esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### XRD Analysis  
Figure 6 shows the XRD patterns of the HSCs samples. The 5-65\({}^{\circ}\) section was used to examine the changes in the crystal phases formed during the hydration reaction, and each sample was distinguished. Because most of the hydration process occurred during the initial curing period of the steam-cured specimens, the XRD patterns observed after steam curing exhibited similar trends. When curing was performed in a constant temperature and constant humidity environment, rather than in an underwater environment, continuous hydration did not occur, because moisture was not directly supplied to the specimen [43; 44]. HSCs with a low w/b ratio could contribute to the continuous hydration reaction until the final hydration process because of their high densities, especially when moisture is supplied continuously even after steam curing [45]. As shown in Figure 6, quartz (SiO\({}_{2}\)), alite (3 CaO-SiO\({}_{2}\)(C\({}_{3}\)S)) and calcite (CaCO\({}_{3}\)) were detected in most specimens. The generation ratio of alite (A) measured in the 32-33\({}^{\circ}\) and 41-42\({}^{\circ}\) sections of specimen P and that of specimens mixed with the pigments differed considerably. Peak C at 29.1\({}^{\circ}\), 42.7\({}^{\circ}\), and 48.2\({}^{\circ}\) was ascribed to calcium hydroxide, which was utilized to detect the carbonates, such as calcite, generated by it. Peak C of the G10 specimen was at 29.1\({}^{\circ}\) and 42.7\({}^{\circ}\). XRD patterns with similar trends were observed after the steam-curing process. Although some differences existed in the proportions of the generated substances, as indicated by the areas under the patterns, the deviations were marginal.  
Figure 3: TGA of samples at three days. (**a**) TGA results of specimen P at the age of three days, (**b**) TGA results of specimens R5 and R10 at the age of three days, and (**c**) TGA results of specimens G5 and G10 at the age of three days.  
Figure 4: TGA of samples at seven days. (**a**) TGA results of specimen P at the age of seven days, (**b**) TGA results of specimens R5 and R10 at the age of seven days, and (**c**) TGA results of specimens GR5 and G10 at the age of seven days.  
Figure 5: TGA of samples at 28 days. (**a**) TGA results of specimen P at the age of 28 days, (**b**) TGA results of specimens R5 and R10 at the age of 28 days, and (**c**) TGA results of specimens G5 and G10 at the age of 28 days.
## 2 Results
### Method Validation
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & & & \multicolumn{2}{c}{**Range of assayed**} & & & \\
**Compound** & **R2** & **LOQ (Hg/L)** & **LOD (Hg/L)** & **concentration (Hg/L)** & **Linearity (Hg/L)** & **Recovery (\%)** & **recovery (\%)** \\ \hline Ethanethiol & 0.9950 & 0.569 & 0.171 & 0.04 & 40.77 & 0.64 & 10.19 & 89.91 & 113.03 \\ Dimethyl-sulfide & 0.9904 & 0.208 & 0.063 & 0.04 & 37.84 & 0.59 & 9.46 & 78.02 & 107.77 \\
1-Propanethiol & 0.9894 & 0.611 & 0.183 & 0.06 & 60.97 & 0.95 & 15.25 & 75.71 & 176.99 \\ Diehy-sulfide & 0.9972 & 0.081 & 0.024 & 0.07 & 69.23 & 0.08 & 34.62 & 96.66 & 109.41 \\
1-Butanethiol & 0.9319 & 0.011 & 0.003 & 0.07 & 67.27 & 0.53 & 16.82 & 87.24 & 133.87 \\ Dimethyl-disulfide & 0.9947 & 0.009 & 0.003 & 0.09 & 94.60 & 0.09 & 94.70 & 96.48 & 109.44 \\ Ethyl thioacetate & 0.9924 & 0.002 & 0.001 & 0.08 & 86.65 & 0.08 & 43.32 & 93.99 & 106.68 \\
1-Pentanethiol & 0.9640 & 0.014 & 0.004 & 0.07 & 70.93 & 0.14 & 8.87 & 86.11 & 129.13 \\ Dippropyl-sulfide & 0.9954 & 0.146 & 0.044 & 0.07 & 72.95 & 0.15 & 36.48 & 97.68 & 105.53 \\ Diehy-disulfide & 0.9932 & 0.116 & 0.035 & 0.09 & 89.56 & 0.12 & 11.19 & 89.01 & 105.88 \\ Thiophenol & 0.9969 & 0.048 & 0.015 & 0.09 & 94.41 & 0.09 & 23.60 & 102.08 & 122.60 \\
