esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Distribution Problems
### Capacitated Network Area Coverage  
Let us consider two finite sets \(I\) and \(J\), where \(I\) is the set of service centers \(1,2,\ldots,m\), and \(J\) is the set of customer locations \(1,2,\ldots,n\).  
Further, \(a_{ij}=1\) means that customer location \(j\) is in a reachable distance to service center \(i\), \(a_{ij}=0\) means that it does not satisfy it, and \(w_{i}\) expresses the weights of service centers (since it is the minimization problem, the greater the weights are, the smaller the coefficient must be).  
Similarly, \(x_{i}=1\) means that service centre \(i\) is selected, while \(x_{i}=0\) means that it is not selected.  
Finally, \(c_{i},\ i\in I\)--capacity of service centre \(i\), \(b_{j},\ j\in J\)--demand of customer location \(j\), \(y_{ij}\in\{0,1\}\)--customer from location \(j\) is assigned or is not assigned to service centre \(i\).  
In [13], we derived the following model for a capacitated network area coverage:  
\[z=\sum_{i\in I}w_{i}x_{i}\rightarrow\min \tag{50}\]  
subject to  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}x_{i}\geq 1 \tag{51}\]  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}y_{ij}=1 \tag{52}\]  
\[\forall i\in I:\ c_{i}x_{i}\geq\sum_{j\in J}a_{ij}y_{ij}b_{j} \tag{53}\]  
\[\forall i\in I:\ \sum_{j\in I}y_{ij}\leq nx_{i} \tag{54}\]  
\[\forall i\in I:\ x_{i}\in\{0,1\} \tag{55}\]  
\[(\forall i\in I)(\forall j\in J):\ y_{ij}\in\{0,1\}. \tag{56}\]  
A necessary precondition for finding a solution is that the sum of all capacities is sufficient to cover all demands, i.e., \(\sum_{i=1}^{m}c_{i}\geq\sum_{j\in I}b_{j}\), with each customer having a reachable distance to at least one center, i.e., \(\forall j\in J:\ \sum_{i\in I}a_{ij}>0\).  
In [13], we then modified the previous model for the domain of telecommunication signals considering signal interference and its nonlinear version linearized as follows:  
\[z=\left(\sum_{i\in I}w_{i}x_{i}\right)/\sum_{i\in I}w_{i}-\left(\sum_{i\in I} \sum_{j\in I}d_{ij}h_{ij}\right)/\left(\sum_{i\in I}\sum_{j\in I}d_{ij}\right) \rightarrow\min \tag{57}\]  
subject to  
\[(\forall i\in I)(\forall j\in I):\ h_{ij}\leq x_{i} \tag{58}\]  
\[(\forall i\in I)(\forall j\in I):\ h_{ij}\leq x_{j} \tag{59}\]  
\[(\forall i\in I)(\forall j\in I):\ h_{ij}\geq(x_{i}+x_{j}-1) \tag{60}\]  
\[(\forall i\in I)(\forall j\in I):\ h_{ij}\in\{0,1\} \tag{61}\]  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}x_{i}\geq 1 \tag{62}\]  
\[\forall j\in J:\ \sum_{i\in I}a_{ij}y_{ij}=1 \tag{63}\]  
\[\forall i\in I:\ c_{i}x_{i}\geq\sum_{j\in I}a_{ij}y_{ij}b_{j} \tag{64}\]  
\[\forall i\in I:\ \sum_{j\in I}y_{ij}\leq nx_{i} \tag{65}\]  
\[(\forall i\in I)(\forall j\in I)(i\
eq j):\ d_{ij}\geq(x_{i}+x_{j}-1)d_{\min} \tag{66}\]  
\[\forall i\in I:\ x_{i}\in\{0,1\} \tag{67}\]  
\[(\forall i\in I)(\forall j\in J):\ y_{ij}\in\{0,1\}. \tag{68}\]  
Another possible modification of the model is to meet the demand by composing parts of the capacities of several centers, but with a fragmentation not lower than a certain threshold. This new approach will be presented in detail in a separate paper.
