esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Nylon Fiber Addition on the Performance of Recycled Aggregate Concrete
## 3 Results and Discussion
### Split Tensile Strenth  
The split tensile strength values of the CAC and RAC mixes with different NF contents at 7, 28, and 91 days are listed in Table 6. As expected, the values of the split tensile strength of concrete were significantly dependent on the added NF content. The strength results showed that there was a great increase in the split tensile strength values with the addition of NF up to 1.2 kg/m\({}^{3}\) in both the CAC and RAC mixes. At 91 days, the maximum strength value obtained in the case of the CAC mixes was 6.3 MPa for 1.2 kg/m\({}^{3}\) NF content, an increase of 23.5% over the CAC1 mix. Similarly, for the RAC mixes, the maximum improvement examined in the RAC3 mix with the same NF content, as compared with the unreinforced RAC1 mix, was 80.6%, which has a split tensile strength of 5.6 MPa. These results were in a good agreement with other studies [15; 27].  
The calculated results of the split tensile strength ratio (SSR) of the concrete mixes at 7, 28 and 91 days were presented in Figure 5a-c, respectively. For both the CAC and RAC mixes, it seems that the addition of NF had much influence on the split tensile strength, especially at the early age of curing. At 7 days, the SSR value for the RAC3 mix was 205% compared to 151% for the CAC3 mix, and similar trends were also observed at 28 and 91 days, as shown in Figure 5b,c.  
Within the scope of the present study, it was confirmed that a higher addition of NF led to an increase in both the compressive and split tensile strengths. Furthermore, this was obvious when NF was applied in the RAC mixes compared to the CAC mixes.  
Figure 4: Compressive strength ratios of concrete mixes at: (**a**) 7 days, (**b**) 28 days, and (**c**) 91 days.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Mixes**} & \multicolumn{3}{c}{**Compressive Strength (MPa)**} \\ \cline{2-4}  & **7d.** & **28d.** & **91d.** \\ \hline CAC1 & 25.6 (0.97) & 36.5 (1.14) & 41.2 (1.12) \\ CAC2 & 37.5 (1.42) & 45.6 (1.02) & 52.4 (0.76) \\ CAC3 & 38.2 (0.88) & 49.8 (1.65) & 56.6 (1.35) \\ RAC1 & 18.6 (1.21) & 25.4 (2.18) & 30.1 (0.90) \\ RAC2 & 27.0 (0.45) & 39.5 (0.98) & 43.6 (2.33) \\ RAC3 & 36.2 (0.81) & 47.2 (1.47) & 52.6 (0.66) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Compressive strength values of concrete mixes with different NF contents (standard deviation in parenthesis).
# Mechanical Properties and Formaldehyde Release of Particleboard Made with Lignin-Based Adhesives
## 3 Results and Discussions
### Free Formaldehyde Emission  
The experimental values of the formaldehyde emission are presented in Table 6.  
