esa-sceva/satcom-chunk-collection huggingface dataset:
# Regional Climate Change in Southeast Mexico-Yucatan Peninsula, Central America and the Caribbean
## 3 Results
### Climate Regional Drivers
#### 3.2.2 Temperatures  
Similar to other studies [16; 72; 73], the results of this study support the significant role that AMO and ENSO play in the variability and trends of precipitation and temperature of CA and Mexico. However, Figure 4 shows a low AMO signal in terms of temperature behavior. This is detected in other regions [74]; there are studies [75; 76] which report that during the AMO warm phase, the occurrence of droughts is associated with higher temperatures.  
AMO and PDO with temperature relations are positive in both cases. The AMO and PDO effects on the Northern Hemisphere climate are similar and could trigger droughts [77]. Both teleconnections have longer frequencies than ENSO for the whole study region. In terms of temperature (Figure 4), the three oscillations are clearly discernible from each other. The extension (variance) of the results is clearly minimal, as expected, with the AMO, given its greater temporal extension and geographic influence as compared to ENSO and PDO. ENSO showed a higher variability, and Figure 4 shows that there is a negative correlation for the Caribbean. This could be due to other geographic factors, such as the small size of the territory and presence of ocean borders. ENSO shows a different signal for the study region temperature, particularly in relation to the Caribbean, while AMO and PDO maintain the same behavior in relation to temperature.  
Figure 3: Taylor diagram for the 1060–2016 precipitation and climate drivers (ENSO-blue, PDO-red, AMO-green). Southern Mexico is represented by the points 1–5, Central America by the points 6–12, and the Caribbean by 13–17.
## 1 Introduction
### Research Contribution  
Some of the headmost contributions of this work are enumerated below:  
* For resolving the connectivity issues in the SDWSN environment, we have performed connectivity-aware network placement, which also overcomes the issue of less energy efficiency.
* For reducing the energy consumption of the SDWSN nodes, we have performed density-based clustering using the DWTMB algorithm based on several metrics. In addition, the resilient cluster leader also was selected based on high residual energy, less mobility, less distance, and high trust score.
* For reducing the unwanted interference and complexity issues, we have performed multi-tier scheduling based on a NCFT approach in which the scheduling is performed in three tiers such as CMs to CL, CLs to LBSs, and LBSs to sink nodes.
* For reducing the cybersecurity threats and link failures during routing, we have performed two-way routing in the SDWSN environment using a MTD approach. Furthermore, security during routing is utilized by the CEA algorithm.
## 2 Materials and Methods
### Data Acquisition and Processing
#### 2.2.4 Normalization Process  
Due to the different spatial and temporal distributions of CPUE and the large differences in the numerical ranges of environmental factors, in this paper, in order to eliminate the influence of anomalous variables and numerical range differences on the forecast results and improve the speed of model optimization and forecast accuracy, the data normalization of CPUE, SST, Chl-a, SSS, and SSH was [0-1]. The calculation formula is shown in (2).  
\[X^{\prime}=\frac{X-X_{min}}{X_{max}-X_{min}} \tag{2}\]  
where \(X^{\prime}\) denotes the normalized values of CPUE and environmental factors, \(X\) denotes the actual initial values, and \(X_{min}\) and \(X_{max}\) represent the minimum and maximum values of CPUE and environmental variables month by month, respectively.
# Deep Learning-Based Optimization for Maritime Relay Networks
###### Abstract  
The complexity and uncertainty of the marine environment pose significant challenges to the stability and coverage of communication links. Due to the limited coverage range of traditional onshore base stations (BSs) in marine environments, relay technology has become an essential approach to extending communication coverage. However, the rapid variations in marine wireless channels and the complexity of hydrological conditions make it extremely difficult to obtain accurate channel state information (CSI). In particular, dynamic environmental factors such as waves, tides and wind speed cause channel parameters to fluctuate significantly over time. To address these challenges, this paper proposes a cooperative communication strategy based on ships and designs a novel channel modeling method to accurately capture the characteristics of marine wireless channels. Furthermore, a deep learning-based optimization scheme is proposed, which formulates the relay selection problem as a spatiotemporal classification task. By integrating the spatial positions of candidate relays and their communication conditions, the proposed scheme enables real-time selection of the optimal relay while evaluating link connectivity probabilities under hydrological influences. Simulation results confirm that the proposed method achieves high accuracy even in rapidly changing marine environments.  
