esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Validation  
[PERSON] et al. [71] conducted a study on the behavior of RC slabs against blast loading. The slab with dimensions of 1000 \(\times\) 1000 \(\times\) 100 mm was exposed to explosions with a scaled distance of 0.079-0.527 m/kg\({}^{1/3}\). Here, to validate the process of blast load calculations in ABAQUS, this concrete slab was modeled, and the blast pressure distribution was compared to the original reference. The _slab-32_ model in reference [71] was modeled under the effect of a blast of 2 kg TNT at a distance of 0.5 m (\(Z\) = 0.3968 m/kg\({}^{1/3}\)). Here, due to the symmetry of the structure and loading, only one-fourth of the concrete slab was modeled and analyzed. The considered RC plate was a square with 500 mm length of side and 100 mm thickness. The finite element model of the RC slab with its support structure prepared here and the definition of the explosive charge are given in Figure 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Thickness (cm)** & **Stand-Off Distance (m)** & **Charge Weight (kg)** & **Scaled Distance (m/kg\({}^{1/3}\))** \\ \hline Model-1 & 15 & 2 & 5 & 1.170 \\ Model-2 & 15 & 5 & 5 & 2.924 \\ Model-3 & 15 & 10 & 5 & 5.848 \\ Model-4 & 15 & 2 & 7 & 1.046 \\ Model-5 & 15 & 5 & 7 & 2.614 \\ Model-6 & 15 & 10 & 7 & 5.228 \\ Model-7 & 20 & 2 & 5 & 1.170 \\ Model-8 & 20 & 5 & 5 & 2.924 \\ Model-9 & 20 & 10 & 5 & 5.848 \\ Model-10 & 20 & 2 & 7 & 1.046 \\ Model-11 & 20 & 5 & 7 & 2.614 \\ Model-12 & 20 & 10 & 7 & 5.228 \\ Model-13 & 25 & 2 & 5 & 1.170 \\ Model-14 & 25 & 5 & 5 & 2.924 \\ Model-15 & 25 & 10 & 5 & 5.848 \\ Model-16 & 25 & 2 & 7 & 1.046 \\ Model-17 & 25 & 5 & 7 & 2.614 \\ Model-18 & 25 & 10 & 7 & 5.228 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Details of the studied models.  
Figure 6: Boundary conditions of the walls: (**a**) fixed top and bottom of the wall; (**b**) fixed sides and bottom of the wall; (**c**) fully fixed BCs.  
Figure 8 shows the time history of the calculated pressure in the middle of the slab in this study compared to the reference [71]. It can be observed that the maximum overpressure difference is equal to 100%\(-\frac{42.36}{44.0}\times\)100% = 3.72%, which is in an acceptable range.
## 2 Construction and Basic Assumptions  
VIG is composed of two pieces of flat glass, sealing solder and supporting pillars (See Figure 1), wherein supporting pillars are arranged in a certain sequence to separate the two glass substrates. In this study, the supporting pillars were distributed in a square and equal space. Due to symmetry, only a single glass flat was discussed. Continuous stress and deformation of the glass panes occurred under atmospheric pressure and the assumptions for supporting characteristics were made as follows.  
1. Elastomer assumption: when the load on the glass does not exceed the limit, it exhibits elastic properties;  
Figure 1: Structural diagram of vacuum-insulated glazing.  
2. Rigid supporting: the elastic modulus of supporting pillars is much larger than that of glass pane, and the glass deformation at the contact position is much larger than that of the supporting pillars. It can be assumed that the supporting pillars are regarded as rigid supports without deformation;
3. Negligible edge displacement: the edge is designed as a fixed constraint after sealing, so that the edge has negligible displacement.
## 2 Materials and Methods
### AMSR-E Data Processing  
The Advanced Microwave Scanning Radiometer (AMSR) for the Earth Observing System (AMSR-E) developed by the Japan Aerospace Exploration Agency (JAXA) was launched in May 2002 on NASA's (National Aeronautics and Space Administration) Aqua satellite [40; 41] and worked until October 2011. AMSR-E was the successor of AMSR, which had lost due to satellite malfunction in October 2003, and nearly had the same characteristics as AMSR. ASMR-2, as the successor of AMSR-E, launched in May 2012 is still working. The detailed characteristics and performance of the AMSR family can be seen from reference [42].  
Figure 1: Study area. The vegetation map was extracted from MODIS land cover product in 2005.  