It was observed that the panels with PMDI and glucose cross-linkers (LO 20 PIG and P2G) had the lowest formaldehyde emission compared to those with pure lignin (L 20), with about 40%. Increasing the amount of PMDI with 2%, led to a decrease of formaldehyde emission with about 28% (LO 20P1 and LO 20 P3 compared to LO 20P1). Because of the PMDI's superior moisture tolerance, it was not necessary to dry the wood particles to levels lower than 4%; the moisture content of wood particles ranged in the limits of 10% to 15% which is ideal for bonding [68]. High moisture content facilitates the hydrolysis speed of wood components and the transport of the formaldehyde out of the particleboard panel [69]. As shown in Figure 7, the high degree of correlation (R\({}^{2}\) = 0.97) between the quantity of formaldehyde released and the PMDI ratio was obtained. The decrease of formaldehyde emission with increase in PMDI level could be explained by the faster curing of the adhesive film formed by PMDI on the particle surfaces, which may have prevented VOCs and the release of formaldehyde from the particle surfaces [69]. These could be also deduced from the microscopic images (Figure 5c,d), which showed the good interface of particles. Several researchers [70; 71; 72] have reported that bio-based formaldehyde scavengers with hydroxyl groups contribute both to the high internal cohesion of boards and the low formaldehyde emission of particleboards. Based on this theory, the D-glucose, which has hydroxyl groups in its composition, may act as formaldehyde scavenger [73], thus lowering the formaldehyde emission, as can be seen for LO 20 PIG and LO 20 P2G panels (Table 5  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type of the Receipt** & **Values** \\ \hline LP 20 (LIGNEX MG F powder) & 0.789 \\ LO 20 (oxidized LIGNEX MG F) & 0.616 \\ LO 20 P1 (oxidized LIGNEX MG F + 1\% PMDI) & 0.553 \\ LO 20 P2 (oxidized LIGNEX MG F + 2\% PMDI) & 0.477 \\ LO 20 P3 (oxidized LIGNEX MG F + 3\% PMDI) & 0.401 \\ LO 20 PIG (oxidized LIGNEX MG F + 1\% PMDI + glucose) & 0.386 \\ LO 20 P2G (oxidized LIGNEX MG F + 2\% PMDI + glucose) & 0.347 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Formaldehyde emission, in mg/ m\({}^{2}\)-h.  
Figure 6: Panel density profiles along the panel thickness leading to very different board properties; (**a**) panel with oxidized lignin (LO20); (**b**) panel with oxidized lignin, 2% PMDI and glucose (PO 20 P2G).  
and Figure 7). Formaldehyde emission values are much lower than the standardized limit specified for E1 class (3.5 mg/m\({}^{2}\).h) [61]. It can be noticed that the formaldehyde values obtained are close to those of some natural wood species (Douglas fir and oak: 0.397 mg/m\({}^{2}\).h and 0.43 mg/m\({}^{2}\).h, respectively) [74].
# Can LiDARs Replace Meteorological Masts in Wind Energy?
## 3 Comparison of LiDAR Measurement with Sonic and Cup Anemometer
### Peak Wind Speed
The two zoom-in figures show the maximum wind speeds for a 24 h period; **(top left)** is for 2018-10-01 and **(top right)** is for 2019-02-01. In wind engineering, peak wind speed (\(\widehat{V}\)) is usually expressed as a function of mean wind speed (\(\overline{V}\)) and turbulence intensity (\(I\)):  
\[\widehat{V}=(1+g\,I)\,\overline{V}=G\,\overline{V}, \tag{7}\]  
where \(g=(\widehat{V}-\overline{V})/\sigma\) is the peak factor and \(G=\widehat{V}/\overline{V}\) is the gust factor. Figure 13 shows the distribution of the gust factor as a function of mean wind speed at 57 m height. The 95 th percentiles of the measured data are shown by lines in these figures. The gust factor shows a similar trend to the turbulence intensity, i.e., the highest value occurs for the lowest mean wind speed for which the variation is also large. For higher wind speeds, \(G\) gradually converges between 1 and 2. For both the sonic anemometer and the profiling LiDAR, \(G\) converges roughly to 1.9, i.e., \(\widehat{V}\) is 1.9 times \(\overline{V}\) during strong wind speeds. One can conclude from these comparisons that LiDAR is able to measure peak wind speeds with a similar accuracy to that of sonic anemometers.
# Optical accelerometers for Detecting Low-Frequency Micro-Vibrations
## 2 Principle and Requirements
### Requirements of Low Frequency Accelerometers  
Low-frequency, high-sensitivity accelerometers have a wide range of applications, and they play an important role in industries and daily life, including inertial navigation, structural health monitoring, active vibration isolation systems, fault detection of wind turbine, and earthquake detection.
