esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Land Use/Cover (LULC) Maps for 1984 and 2018  
The LULC maps in Figure 5 provide the spatial distribution of classes and changes between 1984 and 2018. Visual inspection of the two images reveals that from 1984 to 2018, there has been a reduction in vegetation and cropland area. While there are modest  
Figure 4: Trends in extremely high rainfall events.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class Pair Separation** & **1984** & **2018** \\ \hline Croplands and HDR & 1.01 & 1.52 \\ Croplands and Vegetation & 1.02 & 1.72 \\ LDR and Vegetation & 1.10 & 1.55 \\ Vegetation and Water & 1.26 & 2.00 \\ CBD/Industrial and HDR & 1.36 & 1.27 \\ LDR and Water & 1.41 & 1.96 \\ Croplands and Water & 1.53 & 1.99 \\ Croplands and LDR & 1.61 & 1.45 \\ HDR and LDR & 1.71 & 1.46 \\ HDR and Vegetation & 1.75 & 1.69 \\ CBD/Industrial and Water & 1.76 & 1.99 \\ HDR and Water & 1.77 & 1.99 \\ CBD/Industrial and Croplands & 1.79 & 1.84 \\ CBD/Industrial and LMR & 1.89 & 1.79 \\ CBD/Industrial and Vegetation & 1.95 & 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ROI Separability Test results for ROI classes used in 1984.  
increases in CBD and LDR areas, there has been a substantial increase in area under HDR from 1984 to 2018. The increase in HDR area is mainly at the expense of vegetation and croplands. Vegetation left is predominantly found in the North-East, where LDR are located.
## 2 UWB Patch Antenna Design
### Design Equations  
Several generic equations are required to estimate the antenna parameters, which govern the antenna design process. For a given \(\epsilon_{r}\), Equation (1) expresses the effective dielectric constant.  
\[\epsilon_{ef}=\frac{\epsilon_{r}+1}{2}+\frac{\epsilon_{r}-1}{2}\bigg{[}1+12 \frac{h}{W}\bigg{]}^{-1/2} \tag{1}\]  
In Equation (1), \(h\) and \(W\) represent the substrate's height and width, respectively, while \(\frac{W}{h}\gg 1\) represents the substrate's width-to-height ratio for minimizing the fringing effect.  
Figure 2: The flow chart of the mutual coupling reduction method.  
The \(\epsilon_{ef}\) and \(\frac{W}{h}\) are two important factors that affect the extended length \(\Delta L\) of the patch. Equation (2) gives the normalized extended length of the patch.  
\[\frac{\Delta L}{h}=0.412\frac{\left(\epsilon_{ef}+3\right)\left(\frac{W}{h}+0.264 \right)}{\left(\epsilon_{ef}-0.258\right)\left(\frac{W}{h}+0.8\right)} \tag{2}\]  
The effective length for dominant mode without fringing effect is then given as  
\[L_{ef}=L+2\Delta L \tag{3}\]  
Equation (4) describes how the patch antenna's resonant frequency \(f_{r}\) depends on both its length \(L\) and the speed of light \(v\).  
\[f_{r}=\frac{1}{2L\sqrt{\mu_{0}\epsilon_{0}}\sqrt{\epsilon_{r}}}=\frac{v}{2L \sqrt{\epsilon_{r}}} \tag{4}\]  
In Equation (4), \(\mu_{0}\) denotes the permeability of free space, while \(\epsilon_{0}\) is its relative permittivity. To account for fringing in order to include edge effects, Equation (4) is modified as  
\[f_{rf}=\frac{1}{2L_{ef}\sqrt{\mu_{0}\epsilon_{0}}\sqrt{\epsilon_{ef}}}=\frac{ 1}{2(L+\Delta L)\sqrt{\mu_{0}\epsilon_{0}}\sqrt{\epsilon_{ef}}}f_{rf}=q\frac{ 1}{2L\sqrt{\mu_{0}\epsilon_{0}}\sqrt{\epsilon_{r}}}=q\frac{v}{2L\sqrt{ \epsilon_{r}}} \tag{5}\]  
In Equation (5), \(f_{rf}\) is the resonant frequency due to fringing effect, while the factor \(f_{rf}/f_{r}\) denotes the fringe factor (length reduction factor). Both \(W\) and \(L\) are determined using Equations (6) and (7) [35].  
\[W=\frac{1}{2f_{r}\sqrt{\mu_{0}\epsilon_{0}}}\sqrt{\frac{2}{\epsilon_{r}+1}}= \frac{v}{2f_{r}}\sqrt{\frac{2}{\epsilon_{r}+1}} \tag{6}\]  
\[L=\frac{1}{2f_{r}\sqrt{\epsilon_{ef}}}-2\Delta L \tag{7}\]  
In these expressions, an increase in \(h\) causes an increase in fringing. This produces a significant separation between the radiating edges and the lower \(f_{r}\). Table 1 illustrates the dimensions and design parameters of the UWB patch antenna with the geometric configuration shown in Figure 1.
