esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In designed systems, the laser beams are controlled by computer, and are used to change the color of the denim surface by directing the laser beams at the suitable parameters [17]. Advantages of laser bleaching over other conventional bleaching are: Less pollution of the environment by a reduction of chemical agents and water consumption; process flexibility allows the replication of designs or creation of new finish styles; the finishing process can be applied on parts or assembled garments, or on uncut material as it is; by using automated control of laser-faded technology, the manufacturers can change the finish style to another without retooling; micro graphics can be applied onto the garment; special logos or characters can be applied to denim products [18]. The kapok fiber is a single-cell natural cellulose fiber. Its walls comprise 64% cellulose, 13% lignin, 8.6% water, 1.4-3.5% ash, 4.7-9.7% water-soluble substances, 2.3-2.5% xylan, and 0.8% waxes [19]. This kapok fiber has a very thin cell wall with a huge hollow region full of air, so it has a 90% hollow degree. In addition, kapok fiber has many excellent performances with good hygroscopicity, oil absorption, moisture guide properties and warmth retention property. The high performance and environment friendly character made kapok become a new material of denim fabric. Furthermore, the price of kapok fiber has high market competitiveness, since it is only about half of that of cotton fiber. Kapok fiber length is short, however, and has low intensity, no significant convolution, poor cohesion and a lack of flexibility, so it is difficult to separately spin. Years of research focuses on the blending spinning of kapok fiber and cotton fiber [20]. Our laboratory used an efficient short process embedded spinning technology (Ruyi spinning) to make the blended yarns of kapok fiber and cotton fiber, and thus produced the kapok/cotton denim fabrics. The volume ratio of kapok fiber arrived at 30%. In this work, a new type of kapok/cotton blend denim fabrics were woven and tested for basic physical properties. The kapok/cotton denim fabric and cotton denim fabric were bleached by laser, cellulose enzyme, hydrogen peroxide, sodium hypochlorite and potassium permanganate. After bleaching, the K/S values, tensile strength, air permeability, thickness, color fastness to rubbing and crease recovery angle of these denim fabrics were measured through the spectrophotometer, tensile strength tester, air permeability tester, thickness tester, rubbing fastness tester and fabric crease elasticity tester, respectively. The surfaces of the fabric and the fiber were observed by scanning electron microscopy (SEM). The physical properties of the denim fabric treated by different methods were compared.
## 3 Results and Discussions
### N\({}_{2}\) Adsorption and Desorption  
N\({}_{2}\) adsorption and desorption experiments were performed to analyze the structure of the Ce\({}_{x}\)W\({}_{0.4-x}\)Ti catalysts. All catalysts exhibited typical type III isotherms, indicating the formation  
Figure 1: NO conversion (**a**) and N\({}_{2}\) selectivity (**b**) as a function of reaction temperature over the Ce-W-Ti catalysts. Reaction conditions: [NO] = [NH\({}_{3}\)] = 500 ppm, [O\({}_{2}\)] = 5%, balanced N\({}_{2}\), GHSV = 120,000 mL-g\({}^{-1}\).h\({}^{-1}\).  
of multilayers in the catalysts and relative weak interactions between N\({}_{2}\) and the catalysts [30]. The hysteresis loop was demonstrated to be H3 type for the catalysts, which is associated with the existence of slit-shaped pores formed from the aggregation of plate-like particles [31]. Table 1 gives the information of the specific surface area (S\({}_{\text{BET}}\)), the pore volume and the average pore diameter of the catalysts based on the calculations from the isotherms. Compared with Ce\({}_{0.4}\)Ti and W\({}_{0.4}\)Ti, the Ce\({}_{x}\)W\({}_{0.4-x}\)Ti catalysts possessed larger S\({}_{\text{BET}}\) and pore volume, indicating more surface active sites formed on the Ce\({}_{x}\)W\({}_{0.4-x}\)Ti catalysts for the SCR reaction. The highest S\({}_{\text{BET}}\) (108.1 m\({}^{2}\cdot\)g\({}^{-1}\)) and pore volume (0.121 cm\({}^{3}\cdot\)g\({}^{-1}\)) was observed for the Ce\({}_{0.2}\)W\({}_{0.2}\)Ti catalyst, while further increasing of the loading amount of Ce or W resulted in decreased S\({}_{\text{BET}}\) and pore volume. It is well recognized that larger specific surface area was beneficial for offering more active sites for the NH\({}_{3}\)-SCR reaction [23; 25]. Moreover, Ce\({}_{0.2}\)W\({}_{0.2}\)Ti showed the smallest average pore diameter of 3.9 nm compared to the other samples. [PERSON] et al. also reported that the decrease of average pore diameter and pore volume could improve the performance of the NH\({}_{3}\)-SCR process [32].
