esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Calculation Results and Analysis
### Analysis of Flow Field Characteristics  
As demonstrated by Figure 12, it becomes evident that under the condition of an AOA of 0\({}^{\circ}\), the gas-liquid motion trajectory surrounding the airfoil and the limit of water-drop impact on the wing are both symmetrically distributed. Due to the alterations in AOA, the location at which the fluid comes into contact with the airfoil surface undergoes a corresponding adjustment. As illustrated in Figure 12a,b, a discernible trend is observed in the displacement of the gas-liquid impact area along the airfoil's leading edge as the AOA increases from 0\({}^{\circ}\) to 9\({}^{\circ}\). Figure 12c illustrates that the region of high LWC encircling the airfoil face also increases with an elevation in AOA. Furthermore, the zone with the highest LWC of water drops progressively expands towards the trailing edge of the lower surface of the airfoil. As depicted in Figure 12d, it is evident that the threshold for water-drop impact position has undergone a notable downward shift. Based on the streamline diagram, it is evident that the air bypasses the wings along the wall, and a significant portion of water drops also bypass the surface. Only a minor fraction of the water drops made contact with a small section of the leading edge of the wing. It is clear that air and diminutive water drops are notably susceptible to flow perturbations, resulting in trajectory deviations, whereas certain macroscopic droplets exhibit direct impingement upon the wing wall, owing to their larger inertia. After passing over the wing's leading edge, the fluid stream proceeds smoothly along the wing's surface without making direct contact again. Based on the data presented in Figure 12d, it can be observed that the water-drop impact threshold is confined to a limited area of the wing's leading edge. This phenomenon accounts for the predominant occurrence of wing icing in this particular region.  
Figure 11: Effect of LWC on water-drop collection coefficient.
# Monitoring Forest Diversity under Moso Bamboo Invasion: A Random Forest Approach
## 4 Discussion
### Model Performance  
Predictive model performance metrics were based on three RF predictions. The mean R\({}^{2}\) value assesses the fit of the model, with higher values indicating a better fit of the model to the data. In our study, the average R\({}^{2}\) values indicated that the RF model had a strong predictive ability for the Margalef, Shannon, and Simpson indices, i.e., it could capture abundant information to accurately predict the diversity indices. However, the Pielou index had an average predictive ability with a lower R\({}^{2}\) value than the other indices. To further assess the performance of the model, we calculated the average of the RMSE and MAE, which confirm the findings of the R\({}^{2}\) values and indicate good model performance for the Margalef, Shannon, and Simpson indices. Again, the prediction accuracy was lower for the Pielou index. Moreover, scatter plots were generated to visually present the relationship between predicted and observed values and further assess model predictive performance. Similarly, the model predictions were accurate for the Margalef, Shannon, and Simpson indices, with predicted values being very close to the observed values, indicating that the predictions of the model were generally consistent with the actual data. However, for the Pielou index, the scatterplot showed significant differences between the predicted and observed values, especially for areas with low diversity indices, highlighting the tendency of the model to overestimate or underestimate certain diversity indices under different scenarios. These results provide insights into model performance and its impact on predicting biodiversity indices.
## 3 Study Area and Dataset
### Image Dataset and Pre-Processing  
Two different datasets of HSR multi-spectral satellite images were used to map changes in artificial sprawl. Different datasets were used for each period: RapidEye images for the most recent period (2009) and Indian Remote Sensing (IRS) images for the earlier (1996-1997) period. IRS images were the only HSR remotely sensed data available that covered the study area during the earlier period. Table 1 lists the characteristics of the two datasets. We wanted to use images acquired during spring and summer, _i.e._, when vegetation was in active growth, to limit confusion between bare soils (natural or agricultural) and artificial areas.  
RapidEye images are produced by the remote sensors in a five-satellite constellation in use since  
Figure 1: The study area in the Languedoc Roussillon region, in the South of France. The method was applied at the scale of the four coastal departments.  
2008. This constellation can quickly provide homogenous and recent data covering large areas. Images are delivered in 25 km large orthorectified blocks with a spatial resolution of 5 m. Sixty-five images (acquired during spring and summer, 2009) were required to cover the study area.  