## 2 Low-Resource Languages
### Challenges in Low-Resource Language Processing  
The natural language processing (NLP) tools experienced a drastic change in the 1990s, shifting from rule-based techniques to statistically based approaches, and a new era of artificial intelligence started. Since then, the focus has majorly been on English as an international language, and about 20 languages out of 7000 languages of the world have been considered [10].  
Languages that need a lot of research are often referred to as low-resource languages and face many challenges, as briefly discussed below:  
* Alignment or the projection technique (three levels of alignment, i.e., word, sentence, and document) is a common technique for annotation. It is difficult to adopt the projection technique from HRLs to LRLs because of a lack of resources and different structures of target and source languages [8];
* Creating a bag of words, dataset, and raw text collection for LRLs is difficult but necessary for any natural language processing (NLP) task or mapping technique [8];
* The most important resource for any language is the lexicon of that language; many NLP tasks heavily depend on the textual material available, which is lacking in LRLs, making it a challenging task to produce an efficient lexicon;
* The morphology of LRLs is constantly evolving, with vocabulary easily extended. Developing a comprehensive framework for morphological pattern recognition is difficult because of multiple roots [11];
* The major applications of NLP, such as question-answer systems, sentiment analysis, image-to-text mapping, machine translation, and named entity recognition-based systems, are very difficult to implement in low-resource languages;
* Basic NLP tasks such as stop-word identification and removal, tokenization, part-of-speech tagging, sentence parsing, lemmatization, stemming, etc., are also difficult in low-resource languages;
* The NLP systems of LRLs are time-consuming and comparatively less efficient as a result of a lack of resources, increasing the difficulty of developing a machine learning system [10];
* Many languages are mostly oral, for which very few written resources exist (physical and digital formats). For some, there are written documents but not even a basic resource like a dictionary;
* Integrated and customized systems are always a huge challenge for multilingual systems.  
Dealing with all the challenges faced by low-resource languages requires extensive research in different dimensions. Urdu and Arabic are two huge languages that need a lot of focus in research.
## 4 System Tolerance Analysis  
We assigned tolerances to the two sub-systems of the integrated system, and performed Monte Carlo analysis on each sub-system based on the tolerances. The integrated system is a reflective system, mainly based on tolerances such as radius tolerance, mirror displacement, mirror tilt and other tolerances [15]. The results of the tolerance distribution of the visible light subsystem are shown in Table 3. The analysis shows that the displacement tolerance of the secondary mirror of the system is more sensitive. Through the 500-run Monte Carlo analysis, the MTF value of the system with tolerance is 0.386.  
The primary and secondary mirrors of the long-wave infrared subsystem are shared with the visible light band, so the result of the tolerance distribution of the primary and secondary mirrors remains unchanged. The rear optical system tolerance distribution results in the infrared band are shown in Table 4. The infrared band has a longer wavelength, so the tolerance requirements are relatively loose. Through 500-run Monte Carlo analysis, the MTF value of the system with tolerance is 0.345. It can be seen from the results that the infrared system is less sensitive to various tolerances, and the MTF of the system with tolerances only drops by 0.015, so various tolerance values can be appropriately increased.
## 3 Experimental Results  
### Damage Modes  
Figure 8 shows all tested specimens with their damage locations and failure modes. Table 4 presents the crushing locations of concrete pile specimens after the maximum load decreased by 85%. Further comparisons on the damage of PHC-1 to PHC-4 specimens were also listed in Table 4 based on the reference [28]. It can be observed from Figure 8a-g and Table 4 that the crushing locations of CP-1 to CP-5 were at 3.5 D, 3.6 D, 5.4 D, 3.4 D, 4.3 D, and PC-6 and PC-7 were at 4.4 D and 4.6 D of the buried depths, respectively. It was found that the crushing location became deeper with the increase of reinforcement ratio, buried pile depth and prestressing level. Figure 8h shows the crushing location of the RP-8 specimen was located at 6.2 D of the buried depth. It can be observed that the pile with larger  
Figure 7: (**a**) Overall view of the test setup. (**b**) Detail of the test setup. (**c**) Loading protocol of the test.  
cross-section had a better soil-pile interaction and deeper crushing location as compared to another pile (CP-2) with the same material and reinforcement ratio. For the HP-9 specimen (Figure 8i), only the bending deformation was observed without any sign of buckling phenomenon in the flange and the web of the pile, which indicates that the HP-9 specimen performed a favorable elastic-plastic behavior compared to the CP, PC, RP and PHC specimens.  
