esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Understanding these relationships can enable companies to adjust their operations to reduce their energy consumption and improve efficiency. If autocorrelation is present in the data, it may be necessary to use more advanced techniques to capture the dynamics of the time series accurately. Failure to account for autocorrelation in an analysis properly can lead to biased results and incorrect conclusions. Therefore, it is crucial to properly account for autocorrelation when analyzing time series. To address autocorrelation in the regression model, we propose using an alternative partial expectation-conditional maximization (APECM) algorithm, which is an improved version of the ordinary expectation-maximization (EM) algorithm. This method is particularly useful for mixture modeling when dealing with autocorrelated observations. The APECM algorithm is considered one of the most efficient variants of the original EM because it employs an optimized algorithm for estimating and inverting component covariance matrices in the case of autocorrelated data. Our most recent study highlights the potential of this method for clustering time-series data with autocorrelated observations [18]. In this study, we extend this research by applying a clustering approach to the energy-consumption time series in the context of the plastic industry. A detailed discussion of this motivating case study is presented in the next subsection. This paper is organized as follows. In Section 2, the fundamental aspects of the methodology are discussed. In Section 3, specific details regarding the regression mixture model method for time-series clustering in the presence of autocorrelated observations are provided. In Section 4, a real-world case study related to the energy consumption of the plastic injection molding process is presented, followed by a discussion of the clustering results. The paper concludes in Section 5.
# Chemo-Mechanical Model for the Expansion of Concrete due to Alkali Silica Reaction
## 6 Verification Examples
### Effect of External Alkali on ASR  
An experiment to investigate the characteristic of ASR in different alkali exposure environments was carried out recently [22]. Two size groups of waste glass were incorporated as the reactive aggregates in the specimens, group 1.18-2.36 mm and 2.36-4.75 mm, respectively. The specimens were immersed in three kinds of solution, 1N NaOH solution, 0.5N NaOH solution, and 0.5N NaOH+0.5N NaCl solution at 80 \({}^{\circ}\)C. As shown in Figure 6, the expansion caused by ASR increases with the NaOH concentration of the solution. The expansion was increased significantly due to the addition of NaCl in the NaOH solution.  
Numerical simulations were carried out with the developed model to capture the experimental results. The two size groups of aggregate were equivalently simplified in the model, diameter with 1.77 mm for group 1.18-2.36 mm and 3.56 mm for group 2.36-4.75 mm, respectively. Only 1/8 of specimen had to be modeled, as shown in Figure 6 on the left. The parameters used in the numerical model were calibrated based on the highest expansion curve at 1N NaOH, and are given in Table 2. Then, the other alkali contents were simulated with the same parameters, and the results are shown in Figure 6, along with the experimental expansion data.  
The results indicate that the model can capture the influence of different alkali contents on expansion, except for a small gap in the early period. The expansion increased by NaCl with changes in the swelling capacity of gel was predicted very well. However, it may be noted that we ignored the change of hydroxide ions due to the formation of Friedel's salt, etc.
# Bond Fatigue of TRC with Epoxy Impregnated Carbon Textiles
## 2 Materials and Methods
### Test Setup  
The test setup for the investigation of the bond bearing behavior of the chosen material combination is known as the 'double-sided textile pull-out (DPO) test' (Figure 3a). Initially, the test was developed at RWTH Aachen University, e.g., [22], for the determination of the needed anchorage length. Stuck steel plates define the investigated anchorage length on both ends of the specimen. A small gap between the steel plates works as a predetermined breaking point in the middle of the specimen. By pulling the steel plates apart, tension is initiated in the specimen and hence in the textile as well. If the textile ruptures, the chosen anchorage length was long enough to transfer the complete tensile load. If bond failure occurs and the textile, e.g., pulls out, the anchorage length was too short. The applicability of the test setup for fatigue tests was investigated e.g., in [16].  
Figure 1: Carbon textile impregnated with epoxy resin.  
The tests were carried out in a servo-hydraulic tension testing machine with accuracy class 1 and a load capacity of 100 kN for cyclic tests. During the tests, the machine force was recorded by a load cell. The deformation of the specimen at the predetermined breaking point was measured by two  
Figure 3: Test setup for static and cyclic double-sided textile pull-out (DPO) tests: (**a**) Specim geometry; (**b**) Testing machine with specimen.  
Figure 2: Manufacturing of the specimens: (**a**) Casting of huge panels; (**b**) Sawing out of specimens (exemplary cut).  
extensometers, which were fixed on the steel plates on both sides of the specimens. In the cyclic tests, the number of load cycles was recorded as well.
