esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dynamic Response Analysis of the Test Model  
### Slope Failure Test  
Under the El Centro wave with a peak acceleration of 0.1 g to 0.3 g, the surface soil of the slope had a strong dynamic response. Figure 11a shows that the slope surface exhibited the phenomenon of delamination, the soil particles moved and slid within a small range, and the gravel soil tended to slide and collapse. Figure 11b shows that under the 0.4 gravitational acceleration El Centro wave, the steep section of the slope, which had poor stability, collapsed locally and tension cracks occurred on the slope surface. This failure phenomenon appeared symmetrically and simultaneously on both sides of the central axis of the model, proving the rationality of the slope filling. Figure 11c shows that under the 0.5 g El Centro wave, the chain effect of the slope failure caused a substantial loss of soil in the lower part of the slope, and the gravel soil continued to accumulate near the slope toe, reducing the horizontal resistance of the soil in front of the back-row anti-slide piles and exposing the pile foundation in front of the pier, thereby causing the loss of soil resistance to some extent. As shown in Figure 11d, when the peak acceleration of the seismic wave reached 0.6 g, a large portion of the soil in the sliding section of the slope slid, the slope bed near the crest of the slope was exposed, and the sliding mass formed a significant accumulation behind the back-row anti-slide piles. During the vibration process, the gravel soil constantly showed the phenomenon of slide and shear at the top of the back-row anti-slide piles, and the back-row anti-slide piles played a role in the seismic reinforcement. The front-row anti-slide piles were not very effective as a seismic support structure because of their large distance from the pier, and the landslide thrust was mainly resisted by the back-row anti-slide piles.  
Figure 10: Fourier spectrum of El Centro wave.  
Figure 9: Fourier spectrum of time history curve of accelerometer A4.
## 3 Results: Predicted GNSS-R Altimetry Performance  
### GNSS-R Altimetry Performance and Trade-off between Techniques  
Following the same procedure as in [6], and taking into account the effects discussed in Sections 2.4.1 and 2.4.2, properly compensated for, the altimetry performance is computed for the nominal transmitted powers of the GPS (Figure 13) and Galileo (Figure 14) constellations, for the minimum (330 km) and maximum (460 km) ISS orbital heights, which require the maximum and minimum antenna off-boresight angles to satisfy the \(\pm\)250 km swath requirement.  
The altimetry performance is computed first individually for each band (F1 = L1 or E1, F5 = L5 or E5a), using either the conventional or the interferometric GNSS-R techniques (cGNSS-R or iGNSS-R). Then, the performance of the dual-frequency combination of these observables to compensate for the ionospheric delay (Equation (8)) is computed, that is, using either cGNSS-R or iGNSS-R, at either F1 or F5,  
Figure 12: Example of the impact of multipath in the ISS structure.  
It is worth to note that for the minimum transmitted powers, in all cases, except for Galileo at nadir, the performance of the iGNSS-R technique quickly degrades because of the marginal SNR, while cGNSS-R exhibits a more graceful degradation in terms of range estimation performance. At nadir, the wind speed at which the performance of iGNSS-R and cGNSS-R decreases with increasing height, while at the swath edge it decreases with increasing local incidence angle (e.g., lower height). These results can be used as inputs for assimilation studies to assess the scientific impact of the GEROS-ISS observations.  
Assuming a 1 s incoherent integration time, for a nominal instrument with errors, the altimetric rms error is smaller than 70-90 cm at nadir (38-43 cm if the transmitted power is increased by 3 dB), and smaller than 11-17 m at the swath edge (250 km) for U\({}_{10}\) < 8-9 m/s (71-76 cm if the transmitted power is increased by 3 dB), and for any orbital height h\({}_{\text{ISS}}\) = 330-460 km.  