2-Methyltetrahydrodrothiophene-3-one & 0.9991 & 0.045 & 0.014 & 0.11 & 108.57 & 0.11 & 108.57 & 92.48 & 108.62 \\
3-Thiopheneexorabaldehyde & 0.9984 & 0.053 & 0.016 & 0.11 & 114.68 & 0.11 & 57.34 & 90.37 & 110.20 \\
2-Thiopheneexorabaldehyde & 0.9983 & 0.208 & 0.063 & 0.11 & 111.71 & 0.21 & 55.85 & 92.89 & 106.75 \\ Ethyl 3-(methylthio)propionate & 0.9943 & 0.187 & 0.056 & 0.09 & 95.86 & 0.09 & 47.93 & 99.94 & 109.93 \\ Dib butyl-sulfide & 0.9957 & 0.103 & 0.031 & 0.07 & 75.60 & 0.10 & 37.80 & 92.89 & 112.25 \\ Dippropyl-disulfide & 0.9957 & 0.132 & 0.040 & 0.08 & 81.55 & 0.13 & 10.19 & 94.36 & 110.31 \\ Benzothiazole & 0.9945 & 0.208 & 0.062 & 0.10 & 107.10 & 0.21 & 53.55 & 112.95 & 125.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Limits of detection and quantification, linearity, and recovery of the method.
## 4 Results and Discussions
### Comparison between the RVE Model and the Full-Size Model
#### 4.1.3 Effective Thermal Conductivity  
According to the results mentioned above, the average heat flux on the \(z\)-axis of the RVE model was 56,958 W/m\({}^{2}\), the difference between the constant temperatures of top and bottom surfaces was 10 K, and the thickness of the RVE model was 0.6 mm. In the full-size model, the average heat flux on the \(z\)-axis was 13,968 W/m\({}^{2}\), the difference between the constant temperatures of the top and bottom  
Figure 8: The heat flux fields of the RVE model.  
surfaces was 10 K, and the thickness was 2.8 mm. Therefore, the effective thermal conductivities on the z-axes k\({}_{\rm Z}\) of the two models could be calculated based on [PERSON]'s thermal conduction equation.  
The k\({}_{\rm Z}\) values of the RVE model and the full-size model were 3.42 W/(m\(\cdot\)K) and 3.91 W/(m\(\cdot\)K), respectively. The ETC k\({}_{\rm Z}\) based on the RVE model and the ETC k\({}_{\rm Z}\) based on full-size model had a significant difference. The reason for this was that the thickness of the full-size model was small (2.8 mm) and the periodicity was unsatisfied in the \(Z\) axis direction for the full-size model, leading to a large difference for the periodic RVE model.  
Furthermore, the effective thermal conductivities along the \(x\)- and \(y\)-axes (k\({}_{\rm X}\) and k\({}_{\rm Y}\)) could also be calculated using a similar method. The k\({}_{\rm X}\) values of the RVE model and full-size model were 5.63 W/(m\(\cdot\)K) and 5.62 W/(m\(\cdot\)K), respectively, and the k\({}_{\rm Y}\) values of the two models were 5.28 W/(m\(\cdot\)K) and 5.77 W/(m\(\cdot\)K), respectively. A comparison between the ATCs based on the RVE model and full-size model is shown in Figure 9.
## 3 Modeling of Broken Rock Masses Based on the Coupled DFN-DEM Approach
### Construction of Equivalent Rock Mass Models Using DFN-DEM Coupling Technique  
The discrete element method [35], also known as the distinct element method, is a numerical method based on the mechanics of discontinuous media. Its fundamental principle, grounded in [PERSON]'s second law, involves the consideration of contact surfaces or interfaces between small blocks (particles) that constitute as system, allowing for a more accurate simulation of the mechanical behavior of the blocks and contact interfaces. Currently, widely employed programs for discrete element simulations include the two-dimensional discrete element program UDEC (Universal Distinct Element Code), block discrete element program 3 DEC (3-dimensional Distinct Element Code), and particle flow codes (PFCs). These program tools are particularly well-suited for analyzing the response of fractured rock systems or assemblies of discontinuous blocks under static or dynamic loading conditions.  