# Color Visual Secret Sharing for QR Code with Perfect Module Reconstruction
###### Abstract  
Visual secret sharing is a secret sharing scheme where the decryption requires no computation. It has found many applications in online transaction security, privacy protection, and bar code security, etc. Recently, researches have indicated that combining visual secret sharing with the widely used Quick Response code may provide additional security mechanism to online transaction. However, current methods are either pixel-based, which requires high computational complexity or module-based, which sacrifices error correction capability of the original Quick Response code. Designing module-based visual secret sharing for the Quick Response code without sacrificing error correction capability is a challenging problem. To solve this problem, this paper proposes a (\(3\), \(3\))-threshold visual secret sharing for Quick Response code scheme that fully explores the extra freedom provided by color visual secret sharing and color stacking. The binary secret Quick Response code is encoded into color shares. By stacking all the three shares, a binary color Quick Response code can be reconstructed. After the inherent pre-processing steps in a standard Quick Response code decoder, the original binary secret Quick Response code can be completely reconstructed. Thus, the original error correction capability of the Quick Response code is fully preserved. Theoretical analysis shows that the visual secret sharing for Quick Response code is secure under the condition that the computational device available to the attacker is limited to a decoder for standard Quick Response code. Experimental results verify that the secret Quick Response code cannot be reconstructed from just one share or any two shares. However, it can be 100% reconstructed once the three shares are stacked. The proposed visual secret sharing for Quick Response code is module-based, and it does not sacrifice the error correction capability. Furthermore, No extra pre-processing steps other than the standard Quick Response code decoder are required.  
Keywords:visual secret sharing; Quick Response code; color stacking +
Footnote †: journal: Applied
## 3 Analysis of Influencing Factors of Surface Settlement
### Influence of Tail Void on the Lubricant Controlling Effect  
This section simulated the surface displacement along monitoring Line 3 when the tail void was set to 40 mm. All simulation settings were identical to those in Case 1-2 in Scenario 1, except for the tail void value. Thus, the gate-shaped pipe-roof layout was adopted for the model, with the burial depth fixed at 4 m and the Young's modulus uniformly increased from 200 kPa to 500 kPa. Refer to Figure 7 for the model used in this section.  
Table 6 lists the primary parameters for simulation Scenario 3, and Figure 12 displays the corresponding simulation results. As evidenced in Figure 12, the larger tail void results in greater surface settlement and a steeper settlement trough (Figure 12b); however, surface displacement can be fruitfully controlled by increasing the lubricant Young's modulus (Figure 12a). Additionally, a comparison of Figures 12a and 8b shows that, when other variables are held constant, raising the lubricant elastic modulus from 200 kPa to 500 kPa lowers the maximum vertical displacement for the tail void of 40 mm from 29.6 mm to 21.5 mm--an approximate reduction of 27%--whereas the reduction for the tail void of 20 mm is 17.6%. The foregoing results indicate that although enlarging the tail void weakens the lubricant's ability to control surface settlement, it simultaneously enhances the reduction in surface settlement achieved by augmenting the lubricant Young's modulus.
# RAPT: A Robust Attack Path Tracing Algorithm to Mitigate SYN-Flood DDoS Cyberattacks
## 3 The Proposed Robust Attack Path Tracing Algorithm
### Attack Type  
The DDoS attack that the proposed algorithm aims to stop and handle is of the flooding category. The reason behind this is coming from the fact that a significant portion of DDoS attacks falls under this category [34]. In 2019, it was found that 36% of DDoS attacks are UDP Flooding and 14% SYN Flooding attacks in the year 2019 alone [35]. Ideally, the tracing scheme should be able to identify the source of any piece of information sent over the network. Within the IP protocol framework, a packet is the smallest atomic unit in the data and any smaller division of the data (e.g., byte) is contained in an IP packet. A typical tracing system must accurately identify the source of any random packet, and any larger data unit or data stream that can be separated by searching for any particular packet containing data within that stream.
## 1 Introduction  
The species _Clinopodium chinense_ (Benth.) [PERSON] from the genus Clinopodium of the Lamiaceae family is recorded in the Chinese pharmacopoeia [1]. The aerial parts of _C. chinense_, as well as _Clinopodium polycephalum_, known as duxnucleiu in China, are used as a traditional folk medicine for treating diseases such as hematuria, influenza, and allergic dermatitis [2]. Previous studies of the chemical constituents of _C. chinense_ have indicated that triterpenoid saponins are the major chemical components of _C. chinense_[3]. Triterpenoid saponins exert important pharmacological effects including hemostatic [4], antitumor [5], and anti-hyperglycemic [6] activities. However, it is difficult to extract triterpenoid saponins in sufficient quantities from natural sources. Triterpenoid saponin biosynthetic pathways have not been well characterized. Genome sequencing and transcriptome profiling studies of species such as _C. chinense_ have the potential to significantly improve understanding of these pathways.  
