esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Acquisition and Preprocessing
#### 2.2.2 Preprocessing of Raman Spectrum Data  
When using Raman spectroscopy to analyze data, the initial data often need to be preprocessed due to interference from factors such as cosmic rays, instrument noise, and autofluorescence of the sample itself, in order to prevent noise from affecting the experimental results. Common preprocessing methods include smoothing, scattering correction, baseline correction, and normalization [32]. In this experiment, S-G (Savitzky-Golay) smoothing and normalization were used to preprocess the raw data. S-G smoothing can maximize the preservation of data information while reducing noise and increasing the signal-to-noise ratio. Normalization can reduce the negative effects of large variations in spectral data, allowing the spectra to fall within a specific range. We preprocessed the obtained 40 Raman spectra, and the preprocessed spectral images are shown in Figure 2. It can be seen that the spectral curve is very smooth, and the Raman intensity (y-axis) is concentrated between 0 and 1. This proves convenient for subsequent analysis and model training. Meanwhile, we can also observe that the spectral images of the four microorganisms are very similar, with their peak positions mostly overlapping. Due to the overlapping Raman spectral characteristics of the four microorganisms, special methods are needed to distinguish them.
## 4 Formulation for Optimal Solution
Constraint in Equation (39) ensures that the decision variable \(\psi_{s_{i}}^{s}\) is enforced to 1 when the sink node \(s_{i}\) has the maximum value of both metrics \(EIM_{s_{i}}^{i}(t)\) and \(SLM_{s_{i}}(t)\), and the minimum value of \(DhM_{s_{i}}^{s}\) compared with other sink nodes. Constraints in Equations (40) and (41) are utilized to ensure that the forwarding candidate neighbor set \(FNEN_{x}\) contains only the neighbor nodes that have relaying speeds higher than the desired speed; it has often less members than \(CNEN_{x}\). If no neighbor node in the candidate set \(CNEN_{x}\) satisfies this condition, the \(FNEN_{x}\) will not have a member. Therefore, such constraints are used to ensure the presence of members in the set \(FNEN_{x}\) otherwise, drop control is called. Constraints in Equations (42) and (43) ensure that every neighbor node \(y\) reaches the selected sink node. Any neighbor node \(y\) must be on at least one path to the selected sink. Constraint in Equation (44) is intended to prevent any cycle. For the same source node \(s\) and message \(m\), the use of any neighbor node \(y\) as a relay node has a cost of 1. Constraints in Equations (45)-(48) ensure that only one neighbor node \(y\) meets the maximum relaying speed criterion while real-time data packets are delivered to the selected sink on time. Constraints in Equations (49)-(52) ensure energy consumption balance to prolong the network lifetime. The only neighbor node \(y\) that has the highest value \(ECL_{y}(t)\) must be selected from the final neighbor set of the sensor node \(x\). Constraints in Equations (53)-(56) guarantee that the sensor node \(x\) selects only one neighbor node \(y\) from its final neighbor set that meets the maximum final environmental impact metric condition so that data packets are sent to the selected sink in safe paths away from hazards. Constraint in Equation (57) is utilized to ensure that the decision variable \(\omega_{xy}^{sm}\) is enforced to 1 when: (1) the neighbor node y reaches the selected sink, (2) it has the minimum value of \(Rd_{y}\) compared with other neighbor nodes, and (3) it has the maximum value of \(EIM_{y}^{i}(t)\) and \(ECL_{y}(t)\) compared with other neighbors. Constraints in Equations (58) and (59) are a redundancy constraint, where all \(\sum\limits_{x,y\in L}d_{xy}^{sm}\) and \(\sum\limits_{p\in P_{y}^{t_{i}}}\alpha_{y}^{p}\) must be greater than or equal to 1. Constraint in Equation (60) the decision variables \(\left\{d_{xy}^{sm},a_{y}^{p},\omega_{xy}^{sm},c_{y},o_{y},b_{v},e_{y},v_{n},N_{y}\right\}\) are equal to 0 or 1.
