esa-sceva/satcom-chunk-collection huggingface dataset:
# Spaceborne LiDAR Systems: Evolution, Capabilities, and Challenges
## 6 Applications and Outcomes from Spaceborne LiDAR Data
### Atmospheric Applications  
Spaceborne LiDAR systems provide unparalleled insight into the vertical structure of Earth's atmosphere, especially for aerosols (particles) and clouds.  
Ref. [152] analyzes the Earth's boundary layer height (PBLH) and applies the Different Thermo-Dynamics Stability (DTDS) algorithm to CATS spaceborne LiDAR data, allowing for a direct comparison between satellite- and ground-based LiDAR measurements. The study validates PBLH estimates using data from the Atmospheric Radiation Measurement (ARM) program, ensuring consistent detection at all hours. The refined approach aims to improve the ability to obtain the diurnal variability of PBLH from satellite observations.  
Ref. [119] utilizes data from the CALIOP LiDAR on the CALIPSO satellite, which has been collecting global atmospheric profiles since June 2006. The research aims to characterize the global 3-D distribution of aerosols, including their seasonal and interannual variations, by constructing a monthly global gridded data set of daytime and nighttime aerosol extinction profiles, available as a Level 3 aerosol product. The data set distinguishes between cloud-free and all-sky conditions and reveals that vertical aerosol distributions vary with season due to changes in source strengths and transport mechanisms. The study finds that the mean aerosol profiles in the clear sky and in the all-sky atmosphere are quite similar, indicating a lack of correlation between high semitransparent clouds and aerosols in the lower troposphere. An initial evaluation of the accuracy of aerosol extinction profiles highlights detection limitations, particularly in the upper troposphere. Despite preliminary results, the study provides evidence that monthly mean CALIOP aerosol profiles can quantitatively characterize elevated aerosol layers in major transport pathways. The work forms the basis for an initial global 3D aerosol climatology, which will be extended and improved over time.  
Ref. [153] presents a global and seasonal distribution of cirrus clouds based on measurements from the CALIPSO satellite LiDAR, collected between June 2006 and June 2007. The frequency of occurrence of cirrus clouds is highest near the tropics, particularly in the 100\({}^{\circ}\) to 180\({}^{\circ}\) E longitude band, with a maximum frequency of up to 70%. Cirrus cloud cover shows significant latitudinal movement with changing seasons. The vertical distribution reveals a maximum frequency of occurrence of the highest altitude of the cirrus cloud at approximately 11% at 16 km in the tropics. In the northern and southern midlatitudes (20\({}^{\circ}\) N to 60\({}^{\circ}\) N), the maximum frequency of the top and base altitudes of the cirrus is about 5.1% at 11 km and 8 km, respectively. The study highlights the variability in the distribution of the cirrus cloud horizontally and vertically, influenced by seasonal changes.
###### Abstract  
The ongoing electrification process also requires improvements in the efficiency of power transmission devices, such as transformers, the main part of which is the magnetic core. Despite great progress in the development of core material, losses and audible noise during their operation is still a critical issue to be solved. Currently, a magnetic material used to produce the transformer core is amorphous steel, which is gaining popularity. Compared to traditionally used grain-oriented silicon electrical steel, a significantly larger number of very thin amorphous ribbons is needed to produce the core, which is due to the fact that they are about an order of magnitude thinner, making mechanical stability a challenge. The presented article describes the preparation of a hybrid binder for amorphous steel based on the two types of silanes, tetraethyl orthosilicate and 1,2-bis(triethoxysil)ethane, for which their anticorrosive character and good dielectric properties were confirmed. Using the obtained binders, model toroidal cores were produced and their magnetic and acoustic properties were tested. The obtained results indicate that the applied silane-based hybrid binders improved important functional properties by reducing the magnetic no-load losses and audible noise.  
