esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Spelling Correction
### Error Detection  
The spelling error detection process consists of verifying, given an input text, if it is considered a valid index or not. In a way, efficient techniques have been developed to detect the various types of spelling errors; however, the two most known techniques for this task are \(N\)-gram analysis and dictionary lookup, which are most used by text recognition systems and spell checkers, respectively [68; 69].  
The first approach, \(N\)-grams, are sub-strings of tokens with \(N\)-sequences. The detection process works by counting each \(N\)-gram probability of the input text and looking it up in a pre-built table of \(N\)-gram statistics. If a non-existent or rare \(N\)-gram is found, the token is flagged as a misspelling, otherwise not [70]. The second approach, dictionary, is structured from a given corpus and group words to avoid repetition (called word-types or types), and it is used to check each word in the input text for its presence in the dictionary. If that word is present in the dictionary, it is a correct word;  
Figure 2: Workflow of the spelling correction system.  
otherwise, it is flagged as a misspelling [59]. Furthermore, the frequency of words can also be integrated into the raw dictionary to increase vocabulary knowledge and create ranges for candidate words based on the value of occurrences. In this way, word-types are ordered by frequency of occurrence [71; 72].
# Compression of GNSS Data with the Aim of Speeding up Communication to Autonomous Vehicles
## 2 Background  
### Autonomous Vehicles  
By using autonomous vehicles, we endeavor to reduce human errors (currently responsible for more than 90% of accidents), but there are also machine errors [10].  
One of the causes of safety failure is a delay in information transfer between V2V autonomous vehicles or between autonomous vehicles and infrastructures such as the GNSS (V2I). Unlike traditional vehicles, autonomous vehicles transmit a considerable amount of information over a network. Therefore, it is essential to take care of data compression to reduce the amount of information that needs to be sent and, as a result, increase the speed of information transmission.  
Furthermore, in cases of communication failures, it is necessary to provide error resilience to omissions or mistakes in information with a system that knows how to autonomously operate according to a self-recovery protocol [11; 12].
## 3 Results
### Quantifying Material Properties Using Raman Spectroscopy  
Figure 5 presents the Raman spectra of the samples that were analyzed in this study. In Figure 5a,c,e, the clear Raman spectra can be observed from the pure material PLA and the PLA/Glass mixtures. There are no clear Raman spectra differences due to the additives, with only two minimal changes which are close to the noise level. Only when the Raman spectrum of pure PLA was used for normalization, differences in the Raman spectrum were observed. As is seen in Figure 5b,d,f, the first difference is an increase at 870 cm\({}^{-1}\) (C-COO stretching) and the second at 2945 cm\({}^{-1}\) (C-H2 asymmetric stretching). The related Raman peaks from the pure PLA sample are presented in Table 1 and are validated by the literature.  
Figure 4: Thermal Decomposition of pure PLA and PLA/Glass Fillers Compounds: **(a)** TGA curves for pure PLA, PLA/Glass Beads 3.0 wt.%, PLA/Glass Flakes 3.0 wt.%, PLA/Glass Powder 3.0 wt.%, **(b)** DTGA curves for pure PLA, PLA/Glass Beads 3.0 wt.%, PLA/Glass Flakes 3.0 wt.%, PLA/Glass Powder 3.0 wt.%, **(c)** TGA curves for pure PLA, PLA/Glass Beads 6.0 wt.%, PLA/Glass Powder 6.0 wt.%, **(d)** DTGA curves for pure PLA, PLA/Glass Beads 6.0 wt.%, PLA/Glass Powder 6.0 wt.%, **(e)** TGA curves for pure PLA, PLA/Glass Beads 9.0 wt.%, PLA/Glass Flakes 9.0 wt.%, PLA/Glass Powder 9.0 wt.%, **(f)** DTGA curves for pure PLA, PLA/Glass Beads 9.0 wt.%, PLA/Glass Flakes 9.0 wt.%, PLA/Glass Powder 9.0 wt.%, **(f)** DTGA curves for pure PLA, PLA/Glass Beads 9.0 wt.%, PLA/Glass Flakes 9.0 wt.%, PLA/Glass Powder 9.0 wt.%.  