## 5 Proposed Classification
### Classification of RL-Based Routing Schemes with Regard to Learning Framework  
According to the mentioned classification, RL-based routing approaches can be categorized into single-agent and multi-agent with regard to the learning framework. Table 3 summarizes the advantages and disadvantages of these approaches.  
* **Single-agent RL-based routing approach:** In this type of routing scheme, an agent alone interacts with the network (i.e., learning environment) to learn its best behavior (i.e., the most suitable path) and maximize the rewards obtained from the environment. In this case, the routing methods utilize a single-agent RL system, as shown in Figure 3. In these routing protocols, if the learning environment is complex (i.e., the network is large-scale or includes a very high number of states and actions), the learning capability of the agent is greatly reduced, and the agent requires a longer time to earn the most suitable response. Thus, in this case, the agent suffers from a low convergence rate and may never get an optimal response. However, implementing a single-agent scheme is much easier than the multi-agent method, and it has a low computational cost [35; 36].  
Figure 2: Our classification for RL-based routing approaches in VANETs.  
* **Multi-agent RL-based routing method:** In this type of routing method, several agents (for example, vehicles in the network) interact with the network environment to learn optimal behavior (i.e., the best path). In this case, the routing schemes use a multi-agent RL system. See Figure 4. In these protocols, environment dynamics can be affected by the behavior of other agents on the network. As a result, it is very challenging to coordinate agents with each other in multi-agent RL-based routing protocols. However, these methods are beneficial when agents have a connection link with each other to share their experiences. In this case, the learning ability of the multi-agent systems will be extremely accelerated because agents perform the computing process using a parallel manner [35; 36]. The important advantage of multi-agent routing protocols is that they are fault-tolerance. These protocols are more suitable for the complex environment and have high learning strength. However, they suffer from high computational costs in comparison with single-agent systems so if the number of states and actions increases, then these multi-agent systems experience an exponential increase in their computational complexity [35].
## 2 System Model
From the SI model mentioned in (2) and (4), it is evident that the performance of digital SIC is fundamentally contingent upon the accuracy of the channel response coefficient estimation. Moreover, the power of the residual SI signal remains high after RF cancellation. when the interference-to-signal ratio (ISR) is high, even minimal residuals can severely compromise the separation of the RT signal. Additionally, at higher SNRs, noise ceases to be the predominant factor impacting the estimation of the SI signal. Under such circumstances, the primary source of error in SIC emanates from the RT signal itself. Consequently, in scenarios where the RT signal is known, it becomes essential to employ a joint channel estimator to enhance the precision of both SI and RT signal separations, thereby mitigating the aforementioned challenges. We assume that the original RT signal, \(i[n]\), is known; then, (1) and (2) can be rewritten in [23] as  
\[y[n]=\mathbf{w}^{T}[n]\mathbf{x}[n]+\mathbf{h}^{T}[n]\mathbf{i}[n]+v[n], \tag{5}\]  
where \(h[n]\) denotes the RT channel coefficient, and the interest signal, \(r[n]\) is represented as \(\mathbf{h}^{T}[n]\mathbf{i}[n]\). By merging two channels into a joint channel, according to (5), we can obtain the following expression:  
\[y[n]=\mathbf{c}^{T}[n]\mathbf{u}[n]+v[n], \tag{6}\]  
where \(\mathbf{c}^{T}[n]=[\mathbf{w}^{T}[n],\mathbf{h}^{T}[n]]\) combines the coefficients of both the SI and RT channels, and \(\mathbf{u}[n]=[\mathbf{x}^{T}[n],\mathbf{i}^{T}[n]]^{T}\) is the new input signal. In (5), the estimation of SI is influenced by both \(r[n]=\mathbf{h}^{T}[n]\mathbf{i}[n]\) and \(v[n]\). However, by combining the SI and RT channel as a new estimation parameter, only the noise \(v[n]\) impacts this process. The benefits of this joint channel estimation approach will be detailed in the simulations presented in Section 4.