IRS images are generated by two remote sensors: one sensor (LISS) is used to acquire multispectral images at a spatial resolution of 23 m, and the other (PAN) for panchromatic images with a spatial resolution of 5.8 m. Images from the two sensors (LISS & PAN) were used for our study because panchromatic images are required to accurately delineate artificial areas in addition to multispectral images. Twelve archive image pairs (LISS + PAN) were used to cover the study area. Images from each pair were collected simultaneously. Only eight image pairs were available for spring or summer in the archive catalogue, the three other pairs were acquired in the fall. Images are delivered without geometric correction. Each image was orthorectified using the LPS module of ERDAS IMAGINE(tm) ERDAS, Inc. (GA, USA) software based on a digital elevation model with a spatial resolution of 50 m and orthophotos with a spatial resolution of 0.5 m produced by the French National Geographic Institute (IGN). The images were resampled to match the spatial resolution of the RapidEye images (5 m). Finally, mosaics were built for the whole study area using each image dataset.
## 5 The Case Study
### The Governing Equations
#### 5.3.2 The PDMS Diaphrag Characterization  
The other important characterization process concerns the analysis of the dynamic mechanical behavior of the PDMS membrane properties. Figure 6 depicts an electromagnetic actuated mass-spring-damper equivalent schematic that represents the model adopted for the characterization of the main dynamic parameters. The here-reported model can be fully defined based on three main lumped parameters: the equivalent spring \(k\), the equivalent damping \(c\), and the equivalent mass \(m\).  
The total equivalent mass was obtained from the CAD model of the device and was the result of the sum of the PDMS diaphragm mass plus the mass of the NdFeB permanent magnet. The lumped value of the equivalent spring was instead obtained from the reduced static equilibrium at the mass of the system Equation (12).  
\[F_{mag}=F_{g}+F_{el} \tag{12}\]  
where \(F_{g}\) is the weight force of the total mass, \(F_{el}\) is the force exerted by the equivalent spring, and \(F_{mag}\) is the Lorentz formulation of the magnetic force exerted by an n-turning coil. The resulting expression of Equation (12) is reported below in Equation (13).  
\[n\ l\ i\ \Lambda\ B=\Big{(}m_{diaphragm}+m_{magnet}\Big{)}g+k\ w_{max} \tag{13}\]  
Coupling the resulting formulation with the theory of diaphragm deflection (see Equation (14)) proposed by [PERSON] in Bao (2010), it is possible to trace the formulation of the equivalent winding length and the equivalent stiffness parameter, reported in the set of Equation (15).  
\[w_{max}=\frac{F\ r^{4}}{S64D}\quad D=\frac{E\ h^{3}}{12(1-v^{2})} \tag{14}\]  
\[l=\frac{w_{max}\pi r^{2}64D}{N\ i\ B\ r^{4}}\quad k=\frac{n\ l\ i\ B-\Big{(}m_{diaphrag }+m_{magnet}\Big{)}g}{w_{max}} \tag{15}\]  
The constitutive parameters' nomenclature and the relative values are listed in Table 2.  
\begin{table}
\begin{tabular}{l c c} \hline
**Parameter** & **Value** & **Unit** \\ \hline AC resistance: \(R_{AC}\) & 2 & \([\Omega]\) \\ Stray capacitance: C & \(1.36\times 10^{-10}\) & \([\mathrm{F}]\) \\ Stray inductance: \(L\) & \(4.766\times 10^{-7}\) & \([\mathrm{H}]\) \\ \hline \end{tabular}
\end{table}
Table 1: Estimated R-L-C parameters @ 100 Hz.  
Figure 6: MEMS electromagnetic actuator schematic model.  
Due to the lack of information provided in the referenced article [4], the equivalent damping \(c\) of the device was tuned numerically and obtained a value equal to \(5.53\times 10^{-6}\frac{\left(\frac{\text{N}_{\text{k}}}{\text{mm}}\right)}{\text{mm}}\).
# Developing Occupancy Grid with Automotive Simulation Environment
## 2 Materials and Methods
### Performance Assessment Methods
#### 2.5.2 Proposed KPIs  
Every resultant (the 1-D simplification is presented in Figure 10) cell is compared to ground-truth. The results of grid fusion _D3.2_ are binarized and undergo spatio-temporal alignment against the reference map _D4_. The results are classified as: True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN). The number in a given class is added [17]. Then, the data are used for analysis with the following KPI methods:  
* Probability of detection This indicator corresponds to the True Positive Rate (TPR) produced by the algorithm, and its increase means that the analyzed method is better at detecting actual obstacles, and is therefore expected to be maximized. \[TPR=\frac{TP}{P}=\frac{TP}{TP+FN}\] (31)
* Precision The factor representing Positive Predictive Value (PPV) indicates how good the method is at judging whether the reported occupied areas are actually occupied. This factor is therefore expected to be maximized for the best performance. \[PPV=\frac{TP}{TP+FP}\] (32)
* Probability Of False Alarm The False Positive Rate (FPR) shows how bad the method is at the estimation of a certain cell as free when it is occupied in the scope of all those events and actually free cells in the experiment. For better performance, this factor has to be minimized, since the severity of false positives is significant in the Advanced Driver Assistance Systems from a functional safety perspective [18]. \[FPR=\frac{FP}{N}=\frac{FP}{FP+TN}.\] (33)  
Figure 10: Drawing of the 1D simplification of the reference data generation and comparison with the high resolution reference data along with the resulting event classification for the performance assessment.  
