esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Chemical Activation Process Test Results
#### 3.2.2 Orthogonal Test Extreme Variance and ANOVA Analysis
The F value for CaO at 3 d was 9.16, demonstrating a significant impact. The significance analysis of compressive strength (Figure 19) indicated that the F values for CaO were consistently below nine, reflecting the weakest significance. The confidence intervals for CaCO\({}_{3}\) and CaCO\({}_{4}\cdot\)2H\({}_{2}\)O at 3 d were identical to those of CaO, suggesting that the activator had an insignificant effect on compressive strength at this stage. However, at 7 days, the significance of CaCO\({}_{3}\) and CaCO\({}_{4}\cdot\)2H\({}_{2}\)O increased, both falling within the 19-99% confidence interval. By 28 days, the F values dropped to the 9-19 confidence interval, indicating a relatively significant impact. Through a comprehensive analysis of the range and variance of the orthogonal test, the optimal reagent combination identified to enhance the activity of volcanic rock powder was 6% CaO + 2% CaCO\({}_{3}\) + 2% CaCO\({}_{4}\cdot\)2H\({}_{2}\)O (HS-4). The calculated 28-day flexural and compressive activity indices are 88% and 82%, respectively. In comparison to H-1 (cement + 30% volcanic rock powder), the flexural activity has increased by 22%, while the compressive activity has increased by 21%.
# Methods of Analyzing Microsized Plastics in the Environment
## 5 Emerging Technologies
### Near IR Spectra Analysis Method  
A microplastic analysis method using near IR (NIR) spectroscopy has also been tried. FTIR analyzes the spectrum between 600 and 4000 cm\({}^{-1}\), but NIR analyzes the spectrum over 4000-15,000 cm\({}^{-1}\)[78]. NIR spectra are generally analyzed based on vibration combinations of molecular vibrations of X-H, such as C-H, O-H, and N-H. Furthermore, NIR analysis is not sensitive, and it is difficult to use it for quantitative analysis. However, it can classify and analyze a large number of plastic samples within a short timeframe. Therefore, it is a more desirable analysis method to use when determining the type rather than the amount of a sample [79].
## 5 Results and Discussion
### Packet Delivery Ratio (PDR)  
The findings of PDR values for the proposed RALAR are shown in Figure 5. The results show that the proposed RALAR algorithm delivered a much better overall performance than the LAR protocol and KALAR approach. The proposed RALAR algorithm achieved an average PDR value equal to 0.9888, whereas the KALAR approach and the LAR protocol achieved an average PDR value of 0.9439 and 0.9438, respectively. This is due to using the concept of moving the rectangular zone to estimate the location, where this rectangular zone rejects the bad GPS location data and accepts the good GPS location data only. The results show that the KALAR approach is a bit higher than the LAR protocol.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Speed Limit (km/h) & 70–110 \\ Car Size (m) & 5 \\ Congestion distance (m) & 2 \\ Pedestrian crossing time (s) & 10 \\ Traffic jam distance (m) & 3 \\ Traffic lights & 2 \\ Number of Lanes & 2 \\ Acceleration movement (m/s\({}^{2}\)) & 1 \\ Safe Deacceleration (m/s\({}^{2}\)) & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Node’s mobility model parameters.