## 2 Results
### Direct Functionalization of PLA Backbone: Towards PLA-Based Graft Copolymers
#### 2.2.4 PLA-\(g\)-Halogen  
The halogenation, in particular bromination, of the PLA chain is another method for reactive functionalization of the PLA chain. Usually, bromination is achieved by a free-radical mechanism. Typically, PLA is treated with N-bromo succinimide (NBS) in the presence of BPO over a period of 5 days [91]. There is no detectable chain degradation or crosslinking based on SEC results. Similarly, authors prepared chlorinated and iodinated PLAs. They used short-chain PLA (Mn = 2 kDa) to facilitate polymer characterization. The substitution degree depended on the halogen: from 3.2% for bromination and chlorination to 0.5% for iodination. However, the degree of bromination can be increased to 8% with microwave activation.  
PLA-\(g\)-Br was used as a multisite macroinitiator for the ATRP of methyl methacrylate (MMA) and oligo ethylene glycol methacrylate (OEGMA) (Figure 17). Depending on the PLA-\(g\)-Br/MMA ratio, the side chains had different lengths. Complete bromine consumption was achieved during polymerization [92].  
Figure 17: Formation of PLA-\(g\)-PMMA and PLA-\(g\)-POEGMA by ATRP (from [PERSON] et al. [92], copyright American Chemical Society, reproduced with permission).  
The bromination reaction was also performed on the surface of PLA films by treating the surface with NBS in H\({}_{2}\)O under UV irradiation. The incorporation of up to 3.7% bromine on the surface was achieved. Surface-initiated ATRP of quaternary ammonium methacrylate (QMA) chloride in the presence of CuBr and 2,2\({}^{\prime}\)-bipyridyl (bpy) was then performed, as shown in Figure 18. The cationic grafted surfaces are significantly more toxic to E.coli cells than genuine PLA, but no toxicity to HeLa cells upon contact was found [93].  
A direct surface grafting (without any pre-halogenation) of poly(methacrylic acid) (PMAA) onto PLA was obtained after activation of the surface of a PLA film via photo-oxidation followed by the UV-induced polymerization of methacrylate acid. The grafting was confirmed in particular by FTIR analysis [94]. This method was employed to prepare a nano hydroxyapatite/g-PLA composite.  
PLA nanofibers obtained by electrospinningning were coated with PMMA by plasma polymerization [95]. The coated PLA fibers showed an increase in diameter from 250 nm to 700 nm. MTT assays and cells count showed that the PLA-_g_-PMMA copolymers form intrinsically biocompatible systems.
# Noise-to-Norm Reconstruction for Industrial Anomaly Detection and Localization
## 4 Experiments
### Comparative Experiments  
#### 4.3.1 MPDD  
We compared our proposed method with several state-of-the-art (SOTA) methods on the MPDD dataset, including reconstruction-based methods [12; 21] and feature embedding-based methods [7, 8, 11]. The image-level detection results are listed in Table 1, and the anomaly segmentation results are presented in Table 2. Experiments demonstrated that our proposed method outperformed previous SOTA methods on the MPDD dataset. The partial visualization results of the proposed method on the MPDD [2] dataset are shown in Figure 4.  
Specifically, as shown in Table 1, our proposed method achieved an overall improvement of 8.82% compared to that of the previous best-performing method, CFLOW [11]. The most significant improvement was observed in the tubes that contained multiple instances with a random distribution of positions. These results highlighted the advantages of the proposed method. As shown in Table 2, the best average performance was achieved. However, the proposed method has some limitations. We were unable to achieve satisfactory performance in the brown bracket category. Most defects in the brown bracket category are deformation defects, and our method cannot accurately restore deformations, which hinders the accurate identification of such defects.
# Nodal Accuracy Improvement Technique for Linear Elements with Application to Adaptivity
## 6 Concluding Remarks  
The second-ordered nodal accuracy of linear elements in all 1D to 3D problems is the major obstacle that prevents linear elements from gaining super-convergent solutions in recovery processes, and the resulting absence of effective error estimators has in turn prevented linear elements from being used in most adaptive FE analyses. In this paper, a direct nodal accuracy improvement method for 2D and 3D linear elements is proposed, in which, without changing global stiffness matrices, simple back-substitutions for the residual nodal load vector would produce the corrective nodal displacements of the increment FE solution. On one hand, a simple addition of the increment FE solution to the conventional EEP solution provides an improved EEP solution, for which the nodal values gain fourth-order convergence and interior values gain third-order convergence in maximum norm for all multi-dimensional problems. On the other hand, this super-convergent method provides an effective and reliable error estimator for linear elements and has been successfully incorporated into a two-phased adaptivity algorithm which requires the maximum error of FE displacements to satisfy the user-specified error tolerances for 1D and 2D problems.  