* False Discovery Rate (FDR) The FDR describes a ratio of how many cells identified as occupied were falsely classified. In terms of a better performing method, this factor must be minimized. \[FDR=\frac{FP}{FP+TP}.\] (34)
* F1 Score F1 Score is also known as a measure of accuracy, or a harmonic mean of precision and accuracy. The higher the value of this factor, the better the performance of the method. \[F1=2\frac{PPV\cdot TPR}{PPV+TPR}=\frac{2 TP}{TP=TN+FP+FN}.\] (35)
* Normalized Occupied Map Score (NOMS) The NOMS method is a variation of the method used for grid performance evaluation proposed by [PERSON] et al. [19]. This method relies on non-binary algorithm outputs (a grid composed of cells with a probability gradient) A and a filtered reference grid B--obtained by subjecting a binary reference grid to a spatial filtering that generates gradients of probabilities. The maximization of the score for this method indicates higher performance. \[NOMS=2^{\sum_{i}[\log_{2}(A_{i}B_{i}+A_{i}B_{i})]}.\] (36)  
The set of chosen indicators purposely does not include the methods relying heavily on true negatives due to the sparse nature of the grids. These kinds of indicators were identified as having a bias on the results.
## 3 Results
### Quantification of Caffeic Acid in Food Supplements  
The SPCE/PB/HRP biosensor, the biosensor with the best performance characteristics developed in this study, was used to quantify caffeic acid in three food supplements. Cyclic voltammograms recorded with the SPCE/PB/HRP biosensor in the solution of these products show a pair of redox peaks related to the presence of caffeic acid present in the samples to be analyzed, similar to those observed in pure caffeic acid solutions.  
The amounts of caffeic acid in the food supplements were calculated from the calibration equations for the biosensor (SPCE/PB/HRP). All quantification experiments were performed in triplicate and the results obtained are reported as averages.  
For the voltammetric method, the current corresponding to the specific detection potential for caffeic acid at 1.100 V was taken into account.  
For the calculation of the values reported in Table 4, the dilutions and the amount of food supplements used in the analysis were taken into account. Manufacturers indicate that caffeic acid is present in food supplements, specifying the amount in some cases. The results, expressed in mg of caffeic acid per capsule, are shown in Table 4.  
The values of caffeic acid content obtained with the biosensors are close and the analysis of variance (ANOVA) showed that there is no significant difference between the values at a 99% confidence level (\(p=0.002\)), assuming equals variances. This result demonstrates that the voltammetric method is a sensitive method and has very good accuracy for the determination of caffeic acid in food supplements.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Food Supplement**} & \multicolumn{2}{c}{**Caffeic Acid Content (\%)**} \\ \cline{2-3}  & **Biosensor** & **Declared Content by Producer (\%)** \\ \hline Telom-R Diab & 2.54 \(\pm\) 0.08 & 2.5 \\ Calmogen Plant Somn & 1.20 \(\pm\) 0.002 & - \\ DVR-Stem Glycemo & 2.47 \(\pm\) 0.06 & 2.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Caffeic acid content in food supplements.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Caffeic Acid 20 \(\upmu\)M + Interfering Compound 20 \(\upmu\)M** & **Potential (V)** & **Potential Change (\%)** & **Current (\(\upmu\)A)** & **Current Change (\%)** \\ \hline Caffeic acid & 1.100 & - & 140.2 & - \\ Caffeic acid + catechol & 1.105 & 0.45 & 141.3 & 0.78 \\ Caffeic acid + catechchin & 1.092 & 0.72 & 138.7 & 1.07 \\ Caffeic acid + gallic acid & 1.110 & 0.91 & 143.5 & 2.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Interference study results on the response of biosensor SPCE/PB/HRP immersed in 20 \(\upmu\)M of caffeic acid.