Triterpenoid saponins are a class of structurally diverse specialized metabolites in plants [7] and marine invertebrates including sea cucumbers [8] and sponges [9]. Their biosynthetic routes include the isoprenoid pathway, and isoprenoyl pyrophosphate (IPP) is the precursor of all isoprenoids [10]. IPP is synthesized via the mevalonate (MVA) or 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway [10]. Triterpenoid saponin biosynthesis can be summarized in three main stages: first, IPP is converted to farnesyl pyrophosphate (FPP) by geranyl-diphosphate synthase (GPPS) and farnesyl-diphosphate synthase (FPPS) [11]. Second, 2,3-oxidosqualene is cyclized by 2, 3-oxidosqualene cyclases (e.g., beta-amyrin synthase (\(\beta\)-AS) and lupeol synthase (LS)) to form diverse compounds (e.g., beta-amyrin and lupeol) [12]. Finally, the formation of various triterpenoid saponins is catalyzed by cytochrome P450-dependent monooxygenases (PDMO) and glucosyltransferases (GTs) [13; 14].  
Recently, transcriptome analysis has become an effective approach to identify the biosynthesis of secondary metabolites and determine the functions of genes in plants. RNA sequencing (RNA-seq) has been widely used to screen functional genes and accurately quantify gene expression without a reference genome [15; 16]. Several secondary metabolite biosynthesis pathways in medicinal plants have been analyzed, including terpenoid biosynthesis in _Artemisia argyi_[17], and triterpenoid saponin biosynthesis in _Anemone flaccida_[18] and _Gleditsia sinensis_[19].  
In this study, we conducted a comprehensive transcriptome profile analysis of _C. chinense_, and identified numerous genes related to triterpenoid saponin biosynthesis. These transcriptome data provided new insights to guide further studies on this species.
# Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
## 4 Experimental Results
### Experiment No.2: Performance of Proposed Model \(\chi^{2}\_\)RF on Balance Dataset
\begin{table}
\begin{tabular}{r c c c c c c c} \hline
**SF** & **Hyper.** & **Acc.\_Train** & **Acc.\_Test** & **Sensitivity** & **Specificity** & **F1\_Score** & **MCC** \\ \hline
08 & E:10, D:10 & 91.91 & 88.28 & 92.31 & 70.00 & 88.00 & 0.6114 \\
09 & E:100, D:5 & 90.65 & 83.78 & 54.84 & 95.00 & 84.00 & 0.5709 \\
10 & E:100, D:5 & 94.96 & 88.28 & 63.33 & 97.53 & 88.00 & 0.6901 \\
11 & E:10, D:5 & 94.19 & 90.99 & 67.74 & 100 & 91.00 & 0.7759 \\
11 & E:200, D:5 & 96.21 & 91.89 & 70.00 & 100 & 92.00 & 0.7937 \\
12 & E:200, D:2 & 95.70 & 89.18 & 63.64 & 100 & 89.00 & 0.7426 \\
13 & E:100, D:5 & 95.77 & 92.79 & 100 & 72.41 & 93.00 & 0.8122 \\
14 & E:200, D:5 & 97.22 & 91.89 & 97.65 & 73.08 & 92.00 & 0.7647 \\
10 & E:100, D:10 & 97.72 & 94.59 & 98.00 & 80.77 & 94.00 & 0.8413 \\
12 & E:10, D:2 & 93.56 & 90.89 & 95.26 & 86.00 & 91.00 & 0.8098 \\
13 & E:100, D:10 & 94.56 & 92.99 & 96.15 & 77.31 & 93.00 & 0.8130 \\
14 & E:200, D:2 & 96.71 & 92.82 & 96.52 & 71.00 & 93.00 & 0.8231 \\
14 & E:100, D:5 & 97.22 & 93.69 & 100 & 75.00 & 94.00 & 0.8316 \\ \hline \end{tabular}
\end{table} 
Table 4: Performance of the RF model along with \(\chi^{2}\) feature selection method. Figure 3: Performance comparison based on AUC. In addition, we evaluated the performance of the proposed model in unbalanced data to validate its efficiency in balanced data, as indicated in Table 4. The performances of the proposed model (\(\chi^{2}\_\)RF) on unbalanced data were evaluated using various evaluation metrics, such as accuracy, sensitivity, specificity, F1 score, and MCC. Table 5 shows the performance of the proposed model on unbalanced data, where the highest accuracy of 86.15% was achieved by using only 10 selected features, compared with balanced data, where the proposed model achieved the highest accuracy of 94.59% by using the same number of selected features (10).
