esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Spacecraft structures undergo considerable exposure to the orbital space environment, which is characterized by conditions of ultra-high vacuum and large thermal variations (entailing problems of outgassing from the exposed surfaces) and by the presence of atomic oxygen (AtOx) and high-energy UV radiation, as well as other phenomena that can cause critical degradation in the performance of materials [1; 2; 3]. Atomic oxygen is formed in the low Earth orbital environment (LEO) by photo-dissociation of diatomic oxygen by short-wavelength (<243 nm) solar radiation, which has sufficient energy to break the 5.12 eV O\({}_{2}\) diatomic bond in an environment where the mean free path is sufficiently long (\(\sim\)108 m) so that the probability of reassociation or the formation of ozone (O\({}_{3}\)) is small. The issue of AtOx shielding is of crucial interest in the study of the low Earth orbit environment, because this atomic species is highly aggressive toward the organic materials present on the outer surface of spacecraft, causing physical damage due to erosion and corrosion [4; 5; 6]. When a spacecraft travels in LEO (where crewed vehicles and the International Space Station fly), the AtOx formed from the residual atmosphere can react with the exposed surfaces through high-energy impacts (at relative velocities around 8 km/s), which in turn may critically affect the performance of the systems and subsystems. The main AtOx effects on ceramic materials is erosion via chemical reactions and the release of volatile products, resulting in mass loss. Advanced carbon-based composites, such as carbon/carbon (C/C) or carbon/silicon carbide (C/SiC), are the ceramic materials employed in the aerospace industry to deal with the dramatic thermomechanical stress suffered by spacecraft structures during re-entry into the Earth's atmosphere [7; 8; 9]. Thanks to their very low thermal expansion, such materials benefit from outstanding chemical stabilities at extreme temperatures and are thus the ideal candidates for reusable integrated space components such as TPS, nozzle throats, and ablation thermal-proof structures [10; 11]. In long-term missions, on the other hand, the continuous and extended exposure of spacecraft surfaces to a detrimental oxidative environment must be carefully considered. LEO working conditions, in particular, are responsible for the aging of materials due to the combined effects induced by severe vacuum thermal cycles and harsh UV/AtOx irradiation [12; 13; 14; 15]. Such drawbacks may be sufficient to discourage the use of C/C as a re-entry protection material, because significant surface oxidation may lead to so much TPS damage that the main thermomechanical functionality would be compromised. In this work, the preliminary results on the analysis of LEO's environment effects on a commercial C/C material are presented and discussed. The experimental tests were performed by means of a specific facility able to reproduce LEO conditions in terms of AtOx/UV irradiation, analyzing the dependence of C/C erosion damage on both the sample attitude position and the synergic effects of the combined aging agents.
# Image Noise Reduction by Means of Bootstrapping-Based Fuzzy Numbers
## 7 Conclusions  
Colors are one of the places where the fuzzy concept can be greatly used. If we express the color of each pixel as a fuzzy number in an image, we can separate the color of the pixels more accurately. In this paper, we introduced an approach to determine the fuzzy color value of each pixel and reduce the noise of an image by using convergence in the mean bootstrap (according to the \(SLLN\) in the fuzzy metric space [14]).  
In fact, the \(BCFM\) filter relies on the \(SLLN\) for the bootstrap mean [14], eliminates noise, and as shown in the results, performs better than the \(CFM\) and \(Median\) filters.  
Two reasons can be considered the main factors improving the results. The first reason can be seen in the use of the fuzzy color pixel approach, which, according to the expressed triangular fuzzy number for each pixel, finds a more accurate mean of the central pixel neighborhoods of each window. The second reason for the performance improvement can be expressed by using the bootstrap mean to calculate the mean of the neighbors, which, by increasing the sample, reduces the effect of outliers (noisy pixels) on the mean. In 2007, [PERSON] et al. [15] compared the \(CFM\) filter to other methods, and the results showed that \(CFM\) performed better than the other filters. In this paper, we compared the \(BCFM\) filter with \(CFM\), which shows the better performance of the \(BCFM\) filter compared to the other methods.  
All authors contributed to the study conception and design. The material preparation, data collection, and analysis were performed by [PERSON], [PERSON], A.N. and M.R. The first draft of the manuscript was written by [PERSON], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.  
[PERSON] acknowledges the support of the Generalitat Valenciana under Grant AICO2020-136.  