In Table 4 the crushing length \(L_{ci}\) was measured from the soil surface. For instance, \(L_{c1}\) represents the distance from the crushing location of the CP-1 specimen to the soil surface. \(L_{ci}\)/\(L_{c1}\) is the crushing length ratio of other specimens versus the CP-1 specimen. The damage locations of the CP specimens were in the range of 3.4 D to 5.4 D of the buried depth. For the PC specimens, the damage locations were in the range of 3.6 D to 4.6 D of the buried depth. The damage locations were in the range of 4.2 D to 7.7 D of the buried depth for the PHC specimens. The main damage of the RP-8 specimen was located at 6.2 D of the buried depth. From Figure 8 and Table 4, it can be concluded that the soil-pile system had a better interaction at deeper buried depth and more efficient with the larger reinforcement ratio, buried pile depth, prestressing level, cross-sectional dimension and concrete strength.  
Figure 8: Specimens’ failure modes. (**a**) CP-1 specimen. (**b**) CP-2 specimen. (**c**) CP-3 specimen. (**d**) CP-4 specimen. (**e**) CP-5 specimen. (**f**) PC-6 specimen. (**g**) PC-7 specimen. (**h**) RP-8 specimen. (**i**) HP-9 specimen.
## 2 Materials and Methods
### Electrochemical Study  
The conductivity of the YSZ-rGO ceramics at different temperatures was studied via electrochemical impedance spectroscopy method (Potentiostat/Galvanostat Autolab PGSTAT 302 N). A central sector with width of \(\sim\)2.5 mm was cut from a sample of each composition for the investigation. Measurements were conducted in N\({}_{2}\) atmosphere with residual oxygen pressure less than 10\({}^{-3}\) atm with silver paste electrodes; the data for each 50 \({}^{\circ}\)C in a range from room temperature to 700 \({}^{\circ}\)C were taken. Impedance spectra were analyzed using Autolab NOVA and Impfco software. Activation energies of the conductivity were estimated using Arrhenius equation from linear temperature regressions of the conductivity:  
\[\sigma=\sigma_{0}\exp\left(\frac{E_{a}}{RT}\right) \tag{2}\]  
where \(\sigma\) and \(\sigma_{0}\) are specific conductivities, \(E_{a}\) is activation energy of conductivity, \(R\)--universal gas constant, \(T\) is the temperature.
# Faster MDNet for Visual Object Tracking
## 4 Experiments
### Results on OTB100
#### 4.4.1 Comparisons with State-of-the-Art Trackers  
Figure 3 shows that our tracker performs better than SiamRPN++ [11]. Compared with MDNet [20], our method improves the precision by 1.2% and the success rate by 2.1%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Parameters** & **FLOPs** & **Mean IoU** \\ \hline Baseline & 4,170,370 & 122.14 M & 0.689 \\ +SE Module [31] & 4,203,138 & 122.17 M & 0.674 \\ +GSO Module [32] & 4,480,264 & 252.11 M & **0.695** \\ +CBAM Module [33] & 4,203,236 & 122.17 M & 0.676 \\ +ECA Module [34] & **4,170,375** & **122.14 M** & 0.694 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of adding different attention modules to our model in terms of parameters, FLOPs, and mean IoU value on OTB100. The model without the attention module is considered the baseline. The best-performing value for each metric is shown in bold.