\begin{table}
\begin{tabular}{c c c} \hline
**Wavenumber (cm\({}^{-1}\))** & **Intensity** & **Raman Peak Assignment** \\ \hline
870 & Medium & C-COO stretching [62] \\
1040 & Small & C-CH3 stretching [62] \\
1059 & Small & C\(-\)C asymmetric stretching \\
1126 & Medium & C-O-C stretch [63] \\
1293 & Medium & C-O-C stretch [63] & C-H\({}_{2}\) twisting [64] \\
1413 & Small & C-H\({}_{3}\) deformation [65] & C-H\({}_{2}\) deformation [64] \\
1437 & Medium & C-H\({}_{3}\) deformation [65] & C-H\({}_{2}\) deformation [64] \\
1457 & Medium & C-H\({}_{3}\) symmetric bending [62;63;65] & C-H\({}_{2}\) twisting [64] \\
1770 & Medium & C-O stretching [62;63] \\
2721 & Small & C-O stretching [66] \\
2845 & Major & C-H\({}_{2}\) symmetric stretching [67] \\
2880 & Major & C-H\({}_{2}\) symmetric stretching [67] & C-H symmetric stretching [68] \\
2945 & Major & C-H\({}_{2}\) asymmetric stretching [67] \\
3000 & Medium & C-H\({}_{3}\) asymmetric stretch [68] \\ \hline \end{tabular}
\end{table}
Table 1: Major Raman peaks of pure PLA identified and their related assignments.  
Figure 5: Raman spectra from **(a)** pure PLA, PLA/Glass Beads 3 wt.%, 6 wt.%, 9 wt.%; **(b)** the differences of PLA/Glass Beads from pure PLA; **(c)** pure PLA, PLA/Glass Flakes 3 wt.%, 6 wt.%, 9 wt.%; 9 wt.%; **(d)** the differences of PLA/Glass Flakes from pure PLA; **(e)** pure PLA, PLA/Glass Powder 3 wt.%, 6 wt.%, 3 wt.%, 6 wt.%, 9 wt.%, **(f)** the differences of PLA/Glass Powder from pure PLA.
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c c} \hline
**Biomethane** & **Production in 2020** & **Production in 2030** & **Production in 2050** & **Total Existing** \\
**Production Pathway** & **(TWh/y)** & **(TWh/y)** & **(TWh/y)** & **Potential (TWh/y)** \\ \hline  & 24.7–79.41 [41] & 148.3–369.4 1 [41] & 1088.9 1 [44] & 865.6 1.3 [42] \\ Biogas upgrading & 510.67 1,2,3 [42] & 334.94–467.6 (1) [43] & 1088.9 1 [44] & 500 1.4 [45] \\  & 221.11–278.89 1 [43] & 334.94–467.6 (1) [43] & & \\ \hline Gasification with methanation & - & 885 1.5 [46] & - & - \\ \hline PtM & - & 5.9–13.9 1 [41] & 266.7 1 [44] & - \\ \hline Biogas upgrading and gasification with methanation & 88.61 1 [47] & 200–222.2 1 [47] & - & 1521.4–2467.8 1 [47] \\ \hline \end{tabular}  
* Converted to TWh according to [48]. 2 This is the total production accumulated between 2010 and 2020.
* Considers only agriculture by-products and waste as feedstock. 4 Considers only waste as feedstock. 5 Does not consider the competition in the biomass market, biofuels, or new biochemical production pathways.  
\end{table} 
Table 1: Potential of biomethane production. Figure 3: European biomethane production in EU-27 and Europe. Adapted from EBA [39]. The EU's 2050 long-term strategy includes clear indications about the potential future role of low-carbon gaseous fuels in the EU energy system. It is projected that the share of NG in gaseous fuels will be reduced to 20% or less, with the majority of the remaining 80% of gaseous fuels being of renewable origin (i.e., hydrogen, biogas, biomethane, or synthetic methane) [24]. The upgrading of biogas is essential for using the excess gas as domestic and transportation fuel [20]. Amidst the current energy crisis, driven by conflicts, soaring prices, and energy dependence, biomethane emerges as an essential renewable energy source. Its rapid expansion highlights its importance in the European and global energy discourse, emphasizing the significance of this study in the current European context. The field of biogas enhancement technologies is vast and continually evolving, influenced by numerous factors. This comprehensive review aims to encompass a wide range of contributions to understand these technologies more thoroughly. This work stands out in providing a comprehensive overview of advances in innovative enhancement technologies. The main objective is to examine and conduct an analysis of the recent advancements in physical, chemical, and biological biogas upgrading technologies, with a specific focus on processes for carbon dioxide removal and/or its conversion into methane. The currently available commercial technologies are also summarized. Furthermore, the review aims to provide a comprehensive overview of current biogas production processes and upgrading technologies in this field, thus discussing and evaluating different options to provide a basis for future studies.
