esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Research Results
### Analysis of the Research Results

Table 2: Correlation analysis. The importance of a company's dependence on such an impact should be emphasised since such results may encourage further production companies to implement AM technology. Other relationships basically confirm the assumed benefits of using AM technology. FDM technology consists in the layered joining of plastic polymer material extruded through a nozzle, allowing the production of objects of any shape, with the only restriction being the dimensions of the designed element. The relationship between heat bonding the material and reducing the amount of waste can be justified by the maximum use of raw materials. To define the nature of significant interactions of the influence of AM technology use on the increase manufacturing company's competitiveness--in the context of Polish Manufacturing Companies, the study tests the hypotheses using regression analyses which estimate this impact (Equations (1)-(3)). Based on the analysis, it was noticed that as the use of FDM technology increases, the possibilities for product design (Equation (1), Figure 7). Increasing the possibilities for product design = 0.0682 + 0.2985 \(\times\) FDM  
The results show that as the use of heat bonding increases the level of reduction in post-production waste increases (Equation (2), Figure 8). \[\text{waste reduction}=0.0041+0.2459\times\text{heat bonding} \tag{2}\]  
Figure 7: Interactions between using Fused Deposition Modelling (FDM) and increasing the possibilities for product design. The results obtained showed that with the increase in the use of laser cutting technology, the level of company development increases significantly (Equation (3), Figure 9). \[\text{development}=0.1074+0.6426\times\text{laser cutting} \tag{3}\]  
The statistical results showed that the three AM technologies examined are held to have the strongest positive effect on the company's competitiveness. Figure 10 presents the structural model. Figure 8: Interactions between the use of heat bonding and the reduction of post-production waste. Figure 9: Interactions between the use of laser cutting technology and the future possibilities for increased development within an enterprise. This study explores the influence of AM Technology use on the increase manufacturing company's competitiveness--in the context of Polish Manufacturing Companies. The statistical results showed that the three AM technologies examined have the strongest positive effect on the company's competitiveness. In addition, an analysis of dependencies was then made to assess the impact of the factors defined which determine management decisions to change business processes within an enterprise. The research was carried out using Statistica ver.13.3. The data was thoroughly examined for linearity, equality of variance and normality. No significant deviations were found. Tables 3-5 present the correlation analysis, with only those variables for which the relationships were significant--that is, the production process, the prototyping process and the services--being listed.
## 2 System Model of NR-ODBR-DCSK
### Chaotic Signal Generator  
Since chaotic signals have noise-like properties and excellent secrecy when used as carrier signals, they have received much attention and in-depth study in the field of communication research. Typical chaotic dynamical systems are categorized into discrete chaotic mapping systems, continuous chaotic systems and hyper chaotic systems, and a common method to generate chaotic signals in chaotic digital modulation systems is to utilize chaotic system mapping. In this paper, we will take logistic mapping as an example and focus on the statistical characteristics of chaotic signals. The expression of logistic mapping is as follows:  
\[x_{n+1}=\mu x_{n}(1-x_{n})\ \ \ n=1,2,\cdots \tag{1}\]  
where the control parameter \(\mu\in(0,1]\), and the system is in a chaotic state when \(3.5699\cdots<\mu\leq 4\).  