m +
Footnote †: journal: Applied sciences  
article
# Chemicals from Brominated Flame Retardants: Analytical Methods, Occurrence, Transport and Risks
## 3 Distribution of BFRs in Different Matrices
### Surface Sediment  
[PERSON] et al. [15] detected TBBPA in surface sediment samples from eight rivers and three reservoirs in Western Guangdong, China, with a detection frequency of 85.27%, and the sediment ranged from not detected (ND) to 12.59 \(\upmu\)g/kg. [PERSON] et al. [13] revealed that the average concentrations of TBBPA in sediments from 24 fishing ports in South China Coast ranged from 0.02 to 18.3 ng/g dw and that the detection rates for both TBBPA and HBCDDs were 100%. Tetrabromobisphenol (TBBPA) has been detected from a typical electronic waste (e-waste) dismantling site in Southern China, with concentrations in the range of 19.8-1.52 \(\times\) 10\({}^{4}\) ng/g dw [14]. [PERSON] et al. [57] measured the levels of TBBPA ranging from 0.003-0.31 and not detected (nd) to 1.11 ng/g dry weight in the sediments at Mangrove wetlands in South China. The concentrations of TBBPA in the fishing-port sediments of a cost land in South China were found to be in the range of 0.02-21.5 ng/g dw by [PERSON] et al. [13]. [PERSON] et al. [15] revealed the concentration of HBCDs in the surface sediment of a South China Coast ranged from ND to 6.31 \(\upmu\)g/kg. PBDEs levels in sediment from Guiyu, a typical e-waste dismantling region in China, were in the range of 0.345-401,000 ng/g dw, as reported by [PERSON] et al. [58]. The levels of seven novel brominated flame retardants (NBFRs) in sediment from Guiyu, a typical e-waste dismantling region in China, reported by [PERSON] et al. [58] ranged from 0.581 to 73,100 ng/g dw. The highest concentrations of BFRs are found in e-waste dismantling regions, indicating significant contamination likely due to improper disposal and recycling practices.
# Nanofiltration of Simulated Acid Mine Drainage: Effect of pH and Membrane Charge
## 4 Results and Discussion
### Simulated AMD Solutions  
In the simulated AMD solution, rejection minima were observed at pH 4 (Figure 8). This value is close to the IEP of Membrane A for which the net charge of the membrane approaches zero. At the IEP, size exclusion is the remaining rejection mechanism, therefore, an increased passage of metal ions is registered. The observed rejection minimum (at pH = 4.0) was lower than the IEP obtained from the zeta potential measurement at membrane IEP of pH 4.5-4.7. The observed shift of minimal rejection can be attributed to the fact that the IEP measurements are dependent on the solution chemistry that the membrane is exposed to. Thus, the observed difference in the IEP for the model AMD solution may be due to the adsorption of divalent sulfate anions present in the solution onto the membrane.  
With the exception of copper, metal ions rejection increased with decreasing pH due to stronger electrostatic repulsion brought about by a more positively charged membrane. An opposite trend was observed for copper, for which rejection increases with increasing pH. This is possibly due to the appearance of a negatively charged copper complexes, such as Cu(OH)\({}_{3-}\), which become increasingly  
Figure 8: Apparent metal species rejection as a function of feed pH.  
Figure 7: Effect of salt concentration on apparent sulfate and chloride rejection. Experimental conditions: feed pressure = 10 bar, 25 \({}^{\circ}\)C.  
rejected by the more negatively charged membrane with increasing pH. The permeate flux (shown in Figure 9) remains relatively constant throughout the tested pH range. The permeate pH values were lower than the feed pH ones, especially for feed pH lower than 4 as H\({}^{+}\) couples with anions and passes through the membrane.  
Sodium rejection was lower than that for the larger size divalent cations. In general, sodium rejection increases with increasing solution pH (a more negatively charged membrane). As seen can be in Figure 10, at higher pH, sodium rejection displays a higher rejection value as accompanying divalent anions (SO4\({}^{2-}\)) are highly rejected. However, sodium rejection was lower than expected. This may possibly be due to the permeation of sodium cations with monovalent anions (nitrate and chloride). The anions rejection increases with increasing pH due to a stronger electrostatic repulsion from the negative membrane. Due to their monovalent nature and smaller size, nitrate and chloride expectedly showed lower rejection values than those for sulfate.  
