esa-sceva/satcom-chunk-collection huggingface dataset:
# Comprehensive Annual Ice Sheet Velocity Mapping Using Landsat-8, Sentinel-1, and RADARSAT-2 Data
## 2 Data and Methods
### Automatic Calibration Scheme

Table 1: Ice velocity mapping precision (\(\epsilon\)) for different sensors assuming an error in measured displacement of 0.1 pixel, calculated as \(\epsilon=0.1*ps/laps*365.25\) where \(ps\) is the pixel size in meters and \(laps\) is the repeat cycle in days. RADARSAT-1/&-2 precision may vary depending on the acquisition mode, we use here the values for the most common mode in our dataset. The precision is affected by the nominal repeat orbit as well as the resolution, this table is therefore provided for reference only. We show in our work that actual precision is often better than that provided in Table 1. The utility of the table lies in a cross sensor comparison, that we exploit in our weighting system when we form the velocity mosaic. Text in italics indicates values for sensors used in previous studies [2,22,27]. Figure 1: Distribution of the calibration constants applied to Landsat-8 data along the x (easting) and y (northing) direction (in polar stereographic projection).
## 3 Experimental Testing of Search Performance on IABCBAS Algorithms
### Comparative Ablation Experiments  
The proposed algorithm has been proven to exhibit superior global search capability and local optimization performance. However, additional experiments are required to verify (1) whether the selected mechanism is optimal, (2) whether it represents the optimal improvement for the proposed algorithm, and (3) whether the combination of the two algorithms is generalizable. To enhance the persuasiveness of model improvements, comparative algorithms are selected for experiments by reviewing the latest literature. The ABC algorithm is modified by introducing Bernoulli chaotic mapping, reverse learning mechanism, and KHO algorithm, respectively [29; 30; 31], denoted as ACO1-ACO3; the BAS algorithm is selected to combine with ABC, PSO, and AFSA [32; 33; 34], denoted as BAS1-BAS3. ACO1 and ACO2 were set up to compare with the Tent population Chaos and local random direction selection mechanism. ACO3 and BAS1-BAS3 were set up to compare the two algorithm-combining methods we used, respectively. The results are shown in Table 4.  
It can be seen from Table 3 that compared with the original ABC algorithm, the added mechanisms have improved the algorithm's optimality. The numerical results of ACO1 are all better than those of ACO2 in \(f_{1}\sim f_{6}\) function, indicating that enhancing population richness can effectively improve global search efficiency in high-dimensional functions. The numerical results of ACO2 are slightly better than that of ACO1 in function \(f_{7}\sim f_{9}\), indicating that increasing the random direction can increase local search stability in the low-dimensional function. Comprehensive results show that the results of each two combined algorithms are numerically better than the single-added mechanism results. The numerical results of BAS3 and ACO3 are better than those of BAS1 and BAS2, indicating that the combination of the new swarm intelligence algorithm has a wider search range and better enhancement effect. Meanwhile, the overall value of BAS1 is better than BAS2, indicating that the combination of the bee swarm and the beetle swarm is also good, which side by side verifies the accuracy of the basic algorithm selected. However, compared to the fish swarm and krill swarm algorithms, the beetle swarm algorithm has a simpler structure, giving the IABCBAS algorithm a greater advantage in search and optimization capabilities.  
Figure 4: Comparison of benchmark function convergence curves. (**a**) Schwefel’s function; (**b**) Rosenbrock function; (**c**) Sphere function; (**d**) Schwefel function; (**e**) Foxholes function; (**f**) Schkel function.  
Furthermore, taking the \(f_{1}\) function as an example, the average value of the IABCBAS algorithm is the lowest among the eight algorithms, reaching \(4.076\times 10^{-29}\), which indicates that the algorithm has a better optimization effect on the f1 function. Meanwhile, only the IABCBAS algorithm has a standard deviation of 0, suggesting that the results of multiple runs are highly stable without significant differences.