The statistical properties of the logistic mapping are discussed below, and the probability density function of the logistic mapping is  
\[\rho(x)=\begin{cases}\frac{1}{\pi\sqrt{x(1-x)}},\ 0<x<1\\ 0,\ \ \ \ \ \ \ \ \ \ \ \ \ \ else\end{cases} \tag{2}\]  
The average value is  
\[\overline{x}=\lim_{N\rightarrow+\infty}\sum_{i=1}^{N}x_{i}=\frac{1}{2} \tag{3}\]  
Autocorrelation, and cross-correlation functions, respectively:  
\[\begin{split} R_{xx}(\tau)&=\lim_{N\rightarrow+\infty} \frac{1}{N}\sum_{n=0}^{N}x_{n}x_{n+\tau}=\left\{\begin{array}{ll}0.125,\tau=0 \\ 0,\ \ \ \ \ \ \tau\
eq 0\end{array}\right.\\ R_{xy}(\tau)&=\lim_{N\rightarrow+\infty}\frac{1}{N}\sum_{n=0}^{N}x_{n}y_{n+ \tau}=0\end{split} \tag{4}\]  
The improved logistic mapping expression is  
\[x_{n+1}=1-2x_{n}^{2}x_{n}\in(-1,1) \tag{5}\]  
The probability density function of the improved logistic mapping is  
\[\rho(x)=\begin{cases}\frac{1}{\pi\sqrt{1-x^{2}}},\ -1<x<1\\ 0,\ \ \ \ \ \ \ \ \ \ \ \ \ \ \ \ else\end{cases} \tag{6}\]  
The average value is  
\[\overline{x}=\lim_{N\rightarrow+\infty}\sum_{i=1}^{N}x_{i}=0 \tag{7}\]The improved logistic mapping autocorrelation and mutual correlation functions are respectively:  
\[\begin{split} R_{xx}(\tau)&=\lim_{N\to+\infty}\frac{1}{N} \sum_{n=0}^{N}\ x_{n}x_{n+\tau}=\left\{\begin{array}{ll}0.5,\tau=0\\ 0,&\tau\
eq 0\end{array}\right.\\ R_{xy}(\tau)&=\lim_{N\to+\infty}\frac{1}{N}\sum_{n=0}^{N}\ x_{n}y_{n+\tau}=0 \end{split} \tag{8}\]  
The autocorrelation function of the logistic mapping is plotted in Figure 1a below, and the inter-correlation function of the logistic mapping for initial values of 0.4 and 0.4001 is plotted in Figure 1b below. The reason for the absence of correlation peaks is due to the initial-value sensitivity of the chaotic system, and it is clear that even a small change in the initial chaotic values can lead to significant modifications in the entire chaotic sequence.
## 2 Materials and Methods
### Features Extraction  
This research proposed two categories of feature extraction suite, a very high-resolution suite and an ultra-resolution suite for differentiation and mapping of land cover and vegetation types from a limited number of multi-spectral and panchromatic images. The features extraction flow of the ultra-resolution suite involves pan-sharpening of the multispectral image, color-transformation of the pan-sharpened image, and the generation of panchromatic textural features; whereas the very high-resolution features extraction suite involves the calculation of radiometric indices and color-transformation of the multi-spectral image.  
For the very high-resolution suite, nine radiometric indices were calculated (Table 1) and combined with the multi-spectral image consisting of 8 bands (coastal, blue, green, yellow, red, red edge, near infrared 1, and near infrared 2), which yielded 17 spectral  
Figure 2: Distribution of the ground truth data size for each site.  
radiometric features. In addition, the 8-band multispectral images were transformed into the Hue-Saturation-Value (HSV) color space. For this purpose, by taking 3 out of the 8 bands at a time, 120 permutations were calculated which yielded 336 sets of RGB images (1008 features). OpenCV ([[https://opencv.org](https://opencv.org)]([https://opencv.org](https://opencv.org)), accessed on 15 April 2022)--Open Source Computer Vision Library--was used for the HSV transformations. It produced a large dimension of data in which some features exhibited multicollinearity. To cope with the complexity associated with the multicollinearity, 27 least-correlated features were extracted and combined with the 17 spectral-radiometric features prepared earlier.  
For the ultra-resolution suite, pan-sharpening of the multi-spectral images (8 bands) was carried out with the panchromatic image using the local mean and variance matching method [71] to generate a pan-sharpened image (8 bands) at 0.5 m resolution. The 8-band pan-sharpened image was transformed into the Hue-Saturation-Value (HSV) color space and 27 least-correlated features were extracted from the pan-sharpened image in a manner similar to the HSV transformation of the very high-resolution suite. In addition, twenty-six [PERSON]'s textural images were generated from the panchromatic image using a sliding window of 3 \(\times\) 3 pixels. To cope with the complexity associated with the multicollinearity, out of 18 textural features, 8 least-correlated features were extracted. Orfeo ToolBox ([[https://www.orfeo-toolbox.org](https://www.orfeo-toolbox.org)]([https://www.orfeo-toolbox.org](https://www.orfeo-toolbox.org)), accessed on 15 April 2022)--an open-source C++ library for image processing--was used for pan-sharpening and the calculation of [PERSON]'s textures. A summary of the features extracted for the ultra-resolution and very high-resolution mapping suites is described in Table 2.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
## 3 Passband Compensation of Chebyshev Sharpened Combs from [8; 10; 11]
### Coleman Chebyshev Sharpened Comb [8]  
The fifth-order Chebyshev polynomial coefficients in [8] are [0, 5, \(-\)20, 16]. The decimation factor \(M=16\), and the attenuation in the stopbands is \(-\)100 dB [8].