Data will be made available upon reasonable request.  
The authors have no conflict of interest to declare that are relevant to the content of this article.  
The codes developed during the current study are available from the corresponding author upon reasonable request.
# A Formal Analysis of the Mimblewimble Cryptocurrency Protocol +
## 6 Mimblewimble Implementations
### Discussion
#### 6.3.3 Some Design Decisions  
Emission Scheme  
It is known that BitCoin has a limited and finite supply of coins. Nowadays, new coins come from the process called \"mining\" where miners are paid because of their work of aggregating new blocks to the chain besides of the transaction fees. However, once the maximum amount of coins in circulation is reached, there will not be new coins and the miners will be paid only with the transaction fees.  
Grin has a different approach. It has a static emission rate, where a fixed number of coins is released as a reward for aggregating a new block to the chain. This algorithm has no upper bound for new coins. However, Beam has a capped total supply standing at 262M. The reward algorithm is decreasing over the years [58].  
Parties Negotiation  
Mimblewimble establishes that communication between the parties to construct a new transaction is made off-chain. Parties should collaborate in order to choose blinding factors and construct a valid transaction, in particular, a balanced transaction as in Definition 5. Grin offers this process synchronously. Both parties are connected directly to one another and they should be online simultaneously.  
On the other hand, in order to construct a new transaction, Beam offers a non-interactive negotiation between the parties. The Secure Bulletin Board System (SBBS) [59] runs on the nodes and it allows the parties to communicate off-line. Moreover, Beam also presents a one-side payment scheme. This scheme allows senders to pay a specified value to a particular receiver, without any interaction from the receiver side. The key here is not revealing blinding factors. It is addressed with a process called kernel fusion. Basically, both parties construct a half kernel and both kernels should be present in the transaction.  
Chain Syncronization  
Grin allows partial history syncronization. When a new node wants to enter the network, it is not necessary to download the full history of the chain but it will query the block header at a horizon. The node can increase this limit as necessary. Then, it will download the full UTXO set of the horizon block.  
Beam improves node synchronization using macroblocks. A macroblock is a compressed version of blockchain history after applying the cut-through process. Each node stores macroblocks locally. When a new node connects to the network, it will download the latest macroblock and will start working from that point.
## 3 Data and Methods  
### Data  
We select the crude oil spot price of WTI, Brent, Dubai, and Daqing (China) as empirical objects. The data are monthly month-on-month data obtained from the Wind Database ([[http://www.wind.com](http://www.wind.com)]([http://www.wind.com](http://www.wind.com)) (accessed on 1 March 2022)) and the Energy Information Administration ([[http://www.eia.gov](http://www.eia.gov)]([http://www.eia.gov](http://www.eia.gov)) (accessed on 1 March 2022)). Since international crude oil trading price is based on the price of Brent, WTI, and Dubai crude oil, the monthly spot prices of Brent, WTI, and Dubai crude oil are selected to represent the international crude oil price level. The monthly spot price of Daqing crude oil is chosen to represent the Chinese crude oil price level. The study period is from January 2011 to March 2020; thus, there are 110 observations for each time series during the sample period. Then, we calculate the log return of the crude oil prices. The price indices we use in this paper are CPI, PPI, RPI, PPIRM, price index for means of agricultural production (MAPPI), corporate goods price indices (CGPI), the export price index (EPI), and the import price index (MPI), which reflect the Chinese price levels. The data period is from January 2011 to March 2020, downloaded from the Wind Database. We process the time series with a first-order difference. Based on the ADF test, all the variables are stationary.
# Near Real-Time Extracting Wildfire Spread Rate from Himawari-8 Satellite Data
## 3 Methodology  
The methodology in this study can be divided into two stages (see Figure 2): (i) The extraction of near real-time FSR (H8-FSR), consisting of data preprocessing, near real-time burned area detection, near real-time fire center extraction, and near real-time FSR calculation; and (ii) The comparison of H8-FSR results with those obtained using the CSIRO GFS Model.
