esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Preference Strategy
### Preference Selection Algorithm  
According to the results of the above cluster analysis, we can choose four preference strategies: information transmission, network control, security protection and comprehensive scores. According to the comprehensive scores, the best performances are observed in units 1, 9, 19 and 11. The comprehensive score of unit 19 ranks third, with the highest score of factor 1, but the scores of factor 2 and factor 3 are very low, which means that the information transmission ability of unit 19 is very strong while the network control ability and security protection ability are poor. Therefore, the optimization of network design scheme should be based on a certain preference and refer to other factors.  
The preference selection algorithm proposed in this paper is shown as follows.  
1. According to the preference strategy (information transmission, network control, security protection and integration), perform cluster analysis on the evaluation results and obtain n-level data set (the number of grades can be specified by users based on the cluster analysis results);
2. Select the first two levels of the corresponding cluster analysis as initial ranking;
3. Delete schemes at the lower 2 levels among other preferences;
4. Output the remaining schemes in order as the result.  
Based on the above algorithm, when the preference of comprehensive scores is selected, the optimal results are 1, 9, 11, 10 and 22. When the preference of information transmission  
Figure 5: Cluster analysis of security protection factors.  
Figure 6: Cluster analysis of comprehensive score.  
is selected, the optimal results are 9, 1 and 11. When preference of network control is selected, the optimal results are 1, 11, 9, 10 and 22. When preference of security protection is selected, the optimal results are 1, 9 and 15. Compared with the traditional factor analysis comprehensive score method, the preference selection algorithm proposed in this paper can avoid other low scores except when considering the factors of user preference or comprehensive scores, rendering the output optimization result more reasonable and feasible.
## 2 Literature Review
### Line-Fitting-Based Method  
The following studies utilized the line-fitting-based method to extract structural element lines. The RANSAC-based method is a model-fitting method that can perform well in the presence of outliers [24]. It is particularly suitable for fitting 2D lines or planes of structural elements from a point cloud. [PERSON] et al. [10] created various horizontal slices based on the floor and ceiling to create as-built BIMs. The horizontal slice with the fewest indoor clutter objects was selected for RANSAC application. The 2D line derived from the structural elements was used in the as-built BIM. [PERSON] et al. [15] selected the horizontal slice intended for RANSAC applications 0.3 m below the ceiling. [PERSON] and [PERSON] [14] determined the z-coordinate of the floor and the ceiling to determine indoor height. The horizontal slice for RANSAC application was then extracted from the z-coordinate, which was estimated based on the multiplication of the threshold and the indoor height.  
The RANSAC-based method is directly applicable to both horizontal slices and point clouds. When applied to point clouds, the RANSAC-based method includes fitting the plane of walls or columns as general structural elements. [PERSON] et al. [9] applied RANSAC to a point cloud to fit the plane of structural elements, and the location of the plane after fitting was used in as-built BIM modeling. [PERSON] et al. [16] used RANSAC to detect wall candidates from a point cloud. The detected wall candidate was used in the line segment, and a 2D floor plan was created.  
Several studies applied other line-fitting methods to obtain more accurate lines of structural elements. [PERSON] and [PERSON] [25] applied a voxelization-based method to obtain structural element lines. They utilized the horizontal slice between the floor and ceiling, which is the most clutter-free slice. [PERSON] and [PERSON] [26] adopted morphological operations on the x-y plane of a point cloud to remove indoor noise. [PERSON] et al. [27] developed a Modified Ring-Stepping Clustering (M-RSC) method to extract structural element lines in complex indoor environments. However, their method involved a manual task to remove indoor clutter object data. [PERSON] et al. [28] applied the Maximum Likelihood Estimation SAMple Consensus (MLESAC) method to extract structural element lines. After that, the indoor clutter objects were removed from the structural element point that was obtained from the structural element line.  