Finally, the altimetry performance for different combinations of GNSS-R techniques (iGNSS-R and cGNSS-R) is discussed. In particular, the use of iGNSS-R in both bands, and the use of iGNSS-R at F1 and cGNSS-R at F5, which will make it more robust in front of the severe RFI that it is likely to appear due to DME (Distance Measuring Equipment) and TACAN (TACtical Air Navigation system) systems [21]. It is worth noting that the performance degradation as compared to the use of cGNSS-R is typically a factor between 3 and 4, except for the lowest SNR, when the performance is basically the same (GPS, U\({}_{10}\) = 15 m/s).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & \\ \hline
**GNSS** & **Cross Track** & **P\({}_{\text{T}}\)** & **
## 3 Experimental Results
### Data Preparation
This parameter sweep was designed to systematically evaluate the sensitivity of the noise classification threshold and its effect on imaging quality. Figure 8 shows the corresponding SNR as a function of \(m\). As shown in Figure 8, when \(m\) exceeds 1.4, the noise-attenuated data achieve a higher imaging SNR. Below this threshold, excessive noise attenuation adversely affects signal integrity. Under these conditions, imaging directly with noisy data may produce superior results, since denoising unavoidably compromises valid signals. When the noise is weak, the advantages of noise attenuation do not outweigh the associated signal loss. Therefore, in this test, we selected 1.4 as the noise threshold for dataset preparation. Figure 8: Imaging SNR versus the parameter \(m\). Figure 7: An example of synthetic data: (**a**) noise-free, (**b**) with added noise, (**c**) after noise attenuation. By combining the NA and SA datasets, we created approximately 3.41 million training samples with an equal proportion of weak and strong noise traces. This balanced distribution ensures that the model is adequately exposed to both subtle and severe noise scenarios during training, promoting generalization across a wide range of noise conditions. Figure 9 presents some examples from the training dataset. It can be observed that the Mel-spectrogram features of low-noise data are relatively consistent and simple, whereas those of high-noise traces exhibit greater variability and differ significantly from low-noise signals. The test dataset, composed of NB and SB, contains about 810,000 samples, with strong noise traces accounting for roughly 12%. This unbalanced distribution more accurately reflects real-world data, where strong noise is relatively sparse.
## 2 Protocol Description
### Definition of Quantitative Metrics and Their Relationship with Measurable Functional Parameters  
A critical component of the protocol is the detailed description of parameters, metrics, and characteristics that, when measured or estimated, encompass a complete and comprehensive evaluation of functionality (the flowchart for this phase is shown in Figure 2). This set of data will be referred to as _metrics_, in general. For this process, a catalogue of metrics will be developed from the necessary aspects to be considered in a functional assessment:  
* Metrics related to physical aspects such as body position, balance, speed of movement, bimanual coordination, etc.
* Metrics related to interaction with objects such as fine coordination, object manipulation, hand-eye coordination, etc.
* Metrics related to cognitive aspects such as problem solving, sequencing, following instructions, etc.  
In turn, a series of metrics obtained from wearable devices, such as heart rate, number of steps, energy expenditure, etc., will be also defined, as they can provide relevant information to be used in the functional assessment process.  
The development of these metrics involves, again, close collaboration between engineers and healthcare professionals to ensure technical feasibility, and that they are clinically relevant and accurately reflect people's functional capacity.
## 3 Materials and Methods
### Inorganic Geochemistry  
The analyses of chemical elements (Ag, Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, Ga, Hg, K, Mg, Li, Mn, Mo, Na, Ni, P, Pb, Sb, S, Sc, Sr, Te, Ti, Tl, U, V, W, Y, Zn, and Zr) were carried out on freeze-dried sediment sample after acid digestion employing HF, HClO\({}_{4}\), HNO\({}_{3}\), and HCl and the following determination of the concentration of each element in the eluate solution by Inductively Coupled Plasma-Optical Emission Spectroscopy (Activation Laboratory LTD, Ancaster, ON, Canada). To identify possible anthropic contributions, some pollution indicators, such as the contamination factor (C\({}_{J}\)), the degree of contamination index (C\({}_{d}\)), the enrichment factor (EF), the geoaccumulation index (I\({}_{geo}\)), and the pollution load index (PLI) of heavy metals and other trace elements were calculated [69] (Table 1). Furthermore, element concentrations were compared to the threshold values of the Italian legislation [43], to the general abundance data reported for the marine shale [70] (Table 2) and the ecotoxicological risks with the Sediment Quality Guidelines (SQGs) indicated in [PERSON] and [PERSON], 2015 [71]. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**C\({}_{f}\)** & **Classification** & **EF** & **Classification** & **I\({}_{geo}\)** & **Classification** & **mC\({}_{d}\)** & **Classification** & **PLI** & **Classification** \\ \hline \(<\)8 & Low & \(\leq\)2 & Low & \(<\)0 & Unpolluted & \(<\)1.5 & Very low & \(<\)1 & Unpolluted \\  & & & & & Unpolluted & & & & Unpolluted to \\
8–16 & Moderate & 2–5 & Moderate & 0–1 & to & & & & 1–2 & moderately \\  & & & enrichment & & & polluted & & & & polluted \\
16– & & & High & 1–2 & & Moderately & & & & \\
32 & Considerable & 5–20 & \(\begin{array}{c}\text{High}\\ \text{enrichment}\end{array}\) & 1–2 & \(\begin{array}{c}\text{Moderately}\\ \text{polluted}\end{array}\) & 2 –3 & \(\begin{array}{c}\text{Moderately}\\ \text{polluted}\end{array}\) & 2 \(<\) mC\({}_{d}<\) 4 & Moderate & 2–3 & \(\begin{array}{c}\text{Moderately}\\ \text{polluted}\end{array}\) \\ \(>\)32 & Very high & 20–40 & \(\begin{array}{c}\text{Very high}\\ \text{enrichment}\end{array}\) & 2–3 & \(\begin{array}{c}\text{Moderately}\\ \text{to strongly}\\ \text{polluted}\end{array}\) & 4 \(<\) mC\({}_{d}<\) 8 & High & 3–4 & \(\begin{array}{c}\text{Moderately}\\ \text{to highly}\\ \text{polluted}\end{array}\) \\  & & \(>\)40 & \(\begin{array}{c}\text{Extreme}\\ \text{enrichment}\end{array}\) & 3–4 & \(\begin{array}{c}\text{Strongly}\\ \text{poluted}\end{array}\) & 8 \(<\) mC\({}_{d}<\) 16 & Very high & 4–5 & \(\begin{array}{c}\text{Highly}\\ \text{polluted}\end{array}\) \\  & & & & Strongly to & & & & & \\  & & & extremely & & & & & \\  & & & strongly & & & & & \\  & & & polluted & & & & & \\  & & & & & & & & \\  & & & & & & & & & \\  & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the classification categories of sediment contamination derived from the different applied indicators.
## 2 System Description and Its Mathematical Modeling
### Control-Oriented Open Loop Average Model of the DC-AC Boost Inverter  
The average model of the inverter at the switching period scale can be obtained by substituting the binary signal \(u\) by its duty cycle \(d\) and the instantaneous state variables by their averaged quantities. Therefore the averaged model can be expressed as follows  
\[\frac{d\tilde{i}_{1}}{dt} = \frac{1}{L_{1}}(\overline{v}_{\rm dc}-\overline{v}_{o1}(1-d)), \tag{2}\] \[\frac{d\tilde{i}_{2}}{dt} = \frac{1}{L_{2}}(\overline{v}_{\rm dc}-\overline{v}_{o2}d)\] (3) \[\frac{d\overline{v}_{o1}}{dt} = \frac{1}{C_{1}}((1-d)\tilde{i}_{1}-\tilde{i}_{g}),\] (4) \[\frac{d\overline{v}_{o2}}{dt} = \frac{1}{C_{2}}(d\tilde{i}_{2}+\tilde{i}_{g}),\] (5) \[\frac{d\overline{v}_{\rm dc}}{dt} = \frac{1}{C_{\rm dc}}(2I_{\rm MPP}-\overline{v}_{\rm dc}I_{\rm mpp }-\tilde{i}_{1}-\tilde{i}_{2}),\] (6) \[\frac{d\tilde{i}_{g}}{dt} = \frac{1}{L_{g}}(\overline{v}_{o1}-\overline{v}_{o2}-v_{g}), \tag{7}\]  
where the overbars stand for averaging with respect to the switching period and \(L_{1}\) and \(L_{2}\) are the inductance values of the boost inductors, and \(C_{1}\) and \(C_{2}\) are the capacitance values of the boost capacitors and \(C_{\rm dc}\) is the capacitance of the DC-link capacitor placed at the output of the PV generator. All other parameters and variables that appear in (2)-(7) are shown in Figure 2.
# Spatial Prediction of Soil Contaminants Using a Hybrid Random Forest-Ordinary Kriging Model
## 2 Materials and Methods
### Combined RF-OK Interpolation Model  
The process of combining both techniques involved three steps: prediction using the RF model, application of OK to the residuals of the RF prediction, and totaling the RF model prediction results with the OK results of the RF prediction residuals. This approach combines the strengths of RF and OK. First, the predicted values were calculated using an RF model. Then, the difference between the predicted and actual values was represented as the residuals of the RF model (Equation (4)). RF was used to capture complex patterns in the data, whereas OK was used to adjust for local discrepancies not captured by the RF model. This enhances the overall accuracy and reliability of the predictions.  
\[Z_{RFresidual}(x_{i})=C(x_{i})-\hat{C}_{RF}(x_{i}) \tag{4}\]  
where \(Z_{RFresidual}(x_{i})\) represents the RF residual at location \(i\), \(C(x_{i})\) is the sampled data value at location \(i\), and \(\hat{C}_{RF}(x_{i})\) is the RF model prediction at location \(i\).  
