esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results
### Migration, Weight Increase and Visual Changes of GPPS and HIPS with Food Simulants  
The migration and weight increase from GPPS and HIPS into food simulants at the tested temperatures are depicted in Table 1 to Figure 11. All experiments were carried out in triplicate and the results were averaged. Tabulated data (mean values and standard deviation of each measurement) are reported in the Supplementary material (Tabulated data). Line graphs of the depicted migration curves represent the connection of the relative migration (dependent variable) over time (independent variable). No fitting of the data was done. The relative migration represents the percentage of substance that migrates from polymer to food simulant. It was calculated by dividing the mass of each substance recovered in the food simulant by the initial mass of the same substance determined in the polymer sheets at the beginning of the study (\(C_{p,0}\) given in Table 1). This way, it is possible to compare the two investigated polymers and appreciate the physical-chemical migration behavior for each particular compound considered.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Average Concentration \(\pm\) SD**} \\  & \multicolumn{2}{c}{**[mg/kg]**} \\ \hline
**Substance** & **GPPS** & **HIPS** \\ \hline
**Artificially added “spiked” substances** & & \\ Toluene & 417 \(\pm\) 5 & 763 \(\pm\) 11 \\ Chlorobenzene & 504 \(\pm\) 5 & 821 \(\pm\) 13 \\ Phenyl cyclohexane & 705 \(\pm\) 8 & 1229 \(\pm\) 33 \\ Benzophenone & 610 \(\pm\) 5 & 1002 \(\pm\) 27 \\ Methyl acetate & 718 \(\pm\) 7 & 1179 \(\pm\) 34 \\ \hline
**Styrene** & 352 \(\pm\) 3 & 363 \(\pm\) 6 \\ \hline
**Styrene dimers (sum) 1:** & 730 & 1743 \\
1,3-Diphenylpropane: Oligo-1 & 180 \(\pm\) 6 & 134 \(\pm\) 4 \\
2,4-Di-phenyl-1-butene: Oligo-2 & 550 \(\pm\) 7 & 1609 \(\pm\) 35 \\ \hline
**Styrene trimers (sum) 1:** & 5807 & 30290 \\
2,4,6-Tiphenyl-1-hexen: Oligo-3 & 1389 \(\pm\) 23 & 4323 \(\pm\) 152 \\ isomer of 1-Phenyl4-(1-phenylethyl)-tetraline: Oligo-4 & 1092 \(\pm\) 20 & 6740 \(\pm\) 230 \\ isomer of 1-Phenyl4-(1-phenylethyl)-tetraline: Oligo-5 & 2324 \(\pm\) 44 & 13440 \(\pm\) 455 \\ isomer of 1-Phenyl4-(1-phenylethyl)-tetraline: Oligo-6 & 657 \(\pm\) 14 & 3684 \(\pm\) 146 \\ isomer of 1-Phenyl4-(1-phenylethyl)-tetraline: Oligo-7 & 260 \(\pm\) 5 & 1624 \(\pm\) 60 \\ isomer of 1-Phenyl4-(1-phenylethyl)-tetraline: Oligo-8 & 85 \(\pm\) 2 & 479 \(\pm\) 18 \\ \hline \hline \end{tabular}  
* The styrene oligomers were characterized via their mass spectra and tentatively identified as far as possible by comparison with library spectra and literature data.  
\end{table}
Table 1: Average initial concentration (\(C_{p,0}\)) of the model spiked substances and residual content of styrene monomer and oligomers in the investigated polystyrene (GPPS and HIPS) sheets (\(\pm\)standard deviation of triplicate analysis).
# Analytical Solution for the Problem of Point Location in Arbitrary Planar Domains
## 5 NRA Crossing in Parametric Paths
### Vectorization of Algorithm 4  
Although not necessarily a breakthrough in reducing computational costs, Algorithm 4 on the following page can be easily vectorized for parallel computing, at least for the test of the inclusion of one point. Follows in Figure 11 a fully operational excerpt in Matlab code (listing) that accepts a matrix \(P\) with the polygon vertices (with the last vertex replicated from the first) and a point \(A\). The procedure is vector based and operates with an entire polygon \"at once\".  