## 3 Experiment Setup
### Measuring Building Airtightness  
The airtightness of the building must first be measured to find the relationship between supply and return airflow and leakage. This is because the amount of leakage in a building will vary depending on the airtightness of the building when the pressure difference is the same. To measure the airtightness of the building envelope, which determines the leakage in the building, the air handler fan in the test bed was used. The air handler fan was used to pressurize and depressurize the room to maintain a 10 Pa gap between the indoor and outdoor pressure difference in the range of 10 to 60 Pa. Prior to pressurization, the exhaust diffuser was sealed with plastic to prevent air from escaping through the diffuser when the room was under positive pressure. In contrast, the supply air diffuser was sealed with plastic to prevent outside air from entering the room owing to the formation of negative pressure, as shown in Figure 7. The damper opening-rate condition of the air handler was set to 100% outside air during pressurization and 100% exhaust air with the damper closed to prevent outside air from entering during depressurization.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sensor** & **Specification** \\ \hline Range Accuracy Resolution & \(\pm\)1.0 mm H\({}_{2}\)O to 0–100 mm H\({}_{2}\)O \\ \(\pm\)0.5\% FS of reading Resolution & \(\pm\)0.01 mm H\({}_{2}\)O (0.1 Pa) \\ \(\pm\)0.01 mm H\({}_{2}\)O (0.1 Pa) \\ IPT1500 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: The specifications of the sensor used for the experiment.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case** & **Supply Fan Speed (Hz)** & **Return Fan Speed (Hz)** &
\begin{tabular}{c} **VAV Terminal** \\ **Damper Rate of** \\ **Opening (\%)** \\ \end{tabular} \\ \hline
1–3 & 20 & & 10/55/100 \\ \hline
4–6 & 30 & 20 & 10/55/100 \\ \hline
7–9 & 40 & & 10/55/100 \\ \hline
10–12 & 20 & & 10/55/100 \\ \hline
13–15 & 30 & 0 & 10/55/100 \\ \hline
16–18 & 40 & & 10/55/100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experiment cases.  
A graph (Figure 8) of the logarithmic values of the measurement result data for each of the pressurization/depressurization methods was plotted, and the values of the leakage index, n, and the leakage coefficient, C, were derived through regression analysis. By substituting the values of C and n into Equation (1), the leakage function representing the airtightness performance of the test bed can be expressed as Equation (3). The leakage function of the test bed is shown in Figure 9 as a graph of the leakage according to the pressure difference. The measured airtightness performance of the test bed revealed a CMH50 of 6408 CMH and an ACH50 of 27 times/h.  
\[Q=675.9373(\Delta P)^{0.5654} \tag{3}\]  
Figure 8: Log graph of airtightness measurement results.  
Figure 7: Sealing air diffuser for pressurization and depressurization.
# Compact Photonic-Crystals Based Isolator Using Ni-Zn Gyromagnetic Ferrite Posts
## 3 Photonic-Crystals Isolator
### Numerical Simulation of the PCs Isolator  
In this section, the function and transmission characteristics of the PCs isolator are numerically simulated by the software COMSOL Multiphysics like [10], based on the following equation:  
\[\varepsilon^{-1}\cdot\
abla\times\left([\mu_{r}]^{-1}\cdot\
abla\times \overset{\rightarrow}{E}\right)=\omega^{2}/c^{2}\cdot\overset{\rightarrow}{E} \tag{2}\]  
where \(\varepsilon\) is the relative dielectric constant of the materials, \(\omega\) is the frequency of incident signal, \(c\) is the speed of the light and \(\overset{\rightarrow}{E}\) is the electric field intensity. The parameters of the designed PCs isolator are \(A=4\) mm, \(r_{0}=0.207A\), \(r_{1}=1.28\) mm, \(r_{2}=0.5r_{0}\), \(d=0.5A\), \(\theta=6^{\circ}\), \(w=8.64\) mm and \(h=4.32\) mm. At 26 GHz, the power distribution of the signal in the PCs isolator is shown in Figure 4.  
As shown in Figure 4a, the signal incoming from Port 1 is transmitted stably to Port 2. Nonreciprocal, the signal incoming from Port 2 totally travels into the fan-shaped coupler, thus Port 1 is isolated, as in Figure 4b. Consequently, it perfectly realizes the function of the PC isolator, and its external properties are also numerically calculated as shown in Figure 5.  