# Exploring the Impact of Virtual Reality on Painting Appreciation
## 1 Introduction
### Impact of Exhibition Presentation Methods on Appreciation
[20] explored the experience of art appreciation through desktop VR and mobile VR, comparing it to the appreciation of a physical painting. The findings indicated that there were no significant differences in painting evaluation and the emotions expressed during appreciation across the three conditions. This suggests that participants perceive paintings similarly, whether viewed through VR or in person. Moreover, the study noted that special attention should be paid to a device's specifications and the interaction methods, which do not need to be considered in physical exhibitions but are key to audiences experiencing depth and satisfaction in VR systems.
# Advances in the Biosynthesis and Molecular Evolution of Steroidal Saponins in Plants
## 2 Biosynthesis of Steroidal Saponins
### Biosynthesis of 2,3-Oxysqualene  
The precursor of 2,3-oxsqualene is isopentenyl diphosphate (IPP). There are two ways to synthesize IPP in plants, namely MVA and MEP (Figure 2). The MVA pathway is found in the cytoplasm and mitochondria. Acetyl Co-A is catalyzed by acetyl Co-A acetyltransferase (ACAT) to form acetoacetyl Co-A, which is then catalyzed by 3-hydroxy-3-methylglutaryl-CoA synthase (HMGS), mevalonate kinase (MVA), phosphomevalonate kinase (PMK), and mevalonate-5-diphosphate decarboxylase (MVD) to generate IPP (isopentenyl-5-diphosphate) [1; 3; 20].  
The MEP pathway is found in plastids. Glyceraldehyde-3-phosphate and pyruvate form 1-deoxy-D-xylulose 5-phosphate (DXP) under the catalysis of 1-deoxy-D-xylulose 5-phosphate synthase (DXS). Then, DXP is catalyzed by 1-deoxy-D-xylulose 5-phosphate reductoisomerase (DXR), 2-C-methyl-D-erythnitol 4-phosphate cytidylyltransferase (MCT), 4-diphosphocytidyl-2-C-methyl-D-erythnitol kinase (CMK), 2-C-methyl-D-erythnitol 2,4-cyclodiphosphate synthase (MDS), 4-hydroxy-3-methylbut-2-enyl pyrophosphate (HMBPP) synthase (HDS), and HMBPP reductase (HDR) to generate IPP (isopentenyl-5-diphosphate) [1; 3; 20].  
Subsequently, IPP is catalyzed by isopentenyl diphosphate isomerase (IDI), geranyl pyrophosphate synthase (GPPS), and farnesyl pyrophosphate synthase (FPPS) to synthesize farnesyl pyrophosphate (FPP). Finally, FPP is condensed by squalene synthase (SQS) to form squalene, which is further epoxidized to 2,3-oxidosqualene by squalene epoxidase (SQE) [1; 3; 20].
## 3 Inherent Optical Properties (IOPs)
### Scattering Phase Matrix  
The theoretical development of vector radiative transfer theory may start with the Stokes vector representation \(\mathbf{I}=[I_{\parallel},I_{\perp},U,V]^{T}\), where the superscript \({}^{T}\) denotes the transpose. In terms of the complex transverse electric field components of the radiation field \(E_{\parallel}=|E_{\parallel}|\mathbf{e}^{-i\epsilon_{1}}\) and \(E_{\perp}=|E_{\perp}|\mathbf{e}^{-i\epsilon_{2}}\), these Stokes vector components are given by:  
\[I_{\parallel} = E_{\parallel}E_{\parallel}^{*}\] \[I_{\perp} = E_{\perp}E_{\perp}^{*}\] \[U = 2|E_{\parallel}||E_{\perp}|\cos\delta\] \[V = 2|E_{\parallel}||E_{\perp}|\sin\delta \tag{11}\]  
where \(\delta=\epsilon_{1}-\epsilon_{2}\). The connection between this Stokes vector representation, \(\mathbf{I}=[I_{\parallel},I_{\perp},U,V]^{T}\), and the more commonly used representation \(\mathbf{I}_{S}=[I,Q,U,V]^{T}\), where \(I=I_{\parallel}+I_{\perp}\) and \(Q=I_{\parallel}-I_{\perp}\), is given by:  
\[\mathbf{I}_{S} = \mathbf{DI} \tag{12}\]  
where  
\[\mathbf{D}=\left(\begin{array}{cccc}1&1&0&0\\ 1&-1&0&0\\ 0&0&1&0\\ 0&0&0&1\end{array}\right),\qquad\mathbf{D}^{-1}=\frac{1}{2}\left(\begin{array} []{cccc}1&1&0&0\\ 1&-1&0&0\\ 0&0&2&0\\ 0&0&0&2\end{array}\right). \tag{13}\]The degree of polarization is defined as  
\[p=[Q^{2}+U^{2}+V^{2}]^{1/2}/I \tag{14}\]  
so that \(0\leq p\leq 1\), where \(p=1\) corresponds to completely polarized light and \(p=0\) to natural (unpolarized) light. The degree of circular polarization is defined as  
\[p_{c}=V/I \tag{15}\]  
the degree of linear polarization as  
\[p_{l}=[Q^{2}+U^{2}]^{1/2}/I \tag{16}\]  
and alternatively, when \(U=0\) as  
\[p_{l}=\frac{|Q|}{I}=\frac{|I_{\perp}-I_{\parallel}|}{I_{\perp}+I_{\parallel}}. \tag{17}\]  
The transverse electric field vector \([E_{\parallel},E_{\perp}]^{T}\) of the scattered field can be obtained in terms of the transverse field vector \([E_{\parallel},E_{\perp 0}]^{T}\) of the incident field by a linear transformation:  
\[\left(\begin{array}{c}E_{\parallel}\\ E_{\perp}\end{array}\right)=\mathbf{A}\left(\begin{array}{c}E_{\parallel 0}\\ E_{\perp 0}\end{array}\right)\]  
where \(\mathbf{A}\) is a \(2\times 2\) matrix, referred to as the amplitude scattering matrix, which includes a \(1/r\) dependence of the scattered field. The corresponding linear transformation connecting the Stokes vectors of the incident and scattered fields in the scattering plane is called the Mueller matrix (in the case of a single scattering event). For scattering by a small volume containing an ensemble of particles, the ensemble-averaged Mueller matrix is referred to as the Stokes scattering matrix \(\mathbf{F}\). Finally, when transforming from the scattering plane to a fixed laboratory frame, the corresponding matrix is referred to as the scattering phase matrix \(\mathbf{P}\).
## 3 Experiments and Results
### Synthetic Data
#### 3.1.2 Noise Sensitivity  
In this Section, we add zero-mean Gaussian noise onto the image points. We vary the noise deviation level \(\delta\) from 0 to 2 pixels. At each noise level, we run 50,000 independent trails and report the median focal length, translation, rotation, and reprojection error in Figure 7.  
Based on Figure 7, in terms of the relative focal length error, our proposed method is better than GP4 Pf. In terms of the relative translation and reprojection error, our proposed method and [PERSON]'s method have similar performance. In terms of the rotation error, our proposed method is slightly worse than [PERSON]'s method, but [PERSON]'s method has a multi-solution phenomenon that is up to four solutions and needs a fully calibrated camera.
## 3 Rational for Optimization in WSNs  
### Wireless Sensor Networks and Optimization  
A WSN typically has little or no infrastructure. A sensor network is created with a large number of sensor nodes, which are deployed either inside the monitoring substance or very close to it (as shown in Figure 2) [30]. Unlike traditional networks, a wireless sensor network has its own design and resource constraints. Sensor nodes carry very limited, non-replenishable power sources. As a result, while traditional networks focus more on achieving high quality of service (QoS), sensor network protocols have to focus primarily on power conservation issues. Other resource constraints include low bandwidth, short communication range, and limited processing and storage in each node.  
All the above mentioned issues are directly related to the optimization problem. Maximizing the lifetime, ensuring the QoS along with security is not an easy task. Furthermore, often these three issues contradict each other. If we want to ensure energy efficiency we have to compromise on QoS and security. If QoS is assured, then the other two issues may lack proper awareness.  
Figure 2: Architecture of a general wireless sensor network.  
So, from the optimization point of view of WSNs, the right choice of the optimizer or algorithm for WSN problems is very important. The algorithm chosen for an optimization task will largely depend on the nature of the algorithm, the type of the problem, the desired quality of solutions, the available resources, time constraints, _etc._ The nature of an optimizer may determine if it is appropriate for a particular type of problem For instance, derivative-based algorithms such as hill-climbing are not appropriate for optimization problems whose objective is discontinuous. On the contrary, the type of problem we are trying to solve also can play role in determining which algorithm to choose. If the objective function of the problem is highly nonlinear and multi modal, the classical algorithms are not appropriate, as they are local search algorithms. Most WSNs suffer from huge resource constraints, and most of the problems that are to be optimized are NP-hard problems, so the cost of simulators or mathematical programming engines used for linear, nonlinear and quadratic programming make them unattractive. As the problem size increases, the computational complexity of conventional methods grows exponentially. This is the main inspiration for choosing bio-mimetic algorithms (global optimizers) such as PSO, GA, ACO, CS, _etc._
## 2 Materials and Methods  
### Materials and Ecological Concrete Preparation  
Ecological concrete modules were prepared using Type I ordinary Portland cement conforming to ASTM C150 standards [14], coarse aggregates (5-20 mm), reinforcing agent, and water. Three strength grades (C20, C25, and C30) were designed following modified ACI mix design methods. The mix proportions were optimized with cement content ranging from 358 to 398 kg m\({}^{-3}\), reinforcing agent from 10 to 15 kg m\({}^{-3}\), constant aggregate content at 1650 kg m\({}^{-3}\), and water content from 130 to 145 L m\({}^{-3}\).  
The biochar used in this study was derived from corn straw pyrolyzed at 500 \({}^{\circ}\)C and was sourced from Jiangning District, Nanjing, Jiangsu Province. The modular units were designed with dimensions of 100 \(\times\) 100 \(\times\) 100 mm, featuring cylindrical cavities (\(\varphi\)61 mm \(\times\) H60 mm) on the surface for biochar incorporation. Biochar (30 g) was carefully placed within each cavity to enhance the metal removal capacity of the modules.  
For biochar integration, 30 g of prepared biochar (0.5-2.0 mm particle size, 8-10% moisture content) was simply added to the cylindrical cavity (\(\varphi\)61 mm \(\times\) H60 mm) without compression. This straightforward approach was intentionally chosen to reflect practical field application conditions while maintaining adequate porosity for water penetration and contaminant interaction. The cavity design naturally contains the biochar while allowing sufficient water contact for effective heavy metal removal.
# Radiation-Hardened 16T SRAM Cell with Improved Read and Write Stability for Space Applications
## 3 Simulation Results and Analysis
### Soft Error Robustness Verification and Comparison  
The robustness of the proposed cell against soft errors can be evaluated by injecting a current source into the sensitive node [29]. As shown in Figure 10, the direction of the current source generates a negative transient pulse at the drain of the NMOS and a positive transient pulse at the drain of the PMOS. For instance, in the case of an SEU occurring at node Q storing '1', a negative transient pulse is induced in the off-state transistor N6. Thus, the recovery capability from SEUs can be evaluated by applying current, as shown in Figure 10a. Similarly, for an SEU occurring at node QB storing '0', a positive transient pulse is induced in the off-state transistor P3. Consequently, the recovery capability from an SEU can be evaluated by applying current, as shown in Figure 10b. The equation for the injected current source to evaluate the robustness against soft errors is given as follows:  
\[I_{inj}(t)=I_{0}\Bigg{\{}\exp\!\left(-\frac{t}{\tau_{\alpha}}\right)-\exp\! \left(-\frac{t}{\tau_{\beta}}\right)\Bigg{\}} \tag{1}\]  
where \(I_{0}\) represents the peak current of the current pulse and \(\tau_{\alpha}\) and \(\tau_{\beta}\) are the initial ion track establishment time constant and the collection time, respectively. In this paper, \(\tau_{\alpha}\) and \(\tau_{\beta}\) are set to 200 ps and 50 ps, respectively, for measurement. The critical charge (\(Q_{C}\)) refers to the minimum charge required to flip the data of the cell when the sensitive node is affected by an SEU. To measure \(Q_{C}\), different amplitudes of \(I_{inj}\) are injected into the sensitive node as described above, and the minimum value of \(I_{0}\) and the critical time (\(t_{c}\)) can be determined when the data of the cell have flipped. Subsequently, \(Q_{C}\) can be calculated using the following equation [6]:  
\[Q_{C}=\int_{0}^{t_{C}}I_{inj}(t)\ dt \tag{2}\]  
The critical charge values for the comparison cells are presented in Table 1. As described in Section 2, the recovery simulations for Q, QB, and S1 when the proposed cell initially stores data '1' and the recovery simulations for the SEMNU occurring at the storage node pair Q-QB are shown in Figure 11. Furthermore, the proposed cell can recover normally from SEUs and SEMNUs even at accumulated charges above 100 fC.  
Figure 9: \(H_{\text{PWR}}\) for different comparison cells with supply voltage variation.  
Figure 11: Waveforms presenting the soft error recovery when SEU affects (**a**) node Q, (**b**) node QB, and (**c**) node S0; (**d**) the storage node pair Q-QB.  
Figure 10: Equivalent circuit for generating (**a**) negative and (**b**) positive transient pulses.
## 5 Discussion  
Since a medical data such as BP is mostly limited quantitatively, it is difficult to take advantage of the NN, which shows promising performance when large data is used. In reality, since measurements are obtained with expensive machinery and labels are the fruit of a time-consuming analysis, drawn from the conclusions of human experts, it is difficult to collect the sufficient labeled data for BP measurement which includes data and its label, verified by the expert.  
To address this problem, the data augmentation techniques were utilized for boosting quantitatively the training DB in which one of the representative techniques is the bootstrap algorithm. However, while the previous method such as the bootstrap algorithm does not properly represent the correlations between multi-dimensional features in estimating the BP, the proposed algorithm can express efficiently the correlation between the features. For this reason, the proposed algorithm showed better performance than the conventional algorithm. It is wise to explain the merit of the generator using the MGD, as depicted in Figure 7. As shown in the figure, the generator based on the MGD creates the pseudo data that resembles most closely the actual data. Specifically, when we employ the MGD, the distribution of the pseudo data is similar to the distribution of the original one, while the data distribution of bootstrap is quite different. In addition, since the MGD-based generator creates the pseudo data for sections with a little distribution of actual data, the pseudo data is generated more diversely while maintaining correlation between features than bootstrap. In addition, when we compared the results of 50 pseudo subjects with the results of 100 pseudo subjects, the average performance of each experiment was similar. Thus, it turns out that the 50 pseudo subjects were sufficient to improve the performance of the NN-based BPE. In the case of 100 pseudo subjects, a very small amount of unnecessary data may have been added. Finally, since our DB contains a small amount of hypertension data, we need to collect additional DB for evaluating performance for hypertension in future works.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{**BP**} & \multicolumn{4}{c}{**Baseline (without DA)**} & \multicolumn{4}{c}{**Bootstrap (5 Times)**} & \multicolumn{4}{c}{**Bootstrap (20 Times)**} & \multicolumn{4}{c}{**Proposed (50 Subjects*8)**} & \multicolumn{4}{c}{**Proposed (100 Subjects*8)**} \\ \cline{3-14}  & & **ME** & **SDE** & **r-Value** & **ME** & **SDE** & **r-Value** & **ME** & **SDE** & **r-value** & **ME** & **SDE** & **r-Value** & **ME** & **SDE** & **r-Value** \\