The DFN-DEM coupling technique is a method that integrates DFN techniques for modelling fractured rock masses with the DEM for numerical experiments to determine the mechanical properties of fractured rock masses. In this study, the programs introduced in Section 3.2 are utilized as the DFN generator, while the numerical discrete element analysis is performed using the 3 DEC 7.0 software, chosen for its efficiency in modeling and analyzing large-scale systems. The integration of the DFN simulation tools with 3 DEC 7.0 software facilitates the utilization of the DFN-generated random fracture network to cut the discrete element blocks, constructing a numerical model with statistically similar fracture distributions to the actual rock masses, as demonstrated in Figure 10.
# Polarization Influence on Algorithms of TFBG Sensors Data Analysis for Bending Application
###### Abstract  
In this article we deal with the influence of polarization on determining the bending radius in TFBG (tilted fiber Bragg grating) sensors. The spectrum of TFBG structures changes under the influence of many factors at the same time. In the case of bending radius measurements, additional factors may be the temperature and polarization state of the introduced light. So far, only the cladding mode envelope algorithm has been used to determine the bending radius. An interesting issue seems to be establishing the influence of cross-sensitivity of the spectrum on changes in polarization during bending measurements. In addition to the envelope algorithm, algorithms for spectral length and average deviation from the local mean were examined. As a result of the analysis of experimental data, it was found that the level of polarization's influence on the result may be significant in determining the bending radius. Reducing the influence of polarization can be achieved by using algorithms providing wavelength parameters. Additionally, in order to reduce the influence of polarization, we proposed the use of the PLS (partial least squares regression) algorithm for the processed spectrum.  
b +
Footnote †: journal: Applied Sciences  
1
Footnote 1: institutetext: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland; [EMAIL_ADDRESS]  
2
Footnote 2: institutetext: Department of Electronics and Information Technology, Lublin University of Technology, Nadbystrzycka 38A, 20-618 Lublin, Poland; [EMAIL_ADDRESS]
# Whole Organ Engineering: Approaches, Challenges, and Future Directions
## 6 Supporting Technological Advances
### Genetic Engineering  
CRISPR-Cas-based technologies have gained prevalent use in genetic modification research as a result of their accessible and versatile ability to alter, regulate, and visualize genomes, enabling significant advancement in the field of whole organ engineering. CRISPR-Cas tools have been successfully utilized to accelerate genetics-focused research development, being suitable tools for improving understanding of the genetics of diseases and new organisms. Cas-based biotechnology is developing rapidly, to the extent that there are several clinical trials, both in progress or beginning soon, that depend on Cas9 technology, and the results of which will likely determine how somatic cell editing is used both in research and clinical applications in patients.  
Human pluripotent stem cells such as iPSCs have strong potential in regenerative medicine applications. Despite this potential, applications are limited by the risk of immune rejection arising from an HLA mismatch. Cas9 techniques such as B2M gene knockout or HLA-homozygous iPSC stocks have the potential to address immuno-compatibility concerns, however B2M knockout runs the risk of eliciting natural killer (NK) cell activity and failing to present antigens, and for HLA-homozygous iPSCstocks, it can be difficult to recruit the rare donors needed. [PERSON] and colleagues have developed two approaches for gene editing of iPSCs to make them immuno-compatible [187]. The first is a method of generating HLA pseudo-homozygous iPSCs by using allele-specific editing to genetically modify HLA heterozygous iPSCs. The second method involves disrupting both HLA-A and HLA-B alleles as a means of suppressing the NK cell response while still retaining antigen-presenting capabilities, producing HLA-C retaining iPSCs as a result. This is valuable because iPSCs that retain HLA-C have been shown to evade T cells and NK cells, both in vitro and in vivo. It is estimated that at least 90% of the human population could be compatible with cells produced by combining 12 lines of HLA-C-retained iPSCs with HLA-class II, which greatly facilitates iPSC-based regenerative medicine applications.
## 3 Proposed Model
### Compliance with Security Requirements  
The proposed model is designed to fulfill the most demanding security requirements: integrity, confidentiality, authentication, access control, availability, nonrepudiation, and ultimately, full compliance with the GDPR.  
Data stored in the blockchains cannot be modified once registered due to its immutability. As a consequence, integrity is achieved while at the same time only nonpersonal information is saved on the chain. Academic information is stored off-chain encrypted with a secret key only known by the issuer and the holder. Even if the issuer's or the holder's systems are compromised, private information cannot be recovered unless the decryption keys are also exposed. For the case in which the institution disappears and its information is stored in a restricted access blockchain, being encrypted with a key only known by the holder and the discontinued organization, it can be considered that it remains confidential.  