## 2 Materials and Methods
### 16S rRNA Gene Sequencing for Identification  
A total of 47 bacterial strains (12 strains from Stn. 1 and 35 strains from Stn. 2) were identified by partial 16S rRNA gene sequencing (500 bp). All strains were cultured in ST10\({}^{-1}\) liquid medium for two weeks then centrifuged (8000 rpm) for 5 min to obtain bacterial pellets. Pellets were suspended in phosphate-buffered saline and washed three times to remove the medium. Bacterial DNA was extracted using NucleSpin Tissue XS (TaKaR BIO Inc., Japan) and stored at \(-20\,\mathrm{\SIUnitSymbolCelsius}\). The universal primers 27F (5'-AGAGTTTGATCMTGGCCTAG-3' [49]) and 519R (5'-GWATTACCCGGCKGCCTG-3' [50]) were applied to amplify the 165 rRNA gene fragments using Blend Taq(r)-Plus- (TOYOBO, Japan). The PCR temperature cycling conditions were: initial denaturation at 94 \(\mathrm{\SIUnitSymbolCelsius}\) for 1 min, followed by 30 cycles of denaturation at 94 \(\mathrm{\SIUnitSymbolCelsius}\) for 30 s, annealing at 52 \(\mathrm{\SIUnitSymbolCelsius}\) for 30 s, and extension at 72 \(\mathrm{\SIUnitSymbolCelsius}\) for 1 min; final elongation was for 7 min at 72 \(\mathrm{\SIUnitSymbolCelsius}\). PCR products were purified using ISOSPIN PCR Product (NIPPON GENE CO., LTD., Japan) before sequencing. Both strands of the PCR products were sequenced using the same primers in a cycle sequencing reaction using a sequencing kit (Big Dye Terminator Cycle version 3.1, Applied Biosystems, USA) using a DNA sequencer (ABI 3130, Applied Biosystems, USA). Nucleotide sequences obtained were aligned using Chromas Pro (ver. 1.7.1). Approximately 500 bp of each consensus sequence were analyzed by A BLAST ([[http://blast.ncbi.nlm.nih.gov/Blast.cgi](http://blast.ncbi.nlm.nih.gov/Blast.cgi)]([http://blast.ncbi.nlm.nih.gov/Blast.cgi](http://blast.ncbi.nlm.nih.gov/Blast.cgi))) sequence similarity search to identify similarity with previously reported bacteria.
## 2 Materials and Methods
### Connectivity Analysis
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Metric** & **Level** & **Formula** & **Meaning** & **References** \\ \hline Flux (F) & Global level and Components level & \(S=\frac{\pi}{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\alpha_{i}^{\beta}e^{- \alpha_{ij}}}\) & Sum of potential dispersion from all patches & [41; 42; 43] \\ Equivalent Probability (\(EC\)) & Global level & \(EC=\sqrt{\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\alpha_{i}^{\beta}e^{- \alpha_{ij}}}\) & Sum of points of patches weighted by their interaction & [44] \\  & & & Sum of products of capacity of all pairs of patches weighted by their interaction & \\ Probability of connectivity (\(PC\)) & Global level & \(PC=\frac{1}{\Delta^{2}}\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{n}\alpha_{i}^{ \beta}e^{-\alpha_{ij}}\) & probability, divided by the square of the area of the study zone. This ratio is the equivalent to the probability that two points randomly placed in the study area are connected. & [45] \\ Number of Components (\(NC\)) & Global level & \(NC=nc\) & Number of components of the graph. & [46; 47] \\ Current Flow (\(CF\)) & Local Level & \(CF_{i}=\frac{\pi}{\gamma}c_{i}^{j}\) & Sum of currents passing through the patch i. & \(c_{i}^{j}\) represents the current through the patch i when currents are sent from all patches (except j) to patch j. Patch j is connected to the ground [48]. & [46] \\ \hline  & Where: N: number of patches, nc: number of components, n\({}_{k}\): number of patches in component k, Ni: all patches close to patch I, a\({}_{i}\): capacity of patch i (generally the surface area), ac\({}_{k}\): capacity of component k (sum of the capacity of the patches composing k), A: area of the study zone, d\({}_{i}\): distance between patches i and j (generally the least-cost distance between them), e\({}^{-\alpha_{ij}}\): probability of movement between the patches i and j, \(\alpha\) = brake on movement distance, \(\beta\) = exponent to weight with more or less capacity. & [45] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of connectivity metrics analyzed according to [40].