# Towards Automatic Depression Detection:
## 5 Experiments
### Data Processing  
#### 5.1.1 Audio Features  
For the DAIC-WoZ dataset, the voice of each participant was first segmented from the whole recording, following the timestamps documented in the transcripts. The durations of segmented recordings ranged from 5 to 25 min. To avoid introducing some person specific characteristics, the lengths of segmented recordings were unified by cropping recordings into slices of a certain length. The length of slices was expected to be neither too long, nor too short. It should be long enough to extract subtle speech characteristics displayed by depressed individuals and short enough to ensure that the 1D CNN model is trainable. In order to discover the proper length, an audio length comparison experiment was conducted. Inspired by the exploratory trial in [49], the length of the audio clips was set to 4, 10, 15, 20, and 25 s in the experiment. The results indicate that the 1D CNN model with 15 s audio slices shows the best performance, for which the precision and recall are 0.77 and 0.83, respectively. Therefore, the segmented recordings of each participant were cropped into a number of 15 second slices.  
Resampling was performed on each audio of depressed samples until the number of depressed samples was equivalent to that of the non-depressed ones. Audio slices were converted to Mel spectrograms with 80 Mel filters. After that, a min-max normalization was conducted on all of the Mel spectrograms.  
Considering the question-answer nature of depression detection, the free-form part of AViD-Corpus was utilized. First, the silent part at the beginning and the end of each recording was removed. Next, the processed recordings were split into 15 second slices. The recordings in AViD-Corpus range from 6 s to 4 min. However, our method requires 15 second audio slices for audio feature extraction. Therefore, for recordings less than 15 s, silent audio segments were padded to extend the audio lengths to 15 s. The way of padding the original recording with the silent audio segment was to concatenate the original recording array with a silent segment array. The values in the silent segment array are all 0.001, and the length of the silent segment array is \(sr\cdot(15-l_{rec})\), where \(sr\) is the sample rate and \(l_{rec}\) is the length of the original recording array. The rest of the feature extracted procedure was the same as the procedure for the DAIC-WoZ dataset.  
It should be noted that resampling was only performed on the training set for the depression datasets.
## 4 Results and Discussion
### Case Study 1: Flexible Beam Coupling
#### 4.1.3 Cross-Receptance Components Between Tip and Connection Point
The trends in Figure 18 for \(H^{AB}_{\theta_{2}\theta_{1}}\) closely resemble those observed in rotational receptance components \(H^{AB}_{\theta_{1}\theta_{1}}\) and \(H^{AB}_{\theta_{2}\theta_{2}}\); further confirming the consistency between the two methodologies. Both the GRCFBS and FEA approaches effectively replicate the rotational coupling dynamics, maintaining strong correlations in both resonance peaks and anti-resonance behavior across the frequency range. The similarity in behavior across these indirect receptance components underscores the ability of the GRCFBS method to capture coupled dynamics between rotational and translational degrees of freedom. This is particularly important because such dynamics are often more challenging to model due to the interplay of higher-order effects and phase relationships in coupled systems. Additionally, the consistency observed in the alignment of natural frequencies and resonance peaks further validates the robustness of the GRCFBS method and reinforces the suitability of this approach as a reduced-order alternative to FEA for dynamic modeling of complex coupled systems. A comparison has been made between \(H^{AB}_{y_{1}y_{2}}\), \(H^{AB}_{y_{1}\theta_{2}}\), and \(H^{AB}_{\theta_{1}\theta_{2}}\) for the higher frequency range up to 6000 Hz, covering all four mode shapes and peaks, as shown in Figure 19. Regardless of variations in response characteristics, the alignment on natural frequency peaks indicates consistent resonance predictions across different excitation conditions, like the observations at the tip and connection nodes. Figure 17: Cross-recceptance \((H^{AB}_{y_{1}\theta_{1}}=H^{AB}_{y_{1}\theta_{2}})\): translational disp. at the connection node due to the moment excitation at the tip (case study 1). Figure 18: Cross-recceptance \((H^{AB}_{\theta_{1}\theta_{1}}=H^{AB}_{\theta_{1}\theta_{2}})\): rotational disp. at the connection due to the excitation couple at the tip (case study 1).
# Analysis of Injected Electron Beam Propagation in a Planar Crossed-Field Gap
## 4 Simulation Results
### Effect of Magnetic Field Tilt
To confirm this stability, we drew a best fit line through the complete orbitals from the phase space plots, yielding a slope and intercept of 0.00486 and 0.00637, respectively, for the 36 mA/cm\({}^{2}\) case. From the slope and intercept, the calculated variation is \(\approx\)23% which denotes the beam is nominally stable given the relatively similar variation to the previously considered stable condition. However, for \(J\approx 26\) mA/cm\({}^{2}\) (\(0.26J_{c}\) \(<0.33J_{c}\)), Figure 12a shows unstable propagation and a rotated beam. Similarly, for \(J\approx 43\) mA/cm\({}^{2}\) (\(0.43J_{c}\) \(>0.4J_{c}\)), Figure 12c shows unstable propagation and a rotated beam, which matches previous 1-D PIC simulations [11], confirming the presence of stability bands in 3-D. We did not calculate slopes and intercepts for the final two cases (injected beams of 50 mA and 90 mA) because the propagation instability is evident from the 3-D beam trajectories (Figure 12a,c).