The line-fitting-based method can generate a point cloud of structural elements and 2D floor plans, considering indoor clutter objects in the point cloud. Notably, the RANSAC-based approach has been robustly employed to extract lines or planes of structural elements. However, it struggles to accurately represent the thickness of inner walls or columns due to the challenges in optimizing the right parameters. Furthermore, line-fitting-based methods are required to select an appropriate horizontal slice. However, it is difficult to define the appropriate horizontal slice that is least affected by the indoor clutter object data.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 3 Design of Support Structure for Space Mirror Component  
### Design of Support Structure  
In this study, the secondary mirror of a system is required to withstand the severe mechanical environment and a wide range of temperature change environments, which requires the mirror component not only to have a high degree of rigidity at the same time, but also needs to have a relatively flexible component support structure to offload the temperature change [13]. First, a back three-point support structure is selected, and a new biaxial circular arc flexible hinge support structure is introduced on the basis of ensuring the stiffness of each component.  
The mirror component consists of the mirror blank, nesting, flexible hinges, support seat, and base plate as shown in Figure 5. First, the material of the mirror blank is determined to be ultra-low-expansion glass. Considering the matching of the thermal properties of the material, the inlays connecting the support holes at the back of the mirror and the support structure are selected to be invar steel (4J32) with a lower coefficient of linear expansion, to reduce the effect of the temperature-varying environment on the mirror surface. The nesting, flexible hinges, and support seat are screwed to each other. High specific stiffness, high strength, low density, and mature processing of Titanium alloy (TC4R) is selected for the flexible hinges and supports, while the base plate is made of carbon fiber composite material with high specific stiffness and a low coefficient of linear expansion. The material properties are shown in Table 3[14].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Shape of Stiffener** & **Mass** & **Mirror Frequency** \\ \hline triangular stiffener structure & 13.2 kg & 1267.5 Hz \\ \hline square stiffener structure & 13.5 kg & 1225.6 Hz \\ \hline hexagonal stiffener structure & 13.7 kg & 1239.8 Hz \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparative analysis of lightweight structure of sandwich.  
Figure 4: Honeycomb grid structure form.
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 4 Experiments
### Result  
Table 4 shows the accuracy of various models, including the previously proposed semantic similarity evaluation models. Acc shows the average accuracy of 10 experiments using Manhattan distance. Here, English Acc means the accuracy using the English dataset, and Korean Acc means accuracy using the Korean dataset.  
In Table 4, No. 1 to No. 4 are models using only global features. No. 1 to No. 3 are models proposed in the previous studies [2; 3; 4], and No. 4 and No. 5 are models using simple Bi-LSTM and CNN, respectively. These models are arbitrarily built to compare the accuracy of the RNN series model and the CNN series model. RNN shows higher accuracy than CNN in this task. No. 6 to No. 11 are models using only local features, i.e., the capsule network. No. 6 and No. 7 are experiments to verify the effect of dynamic routing when using a capsule network. In both English and Korean, it can be seen that dynamic routing causes performance degradation. No. 8 to No. 11 are models applying the capsule network to RNN-related models, but they do not use the last hidden state of LSTM. No. 10 and  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Model** & **English Acc** & **Korean Acc** \\ \hline
1 & LSTM [2] & 80.99\% & 87.25\% \\
2 & Convolutional neural networks (CNN) + LSTM [3] & 80.44\% & 83.76\% \\
3 & Group CNN + bidirectional gated recurrent unit (Bi-GRU) [4] & 82.18\% & 89.52\% \\ \hline
4 & Bi-LSTM & 82.9\% & 90.28\% \\
5 & CNN + Max-Pooling & 80.58\% & 88.26\% \\ \hline
6 & Capsule Network & 81.57\% & 88.85\% \\
7 & Capsule Network + Dynamic Routing & 78.82\% & 86.48\% \\ \hline
8 & LSTM + Capsule Network & 82.07\% & 90.25\% \\
9 & Bi-LSTM + Capsule Network & 82.15\% & 90.56\% \\
10 & LSTM + Self-Attention + Capsule Network & 82.78\% & 90.61\% \\
11 & Bi-LSTM + Self-Attention + Capsule Network & 82.9\% & 91.41\% \\ \hline
12 & Proposed Model & 83.51\% & 92.08\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Model accuracy comparison according to experiments.  
Figure 2: (**a**) English dataset accuracy according to \(\alpha\); (**b**) Korean dataset accuracy according to \(\alpha\).  