## 2 Materials and Methods
### Evaluation Metrics  
To quantitatively assess the efficacy of the network model detailed in this study, four fundamental evaluation metrics are employed: the Pixel Accuracy (PA), Intersection over Union (IoU), Recall rate (Recall) and F1-Score. PA serves as a metric delineating the precision of water body extraction, while IoU stands as the standard figure of merit for semantic segmentation, epitomizing the concordance between actual and predicted values. Recall serves as a metric assessing the completeness of water body extraction, indicating the proportion of actual water bodies that have been correctly identified. Meanwhile, the F1-Score stands as a balanced measure of performance for semantic segmentation, providing a comprehensive evaluation of the agreement between actual and predicted values. A higher value for these metrics signifies a superior model performance. The calculation methods are succinctly summarized below:  
\[\text{PA}=\text{TP}/(\text{TP}+\text{FP}) \tag{4}\]  
\[\text{IoU}=\text{TP}/(\text{TP}+\text{FP}+\text{FN}) \tag{5}\]\[\text{Recall}=\text{TP}/\left(\text{TP}+\text{FN}\right) \tag{6}\]  
\[\text{F1-Score}=2\text{TP}/\left(2\text{TP}+\text{FP}+\text{FN}\right) \tag{7}\]  
where TP represents the count of pixels correctly classified, FP represents the count of pixels incorrectly classified, and FN represents tthe count of pixels incorrectly classified as background.
## 4 Magnetic Field Analysis under MTPA and Field Weakening Control Strategy
Points were taken at the typical position of the tooth and yoke, and analyze different conditions, namely no-load, constant torque operation (0-3200 rpm) and constant power operation (3200-6000 rpm)), the radial flux density of the motor teeth, and the tangential flux density of the yoke change, as shown in Figure 9.  
Figure 6: The distribution of magnetic field lines and air gap radial flux density when the motor was under no-load and field weakening control. It can be seen from Figure 9 that similar to the air gap flux density, the no-load air gap flux density of the tooth was a trapezoidal wave. In the constant torque region, the winding current amplitude and phase were the same. The flux density waveform was basically the same. In the constant power zone  
Figure 8: The air gap flux density under the armature reaction with considering the magnetic field of the permanent magnet. Figure 7: The air gap flux density under the armature reaction without considering the magnetic field of the permanent magnet. Figure 9: Changes in the radial flux density of the teeth and the tangential flux density of the yoke of the motor under different working conditions. (field weakening control zone), as the speed increased, the air gap flux density of the motor gradually decreased. Perform Fourier analysis on the radial flux density of the tooth and the tangential flux density of the yoke, and the amplitude of each harmonic component is shown in Figure 10.  
It can be seen from Figure 10 that under the field weakening control strategy, as the speed increased, the d-axis current increased, the magnetic field was weakened, and the amplitude of the fundamental wave decreased. But as the speed increased, the influence of space harmonics increased, and the proportion of harmonic components of flux density gradually increased.
## 4 Results and Analysis
### Results of the EEG Emotion Dataset  
#### 4.2.1 Result of Filter-Based Feature Selection on the EEG Emotion Dataset  
Table 3 shows the experimental results. In the pursuit of optimizing machine learning model performance, the experimentation focused on filter-based feature selection using SelectKBest with an ANOVA \(F\)-test. The initial assessment without feature selection provided a baseline for comparison across multiple metrics. RF achieved an accuracy of 98.78%, logistic regression achieved an accuracy of 96.72%, XGBoost demonstrated exceptional performance at 99.30%, and SVM showed an accuracy of 96.67%. Subsequent evaluations with feature selection at different numbers of features (100, 500, 1000, 1500, and 2000) offered insights into the impact on model performance. With 100 features, the models displayed a balance between accuracy and, with RF at 98.82%, logistic regression at 96.80%, XGBoost at 99.20%, and SVM at 96.75%. As the number of features increased, the models exhibited varying degrees of performance, reaching peaks and plateaus. Notably, XGBoost, with 1000 features achieved an improved accuracy of 99.33% compared to the model without feature selection, demonstrating the model's robustness in handling an extensive feature set. This performance can be attributed to the combination of SelectKBest and aspects of the XGBoost design that make it particularly effective for high-dimensional data.  