silicars; TEOS; BTSE; amorphous steel; magnetic properties; electrical devices; no-load loss; noise analysis Article
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 3 Improved Genetic Algorithm
### Improved GA  
The basic GA is usually employed to optimize the polynomial coefficient or the path length in some problems, where chromosomes are assigned a category of meaning. Referring the composition mechanism of chromosomes studied in [22], an IGA was developed. Chromosomes were assigned three categories of meanings. Each IGA chromosome consists of three parts, as shown in Figure 4, where \(\left\{W_{1}\text{, }W_{2}\text{,..., }W_{N}\right\}\) denotes the waypoint sequence, \(\left\{C_{1}\text{, }C_{2}\text{,..., }C_{N}\right\}\) denotes the sequence of joint configurations, and \(OPC\) denotes a special value corresponding with the cubic coefficients of the arguments in the piecewise-sine functions. Joint configuration \(C_{k}\left(k=1,...,N\right)\) corresponds with waypoint \(W_{k}\).  
Since the time period employed by the space robot for each subpath is positive, value \(\left\{\frac{2\left[a_{0j}^{i}-arcsin\left(\frac{a_{jf}^{i}}{A}\right)\right] }{a_{3j}^{i}}\right\}\) in (18) is positive. Therefore, \(a_{3j}^{i}\) is expressed as:  
\[a_{3j}^{i}=sign\left(a_{0j}^{i}-arcsin\left(\frac{q_{jf}^{i}}{A}\right)\right) \cdot abs\left(a_{3 spe}\right) \tag{19}\]  
In [24], \(a_{3 spe}\) denotes the special value after decoding \(OPC\), where \(abs\left(\cdot\right)\) denotes the absolute value of \(\left(\cdot\right)\).  
The encoding mechanism is an essential component of the IGA, which is studied first. Then, the IGA updating mechanism, including initialization, reproduction, crossover, and mutation, is studied. Finally, the IGA work mechanism is developed.  
Figure 3: Flowchart of basic GA.
## 2 Data and Methods
### Network Structure of SAFCSRGAN  
The generator network structure of the SAFCSRGAN (Spatial Attention of Fused Channels Super-Resolution GAN) proposed in this paper is shown in Figure 2. The generator mainly includes a shallow feature extraction module and a deep feature extraction and upsampling module [19]. The feature extraction module is responsible for learning the super-resolution features from low-resolution (LR) to high-resolution (HR) images, while the upsampling module is responsible for generating the final HR images based on the learned mapping relationship. The first layer of the network is a convolutional layer used for shallow feature extraction, and its output is also used as the input for both the upsampling module and deep feature extraction module. The core structure consists of a series of Residual in Residual Concatenation Attention Blocks (RRCASB), which can effectively achieve feature extraction and fusion, ensuring the training stability of the deep network. The residual connections within RRCASB help to maintain the flow of gradient information, thus avoiding the problem of gradient vanishing. After passing through a series of RRCASB, the feature maps are then reconstructed at a super-resolution by the upsampling module. The upsampling technique employed in this paper is PixelShuffle [20], also known as sub-pixel convolution.  
Figure 1: Coronal images are captured by different devices and the position of high-resolution images within low-resolution ones is determined through image registration.  
Our proposed method, compared to ESRGAN, removed some of the redundant dense connections. As shown in Figure 3, a basic block originally contained 9 connection operations. We believe that an excess of connections could potentially weaken model performance, as they might result in the preservation of insignificant features. Therefore, while retaining some of the necessary connections, we introduced residual connections. Experiments have confirmed that this improvement not only significantly reduced the number of model parameters but also enhanced model performance.
## 5 Biofertilizers  
Continuous farming and use of agricultural lands, over a period of time, lead to depletion of the nutrients contained in the soil. Hence, the frequent resuscitation of the lands using fertilizers is necessary. When the soil is impoverished, there is low crop production, which leads to poor harvest, hunger and malnutrition. Farmers find it easy and convenient to use chemical fertilizers for crop improvement, but their cost and inherent environmental pollution calls for urgent attention. Biofertilizers or green fertilizers are natural ways of enriching the soil by using dead plant materials or animal wastes, which are fed on by microbes to give the required nutrients for efficient crop production.  