# Teletraffic Analysis of DoS and Malware Cyber Attacks on P2P Networks under Exponential Assumptions
## Appendix A Algorithms
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* [PERSON] and [PERSON] (2006) [PERSON]; [PERSON] Exploiting P2P systems for DDoS attacks. In Proceedings of the 1 st International Conference on Scalable Information Systems, Hong Kong, China, 30 May-1 June 2006; p. 47. [CrossRef]
* [PERSON], [PERSON], A.K.F.J.G.]. Blockchain-based DDoS attack mitigation protocol for device-to-device interaction in smart home. _Digit. Commun. Netw._**2023**, 1-15. [CrossRef]

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# Laser Inter-Satellite Link Visibility and Topology Optimization for Mega Constellation
## 4 Results and Discussion  
### Solving for the Optimal Phasing Factor  
Based on the constellation data in Table 1, it can be observed that the Starlink constellation and the OneWeb constellation are expected to deploy a large number of satellites, whereas other constellations such as Telesat are not in the same order of magnitude; therefore, no comparison is produced. This paper examines the variation of the shortest distance under various phasing factors for the OneWeb and Starlink constellations, as depicted in Figure 6.  
Figure 6 illustrates the variation of the OneWeb constellation, where \(0\leq F\leq 17\), due to the absence of cross-links, the shortest distance of LISLs is always greater than 0, collisions do not occur, and the shortest distance appears when F = 9. Figure 6 shows the variation of the Starlink constellation, where \(0\leq F\leq 23\), when the shortest distance of LISLs is 0, the two satellites will collide, thereby even values of \(F\) should be avoided. In order to satisfy the two basic optimization criteria, it is necessary to select the peak value so that the optimal \(F\) for the LEO layer in the first phase of the Starlink constellation is obtained as 11 by solving.
## 1 Introduction
It has high spectrum utilization and can use a fast discrete Fourier inverse/forward transform (IFFT/FFT) algorithm to realize signal modulation and demodulation. It has been widely used in space laser communication systems [17; 18; 19; 20]. This paper introduces the QD multi-spot position detection method based on OFDM. Initially, a mathematical model is formulated to assess the accuracy of QD position detection, drawing insights from the operational principles of OFDM and QD multi-beam position detection. Subsequently, an analysis of the SNR of the photocurrent signal in the OFDM system after FFT demodulation is presented. The Monte Carlo method is employed to simulate the practical scenario of light spot incidence on the QD in real-world systems. The proposed mathematical model is validated using LabView, demonstrating the congruence between simulation and formula-based results. This substantiates that the proposed mathematical model is capable of directly computing the accuracy of multi-spot position detection within specified parameters. Finally, the QD multi-spot position detection method based on OFDM is shown to facilitate efficient one-to-many laser communication. The primary contributions of this paper are delineated as follows:  
1. The presented mathematical model establishes a connection between the position detection accuracy and key factors such as the Gaussian spot radius, spot center position, SNR, and the quantity of FFT points. This correlation facilitates the concurrent detection of multiple spot positions, ultimately enabling efficient one-to-many laser communication. 2. The incorporation of FFT operation results in an enhancement of the SNR in the photocurrent signal, thereby elevating the precision of spot position detection. The remainder of this paper is organized as follows: The working principle of the OFDM system is introduced in Section 2. Section 3 shows the construction and analysis of the simulation system. The analysis of the simulation results is presented in Section 4. Finally, Section 5 provides the concluding remarks for this paper.