# Development of an Automatic Rock Mass Classification System Using Digital Tunnel Face Mapping
## 4 Tunnel Digital-Mapping-Based Online Rock Mass Evaluation
### Discussion  
Currently, the assessment of rock mass quality at the tunnel excavation face is only conducted when the support pattern requires modification or additional reinforcement is needed due to alterations in the rock mass conditions relative to the parameters used during the tunnel's design phase. In such instances, upon solicitation from the construction manager, the contractor, along with the responsible supervisor and pertinent tunnel experts, performs an on-site inspection of the tunnel face. This team reviews the materials present at the site to determine the requisite modifications to rock support patterns and the incorporation of additional reinforcements.  
Implementing digital excavation face mapping enhances efficiency and the streamlining of production, management, transmission, and reporting of mapping results. This digital approach can substantially reduce the time frame required for critical decision-making processes, such as rock quality evaluation and adjustments to support patterns. Utilising face mapping derived from image data is a promising opportunity to transition from the traditional offline rock quality evaluation to an online platform, thus enabling more realistic and timely data to be obtained.  
To effectively establish an online rock mass quality assessment system, it is imperative to provide comprehensive 3D geological profiles, 3D models of the tunnel face, and pertinent monitoring data and information on design and construction materials. This integration is vital for facilitating accurate evaluations of rock mass quality, thus allowing assessment committee members to engage with the rock mass characteristics remotely, even in the absence of physical site visits.  
Consequently, there is a need to establish a digital platform that serves as a repository for digital face mapping materials alongside essential data from the design and construction phases. This platform should be designed to enable remote access by assessment committee members, thereby equipping them with the necessary information for informed decision making regarding rock mass quality assessment, as illustrated in Figure 14.  
In future research, we will enhance the current system by integrating a three-dimensional geological profile map. Additionally, we plan to incorporate wedge analysis and implement an automated extraction system for identifying joints from photographs of tunnel faces using artificial intelligence techniques, including deep learning. These advancements are intended to improve the accuracy and efficiency of geological assessments within tunnel environments.
# Analysis of Potent Odour-Active Volatile Thioids in Foods and Beverages with a Focus on Wine
###### Abstract  
Certain volatile thiols are some of the most potent odour-active molecules that are found in nature. Thiols play significant roles in the aroma qualities of a range of foods and beverages, including wine, with extremely low odour detection thresholds (nanogram per litre range). A fundamental understanding of their formation, fate, and impact essentially depends on the development of suitable analytical methods. The analysis of volatile thiols in foods and beverages is a challenging task when considering (1) the complexity of food and beverage matrices and (2) that thiols are highly reactive, low molecular-weight volatiles that are generally present at trace to ultra-trace concentrations. For the past three decades, the analytical evaluation of volatile thiols has been intensively performed in various foods and beverages, and many novel techniques related to derivatisation, isolation, separation, and detection have been developed, particularly by wine researchers. This review aims to provide an up-to-date overview of the major analytical methodologies that are proposed for potent volatile thiol analysis in wine, foods, and other beverages. The analytical challenges for thiol analysis in foods and beverages are outlined, and the main analytical methods and recent advances in methodology are summarised and evaluated for their strengths and limitations. The key analytical aspects reviewed include derivatisation and sample preparation techniques, chromatographic separation, mass spectrometric detection, matrix effects, and quantitative analysis. In addition, future perspectives on volatile thiol research are also suggested.  
