esa-sceva/satcom-chunk-collection huggingface dataset:
# Distributed Management Systems for Infocommunication Networks: A Model Based on TM Forum Framework
## 3 Methods
### Simulation Results of Request Service Procedure  
An example of the graph of the request implementation procedure is shown in Figure 18. The memory distribution among the elements of a BP (the so-called business components, BCs) is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Elements of Business Process**} \\ \cline{2-7}  & **1** & **2** & **3** & **4** & **5** & **6** \\ \hline Size of BC, in Kb & 3000 & 2000 & 1500 & 1000 & 700 & 500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Memory distribution among business components (BCs).  
Figure 18: Graph of request implementation procedure.  
The mean number of control transfers \(g_{ij}\) can be obtained using a program monitor of this system. The results of measurements are combined in Table 2.  
Here the desired variables are the elements of the matrix \(L=\left\|{{{l_{ij}}}}\right\|_{l,j=1}^{n}\), where \(l_{ij}=\left\{{\begin{array}{*{20}{c}}1&1&1&1&1&1&1&1\\ 30&0&1&1&1&1&1&1\\ 100&1&1&0&0&1&1&1\\ 200&1&1&1&1&0&0&1\\ 1000&1&1&1&1&1&0&0\\ 2000&0&0&0&1&1&1&1\\ \end{array}}\right.\).  
Clearly, the optimal combination consists of the following PCs: 1-5, 1-6, 1-4, 2-3, 2-5, 2-6, 4-6, 5-6. All PCs of a business process have to be divided into two segments: PCs 1-4-5-6 and 2-3, or PCs 1-4 and 2-3-5-6. The execution time of the business process (service implementation) has been reduced from 4.416 to 3.681 ms, which is 17%.  
The main limitations of the developed models, which are related to the accuracy of the research results, are determined by the assumption that the phases of receiving and processing packages are independent.
# Human Activity Recognition Using CSI Information with Nexmon
## 8 HAR Classification and Performance Analysis
### Deep Neural Network
#### 8.3.2 Long Short-Term Memory (LSTM)  
LSTM is a deep learning network that uses RNN methodology for learning. The RNN experiences gradient explosion or shrinking in long-term relationships while training, which resulted in low effectiveness and accuracy. LSTM uses additional gates to influence and better guide the next state's learning to overcome this problem. Gates helps to decide whether the previous state's output is helpful for the output of the next state or it is better to forget the previous state weights for better improvement of the learning and training.
## 4 Comparison of Wavefields of \"Perfect Beams\" and SPPs
### Comparison of Perfect Beams Generated with Different Axicons  
Since the field distributions in \"perfect beams\" created by binary axicons are quite far from ideal rings, we calculated the fields of Bessel beams and their images in the focal plane of a lens for all three axicons within the scalar diffraction theory. The calculation results are shown in Figure 6.  
As seen from the third row in Figure 6, the calculated value of the ring radius in the focal plane of a lens with \(f=100\) mm for an ideal Bessel beam with a transverse wavenumber \(\kappa=3.1\) (see Equation (4)) is 6.9 mm. The inner radius of all three rings in the figure is 6.6 mm. The radius of the maximum intensity of the ring created using the holographic axicon coincides well with the calculated value of 6.9 mm. The rest of the rings are much wider, and the numbers of spiral segments they have are 9 and 18, respectively. Using these results and adding to them the results of calculations for the same axicons with a topological charge of 3, we collected in Figure 7 the cross-sections of perfect beams produced with different axicons. Now, we summarize in Table 1 the main characteristics of perfect beams created using different types of axicons and discuss the results obtained.  
Figure 6: Top row: binary, kinoform, and holographic axicons for generation of quasi-Bessel beams with topological charge \(\ell=+9\) (\(D=50\) mm, (\(\kappa=3.1\) mm\({}^{-1}\)), \(Z_{L}=120\) mm). Middle row: Bessel beams’ intensity distributions; phase distributions are shown with a color map (\(\lambda=141\) μm). Bottom row: the same for a lens focal plane (\(f=100\) mm). Frame size is \(20\times 20\) mm\({}^{2}\).  
Figure 7: Calculated cross-sections of rings of perfect beams for three types of axicons and two values of topological charge (\(f=100\) mm). For other parameters, see Figure 6.