# Dynamic Changes in the Export Competitiveness of the South Korean Oil Refining Industry
## 2 Current Situation of the South Korean Oil Refining Industry and Literature Review  
### Current Situation of the South Korean Oil Refining Industry  
Petroleum products are South Korea's top five export items after semiconductors, petrochemicals, general machinery, and auto parts as of 2021, occupying an important position in the national economy and competing with global companies. The South Korean oil refining industry is a typical oligopoly market composed of four companies: SK Energy, GS Caltex, S-OIL, and Hyundai Oilbank. SK Energy ranks first in the domestic market with a market share of over 30%, and the remaining three companies have an even market share of around 20%. The market share of the four companies has been maintained without significant change since 2000. The South Korean government even created a government-led retail supply chain called \"thrifty gas stations\" to break the oligopoly and promote competition, but it did not produce any meaningful results.  
Table 1 presents the export volume and export value statistics of the South Korean oil refineries. Exports, which were only 341 million barrels (29,814 million dollars) in 2020, achieved the best performance at 494 million barrels (39,978 million dollars) in 2018. However, due to the impact of the pandemic that started in 2020, exports in 2021 decreased to 420 million barrels, but due to the price increase of petroleum products, exports in 2021 returned to the pre-pandemic level.  
Table 2 presents South Korea's export volume by country as of 2021. Exports to China account for the largest share at 21.5%, followed by Japan, Singapore, the United States, and Australia. Petroleum product exports to these five countries accounted for 66.5%, indicating a high degree of export dependence on these countries.
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 1 Introduction  
Mass digitisation of people and things has opened the doorway to the Internet of Things (IoT), critical in many social and industrial applications [1, 2]. In particular, IoT is envisaged to deliver the vital data to inform digital twins and improve infrastructure maintenance and safety. In many cases, wireless IoT sensors that measure physical signals (e.g., gas pressure, water contamination) are buried underground [3, 4]. Encrypting the critical infrastructure information is important for national security, commercial sensitivity, and anti-tampering requirements. Many current IoT wireless transmissions (e.g., LoRaWAN [5], ZigBee) are vulnerable to eavesdropping. Authentication (e.g., over-the-air activation session keys in LoRaWAN, elliptic curve Diffie-Hellman in Bluetooth) verifies the user's identity and prevents malicious users from accessing the network. Encrypted wireless transmission protects data integrity and confidentiality [6].
## 4 Results
### Numerical Analysis  
The interpolation of the point cloud into a regular matrix allows us, for the first time, to calculate new parameters based on field-measured data of the river section under investigation, that would otherwise only be possible to derive from numerical modeling. Each 3D point in the matrix represents a known volume, known as a voxel (analogous to a pixel in a 2D raster space). Because of this property, it becomes possible to calculate the total volume of water in the AOI: a voxel size of 3 \(\times\) 3 \(\times\) 0.2 equals a volume of 1.8 \(\mathrm{m}^{3}\), so the total number of voxels times 1.8 gives a fairly good estimate of the total volume of water in the river reach at a given stage of the flood. Furthermore, it becomes possible to evaluate the volume of water moving at a certain velocity or a range of velocities. That way, the high-velocity core can be quantified with reasonable accuracy. Table 2 lists total volumes, along with volumes of water moving at velocities of <0.5 \(\mathrm{m}^{3}\), 0.5-1.0 \(\mathrm{m}^{3}\), 1.0-1.2 \(\mathrm{m}^{3}\) and >1.2 \(\mathrm{m}^{3}\). Figure 13 shows the flow velocity distribution for each measurement day as histograms.  
The data presented in Table 2 and Figure 13 show that the velocities are largely normally distributed and that the total volume of water in the meander bend AOI increases during the rising stage of the flood, along with water levels and discharge, as one would expect. However, the peak velocities of the whole measurement period are concentrated on 18 and 19 May, reaching 1.8 \(\mathrm{ms}^{-1}\) with a mean velocity exceeding 1 \(\mathrm{ms}^{-1}\). Thereafter, while water levels and, as a consequence, total volumes of water increase until May 23, the peak velocities decrease, and most of the water is moving at speeds slower than 1 \(\mathrm{ms}^{-1}\). This is reflected in the discharge values measured on these days. Discharge peaks on May 19 at 66 \(\mathrm{m}^{3}\mathrm{s}^{-1}\) and then drops overnight by almost 20 \(\mathrm{m}^{3}\mathrm{s}^{-1}\), while the water level only decreased by 5 cm during the  
Figure 13: Histogram plots of 3D interpolated flow velocity lattice illustrating the changes in magnitude and flow speed distribution during the measurement period. All plots use the same scale. One frequency unit represents 1.8 \(\mathrm{m}^{3}\) of water, as per the 3D interpolation. Only measured volume is represented here. The top layer of water that is not captured by the ADCP, the local speeds of which are unknown, is not included in this analysis.  