Comparing the initial machine learning models with 2548 features, the models with 100 selected features demonstrated an increase in accuracy, precision, recall, and F1 scores across most models: RF (0.04%, 0.01%, 0.02%, 0.02%), logistic regression (0.08%, 0.02%, 0.09%, 0.09%), XGBoost (\(-0.1\%\), \(-0.1\%\), \(-0.1\%\), \(-0.1\%\)), and SVM (0.08%, 0.09%, 0.09%, 0.09%).  
Figure 5: FFT-based frequency analysis of the EEG dataset: randomly selected FFT of a sample with (**a**) positive and (**b**) negative emotion levels; emotion level analysis of the DEAP dataset: (**c**) neutral labels from the EEG Emotion dataset, (**d**) valence level, and (**e**) arousal level from the DEAP dataset.  
Training with 2000 selected features revealed noteworthy changes in model performance. The RF, logistic regression, and SVM models show substantial improvements in accuracy (RF: 0.06%, logistic regression: 0.40%, SVM: 0.11%), precision (RF: 0.04%, logistic regression: 0.27%, SVM: 0.05%), recall (RF: 0.06%, logistic regression: 0.35%, SVM: 0.09%), and F1 score (RF: 0.06%, logistic regression: 0.35%, SVM: 0.11%). These findings underscore the practical benefits of feature selection, particularly for logistic regression and SVM, in enhancing model performance. These results suggest that the selected features have essential information for classification, facilitating more streamlined and resource-efficient model training.
# Application of Image Recognition Methods to Determine Land Use Classes
## 3 Materials and Methods
### Satellite Data Acquisition  
The first step in our methodology involves obtaining Sentinel-2 satellite imagery. Sentinel-2, which is managed by the European Space Agency (ESA), provides multi-spectral data with resolutions ranging from 10 m to 60 m. To ensure high classification accuracy, we  
Figure 1: A schema of the hybrid ML, cloud interpolation, and vegetation indices-based approach.  
utilized Level-2A data, which are atmospherically corrected and pre-processed to reduce noise. The study focused on the territory of Lithuania, where Sentinel-2 data were retrieved from 2022 to 2024. Strict filtering criteria were applied to enhance the quality of data used for classification, ensuring that only images with cloud coverage below 20% were selected. This choice of threshold is supported by previous research findings that have shown that images with less than 20% cloud cover provide an optimal balance between data quality and availability (especially for model training), thereby minimizing spectral distortions or potential errors in the training sets. It is strongly recommended that cloud contamination be limited to this level to ensure the reliability and accuracy of analyses in land use remote sensing applications.  
Additionally, metadata such as acquisition time, sensor angle, and cloud masks were analyzed to identify the most suitable images for classification. The detailed data acquisition schema is presented in Figure 2.  
As shown in Figure 2, Figure 3 provides a detailed representation of the satellite data download process.  
Figure 2: Detailed data acquisition schema. Dotted line—message flow; X—exclusive gateway; +—parallel gateway.
## 1 Introduction  
In recent years, emerging applications have continued to challenge radio frequency (RF)/microwave filters' designers with stringent simultaneous requirements such as high performance, light weight, low cost and miniaturization. As the electromagnetic spectrum is limited and has to be shared, small sized band-pass filters with narrow frequency response and high selectivity are used to confine the signals within assigned spectral limits and to reject the noise and interferences from adjacent channels.  
To minimize the filter size, a practical strategy is to reduce the resonator circuit by modifying its physical structures [1; 2; 3; 4; 5; 6; 7]. Starting from the conventional parallel coupled band-pass filter [8; 9; 10; 11] which has a simple synthesis procedure, and ending with U-shape resonators and open loops [12; 13], hairpin filters [14] has helped progress in size reduction. However, with the rapid evolution of modern communication systems [15], the sizes of these resonators are still not small enough to be used.  
In this work, a novel low thickness band-pass filter based on a simple and compact size electromagnetic resonator without vias is presented. The novelty of this contribution relies on taking advantage of the predicted electromagnetic band-gap (EBG) properties of a resonator unit-cell with improved performance (by means of its dispersion diagram) to achieve the intended band-pass filter behavior. Moreover, the filter exhibits a narrow passband, high out-of-band rejection level, and second harmonic suppression. The paper is organized as follows. Section 2 outlines the study of the unit-cell resonator in terms of dispersion diagram. In Section 3, the design of the filter is presented based on the unit-cell resonator. Also, the possibility of suppressing of the second harmonic is discussed. Two filters are manufactured and measurements concerning the transmission coefficient are provided to show their performance results.