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 2 VMD Parameter Optimization and Wavelet Noise Reduction
### Optimize VMD Based on RIME  
RIME is a heuristic optimization method introduced by [PERSON] et al. in 2023 [23]. It exhibits efficient global search and local refinement capabilities, making it resilient against noise and multi-modal function challenges. In comparison to other heuristic algorithms like PSO and WOA, RIME boasts a simpler mechanism, fewer parameters, robust optimization capability, fast convergence speed, and excellent black-box properties. Hence, this paper opts for the RIME algorithm to optimize VMD parameters.  
The RIME algorithm draws inspiration from the natural phenomenon of time, simulating the growth process of time. It is developed through a search process that mimics this natural phenomenon. RIME comprises four primary processes:
## 5 MIMO Diversity Analysis
### Tarc  
The TARC is a parameter used to validate the performance of an MIMO antenna in a rich multipath fading environment where the phase angle of every path keeps on changing. TARC can be calculated using Equation (3) [26]:  
\[\Gamma=\frac{\sqrt{\left(\left|S_{ii}+S_{ij}e^{i\theta}\right|^{2}\right)+\left( \left|S_{ji}+S_{jj}e^{j\theta}\right|^{2}\right)}}{\sqrt{2}} \tag{3}\]  
where \(\theta\) is the input phase angle that is varied from \(0^{\circ}\) to \(180^{\circ}\) with an interval of \(30^{\circ}\), \(S_{ii}\) and \(S_{jj}\) are the reflection coefficients of port one and port two, respectively, and \(S_{ij}\) and \(S_{ji}\) are the port isolation (dB) between antenna elements.  
Figure 12 illustrates the TARC values, which are almost stable throughout the operating bands, which ensures that the proposed four-element mobile MIMO antenna system has achieved high port isolation as well as very stable performance under a multipath fading scattering environment.  
Figure 11: ECC and DG of 4-element mobile MIMO antenna.  
Figure 12: TARC of 4-element mobile MIMO antenna.
## 2 Related Works
### Brain Tumor Segmentation with Transformer  
A transformer was first used in natural language processing; the feature extraction of the transformer is based on a self-attention mechanism which facilitates the learning of long-range dependencies in sequences. [PERSON] et al. [14] propose to divide the image into patches, then convert patches into a token sequence using a transformer-based model for feature extraction, and it achieved good performance. After that, the performance of transformer-based models has outperformed convolutional models in natural image processing [15; 16; 29]. A number of studies [30; 31; 32] have applied transformer models to medical image analysis, including brain tumor segmentation [33; 34; 35]. [PERSON] et al. [34] propose employing a 3D transformer block for encoding bottleneck layer features with the encoder and decoder still using convolutional modules. [PERSON] et al. [35] propose building their encoder with a 3D transformer to encode images directly, then using a CNN decoder to catch global information. [PERSON] et al. [36] propose using bi-level transformer blocks for encoding two levels of bottleneck features with the rest of network remaining a convolutional neural network. To summarize the studies mentioned above, some apply the transformer blocks to the bottleneck layer for feature extraction [34; 37] while others use the transformer blocks as additional modules after the convolutional layers [38; 39]. However, these efforts do not leverage the spatial context advantages of transformers. Inspired by this, a pure transformer segmentation network is proposed in this paper to fully exploit the feature extraction ability of a self-attention mechanism.  
Medical image analysis, like segmentation, requires models to extract multi-scale features for dense prediction. Skip connection-based architectures such as the U-Net [27] and pyramid network [40] are widely adopted to leverage hierarchical features; this type of structure allows the final recovered feature map to incorporate more low-level features, and it also enables the fusion of features from different scales, thus providing multi-scale prediction. The self-attention mechanism in a vision transformer [14] is computed based on a single patch size, which results in its feature extraction lacking the inductive bias inherent in CNNs; therefore, it is not appropriate for a brain tumor image with rich structural information and a high variation of regions of interest. Soon after, Swin Transformer [15] introduced a hierarchical architecture for the transformer, which is more suitable for dense prediction tasks. Therefore, the segmentation model proposed in this paper takes the Swin Transformer as a basic unit, incorporating skip connection with a U-shape encoder-decoder structure.