In Figure 5a, the numerical results suggest that the optimal isolation of the PCs isolator is 44.20 dB at 25.81 GHz, and the isolation remains above 15 dB within the range of 25.66 to 25.95 GHz. In addition, the insertion loss is low to 0.50 dB at 26.06 GHz with the transmission efficiency of above 50% (3 dB) over a wide frequency range from 25.03 to 26.92 GHz, revealing an excellent forward transmission characteristic of the proposed PCs isolator. The curves of the forward and backward reflected powers of the PCs isolator with frequency are basically coincident, as shown in Figure 5b. It means that the return losses of the two ports are almost the same, which are reflected by the symmetrical structure. The peaks of the S parameters S11 and S22 respectively are \(-21.87\) dB and \(-21.83\) dB at 26.02 GHz. The S parameters play important roles in describing the performance of the devices, which are the ratios between the powers of waves between these ports. In our design, the parameters S11 and S22 represent the ratio of the reflected power to the input power of the Port 1 and Port 2, respectively. The parameter S21 represents the insertion loss of the isolator, which is the logarithmic value of the ratio of the transmission power of the Port 2 from the input power of the Port 1. Moreover, the parameter S12 is used to measure the isolation performance of the isolator, which is described in detail in [10].  
Figure 4: The power distribution in the PCs isolator at 26 GHz. (**a**) signal incoming from Port 1 is transmitted stably to Port 2. (**b**) signal incoming from Port 2 totally travels into the fan-shaped coupler.
## 3 The PSOTSC Algorithm
### Particle Swarm Segmentation Optimization  
Particle swarm optimization is an evolutionary computation technology based on swarm intelligence. It originated from biologists' observation and research on the behavior of birds' foraging process. It has the characteristics of fast calculation speed, good robustness, strong global convergence ability, being easy to understand, easy to realize, etc. In the PSO algorithm, the potential solution of each optimization problem is a bird in the search space, which is called the \"particle\". The particle has a fitness value representing whether it is close to the target and a speed value representing its update speed and search direction. The flow of PSO algorithm is shown in Figure 3.  
The algorithm is initialized as a group of random particles (random solution) and finds the optimal solution through iteration. The quality of the solution is evaluated by the fitness of the particles. In each iteration, each particle tracks two \"optimal particles\" to update itself; one is the local optimal particle found by itself, the other is the global optimal particle found by the swarm. The particles follow them to search in the solution space. The advantage of the PSO algorithm is that there is no crossover and mutation operation, and it relies on particle speed and fitness evaluation to complete the search. In the iterative evolution, only the optimal particle transmits information to other particles, therefore the optimization speed is fast and the solution accuracy is high. At the same time, the structure of the algorithm is simple and easy to implement. _PSOSegSearch_ method is designed to realize the application of PSO in trajectory segmentation solution search, which is the core of the PSOTSC algorithm shown in Algorithm 2. _PSOSegSearch_ method needs to solve two key problems: one is how to establish the mapping relationship between the trajectory and the particle; the other is what kind of driving mechanism particles use to update and quickly optimize. To solve the first problem, _PSOSegSearch_ method maps the compressed trajectory to the particle, which is expressed as a one-dimensional sequence with the same length as the original trajectory. The original trajectory points to be retained and discarded in the sequence are assigned different values. In this way, the problem is simplified, and the process seems simple and straightforward. However, if the length of the original trajectory is very long, it will increase the length of the particle and affect the effect of the algorithm, which leads to the second problem mentioned above. _PSOSegSearch_ method proposes a NARJ particle  
Figure 3: Basic flow of PSO. update strategy, which can speed up the search of segmentation solution and improve the efficiency of the PSOTSC algorithm. The content of NARJ will be described in detail in Section 3.3.
## 3 Methodology
### Feature Selection (FS)  
A robust feature selection methodology was employed that combined the outcomes of six FS techniques: two filter algorithms (Pearson correlation [20] and Chi-2 [21]), one wrapper (with logistic regression [22]) and three embedded ones (logistic regression L2 [23], random forest [24] and LightGBM [25]). Feature ranking was decided on the basis of a majority vote scheme. Specifically, we performed all six FS techniques separately, each one resulting into a selected FS. A feature receives a vote every time it has been selected by one of the FS algorithms. We finally ranked all features with respect to the votes received.  
The proposed feature selection proceeds along the following steps as shown in Figure 6.
