esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Methodology
### Technology Acceptance Model (TAM)  
Technology Acceptance Model (TAM) questionnaire was used as a satisfaction measurement tool to evaluate the effective implementation of the TQIM-H conceptual framework to develop quality innovation projects in healthcare; ease of use, the comparison of the quality and innovation project development in healthcare through the TQIM-H conceptual framework, and the traditional developed innovation project in healthcare without the conceptual framework; and the practicality of the final TQIM-H conceptual framework.  
Healthcare innovators or healthcare members who were related to healthcare innovation project development from the hospital in Southeast Asia participated in this study. They were selected based on their experience in developing healthcare innovations that allowed them to be able to evaluate the framework used in quality innovation development. The participants were familiarized with the developed TQIM-H conceptual framework. Then, they were asked via questionnaire for their opinions on the conceptual framework's ability to develop an effective innovation project. The process of TAM methodology contains four steps:  
Step 1. We provided the TQIM-H framework and related information to participants. Fifty participants were familiarized with the TQIM-H conceptual framework thoroughly.  
Step 2. The participants evaluated the conceptual framework using the TAM questionnaire. TAM questionnaire, which asked for the acceptance and efficacy of the TQIM-H when it was applied to real situations, was answered by the participants after they were familiarized with TQIM-H conceptual framework Step 3. The participants evaluated the TQIM-H efficiency. All the tested constructs, except objective usability, were measured using 5-point Likert-type scales ranging from \"strongly disagree\" to \"strongly agree\".  
Step 4. We analyzed the TAM results. TQIM-H conceptual framework acceptance and utilization to develop a healthcare innovation project were analyzed by descriptive statistics and expressed with means and standard deviation.
# Spectroscopic Ellipsometry and Optical Modelling of Structurally Colored Opaline Thin-Films
## 3 Results  
### Experimental Ellipsometry  
Experimental spectroscopic data showing ellipsometric parameters delta (\(\Delta\)) and psi (\(\Psi\)) are shown in Figure 2. In each case, broad resonant features are seen around the structural colour peak at \(\lambda\approx 460\) nm (\(\theta_{I}=50^{\circ}\)). The sharpness and intensity of these features, as inferred from a Lorentzian FWHM fitting of \(\Psi(\lambda)\), increases markedly as the structural order is improved by BIOS processing (Table 1); and the effect of sequential ordering up to 40 BIOS shear-cycles is evident.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \(\Psi\) & & \(\Delta\) & \\ \hline \multirow{2}{*}{**Sample**} & **Experimental** & **Simulation** & **Experimental** & **Simulation** & **Disorder (\(\delta\))** \\  & **FWHM (nm)** & **FWHM (nm)** & **FWHM (eV)** & **FWHM (eV)** & **Factor for Best Fit** \\ \hline
0-BIOS & 48.5 (\(\pm 1.7\)) & 47.9 (\(\pm 0.6\)) & 0.298 (\(\pm 0.034\)) & 0.351 (\(\pm 0.004\)) & 19 \\
5-BIOS & 28.8 (\(\pm 1.6\)) & 28.3 (\(\pm 0.3\)) & 0.166 (\(\pm 0.017\)) & 0.124 (\(\pm 0.002\)) & 10 \\
20-BIOS & 19.6 (\(\pm 1.5\)) & 18.7 (\(\pm 0.2\)) & 0.066 (\(\pm 0.005\)) & 0.086 (\(\pm 0.003\)) & 4 \\
40-BIOS & 22.6 (\(\pm 3.5\)) & 20.2 (\(\pm 0.1\)) & 0.077 (\(\pm 0.011\)) & 0.097 (\(\pm 0.003\)) & 5 \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of experimental and simulation outputs relating to the \(\Psi(\lambda)\) and \(\Delta(\lambda)\) ellipsometric parameters for the range of samples studied, with one-sigma confidence intervals. The corresponding nearest matching Gaussian disorder factor is listed in each case.  
A notable artifactual feature in these spectra is the periodic modulation phenomena at the long wavelength end (near-IR). The wavelength spacing of these fringes (of order 30 nm) is indicative of thin-film interference from structures of thickness order tens of microns, and amplitudes apparently become more pronounced as the BIOS processing of samples is increased. Additionally, all samples show absorptive behaviors at the short wavelength end, well above the structural color feature.
## 5 Optimization Control Strategies in Bems
### Uses of Optimization Algorithms in BEMS
#### 5.2.4 Genetic Algorithm (GA)  
GA is an iterative optimization technique inspired by live creatures' genetic processes. New genes are created that inherit the characteristics of their parents [153]. Chromosome representation and algorithmic flows are the two main components of a GA. An algorithmic flow is an iterative technique for generating and evolutionarily selecting chromosomes to obtain high-quality solutions, while a chromosomal representation is a scheme for modeling a solution [154]. GA was used to optimize the scheduling of ESS and plug-in electric vehicle (PEV) operations in a home energy management strategy in order to reduce daily electrical energy expenditure for the user [155]. Residential load management solutions may necessitate appliance scheduling to achieve specific goals such as load factor reduction, a PAR ratio reduction, or energy cost reduction. This problem is solved using GA [156].
# IoT-Enabled Vehicle Speed Monitoring System
###### Abstract  
Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed of all vehicles on roads at all spatial points. They can only detect the speed of the vehicle the line of sight of the camera. A solution is to deploy a huge number of speed guns at different locations on the road to detect and report vehicles that are over speeding. However, this solution is not feasible because it demands a large amount of equipment and computational resources to process such a big amount of data. In this paper, a speed detection framework is developed to detect vehicles' speeds with only two speed guns, which can report speed even when the vehicle is not within the camera's line of sight. The system is specifically designed for an irregular traffic scenario such as that of Pakistan, where it is inconvenient to install conventional systems. The idea is to calculate the average speed of vehicles traveling in a specific region, for instance, between two spatial points. A low-cost Raspberry Pi (RPi) module and an ordinary camera are deployed to detect the registration numbers on vehicle license plates. This hardware presents a more stable system since it is powered by a low consumption Raspberry Pi that can operate for hours without crashing or malfunctioning. More specifically, the entrance and exit locations and the time taken to get from one point to another are recorded. An automatic alert to traffic authorities is generated when a driver is over speeding. A detailed explanation of the hardware prototype and the algorithms is given, along with the setup configurations of the hardware prototype, the website, and the mobile device applications.  
Internet of Things (IoT); artificial intelligence; Raspberry Pi (RPi) module
###### Abstract  
Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.  
Keywords:hesitant fuzzy entropy; heterogeneous clustering; wireless sensor networks; opportunistic routing; multi-attribute decision modeling; data fusion +
Footnote †: journal:  
_Article_
# An Embedding-Based Approach to Repairing OWL Ontologies
## 5 Experiments
### Results of Comparing Different Models
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Ontology**} & \multicolumn{4}{c}{**ThrCosAlg**} & \multicolumn{4}{c}{**ThrEosAlg**} \\  & **m1** & **m2** & **m3** & **m4** & **m1** & **m2** & **m3** & **m4** \\ \hline ALOD2 Vec-confof-edas & 5 & 3 & 3 & 1 & 1 & 2 & 3 & 3 \\ ATRON-cntof & 2 & 2 & 1 & 2 & 1 & 2 & 2 & 2 \\ CHEM-A & 3 & 3 & 4 & 3 & 1 & 3 & 2 & 1 \\ dbpeda\_2014 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ Economy & 36 & 8 & 43 & 8 & 20 & 22 & 11 & 14 \\ Geography & 11 & 11 & 15 & 11 & 13 & 14 & 15 & 15 \\ km1500-3500 & 38 & 23 & 29 & 23 & 30 & 32 & 33 & 27 \\ koala & 3 & 1 & 3 & 1 & 3 & 2 & 2 & 1 \\ Lily-cnn-conference & 2 & 1 & 2 & 1 & 2 & 1 & 4 & 1 \\ Lily-cds-ekaw & 8 & 9 & 8 & 9 & 7 & 5 & 5 & 5 \\ MGED & 7 & 5 & 8 & 5 & 7 & 20 & 6 & 10 \\ miniTambis & 3 & 3 & 3 & 3 & 3 & 3 & 3 \\ pizza & 3 & 3 & 3 & 3 & 3 & 3 & 2 & 2 \\ proton & 12 & 12 & 13 & 13 & 11 & 9 & 9 & 9 \\ Terroirism & 1 & 1 & 1 & 1 & 4 & 5 & 5 & 4 \\ Transportation & 19 & 26 & 21 & 19 & 20 & 17 & 14 & 16 \\ University & 3 & 3 & 4 & 3 & 4 & 4 & 4 & 4 \\ VeAlign-els-laset & 2 & 3 & 9 & 4 & 3 & 2 & 2 & 3 \\ Wiktionary-cnt-confof & 4 & 5 & 5 & 6 & 8 & 7 & 8 & 7 \\ Wiktionary-confof-edas & 5 & 6 & 4 & 1 & 1 & 4 & 4 & 4 \\ \hline Total & 168 & 129 & 180 & 118 & 143 & 158 & 135 & 132 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Number of axioms removed by two threshold-based algorithms using different models. Figure 5: Time in milliseconds (y-axis) for each incoherent ontology to compute a diagnosis by the threshold-based algorithm with different models and distance metrics while keeping the same threshold of 0.5.