Despite the possibility of some finer tuning, the vectorized approach, nevertheless, forces all the operations to be performed through the entire chain of tests. Some tests could be dismissed early in a sequential approach, but the vector approach forces all the operations for all cases. Hence, it may not be suited for better performance in all computational setups.  
```
1:\(a=(a_{x},a_{y})\), \(P_{n}=(x_{n},y_{n})\) for \(n=1,2,\ldots,M\)
2:\(P_{n}\gets P_{n}-a\) for \(n=1,2,\ldots,M\)
3:\(I_{\mathbb{C}}\gets 0\) // initialize NRA Intersection counter
4:for\(n=1\)to\(M\)do
5:\(Y_{1}\gets y_{n}\) ; \(Y_{2}\gets y_{(n\bmod M)+1}\)
6:\(X_{1}\gets x_{n}\) ; \(X_{2}\gets x_{(n\bmod M)+1}\)
7:if\(\operatorname{sgn}(Y_{1})==\operatorname{sgn}(Y_{2})\)then
8:continue //no NRA crossing: horizontal line, etc.
9:endif
10:\(\Delta Y\gets Y_{1}-Y_{2}\), \(\Delta X\gets X_{1}-X_{2}\)
11:if\(\Delta Y>0\)then
12:if\(Y_{1}<0\)then
13:\(//\sigma Y_{2}>0\) redundant
14:continue //no NRA intersection
15:endif
16:endif
17:else
18:if\(Y_{2}<0\)then
19://or\(Y_{1}>0\) redundant
20:continue //no NRA intersection
21:endif
22:endif
23:else
24:if\(Y_{1}\times\Delta X\geq X_{1}\times\Delta Y\)then
25:continue //no NRA intersection
26:endif
27:endif
28:\(I_{P}\leftarrow\operatorname{sgn}(\Delta Y)\) //There is NRA intersection
29:if\(Y_{1}==0\) or\(Y_{2}==0\)then
30:\(I_{P}\gets I_{P}\times 0.5\) //touch NRA only
31:endif
32:\(I_{\mathbb{C}}\gets I_{\mathbb{C}}+I_{P}\) //Update NRA crossing counter
33:endfor
34:if\(I_{\mathbb{C}}\
eq 0\)then
35:return true //point \(a\) is included
36:else
37:return false //point \(a\) is not included
38:endif
```  
**Algorithm 4**An optimized version of Algorithm 3
## 3 Methodology and Model
### RepViT  
The primary framework of the network adopts RepViT, which notably enhances the block structure originally found in MobileNetV3-L by segregating the token mixer and the channel mixer. In the original MobileNetV3-L block structure, a \(1\times 1\) extension convolution precedes a deep convolution and a subsequent \(1\times 1\) projection layer. These components are connected through residual concatenation. The \(1\times 1\) expansion convolution and \(1\times 1\) projection layer foster channel interaction, while the deep convolution encourages the amalgamation of spatial information. In the MobileNetV3 block, the former and latter functionalities correspond to the channel mixer and token mixer, respectively, and are intertwined. Building upon this foundation, RepViT advances the depth convolution, relocating the squeeze-and-excitation module to follow the depth-wise filters. This restructuring is pivotal, as the module relies on spatial information interaction. Consequently, separating the channel mixer and token mixer and integrating the SE layer into cross-blocks across all stages optimizes accuracy gains with minimal additional latency. To enhance performance, a multi-branch topology is introduced for the depth filter during training. The structural layout of the RepViT model is illustrated in Figure 2.