The microorganisms are key in this process of natural fertilization because, without them, the plant or animal materials are not in the absorbable form by the growing crops and therefore are of no use to the soil [60]. Alternatively, biofertilizers can be microorganisms that activate the soil and plant natural processes for efficient nutrient uptake, high crop yield and quality, and tolerance to abiotic stress. Such microorganisms are majorly bacteria that are contained in the soil and are called plant growth-promoting rhizobacteria, including blue-green algae, phosphorus-potassium solubilizing organisms, azotobacter, and Rhizobium [60]. They fix the atmospheric nitrogen and interact with the decaying organic matter to make available nutrients for the plant growth. They are symbiotic in behavior, as plants cannot grow well without them, and they also depend on plants for existence. Apart from making nutrients available for plants, they also get involved in protecting the plants against some pathogenic attack.  
Other methods of the biofertilization involve the rotational practice of cereal-legume with combined crop-livestock agricultural system. This method works to enhance the soil nutrients through fixing of the atmospheric nitrogen by legumes and the livestock wastes combines with the cereal-legumes to improve the soil texture and provide appropriate environment for the action of the microbes. Organic farming or biofertilization generally gives high-value crops but can be practiced in a small way, especially by rural farmers and the prices of the produce are high.
## 5 Results
### Results for Zurich Summer Dataset  
According to the quantitative comparison of the results, CNN-MRS model usage increased the accuracies and performance metrics. Applying the spatial filter and MRS on U-Net and SegNet classification increased the overall accuracies for all patch sizes. The highest evaluation metrics belong to SegNet-MRS models (Tables 15 and 16). These refinement values of the proposed approach are also compatible with the experiments conducted on the Sentinel dataset (Tables 9, 11 and 13).  
In the experiment, four bands were used. Like other studies in the literature, background was not considered in classification. In the CNN-MRS model, median filter size was 3 \(\times\) 3. The ZH-16 and ZH-17 images were used as test images, and the ZH-20 image was used as validation data for the experiments. The proposed approach as CNN (SegNet)-MRS model had better performance on the public shared dataset than SegNet model in all metrics for the test images (Figure 18, Figure 19 and Table 15, Table 16). In CNN-MRS application, the object majority voting was applied as \(k\) = 80% for ZH-16 and ZH-17 images. Determining \(k\) threshold was dependent on the image, object size and category mixing. If there were more than \(k\) value segmentation predictions as classes in an object, then all the pixels were assigned the same class labeling of majority class, if not, first prediction class labels were preserved. Thus, the salt-pepper effect was decreased by applying the CNN-MRS model on the public VHR imagery dataset (Table 15).  
Experiments on Zurich Summer dataset were compared with other state-of-the-art CNN models in the literature (Table 17). All methods were run on the same training, validation, and test data. The SegNet-MRS (128 \(\times\) 128 patch) method in this study had the highest overall accuracy and weighted IoU in the ZH-16 test image, while it had the highest F1-score in the ZH-17 image.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**ZH-16 Image**} & \multicolumn{4}{c}{**ZH-17 Image**} \\ \hline
**Method** & **OA** & **F1-Score** & **Weighted IoU** & **OA** & **F1-Score** & **Weighted IoU** \\ \hline FCN [60] & 81.02 & 66.13 & 69.92 & 77.14 & 67.42 & 62.81 \\ DeeplabV3 + ResNet50 [61] & 88.02 & 69.54 & 79.38 & 89.21 & 81.36 & 80.74 \\ DeeplabV3 + Xception [62] & 90.29 & 76.02 & 83.05 & 90.21 & 82.11 & 82.33 \\ CNN–MRS * & 92.03 & 72.79 & 85.88 & 89.94 & 87.76 & 81.52 \\ \hline \hline \end{tabular}  
* Our implementation of the proposed methodology.  
\end{table}
Table 17: Comparison with other approaches on Zurich Summer dataset (The values are given as %).  
Figure 19: Results of Zurich dataset (ZH-17): (**a**) original image; (**b**) ground truth; (**c–f**) 32 \(\times\) 32 patch size; (**g–i**) 64 \(\times\) 64 patch size; (**k–n**) 128 \(\times\) 128 patch size.