In this study, the AMSR-E/Aqua ascending daily global 0.25-degree Tb data from 2003 to 2010 were provided by the National Snow and Ice Data Center (NSIDC) ([[http://nsidc.org/](http://nsidc.org/)]([http://nsidc.org/](http://nsidc.org/))). For AMSR-E data preprocessing, first, we conducted a simple atmospheric correction for the L-band, C-band, and X-band on the global scale by using Earth Vegetation Atmosphere (EVA) model [43]. Second, we used spectral difference statistics (means and standard deviations) to identify pixels contaminated by radio frequency influence (RFI) and used RFI of K-band (RFIK) to shield high-frequency RFI for radio frequency interference correction [44; 45]. Finally, we aggregated daily Tb data into monthly by using the average method.
# Cyberattack Models for Ship Equipment Based on the MITRE ATT&CK Framework
## 5 Conclusions  
For the safe navigation of ships, cybersecurity regulations enforced by the IMO as well as the guidelines of governments (e.g., USCG) and related associations (e.g., BIMCO) should be observed. Shipbuilders and ship equipment manufacturers also need to understand the threats in threat libraries and implement appropriate measures.  
This study analyzed ship cybersecurity research cases based on the MITRE ATT&CK framework and presented mitigation plans based on the analysis results.  
In this study, we investigated and analyzed potential cybersecurity threats on ships. It was established that while ICT-based ships are vulnerable to sophisticated cyberattacks, the industry is not yet fully prepared. Future research on ship cybersecurity is required, and future research trends are as follows:  
1. Design of cybersecurity risk management and risk assessment framework for various ship types [33].
2. Threat analysis and security requirements analysis for NMEA 0183, 2000 protocol [34].
3. A honeypot system to detect new cyberattacks onboard and reduce false positives in IDS [35]. In particular, a honeypot system has been developed [36] that can be installed in an L2 layer switch environment using VLAN when the deployment location of the honeypot is the ship's internal network. For analyzing the attack type on the ship's satellite communication system from outside the ship, a cloud-based ICS/SCADA honeypot system has been proposed [37].
4. Artificial intelligence-based ship cyber threat response technology for an independent system of an autonomous vessel at sea to recognize an attack and respond by itself.
5. The ship equipment whitelisting technique and data set [38].
6. Maritime cyberthreat intelligence: technology for the classification and evaluation of threat information tailored to ships, the maritime industry, and stakeholders [39].
7. Ship digital forensic technology to collect and analyze digital evidence in security incidents of onboard systems.  
Cybersecurity is an important issue for ICT-based ships. There is an urgent need to increase awareness regarding the various threats posed to systems such as ECDIS, VDR, ECS, VSAT, and NMEA, as various vulnerabilities are being discovered in IT and OT every day. From a cybersecurity perspective, this awareness is critical to ensure the dependability of a ship at sea. We believe that our research will instigate future research directions and provide motivation for further research on ship cybersecurity.  
Conceptualization, Y.J.; Methodology, Y.J.; Validation, D.H.L.; Resources, J.Y.; Writing--original draft preparation, Y.H; Writing--review and editing, Y.J., D.H.L., J.Y., Y.C. and O.C.; Supervision, Y.C. and D.H.L.; Project administration, O.C.; Funding acquisition, Y.J. All authors have read and agreed to the published version of the manuscript.  