The residuals were calculated using this method and subjected to hyperparameter tuning following previously defined options and parameter grids for OK. This involved searching for the optimal choices for the RF model residuals and conducting OK based on the optimal options and parameters obtained. This allows RF predictions and RF residuals to be spatialized to account for spatial autocorrelation that does not exist in the sampling data, thus compensating for the shortcomings of both techniques. Subsequently, a model that combines these two techniques was developed. This was achieved by combining the prediction values for the entire study area calculated using the trained RF model, with the residual prediction values for the entire study area obtained by applying OK to the RF model's prediction residuals. This combined methodology is mathematically represented by Equation (5).  
\[\hat{Z}_{RFOK}(x_{i})=\hat{Z}_{RF}(x_{i})+\hat{Z}_{OK}(x_{i}) \tag{5}\]  
where \(\hat{Z}_{RFOK}(x_{i})\) represents the RF-OK model prediction at location \(i\), \(\hat{Z}_{RF}(x_{i})\) is the RF model prediction at location \(i\), and \(\hat{Z}_{OK}(x_{i})\) denotes the OK estimation of the RF residual at location \(i\).
# Lagrangian Modeling of Marine Microplastics Fate and Transport: The State of the Science
## 6 Conclusions and Future Research Directions  
The current study aims, as a worthwhile endeavor, to systematically review some well-known Lagrangian numerical particle-tracking models that have been used for predicting the fate and transport of microplastics. In this regard, four Lagrangian particle-tracking models, i.e., D-WAQ PART, Ichthyop, TrackMPD, and CaMPSim-3D, have been investigated in terms of addressing physical transport processes (advection, diffusion, windage, beaching and washing-off, and resuspension) and physical transformation processes (biofouling and degradation) with consideration of the effects of the physical properties (mainly size, diameter, and shape) of microplastics on their fate and transport. Moreover, a numerical scheme of each model is described based on its spatio-temporal interpolation scheme and the structure of the adopted mesh. Accordingly, it has been shown that some of these models are more powerful than others in capturing the physical properties of particles and predicting their behavior under different physical transport and transformation processes. Some concluding remarks for the current study are summarized below:  
* With respect to the consideration of the physical properties of microplastics, D-WAQ PART and Ichthyop use simplified equations for calculating the settling velocity as a function of the size and density of plastic particles, while TrackMPD and CaMPSim-3D employ more accurate equations to meet the effect of particle shape as well. In other words, D-WAQ PART utilizes an equation for calculating the settling velocity of plastic particles with different shapes and types, while Ichthyop is suited for considering the settling velocity of prolate spheroids. TrackMPD can differentiate spherical and cylindrical particles in the simulation of the settling velocity. CaMPSim-3D can be applied for simulating particles with different shapes, including spherules, films, fragments, and fibers. * Among the transformation processes reviewed in this study (i.e., biofouling and degradation), although biofouling has been regarded as one of the important processes in the fate of microplastics, D-WAQ PART and Ichthyop are unable to predict the behavior of particles under the influence of this process. However, TrackMPD and CaMPSim-3D can simulate fouled particles using empirical equations. Unlike D-WAQ PART and Ichthyop, TrackMPD, and CaMPSim-3D can predict the degradation of microplastics based on the proposed relationship in the literature. * Four particle-tracking models employ generally universal advection-diffusion equations, while the equations for parameterizing various physical processes may be different. However, only D-WAQ PART, TrackMPD, and CaMPSim-3D consider the effect of wind
## 4 Results and Analyses  
### Backward Trajectory Clustering  
TrajStat was used to process airflow data in Weifang in different seasons to obtain transport trajectories in different seasons. However, from these airflow trajectories, it is not possible to determine the exact number of trajectories from different directions. Therefore, according to the consistency in the spatial distributions of various airflow trajectory types, trajectories in spring, summer, autumn, and winter are integrated into four, five, two, and two clusters, respectively (Figure 4).  