## 3 Results and Discussion  
### Crack Propagation and Modes of Failure  
During loading of the beams, the first vertical cracks in the critical sections occur at similar loads. Generally, new cracks in the midspan can occur until failure, while new cracks in the middle support area stop appearing at a load corresponding to 50% of the failure load. The crack formation for the two series of beams with different longitudinal GFRP reinforcement differed significantly. The small number of wide cracks in beams series 2 in both the midspan and the middle support areas indicates poor adhesion conditions between the GFRP reinforcement and concrete. The main reason for this occurrence is probably the different surface treatments used on the ribbed GFRP bars in the beams series 1 and on the wrapped GFRP bars for beams series 2. Regarding beams series 1, the highest number of cracks in the middle support area appeared in beam G1-0, while the highest number of cracks in the midspan appeared in beam G1-25. A number of short, slightly sloping, and horizontal cracks were particularly pronounced in the beam midspan, indicating large deformations of beam G1-25. Characteristically for beam G1-25, just before failure, cracks appeared in the immediate vicinity of the end supports, resulting from the adopted reinforcement, which was significantly larger in the midspan compared to the support, as a consequence of designed redistribution of internal forces. Because of this, the beam G1-25 provided the highest load-carrying capacity out of the beams series 1. Shear cracks occurred at a load level above 60% of the failure load, at the hogging moment region in the beam G1-0, due to high reinforcement axial stiffness. The crack patterns at failure for all beams are shown in the Figure 4.  
Figure 4: Crack patterns of experimental beams at failure (P—load at failure).  
The mode of failure for all beams was concrete crushing in compression in combination with the shear. In beams G1-0, G1-25, and G2-0, failure took place in the midspan, in beams G1-15 and G2-15 in the middle support, while in the beams G2-25 failure appeared simultaneously in the midspan and at the middle support. It was characteristic that failure in beams G1-15 and G2-0 was destructive, followed by rupture of compressed and tensioned bars by the dowel effect. The modes of failure of all tested beams are shown in the Figure 5. A detailed description of modes of failure and crack propagation of the beams was given in previous work [26].
## 1 Introduction
In order to comprehensively consider the system's reliability and transmission rate performance, CPS technology is added on the basis of the above method to make up for a certain amount of rate loss. In the optical domain, the LCLV is combined with the PMT. Based on the magnitude of the incident light's power, it judges whether the PMT has reached saturation and then determines whether to change the transmittance of the LCLV. The simulation results show that the proposed algorithm improves the reliability and efficiency of the system. The use of LCLV increases the dynamic range of the PMT by about 10 dB and expands the dynamic range of the communication distance of the system.
## 2 Design of Real-Time Communication Subsurface Mooring System  
### General Descriptions of the Real-Time Communication System  
The real-time communication subsurface mooring system designed in this paper includes satellite communication buoy (hereinafter referred to as SCB), inductively coupling communication cable [10; 11], conductivity-temperature-depth sensors with an inductive modem (CTD-IM) and moorings, as shown in Figure 1. For the operation of the system, the SCB on the sea surface collects various types of sensor data through the underwater inductive coupling cable [12], and the minimum collection interval is around 3 min. Then, the data will be verified, compressed, and encrypted through the processing unit, and finally transmitted to the shore station receiver through satellite communication, so as to achieve the purpose of real-time acquisition of underwater sensor data [13]. Because of the two-way communication function of the SCB, the shore station receiver can complete the operations of querying the status of the SCB and underwater sensor, changing the sampling mode, and returning historical data by remotely issuing commands.
