast errors (RMSE) increase over time for all methods, with the NO method consistently exhibiting the highest RMSE value (Figure 6). In contrast, DRL-AST achieves the lowest RMSE value, indicating that its variance adjustment strategy results in more accurate initial conditions in the VarDA process. The ACC results further highlight differences in forecast skill deterioration. While all methods experience a decline, the NO method degrades the fastest, whereas DRL-AST maintains the highest ACC, suggesting that it better preserves the structural integrity of the initial conditions, which benefits medium-range to long-range forecasts. Overall, DRL-AST outperforms other **B**-matrix variance adjustment strategies (CON and CUTE-ST) across different observational error levels. By optimizing the assimilation-derived initial conditions, DRL-AST improves the quality of the initial conditions for numerical models, contributing to reduced forecast errors and improved predictive skills over extended lead times.
## 4 Hardware Description
### Description of Hardware Components
#### 4.2.4 Flow Sensor  
The sensor is used for water flow measurement and is positioned according to the water supply [40]. Flow measurement by use of a sensor pinwheel. A magnet is fixed to the pinwheel, and a Hall effect magnetic sensor on the opposite side of the plastic tube measures the spins the pinwheel has made through the plastic wall. There are three wires of the sensor: red, black, and yellow (Hall effect pulse output). The motion of the fluid is monitored by measuring the pulses from the sensor output. Every pulse is 2.25 mL.
# Rotational Speed Control Using ANN-Based MPPT for OWC Based on Surface Elevation Measurements
## 2 Theoretical Considerations  
The work presented in this paper requires the understanding of the system to be controlled and the Artificial Neural Networks to be used. In this sense, this part introduces the modeling of the different parts constituting the studied Wave Energy Converter (WEC) which is the Oscillating Water Column (OWC) illustrated in Figure 1, this would include the mathematical models of the waves, capture chamber, Wells turbine and the generator. In addition, a brief background on the Artificial Neural Networks.  
Figure 1: Scheme of an Oscillating-Water-Column system and the sea wave.
## 2 Extracted Beams
### Mechanical Characteristics of the Beam Materials
#### 2.2.2 Test on Steel Rebar  
The mechanical properties of the beam reinforcement were evaluated by DT performed on rebar pieces extracted by the retrofitted beams after the failure tests (SS4.3). These rebars had smooth surfaces and mechanical properties comparable to the ones of Italian Steel grade rebar with yield stress of 430 MPa. 14 rebar specimens (RT) were taken from the two-beam reinforcement to be tested in tension (Table 1): 8 rebar specimens with diameter \(\phi\) 6 mm (3 from stirrups of DB1 and 5 from stirrups of DB2) and 6 rebar specimens with diameter \(\phi\) 10 mm (3 from longitudinal reinforcement of DB1 and 3 from longitudinal reinforcement of DB2). The yield stresses values (f\({}_{0.2\%}\)) obtained by these tests are shown in Table 1. The existing rebar extracted by the beams were carefully chosen to be without any sign of yielding, after the beam failure tests. This was confirmed by tensile tests during which no plastic deformation seemed to preexist.  
The mechanical properties of the steel rebars used to improve the flexural strength of the extracted beams at the midspan (SS3), were also tested. 4 rebar pieces with diameter \(\phi\) 20 mm, Italian Steel grade with yield stress of 430 MPa and ribbed surface were tested under monotonic tensile load until failure. In Table 2, the mean value (mean) and the CoV of yield (f\({}_{0.2\%}\)) and maximum stresses (f\({}_{\text{su}}\)) are given considering groups of rebars with the same diameter. The results obtained for the ribbed rebars used for retrofitting, showed very small values for the CoV whereas the results of the smooth rebars were wide scattered, especially in the case of the \(\phi\)6 mm rebars.
## 3 Results and Discussion
### Antioxidant Activities and Total Phenolic Content  
The total phenolic content and antioxidant activity of the _Z. kerrii_ rhizome extract were evaluated using DPPH, ABTS, and FRAP assays. The total phenolic content of the extract was low, at 2.2 \(\pm\) 0.1 mg GAE/100 g extract. Regarding antioxidant activity, the extract showed IC\({}_{50}\) values of 143.5 and 169.7 ug/mL using the DPPH and ABTS assays ascorbic acid was used as a positive control, with IC\({}_{50}\) values of 1.6 \(\pm\) 0.8 and 5.2 \(\pm\) 0.8 ug/mL, respectively. The FRAP were also low, at 0.3 \(\pm\) 0.01 mM AAE/g extract, as shown in Table 2.  
Phenolic compounds are secondary metabolites in plants that show strong antioxidant activities [38]. They play important roles in neutralizing free radicals, thereby preventing oxidative damage. Previous research revealed that a relationship between the phenolic content and the antioxidant activity. In this study, the _Z. kerrii_ rhizome contained a small amount of phenolic compounds. The chemical compositions of the _Z. kerrii_ essential oils showed that terpenoid was a main compound. Similarly, previous studies reported that the phenolic content in leaves was higher than in rhizomes [39], providing the reason why the lowest antioxidant activity was indicated by those assays.
###### Abstract  
Monitoring soil tillage activities, such as growing and cultivating, is essential for aligning agricultural practices with environmental standards for soil health. Detecting these activities presents significant challenges, especially when relying on remotely sensed data. This paper addresses these challenges within the framework of the Common Agricultural Policy (CAP), which requires EU countries to enhance their environmental monitoring and climate action efforts. We used remote sensing data from Sentinel-1 and Sentinel-2 missions to detect soil tillage practices in 73 test farms in Portugal. Three approaches were explored: a rule-based method and two machine learning techniques based on XGBoost (XGB). One machine learning approach utilized the original imbalanced dataset, while the other employed a SMOTE (Synthetic Minority Oversampling Technique) approach to balance underrepresented soil tillage operations within the training set. Our findings highlight the inherent difficulty in detecting soil tillage operations across all methods, though the XGB-SMOTE approach demonstrated the most promising results, achieving a recall of 67% and an AUC-ROC (area under the receiver operating characteristic curve) of 74%. These results underscore the need for further research to develop a fully automated detection model. This work has potential applications for monitoring compliance with CAP mandates and informing environmental policy to better support sustainable agricultural practices.  
Common Agricultural Policy; change detection; remote sensing; XGBoost; environmental monitoring