## 2 Materials and Methods
### Force Variables  
Measures for the force-time curve were automatically obtained using the manufacturer's software for windows (Chronojump 1.9.0, Barcelona, Spain). Results from each of the three trials were used for the intra-session analysis, whereas the trial with the highest values was used for the test-retest  
Figure 1: Isometric knee extension test set-up. The portable strain gauge secured at one end to the bench at 45 cm from the floor, and attached at the other to a chain connected to a resistant padded ankle. Visual feedback was given in real time using a computer software.  
reliability analysis [9]. This software provides values in raw (original signal) and fitted (inverse monoexponentially function that better fits the raw data). This fitting is made by adjusting the Fmax (maximum force, i.e., MVC) and tau (\(\tau\), the time necessary to reach the 63.2% of the Fmax), as follows: \[\text{F}=\text{Fmax}\times(1-\text{e}^{*}(1/\tau))\] The following variables were calculated (Figure 2):  
* Maximal voluntary contraction force (MVC): instantaneous maximal isometric muscle strength in Newtons (N).
* Rate of force development (RFD): the contractile RFD was obtained from the slope of the force-time curve (\(\Delta\)Force/\(\Delta\)time) expressed in N-s\({}^{-1}\); thus, the instantaneous RFD peak (RFDmax) was the highest slope of the curve [17]. Average RFD was calculated for three overlapping periods in milliseconds to collect measures in three different phases of the contraction: 0-50 ms (RFD\({}_{0-50}\)), 0-150 ms (RFD\({}_{0-150}\)) and 0-250 ms (RFD\({}_{0-250}\)) [9; 27].
* Impulse: the impulse was calculated through integration of force over time (i.e., cumulated area under the force-time curve) expressed in N-s [17]. Average impulse was calculated for the same three overlapping RFD periods in milliseconds (Impulse\({}_{0-50}\), Impulse\({}_{0-150}\) and Impulse\({}_{0-250}\))
## 4 Experimental Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No** & **Title** & **Year** & **Ref** & **Accuracy** \\ \hline
1 & Artificial Intelligence-Driven Real-Time Automatic Modulation Classification & 2021 & [1] & **97** \\  & Scheme for Next-Generation Cellular Networks. & 2021 & [3] & **99** \\
2 & Machine Learning Based Automatic Modulation Recognition for Wireless & 2021 & [4] & **95** \\  & Communications: A Comprehensive Survey & 2019 & [4] & **95** \\
3 & Faster Maximum-Likelihood Modulation Classification in Flat Fading & 2020 & [5] & **96** \\  & Non-Gaussian Channels & 2021 & [6] & **98.7** \\
4 & Deep Convolutional Neural Network with Wavelet Decomposition for & 2020 & [7] & **99.7** \\  & Automatic Modulation Classification & 2021 & [19] & **98.7** \\
5 & Deep Learning-Based Robust Automatic Modulation Classification for Cognitive & 2020 & [19] & **98.7** \\  & Radio Networks & 2021 & [19] & **93** \\
6 & An Efficient CNN Architecture for Robust Automatic Modulation Classification & 2020 & [32] & **93** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Compare the proposed method against a number of previously trained CNN. Figure 8: The performance of the pre-trained models and the proposed model.
## 3 Results and Discussions
### Optical Properties
#### 3.3.3 Calculation of Optical Band Gap and Urbach Energy  
To go deeper inside the absorption behavior of the films, we calculated the optical band gap, \(E_{\rm g}\), and Urbach energy, \(E_{\rm U}\); the values are presented in Table 2. A Tauc plot [39] was used for calculation of \(E_{\rm g}\), assuming direct transition between valence and conduction bands. \((\alpha E)^{2}\) is plotted versus light energy \(E\), and from the linear part of the curve, \(E_{\rm g}\) is calculated at \((\alpha E)^{2}=0\) (\(\alpha\) is the absorption coefficient in cm\({}^{-1}\), calculated from the previously determined extinction coefficient \(k\), using relation \(\alpha=4\pi\)k/\(\lambda\), and \(E\) is the photon energy in eV; the power degree is 2 because of the assumption of direct transitions in ZnO). The values of \(E_{\rm g}\) in Table 2 indicate an increase in the optical band gap after doping with 40-60 meV. The widening of the optical band of ZnO after doping with Al and Co has been observed by many authors [10; 17; 37; 40]. The increase in \(E_{\rm g}\) can be attributed to the Burstein-Moss effect, in which the increase in carrier concentration generated from substitution of Zn ions leads to a shift of Fermi level toward the conduction band.  