# Unity Power Factor Operation in Microgrid Applications Using Fuzzy Type 2 Nested Controllers
## 3 Proposed System  
### Fuzzy Type 2 Control  
The use of FLC for a system is fundamentally based on rules derived from human skills and knowledge. Type 2 fuzzy sets (Fuzzy 2) are an enhanced version of ordinary fuzzy sets, namely, Fuzzy type 1 sets (Fuzzy 1) [42]. Expert knowledge of the problem should be used to define a knowledge base that includes information on the attribute values, fuzzy subsets identifying them, and the rules linking these variables to determine the output. The controller can analyze these outputs by fuzzifying the actual inputs and using the fuzzy control rules. Defuzzification transforms the fuzzy variables that are the intermediate results of evaluating fuzzy rules into non-fuzzy control information for the ultimate procedure [43]. Figure 1 groups the five primary components of a Fuzzy 2 logic set: Defuzzifier, Fuzzifier, Rules Base, and Type Reducer are all components of a Fuzzy inference engine. The controller receives its input from the clear deviations that can be seen between the measured outputs of the plant and its reference inputs. The outputs of the controller are precise control signals that are transmitted to the plant actuators. The determined plant outputs are forwarded to the fuzzy controller to alter the error input.  
Figure 1: Regular Fuzzy 2 structure.  
The fuzzy 2 controller has four fundamental elements:  
1. Fuzzification is the layout that transforms input values from crisp to fuzzy. This operation allows us to determine the degrees to which each belonging function of each input applies to the fuzzy sets.
2. Linguistic rules contain human control problem knowledge. Expert knowledge is turned into logic to develop rule bases.
3. On the grounds of the knowledge base, an inference engine produces fuzzy controller inputs.
4. An interface for defuzzification that transforms fuzzy outputs into crisp outputs suitable for operating system actuators.
# Stability of Paracetamol Amorphized by Co-Melting with Various Cellulose Derivatives
## 1 Introduction
Our previous research and those of other researchers indicate that paracetamol can be reduced in crystallinity when homogenized with polymeric excipients, like cellulose derivatives (especially hypromellose) [23; 24]. Currently, many types of HPMC are used, which differ in the degree of substitution with hydroxypropyl and hydroxymethyl groups and the average chain length in the molecule. Most often they have the function of fillers in the solid forms of the drug. Complete amorphization of paracetamol can be achieved by co-melting paracetamol with HPMC. Amorphization is possible even when the polymer addition is less than 30% of the mixture. Previous studies have proven that there is no statistically significant difference in the effect of HPMCs of different chain lengths on the amorphization of paracetamol [24]. Thus, a study was undertaken to determine whether the amorphization of paracetamol under the influence of different cellulose derivatives: HPMC with different average molecular weights and methyl cellulose would be stable. Moreover, the purpose of this study is to determine whether co-melting paracetamol with various cellulose derivatives in different proportions of components will result in full amorphization of paracetamol. Additionally, it was checked whether recrystallization of the active ingredient would occur when the samples were stored at room temperature. We also decided to determine whether the stability of the amorphous form and possible recrystallization time depend on the polymer used in the mixture. These assays were based on using thermal (differential scanning calorimetry (DSC)) and spectroscopic methods, including Fourier transform infrared (FTIR) and Raman spectroscopy. It should be also noted that to achieve the amorphization of paracetamol by co-melting, the increased temperature was used. This factor can induce potential changes in the active substance and cause its decomposition. Ultrahigh-performance liquid chromatography (UHPLC) was used to determine whether the decomposition of paracetamol occurs during heating-induced amorphization.