## Literature Review
### Laser Scanning for Construction Quality Control  
Laser Scanning is a novel surveying technology. A laser scanner sweeps the surrounding space with laser light to acquire 3D data points. A laser scanner emits a pulse of light, which measures the amount of time it takes to travel from the scanner to the object and back, allowing the scanner to calculate the distance. Point clouds provided by laser scanners can be used directly for measurement and visualization [20]. One of the critical applications of laser scanning is construction quality control [21; 22; 23]. [PERSON] et al. [21] proposed a first formalization for integrating project 3D models and sensor systems to defect detection and characterization for construction quality control. [PERSON] and [PERSON] [20] then presented the implementation of such a system, called the Scan-vs-BIM principle. This enables the system to automatically match 3D data points to each BIM model object.  
Point cloud data that capture facilities' existing conditions are adopted for the quality inspection of buildings and civil infrastructures. According to the different types of geometry quality, research efforts on geometry quality inspection can be divided into three categories, namely (1) dimensional quality inspection, (2) surface quality inspection, and (3) displacement inspection [24]. The dimensional quality inspection has covered dimensions of prefabricated elements and dimensions of building facade elements, such as size [25; 26], shape [27], position [28], and orientation [28]. For surface quality inspection, most research works are focused on surface crack, spalling, flatness, and deformation/distortion. For surface cracks and spalling, local geometric features are commonly used to detect the target problems, such as the local curvatures [29] or normal vectors [30]. Displacement inspection is focused on the change of relative position of a structure or elements.  
These research target problems include displacement of highway retaining walls [31], displacement of dams [32], deflection of bridges [33], and displacement of landslides [34]. These research works often need to collect point cloud data to monitor the displacement of structures by comparing point cloud data captured at different construction processes [25]. In the fabrication phase, the components such as precast concrete elements or prefabricated pipe spools are inspected to ensure smooth installation and assembly on-site [35].  
Laser scanning can contribute to reconstructing the structure or components of a construction project. It can be integrated with BIM technology to ensure smooth control of the construction quality.
# Fault-Resilient Manufacturing Scheduling with Deep Learning and Constraint Solvers
## 2 Related Work
### Comparison with Existing Approaches
#### 2.3.4 Energy-Aware Scheduling  
[PERSON] et al. [21] discussed energy-aware scheduling in data centers using machine learning techniques. They proposed a framework that optimizes resource allocation by predicting power consumption and workload behavior, aiming to improve both energy efficiency and Service Level Agreement (SLA) compliance through workload consolidation and dynamic resource management. While their focus was on real-time machine learning-driven decisions for energy efficiency, our work contrasts by combining fault prediction and scheduling optimization to minimize machine faults in edge computing environments, thereby enhancing overall system efficiency.  
The reviewed studies highlight the diverse methodologies employed in job scheduling and predictive maintenance, including SMT-based optimization, machine learning models, reinforcement learning techniques, and hybrid approaches. However, most existing methods focus primarily on either scheduling optimization or fault prediction, without fully integrating both in a dynamic and adaptive manner. Additionally, while SMT-based scheduling provides powerful optimization capabilities, it often lacks real-time adaptability, and machine learning-based fault prediction methods do not inherently optimize job scheduling. Our research addresses this gap by proposing a hybrid scheduling framework that seamlessly integrates Predictive Scheduling (fault prediction) with Proactive Scheduling (dynamic job reassignment). By leveraging deep learning for real-time fault anticipation and SMT-based optimization for dynamic job allocation, our approach enhances both efficiency and reliability in dynamic production environments, particularly in real-time edge-based production environments.
## 3 Results and Discussion
### Effects on Land Variables
The mean GPP values of the NEW run for the five typical regions were 2.94 Pg C year\({}^{-1}\), 15.33 Pg C year\({}^{-1}\), 9.46 Pg C year\({}^{-1}\), 3.95 Pg C year\({}^{-1}\), and 4.90 Pg C year\({}^{-1}\), with the highest GPP in R2 (located in South America), followed by R3 in South Africa; South Africa is fairly humid (with annual precipitation over 800 mm) and mainly covered by broadleaf trees, grass, and crops. These values were closer to MTE_GPP, with 2.99 Pg C yearyear\({}^{-1}\), 15.33 Pg C year\({}^{-1}\), 10.67 Pg C year\({}^{-1}\), 4.01 Pg C year\({}^{-1}\), and 4.87 Pg C year\({}^{-1}\). Table 2 shows the statistical characteristics of the two simulations. Clearly, the NEW run did not show apparent improvement in the correlation coefficient, but significantly reduced the MBEs and RMSEs of GPP for the five typical regions. This collection of results proved that the GPP simulation could be improved by the NEW run, reflecting the success of the dynamic rooting scheme in the coupled model. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**R1**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**R3**} & \multicolumn{2}{c}{**R4**} & \multicolumn{2}{c}{**R5**} \\ \hline  & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** \\ \hline \(R\) & 0.86 * & 0.88* & 0.75 & 0.77 & 0.59 & 0.60 & 0.85* & 0.86* & 0.76 & 0.77 \\ MBE & \(-\)37.61 & \(-\)20.89 & \(-\)208.6 & \(-\)98.4 & \(-\)60.1 & \(-\)30.3 & \(-\)18.1 & \(-\)36.6 & 70.2 & 81.5 \\ RMSE & 51.3 & 35.7 & 226.5 & 130.1 & 151.3 & 95.3 & 41.3 & 91.5 & 112.1 & 191.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical characteristics of annual ET for five typical regions compared to MTE data. (Values with asterisks represent \(R\) with significance at the 95% confidence level (\(p<\) 0.05). Units for mean biases (MBE) and root mean square errors (RMSE) are in mm year\({}^{-1}\).) \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**R1**} & \multicolumn{2}{c}{**R2**} & \multicolumn{2}{c}{**R3**} & \multicolumn{2}{c}{**R4**} & \multicolumn{2}{c}{**R5**} \\ \hline  & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** & **CTL** & **NEW** \\ \hline \(R\) & 0.98 * & 0.98 * & 0.70 & 0.82 & 0.60 & 0.50 & 0.98 * & 0.98 * & 0.96 * & 0.97 * \\ MBE & 1.09 & 0.05 & \(-\)7.28 & \(-\)5.04 & \(-\)2.41 & \(-\)1.21 & 2.77 & 0.06 & \(-\)1.17 & 0.03 \\ RMSE & 2.29 & 0.25 & 8.01 & 6.06 & 2.44 & 1.25 & 2.79 & 0.46 & 1.22 & 0.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical characteristics of annual GPP for the five grouped regions compared to MTE data. (Values with asterisks represent \(R\) values with significance at the 95% confidence level (\(p<\) 0.05), and the units for MBE and RMSE are g C m\({}^{-2}\) year\({}^{-1}\).)
## Materials and Methods
### Analytical Methods
#### 2.4.4 Vitamin C Content  
The iodometric titration method described by [PERSON] et al. [25] was used as a general norm for vitamin C content determination. Briefly, 3 g of six selected extracts (coded as: C1, C5, and C8, according to Table 1 and U1, U5, and U10, according to Table 3, Section 3.2) were mixed with 20 mL of HCl 2% solution (\(v/v\)) to avoid the oxidation, and the mixture was completed with distillate water to the final volume of 100 mL. After 10 min of reaction, the mixture was filtered using a narrow-pore, dense paper filter with 0.16 mm thickness for slow filtering. Then, the filtrate (\(n=3\)) was mixed with 30 mL of distillate water, 5 mL of KI 1% solution (\(v/v\)), and 1% starch solution (\(v/v\)), homogenized, and then titrated with 0.002 mol/L KIO\({}_{3}\), previously standardized, until the mixture became dark blue and the color persisted for more than 60 s. All of the solutions were prepared and standardized daily. Each mL of 0.002 mol/L KIO\({}_{3}\) is equivalent to 0.8806 mg of L-ascorbic acid.  
The vitamin C content was calculated with the Equation (1):  
\[\frac{\text{mg ascorbic acid}}{\text{100 g d.w.}}=\frac{\text{100 t-V-f}_{ \text{d}}}{\text{m}} \tag{1}\]  
where t is the titre of 0.002 mol/L KIO\({}_{3}\) solution reported for the ascorbic acid (0.8806 mg); V is the volume of 0.002 mol/L KIO\({}_{3}\) solution used for the titration, mL; f\({}_{\text{d}}\) is the dilution factor; and m is the mass of the sample, g.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Independent Variables**} & \multicolumn{3}{c}{**Response Variables**} \\ \cline{2-7}
**Sample** & **T** & **t** & **EtOH** & **TPC** & **TFC** & **TAA** \\
**Code** & (\({}^{\circ}\)C)** & **(min)** & **(\%)** & **(mg GAE/g dw)** & **(mg QE/g dw)** & **(mg TE/g dw)** \\ \hline C1 & 50 & 30 & 60 & 26.68 \(\pm\) 0.52 & 1.40 \(\pm\) 0.01 & 28.6 \(\pm\) 1.5 \\ C2 & 40 & 15 & 100 & 2.51 \(\pm\) 0.10 & 0.23 \(\pm\) 0.02 & 5.23 \(\pm\) 0.38 \\ C3 & 50 & 30 & 100 & 1.8
## 2 Mathematical Optimization Model
### Interpretation of the Mathematical Model  
The optimization model (1)-(7) that represents the problem of the optimal siting and sizing STATCOMs in power systems independent of the grid topology (i.e., radial or meshed with multiple generators) is interpreted in the following manner: Equation (1) corresponds to the objective function for the annual operating costs of the power systems incurred with the minimization of the cost of the energy losses and investment. Note that the problem can be solved using a single-objective function, i.e., \(A_{\text{cost}}\), or a multi-objective approach by minimizing \(f_{1}\) and \(f_{2}\) simultaneously. Equations (2) and (3) represent the most complicated constraints of the optimization model, as these are equality constraints related with the active power balance equations in all the nodes of the system for all periods of time. These constraints are complicating, as they are non-affine and non-convex due to the products of the voltages and trigonometric functions. The box-type constraint (4) ensures that the voltage profile performance of the grid fulfills the voltage regulation bounds demanded by regulatory entities (in Colombia, these bounds are typically \(\pm 10\)% for transmission and distribution grids). Restriction (5) determines the maximum size of the STATCOM that can be installed at node \(k\) if the binary variable \(x_{k}\) is activated. The box-type constraint (6) guarantees that the reactive power provided by the STATCOM can be capacitive or inductive between its nominal rates as a function of the grid requirements for each period of time. Finally, the inequality constraint (7) bounds the number of STATCOMs that can be installed inside power system.  
**Remark 3**.: _The mathematical optimization model (1)-(7) exhibits an MINLP structure based on the existence of binary variables with regard to the siting of the STATCOMs in a power system. These are added with the continuous variables associated with active and reactive power generation, voltage magnitudes, and angles, respectively. The nonlinear structure is identified in the presence of sine and cosine functions as well as products of voltage magnitudes in constraints (2) and (3)._  
Based on the complex MINLP structure exhibited by the optimization model (1)-(7), in this paper, the GAMS optimization package is adopted to solve this model, with the main advantage being that the computational effort is minimum (in the order of seconds) with the possibility of being applicable to high- and medium-voltage networks.
## 3 Results and Discussion
### Synthesis of MIP on Terbium Nitrate
#### 3.2.2 Scanning Electron Microscopy  
SEM provides an extensive view of the surface properties of the MIP material, including the size, shape, porosity and density. SEM can be used to analyze the quality of the MIP polymer, such as the damage, cracks in the structure, regularity and smoothness of the surface of the MIP material.  
In addition, SEM analysis can help establish a connection between the features of the MIP surface and its properties, such as adsorption and desorption. This type of analysis can help develop more efficient methods for selectively extracting and separating target molecules from complex mixtures.  
According to the obtained SEM pictures of the MIP (Figure 8) and NIP (Figure 9), it is visually noticeable that the MIP has more pores compared to the NIP. It indicates that the presence of the template (terbium salt) during the synthesis of the MIP leads to the occurrence of cavities (pores) in the structure of the polymer. The size of these pores ranges from 0.779 \(\upmu\)m to 1.874 \(\upmu\)m. This proves once again that the synthesis was successful.  
Figure 7: Schematic representation of the chemical reaction process for MIP production.
## 3 Mathematical Model
\right.\) The mathematical model can be stated as follows: Minimize: \[\sum_{j=1}^{n}T_{j}.\] (1) Restricted to: \[\sum_{b=1}^{r}\left(\alpha_{jb}+\beta_{jb}\right)=1,\ \forall j=1,\ 2,\ldots n\] (2) \[\sum_{b=1}^{r}CF_{jb}\left(\alpha_{jb}+\beta_{jb}\right)=1,\ \forall j=1,\ 2,\ldots n\] (3) \[\alpha_{jb}+\beta_{jb}\leq\beta_{kb}+1-\theta_{jk},\ \forall j=1,\ 2,\ldots n;\ k=1,\ 2, \ldots n;\ b=1,\ 2,\ldots r\] (4) \[\sum_{j=1}^{n}\alpha_{jb}\leq 1,\ \forall b=1,\ 2,\ldots r\] (5) \[\sum_{b=1}^{r}\alpha_{jb}+\sum_{k=1}^{n}\theta_{kj}=1,\ \forall j=1,\ 2,\ldots n\] (6) \[\sum_{k=1}^{n}\theta_{jk}\leq 1,\ \forall j=1,\ 2,\ldots n\] (7) \[C_{k}\geq C_{j}+M_{jk}+\sum_{b=1}^{r}p_{k}\beta_{kb}+G\left(\theta_{jk}-1 \right),\ \forall j=1,\ 2,\ldots n;\ k=1,\ 2,\ldots n\] (8) \[C_{j}\geq p_{j}+A_{j}\sum_{b=1}^{r}\alpha_{jb},\ \forall j=1,\ 2,\ldots n;\] (9) \[C_{j}\leq d_{j}+T_{j},\ \forall j=1,\ 2,\ldots n;\] (10) \[\sum_{b=1}^{r}\left(\alpha_{jb}+\beta_{jb}\right)=1,\ \forall j=1,\ 2,\ldots n\]0 (11)  
Equation (1) represents the objective function, which is to minimize the sum of all tardiness. A job is considered tardy if its completion time is higher than its due date; and in that case, the difference between its completion time and its due date is added to the sum. Equation (2) ensures that each job must be assigned to one machine. Equation (3) checks compatibility, which means that a job can only be scheduled on a compatible machine. Equation (4) enforces a job and its direct successor in the processing sequence to both be produced on the same machine. Equation (5) ensures that, for each machine, there can only be one job scheduled first. Equation (6) states that if a job is not the first to be scheduled on any machines, then it must have a predecessor. Equation (7) says that for each job, there can only be one other scheduled immediately before it or none in case it is the first job. Equation (8) forces the completion time of a certain task to be bigger than its processing time plus the setup and the completion time of its predecessor. Equation (9) computes the completion time of the first job processed by each machine as its processing time plus the setup time. Equation (10) provides the completion time of a job smaller or equal to the sum of its due date and the tardiness. Equation (11) is the non-negativity constraint.
# Thermal Properties of Ultrasound-Extracted Okra Mucilage
## 3 Results and Discussion  
### Yield of the Ultrasound-Assisted Extraction of Okra Polysaccharides (UAEOP)  
The yield (%) values calculated for the extracts obtained using different solid-to-solvent ratios and by applying different extraction times are shown in Table 1.  
The highest yield was found at a solid/liquid ratio of 1:50 (\(p<0.05\)). The yield increased as the okra/distilled water ratio decreased, but time had no significant effect on the yield (\(p>0.05\)). At 1:10 and 1:25 solid/liquid ratios, it can be seen that the yield decreased as time increased (\(p<0.05\)). Because the 1:10 and the 1:25 consistencies are very dense, the ultrasound could not work for long, and the temperatures increased excessively. [PERSON] et al. [4] reported a yield of 6.37% in their study investigating the effects of different temperatures on the yield of ultrasound-assisted extraction of polysaccharides from okra. Similarly, our study suggests that as the okra density increases, ultrasound-induced molecular vibrations may be accelerated, leading to an increase in temperature. According to [PERSON] et al. [19], the yield of asparagus increased over the extraction temperature range of 60 to 80 \({}^{\circ}\)C. On the other hand, unnecessarily prolonging the extraction time at a high temperature (80 \({}^{\circ}\)C) may cause polysaccharide breakdown and, as a result, reduce the yield of asparagus by 2.151 percent. When the bubbles collapsed as a result of the compression's increase in both pressure and temperature, a powerful shock wave was created that cut through the solvent and improved the mass transfer through the entire system. Once the cell walls were breached, an ultrasonic wave passed through them and rinsed out the contents of the cells. In another study, [PERSON] et al. [3] found a yield of 8.6% when testing different extraction techniques for okra polysaccharide extraction. Okra mucilage yields reported in the literature vary. [PERSON] et al. [20] were reported to yield approximately 2.76 wt%. [PERSON] et al. [21] obtained about 0.5%.  
By varying factors such as extraction time, temperature, number of cycles, and solvent/material ratio, many researchers have attempted to determine the most appropriate extraction conditions. The water-based extraction method employed by [PERSON] et al. [22] resulted in a yield of 2.38% for the extraction of mucilage from okra. [PERSON] [23] utilized water exclusively as a solvent for the extraction of okra mucilage, which yielded a remarkably high percentage of 17%.  
The variations in the values obtained could be attributed to several factors, including the region where the fruit is grown, the type of okra used, the portion of the okra plant utilized (i.e., calyx or pulp), the maturity stage of the fruit (fresh/ripe okra), and the physical state of the okra (dried powder or aqueous seed). Additionally, the choice of solvent employed may also have an impact on the percentage yield. When the studies in the literature are examined, it can be seen that they are in parallel with the results we found.