derivatisation; sample preparation; gas chromatography; high performance liquid chromatography; mass spectrometry; untargeted identification; targeted quantitation; matrix effect; stable isotope dilution assay
## 2 Methodology
### Bayesian Network Improvement
#### 2.7.1 Construction of a Reliability Bayesian Network Based on FTA  
FTA has advantages such as qualitative reasoning of initial failures and event impacts in complex systems, as well as simple modeling, making it widely used in the field of reliability analysis. In this study, the qualitative deductive reasoning analysis of FTA is integrated into the Bayesian network to construct FR-BN. Taking a key component of the ship lubricating oil system as an example, the construction process of FR-BN is illustrated. First, based on a fault analysis of the component, a fault tree model is established, which consists of three main parts: Top-level event (E): represents the fault state of the component. Intermediate events (u): represent the set of fault sub-nodes of the component, denoted as \(u=\{u_{1},u_{2},\ldots,u_{N}\}\), where \(u_{i}(i=1,2,\ldots,N)\) represents the \(i\)-th fault type, and \(N\) is the total number of fault types for the component. Basic events (\(v\)): represent the set of root nodes that reflect the symptoms of the component, denoted as \(v=\{v_{1},v_{2},\ldots,v_{n}\}\), where \(v_{i}(i=1,2,\ldots,n)\) represents the \(i\)-th symptom, and \(n\) is the total number of possible symptoms reflecting the component's condition.
# Real-Time Precise Point Positioning during Outages of the PPP-B2b Service
## 4 Discussion
At stations with high-quality PPP-B2b positioning services, such as BJ01, JFNG, and HKSC, the decline in accuracy was gradual, with positioning accuracy dropping by approximately 10 cm when the interruption time reached 20-30 min. In contrast, at stations with lower-quality PPP-B2b positioning services, such as KSKT and MSSA, the decline was more severe, with positioning accuracy dropping by approximately 10 cm when the interruption time reached 10-20 min. In this experiment, the interruption time was kept below 10 min, which allowed all stations to maintain their real-time positioning accuracy. Additional kinematic experiments were conducted to assess the performance of the extended correction during the PPP-B2b service interruptions. As shown in Figure 15, while positioning accuracy decreased as the interruption time increased, the RMSE of 3D positioning accuracy during the interruption was 0.1 m--slightly worse than the 0.07 m and 0.09 m achieved before and after the interruption. These experiments demonstrate that the use of extended corrections during PPP-B2b service outages can compensate for interruptions within 10 min.
## 5 Conclusions  
The static bending and free vibration of FG porous plates were numerically analyzed by making use of the (3,3,2) hierarchical model and 2D NEM. The power-law function was adopted to express the thickness-wise metal and ceramic volume fractions, and five different porosity distributions were considered. The proposed numerical method was demonstrated and verified through the benchmark experiment. Moreover, the central deflection and the fundamental frequency were parametrically investigated with respect to the combination of the material composition distribution, the porosity distribution and the \(w/t\) ratio, and to the boundary condition. From the numerical results, the following main observations are drawn:  
* The maximum relative difference in the calibrated central deflections and fundamental frequencies between the present numerical method and the reference is 5.336%.
* The calibrated central deflection increases with the porosity parameter, and the calibrated fundamental frequency also increases with the porosity parameter, except for the even distribution with \(n=0.6\).  
Figure 8: Dependence of the calibrated fundamental frequency on the porosity parameter \((a/h=20,n=0.3)\): (**a**) for different boundary conditions, (**b**) for cosine-type.  
Figure 7: Dependence of the calibrated fundamental frequency on the porosity parameter: (**a**) for different ceramic indices \((a/h=20)\), (**b**) for different width-thickness ratios \(n=0.3\).  
* The even porosity distribution leads to higher central deflection than the uneven porosity distribution, and this relative difference between two porosity distributions uniformly increases with the porosity parameter and the ceramic index, but it is not affected by the plate width-thickness ratio.
* The relative order in the magnitude of calibrated fundamental frequency is dependent on the ceramic index and the width-thickness ratio. The relative difference between even and uneven porosity distributions uniformly increases with the ceramic index and the width-thickness ratio.
* The order in the magnitude of calibrated central deflection among four boundary conditions is SFSF > SSSS > SCSC > CCCC, but this relative order becomes completely reversed for the calibrated fundamental frequency.
* Regarding cosine-type porosity distributions, the unsym-1 provides the highest calibrated central deflection while the sym leads to the lowest level, but this relative order becomes completely reversed for the calibrated fundamental frequency.