## 6 Results and Evaluation
### Results Discussion
In our proposed evaluation, we study RPL routing protocol using hop count and ETX in terms of overhead traffic, end-to-end delay, PDR and power consumption through two different network topologies. From our experimental and simulated scenarios, we found the following:  
1. Compared to the mentioned related work [7; 9; 12; 17], we tested the performance using several scenarios and network parameters like network density, routing metrics, physical topologies. 2. In contrary to [7], we figured out that RPL performance strongly depends on the physical network topology. For instance, in the random topology case, we experienced performance degradation in terms of traffic overhead, latency, power consumption and PDR compared to the uniform topology. 3. ETX is outperforming hop count as it introduces little traffic overhead, latency, and power consumption while increasing the PDR value than hop count. 4. For large scale networks, ETX introduces higher latency than Hop Count, thus ETX is only not applicable for low latency applications. Table 6 summarizes the comparison of different RPL evaluation environments.
###### Abstract
In this study, forest structural attributes such as diameter at breast height (DBH), mean tree height (MTH) volume and basal area (BA) were investigated for the same species and in the same geographical area but results were different. In terms of coefficient of determination, results were as high as 0.64 for DBH. Advancements in remote sensing made it possible to estimate LAI timely with acceptable accuracy. A number of previous studies estimated LAI with acceptable accuracies using spectral reflectance; however, due to saturation of vegetation indices at LAI values above three, texture measures were utilized as an alternative ([PERSON] et al., 2013). Image texture is the measure of the spatial variation in the grey levels in the image as a function of scale. Image texture was demonstrated by [PERSON] et al. (2013), [PERSON] (2015) and [PERSON] et al. (2019) to improve the estimation of LAI. This paper reviews previous work on remote sensing methods of estimating LAI across different forest ecosystems, crops and grasslands. These three landcover types are distinct in characteristics and, therefore, are studied using different methods, equipment and models. Many different types of forests exist but they are generally characterized by different tree species with distinct upper and lower canopy layers. These forests can be either planted or grow naturally. The different types of forests include tropical, temperate, boreal and savanna forests. Crops are a cultivated type of vegetation that are planted on a large scale. Crops are grown and harvested for profits or subsistence. Examples of crops include but are not limited to rice, wheat and maize [9]. A grassland is a large open area where vegetation is dominated by grasses. Grasslands generally grow naturally and are found in most regions of the world. Grasslands and crops are relatively shorter plants compared to most types of forests [18]. This review investigates the current trends and accomplishments and direction of research in terms of different remote sensing platforms, sensors and models that are utilized to estimate LAI. To achieve this, the paper reviewed published papers on LAIs estimated using remote sensing from the past ten years. Remote sensing was used by a number of researchers to estimate LAI globally and the availability of different remote sensing imagery and increases in diversity of spatial, spectral, temporal and radiometric characteristics led to more research and, thus, better accuracy in estimating LAI [1]. There was a sharp increase in the number of LAI studies using remote sensing between 2010 and 2020; this review, therefore, investigated the contributing factors to this increase, which were also reported in the results of a review paper by [PERSON] et al. [12].
# Image Degradation Modeling for Real-World Super Resolution via Conditional Normalizing Flow
## 2 Related Work  
### Real-World Super Resolution  
In recent years, CNN-based SR methods have been the mainstream in the field. Since [PERSON] et al. [1] proposed SRCNN, numerous CNN architectures have been proposed for SR [2; 3; 6; 13]. Even though these methods achieve impressive performance in terms of fidelity, they would generate visually blurry images, especially for a large-scale factor. To tackle this drawback, various Generative Adversarial Network (GAN)-based SR methods [4; 14; 15] (e.g., ESRGAN [4]) are proposed. They introduce adversarial loss as well as perceptual loss to further enhance the texture details of SR images. However, these methods are trained on synthetic clean data that do not contain any noisy or blurry data, leading to poor performance in real-world scenarios.  