To further clarify the light absorption in the films, we calculated the Urbach energy, \(E_{\rm U}\), which is the width of the band tail (therefore also called the Urbach tail) and is commonly associated with structural disorder in the material. We used the dispersion curve of the extinction coefficient (the absorption coefficient \(\alpha\)) in the spectral range, where it obeys exponential decay [39]:  
\[\alpha=\alpha_{o}\mathrm{exp}(\frac{E}{E_{\rm U}}) \tag{3}\]  
where \(\alpha_{o}\) is a constant and \(E\) is the photon energy in eV. The slope of the linear fit of the plot ln(\(\alpha\)) versus \(E\) gives the reciprocal value of \(E_{\rm U}\). From Table 2, it can be seen that doping with Co leads to an increase in the Urbach energy to 117 meV for CZO and 19 meV for CAZO films compared to pure ZnO films. This result is expected and explains the observed increase in the slope of the fundamental absorption edge in transmittance spectra (Figure 5) and the tail in extinction spectra for the CZO and CAZO films (Figure 6b). Our results are consistent with the results of other authors. For example, [PERSON] and [PERSON] [24] have found that the Urbach energy for CZO films obtained by the chemical bath deposition method increases from 88 meV for pure ZnO to 103 meV and 107 meV for 6% and 9% cobalt-doped films, respectively. [PERSON] et al. [26] have reported an increase in \(E_{\rm U}\) from 62 eV for ZnO to 266 eV for 6% Co-doped ZnO films obtained by the sol-gel and spin-coating methods.
# TSNetIQ: High-Resolution DOA Estimation of UAVs Using Microphone Arrays
## 2 System Model and Problem Formulation
### Problem Formulation  
Most deep learning-based DOA estimation methods formulate angle prediction as a classification task by discretizing the angular space. However, these approaches fail to capture the continuity of angles, particularly near category boundaries, leading to discretization errors and limited generalization. To address this issue, this paper frames DOA estimation as a regression problem, directly predicting continuous angles between 0\({}^{\circ}\) and 360\({}^{\circ}\). This eliminates discretization artifacts and improves accuracy.  
Conventional inputs for deep learning DOA models include raw phase features, hand-crafted features, hybrid representations, or covariance matrices. This article evaluated  
Figure 1: The structure of UCA.  
multiple input representations (raw signals, phase-based features, STFT/FFT spectrograms) and observed consistently lower accuracy compared to \(IQ\) components. The \(IQ\) representation inherently preserves amplitude and phase information, enabling the learning of signal envelopes and instantaneous phases. Crucially, it avoids (1) information loss from Fourier transforms numerical errors, and (2) complex preprocessing (e.g., covariance matrix computation). Thus, \(IQ\) components are adopted as inputs to enhance feature richness while streamlining processing.  
Specifically, this paper proposes a regression model based on the \(IQ\) components of the signal, which directly outputs continuous angle values through deep learning.  
The angle \(\theta\) is defined as a continuous value representing all possible directions of arrival, satisfying  
\[\theta\in[0,360^{\circ}) \tag{2}\]  
Our objective is to formulate a regression problem that maps the \(IQ\) components of signal data to a continuous, real angle \(\theta\) via a neural network.  
Given the complex-valued time-domain signal samples collected across \(M\) sensors at a given sampling frequency \(f_{s}\) and a fixed sampling time \(T\), our goal is to learn a mapping from the \(IQ\) components of signal input to continuous-angle variables \(\theta\in[0,360^{\circ})\).  
Formally, the task is to learn a function defined as  
\[f_{\Theta}:\mathbb{R}^{2M\times(f_{s}\times T)}\rightarrow\mathbb{C}^{1} \tag{3}\]  
where \(\mathbb{C}^{1}\) denotes the unit circle manifold representing the continuous-angle space, and \(f_{\Theta}\) is a nonlinear function approximated by a deep neural network with learnable parameters \(\Theta\).  
Deep learning is used to approximate this nonlinear \(f_{\Theta}:\mathbb{R}^{2M\times(f_{s}\times T)}\rightarrow\mathbb{C}^{1}\) mapping  
\[\hat{\theta}=f_{\Theta}\big{(}X_{IQ}\big{)} \tag{4}\]  
where \(\hat{\theta}\) is the predicted DOA angle. Thus, the problem definition is completed.