## 6 Discussion
Peak Signal-to-Noise Ratio (PSNR) statistical significance was determined using Student's \(t\)-test. Probability tests showed that the algorithm is robust. The tables above show the results. The obtained \(p\)-values are statistically significant. Furthermore, a random noise stability test was conducted on the neural networks that were used. A noise stability test of the neural network was conducted to evaluate the robustness of the MR image reconstruction process in the presence of realistic noise. The local robustness of the Gaussian noise in the trained neural network was empirically determined by measuring the greatest ratio between variations in the output space and variations in the input space. Neuroimaging techniques were employed in this trial. An initial batch of 1000 brain scans were utilized as magnetic resonance (MR) pictures, and their associated k-space data were produced. The second set of 1000 MR images had additive white Gaussian noise randomly applied to their associated k-space data, with a signal-to-noise ratio of 20-30 dB. The phase images of the brain scans remained consistent throughout the pairings of brain scans. The noise-corrupted trained model was used to recreate both k-space data. An output-to-input variation ratio of 2.5 was observed when the Gaussian noise ranged from 20 to 30. This study proposes a new magnetic resonance image reconstruction framework to improve resolution. Compressed raw data and an advanced GAN architecture are used in the suggested system. For low-resolution magnetic resonance (MR) image preprocessing, it includes denoising and deformable motion estimation modules. The network processes low-resolution and noisy magnetic resonance (MR) data and reconstructs high-resolution MR images with super-resolution. The technology helps solve a problem where CMR artifacts and noise lower the Peak Signal-to-Noise Ratio (PSNR), reducing the GAN efficacy. Our technique has higher-quality ratios than existing methods, indicating that it yields better picture reconstruction. Thus, this can help doctors diagnose more accurately. This research received no external funding  
**Institutional Review Board Statement:** Not applicable  
**Informed Consent Statement:** Not applicable  
**Data Availability Statement:** The raw data supporting the conclusions of this article will be made available by the authors on request. **Conflicts of Interest:** The author declares no conflict of interest.
## 2 Stimulus Generation Based on Noise-Vocoded Speech  
### Speech Data in Evacuation Announcements  
We utilized the recorded speech data from actual evacuation announcements in our experiments. These were recorded when a tsunami (tidal wave) was predicted after an earthquake off the coast of Fukushima, Japan on 22 November 2016 [16]. The announcements were spoken by multiple professional announcers with various speaking styles on different TV channels. We selected one real speech dataset (the same speaker, the same sentence) by a professional male speaker (the same used by [PERSON] & [PERSON] [16]) as the original stimuli, which was/i/ma/su/gu/ni/ge/te/ku/da/sa/i/(\"Please evacuate now\" in English). This announcement was made at four different levels of urgency (\"A,\" \"B,\" \"C,\" and \"D\", corresponding to noise-vocoded stimuli labeled \"a,\" \"b,\" \"c,\" and \"d\") to determine how the urgency was perceived for each.
## 3 Laboratory Study
### Materials
#### 3.1.2 Kraft Lignin  
The Kraft lignin utilized in this study is a sticky brown fine powder, derived from softwood and provided by FPInnovations (Figure 1a). Kraft lignin is a valuable and sustainable raw material produced as a byproduct of the paper industry. The main properties of this powder are presented in Table 3. The elemental analysis provided by the supplier indicated that the Kraft lignin was composed primarily of four (4) main elemental components, including carbon, hydrogen, oxygen, and sulfur, with the remaining 3% composed of other elements (Figure 1b).  
The particle size distribution is a critical property that can influence the performance of materials in various applications. To determine the particle size distribution of Kraft lignin, the laser diffraction technique was employed using a Mastersizer 3000 instrument equipped with a Hydro EV dispersion unit. The measurements were conducted over a range of 0.01-500 \(\upmu\)m to ensure a comprehensive analysis of the particle size distribution. The resulting data showed that the particle size of the Kraft lignin powder fell within the range of 1 to 100 \(\upmu\)m, with 80% of the particles between 5.47 \(\upmu\)m (D10) and 48.7 \(\upmu\)m (D90), as shown in Figure 2.