## 4 Numerical Simulation and Discussion
### Case Study 1: Non-Smooth Reference Trajectory
Figure 6: Tracking performance of the suggested controller in case of step reference trajectory. the SESO-BC controller and DESO-BC controller must produce much higher control efforts to track the step reference trajectory. On the contrary, since the filtered reference trajectory is smooth, a remarkably less control endeavor is required to ensure the predefined tracking performance. The results indicate that the trajectory planner plays an important role in reducing the tracking overshoot and the control effort and achieving the prescribed tracking qualification. Additionally, adopting a trajectory planner can be considered as an effective way to avoid the critical input saturation problem which naturally exists in real applications. The estimation performances of angular velocity and both lumped unmatched and matched disturbances are presented in Figure 8, Figure 9 and Figure 10, respectively. As shown in Figures 8 and 9, with a similar bandwidth, the constructed ESMO can estimate the velocity and lumped mismatched uncertainties more accurately compared to the well-known ESO, hence, better disturbance rejection capability is attained by the proposed control approach. Although the SESO-BC possesses the cost-effectiveness by employing position sensor only, due to the nonexistence of mismatched disturbance estimation and compensation mechanism, the SESO-BC control approach exhibits the worst estimation performances of immeasurable angular velocity and lumped matched disturbance. Therefore, the overall tracking ability of SESO-BC controller is considerably deteriorated compared to the remaining control approaches.
# Image Anomaly Detection Using Normal Data Only by Latent Space Resampling
## 2 Related Work
### Feature Extraction Based Method  
Feature extraction-based methods generally map images to the appropriate feature space and detect anomalies based on distance. Generally, feature extraction and anomaly detection are disjointed. [PERSON] et al. [16] proposed a PCA-based method for concrete bridge deck crack detection. They first use PCA to get dominant eigenvectors of tested image patches and then calculate the European distance from the dominant eigenvectors of normal image patches. [PERSON] et al. [17] used SVDD to detect defects in TFT-LCD array images. They described an image patch with four features including entropy, energy, contrast, and homogeneity and trained an SVDD model using normal image patches. If a feature vector lies outside the hypersphere found by SVDD during testing, the image patch corresponding to this feature vector is considered anomalous. Similar works can be found in [18; 19; 20]. Compared to these traditional dimensionality reduction models, a convolutional neural network (CNN) provides nonlinear mapping and is better at extracting semantic information. There are two common practices for applying CNN for feature extraction, one is to use a pre-trained network, such as VGG or ResNet, and the other is to develop a deep feature extraction model specifically for the purpose. For example, [PERSON] et al. [21] used a pre-trained ResNet-18 to extract feature vectors from the scanning electron microscope (SEM) images to construct a dictionary. In the prediction phase, a tested image is considered anomalous if the average Euclidean distance between its feature and its \(m\) nearest neighbors in the dictionary is higher than the threshold. The other is the Deep SVDD model developed by [PERSON] et al. [22] who combine CNN with SVDD. They trained the deep neural network by minimizing the volume of the hypersphere containing feature vectors of the data.
# Aircraft Target Interpretation Based on SAR Images
## 2 Related Work
### Inception Network  
The inception network is a milestone in the development of convolutional neural networks. Before, the network often increased the depth to achieve better performance, but in this way, the model is easier to overfit and brings lots of computation. Inception is used to solve the problem of convolutional layer stacking. It sets up multiple channels in the same layer, which are obtained by operation with convolution kernels of different sizes, and it expands the width of the network. Inception structure can avoid redundant computation, reduce the number of parameters, and extract high dimensional features of SAR images more effectively. Inception structure can enhance the generalization ability and feature expression ability of the network, and the feature learning ability of the convolutional neural network is greatly improved.  
The inception structure performs multiple convolution operations with different scale kernels at the input feature map in parallel to obtain four branches. All the branches are concatenated into one feature map in the output section. Figure 3 shows the architecture of the inception network. It contains three convolution kernels with different scales and one maximum pooling layer. Each branch performs its convolutional operations, which can extract much richer features and reduce computational complexity. Inception structure can fuse the different scale features of the SAR image, obtain receptive fields of different scales, and improve performance.
## 2 Materials and Methods  
### Satellite Constellation and RadCalNet Sites  
#### 2.1.1 The Zhuhai-1 Constellation and Hyperspectral Sensors  
To maintain lower costs, Zhuhai-1 micro-nano satellite hyperspectral sensors have a gradient filter spectroscopic design. Compared to a traditional narrow-band filter, a linear gradient filter has a nearly continuous spectral channel; therefore, a higher spectral resolution can be obtained by using a linear gradient filter to split light. In this study, the camera was composed of three CMOS sensors. Figure 1 shows the splicing method used with this hyperspectral camera. The number of pixels in the sensor was 5056 \(\times\) 2968, and the pixel size was 4.25 \(\upmu\)m. The light in the wavelength range of 400 nm-1000 nm was equally divided into 32 spectral bands (see the spectral response function (SRF) of OHS-F CMOS2 in Figure 2). The specific row position of each spectral band on the sensor was determined using the width of the slit (i.e., area of interest, AOI), and each spectral band used up to eight rows of pixels for eight-level integral imaging. Therefore, we defined eight integration stages of a band as 1 to 8, which were used to adapt to different irradiation stages during imaging. According to changes in the Sun's altitude angle, four groups of common integration stage configurations were set for the satellite on-orbit operation to achieve optimal imaging under the conditions of different solar incident angles. The integration stage settings in a single CMOS were basically the same, but they were different between different satellites or CMOSs. The spectral assignment of sensor pixel rows is shown in Figure 3. When the hyperspectral camera on board Zhuhai-1 micro-nano satellites observed the Earth in orbit, 32 bands of the sensor along the flight direction of the satellite were sequentially imaged, as shown in Figure 3. The integrated imaging principle of each band of the sensor is shown in Figure 4.  