Figure 10: Apparent species rejection as a function of feed pH.  
Figure 9: AMD permeate flux as a function of feed pH.  
While not substituting the necessity of performing validation tests using real acid mine drainage water in a defined case, the results obtained with the simulated AMD solutions can be used as a fast and preliminary evaluation tool of expected performance of the membrane in nanofiltration of real water with a known ionic composition and pH.
# Comparing EEG-Based Epilepsy Diagnosis Using Neural Networks and Wavelet Transform
## 3 Methods and Materials
### Signal Preprocessing  
In the preprocessing step, a 70 Hz low-pass filter was applied to the raw EEG signal collected in both stages, with and without the use of wavelet transform. The FDATOOL toolbox was used to design the filter, and the wave menu tool in the MATLAB 2020 software was used for wavelet transform. To remove noise using wavelet transform, the de-noise section of the wavelet transform toolbox was used. This section allows the wavelet to independently identify noise by comparing the daughter signal (EEG signal) to the mother signal (wavelet signal). If they do not match, the details from the lower levels are removed while preserving the generalities.
## 1 Introduction
### Deep Neural Networks for Electrocardiogram Signal Processing  
Advancements in wearable devices with integrated biosensors have enabled heart rhythm diagnostics using single-lead electrocardiograms (ECGs). One-dimensional (1D) convolutional neural networks (CNNs) are widely used for classifying heartbeats [33; 34], short-term arrhythmias [35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46], and noise affecting interpretation [35; 36; 37; 38]. CNN models can distinguish various rhythms like normal sinus, atrial fibrillation [41; 42; 43; 44; 45; 46], atrial flutter, and ventricular fibrillation [39; 40] and show promise in detecting conditions like heart failure [47] and myocardial infarction [48; 49]. Despite high accuracy, most ECG diagnostic CNN models are tested on GPU-based development platforms.  
A few studies present ECG processing on FPGA platforms for wearable devices using various neural architectures, such as 1D CNNs [50; 51], probabilistic neural networks [52], artificial neural networks [53], spiking neural networks [54], multilabel deep CNNs [55], and sparse CNNs [56]. Recently, CNN models for ECG analysis have been embedded in ARM-based SoCs through (i) the CMSIS-NN library on Nordic nRF52832 with an ARM Cortex-M4 processor [57] and (ii) LiteRT for microcontrollers on ARM Cortex-M4F using X-CUBE-AI [58], Raspberry Pi 3 (ARMv8) [7], and ARM Cortex-M4 (nRF52840) [59]. Studies also explored ECG classifications using optimized 2D CNN and LSTM models with STM32 Cube.AI on ARM Cortex cores [60; 61]. A real-time myocardial infarction detection neural network was also developed for a Cortex-M4 microcontroller [13]. A key aspect of all referenced studies involving embedded systems is their focus on providing detailed information about their specific implementations.  
The focus of this study is on a critical application of 1D CNNs, detecting cardiac arrest rhythms during out-of-hospital cardiopulmonary resuscitation using ECG signals from defibrillator pads. Numerous studies demonstrate that CNNs can effectively distinguish between pulseless and pulsatile rhythms [62] and accurately detect shockable rhythms, including ventricular fibrillation, under various conditions, including the following: artefact-free ECGs [63; 64; 65; 66; 67], ECGs with chest compression artifacts [68; 69; 70; 71; 72], and ECGs during continuous cardiopulmonary resuscitation with ongoing chest compressions and insuffations, without the need to identify the artifacts [73]. Although DNN technologies have the potential for improving current resuscitation practices, they are mostly tested offline with databases in GPU-based development platforms. One recent study [74] took a further step toward embedding computationally efficient deep CNN models in the setting of a digitally connected automated external defibrillator (AED). The authors reported performance evaluated under specific embedded system constraints; however, they do not present details for the actions needed for the implementation of the CNN in the AED embedded microcontroller.