# Versatile High-Throughput Platform for Focused Ultrasound In Vitro Application
## 3 Results
### Ultrasound Sound Pressure Distribution and Output Homogeneity  
3.1.1 LIFU Platform  
The LIFU sonication device was characterized in four frequencies between 0.5 and 2.0 MHz with respect to homogeneity by measuring an xy-pressure field distribution 10 mm in front of the applicator, viewed on dB-scaled intensity maps (Figure 7a). All setups maintained the voltage applied to a single transducer at 25 Vrms by electrical adjustments. The maximum pressure output for each well position was extracted from the data to serve as a metric for qualitative assessment. Well center positions, defined based on the known well grid pitch and maximum pressure values within areas corresponding to the well floor (circles of 6.5 mm in diameter), were identified for each well position. For each frequency, the average effective pressure and the corresponding standard deviation were calculated (Figure 7b).  
When driven with 25 Vrms, the output pressure for all applicators ranged from 145 kPa to 300 kPa. The 0.5 MHz, 1 MHz, and 2.0 MHz models had an average output of around 160 kPa, while the 1.5 MHz model exceeded them with 300 kPa. The 0.5 MHz model presented the highest level of homogeneity with a relative standard deviation of 7%, while the other applicators varied between 27 and 51%. All applicators achieved a high yield of over 70%, defined as the number of elements whose output exceeded \(-\)6 dB of the average output pressure. Notably, the 500 kHz and 2 MHz models showed a very high yield, exceeding 95%, with minimal elements outside the \(-\)6 dB limit. To measure the impact of the excitation voltage on the maximum sound pressure and the intensity, the excitation voltage applied to the LIFU platform was varied between 6 and 48 Vpeak. The filling volume in the wells was 250 uL. The maximum sound pressure amounted to 605 kPa (pmax) with maximum IsPPA values between 0.78 and 12.38 W/cm\({}^{2}\) (Figure 8).  
Figure 7: Homogeneity studies with the LIFU platform at frequencies between 0.5 and 2.0 MHz. (**a**) dB-scaled xy intensity distribution in front of the applicator (z = 10 mm) obtained through an xy-scan of a calibrated hydrophone; (**b**) Statistical assessment of the output homogeneity and the manufacturing yield for the different applicator versions.
# 3D Point Cloud Completion Method Based on Building Contour Constraint Diffusion Probability Model
## 3 Methods
### Point Encoding  
As shown in Figure 9, to enable the fusion of contour constraint regions with the shape latent code of the original point cloud, we use the same encoder as the one used to encode the original building point cloud into shape latent codes to encode the contour constraint regions.  
Inputting the original building point cloud \(X^{(0)}\) into the trained encoder \(\varphi\), we map the point cloud from sample space to latent space, generating the corresponding shape latent code \(h\). The process is described as follows:  
\[q_{\varphi}\Big{(}h\mid X^{(0)}\Big{)}=\mathcal{N}\Big{(}h\mid\mu_{\varphi} \Big{(}X^{(0)}\Big{)},\Sigma_{\varphi}\Big{(}X^{(0)}\Big{)}\Big{)}, \tag{7}\]  
where \(\mu_{\varphi}(X)\) and \(\Sigma_{\varphi}(X)\) are estimated mean and variance obtained through pre-training with PointNet [29].  
To enable the fusion of building contour constraints with the shape latent code of the incomplete point cloud, we use the same encoder \(\varphi\) to map the contour constraint shape point clouds \(X_{C}\) and \(X_{M}\) into the same low-dimensional feature space, generating the corresponding shape latent codes \(C\) and \(M\). The process is described as follows:  
\[C =\varphi(X_{C}), \tag{8}\] \[M =\varphi(X_{M}), \tag{9}\]  
where \(\varphi\) is the encoder.  
By merging the shape latent codes \(h\) of the incomplete building point cloud, the contour constraint shape latent codes \(M\), and the intersection of shape latent codes E, we obtain the building shape latent code \(H\):  
\[H=h\cup M\cup(h\cap C), \tag{10}\]  
Figure 8: Diffusion process.  
Figure 9: Point encoding process.
## 4 Prototype Fabrication and Measurements
### Measured Results  
Figure 8a,b specify a comparison of the simulated and measured SE of the fabricated FSS panel for the TE and TM polarization modes under normal incidence. Since the cell design is compact and fourfold symmetric, it manifests consistent performance for both the x- and y-polarized waves. The results demonstrate that the FSS efficiently shields EM waves, with adequate suppression of 35 dB at its operating frequency. A minor shift in the resonant frequency is observed in the measured results, which is associated with a finite array size, fabrication tolerance, material parameter differences, and measurement errors. In addition, the FSS is also tested for oblique angles up to 60\({}^{\circ}\), with a step size of 30\({}^{\circ}\). Figure 8c,d illustrate the SE-versus-frequency curves as a function of the oblique angle for the TE and TM wave modes, respectively. It can be noticed that the suppression level and the stop bandwidth improve with angle, ensuring wide-angle stability and polarization-insensitive spectral response for the proposed structure, making it suitable for selective shielding applications.  