# Detection of Pneumatic Conveying by Acoustic Emissions
## 3 Ensemble Empirical Mode Decomposition  
With the development of modern digital signal processing technology, signal analysis has become an important tool for the study of pneumatic conveying characteristics. [PERSON] et al. [23] proposed an empirical mode decomposition (EMD) method, which is based on the step-wise decomposition of the non-linear non-stationary signal itself. As mentioned above, in real world monitoring of pneumatic conveying, particle size is uncertain, which possess a challenge when defining an effective frequency range. Thus, the EEMD algorithm is suitable for signal decomposition since it utilizes the raw signal, and the EMD method has good adaptability. Each order of the intrinsic mode function (IMF) should satisfy the following two conditions:  
1. In the entire data sequence, the number of extreme points and the number of zero-crossings should be equal or differ at most by one;
2. The mean value of the envelopes determined by the local maxima and local minima at any given data point should be zero.  
However, the EMD method can misrepresent the envelope due to the addition of the boundary value, and the local fluctuation can result in mode confusion. Considering these EMD disadvantages, [PERSON] [24] proposed an improvement to the ensemble empirical mode decomposition method, adding a Gaussian-white-noise background to the signal. After several decompositions, the additional noise is eliminated and only the signal remains. The EEMD algorithm decomposition steps are as follows:  
1. Add white noise to the raw data \(x(t)\): \[X(t)=x(t)+n_{m}(t)\] (4) where \(n_{m}(t)\) is the white noise, and \(X(t)\) represents the data (with noise) for the \(m\)th trial.
2. Decompose the noise-added data \(X(t)\) into several IMFs (total number I);
3. Repeat steps (1) and (2) several times, each time adding independent random white noise, until the trial number reaches a pre-determined value, \(m=M\), \(M\) is the number of trials;
4. Calculate the ensemble mean \(\overline{c}_{i}\) of the \(M\) trials for each IMF: \[c_{i}=\frac{1}{M}\sum_{m-1}^{M}c_{i,m}\;i=1,2,\ldots,1;\;m=1,2,\ldots,M\] (5)
5. Finally, decompose the raw data into several IMFs: \(\overline{c}_{1},\overline{c}_{2},\ldots,\overline{c}_{I}\) and a residual component \(r\): \[x(t)=\sum_{i-1}^{I}c_{i}+r\] (6)  
The accuracy of the EEMD method depends on both the chosen amplitude for the added white noise and the number of specified trials. A small amplitude for the added noise will have no influence on the raw signals, whereas a high amplitude will mask the raw signal.  
The number of Gaussian white noise to the EEMD obeys the statistical law [25]:  
\[\varepsilon_{n}=\frac{\varepsilon}{\sqrt{M}} \tag{7}\]  
where \(\varepsilon\) is the amplitude of the white noise. Generally, the value of \(\varepsilon_{n}\) is 0.2, and the value of \(M\) is 100. In order to ensure that the algorithm converges quickly and results in efficient detection, \(\varepsilon\) should not be too small.  
Finally, the raw signal \(x(t)\) can be decomposed as follows:  
\[x(t)=\sum_{i=1}^{I}c_{i}(t)+r_{I}(t) \tag{8}\]
# Enhancing Sign Language Recognition Performance Through Coverage-Based Dynamic Clip Generation
###### Abstract  
Sign Language Recognition (SLR) has made substantial progress through advances in deep learning and video-based action recognition. Conventional SLR systems typically segment input videos into a fixed number of clips (e.g., five clips per video), regardless of the video's actual length, to meet the fixed-length input requirements of deep learning models. While this approach simplifies model design and training, it fails to account for temporal variations inherent in sign language videos. Specifically, applying a fixed number of clips to videos of varying lengths can lead to significant information loss: longer videos suffer from excessive frame skipping, causing the model to miss critical gestural cues, whereas shorter videos require frame duplication, introducing temporal redundancy that distorts motion dynamics. To address these limitations, we propose a dynamic clip generation method that adaptively adjusts the number of clips during inference based on a novel coverage metric. This metric quantifies how effectively a clip selection captures the temporal information in a given video, enabling the system to maintain both temporal fidelity and computational efficiency. Experimental results on benchmark SLR datasets using multiple models-including 3D CNNs, R(2+1)D, Video Swin Transformer, and Multiscale Vision Transformers demonstrate that our method consistently outperforms fixed clip generation methods. Notably, our approach achieves 98.67% accuracy with the Video Swin Transformer while reducing inference time by 28.57%. These findings highlight the effectiveness of coverage-based dynamic clip generation in improving both accuracy and efficiency, particularly for videos with high temporal variability.  