## 3 Results
### Diffuse Transmission Spectroscopy Analysis
#### 3.1.3 Statistics of SSC in Transmittance Samples  
The Kennard-Stone (K-S) algorithm is particularly beneficial in fields such as spectral analysis, where it markedly improves model performance and practical applicability by providing a more accurate and representative dataset for model training and evaluation. In this study, the K-S algorithm was employed for sample partitioning. The K-S algorithm selected representative samples with precision to ensure that both the calibration set and the prediction set comprehensively reflected the characteristics and distribution of the entire sample set. This approach enhanced the reliability and predictive capability of the model.  
As shown in Table 2, the total sample set comprised 251 samples, with SSC ranging from a minimum of 5.00 \({}^{\circ}\)Brix to a maximum of 17.60 \({}^{\circ}\)Brix and an average SSC of 11.18 \({}^{\circ}\)Brix. The samples were partitioned into calibration and prediction sets at a 3:1 ratio. The calibration set included 188 samples, with SSC values ranging from 5.00 to 17.60 \({}^{\circ}\)Brix and an average SSC of 12.03 \({}^{\circ}\)Brix. The prediction set consisted of 63 samples, with SSC values ranging from 5.20 to 16.80 \({}^{\circ}\)Brix and an average SSC of 8.66 \({}^{\circ}\)Brix. This distribution ensures that both the calibration and prediction sets are representative of the overall sample set, providing a robust basis for model development and evaluation.  
The distribution of samples in the calibration set, which includes those from the prediction set, contributes to the development of a robust predictive model. By employing the K-S algorithm for partitioning, the representativeness of both the calibration and prediction sets was ensured, thereby enhancing the model reliability and predictive capability. The K-S  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Before and After Outlier Removal** & **No. of Samples** & **LVs** & \(R_{\text{c}}^{2}\) & \(R_{\text{c}}^{2}\) & **RMSEC (\({}^{\circ}\)Brix)** & **RMSEP (\({}^{\circ}\)Brix)** \\ \hline Before & 255 & 11 & 0.99 & 0.95 & 0.34 & 0.87 \\ After & 251 & 11 & 0.99 & 0.96 & 0.39 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation of PLSR model metrics before and after removing outlier samples.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Sample Sets** & **No. of Samples** & **Min. (\({}^{\circ}\)Brix)** & **Max. (\({}^{\circ}\)Brix)** & **Mean (\({}^{\circ}\)Brix)** & **STD. (\({}^{\circ}\)Brix)** \\ \hline Whole sample & 251 & 5.00 & 17.60 & 11.18 & 4.33 \\ Calibration set & 188 & 5.00 & 17.60 & 12.03 & 4.10 \\ Prediction set & 63 & 5.20 & 16.80 & 8.66 & 4.04 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Statistics of kiwifruit SSC for establishing the diffuse transmittance PLSR model.  
algorithm demonstrates significant advantages in fields such as spectral analysis, where it effectively improves both model performance and practical applicability.
# Effects of Aerosols on Gross Primary Production from Ecosystems to the Globe
## 2 Materials and Methods
### Aerosols Contribution to Changes in GPP  
In order to represent different biomes on the global spatial scale, the global potential natural vegetation (PNV) map at 1 km spatial resolution (Available online: [[https://doi.org/10.7910/DVN/QQHCIK](https://doi.org/10.7910/DVN/QQHCIK)]([https://doi.org/10.7910/DVN/QQHCIK](https://doi.org/10.7910/DVN/QQHCIK)), accessed on 18 February 2021) was used for the prediction of GPP. The PNV map includes 10 vegetation types. We used PVN vegetation classification because it assumes an undisturbed natural vegetation state and can well reflect the zonal characteristics of vegetation [46]. The aerosol-radiation effect has a higher latitudinal direction in the influence of GPP, and the use of PVN can better show the influence of aerosol on GPP in different latitudes vegetation types.  