same time period. The total volume of water in the meander bend AOI increases by 99 \(\mathrm{m}^{3}\), but the volume of water moving at speeds over 1 \(\mathrm{ms}^{-1}\) drops by about 5000 \(\mathrm{m}^{3}\), while the volume moving at over 1.2 \(\mathrm{m}^{3}\mathrm{s}^{-1}\) drops by nearly 200 \(\mathrm{m}^{3}\) to almost zero overnight.
# Evaluation of the Long-Term Performances of SMA-13 Containing Different Fibers
## 3 Test Scope and Methods
### FTIR Test  
The Fourier transform infrared spectrometer (FTIR) adopted in this paper is made by Perkinelmer Instruments Co., Ltd., Shanghai branch, Shanghai, China. The asphalt in the SMA-13 mixtures under different aging degrees was extracted from the mixtures using the vacuum extractor (the medium is trichloroethylene) and tested using the FTIR to find the changing pattern of the chemical functional group in the asphalt to better explain the effect of the aging degree on the chemical composition of the asphalt used in the mixtures.
## 3 Results and Discussion
### Changes in Characteristics of \(H\). fusiformis after Base Treatment  
FE-SEM micrographs of (a) untreated \(H\). _fusiformis_ and (b) after treatment with 1 M NaOH are shown in Figure 3. The crystal precipitates disappeared after NaOH treatment, but the surface of treated \(H\). _fusiformis_ was degraded, showing more cracks and greater porosity. This led to improvements in surface area and pore volume, which indicated the potential higher adsorption capacity of the adsorbents [5]. After NaOH treatment, the more active sites absorbed more heavy metals.  
Figure 1: Comparison of metal removal capacity of biomass from different species of algae.  
FTIR was used to identify the functional groups on the surface of untreated and treated algae and to investigate the contribution of functional groups to the biosorption of metals by algae. The FTIR spectra of _H. fusiformis_ is shown in Figure 4. The broad and intense absorption peaks at 3249-3292 cm\({}^{-1}\) corresponded to the C-O stretching vibrations present in cellulose, pectin, absorbed water, hemicellulose, and lignin [42]. The small bands in the range of 2920-2935 cm\({}^{-1}\) are attributable to the C-H stretching vibrations of methyl, methylene, and methoxy groups [43]. The peaks at 1604-1618 cm\({}^{-1}\) corresponded to the ester carbonyl (C = O) groups and carboxylate ion (COO-) stretching band of pectin [25; 44]. The vibrations at 1430-1455 cm\({}^{-1}\) are attributable to aliphatic and aromatic (C-H) groups in the plane deformation vibrations of methyl, methylene, and methoxy groups. The bands in the range of 1000-1300 cm\({}^{-1}\) were assigned to the C-O stretching vibration of R-COOH and R-OH [2]. The change in the vibrational frequency of functional groups after treatment showed that treatment with NaOH could increase the number of ester (1160 cm\({}^{-1}\)) and carboxylic groups (1419 cm\({}^{-1}\)), effectively absorbing heavy metals in an aqueous solution [45]. The peaks of _H. fusiformis_ at 1082 cm\({}^{-1}\) and 890 cm\({}^{-1}\) represent C-O stretching and C = C bending.  
Figure 3: FE-SEM image of **(a)** untreated _H. fusiformis_ and **(b)**_H. fusiformis_ treated with 1 M NaOH (Scale bar: 10 \(\upmu\)m).  
Figure 2: Effect of HCl and NaOH treatment on the pretreatment on _H. fusiformis_ (HR: raw _H. fusiformis_ without treatment; HH_0.1M and HH_1M: 0.1 M and 1 M HCl-treated _H. fusiformis_; HN_0.1M and HN_1M: 0.1 M and 1 M NaOH-treated _H. fusiformis_).