###### Abstract  
The crop water stress index (CWSI) is one of the parameters measured in deficit irrigation and it is obtained from crop canopy temperature. However, image segmentation is required for non-leaf region exclusion in temperature measurement, as it is critical to obtain the temperature values for the calculation of the CWSI. To this end, two image-segmentation models based on support vector machine (SVM) and deep learning have been studied in this article. The models have been trained with different parameters (encoder depth, optimizer, learning rate, weight decay, validation frequency and validation patience), and several indicators (accuracy, precision, recall and F1 score/dice coefficient), as well as prediction, training and data preparation times are discussed. The results of the F1 score indicator are 83.11% for SVM and 86.27% for deep-learning models. More accurate results are expected for the deep-learning model by increasing the dataset, whereas the SVM model is worthwhile in terms of reduced data preparation times.  
deficit irrigation; CWSI; thermography; image segmentation; clustering; SVM; deep learning; model training Article
# New Treatment Addressing the Pathogenesis of Psoriasis
## 3 Treatment
### Sphingosine-1-Phosphate (S1P) Agonist  
Sphingosine is created from ceramide, which is composed of membrane lipids, through ceramidase [94]. S1P is created from sphingosine through sphingosine kinase [94]. S1P is a lipid mediator and associated with cellular proliferation, survival, migration, inflammation, immune cell trafficking, angiogenesis, vascular integrity, and adhesion in the immune and vascular systems [95]. S1P acts on five specific G protein-coupled receptors named S1P receptor (S1 PR)1-5 [95].  
S1 PR1 is expressed on lymphocytes and controls their egress from thymus and secondary lymphoid organs [96, 97]. S1 PR1 modulators induce internalization of this receptor and the majority of circulating lymphocytes are sequestered in lymph nodes, decreasing peripheral lymphocyte counts and trafficking of lymphocytes to peripheral tissues (Figure 2) [98, 99]. Furthermore, it has been reported that S1P inhibits the growth of epidermal cells, induces differentiation of keratinocytes, and shows antiproliferative and anti-inflammatory effects in mouse models of psoriasis [100, 101, 102, 103, 104].  
Ponesimod (ACT-128800) is an orally selective S1 PR1 agonist, which blocks the egress of T cells from lymphoid organs [105, 106]. Ponesimod is excreted within 1 week after discontinuation, and this rapid elimination is beneficial in cases including vaccinations and pregnancy [107, 108].  
In the phase 2 clinical trial, the PASI75 response to ponesimod by week 16 was 46.0% (20 mg) and 48.1% (40 mg), as compared to 13.4% for the placebo. By week 28, PASI75 was achieved in 71.4% (20 mg) and 77.4% (40 mg), respectively [108]. During the trial, dyspnea, increased liver enzymeconcentrations, headache, nasopharyngealrigitis, bradycardia, pruritus, and dizziness were observed [108]. However, the phase 3 clinical trial of ponesimod in psoriasis has not been started yet, possibly because severe side effects such as lymphopenia, bradycardia, and dyspnea were observed in other S1 PR1 modulators [108; 109].