This research was a part of the project titled 'Development of maritime cyber-threat intelligence', funded by the Ministry of Oceans and Fisheries, Korea (No. 20210038).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results
### Chemical Characterization of the Three Propolis Extracts
#### 3.1.2 Total Phenol and Flavonoid Content of the Propolis Extracts  
The GAE and QUE curves were found to be linear (Figure S2, Supplementary Materials). All absorbance determinations were made thrice at 765 nm for GAE and 425 nm for QUE. The mean values of the three measurements were used for constructing the calibration plot. Correlation coefficient (r\({}^{2}\)) values of 0.99 and 0.99 were found for GAE and QUE, respectively. The linearity data of each isolated compound are presented in Table 2.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Chemical Marker** & **PPF** & **PSDE** & **MPE** \\ \hline  & **Mean (mg/g) \(\pm\) SD** & **Mean (mg/g) \(\pm\) SD** & **Mean (mg/g) \(\pm\) SD** \\ \hline Caffeic acid & 168.647 \(\pm\) 17.4 ***(a) & 7.045 \(\pm\) 0.0274 **(a) & 0.795 \(\pm\) 0.0044 *(a) \\ \(p\)-Coumaric acid & 50.989 \(\pm\) 3.9 ***(a) & 6.982 \(\pm\) 0.0132 **(a) & 4.667 \(\pm\) 0.0317 *(a) \\
3,5 Dicaffeoylquinic acid & ND & 7.289 \(\pm\) 0.030 **(b) & 6.623 \(\pm\) 0.035 *(b) \\
4,5 Dicaffeoylquinic acid & ND & 4.741 \(\pm\) 0.0429 **(b) & 11.844 \(\pm\) 0.2153 *(b) \\ Aromadendrin-4-O\({}^{\prime}\)-methyl-ether & ND & ND & 2.619 \(\pm\) 0.038 *(b) \\ Drupanin & ND & 18.945 \(\pm\) 0.134 **(b) & 7.715 \(\pm\) 0.040 *(b) \\ Chrysin & ND & 1.963 \(\pm\) 0.035 **(b) & 1.225 \(\pm\) 0.029 *(b) \\ Galangin & ND & ND & 3.613 \(\pm\) 0.089 *(b) \\ Artepillin C & ND & 44.970 \(\pm\) 1.078 **(b) & 18.850 \(\pm\) 0.373 *(b) \\ Baccharin & ND & ND & 2.581 \(\pm\) 0.071 *(b) \\ \hline \end{tabular}
\end{table}
Table 1: Chemical profile of polar propolis fraction (PPF), propolis soluble dry extract (PSDE), and microencapsulated propolis extract (MPE). Data obtained based on HPLC/DAD methodology ([PERSON], 2019) (\(n=3\) samples analyzed for each product).  
An evaluation of the phenolic and flavonoid contents of the three extracts is presented in Table 3. PPF, PSDE and MPE contain 94, 80 and 40% \(w/w\) of propolis dry matter, respectively. The three extracts all had a greater concentration of phenolics than flavonoids. PSDE is a more concentrated extract, with higher contents of both compound classes, compared with PPF and MPE; these other two extracts contained around one third of the total phenolics of PSDE and approximately one half of the flavonoids. The quantities of total flavonoids were almost proportional to the propolis dry matter content of each extract; higher concentrations of propolis resulted in larger amounts of flavonoids. In the evaluation of MPE and PSDE, when normalized for the same concentration of propolis, the quantity of flavonoids was almost equivalent, different from the total phenolics, which apparently were impacted by the different processes used to produce MPE and PPF. An extensive evaluation and discussion of chemical differences between these three extracts is available in [PERSON] et al. [53].
## 3 Measurement Setup  
The evaluation of the proposed technique was done using three different testing setups. Initially, a lightly contaminated signal recorded from a simple rolling element bearing was used to evaluate the performance of the cyclostationary analysis, as well as its combination with EEMD.  
Next, a more complex scenario was considered with a signal recorded from an input shaft of a 3-stage gearbox in a mock-up wind turbine. Finally, the data recorded from the gearbox of a real wind turbine, during full load operation, was used to evaluate the performance of the technique. Each of these three setups is detailed below.
# Deep Learning-Based Network Intrusion Detection Using Multiple Image Transformers
## 2 Related Work
### Threshold/Pattern-Matching-Based NIDS  
Early NIDS used simple rules to detect or block intrusion based on a simple analysis of traffic. For example, if an ICMP echo response packet is received at over 10 per second, it is detected as a Smurf attack, or if the number of half-open sessions is over five per second, it is evaluated as a synchronize sequence number (SYN) packet scan attack [18; 19].  
Gradually, as network attacks gradually shift from layer 3 and layer 4 attacks, NIDS requires deep packet inspection [20]. A NIDS which supports deep packet inspection attempts to match pre-implemented patterns for the application layer payload, extract information regarding the application layer through matching results, and use them to determine whether to attack and control traffic accordingly. A typical example of deep packet inspection is malicious code detection. To perform a cross-site scripting attack (XSS), specific code is injected into the website, which is then found by pattern matching using regular expressions [21].  
Threshold- and pattern-matching-based approaches can efficiently reduce false positives as they leverage methods designed to identify network intrusions based on actual data. It can easily optimize pattern search using an advanced pattern-matching algorithm that can efficiently search for multiple rules when constructing set pattern-matching rules. \"Aho-Corasick algorithm [22]\". Therefore, although the threshold-based and pattern-matching methods are initial methods, they are still employed in recent NIDS. In particular, the pattern matching-based NIDS is being expanded so that it can use behavioral characteristics as a pattern, not just data characteristics; thus, the NIDS is further improved [23].  
However, because thresholds and patterns are created based on information from prior attacks, an attacker can estimate thresholds and patterns in reverse. An attacker can also modify the attack pattern so that it cannot be detected by exploiting the estimated result. Here, network administrators should steadily collect information regarding variant attacks and add new thresholds and patterns. Consequently, the administrator is overwhelmed, resulting in a sluggish response to new attacks.
# An EMD-SARIMA-Based Modeling Approach for Air Traffic Forecasting
## 3 The Hybrid EMD-SARIMA Forecasting Framework
### Stage 1: EMD Modeling  
The EMD model was first proposed by [PERSON] et al. [39] in 1998. As a novel method of decomposing a signalinto intrinsic mode functions (IMFs), it is extremely useful for analyzing non-stationary and non-linear time series, and has aroused a lot of attention from both industry and academia. An IMF is an independent componentthat has variable amplitude and frequency, and it is acquired from a sifting process of the original time series. The sifting process is an iterative one, and is utilized to extract the different IMF components. The procedures of the sifting process can be summarized as follows:  
1. Identify all the local extrema (i.e., local maxima and minima) in the original time series \(x(t)\).
2. Interpolate all the local maxima (minima) by a cubic spline from the upper envelope \(e_{\text{max}}(t)\) and lower envelope \(e_{\text{min}}(t)\).
3. Calculate the mean of the envelope \(m(t)\) from the upper and lower envelope. \[m(t)=\frac{e_{\text{max}}(t)+e_{\text{min}}(t)}{2},\] (1)
4. Extract the mean of the envelope from the original signal to obtain a new signal \(h(t)\). \[h(t)=x(t)-m(t),\] (2)
5. Check whether \(h(t)\) is an IMF: (1) If \(h(t)\) is an IMF then set \(d(t)=h(t)\) and replace \(x(t)\) with the residual \(r(t)=x(t)-d(t)\); (2) if \(h(t)\) is not an IMF, replace \(x(t)\) with \(z(t)\) then repeat the steps from II to IV until the following stopping criterion is met in the iterative process. As has been suggested by [PERSON] et al. in literature [39], a typical value of \(\delta\) is between 0.2 and 0.3. \[SD=\sum_{t=1}^{m}\frac{\left[h_{i-1}(t)-h_{i}(t)\right]^{2}}{\left[h_{i-1}(t) \right]^{2}}\leq\delta\quad(i=1,2,\cdots,m)\] (3)  
After this sifting process, the original time series can be written as Equation (4) shows, from where \(r(t)\) is the residual after the IMFs are derived, and \(d\dot{t}(t)\) is the IMF obtained from EMD:  
\[x(t)=r(t)+\sum_{i=1}^{n}d^{i}(t). \tag{4}\]  
The IMF is defined as a component that satisfies the following conditions: (1) the number of extrema and the number of zero-crossings must either be equal or differ at most by one; (2) at any point, the mean value of the envelope determined by the local maxima and the envelope determined by the local minima is zero.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{l l c c c c c c c c} \hline
**Peak** & **Identification** & **[M-H]-** & **[M+H]\({}^{*}\)** & **Elemental Composition** & **Error (ppm)** & **MSn ions** & **Rt (min)** & **C.** _setwins_ & **L.** _sicraria_ \\ \hline
**Sesquiterpenes** & & & & & & & & & \\
39 & Cynnoside A & 443.1918 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 0.7 & 281, 237, 219, 189, 161, 131, & & & \\
40 & Unknown & 429.1394 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 1.8 & 205, 119, 113, 897, 71, 59 & 10 & + & + \\
41 & Unknown & 407.1553 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 1.8 & 205, 119, 113, 897, 873, 71 & 13 & + & - \\
42 & Unknown & 409.1716 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 1.3 & 243, 188, 119, 101, 89, 87, 73 & & + & - \\
43 & Unknown & 409.1716 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 0.2 & 269, 258, 987, 59 & 142 & + & - \\
44 & Unknown & 461.2021 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 1.6 & 343, 161, 155, 101, 87, 73, 71 & 16.5 & + & - \\
**Alkyl glycosides** & & & & & & & & \\
44 & Butanol-O-pentosyl-heoside & 367.1608 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 0.5 & 235, 203, 161, 131, 113, 101, & & & \\
45 & Benzyl-O-pentosyl-heoside & 401.1448 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 1.8 & 260, 161, 131, 113, & & + & - \\
46 & Hexanol-O-pentosyl-heoside & 395.1928 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 1.4 & 263, 161, 131, 101, 71 & 132 & + & - \\