# Cramer-Rao Lower Bounds on 3D Position and Orientation Estimation in Distributed Ranging Systems
## 4 Bounds on Orientation Estimation
### Two-Dimensional CRLB on Orientation  
The probability density function [52] of 2D vectors \(\overline{b}=[\overline{b}_{1},\overline{b}_{2},\cdots,\overline{b}_{M}]\) with mean \(\mu_{k}=\mathbf{R}(\theta)\overline{v}_{k}\), relative orientation \(\theta\), proper 2D rotation matrix \(\mathbf{R}(\theta)\), ground node vectors \(\overline{v}=[\overline{v}_{1},\overline{b}_{2},\cdots,\overline{v}_{M}]\), and concentration \(\kappa=[\kappa_{1},\kappa_{2},\cdots,\kappa_{M}]\) is  
\[p_{\overline{b}}(\overline{b};\overline{v},\theta,\kappa)=\left(\prod_{k=1}^{ M}\frac{1}{2\pi I_{0}(\kappa_{k})}\right)\exp\left\{\sum_{k=1}^{M}\kappa_{k} \,\overline{b}_{k}^{T}\mathbf{R}(\theta)\overline{v}_{k}\right\} \tag{28}\]  
where the normalizing constant \(I_{0}(\cdot)\) is a modified Bessel function of the first kind at order 0. The FIM \(\mathbf{I}(\theta)\) is computed as  
\[\mathbf{I}(\theta)=-\mathrm{E}\bigg{[}\frac{\partial^{2}}{\partial\theta^{2}} \mathrm{ln}f(\overline{v};\overline{v},\theta,\kappa)\bigg{]}=\sum_{k=1}^{M} \kappa_{k}\,\overline{b}_{k}^{T}\mathbf{R}(\theta)\overline{v}_{k}, \tag{29}\]  
since \(\mathbf{R}^{\prime\prime}(\theta)=-\mathbf{R}(\theta)\). Thus, the CRLB on an unbiased orientation estimator \(\hat{\theta}\) is  
\[\sigma_{\theta}^{2}\geq\mathbf{I}^{-1}(\theta)=\frac{1}{\sum_{k=1}^{M}\kappa _{k}\,\overline{b}_{k}^{T}\mathbf{R}(\theta)\overline{v}_{k}} \tag{30}\]  
It is important to note that the lower bound is independent of the rotation angle \(\theta\) and only depends on the concentration factor \(\kappa\) and the number of observations \(M\).
## 4 Experimental Stations
### 2D X-ray Detectors
#### 4.3.2 Detector Geometry  
The development of the JUNGFRAU detector reuses, whenever possible, design blocks which have already been developed and successfully used for other PSI detectors in order to minimize the development time and to keep the project cost at an affordable level. In particular the geometrical dimension of the EIGER project [189] has been inherited by JUNGFRAU, so that a common silicon sensor can be used for the two projects.  
Geometrically, the final JUNGFRAU chip comprises \(256\times 256\) pixels of \(75\times 75\,\mathrm{\SIUnitSymbolMicro m}^{2}\) each. Arrays of \(2\times 4\) chips (\(512\times 1024\) pixels) are tiled to form modules of \(38.4\times 76.8\,\mathrm{mm}^{2}\) sensitive area, i.e., about 500 kPixel per module. The chips are coupled to \(320\,\mathrm{\SIUnitSymbolMicro m}\) or \(450\,\mathrm{\SIUnitSymbolMicro m}\) thick \(p+\) on \(n\) silicon sensors (Hamamatsu Photonics, Hamamatsu, Japan). Multi-module systems of 1 MPixel (2 modules), 4 MPixel (8 modules, Figure 19), 8 MPixel (16 modules), 16 MPixel (32 modules) are envisioned and can be tiled in application specific geometries. The gap between two adjacent JUNGFRAU modules is about \(500\,\mathrm{\SIUnitSymbolMicro m}\) (short) and \(2.7\,\mathrm{mm}\) (long side) which corresponds to an insensitive area of about 7 pixels and 36 pixels respectively. The total dead area is 7% on a unit cell, or 5% on a typical 4 M detector.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detector** & **Pixel Size** & **Electronic** & **Single Photon** & **Single Photon** & **Dynamic Range** & **Repetition Rate** \\