Figure 4 shows that, in spring, the airflow trajectories originating from northern inland areas (Trajectories 1-3) dominate. More specifically, airflows originating from Russia, Mongolia, central Inner Mongolia, and Hebei account for 36.91% of the total trajectories, and those from the Yellow Sea and central Shandong account for 29.38%. In summer, the airflow trajectories are shorter and follow a star-shaped distribution. Influenced by warm and humid airflows from the ocean, these trajectories are dominated by southerly and southeasterly winds and account for 67.1% of the total trajectories. In autumn, as cold air masses move southward, the airflow trajectories from the southeast weaken. As a result, the airflow trajectories originating from southeastern Mongolia, central Inner Mongolia, and northeastern Hebei account for 59.07%. The airflow trajectories from northeastern Hebei are shorter, accounting for 40.30%. They pass over the waters of the Bohai Sea and finally return to Weifang via the inland areas of Shandong. In winter, under the influence of the Siberian cold current, the airflow trajectories originating from the northwest dominate (68.91%). These trajectories are longer, and the air masses move more quickly. Moreover, in winter, the airflow trajectories from southwestern Shandong account for 31.06% and are shorter and slower.
# Stents in Congenital Heart Disease: State of the Art and Future Scenarios
## 4 Stents in the Treatment of Congenital Heart Disease  
The transcatheter stenting procedure has been introduced as a less invasive alternative to open-heart surgery in CHD treatment since the 1990s. The first attempts of ductal stenting in humans adopted early-generation rigid stents, with relatively bulky, stiff wires, balloons, and sheats, causing negative outcomes including cyanosis or shock, bleeding, vessel rupture, duct spasm, tissue prolapse, or thrombosis [7; 63; 43]. Within this context, it is relevant to mention the experience of [PERSON] and coworkers, who were the first to investigate the effectiveness of ductal stenting in the palliation of HLHS [43]. They implanted Palmaz-Schatz stainless steel biliary stents in four newborn patients using femoral access, performing balloon expansion in quite straight ducts. However, wrong stent positioning related to the shortening during dilation resulted in incomplete ductal covering and distal ductal stenosis. The same authors were also the first to assess the technical feasibility of ductal stenting in pulmonary atresia [7]. The same stent design was adopted; however, the implantation required multiple interventions to achieve ductal patency, due to the more complex anatomy of the ductus arteriosus. Some cases were reported in which implantation failed due to duct tortuosity [49]. In the same years, the first attempts of stent implantation for treatment of aortic and pulmonary artery stenosis were reported due to the unsatisfactory results of surgery and balloon angioplasty, with acute complications, including vessel wall disruption, stent migration, aneurysm formation, and stent obstruction [64; 65; 66]. Since then, thanks to several innovations in device design and fabrication methods as well as in operative techniques and equipment, the stenting procedure has evolved progressively, establishing as a gold standard in many centers [67]. In the following, the pathologies requiring a similar stenting procedure with comparable related issues are grouped together, distinguishing between stenting of the ductus arteriosus and stenting of the great vessels. Common choices for device selection reported in the literature are reviewed, highlighting existing trends.
# Diabetic Macular Edema Characterization and Visualization Using Optical Coherence Tomography Images
###### Abstract  
Diabetic Retinopathy and Diabetic Macular Edema (DME) represent one of the main causes of blindness in developed countries. They are characterized by fluid deposits in the retinal layers, causing a progressive vision loss over the time. The clinical literature defines three DME types according to the texture and disposition of the fluid accumulations: Cystoid Macular Edema (CME), Diffuse Retinal Thickening (DRT) and Serous Retinal Detachment (SRD). Detecting each one is essential as, depending on their presence, the expert will decide on the adequate treatment of the pathology. In this work, we propose a robust detection and visualization methodology based on the analysis of independent image regions. We study a complete and heterogeneous library of 375 texture and intensity features in a dataset of 356 labeled images from two of the most used capture devices in the clinical domain: a CIRRUS(tm) HD-OCT 500 Carl Zeiss Meditec and 179 OCT images from a modular HRA + OCT SPECTRALIS(r) from Heidelberg Engineering, Inc. We extracted 33,810 samples for each type of DME for the feature analysis and incremental training of four different classifier paradigms. This way, we achieved an 84.04% average accuracy for CME, 78.44% average accuracy for DRT and 95.40% average accuracy for SRD. These models are used to generate an intuitive visualization of the fluid regions. We use an image sampling and voting strategy, resulting in a system capable of detecting and characterizing the three types of DME presenting them in an intuitive and repeatable way.  
Keywords:optical coherence tomography; macular edema; intraretinal fluid accumulations; feature selection; medical image characterization +
Footnote †: journal: Applied Sciences