# Feature Residual Analysis Network for Building Extraction from Remote Sensing Images
## 2 Methodology  
The overall texture and image background of high-resolution remote sensing images may be very complex, such as spatial resolution, building shape, and scale. It is still a challenging task to extract high-precision buildings from high-resolution remote sensing images. In the field of deep learning, in order to be able to extract more detailed building features from remote sensing images, more complex convolution operations and larger network models are usually used for building segmentation. On the one hand, the segmentation accuracy has indeed been significantly improved, but the segmentation speed is not satisfactory, and there is still a big gap for the actual use [31]. At present, the scale of high-definition remote sensing data of many satellites is very large, so computational efficiency becomes very important. Although the operation efficiency can be improved by hardware, it is also very important to improve the operation efficiency by changing the algorithm structure (which is why the lightweight model is an important research direction in the image field). In line with the starting point of balancing the segmentation accuracy and segmentation speed, this paper combines the improved spatial pyramid pooling structure with the encoder-decoder and proposes the FRA-Net. Figure 1 shows the FRA-Net proposed in this paper. The entire network includes two stages of encoding and decoding. Among them, the encoding stage is still used as a cascaded downsampling network for extracting building features from remote sensing images, and the decoding stage is responsible for reconstructing the extracted building features into an image with the same resolution as the input image.  
As shown in Figure 1, in order to reduce the disappearance of the gradient in the training process of the model, the deep residual learning method is used in the coding stage to extract the building features in the remote sensing image. At the same time, in order to reduce the explosive growth of parameters caused by larger convolution kernels, this paper introduces a deep separable convolution to improve efficiency and designs an SRM. In addition, in order to obtain multi-scale information of remote sensing images of buildings, this paper improves the pyramid pooling module proposed in PSPNet and designs an FPP module that is more effective in contextual attention and extracting multi-layer features. In the decoding stage, on the one hand, the SRM is used to gradually restore the spatial information to generate the final building segmentation result; on the other hand, the idea of an attention mechanism is introduced, and an MFA is designed to recover the context information learned in the coding stage from multi-scale in an aggregated manner.  
In Section 2.1, the SRM proposed in this paper will be introduced. In Sections 2.2 and 2.3, the FPP module and MFA module designed in this paper will be introduced, respectively, and then the architecture of the FRA-Net proposed in this paper will be briefly summarized.
## 2 Materials and Methods
### Gathered Data
#### Features
The audience calculated in such a way is simplified since there is no mechanism to assess the number of people jointly watching the TV screen. In Formula (2), the simplified audience is assessed as the number of active devices divided by all of the running devices. \[channel\ viewership_{CH}^{TI}=\frac{active\ devices_{CH}^{TI}}{all\ active\ devices_{CH}^{TI}} \tag{2}\]  
The \(edt_{n}\) (Figure 5) expressed in Formula (3) determines the playback time in seconds for the currently played bitrate level. It is calculated to measure how long the STB plays the video quality. Extended \(edt_{n}\) for decreased bitrate indicates that there can be significant  
Figure 4: The example of bitrate change sequence, green—improvement, red—degradation, black—stall. that passed since the last problem occurred. In the given proposal, the \(bc_{c}\) (bitrate change counter) (6) is increased every time the negative bitrate switch occurs, and the stalls are considered zero value bitrate. The counter is not increased when the current bitrate level is equal to the maximum bitrate or greater than the previous sample's video quality. The counter can increase during the watching session. When the customer power off the device, the counter is reset. It is related to the soft reset of STB, or ONT, which can often help with network environment problems. \[bc_{c}=\left\{ \begin{aligned} bc_{c},& bitrate_{ kbps}^{N}>bitrate_{kbps}^{N-1}\\ bc_{c}+1,&(bitrate_{kbps}^{N}\
eq ref_{bitrate}) \leq\ bitrate_{kbps}^{N-1}\\ bc_{c},&(bitrate_{kbps}^{N}=ref_{bitrate})\end{aligned} \right. \tag{6}\]  
The _tslbc_ (time since last bitrate change) is the time passed from the last problem occurrence. The _tslbc_ updates when the currently played bitrate is lower than the bitrate read in the previously gathered sample. \[tslbc=\left\{ \begin{aligned} t_{n-x},& bitrate_{ kbps}^{N}>bitrate_{kbps}^{N-1}\\ t_{n},& bitrate_{kbps}^{N}\leq\ bitrate_{ kbps}^{N-1}\end{aligned} \right. \tag{7}\]  
The proposed VSBCT parameter decreases when the bitrate quality switches and stalls frequently occur over a short interval. Mentioned device behavior may mean significant problems with the customer's TV set (ONT + STB). The VSBCT will highly decrease if the number of problems is excessive. On the other hand, if the impairment was intensive, but the device recovered the appropriate parameters, the VSBCT will tend to the maximum 0 value. The primary purpose of the proposed parameter is to detect situations when high degradation occurs at the beginning (Figure 6). After a specific time, the STB plays well, and degradation disappears. \[VSBCT=\left\{ \begin{aligned} 0,& bc_{c}=0\\ -1*\frac{log_{2}(1+bc_{c})}{tslbc},& bc_{c}>0\end{aligned} \right. \tag{8}\]  
Figure 6: The example of bitrate change counter (\(bcc\)), and time since last bitrate change evaluation (\(tslbc\)), green—improvement, red—degradation, black—stall.