To alleviate this issue, methods [6; 16] have been proposed by introducing synthetic noisy and blurry data into model training and testing. Though these methods enhance the real-world performance and robustness of the SR model, they need to take the prior about blur/noise as part of the input, therefore limiting the scope of application. [PERSON] et al. [17] proposed ZSSR, which learns the internal statistics of test images during testing. Meanwhile, some recent works have focused on collecting paired HR-LR real-world images. [PERSON] et al. [7] captured the images of 100 city scenes based on two imaging systems and obtained a real-world paired dataset known as City100 [7]. [PERSON] et al. [8] collected paired HR-LR images on the same scene by adjusting the focal length of a digital camera and proposed a RealSR [8] dataset (559 scenes). However, it requires a high degree of manual costs and strict conditions to collect such paired datasets. The number and diversity of these datasets are limited.
# Full-Field Super-Resolution Terahertz Imaging Based on Rotating Coherent Scattering Microscopy
## 2 Principle of THz ROCS
At this time, the spatial frequency information in the red region can be received by the system. It is worth noting that when evanescent wave illumination is used, the frequency shift of the passband can exceed \(k_{0}\), then super-resolution imaging can be achieved. Figure 1: Top view and side view of the TIR schematic based on a hemispherical lens. When the direction of the incident evanescent wave rotates relative to the sample, the frequency shift direction will be changed accordingly, and the corresponding passband will rotate around the center. Specifically, the direct intensity imaging results under different directions of illumination with the number \(N\) are recorded by rotating the hemispherical lens. Here, the ROCS principle is considered for reconstruction, and the super-resolution image is obtained by the direct superimposition of frequency-shifted images, which is recorded as \(I_{final}\)[38]:  
\[I_{final}=\frac{1}{N}{\sum_{m=1}^{N}}{\left[f(\mathbf{r})\cdot E_{m}(\mathbf{r},\phi_{ m})\otimes h_{c}(\mathbf{r})\right]}^{2}, \tag{2}\]  
where \(\mathbf{r}\) denotes the lateral position in the \(x\)-\(y\) plane; \(f\) is the field distribution of the sample; \(E_{m}\) is the electric field function of the evanescent wave incident from the direction of illumination \(\phi_{m};h_{c}\) is the coherent point spread function (PSF) of the imaging system; and \(\otimes\) denotes the convolution operation. Consider the sample as the sum of point scatterers at different positions, then  
\[f(\mathbf{r})=\sum_{j}f_{j}(\mathbf{r})=\sum_{j}\delta(\mathbf{r}-\mathbf{r}_{j}), \tag{3}\]  
and set the field image of the \(j\)th scatterer to be  
\[F_{m,j}(\mathbf{r},\phi_{m})=f_{j}(\mathbf{r})\cdot E_{m}(\mathbf{r},\phi_{m})\otimes h_{c }(\mathbf{r}). \tag{4}\]  
This leads to the intensity under the illumination of the one-directional evanescent wave being:  
\[I_{m}=\left[\sum_{j}F_{m,j}(\mathbf{r},\phi_{m})\right]^{2}. \tag{5}\]  
Figure 2: The relationship between the incidence angle, the aperture angle, and the passband in the wave vector space under the tilted plane wave illumination and the TIR mode, respectively. At this time, the total intensity of the final image is:  
\[I_{final}=\frac{1}{N}\sum_{m=1}^{N}\left[\sum_{j}\abs{F_{n,j}(\mathbf{r},\phi_{m})} \right]^{2}+\sum_{j\
eq k}2\abs{F_{m,j}}^{2}\cdot\abs{F_{m,k}}^{2}\cdot\sum_{m= 1}^{N}\cos(\varphi_{m,j}-\varphi_{m,k})\right]. \tag{6}\]  
where \(\varphi(\mathbf{r},\phi_{m})\) is the phase of the field image that depends on the position \(\mathbf{r}\) of the scatterer and the direction of illumination \(\phi_{m}\). The former term of the above equation represents the incoherent images of different scatterers, and the latter term represents the cosine sum of the phase differences of the point scatterers in all illumination directions.