As is shown in Figure 3, the light wave ranging from 400 nm to 1000 nm was divided into 32 spectral segments on average, and each spectral band accounted for approximately 92 lines on the sensor. Due to low resolution and low signal-to-noise ratio, only eight lines of pixels were used for eight-level integration imaging in each spectral band.  
Figure 1: Focal plane arrangement for the hyperspectral camera. The width of each CMOS image is 50 km, and the inter slice overlap is 50 pixels.  
Figure 3: Sensor spectrum distribution.  
Figure 2: The spectral response function of OHS-F COMS2 sensor.
## 5 Problems and Suggestions for Improvement
### Rationalization of Weights  
The process of selecting weights should be conducted with a rigor that not only corresponds to established evaluative frameworks but also adheres to core developmental principles.  
Among the methods for subjective weighting, both the ESM and the Delphi method are widely utilized. When data is scarce or difficult to quantify, such as in the evaluation of cultural heritage protection, or when it is necessary to integrate expertise from multiple fields or in policy-driven planning, subjective weight determination methods are often employed. However, these subjective weights are influenced by the selection preferences of experts, which can lead to significant biases [145]. Furthermore, consistency testing of these weights is relatively challenging, as well as being time-consuming and labor-intensive. On the other hand, objective weighting methods commonly include the AHP, EWM, and the TOPSIS. Objective weight evaluation necessitates sufficient and high-quality data, aiming to minimize the influence of subjective factors. For instance, when the relationships among indicators are complex, quantitative analysis becomes essential. However, objective weight evaluation is not without its shortcomings. It tends to amplify sensitivity to outliers and may overlook the semantic significance of indicators, and static weights fail to dynamically adapt to environmental changes.  
By combining these methodologies, one can effectively apply expert insights along with data objectivity, thus improving the accuracy and reasonableness of GSE [114; 146]. Recent investigations have witnessed ongoing improvements in weight determination techniques, particularly through enhanced versions of AHP and TOPSIS, which now integrate both subjective and objective weight elements to refine the accuracy and rationale of the evaluations further [52; 147]. Evaluating the reasonableness of weights is a vital part of this methodology, with the robustness and credibility of weights subject to further examination using methods, such as consistency checks and sensitivity analysis. Recent studies have increasingly adopted AI-driven and hybrid methods (e.g., machine learningtechniques, like random forests and Bayesian networks) to address subjective weight assignments by analyzing historical data and automating weight optimization. [148].
## Abstract  
In order to analyse surface electromyography (EMG) signals, it is necessary to extract the features based on a time or frequency domain. These approaches are based on the mathematical assumption of signal stationarity. Stationarity of EMG signals is thoroughly examined, especially in isotonic contractions. According to research, conflicting results have been identified depending on varying window sizes. Therefore, in this study, the authors endeavoured to determine the suitable window size to analyse EMG signals during isotonic contractions utilising stationary tests, reverse arrangement (RA), and modified reverse arrangement (MRA). There were slight differences in the average percentages of signal stationarity for RA and MRA tests in 100 ms, 500 ms, and 1000 ms window sizes. However, there was none for the 200 ms window size. On average, a window size of 200 ms provided stationary information with 88.57% of EMG signals compared to other window sizes. This study also recommended the MRA test to determine EMG signals stationarity for future studies, as the performances were better in comparison to RA tests. However, the following recommendation is only valid for window sizes greater than 200 ms. For a real-time application, the size of the analysis window together with the processing time should be less than 300 ms and a window size of 200 ms is applicable for isotonic contractions.  
EMG signals; isotonic contractions; stationary; windows size +
Footnote †: journal: Applied Sciences