The adaptivity analysis for 3D problems remains to be explored, which calls for future research on a flexible, sophisticated and mature mesh generation algorithm suitable for the 3D EEP algorithm.  
Conceptualization, S.Y.; Methodology, Z.H., S.Y. and Q.X.; software, Z.H.; validation, Z.H.; formal analysis, S.Y. and Q.X; investigation, S.Y. and Q.X; resources, S.Y.; data curation, Z.H.; writing--original draft preparation, Z.H.; writing--review and editing, S.Y. and Q.X.; visualization, Z.H.; supervision, S.Y. and Q.X.; project administration, S.Y.; funding acquisition, S.Y. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (No. 51878383, 51378293). The authors are solely responsible for the content.  
Figure 23: Convergence rates of uniform refinement and adaptive refinement.
## 6 Experimental Validation of the Calculation Result
### Measurement of Orifice Diameter  
An optical image measuring instrument (Hexagon Optiv Classic 321 GL tp) is employed in this research for orifice diameter measuring, as shown in Figure 8a. It has a CCD (Charge Coupled Device) image system to capture the image of the tiny object to be measured, which is capable to measure the contour and dimensions of various complex parts accurately and efficiently. Figure 8b presents a tested image of the orifice. As can be seen from the figure, the round outline of the orifice is clear, and no obvious burr can be observed. These orifices were acquired by micro-drilling machining. The image of the orifice indicates that excellent shape accuracy can be acquired by micro-drilling machining.  
Table 2 lists the tested data of eight orifices. It shows that the actual diameter of the orifice diameter has a good consistency. The nominal size of the orifice diameter is 0.15 mm, and all the measured orifice diameter is around 0.15 mm with a dimension deviation less than \(\pm\)5 \(\upmu\)m, which indicates that the orifices have high dimensional accuracy.
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Network-Level-Focused Energy Conservation Schemes
#### 4.2.1 Topology Control  
Topology control is a duty cycling scheme at the network level that takes advantage of network redundancy to prolong network longevity by dynamically adapting the network topology based on the application needs to minimize the number of active nodes [34]. It leads to turning off a subset of nodes while maintaining another subset of active nodes. This is because when sensors are redundantly distributed to provide good coverage, not all the nodes will be required to maintain network operation and connectivity [57]. It is thus possible to turn off the nodes that are not needed to ensure coverage or connectivity in order to prolong the lifetime of the WSN [93].  
Topology control protocols can be generally classified into two main categories: location-driven and connectivity-driven approaches [36]. In the location-driven approaches, the activation or deactivation of nodes is determined by their specific locations. The assumption underlying these approaches is that the nodes' positions are already known, enabling them to collaborate in deciding which nodes within a specific area should be deactivated without compromising the coverage of that particular area. Connectivity-driven protocols dynamically enable or disable sensor nodes to ensure the fulfillment of network connectivity or complete sensing coverage.  
Topology control (location-driven and connection-driven) is not suitable in thin LWSNs because of the absence of node redundancy imposed by the linear structure. As such, all the nodes, not a subset of the nodes, are needed to maintain coverage.
## 2 Materials and Methods
### Main Study
#### 3.3.2 Results without the Substrate Influence  
For practical use in construction, it is interesting to place the measured values of individual screeds against each other without the influence of the substrate material. The above values were, therefore, averaged, and the result is an unbiased comparison of individual types of screeds (Table 4).  
When comparing the overall tested screeds, it can be seen from Table 4 that the greatest total adhesion is achieved by polymer screeds, which, at the same time, have the lowest durability during the freezing cycles. Bitumen screeds and silicate (mineral) screeds are very similar in the total values. However, they differ in their ability to withstand freezing cycles over time. In the case of bitumen screeds, the first 15 freezing cycles have the greatest effect, and there is no significant loss of strength. In contrast to this, silicate (mineral) screeds have a linear decrease in tensile strength due to freezing (Figure 18).  