No. 11 are experiments in which self-attention is added to No. 8 and No. 9, respectively. Finally, No. 12 is the proposed model using both global and local features.
## 4 Discussion
### Limitations and Future Outlook  
ENBs with preconditioning sonication showed a similar degree of neuroprotection as EPO over 24 h with respect to neuronal cell viability, EPO receptor activation, and proapoptotic and apoptotic pathway deactivation. However, regarding the activation/deactivation of the EPO receptor and proapoptotic and apoptotic pathways, neither TG + EPO nor TG + ENB pretreatment were compared to TG-only treatment (the negative control) in terms of the relative neuroprotective potency; thus, it is not certain how the neuroprotective effects of ENBs were substantially different after TG treatment. Moreover, although the ENBs activated the EPO receptor and deactivated the proapoptotic and apoptotic pathways to a lesser extent than EPO at 12 h and to nearly the same degree as EPO at 24 h, the neuroprotective actions of ENBs (the controlled release of EPO from ENBs by preconditioning sonication [50-60 kHz] for 1 h was most effective until 24 h after treatment), which have a therapeutic time window different from that of EPO in vitro/in vivo (6-8 h after insult) [5, 6, 7], raise concerns over their sufficiency to meet the clinical requirements. However, considering that a single systemic circulation cycle usually takes no more than 5 min, the achievement of enhanced and fast EPO release from the ENBs by adjustment of the preconditioning sonication conditions (for instance, a much longer duration or a lower frequency than the parameters in the current study) may ameliorate the therapeutic time window discrepancy (24 h versus 6 to 8 h), which would provide the current ENBs with approximately 96 chances for delivery to the injured CNS area over 8 h. However, if the breakdown of ENBs starts too early, before the ENBs arrive at the injured CNS area, the exposed MNPs could be engulfed by the mononuclear phagocytic system [64; 65], the MNP-mediated targeted delivery capabilities, such as magnetic navigation [66], self-propulsion [67], and propulsion by tagged bacteria [68], may be weakened or lost. As alternatives to preserve the targeted delivery capabilities, the breakdown of ENBs may be postponed with the aid of in situ strategies, such as on-demand sonication [20], thermal conversion (via inverse piezoelectricity) [69], or the application of a high-frequency alternating magnetic field [70] after ENB arrival at the injured CNS area.
# Inactivation of Thioglucidase from _Sinapis alba_ (White Mustard) Seed by Metal Salts
## 3 Discussion
Thus, the degradation of glucosinolates can take place catalytically through the formation of a complex structure [26]; however, the full confirmation of this hypothesis still requires further research. Measurements of myrosinase activity are usually done by utilizing isolated enzyme and water environment however to establish the best blocking agent for myrosinase it is needed to study this phenomenon in cellular conditions [28]. But-3-enenenitrile and 3-isothiocyanatoprop-1-ene are formed from synigrin. 2-phenylacetonitrile and isothiyanatomethylbenzene are obtained from glucotropaeolin. These two glucosinolates were artificially added to the vegetable. Additionally, kohlrabi contains native glucosinolates (glucoerucin, glucoiberverin, glucoraphanin [29]) which form the corresponding volatile products (4-methylsulfonylbutanenitrile, 1-isothiocyanato-3 methylsulfanylpropane, 1-isothiocyanato-4-methylsulfanylbutane). 5-methylsulfanyl pentanenitrile derived from glucoberteroin. In kohlrabi bulb tissue several volatiles were observed that are related to the action of lipoxygenase (e.g., hexanal, (E)-hex-2-enal, pent-1-en-3-ol, (E)-hex-2-enal, 2-pentylfuran, heptan-2-one, (Z)-hex-3-en-1-ol, (E)-non-2-enal) [30], therefore, we can additionally evaluate the effectiveness of the metal salts used to block this enzyme. According to the preliminary experiment, the strongest inhibition effect of thioglucosidase inactivation in the kohlrabi matrix has a silver and iron(III). However, the iron(III) salt causes an important increase in some volatile compounds derived from lipid oxidation, primarily hexanal, 2-pentylfuran and heptan-2-one, which may distort the results of the analyzes (profile of volatiles). Enzyme inactivation can be caused by changing the conformation of the enzyme protein and destroying the key and lock enzyme system. Likewise, in the kohlrabi matrix environment of copper and iron(II) salts, analogous products are formed as in the model system. The change of the pathway of glucosinolates degradation by these salts is most likely caused by the catalytic decomposition of an intermediate complex compound.