To isolate the contribution of aerosols to GPP, we also simulated GPP with a global spatial resolution of 0.1\({}^{\circ}\) and a monthly temporal resolution from 2000 to 2019 using the EC-LUE model. The simulation based on the AOD-LUE model considered aerosol radiation effects and was referred to as S0 hereafter, while the simulation based on the EC-LUE model ignored aerosol radiation effects and was referred to as S1 hereafter. Two metrics: \(\Delta\)GPP (Equation (8)) and \(\Delta\)GPP (%) (Equation (9)) were used to measure the differences in GPP between the S0 and S1 simulations:  
\[\Delta\mathrm{GPP}=\mathrm{GPP_{S0}}-\mathrm{GPP_{S1}} \tag{8}\]  
\[\Delta\mathrm{GPP}(\%)=\frac{\Delta\mathrm{GPP}}{\mathrm{GPP_{S1}}} \tag{9}\]
# Single-Stage Step-Down Power Factor Corrector without Full-Bridge Rectifier
## 4 Design Considerations
### Design of Output Inductor
 \tag{10}\]  
The amplitude of the ideal input AC current, called \(I_{im}\), is determined by the input power \(P_{in}\), as shown in (11):  
\[I_{im}=\frac{\pi P_{in}}{2V_{in,pk}}\cdot\frac{1}{(\frac{\pi}{4}-\frac{\cos \theta_{0}\sin\theta_{0}}{2}-\frac{\theta_{0}}{2})} \tag{11}\]  
In addition, the average input power is obtained from the output power, and the prescribed efficiency, i.e., \(P_{in}=P_{out}/\eta\), (11) can be rewritten as  
\[I_{im}=\frac{\pi\cdot P_{out}/\eta}{2V_{in,pk}}\cdot\frac{1}{(\frac{\pi}{4}- \frac{\cos\theta_{0}\sin\theta_{0}}{2}-\frac{\theta_{0}}{2})} \tag{12}\]  
By substituting the rated output load \(P_{o,rated}\), the peak value of the minimum input AC voltage \(v_{in,min}\), and the results of (8) into (12), the ideal input AC current amplitude \(I_{im}\) can be obtained as follows:  
\[I_{im}=\frac{\pi\times\frac{90}{0.95}}{2\times 90\sqrt{2}}\cdot\frac{1}{(\frac{ \pi}{4}-\frac{0.63\times 0.78}{2}-\frac{0.68}{2})}=5.83\text{A} \tag{13}\]  
By setting the conduction angle \(\theta\) in (7) to \(\frac{\pi}{2}\), the peak value of the input AC current, called \(I_{in,pk}\), can be obtained to be  
\[I_{in,pk}=I_{im}\cdot(1-\sin\theta_{0}) \tag{14}\]  
Substituting the results of (8) and (13) into (14) yields  
\[I_{in,pk}=5.83\times(1-0.63)=2.16\text{A} \tag{15}\]  
The average value of the inductor current \(i_{L}\), called \(I_{L}\), is  
\[I_{L}=\left\langle i_{L}(t)\right\rangle_{T_{s}}=\frac{i_{in}}{D} \tag{16}\]  
By substituting \(I_{in,pk}\) into (16), the peak value of the average value of the inductor current, called \(I_{L,avg,pk}\), can be found as  
\[I_{L,avg,pk}=\frac{I_{in,pk}}{D} \tag{17}\]  
The ripple current through the inductor \(L\) can be expressed as  
\[\Delta i_{L}=\frac{(v_{in}-V_{o})DT_{s}}{L}=\frac{V_{o}(1-D)T_{s}}{L} \tag{18}\]  
Therefore, if the inductor \(L\) is to be operated in the current discontinuous mode (DCM), the value of inductor \(L\) must satisfy the following inequality:\[ \begin{split} I_{L,avg,pk}&\leq\frac{\Delta i_{L}}{2}\\ \Rightarrow\frac{I_{ln,pk}}{D}&\leq\frac{V_{o}(1-D)T_{ z}}{2L}\\ \Rightarrow L&\leq\frac{V_{o}D(1-D)T_{z}}{2I_{ln,pk} }\\ \end{split}  \tag{19}\]  
Substituting the system specifications shown in Table 1 and the results of (8) and (15) into (19) yields  
\[L\leq\frac{80\times 0.63\times(1-0.63)\times 10\ \mu}{2\times 2.16}=43.2\ \mu\text{H} \tag{20}\]  