Figure 8: FSS shielding performance: (**a**) TE mode at normal incidence; (**b**) TM mode at normal incidence; (**c**) SE-versus-frequency curves for the TE mode up to 60\({}^{\circ}\); (**d**) SE plot for TM mode as a function of incident angle, ensuring angular stability.
# A Learning-Based Image Fusion for High-Resolution SAR and Panchromatic Imagery
###### Abstract  
Image fusion is an effective complementary method to obtain information from multi-source data. In particular, the fusion of synthetic aperture radar (SAR) and panchromatic images contributes to the better visual perception of objects and compensates for spatial information. However, conventional fusion methods fail to address the differences in imaging mechanism and, therefore, they cannot fully consider all information. Thus, this paper proposes a novel fusion method that both considers the differences in imaging mechanisms and sufficiently provides spatial information. The proposed method is learning-based; it first selects data to be used for learning. Then, to reduce the complexity, classification is performed on the stacked image, and the learning is performed independently for each class. Subsequently, to consider sufficient information, various features are extracted from the SAR image. Learning is performed based on the model's ability to establish non-linear relationships, minimizing the differences in imaging mechanisms. It uses a representative non-linear regression model, random forest regression. Finally, the performance of the proposed method is evaluated by comparison with conventional methods. The experimental results show that the proposed method is superior in terms of visual and quantitative aspects, thus verifying its applicability.  
image fusion; random forest regression; SAR image; panchromatic image; high-resolution 2020
# Spatial Variation of Water Chemistry in Aries River Catchment, Western Romania
## 2 Methodology
### Graphical Approaches of the Quality Status  
Piper and Durov diagrams were generated by using AqQa software (free version). The studied water samples were classified, based on the specific concentrations of physico-chemical parameters. For the Piper diagram, specific indicators such as Cl\({}^{-}\), SO\({}_{4}\)\({}^{2-}\), Ca, Mg, Na, K, HCO\({}_{3}\)\({}^{-}\) and CO\({}_{3}\)\({}^{2-}\), concentrations were used, while for the Durov plot, the pH and TDS content were added [42; 43].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter** & **Unit** & \(S_{i}\)* & \(w_{i}\) & \(W_{i}\) \\ \hline pH & - & 6.5–8.5 & 3 & 0.06 \\ TDS & mg/L & 1300 & 5 & 0.10 \\ Ca & mg/L & 300 & 4 & 0.08 \\ Mg & mg/L & 200 & 4 & 0.08 \\ Na & mg/L & 200 & 2 & 0.04 \\ Cl\({}^{-}\) & mg/L & 300 & 4 & 0.08 \\ NO\({}_{3}\)\({}^{-}\) & mg/L & 11.2 & 5 & 0.10 \\ SO\({}_{4}\)\({}^{2-}\) & mg/L & 300 & 3 & 0.06 \\ As & mg/L & 0.100 & 3 & 0.06 \\ Mn & mg/L & 0.100 & 3 & 0.06 \\ Fe & mg/L & 0.200 & 3 & 0.06 \\ Pb & mg/L & 0.050 & 3 & 0.06 \\ Ni & mg/L & 0.100 & 3 & 0.06 \\ Zn & mg/L & 1.00 & 3 & 0.06 \\ Cu & mg/L & 1.00 & 3 & 0.06 \\  & & & \(\sum w_{i}=51\) & \(\sum W_{i}=1.00\) \\ \hline \hline \end{tabular}  
* Ministerial Order 161/2006/OD and Directive 2008/32/CE [34; 35].  
\end{table}
Table 2: List of the parameter values (\(S_{i}\)), weights (\(w_{i}\)) and relative weights (\(W_{i}\)) used for the calculation of the WQI.  
To highlight the correlations and similarities among the physico-chemical indicators, the agglomerative hierarchical cluster analysis (HCA) based on the Ward method (Euclidean distance) was assessed. The method was Appl. using XLSTAT software.