* The order in the peak axial stress is unsym-1 > sym > unsym-2 and the order in the peak shear stress is unsym-1 > unsym-2 > sym, respectively.  
The current work dealt with metal-ceramic functionally graded plates. Thus, the extension of current work to more advanced materials, such as carbon nanotubes (CNTs) and CNT-reinforced composites, would be worthwhile, and represents a topic for future work.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the South Korean government (MSIT, 2020R1A2C1100924).  
The author declares no conflict of interest.
## 2 Materials and Methods
### PCA-Based Unsupervised FE with Optimized SD  
In our work, we used the PCA-based unsupervised FE with an optimized SD method. In the literature, few proposed methods have dealt with PCA-based unsupervised feature extraction; such methods can be found in previous studies [13; 14]. It is briefly described in the following section.  
Suppose histone modification profiles are formatted as a matrix \(x_{ij}\in\mathbb{R}^{N\times M}\) that represents histone modification of \(i\)th regions in \(j\)th samples. Here, we assume that \(x_{ij}\) is normalized as  
\[\sum_{i=1}^{N}x_{ij} = 0, \tag{1}\] \[\sum_{i=1}^{N}x_{ij}^{2} = N. \tag{2}\]  
Applying PCA to \(x_{ij}\) should obtain  
\[x_{ij}=\sum_{\ell=1}^{\min(N,M)}\lambda_{\ell}u_{\ell i}v_{\ell j}, \tag{3}\]  
where  
\[\sum_{i}u_{\ell i}u_{\ell^{\prime}i}=\sum_{j}v_{\ell j}v_{\ell^{\prime}j}= \delta_{\ell\ell^{\prime}}. \tag{4}\]  
To obtain \(u_{\ell i}\), the eigenvalues and vector of \(\sum_{j}x_{ij}x_{i^{\prime}j}\in\mathbb{R}^{N\times N}\) must be obtained as  
\[\sum_{i^{\prime}}\Biggl{(}\sum_{j}x_{ij}x_{i^{\prime}j}\Biggr{)}u_{\ell i^{ \prime}}=\lambda_{\ell}u_{\ell i}, \tag{5}\]  
and \(v_{\ell j}\) can be obtained as  
\[v_{\ell j}=\sum_{i}u_{\ell i}x_{ij}. \tag{6}\]  
To select regions of interest, \(i\), first, the \(v_{\ell j}\) associated with the desired property is identified. In this study, the property of interest is histone modification that is independent of samples (i.e., biological replicates); that is, \(v_{\ell j}\) should be independent of \(j\) (biological replicates) as well. When samples are composed of treated samples and controls, \(v_{\ell j}\) associated with distinction between controls and treate samples are employed. After identifying \(\ell\) of interest, an optimal SD of \(u_{\ell i}\) is obtained such that \(u_{\ell i}\) obeys the Gaussian distribution (null hypothesis) as much as possible.  
SD optimization can be performed as follows. First, \(p\)-values are attributed to the \(i\)th region as  
\[P_{i}=P_{\chi^{2}}\Biggl{[}>\left(\frac{u_{\ell i}}{\sigma_{\ell}}\right)^{2} \Biggr{]}, \tag{7}\]where \(P_{\chi^{2}}[>x]\) is the cumulative \(\chi^{2}\) distribution where the argument is larger than \(x\) and \(\sigma_{\ell}\) is the SD. Then, the histogram \(h_{s}\) of \(P_{i}\) is computed, which is the number of _i_s that satisfy  
\[\frac{s}{N_{s}}\leq 1-P_{i}\leq\frac{s+1}{N_{s}},0\leq s\leq N_{s}-1. \tag{8}\]  
\(h_{s}\) is expected to have constant values for \(s\leq s_{0}\), with some \(s_{0}\), and a sharp peak exists at \(k_{0}<k\). In this case, _i_s included in \(h_{s},s>s_{0}\) are selected based on association with significant histone modification. \(N_{s}\) is the total number of bins taken to be 100. Next, \(P_{i}\)s are recomputed with optimized SD, the obtained \(P_{i}\)s are corrected using the BH criterion [24], and _i_s with adjusted \(P_{i}\)s less than 0.01 are selected.