Accordingly, the MPP powder iron core, manufactured by CSC Co. (Beijing, China) with a model name of CM270125, is used herein and its basic characteristic parameters are shown in Table 2. Therefore, the number of turns can be calculated as below:  
\[N=\sqrt{\frac{L}{A_{L}}}=\sqrt{\frac{43.19\ \mu}{157\text{n}}}=16.6 \tag{21}\]  
In order to ensure that the required inductance value is sufficient for the converter to operate in DCM under any load condition, the number of turns is taken as 16. Therefore, the actual value of the inductor \(L\) is  
\[L=A_{L}\cdot N^{2}=157\text{n}\times 16^{2}=40.2\ \mu\text{H} \tag{22}\]
## 3 Results  
### XO Inhibitory Activity of C. morifolium Fractions  
The yield of fractions and sub-fractions of the bioassay-guided fractionation is presented in Figure 1. All the C. _morifolium_ fractions and sub-fractions were analysed for their XO inhibitory activity and the results were tabulated in Table 1. One-way ANOVA analysis demonstrated that EtOAc fraction exhibited the highest XO inhibitory activity among the fractions. The activity was lower than the standard drug, allopurinol (positive control), which showed nearly 100% inhibition at the same concentration.  
The inhibitory mechanism of the C. _morifolium_ crude extract and its fractions were studied by using a double-reciprocal plot of enzyme kinetic. Lineweaver-Burk plots of XO activity in the presence and absence of inhibitor were shown in Figure 2. The results indicated that the mode of XO inhibition for the RS fraction was competitive, whereas the crude extract, EtOAc, and PE fractions were shown to be non-competitive. The most bioactive EtOAc fraction was fractionated through open column chromatography, and 15 sub-fractions were obtained. The XO inhibitory activity of the 15 sub-fractions showed that F7, F8, F9, F10, and F11 possessed high XO inhibitory activity (>75%) at 100 \(\upmu\)g/mL, whereas other sub-fractions exhibited low to moderate inhibitory activity (Figure 3). The most bioactive subfraction F10 was chosen for all the subsequent cell culture experiments.  
\begin{table}
\begin{tabular}{c c} \hline
**Samples** & **Xanthine** **Oxidase Inhibition (\%)** \\ \hline PE & 28.09 \(\pm\) 2.09 \({}^{\text{c}}\) \\ RS & 33.86 \(\pm\) 3.25 \({}^{\text{c}}\) \\ EtOAc & 51.77 \(\pm\) 0.98 \({}^{\text{b}}\) \\ Crude & 11.55 \(\pm\) 1.21 \({}^{\text{d}}\) \\ Allopurinol & 99.79 \(\pm\) 0.21 \({}^{\text{a}}\) \\ \hline \end{tabular} Values are mean \(\pm\) SEM (\(n=3\)). Means with a different superscript letter showed significant difference (\(p\leq 0.05\)). Percentage of inhibition was measured at 100 \(\upmu\)g/mL. PE, petroleum ether fraction; RS, residual fraction; EtOAc, ethyl acetate fraction.  
\end{table}
Table 1: Xanthine oxidase inhibitory activity of different fractions of the C. _morifolium_ crude extract and fractions.
## 3 Results
### Decision Procedure Design  
In this section, we aim to develop a CNN-based decision element for a decision procedure according to Figure 2. This process is especially based on training and testing of the implemented CNN. Feedforward CNNs consist of multiple layers arranged in a feed-forward manner. The first layers (convolutional and max-pooling, typically combined with ReLU) perform feature extraction from the input data. