esa-sceva/satcom-chunk-collection huggingface dataset:
# From Service Composition to Mashup Editor: A Multiperspective Taxonomy
## 4 Taxonomy for Services Composition
### Services Composition Using BPEL
#### 4.1.4 Horizontal vs. Vertical Compositions  
Several recent research efforts have dealt with the Web service composition problem by trying to divide it into two or more sub-problems, introducing vertical/horizontal service compositions and abstract/concrete services concepts. In [48], authors argue that automatically composing Web services involves two main processes of composition, i.e., vertical and horizontal compositions. Vertical composition aims at finding the \"best\" combination of abstract Web services, namely the abstract workflow, to achieve the main objective, while satisfying all restrictions interdependently. Abstract services refer to each of the sub-tasks (abstract functionality) that, when joined together, represent the main objective of the composite services. Each abstract service can be executed by many equivalent Web services called concrete services. Consequently, the horizontal composition goal is to find the \"best\" concrete Web service among a set of functionally equivalent services available on the Web. These functionally equivalent services represent a Web service community (a concept introduced in [49; 50; 51]). The choice of a concrete Web service is made based on functional attributes such as inputs' types and/or non-functional attributes, such as QoS constraints [52; 53; 54]. In this case, the actual binding of the composite service can be performed dynamically (at the execution time) [55]. For instance, authors in [56; 57] proposed an evolutionary approach for QoS-aware composite Web services, whereas, in [58], an optimization approach is proposed. These QoS indicators need to be monitored, such as the method proposed in [59]. Non-functional attributes include SLA and pricing consideration. For instance, the authors in [60] proposed a service selection approach from a cloud service market. Other approaches, such as a multi-agent service framework [61], are considered as part of the structural approach too.  
The main advantage of distinguishing between these two processes of composition is to simplify the Web service composition problem to reduce the computational complexity. It provides an easier way to consider user intervention, so the user is able to modify/adapt the abstract workflow where necessary [62].
## 4 Empirical Analysis
### Reliability and Validity Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Construct** & **No. of Items** & **Factor Loading** & **CR** & **AVE** & **Cronbach’s \(\alpha\)** \\ \hline \multirow{3}{*}{e-SQ} & \multirow{3}{*}{3} & 0.953 & & & \\ \cline{3-6}  & & 0.863 & & & \\ \cline{3-6}  & & 0.636 & & & \\ \hline \multirow{3}{*}{CS} & \multirow{3}{*}{4} & 0.829 & & & \\ \cline{3-6}  & & 0.844 & & & \\ \cline{3-6}  & & 0.915 & & & \\ \cline{3-6}  & & 0.859 & & & \\ \hline \multirow{3}{*}{e-Trust} & \multirow{3}{*}{4} & 0.791 & & & \\ \cline{3-6}  & & 0.776 & & & \\ \cline{3-6}  & & 0.772 & & & \\ \cline{3-6}  & & 0.800 & & & \\ \hline \multirow{3}{*}{CUI} & \multirow{3}{*}{4} & 0.904 & & & \\ \cline{3-6}  & & 0.931 & & & \\ \cline{3-6}  & & 0.753 & & & \\ \cline{3-6}  & & 0.717 & & & \\ \hline \end{tabular}  
* Indices of the measurement model: \(\chi^{2}\) = 182.829; d.f. = 84; \(\chi^{2}\)/d.f. = 2.177; TLI = 0.962; CFI = 0.970; RMR = 0.022; RMSEA = 0.062. Abbreviations: e-SQ = e-service quality; CS = customer satisfaction; CUI = continuous usage intention; CR = composite reliability; AVE = average variance extracted.  
\end{table} 
Table 2: Reliability and convergent validity. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Construct** & **e-SQ** & **CS** & **e-Trust** & **CUI** \\ \hline e-SQ & 0.877 & & & \\ CS & 0.394 *** & 0.927 & & \\ e-Trust & 0.424 *** & 0.656 *** & 0.872 & \\ CUI & 0.479 *** & 0.677 *** & 0.590 *** & 0.897 \\ \hline \hline \end{tabular}  
* Note: ***\(p\) < 0.001. The diagonal value is the square root of the AVE.  
\end{table} 
Table 3: Correlation coefficients between constructs and discriminant validity.
# Solanesol Biosynthesis in Plants
## 3 Biosynthetic Pathway of Solanesol
### Formation of C5 Units  
IPP and its isomer DMAPP are synthesized via two pathways that occur in different subcellular spaces; i.e., the mevalonate pathway located in the cytoplasm that uses acetyl coenzyme A as a building block, and the 2-C-methyl-D-erythitol 4-phosphate (MEP) pathway in plastids that uses pyruvate and glycerol-3-phosphate. These two pathways are not independent of each other, as IPP is shuttled between the cytoplasm and plastids in plants such as tobacco, _Arabidopsis thaliana_, and snapdragon [17; 18].  
Solanesol is biosynthesized in plastids through the MEP metabolic pathway (Figure 2) [3; 6]. 1-deoxy-D-xylulose 5-phosphate synthase (DXS) catalyzes the formation of 1-deoxy-D-xylulose 5-phosphate (DXP) from pyruvate and glycerol-3-phosphate, and DXP undergoes molecular re-arrangement and reduction to form MEP through the catalytic action of 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR) (Figure 2). MEP then forms IPP and DMAPP through the successive catalytic actions of 2-C-methyl-D-erythnitol 4-phosphate cytidylyltransferase (IspD), 4-diphosphocytidyl-2-C-methyl-D-erythnitol kinase (IspE), 2-C-methyl-D-erythnitol 2,4-cyclodiphosphate synthase (IspF), 1-hydroxy-2-methyl-2-(_E_)-butenyl 4-diphosphate synthase (IspG), and 1-hydroxy-2-methyl-2-(_E_)-butenyl 4-diphosphate reductase (IspH) (Figure 2). The interconversion between IPP and DMAPP is catalysed by isopentenyl diphosphate isomerase (IPI) [3; 6].
# Radiometric Method for Determining Canopy Stomatal Conductance in Controlled Environments
## 4 Discussion
### Canopy Stomatal Conductance
A smaller g\({}_{\rm A}\) for soybean compared to wheat reflects a larger canopy boundary layer associated with the broader soybean leaves. In this study, G\({}_{\rm S}\) values of wheat and soybean were measured at an elevation of 1460 m (4800 ft), a barometric pressure of 86 kPa, and 400 \(\upmu\)mol mol\({}^{-1}\) CO\({}_{2}\) (Table 2). For wheat, G\({}_{\rm S}\) was 2.3 mol m\({}^{-2}\) s\({}^{-1}\) or 18 s m\({}^{-1}\), which is slightly higher than field G\({}_{\rm S}\) values (1.8 mol m\({}^{-2}\) s\({}^{-1}\) or 22.7 s m\({}^{-1}\)) reported by [PERSON] [47] at sea level, under optimal available soil water. The G\({}_{\rm S}\) of soybean was also slightly higher than typical conductances measured in field crops [46]. Soybean G\({}_{\rm S}\) at 400 \(\upmu\)mol mol\({}^{-1}\) CO\({}_{2}\) was 1.1 mol m\({}^{-2}\) s\({}^{-1}\) or 37 s m\({}^{-1}\), nearly one-half the value found in wheat probably due to less leaf area and because it was measured at a lower PPF\({}_{\rm o}\). The G\({}_{\rm S}\) values of this study are expected to be higher than those measured at sea level because, at lower atmospheric pressures, the diffusion coefficients of water vapor and CO\({}_{2}\) in air increase, so G\({}_{\rm S}\) also increases [48; 49].
# A Comparative Study of Indoor Accuracies Between SLAM and Static Scanners
## 2 Materials and Methods
### Reference Cloud Acquisition  
Prior to scanning, both spherical and black and white targets were placed within the area and georeferenced using the total station. Subsequently, scanning with the Leica ScanStation P40 with settings of point density of 3.1 mm per 10 m, normal sensitivity, and full field of view was carried out from 11 stations. The measurement from each station took approximately 3.5 min. After scanning from each station, reference points for Leica P40(black and white targets; Figure 2) were identified in the cloud using a built-in feature (the targets were scanned with an improved density and their centers automatically detected). To verify the accuracy of the reference cloud, 58 control points on the walls and floor were georeferenced using the total station (from five positions). The root mean square error (RMSE) was calculated in CloudCompare using the Cloud-to-Cloud function (comparing the 58 points to the reference P40 cloud).
# Metallurgical Alchemy: Synthesizing Steel Microstructure Images Using DCGANs
## 2 Materials and Methods
### Image Generative Models  
In this study, we utilized DCGAN [35] for generating high-fidelity images of low-carbon steel microstructures. DCGANs consist of two neural networks: a generator and a discriminator. The generator creates synthetic images that mimic real optical microscopy images of low-carbon steel microstructures, while the discriminator evaluates their authenticity. Through iterative training, the generator improves its capability to produce realistic images. The DCGAN architecture was chosen for its effectiveness in producing high-quality and high-resolution images, closely resembling those obtained via traditional optical microscopy techniques.  
Figure 3 illustrates the schematic diagram of the DCGAN architecture used in this study. The generator network takes random noise as input and progressively transforms it into realistic images. In contrast, the discriminator network takes both real and generated steel images as input and functions as a classifier to distinguish between real and synthetic images. During the training process, both networks are updated iteratively. The discriminator is first trained on real images and synthetic images from the generator, with the goal of minimizing its classification error using a binary cross-entropy (BCE) loss function [36]. The generator is then trained to maximize the discriminator's classification error by producing more realistic images, also using a BCE loss function but with the goal of making the discriminator classify synthetic images as real. This adversarial min-max optimization framework continues over many epochs, progressively enhancing the generator's ability to produce highly realistic low carbon steel images that the discriminator cannot easily distinguish from real ones.  
Figure 3: Schematic diagram of the DCGAN architecture used for generating low-carbon steel microstructure images.
# Polyhydroxyalkanoates (PHAs): Biopolymers for Biofuel and Biorefineies
## 4 Parameters Affecting HAME-Based Biofuels Production
### Catalyst Type and Concentration  
In general, acid- and base-catalyzed systems are used for the esterification of polyesters. Acid-catalyzed systems are mostly used to produce HAME biofuels, for which H2 SO4 is the most frequently used catalyst. However, other acids such as H\({}_{3}\)PO\({}_{4}\), organic sulfonic acids, and HCl are also used. [PERSON] et al., [69], [PERSON] et al., [75], [PERSON] et al., [72], and [PERSON] et al., [71] investigated the esterification of PHAs to produce HAME using H2 SO4 as catalyst. [PERSON] et al., studied the effect of different types of acids, including H\({}_{3}\)PO\({}_{4}\), H\({}_{2}\)SO\({}_{4}\), and HCl, on the conversion of P3 HB to HBME. Their findings suggested that an acid-catalyzed system containing H\({}_{2}\)SO\({}_{4}\) as catalyst gives maximum yield.  
Catalyst concentration affects the yield of HAME biofuels. In general, the product yield increases when a 10% (\(v/v\)) acid concentration is used, but decrease upon a further increase in catalyst concentration. [PERSON] et al., [69], studied PHAs conversion to HAME using 15% H\({}_{2}\)SO\({}_{4}\) and reported a 52% conversion. In contrast, [PERSON] et al., [75], [PERSON] et al., [72], [PERSON] et al., [71], and [PERSON] et al., [70] carried the conversion of P3 HB to HBME and reported maximum yield at this concentration.
## 3 Results and Discussions
### Physicochemical Characteristics of Wines  
Physicochemical characteristics showed that the content of alcohol (12.07-12.56%) and total acids (5.20-5.81 g/L) were not significantly different among the wines (Table 2). Compared with SCw (0.68 g/L), the volatile acids in wines (0.49-0.65 g/L) with added \(\beta\)-glucosidase, except H-Ew (0.68 g/L), decreased, which had no relation with the adding strategies of \(\beta\)-glucosidase (Table 2). The volatile acid content of wines also decreased after being fermented by yeast strains with \(\beta\)-glucosidase activity [20; 21]. Excessive volatile acids (>1.2 g/L) would bring undesirable flavors to wine [22]. These results indicate that adding SLY-4E, F2-24E and HX-13 into must would improve flavors by decreasing the volatile acid content. The reason for the effect of crude extracts of \(\beta\)-glucosidase on the volatile acid content in wines is still unclear. Further, the addition of SLY-4E, F2-24E and HX-13E in wines could also decrease the content of anthocyanin (163.30-184.96 mg/L)  
Figure 1: Stability of crude extracts of \(\beta\)-glucosidase in modified P-C buffer.  
(Table 2). In particular, it shows that the anthocyanin content of H-Ew was 163.30 mg/L, which was lower than S-Ew and F-Ew. However, the anthocyanin contents of S/H-Ew (173.08 mg/L) and H/F-Ew (172.17 mg/L) and S/H/F-Ew (184.96 mg/L) were higher than H-Ew, which showed that the combinational addition of SLY-4E, F2-24E and HX-13E could attenuate the declining trend. Consistent with the result of [PERSON] et al. (2011) [21], the wines fermented by _S. cerevisiae_ with higher \(\beta\)-glucosidase activity had lower anthocyanins concentrations. \(\beta\)-glucosidase can hydrolyze the glycosidic bond of anthocyanin to release free anthocyanins, and free anthocyanins easily degraded into colorless compounds, which would have a negative effect on the color of wine [23; 24]. The results showed that adding crude extracts and purified \(\beta\)-glucosidase from yeasts could decrease the anthocyanin content of wines, and those from different yeasts would exhibit different hydrolyzing activity of anthocyanins.