According to the manufacturer's technical data sheets for the used screeds, the manufacturers declare a minimum strength of 0.5 MPa for all types. All the tested screeds have manifested a greater adhesion strength even after 30 freezing cycles.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{2}{c}{**Average Adhesion Strength without**} & \multicolumn{2}{c}{**Average Residual Strength after**} \\  & \multicolumn{2}{c}{**the Influence of the Substrate (MPa)**} & \multicolumn{2}{c}{**30 Freezing Cycles without the Influence of**} \\ \hline
**Type of Screed** & **Reference** & **15 Freezing Cycles** & **30 Freezing Cycles** & **the Substrate (\%)** \\ \hline Bituminous Screeds & 1.35 & 1.08 & 0.97 & 81.1\% \\ Polymer Screeds & 1.91 & 1.79 & 1.43 & 74.6\% \\ Silicate (Mineral) Screeds & 1.28 & 1.22 & 1.03 & 80.8\% \\ \hline \end{tabular}
\end{table}
Table 4: Total adhesion strength and total residual strength after 30 freezing cycles.
## 3 Results and Discussion
### Effect of Circulating Velocity  
Ionic migration in CDI devices is dependent on the circulation of the solution. The effect of circulating velocity on the desorption process was studied at different circulating velocities of the solution, and the result is shown in Figure 4c. The desorption efficiencies after 5 h under circulating velocities of 75, 112, 140, and 168 mL min\({}^{-1}\) were approximately 94%, 100%, 100%, and 100%, respectively. It is worth noting that the higher circulating velocity caused a higher desorption efficiency. This trend seems to be in good agreement with other reports [19; 57]. Circulating velocity increases convection, thereby reducing stagnant or boundary layer thickness that tends to accelerate ion transfer [58]. When the circulating velocity is 112 mL min\({}^{-1}\), the desorption efficiency reached the maximum of 100%, and the difference in desorption efficiency is not obvious when the circulation velocities are greater than 112 mL min\({}^{-1}\). This can be attributed to the electric field. Due to the electric field external to the electrode, mass transfer through the boundary layer will be enhanced [59; 60]. Thus, the optimal circulating velocity is 112 mL min\({}^{-1}\).
# Lacase in Biorefinery of Lignocellulosic Biomass
## 2 Pretreatment of Lignocellulosic Biomasses
### Acidic Pretreatment for Lignocellulosic Biomasses  
Acidic pretreatment involves the use of acid at various concentrations, which acts on the hemicellulose fraction of the lignocellulosic biomass, thus, leaving behind a more amorphous cellulose fraction to be accessible to enzymes. When hemicellulose is removed from tightly packed biomass, lignin and cellulose become loosely packed. The most used acid for this purpose is sulfuric acid, although other acids, such as hydrochloric acid and nitric acid, can also be used [14]. The conditions for acidic pretreatment exhibit a temperature range of 120-200 \({}^{\circ}\)C and a pH range of 1-2.5. The disadvantages of acidic pretreatment include the generation of inhibitory compounds, such as furfural and 5-hydroxymethylfurfural (HMF), which can inhibit downstream enzymatic hydrolysis [15].  
Various types, as well as various concentrations of acids may be employed for pretreatment methods that can be used to treat lignocellulosic biomasses, and can be categorized based on the concentration as dilute acid pretreatment, concentrated acid pretreatment, sulfite pretreatment, etc. Dilute acid pretreatment is a method that uses a low concentration of acid (usually sulfuric acid) at a moderate temperature (around 120-170 \({}^{\circ}\)C) for a short period (around 10-60 min) [16]. This method is effective in breaking down the hemicellulose component of the biomass and releasing monomeric sugars. Concentrated acid pretreatment is a method that uses a high concentration of acid (10-70% usually sulfuric acid) at a high temperature (around 180-220 \({}^{\circ}\)C) for a long period (around 1-5 h). This method is more effective in breaking down both the hemicellulose and lignin components of the biomass, making the cellulose component more accessible [17].  
There is a sulfite pretreatment that uses a mixture of sulfite and acid to pretreat the biomass, which is effective in breaking down the lignin component of the biomass, and also lowers the hydrophobicity of lignin by sulfonation, making the cellulose component more accessible [18].