## 3 Selected Methods and Experimental Approaches for Analysis of HS Structure
### Solid State Investigations
One example is showed in Figure 5 where HA from Leonardite was observed with SEM after a dispersion in alcohol and adsorption/dehydration on mica surface, compared with the same sample of HA dispersed in alcohol observed with TEM. In both cases, the alcohol dehydration favours the collapse of HA molecules into fibrous structures. Comparing AFM images with SEM and TEM, the solution conditions of Figures 2-4 for AFM are very similar and the results are comparable. The hydrophobic effect has been invoked to justify the formation of similar ring-shaped structures observed with AFM at low salt concentration and low pH [102; 103]. Figure 5: (**A**,**B**) SEM microtopographies of HA dispersed in alcohol and then adsorbed and dried on mica surface. (**C**,**D**) TEM microtopographies of HA dispersed in alcohol in forward film. By contrast, it is well known that the sample for SEM and TEM analysis must be treated under high vacuum conditions and on dehydrated conditions. Thereby, the first step with alcohol treatment followed by spraying carbon in the vacuum tank, changes the morphology of HA. However, it is worth noting that the sizes of the fibrous aggregates in Figure 5 are similar in both SEM and TEM images. This may be attributed to the strong alcohol dehydration conditions. The influence of Na+ on the macromolecular structure of HA at pH 4.0-6.0 favours pseudo-spherical colloids, while the alcohol dehydration stabilizes elongated fibrous structures. Figures 3 and 4 show that dimensional parameters such as particle diameter, length and height of the irregular aggregates are much larger than those observed with AFM. The dehydration treatment with alcohol could be responsible for this effect, which may favour the formation of bridges between cation ions and carboxyl groups or other polar groups. This behavior confirms that the dispersion in water used for AFM observation is very useful for obtaining an in-depth analysis of the HS structure analogous to that occurring in natural aqueous environments. The amphiphilic properties of different chemical groups present within the structure of HA such as, e.g., Leonardite, can give rise to the formation of micellar-like aggregates in solution and also to assemblies of various shapes onto mineral surfaces.
## 3 Results and Discussion
### SEM and EDS Analyses  
Scanning electron microscopy (SEM) was used to examine the surface of collagen and energy-dispersive X-ray spectroscopy (EDS) was used to examine the most abundant chemical elements in the samples. The results suggested a similar morphology surface between the collagens, with the \"cellular\" structure typically obtained upon freeze-drying of polymeric solutions. In all cases, the cell walls are just a few microns thick, with slight differences in porosity and mean pore size, apparently increasing in the sequence \(COLRp\_1\)\(<COLRp\_II\)\(<COLRp\_III\) (Figure 4A). In addition, the three highest elements detected (EDS does not detect hydrogen) in the collagen samples were carbon, oxygen, and nitrogen, with weights around 57-59%, 22-24%, and 15-17%, respectively, which are in agreement with the contents of these elements in proteins. Interestingly, other elements such as chlorine, sulfur, aluminum, and sodium were also identified, most probably due to the presence of salts in the samples (Figure 4B).
# Audio Anti-Spoofing Based on Audio Feature Fusion
## 4 Results
### Comparison with Models Using the Pre-Trained Model  
Since our model uses the wav2 vec2 pre-training model, we compared it with other models [12; 37] that also use wav2 vec2. We used the same wav2 vec2 pre-trained model for feature extraction as these models. We differed in using our respective back-end classification modules and different data augmentation techniques. As can be seen from the results in Table 2, the model in [12] had an EER of 7.18% on the ASVspoof 2021 LA partition, and that in [37] had an EER of 3.54% on the ASVspoof 2021 LA partition, both of which are higher than our EER of 1.18%. As can be seen in Table 3, the model in [37] had an EER of 4.98% on the ASVspoof 2021 DF partition, which is also higher than ours of 2.62%. Therefore, our back-end classification module showed an improvement compared to the other back-end classification modules.  
SSL_Anti-spoofing [13] is a model proposed in 2022, which is a synthetic speech recognition model that also uses Rawboost data augmentation techniques and wav2 vec2 pre-trained models. Our model differs from it in the back-end used. According to the latest literature on ASVspoof 2021 [15], it is the best-performing model for both LA and DF partitions. No model has so far been able to surpass it in effectiveness. Our model also failed to reach its performance on the ASVspoof 2021 LA partition.  
However, the proposed model yielded highly satisfactory results on the ASVspoof 2021 DF partition, exhibiting an EER of merely 2.62%, which was slightly higher, by 0.23%, than the SSL_Anti-spoofing model and outperformed all the models mentioned in [15], as displayed in Table 3.