# Extension of DBSCAN in Online Clustering: An Approach Based on Three-Layer Granular Models
## 3 Framework and Necessary Preparation
### Data Preprocessing  
Before the modeling process, some issues are required to be stated in advance, e.g., data preprocessing, which is a must in data mining and data analysis. There are several necessary data preprocesses, e.g., complement of missing data, correction of abnormal data, de-noising, and normalization. Considering that the raw data in the real world often has different units and magnitudes in clustering, normalization processing is emphasized here. Many normalization methods were proposed in the literature [44], e.g., linear function transformation, logarithmic function transformation, cotangent function transformation, and so on. The max-min function transformation is used the most, and is good at limiting the inputs into a space of [0,1]\({}^{n}\). Therefore, it is applied for clustering in this paper, and its formula is defined in (1):  
\[y=\frac{x-x_{\min}}{x_{\max}-x_{\min}} \tag{1}\]  
Figure 1: Brief framework of the proposed method. red points: prototypes of granules; black points: normal data.  
where \(x\) is the original data, and \(y\) is the value after normalization; \(x_{max}\) and \(x_{min}\) are the maximum and minimum values. Through Equation (1), the dataset \(\{y\}\) will be friendlier in clustering.
## 1 Introduction
### BIM and AECO  
Productivity within construction processes has declined over the past years, compared to other engineering fields [18]. Numerous attempts have been made to change this situation, such as the development of collaborative methodologies for project management, like Virtual Design and Construction (VDC), with its elements of Integrated Concurrent Engineering (ICE), Project Production Management (PPM), and BIM. Specifically, BIM supports improving the flow of information, delivering a constructible design, enhancing cost estimation and control, meeting schedule deadlines on time, mitigating conflicts and disputes, increasing productivity, and reducing safety incidents [19,20].  
In the past several years, BIM adoption has grown significantly within the AECO industry worldwide [21,22]. From 2011 to 2020, the BIM adoption level significantly changed. According to the NBS [21], while the active use of BIM has increased from 13% to 73% among respondents worldwide, BIM awareness has decreased from 45% to 26% because it has become merely a natural workflow. In 2011, 43% of the respondents were neither aware of nor using BIM; by 2020, this percentage had dropped to 1%. In Latin American countries, BIM adoption has remarkably grown since the end of the last decade, starting with the initiatives from the government of Chile in 2019, with the creation of its public project BIM standard, followed by Argentina, Brazil, Peru, and Colombia [23].  
Consequently, the demand for civil engineering professionals with BIM competencies has increased exponentially. This growth of BIM in the AECO industry has motivated its inclusion in education [24]. Moreover, as education is essential to promote productivity in the industry [25,26], universities play an important role in providing BIM-equipped professionals to the industry [27]. However, BIM adoption in education has been solely focused on software operations and skill applications [28; 29], even though this change implies not only moving from pedagogical tools in 2D design to 3D visualization [30].  
In reality, BIM implementation implicates the way different parties of the project work in collaboration, from the early stages, to coordinate their inputs and improve the quality of the delivered project while ensuring reliability in its schedule and cost [30]. Therefore, communication and collaboration are key factors in BIM implementation. Unfortunately, to this day, only some approaches in education fully integrate these in BIM training throughout the curriculum while facing this process from an active learning approach [27; 31], that is, a learning-centered or constructivist pedagogical model [32; 33].
## 9 Performance Evaluators
### The Relation between Number of Iterations and Estimation Errors  
To evaluate the performance of the proposed algorithm, we have also done the simulation on the basis of the number of iterations to see the effect on the estimation error of localization and synchronization. We have made a comparison of the proposed algorithm with other techniques like RJSL [14] and TSLA [16]. According to the simulation setting, we execute our considered algorithm almost 20 times to analyze the stability in terms of location estimation errors and clock estimation errors. As a result of simulations, it is clear from the figure that localization and synchronization errors are gradually moving towards stability as the number of iterations increases. This also indicates that by the increase of the number of iterations the error of skew, offset and location decreases. As indicated by Figures 11-13 we can say that a greater number of iterations can bring stability in location, clock skew, and errors respectively. It is also  
Figure 11: Relationship between number of iterations and Localization RMSE (m).  
Figure 12: Relationship between number of iterations and clock skew.
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 4 Discussion
### Electrodeposition  
Although removal by floc adsorption and sedimentation is typically the primary pathway, another possible mechanism for Se and Sr removal from water is electrodeposition, where these elements deposit on the electrode surface during the EC process. Se species, such as selenate or selenite, can undergo reduction at the cathode, as described by Equations (4) and (5):  
\[SeO_{4}^{2-}+4H_{2}O+6e^{-}\to Se(s)+8 OH^{-} \tag{4}\]  
\[SeO_{3}^{2-}+4H_{2}O+4e^{-}\to Se(s)+6 OH^{-} \tag{5}\]  
This process leads to the deposition of elemental Se on the cathode surface. Selenium reduction is more likely under neutral-to-alkaline conditions, where hydroxide ions are abundant. Sr does not undergo direct electrochemical reduction due to its chemical nature. However, localized increases in pH near the cathode (due to the generation of hydroxide ions) can lead to the precipitation of compounds such as SrCO\({}_{3}\) and Sr(OH)\({}_{2}\). These precipitates may adhere to the electrode surface, especially the cathode, where hydroxide ions are generated. Also, if a passive oxide or hydroxide layer forms, Se and Sr can physically adsorb onto the layer on the electrode surface. Furthermore, in the presence of other ions, like CO\({}_{3}\)\({}^{2-}\) and HCO\({}_{3}\)\({}^{-}\), scaling may occur on the electrode surface, with strontium contributing to hard deposits.  
Neutral-to-alkaline pH increases the likelihood of Se reduction and Sr precipitation near the cathode. On the other hand, acidic pH reduces the chances of deposition since the hydroxide and carbonate species required for Sr precipitation are scarce. A high current density can enhance the reduction of Se species at the cathode but may also lead to excessive gas evolution (e.g., hydrogen), which reduces the adherence of deposits.  
To investigate the relevance of this mechanism, the electrodes were mechanically cleaned after the experiment, and the formed layer was analyzed using semi-quantitative ICP-OES analysis. The results show that while some Se was present, Sr was present in significant quantities. The deposition of Se and Sr can reduce the efficiency of the electro-coagulation process by impeding the current flow and mass transfer. However, periodic physical or chemical cleaning can remove these deposits and restore electrode functionality.
## 4 Conclusions  
In this work, the influences of pre-loading degree and pre-loading age on compressive strength development were studied. The influence of the addition of supplementary cementitious materials (SCMs) on compressive strength development was investigated. The acoustic emission signals were tested during the uniaxial compression tests and analyzed. The main conclusions can be drawn as follows:  
1. The pozzolanic reaction healed the damage caused by the pre-loading. The compressive strength of group B concrete at 270 d after pre-loading was higher than that of the group A concrete.
2. The damage threshold of early-age concrete is not static and is closely related to the age and concrete mix ratio, which influences the acoustic emission signals. The experimental results indicate that the 80% pre-loading degree at 3 d exceeds the damage threshold, which cannot be effectively healed following the curing process. For 7 d and 14 d concrete, the damage caused by 80% pre-loading degree can be effectively cured. For 28 d concrete, the 80% pre-loading might not exceed the damage threshold, but due to the high hydration degree, there was not enough cement for further hydration to heal the damage.
3. The acoustic emission characteristics of concrete during uniaxial compression can be divided into the initial stage, stable stage, and active stage. The peak frequency of the uniaxial compression acoustic emission of concrete can be divided into four frequency intervals to correspond to different damage mechanisms of concrete, namely: interval1 (12 \(\pm\) 5 kHz), interval II (38 kHz \(\pm\) 5 kHz), interval III (171 kHz \(\pm\) 5 kHz), interval IV (259 kHz \(\pm\) 5 kHz).  
It is suggested that the acoustic emission method can be reliable for studying the healing effect of concrete incorporated with SCMs. Also, for field practice, it is recommended that the SCMs can be added to a raw concrete mixture as an effective crack healing agent.  
Conceptualization, Z.C. and R.H.; investigation, X.J.; data curation, Z.C.; methodology, R.H.; visualization, R.H.; formal analysis, R.H.; writing--original draft, Z.C. and R.H.; writing--review and editing, Z.C., X.J. and R.H.; project administration, R.H.; funding acquisition, X.J.; validation, R.H.; resources, R.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The financial support from the National Basic Research Program (973 Program) (Grant No. 2015 CB655103) of the China is gratefully acknowledged.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.