## 4 Simulation Experiment Analysis
### Experimental Analysis of the CEC2017 Test Function  
To verify the processing capability of the CG-TSA for complex feature functions and its applicability to different complex optimisation problems, this study used the CEC2017 test function [37] to test its performance. The CEC2017 test function was introduced as shown in Table 5, where the function types contain Simple Multimodal Functions (SMF), Hybrid Functions (HF), and Composition Functions (CF). In this paper, the proposed CG-TSA was compared with the standard PSO algorithm, GWO, SCA, and TSA for the performance of the search. The experimental parameters were taken as population size \(N\) = 100, dimension \(d\) = 10, and the maximum number of iterations \(T_{\text{max}}\) = 5000, and each function was run 50 times independently to obtain the mean and standard deviation. The comparison of the results of each algorithm run is shown in Table 6.  
As seen in Table 6, the standard PSO algorithm performed well on single peaks, but for multipeak, mixed, and composite functions, the CG-TSA had a clear advantage. In particular, for CEC09, CEC11, CEC16, CEC17, and CEC20, the CG-TSA was able to converge to near the theoretical value. The CG-TSA mean was lower and closer to the theoretical value for higher dimensions on CEC23, CEC24, CEC25, CEC26, CEC28, and CEC29 than the other algorithms. Overall, the proposed algorithm had a more prominent advantage in the CEC2017 test function compared to the other four algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Fun No.** & **Dim** & **Function Type** & **Range** & **Optimal Value** \\ \hline CEC04 & 10 & SMF & \([-100,100]\) & 400 \\ CEC05 & 10 & SMF & \([-100,100]\) & 500 \\ CEC06 & 10 & SMF & \([-100,100]\) & 600 \\ CEC07 & 10 & SMF & \([-100,100]\) & 700 \\ CEC08 & 10 & SMF & \([-100,100]\) & 800 \\ CEC09 & 10 & SMF & \([-100,100]\) & 900 \\ CEC10 & 10 & SMF & \([-100,100]\) & 1000 \\ \hline CEC11 & 10 & HF & \([-100,100]\) & 1100 \\ CEC13 & 10 & HF & \([-100,100]\) & 1300 \\ CEC16 & 10 & HF & \([-100,100]\) & 1600 \\ CEC17 & 10 & HF & \([-100,100]\) & 1700 \\ CEC20 & 10 & HF & \([-100,100]\) & 2000 \\ \hline CEC21 & 10 & CF & \([-100,100]\) & 2100 \\ CEC22 & 10 & CF & \([-100,100]\) & 2200 \\ CEC23 & 10 & CF & \([-100,100]\) & 2300 \\ CEC24 & 10 & CF & \([-100,100]\) & 2400 \\ CEC25 & 10 & CF & \([-100,100]\) & 2500 \\ CEC26 & 10 & CF & \([-100,100]\) & 2600 \\ CEC27 & 10 & CF & \([-100,100]\) & 2700 \\ CEC28 & 10 & CF & \([-100,100]\) & 2800 \\ CEC29 & 10 & CF & \([-100,100]\) & 2900 \\ \hline \end{tabular}
\end{table}
Table 5: Part of the CEC2017 test function.