**System** & \(\mathrm{\SIUnitSymbolMicro m}\times\mathrm{\SIUnitSymbolMicro m}\) & **Noise**\(\mathrm{e}^{-}\) & **Sensitivity** & **Sensitivity** & **Photons Per Pulse** & **kHz** \\  & & & **@ 6 keV** & **@ 2 keV** & **Per Pixel** & \\ \hline CSPAD & \(110\times 110\) & \(\sim\)330 & Yes \(\mathrm{{}^{\dagger}}\) & No & \(>\)2.5 \(\times\) 10\({}^{3}\) (@ 8 keV) & 0.12 \\ ePix100 & \(50\times 50\) & \(<\)60 & Yes & Yes & 100 (@ 8 keV) & \(\sim\)1 \\ ePix100k & \(100\times 100\) & \(\sim\)120 & Yes & No & 10,000 (@ 8 keV) & \(\sim\)1 \\ AGIPD & \(200\times 200\) & \(\sim\)265 & Yes & No & \(>\)10\({}^{4}\) (@ 12 keV) & 4500 burst \\ LTD & \(500\times 500\) & \(\sim\)1000 & No & No & \(10^{5}\) (@ 12 keV) & 4500 burst \\ DSSC & Pitch 200 \(\mathrm{{}^{\ast}}\) & \(<\)50 & Yes & Yes & \(>\)6 \(\times\) 10\({}^{3}\) (@ 1 keV) & 1000 burst \\ SOPHIAS & \(30\times 30\) & \(\sim\)150 & Yes & No & \(\sim\)2000 (@ 12 keV) & 0.06 \\ JUNGFRAU & \(75\times 75\) & \(\sim\)65 C0 or & Yes & Yes & \(>\)10\({}^{4}\) (@ 12 keV) & \(\sim\)2.4 \\  & & \(\sim\)50 HG0 & & & & \\ \hline \hline \end{tabular}  
\({}^{\dagger}\) at \(>\)5 keV, for CSPAD in high gain, \({}^{\ddagger}\) in low gain, \({}^{\ast}\) hexagonal pixels.  
\end{table}
Table 4: Comparison of the main characteristics of the current state-of-the-art pixel detection systems for XFEL hard X-ray light sources. The design characteristics which would limit the applicability of the detection system to SwissFEL are highlighted in yellow.
# Efficient Gene Transfection by Electroporation--In Vitro and In Silico Study of Pulse Parameters
## 3 Results
### Modeling the Probability of pDNA and Cell Membrane Contact during GET
#### 3.6.1 200 ns and 500 ns Pulses
Such an analysis effectivly allows us to infer and isolate the roles of electrophoresis and diffusion for different kinds of pulses/pulse durations used for GET as a function of concentration and will allow us to draw more general conclusions. \(PSC(t)\) is evaluated for the 200 ns pulse and 500 ns pulse at the corresponding electric field intensities (Table 1 for C2C12 cells) using \((B_{L},B_{R})\) ranging from 77 nm to 433 nm (corresponding to concentrations ranging from 2500 \(\upmu\)g/mL to 15 \(\upmu\)g/mL, respectively) and is shown in Figure 13A-D. For all ranges of \((B_{L},B_{R})\) tested, \(PSC(t)\) rises post pulse termination (for both 200 ns and 500 ns pulses) through the diffusive process. As seen from Figure 13A,C (without the free energy barrier), \(PSC(t)\) starts to rise from \(t\ \sim\) 0.2 ms for the lowest \((B_{L},B_{R})\) = 77 nm. The time at which \(PSC(t)\) begins to rise depends on \((B_{L},B_{R})\), and \(PSC(t)\) starts to rise earlier for lower \((B_{L},B_{R})\), since the pDNA molecule is initially located close to the cell membrane and thus has a higher probability of reaching the cell membrane (through diffusion) first. Similar profiles (rise times) of \(PSC(t)\) can be seen for 200 ns and 500 ns pulses as \(PSC(t)\) rises predominantly by diffusion with little contribution from electrophoresis. For the case with free energy barrier (Figure 13B,D), the rise in \(PSC(t)\) is delayed. \(PSC(t)\) starts to rise from \(t\ \sim\) 10 ms for the lowest \(((B_{L},B_{R})\) = 77 nm) and attains a lower final \(PSC(t)\) at \(t\) = 100 ms compared to the case without a free energy barrier. Similar profiles of \(PSC(t)\) were obtained for both the 200 ns and 500 ns pulse in the presence of the free energy barrier as well, further indicating the dominating effect of the diffusive process with little contribution from electrophoresis. Figure 13: Evolution of \(PSC(t)\) for different \((B_{L},B_{R})\) ranging from 77 nm to 433 nm, corresponding to concentrations ranging from 2500 \(\upmu\)g/mL to 15 \(\upmu\)g/mL, respectively. (**A**,**B**) The 200 ns pulse at 15.8 kV/cm: (**A**) without free energy barrier, (**B**) with free energy barrier. (**C**,**D**) The 500 ns pulse at 4.1 kV/cm: (**C**) without free energy barrier, (**D**) with free energy barrier. Dashed lines represent the time at which nanosecond pulse ends. Insets in (**B**,**D**) show zoomed-in plots for rise in \(PSC(t)\) from 1 ms.  