## 3 Materials
### Feature Extraction
#### 3.2.7 Discrete Cosine Transform (DCT)  
DCT converts a sequence of data points (such as audio samples or image pixels) from the time or spatial domain to the frequency domain. It achieves this by expressing data as a linear combination of cosine functions with different frequencies and amplitudes.
# RIS-Assisted Fixed NOMA: Outage Probability Analysis and Transmit Power Optimization
## 2 Related Work
* Extensive MC simulations are used to corroborate the resulting analytical outage expressions. The accuracy of derived analytical expressions is proved using the strong correlation between the theoretical and simulation results.
# Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea
## 3 Results
### Identification and Quantification of the Aroma Compounds in Green Tea Samples by HS-SPME/GC-MS  
GC-MS analysis showed significant differences in the volatile components of samples under various light conditions. The concentration of aroma and the number of volatile compounds differed significantly across various treatments. The total volatile concentration in MBL (13,463.39 \(\pm\) 70.28 \(\upmu\)g/kg) treatment was notably higher than in the DARK treatment (11,223.39 \(\pm\) 58.37 \(\upmu\)g/kg). In contrast, HBL (10,921.94 \(\pm\) 47.52 \(\upmu\)g/kg) treatment had a reduced aroma concentration, while LBL (11,234.43 \(\pm\) 51.91 \(\upmu\)g/kg) treatment matched that of the DARK treatment (Figure 3A). All green tea samples treated with blue light [HBL (83), MBL (85), and LBL (87)] had a greater number of aroma compounds detected than those treated with DARK treatment (73). Although the total aroma compound concentration of LBL treatment did not have statistically significant differences with the Dark treatment, the number of aroma compounds was the highest (Figure 3A). A Venn diagram (Figure 3B) was used to illustrate the logical connections among the four treatments, focusing on the key aroma compound. There were 44 shared aroma compounds found across the 4 treatments. Only 15, 4, and 2 aroma compounds were identified in HBL, LBL, and DARK, respectively. In short, the Venn diagram showed significant differences in the constitution of aroma compounds among treatments. All 4 green tea samples shared 116 volatile compounds, as detailed in Table 1. Additionally, these compounds were sorted into 7 different chemical classes according to their chemical structure, including alcohols, aldehydes, ketones, esters, acids, hydrocarbons, heterocyclic compounds, and others, as depicted in Figure 4A. An analysis of the distribution of volatile compounds in each category showed that in green teas under different light treatments, the largest groups were hydrocarbons (29) and alcohols (21), followed by aldehydes (17), esters (15), heterocyclic compounds (13), and ketones (12). Conversely, acids (6) and others (3) had the fewest subclasses. Alcohols and hydrocarbons were the primary groups of aroma compounds, making up nearly 43% of the volatile compounds in green tea. Figure 3: Total amount and quantity of volatile substances. (**A**) Overall aroma concentration (upper, \(\upmu\)g/kg tea leaves relatives to internal standard (ethyl caprate) and the number of compounds (lower) conducted in the tea leaves spread in different light conditions; (**B**) Venn diagram of aroma compounds of green tea under different light conditions. Different letters in the same column indicate a significant difference between light conditions (\(p\) < 0.05). \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.
## 1 Introduction
When the system is expanded to include heat storage, excess heat will be produced [24]. In the case of electricity production, the situation is more complex, as each building has different generation and storage options [25]. Diverse load profiles are also different, depending on the type of building or the intensity of use of a particular building. Recent studies [12; 13; 20; 21; 22] have shown that it is possible to achieve a zero-energy concept in renovated residential buildings. If there are no renovated buildings in the selected area, the need for energy increases significantly, which can no longer be covered by PV energy production alone. And if there are administrative or hotel-type buildings in the given area, it is necessary to increase the total production of other buildings [24]. Energy sector coupling is also important [23]. It is possible to achieve a transition to purely renewable energy by appropriately connecting individual heating and electricity production systems in combination with energy storage. Another benefit of creating such energy units is also the reduction of the total costs of operating energy networks, which is positively reflected in the final price for the consumer [13; 14; 15; 16; 17]. Yet, the important question that is present in countries with lower amounts of solar energy during winter months is, what should be the installed power output of solar photovoltaic power plants to cover the energy demand? For Slovakia, with only 94 sunny days in a year, the higher installed power can lead to an energy surplus during summer days, when most of the sunny days are present.