Apart from data security, the designed model achieves privacy preservation by anonymity if the holder uses different accounts to avoid linkability among the diverse issued data on the chain to him/her.  
As in any other interconnected system, information exchanges must be carried out using secure connections. The security of the nodes that are part of the blockchain and their interconnection with the access points to educational institutions must be properly secured and every private key should be carefully handled and protected.  
With this model, only authorized third parties by the holder can access nonpersonal information to validate or recover the educational information, and every operation is tamperproof logged in the system to provide accountability.  
To maintain trust in the system, it is important that only recognized entities and organizations are allowed to be part of the private blockchains and the main one, but that is outside the limits of the designed model since it is a matter of adding or not certain academic organizations. In any case, if any inappropriate behavior is detected, the institution could always be annulled and, with it, the related academic data.  
Availability is a key feature of blockchain since, by its own design, it replicates the information; therefore, this security service, as long as any node of the network remains operational, will be fulfilled.  
Nonrepudiation can be verified since to carry out any operation, as described in the model, it is necessary by the different actors to use their private keys and the information, and the result will be permanently registered in the blockchain, and hence, it cannot be repudiated.
# Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks
## 2 Flying Ad Hoc Networks
### Dynamic Topology in FANETs  
The topology of a FANET can be described as a time-dependent graph, where the connectivity among UAVs is subject to change owing to the mobility of UAVs and their limited battery capacity. Frequently, the FANET mission execution time (\(T\)) is divided into sufficiently small time slots (\(t\)) of equal length, and in each time slot, the mobility of the UAVs is considered static. Thus, in FANETs, the network topology can be expressed as a time-dependent undirected graph: \(G(t)=(V(t),E(t))\). Here, the vertex \(V(t)\) consists of \(u_{i}\in U(t)\) UAVs and single or multiple BSs, and \(E(t)\) represents the network edges. In each \(t\), if the distance between two UAVs is \(d_{ij}(t)\leq R\), where \(R\) is the LoS transmission range of each UAV, a direct edge between two UAVs \(\left(u_{i},u_{j}\right)\in E(t)\) is considered. In each \(t\), UAVs can leave the aerial network to obtain energy replenishment at charging stations located in the BS and rejoin the aerial network after receiving energy replenishment. The sensor coverage radius (\(R_{c}\)) of an UAV depends on the UAV height (\(h\)) and sensor field of view (\(\alpha\)) (see Figure 1). With increasing \(h\), \(R_{c}\) and the probability of obtaining an LoS to the ground terminal increase. Simultaneously, the path loss to the ground terminal increases. Thus, the height of UAVs must be optimized according to the distribution of GUs, mobility of GUs, mission environment, and the application type of FANETs.
## 1 Introduction
This method quantifies VMD results based on the comprehensive consideration of permutation entropy, frequency domain extremum, kurtosis criterion and energy loss coefficient. Recently, scholars suggested that the initial center frequency and balancing parameter of reconstructed specific subcomponents could be determined adaptively according to the dominant frequency of the remaining signals decomposed by the previous iteration, making VMD an adaptive signal decomposition algorithm [26]. Although the above methods improve the VMD performance to some extent, they still have shortcomings in maximizing the periodic pulse information because the bearing fault impact signal consists of transient and fluctuation, and the existing research work only focuses on the transient characteristics. Another important research topic of VMD for bearing fault diagnosis is high-frequency mode noise elimination. This is different from the general idea of noise elimination because the signal is in the high-frequency band, and the waveform shows certain oscillations, while the noise component is attached to the oscillation waveform, showing overall sparsity and smoothness. Therefore, after deeply discussing the FE guided-VMD, we propose a neighborhood statistical de-noising (NSD) method based on the second-generation wavelet transform (SGWT) and FE, which not only considers the neighborhood statistical property but also makes full use of the oscillation attenuation and tight support of the SGWT. The contributions of this paper are mainly reflected in: (1) a new measure of fault information called fluctuation entropy is constructed. This measure comprehensively considers the transient and fluctuation attributes of fault impulse signals, which can lead to a more reasonable decomposition of VMD. (2) We deduce a new NSD method based on SGWT and FE, thereby highlighting the periodic impulses. Compared with the traditional wavelet threshold method, the NSD algorithm using FE characteristics of the signal to estimate non-Gaussian components makes comprehensive use of coefficient correlation and impulse fluctuation statistics and has good universality and applicability, which can better improve the detection performance of periodic impulses. (3) An enhanced bearing fault detection approach based on FE-guided VMD and NSD is proposed and compared with related methods. This paper is organized into the following sections: Section 2 presents the FE guided-VMD method, including theoretical background, FE establishment using phase space and fitness function construction. Section 3 describes the neighborhood statistical de-noising method based on SGWT. Next, the method is validated on simulated signals in Section 4 and experimental data of bearing fault data set in Section 5. Finally, concluding remarks are given in Section 6.