47 & Unknown & 529.2643 & C\({}_{2}\)H\({}_{4}\)O\({}_{1}\)\({}^{-}\) & 2 & 397, 347, 89 & 15.1 & + & - \\ \hline
**Pavonoids** & & & & & & & & \\
48 & Isovitcoin-O-heoside & & & & & & & \\
49 & (Saponarin) & & & & & & & \\
49 & Iso/eitretin & 447.0933 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 1 & 429, 360, 357, 327, 297, 285 & 12.1 & + & + \\
50 & Iso/eitvin & 431.0974 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 2.2 & 341, 318, 269 & 13.3 & + & + \\
51 & Quercetin-O-heoside & 463.0878 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 0.8 & 300, 271, 255, 179, 151 & 13.5 & + & + \\
52 & Iso/eitvin-O-fenylhevoside & & 771.2141 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\)\({}\)\({}^{*}\) & 1.3 & 753, 595, 433, 459, 397, 337, & 13.5 & + & - \\
53 & Kaeemperf & & & & & & & & \\
54 & Iso/eitvin-O- & & & & & & & & \\
55 & country/hoseoside & & & & & & & & \\
55 & Isdonnmentin-O-trinoside & & & & & & & & \\
56 & Quercetin & & & & & & & & \\
57 & Kaempfer-O-heoside & 447.0927 & C\({}_{2}\)H\({}_{3}\)O\({}_{1}\)\({}^{-}\) & 1.3 & 284 & 14.3 & + & + \\
58 & Isonhmentin-O-hesoside & 477.1031 & C\({}_{2}\)H\({}_{3}\)O\({}_{0}\)\({}^{-}\) & 0.3 & 255, 151, 133 & 14.4 & + & + \\
59 & Luleolin & & & & & & & & \\
60 & Isoharmentin & & & & & & & & \\
61 & Luteolin-O-malonylheoside & & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Cont.
# A Review of Recent Research into the Causes and Control of Noise during High-Speed Train Movement
## 4 High-Speed Train Noise Control Methods
### Acoustic Metamaterials Applied to High-Speed Train Noise Control
#### 4.2.3 Application of Metamaterials in Train Car Bodies
The frequency variation of the main noise sources also deserves attention; compared with the noise in the range of 2000 Hz-4000 Hz, the improvement is more obvious in the range of 500 Hz-2000 Hz. The pantograph noise changes more significantly with speed, and the noise level in the 500 Hz-1500 Hz range reaches nearly 100 dB(A) after the speed reaches 386 km/h. The significant increase in noise levels in the mid-frequency and low-frequency can seriously affect passenger comfort. Traditional acoustic materials mainly use porous structures, such as acoustic sponges, felt, glass fiber, polyurethane foam, etc. When sound waves pass through the pores of various materials, their kinetic energy is converted into thermal energy, leading to the dissipation of sound wave energy to attenuate noise; however, porous materials have poor absorption capacity for low-frequency noise, and the material thickness is strictly limited due to the size of the train, which makes it difficult for the current acoustic insulation car body design to deal with low-frequency noise. In contrast, metamaterials, with their unique material properties, can absorb low-frequency noise while maintaining a small thickness. According to the latest research on acoustic metamaterials shown in Figure 8, it can be found that acoustic metamaterials can cope with low-frequency and mid-frequency noise better and ensure a high noise absorption level in a thinner case. Figure 8d,e of the absorption coefficient curve can be found that in acoustic metamaterials in a wide range of frequencies, the sound absorption ability can reach more than 80%, and this acoustic characteristic can just meet the noise problems faced by high-speed trains. At present, acoustic metamaterials have been widely used in the fields of aerospace, ships, automobiles, and buildings. It is believed that acoustic metamaterials, with their excellent acoustic properties, will also become an important measure to solve the noise problem of high-speed trains. Figure 8: **(a)** Schematic of perforated honeycomb-corrugation hybrid (PHCH) metamaterial [116]. **(b)** Schematic of the broadband metamaterial unit [115]. **(c)** Schematic diagram of a hybrid absorber with microperforated plates and coiled channels [119]. **(d)** Schematic representation of a tunable multi-cavity coupled-resonator with polyurethane-filled slits [121]. **(e)** Schematics of the composite honeycomb sandwich panels [117]. **(f)** Multiband quasi-perfect low-frequency sound absorber based on double-channel Mie resonator [113]. Figure 9: The main noise frequencies characteristics of high-speed trains: (**a**) wheel-rail area; (**b**) inter-coach spacing; (**c**) pantograph [47].