## 3 Results and Discussion
### Evaluation of Commercial EC Product  
Six commercial EC solution products that were purchased randomly were analyzed using the method of liquid and aerosol samples after spiking (Supplementary Table S3). None of our target TSNAs were detected in the analysis of commercial EC solutions. This is probably because of the low content of nicotine in the commercial EC products. In previous studies, the amount of TSNAs was highly correlated with the nicotine and nitrite content in the E-solution cartridge. The amount of the four TSNAs in the EC solution is extremely small, which is consistent with the low amount of nicotine in the drug product: 8.18 ng g\({}^{-1}\) of four total TSNAs on 16 mg of nicotine in cartridge [35; 49; 50]. This implies that the six types of EC solution that were tested in this study probably rely on using a small amount of nicotine with the impurities removed. It is therefore necessary to examine the connection between TSNAs and nicotine by analyzing EC solutions with high nicotine content.  
Some TSNAs in cigarette mainstream are known to be synthesized during smoking [51]. However, it should be noted that there is no study on the formation of TSNAs in the process of aerosolization from  
Figure 2: Comparison of relative recovery (RR) for the four TSNAs between quartz wool (QW) filter and Cambridge filter pad (CFP). RR (%) = (Concentration of spiked sample detected by LC-MS/MS system)/(Theoretical concentration of spiked sample) \(\times\) 100.  
EC solution. Total TSNA and nitrate levels in 1 mL of EC solution were at least 400 and 1300 times lower than the contents of TSNAs and nitrate per gram of common cigarette, respectively [52]. The amount of total TSNAs in EC vapor when puffing 99 times was at least 27 times lower than the quantity in mainstream per cigarette [53]. In addition, the yield of nicotine that was vaped from EC was 85% lower than that of traditional cigarettes [53], supporting the notion that the low concentration of TSNAs in the aerosol is similar to or even less than the TSNA level in E-liquid. Based on comparison of the EC solution and aerosol, TSNAs did not form during the generation of aerosol in the heating coil.  
With EC, it is possible to increase the number of puffs as the EC can be refilled with E-solution, unlike tobacco with a limited number of puffs. According to a survey, EC users inhaled more than four times more aerosols than smokers for 10 min and had an average of 225 \(\pm\) 59 puffs per day [54; 55]. Therefore, further experiments will be needed to understand the different behavior between EC liquid and aerosol based on the quantitation of TSNAs present in the aerosol of EC with consideration of the number of puffs.
###### Abstract  
This research presents the analysis of using different weighting functions for the GPS and Galileo observations in Precise Point Positioning (PPP) performance for globally located stations for one week in 2021. Eight different weighting functions of observations dependent on the elevation angle have been selected. It was shown that the use of different weighting functions has no impact on the horizontal component but has a visible impact on the vertical component, the tropospheric delay and the convergence time. Depending on the solutions, i.e., GPS-only, Galileo-only or GPS+Galileo, various weighting functions turned out to the best. The obtained results confirm that the Galileo solution has comparable accuracy to the GPS solution. Also, with the Galileo solution, the best results were obtained for functions with a smaller dependence on the elevation angle than for GPS, since Galileo observations at lower elevation angles have better performance than GPS observations. Finally, a new weighting approach was proposed, using two different weighting functions from the best GPS-only and Galileo-only for GPS+Galileo solution. This approach improves the results by 5% for convergence time and 30% for the troposphere delay when compared to using the same function.  
PPP; Galileo; stochastic modeling; weighting functions; elevation angle; convergence time 1
# Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery
## 2 Materials and Methods
### Image Processing
#### 2.4.6 Classification
Validation is assessed for the main results using error matrices, as recommended in [25]. These matrices provide information on both omission errors (validation pixels that were not correctly classified to their true class) and commission errors (validation pixels that were incorrectly classified to another class). For some of the comparison results (comparing classifiers, resolutions, band inputs) where space would prohibit printing all the error matrices, the kappa value is used to provide a summary of the classification accuracy, as it includes information both of correctly and incorrectly assigned validation points [25]. Values of kappa over 0.75 are generally regarded as being indicative of excellent agreement between actual and predicted classes.
# Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
## 3 Methodology and Data
### Data Used in the Study
* _Stringency index_: This index measures the strictness of lockdown policies primarily affecting people's behaviour. It includes all common containment and closure policy indicators as well as an indicator for public information campaigns. * _Economic support index_: This index documents measures like income support and debt policies, calculated using all common economic policy indicators. Additionally, we considered the published numbers of confirmed COVID-19 cases and deaths [49].