For the 200 ns and 500 ns pulses, and for both - without and with free energy barrier, the final \(PSC(t)\), i.e., \(PSC(t=100\) ms) depends upon \((B_{L},B_{R})\); thus, we can expect GET to depend on concentrations ranging from 2500 \(\mu\)g/mL to 15 \(\mu\)g/mL.
# Supply Chain Management Maturity and Business Performance: The Balanced Scorecard Perspective
## 5 Results  
### Partial-Least-Squares Structural Equation Modelling  
PLS-SEM analysis was used to evaluate the proposed model. Table 1 and Figure 2 reveal the obtained results of the PLS-SEM path analysis. When assessing a reflective measurement model, the factor loadings are examined first. An indicator loading above 0.70 assures acceptable item reliability [92]. The factor loadings show the degree to which a given factor affects each variable. Thus, loadings close to \(-1\) or \(1\) denote that the factor strongly influences the variable. The factor loadings are shown in Tables 2-4. After determining the factor loading indicators, internal reliability is checked using Cronbach's Alpha and the composite reliability of [93], judging values between 0.60 and 0.70 to be acceptable for exploratory research. Furthermore, convergent validity needs to be checked; this parameter will show the degree of closeness among the constructs, thus explaining the variance of their items. We calculate the average variance extracted (AVE) by squaring the loadings of each indicator on a construct. In the PLS-SEM approach, higher factor loadings denote sufficient variance from that variable. Discriminant validity is also used to determine the degree to which a construct differs from other constructs in the structural model. Ref. [94] suggested that each construct's AVE should be compared to the squared inter-construct correlation, and the AVE values should be higher than those inter-construct correlations. The collinearity test shows whether the method and the construct items are biased, and low VIF values indicate that the construct is free from bias. The indication of collinearity is determined if a VIF value is greater than 3.3, at which point the model construct might be biased [95]. If all VIFs from the collinearity test are equal to or lower than 3.3, the model can be considered free of common method bias. The path measurement shows that FINANCE R\({}^{2}\) is 0.233, CUSTOM R\({}^{2}\) is 0.491, INNO_LEARN R\({}^{2}\) is 0.516, and INT_PROC R\({}^{2}\) is 0.454. The R\({}^{2}\) of the constructs explain the degree to which the exogenous latent variables explain the endogenous latent variables. Table 2 shows the factor loadings for the FOND, STRUCT, VISION, INTEGR, and DYNAM items used in the first construct of SCMM as the independent variable. As can be seen, most loadings are greater than 0.5., which is the recommended threshold of average variance extracted (AVE). The FOND construct items range from 0.664 to 0.795. The STRUCT construct items range from 0.750 to 0.878. The VISION construct items range from 0.763 to 0.885. The INTEGR construct items range from 0.743 to 0.866. The DYNAM construct items range from 0.600 to 0.896. All the items are over the recommended