# Context-Aware Gossip-Based Protocol for Internet of Things Applications
## 6 Effects on Application Requirements
### Comparative Assessment Results
#### 6.4.4 Network Lifetime  
The average longevity of the network for each of the five protocols is shown in Figure 9. The effect of each assessed protocol is plotted for networks with 50 to 250 nodes. The performance of the MFWF designed for energy-intensive IoT applications (see Equation (11)) is the best as the number of nodes increases to 150, 200, and 250. This, of course, highlights the suitability of this equation for ensuring an increased network lifetime for energy-efficient applications. Interestingly, the performance of the equation developed for critical IoT applications (see Equation (10)) also proves adequate, as the number of rounds until the death of the network is maximised for smaller networks (50 and 100 nodes). Nevertheless, its performance becomes stagnant as the number of nodes in the network increases to 250. FELGossiping causes the network to expire much earlier than the constructed MFWFs do (see Equations (10)-(12)). As expected, the traditional gossiping protocol is the most ill suited for preserving the network lifetime.  
Figure 8: Percentages of saved rebroadcasts for the three constructed protocols, the traditional gossiping protocol, and FELGossiping with an increasing number of nodes in the network.  
Figure 9: Network lifetime performances of the three constructed protocols, the traditional gossiping protocol, and FELGossiping with an increasing number of nodes in the network.
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 2 The POSEIDON System
### System Operations
#### 2.3.4 Data Flow, Field Validation and Remote Monitoring
Panel (**b**) also includes the delayed mode data after the retrieval form the internal logger of the instrument and the process. Panel (**c**) presents the data after the first automated quality control, and panel (**d**)—the final data product after all the delayed quality control procedures and the field validation comparison. Using satellite communication during the gliders operation, the piloting team can remotely control and correct, if needed, the settings of the vehicle and the scientific payload. Changes to the parameters of the mission or emergency situations can be handled too. Apart from the manufacturer-provided communication software, an additional, dedicated inspection tool has been developed. The trajectory of the glider's mission is visualized on a map along with the instant display of the most important communication metadata, while all the transmitted navigation and scientific information is available for visualization by the pilots. The plots can provide additional information, such as minimum and maximum boundaries, safety bands of operation, combinations of data series and derived parameters, which should be calculated by the direct measurements received by glider, such as the salinity and the dissolved oxygen. The FB system sends automated generated alerts and emails, in case of failure or misfunctions. The alert reports include the time and the position of the events and metadata information for the system modules.
## 4 Effects of the Parameters of Oceanic Turbulence and Mathieu-Gaussian Beam
### Influence of Oceanic Turbulence  
To comprehend the effects of turbulence parameters on the transmitting probability of signal vortex modes of the MG beam, we investigate the effects of the dissipation rate of kinetic energy per unit mass of fluid \(\varepsilon\) and the inner scale of turbulence \(\eta\) on the transmitting probability of signal vortex modes of the MG beam. From Figure 5, one can see that the value of the transmitting probability of signal vortex modes increases with increase of the \(\varepsilon\) and \(\eta\), as expected [5; 8; 34]. The former is because the strength of oceanic turbulence decreases with the increasing of \(\varepsilon\). The latter is because a larger \(\eta\) corresponding to a weaker scattering.  
Figure 4: The normalized transmitting probability of the signal vortex modes versus wavelength \(\lambda\) for different values of ellipticity parameter \(q\).  
Figure 5: The normalized transmitting probability of the signal vortex modes versus dissipation rate of kinetic energy per unit mass of fluid \(\varepsilon\) and inner scale of turbulence \(\eta\).  
In Figure 6, we investigate the effects of the temperature-salinity contribution ratios \(\varpi\), dissipation rate of the mean-squared temperature \(\chi_{t}\) and an-isotropic factor of turbulence \(\zeta\) on the transmitting probability of signal vortex modes of the MG beam. When \(\zeta\) equals 1, the an-isotropic oceanic turbulence can degrade into the isotropic oceanic turbulence. It is clear from the Figure 6 that the magnitude of the transmitting probability of signal vortex modes decreases with increasing \(\varpi\), and \(\chi_{t}\), as well as, decreasing \(\zeta\). This result also is consistent with the result in Ref. [5; 8; 34].