## 2 Reliability Analysis Method
### Method Validation
#### 2.4.1 Example 1  
Example 1 explores a series system consisting of four branches [28], and its real response surface is shown in Figure 3. The change in color gradient (from bottom to top) represents the increasing reliability of the system. The limi  
Figure 2: Reliability analysis flow chart.  
composed of four connected line segments, which have significant abrupt changes and high nonlinearity. The formula is as follows:  
\[G(x_{1},x_{2})=min\left\{\begin{matrix}3+0.1(x_{1}-x_{2})^{2}-\frac{(x_{1}+x_{2}) }{\sqrt{2}}\\ 3+0.1(x_{1}-x_{2})^{2}+\frac{(x_{1}+x_{2})}{\sqrt{2}}\\ (x_{1}-x_{2})+\frac{7}{\sqrt{2}}\\ (x_{2}-x_{1})+\frac{1}{\sqrt{2}}\end{matrix}\right. \tag{17}\]  
where \(x_{1}\) and \(x_{2}\) are two independent standard normal random variables.  
For this serial numerical example, the prediction results of each method are shown in Table 1. The ISSA-Kriging-MCS model was compared with the binomial response surface method, importance sampling (IS), IS-Kriging model, and Kriging-MCS model. Although the binomial response surface method had relatively few calls, it performed poorly in terms of fitting accuracy with a relative error of over 50%. The IS method performed relatively well in terms of computational accuracy, but due to too many calls, it resulted in excessive computational complexity and low computational efficiency. The number of calls to IS-Kriging was nearly 30% less than that for the ISSA-Kriging-MCS model, but the relative error increased by about 70%. The Kriging-MCS model had a relatively large error compared to the ISSA-Kriging-MCS model when the number of calls was the same. In summary, taking into account both the number of calls and relative error, the ISSA-Kriging-MCS model demonstrated its advantages, effectively balancing the relationship between computational accuracy and cost.
# The Role of Fungi in Food Production and Processing
## 2 Understanding the Research Prospects
### Fungi as Food Spoilage Agents  
#### 2.3.1 In-Depth Analysis of Fungi Responsible for Food Spoilage  
Fungal contamination continues to pose a significant challenge in the food industry, resulting in financial losses and potential health hazards for consumers [85]. This scientific investigation provides an in-depth analysis of the fungal species responsible for food spoilage, elucidating their ecological characteristics, metabolic activities, and mechanisms of action. Through a comprehensive literature review and critical analysis, this manuscript aims to enhance our understanding of fungal spoilage in food products and inform strategies for prevention and control. Fungi play a pivotal role in the spoilage of various food products, leading to changes in the taste, texture, odor, and nutritional quality. While some fungal species are benign, others possess enzymatic capabilities and metabolic activities that promote food degradation and deterioration. Understanding the diversity and behavior of spoilage fungi is crucial for implementing effective control measures and ensuring food safety and quality.
# Modeling and Analysis of Staged Constellation Deployment from a Single-Unit System
###### Abstract  
A novel satellite architecture and deployment method is proposed to reduce the logistical cost and complexity of launching and dispersing satellite constellations. The architecture consists of a primary satellite that separates into multiple smaller units, which are subsequently dispersed using differential drag. An algorithm is developed to determine the required disengagement velocities and optimal timing for separation maneuvers. Two case studies with orbital simulations demonstrate the feasibility of this approach for constellation deployment and phasing. The results indicate that while mission-specific factors influence deployment dynamics, informed selection of the disengagement velocities is crucial for minimizing phase times and mitigating potential delays. The findings confirm the feasibility of the proposed architecture and dispersal method, offering a cost-effective alternative to traditional deployment strategies for future satellite constellations.  
Mobile and Multi-Scale Modeling and Analysis of Staged Constellation Deployment