Keywords:sign language recognition; dynamic clip generation; deep learning; computational efficiency +
Footnote †: journal: Applied Sciences
## 4 Experimental Proof of Concept  
### Materials and Methods  
In order to experimentally prove our radar antenna concept, we used the CST model shown in Figure 1 to design a prototype, which was manufactured by PCBWay in Shenzhen, China [52] in aluminium (AlSi10 Mg) using Selective Laser Melting (SLM) technology. The resulting prototype, shown in Figure 10, weighs 163 g.  
We measured the prototype shown in Figure 10 in the anechoic chamber at the Antennas and Propagation Laboratory (APL) on the Universitat Politecnica de Valencia (UPV) campus. The chamber's measurement space is 3 m long, 2 m wide, and 2 m high, and it is equipped with a spherical measurement system capable of characterising antenna scattering parameters and radiation patterns up to 50 GHz.  
In light of the antenna's symmetry, we measured the radiation pattern for a single boresight angle. Using the experimental setup shown in Figure 11, we measured the scattering parameters between the ports defined in Figure 2, along with the horizontal and vertical cuts of the radiation pattern and the antenna gain. These results are presented in Section 4.2 below.  
Figure 8: (a) 3D \(\Delta_{\theta}\) radiation pattern in dBi; (b) 2D cut in a vertical plane \(\Delta_{\theta}(\varphi=45^{\circ},\theta)\).  
Figure 9: Overlapped monopulse azimuthal coverage.
# Automating Stimulation Frequency Selection for SSVEP-Based Brain-Computer Interfaces
## 5 Experimental Evaluation  
### The Experiment Description  
#### 5.1.1 Subjects  
In this study, eight volunteers participated: six men and two women. All of them were 2-3 year bachelor's degree students at Novosibirsk State Technical University. The average age of the participants was 20.8 years. Each participant was pre-selected for the absence of any neurological diseases, including epilepsy. Volunteers were informed about the aims and objectives of the experiment, as well as the possible risks and benefits of participation. All participants gave their informed consent to participate in the study.
## 3 Materials and Methods
### Description of the Measurement Campaign
#### 3.1.3 Measurement Methodology  
The measurements were based on the transmission and reception of the signal, with the transmitter mounted on a DJI Inspire 1 drone located outside the forest at different heights (6 m, 12 m, and 18 m) and the receivers mounted on a pole with a height of 2 m, being moved by a person walking inside the forest. Both transmitter and receivers had antennas in both vertical and horizontal polarizations, as illustrated in Figure 4.  
Considering the morphology of the forest, predominantly composed of the Pau-Mulato tree species, a typical tree of the Amazon [35] has an average height of 24 m. The transmitter heights were defined at 6 m (tree trunk level), 12 m (beginning of the tree canopy), and 18 m (middle of the tree canopy). The aim was to analyze the influence of transmitter heights and antenna polarizations in the forest. Table 2 shows all possible combinations of transmission and reception parameters.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operating** & **Effective** & **Spreading** & **Bandwidth** & **Coding Rate** \\
**Frequency** & **Radiated Power** & **Factor** & **Bandwidth** & **Coding Rate** \\ \hline
915 MHz & 5 dBm & 12 & 125 KHz & 4/5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of the measurement setup parameters.  
Figure 4: Measurement methodology scenario.  
Given that there are one SF, three heights, and two polarizations in a non-line-of-sight (NLOS) scenario, six combinations were tested. For each combination, the 250 m route inside the forest was traveled six times, totaling 36 repetitions of the same route. Approximately 4.525 samples were collected in total, averaging about 358 samples per route, which corresponds to approximately one sample every 2 m while walking at an average speed of approximately 1.67 m/s. Each sample comprised latitude and longitude values, RSSI, and SNR.