## 2 Experimental Overview
### Experimental Scheme  
In the previous experimental phase, this study utilized phosphogypsum to enhance the stability of lime-ash stabilized red clay. Samples were prepared with a lime-to-fly ash ratio of L:F = 1:2, along with lime-to-phosphogypsum ratios ranging from L:P = 1:0, 1:0.5, 1:1, 1:2, and 1:3. Compaction tests and UCS tests with a 7-day curing period were conducted, and the results are depicted in Figures 2 and 3, respectively.  
Figure 3 clearly illustrates that with an increase in the phosphogypsum content, the UCS of the lime-ash-stabilized red clay initially rises, then declines, and eventually levels off. According to the 'Technical Guidelines for Construction of Highway Roadbases' (JTG/T F20-2015) [18], the 7-day UCS of lime-fly ash stabilized materials in highway subbases should be greater than or equal to 0.5 MPa. Based on the test results, the 7-day UCS of the lime-ash stabilized red clay with an 8% lime content meets this requirement. In the saturated condition test after 7 days of curing, it was observed that the samples with 8% lime content had fewer attached air bubbles on the surface, no particle detachment, and exhibited superior overall integrity.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Natural Moisture** & **Optimal Moisture** & **Maximum Dry** & **Liquid** & **Plastic Limit/\%** & **Plasticity** & **CBR/\%** \\
**Content/\%** & **Content/\%** & **Density/(g:cm\({}^{-3}\))** & **Limit/\%** & **Limit/\%** & **Index** & **CBR/\%** \\ \hline
29.78 & 26.7 & 1.518 & 66.87 & 33.96 & 32.91 & 8.3 \\ \hline \end{tabular}
\end{table}
Table 1: Basic physical indicators of red clay.  
Figure 1: Particle size grading curve of red clay.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**CaO** & **P\({}_{2}\)O\({}_{5}\)** & **SO\({}_{3}\)** & **MgO** & **Na\({}_{2}\)O** & **pH** & **Radioactivity** \\ \hline
38.74\% & 0.57\% & 52.46\% & 0.18\% & 0.31\% & 3.6 & None \\ \hline \end{tabular}
\end{table}
Table 3: Main chemical composition and basic physical indexes of phosphogypsum.  
In summary, this study utilized an 8% lime content, 16% fly ash content, and lime-to-phosphopysum ratios of 1:0, 1:0.5, 1:1, 1:2, and 1:3 to prepare samples. Road performance tests and swelling characteristics tests for phosphopysum-modified lime-ash stabilized red clay (referred to hereafter as 'lime-ash stabilized soil') were conducted following the 'Test Methods of Materials Stabilized with InorganicBinders for Highway Engineering' (JTG E51-2009) [19]. Finally, scanning electron microscopy (SEM) tests and X-ray diffraction (XRD) tests were employed for microstructural and quantitative analyses of the optimal lime-ash stabilized soil content, unveiling the micro mechanisms of phosphopysum-modified lime-ash stabilized red clay.
## 3 Model and Calculation Method
### Calculation Method  
The power flow calculation method of the track structure refers to the work in [24; 25; 26]. Based on the vehicle-track dynamic coupling model, the node velocity and spring force in the time domain are obtained, and through the Fourier transform, the node velocity and spring force in the frequency domain are obtained. According to Equation (7), the power flow can be obtained as follows:  
\[P_{i}(k)=\frac{1}{2}Re[F_{i}(k)V_{i}^{*}(k)] \tag{7}\]  
where \(F_{i}\) denotes the complex force of the \(i\)th node, \(Vi\) denotes the complex velocity of the \(i\)th node in the frequency domain, \(V_{i}^{*}\) denotes the conjugation of \(V_{i}\), \(k\) denotes the sample frequency point, and \(Re\) represents the real part of the power flow value. It is necessary to pay attention to the calculation of the power flow; the node velocity and the spring force should correspond exactly. In order to investigate the influence of the composite steeper-ballasted track under the wheel-rail interaction, it is necessary to choose a certain area. This paper selects the area of a sleeper for calculation. A rail and ballast bed connected by a single sleeper are used as the calculation area. The rail power flow is the total power flow of the rail nodes connected to the fasteners, and the sleeper input power flow is the total power flow of the sleeper nodes connected to the fasteners; similarly, the sleeper output power flow is the total power flow of the sleeper nodes connected to the ballast bed, and the ballast bed power flow is the sum of the ballast bed nodes. The power flow transmission of the track structure is shown in Figure 6, and the total power flow of each structural layer is calculated as follows:  
\[P(k)=\frac{1}{2}\sum_{i=1}^{n}Re[F_{i}(k)V_{i}^{*}(k)] \tag{8}\]  
where \(n\) is the total number of nodes and \(i\) is the sequence number corresponding to the node.  