## 3 Results and Discussion
### Reusability of SA/PVP/TiO\({}_{2}\) Nanocomposite  
Five consecutive experimental runs were performed under optimal conditions using the same set of beads to evaluate the reusability of SA/PVP/TiO\({}_{2}\) nanocomposites as indicated in Figure 8, which permits the process to be considered a cost-effective degradation process for MB. The SA/PVP/TiO\({}_{2}\) nanocomposite beads were recovered and used five times by washing with 0.1 M HCl solution. The obtained data reveal that the MB decay efficiency remained practically unchanged as the cycle number increased. This result may be due to the stability of TiO\({}_{2}\) nanotubes in the SA/PVP polymer matrix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Nanocomposite Material**} & \multicolumn{2}{c}{**Pseudo-First Order**} & \multicolumn{2}{c}{**Pseudo-Second Order**} \\ \cline{2-5}  & \(q_{e}\) & \(K_{1}\) & \(R^{2}\) & \(K_{2}\) & \(R^{2}\) \\  & \(\mathbf{mg\ g^{-1}}\) & \(\mathbf{s^{-1}}\) & & \(\mathbf{g\ m\ g^{-1}\ s^{-1}}\) & \(\mathbf{R^{2}}\) \\ \hline SA/PVP/TiO\({}_{2}\)-1 in dark & 71.4 \(\pm\) 0.2 & 0.051 \(\pm\) 0.001 & 0.96 & 0.0004 \(\pm\) 10\({}^{-5}\) & 0.91 \\ SA/PVP/TiO\({}_{2}\)-3 in dark & 73.6 \(\pm\) 0.1 & 0.059 \(\pm\) 0.001 & 0.93 & 0.0005 \(\pm\) 10\({}^{-5}\) & 0.98 \\ SA/PVP/TiO\({}_{2}\)-1 in light & 91.9 \(\pm\) 0.3 & 0.036 \(\pm\) 0.001 & 0.96 & 0.0003 \(\pm\) 10\({}^{-5}\) & 0.99 \\ SA/PVP/TiO\({}_{2}\)-3 in light & 98.3 \(\pm\) 0.1 & 0.038 \(\pm\) 0.001 & 0.98 & 0.0004 \(\pm\) 10\({}^{-5}\) & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Kinetic parameters determined for the pseudo-first order and pseudo-second order models.  
Figure 8: Reusability of SA/PVP/TiO\({}_{2}\) nanocomposites for the removal of MB during 5 consecutive cycles (at 120 min; pH 7; 50 mg L\({}^{-1}\) initial MB concentration).
## 2 Methodology
For the models corresponding to a collapsed building in one direction, the corresponding \(w_{d}\) can be evaluated as follows:  
\[w_{d,1}=w_{d,max}=a\cdot(\varepsilon-1) \tag{4}\]  
\[w_{d,2}=0 \tag{5}\]  
where \(w_{d,2}\) describes the case in which the debris distribution, which does not affect the analyzed road network segment. For the collapsed building in two directions, the relevant \(w_{d}\) can be calculated as follows:  
\[w_{d,3}=\frac{w_{d,max}}{2}=\frac{a\cdot(\varepsilon-1)}{2} \tag{6}\]  
Based on observations from past earthquakes, the main debris distribution of RC buildings is due to the collapse of nonstructural elements. For these buildings (Figure 3b), it is therefore assumed that the maximum debris area is reached in the life safety limit state (SLS), beyond which the debris area remains constant, while debris volume increases up to the maximum \(DF\) value. This assumption is justified by the fact that, according to NTC 2018, in meeting the SLS, the failure of nonstructural components is expected. In the transition from OLS to SLS, it is assumed that the debris area grows linearly with increasing \(IM\). The infill panels may not collapse on the analyzed road network segment, or they may collapse with complete or partial overturning. To take into consideration the randomness of these collapse mechanisms, in this case it is also possible to assign different graphs of the debris widths. In order to calculate the debris widths expected in SLS, the following expressions are suggested:  
\[w_{d,A}=\Delta h \tag{7}\]  
\[w_{d,5}=0 \tag{8}\]  
\[w_{d,6}=\frac{\Delta h}{2} \tag{9}\]where \(\Delta h\) is the average height of the infill panels or approximately the average floor height of the structure. Equations (7) and (9) correspond to collapse mechanisms with complete or partial overturning, respectively, while Equation (8) considers the case in which the overturning of the infill panels does not affect the analyzed road network segment. For example, this case can also be found in pancake collapses in which there is a minimal debris area. For each assigned vulnerability function, three graphs of debris width should be constructed in order to take into account the possible debris distributions around the damaged building. For each debris distribution, the graph is built based on the debris widths and _IM_ values in which the considered building performance levels are reached. These performance levels can be identified with the _DF_ threshold values shown in Table 1 on the _DF_ values axis of the vulnerability function. According to the assumptions of the proposed methodology, for each _IM_ in which _DF_\(\leq\) 7%, the debris width can be assumed equal to zero (\(w_{d}\) = 0), while for each _IM_ in which _DF_\(\geq\) 80% (for a masonry building) or _DF_\(\geq\) 50% (for an RC building), the debris width can be defined using Equations (4)-(6) for the masonry building, or Equations (7)-(9) for the RC building. Based on these boundary conditions, a reasonable assumption can be made that for the other _IM_ values, the debris width varies with a linear law, and the generic graph of debris width can be defined.