# Pulse Peak Delay-Total Focusing Method for Ultrasonic Tomography on Concrete Structure
## 2 Pulse Peak Delay Calibration  
### Pulse Peak Delay  
The central frequency of an ultrasonic transducer is determined by the thickness of the active element constituting the ultrasonic transducer [21], and the backing material in the ultrasonic transducer determines the bandwidth [22]. Each transducer in the MIRA device is an ultrasonic shear wave DPC (Dry Point Contact) transducer that has a central frequency at 50 kHz and \(-\)3 dB frequency bandwidth of 20 kHz [23], as shown in Figure 1a. The ultrasonic pulses of the MIRA's transducer were simulated by the estimated frequency response function, as shown in Figure 1b. Input signals with three different frequencies of 30, 50, and 70 kHz were fed to the estimated frequency response function. A square wave which is a form of the MIRA device's input signal was used as an input signal in the pulse simulation.  
The TFM algorithm does not consider the pulse duration when calculating the time-of-flight of the pulse. As a result, an intense reflection will be formed and shifted further against the ultrasonic array as much as about half the pulse duration in the TFM algorithm. The duration of the generated ultrasonic signal is about 5 times longer than the duration of the input signal, which results in more severe sagging phenomenon by the long pulse duration on resulting images. Therefore, the long pulse duration should be considered in the image reconstruction algorithm, TFM. As intense reflection on the resulting image is formed by summing peak amplitudes of signals, a pulse peak delay should be included in the term of the delayed time in the image reconstruction algorithm.  
The PPD (Pulse Peak Delay) is the time interval between the beginning and the peak of the generated ultrasonic pulse. The measured PPDs are 43, 45, and 40 us for the input frequency of 30, 50, and 70 kHz, respectively, as shown in Figure 1b. If the ultrasonic wave speed is assumed as 2300 m/s, the PPD distances of the generated ultrasonic pulse will be 98.9, 103.5, and 92 mm, respectively, for 30, 50, and 70 kHz of input frequencies. As the frequency response function of the transducer is a constant property, the generated ultrasonic pulse is rarely affected by the input frequency. In addition, the PPD is not proportional to the input frequency. Therefore, the PPD should be calculated by simulating the ultrasonic pulse with an estimated frequency response function of the transducer and input signal.
# New Insight on the Formation of 2-Aminoacetophenone in White Wines
## 2 Materials and Methods
### Surveys of 2-Aminoacetophenone in Commercial Varietal Wines  
Two surveys of 2-AAP content in commercial varietal wines were carried out. The first survey consisted of analyzing 2-AAP concentration in 86 commercial Austrian white wines from the 2021 vintage prior to and after ageing at 40 \({}^{\circ}\)C for four days. This set of wines was sourced from producers and was exclusively composed of four varieties: Gruner Veltliner (20), Chardonnay (16), Riesling (25) and Muscat Ottnel (25) (Supplementary Data S1). The choice of the varieties was based on their importance for the Austrian wine market and by considering a preliminary study highlighting several trends of higher 2-AAP in some of the selected varieties. Ageing parameters were selected based on a recent study from [PERSON] et al. [3] showing that ageing different white varieties for 3 and 6 days at 40 \({}^{\circ}\)C was enough to substantially increase the level of 2-AAP. After 4 days of artificial ageing, samples were stored at 4 \({}^{\circ}\)C for a few days prior to analysis by gas chromatography coupled to a triple-quad mass detector (GC-MS/MS).  
A second commercial white wine screening consisted of 45 wine samples from Slovenia (40) and Croatia (5) purchased from shops and producers. Samples from vintages 2018, 2019, 2020 and 2021 were analyzed in 2022 as in the first survey. Selected varieties were the same as for the Austrian survey, however, Gruner Veltliner was excluded (rarely found in Slovenia and Croatia) and replaced by the locally more important varieties of Zelen (Slovenia) and Welschriesling (Croatia) (Supplementary Data S1). The 2-AAP concentration was measured in these wines without artificial ageing.  
In the same wines, the level of 2-AAP precursors tryptophan, anthranilic acid, indolelectic acid (ILA), 3-indoleacetic acid (3-IAA) and kynureine were measured by liquid chromatograph coupled to a triple-quad mass detector (LC-MS/MS).  
In order to investigate the influence of ILA on 2-AAP formation, 2-AAP was analyzed in a 2021 white blend from Slovenia (wine A) before and after artificial ageing for 4 days at 40 \({}^{\circ}\)C, and in the same wine spiked with 3-IAA and ILA at 100 \(\mu\)g/L after artificial ageing.