In order to make the comparison obvious, the relative power flow should be calculated. The calculation formula for the relative power flow is as follows:  
\[P_{r}(k)=10\log\left[\frac{P(k)}{P_{0}}\right] \tag{9}\]  
where \(P(k)\) is the total power flow and \(P_{0}\) is the reference value, which is equal 1.0 \(\times\) 10\({}^{-12}\) N\(\cdot\)m/s.  
Figure 6: The transfer of the power flow.
## 6 Results of Uncertainty Simulations for Multiple Cases of Use of Radiation Thermometry
Based on the spectral range of detectors, the uncertainty contribution due to emissivity was calculated and compared to both Monte Carlo (MC) simulation results and analytically (A) calculated results in Figure 19.  
The mismatch in results of both Monte Carlo and analytical simulation compared to the Technical Guide 22 is plotted in Figure 20 and is within 0.073 \({}^{\circ}\)C in the range between \(-\)30 \({}^{\circ}\)C and 500 \({}^{\circ}\)C. While the Monte Carlo simulation results exhibit a mismatch mostly due to statistical noise, there is also an evident discrepancy between the two sensor types in the same spectral range (TRT LOW and FLIR VOx), behaving differently due to different spectral sensitivities within the same spectral range. The TG22 calculation method does not differ between these two detectors, as it only accounts for the spectral range and not for the spectral sensitivity of the instrument, within that range. Table 11 presents a comparison of simulated measurement uncertainty contributions due to the influence of emissivity uncertainty with those calculated using TG22 [25]. Figure 19: Monte Carlo (MC) and analytically (A) simulated standard uncertainty contribution for influence of emissivity on various radiation thermometers, compared with TG22 [25]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Measured** & **Emissivity** & **Simulated** & **TG22** & **Simulation** \\
**Temperature** & **Deviation** & **Deviation** & **Deviation** & **Mismatch** \\ \hline
250 \({}^{\circ}\)C, 8–14 \(\upmu\)m & \begin{tabular}{c} 0.0116 \\ (0.02/\(\sqrt{3}\)) \\ \end{tabular} & \
\begin{tabular}{c} 1.90 \({}^{\circ}\)C \\ \end{tabular} &
\begin{tabular}{c} 1.92 \({}^{\circ}\)C \\ \end{tabular} & \(-\)0.84\% \\
250 \({}^{\circ}\)C, 3.87 \(\upmu\)m & \begin{tabular}{c} 0.0116 \\ (0.02/\(\sqrt{3}\)) \\ \end{tabular} & \begin{tabular}{c} 0.89 \({}^{\circ}\)C \\ \end{tabular} & \
\begin{tabular}{c} 0.89 \({}^{\circ}\)C \\ \end{tabular} &
\begin{tabular}{c} 0.073\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Measurement uncertainty result comparison for emissivity uncertainty interval [0.93; 0.97] influence on uncertainty of temperature measurement. Figure 20: Mismatch of emissivity simulation results to the TG22 results. Uncertainty contribution due to emissivity was further simulated in Figure 21 as dependent on operating spectrum, as measured by a close-to-narrow spectral band radiation thermometer. For radiation thermometers, sensitive in a wider spectral range (wide-band), e.g., 8-14 \(\upmu\)m, the highest uncertainty value within the operating spectral interval should be used.