# Research on the Jiamusi Area's Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model
## 2 Overview of the Study Area  
Jiamusi City is located in the hinterland of the Sanjiang Plain, which is formed by the confluence of the Songhua River, Heilongjiang River, and Ussuri River at the northeast border of China [14]. It faces Heilongjiang in the northeast, Russia in the east, and Raohe County and Fuyuan City in the east. Yilan and Boli Counties border the southwest border. It is adjacent to Qitaihe City and Shuangyashan City. Yichun City is connected with the northwest border. The city of Hegang is to the north [15]. The area is 388 km long from east to west and 150 km wide from north to south, both long and narrow. There are more than 130 large and small rivers in the area, and the main water systems are the Heilongjiang, Songhua, and Wusuli Rivers. These rivers are the Songhua River, the Black Dragon River, and the first and second tributaries of the Wusuli River, distributed in the county (city) of Jiamusi City. Its terrain slopes roughly from southwest to northeast, with the highest point in the southwest and the lowest point in the northeast. This area is mainly composed of three different types of landforms: accumulation plain, erosion accumulation front, erosion denudation low hills and hills [16]. Due to its complex geological structure, many factors affect the groundwater level, mainly the lateral replenishment of atmospheric precipitation and the Tongjiang-Fuyuan section of the Heilongjiang and the Jiamusi-Tongjiang section of the Songhua River. The average annual precipitation in this area is 540 mm, and atmospheric precipitation is the main source of groundwater recharge. The distribution of precipitation is uneven within the year, and the precipitation in July, August, and September accounts for about 59.5% of the annual precipitation, which is the main recharge period for groundwater. In addition, the groundwater extraction in Jiamusi area is large. According to the introduction of [PERSON], director of the Jiamusi Water Bureau, the well irrigation area of the Jiamusi area reaches 5 million mu, accounting for more than 73.5% of the actual irrigation area of the whole area. According to the average annual water consumption of 750 cubic meters per mu, the annual demand for groundwater resources in the well irrigation area is 3.75 billion cubic meters, while the average annual groundwater resources are 3.307 billion cubic meters, and the groundwater over-extraction is more than 443 million cubic meters. A summary is shown in Figures 1 and 2. Two basins were divided using the SWAT model, which served as the basis for subsequent simulation studies.
## 4 Simulation of the Proposed Control  
To verify the effect of the proposed control strategy, an off-shore DFIG wind farm connected to the grid via an MMC HVDC system was studied. In Figure 6, the overall simulation system is depicted. For an on-shore system, the on-shore grid was expressed as an aggregated voltage source over equivalent reactance. The MMC HVDC connected the on-shore system to the off-shore wind farm. Aggregated DFIG consisted of the off-shore wind farm and RSC, and a GSC average model was used. In the proposed control strategy, a phase-shifted pulse-width modulation (PSPWM)-based MMC HVDC was used. For the DFIG system, only an outer current controller was used; an inner voltage controller was assumed to operate ideally; and an average model-based DFIG turbine was used based on a PSCAD/EMTDC program. The parameters used in simulation is described in Tables 1 and 2.  
Figure 5: Off-shore DFIG wind farm with the MMC HVDC controller scheme.  
Figure 6: Studied system model of the off-shore wind farm.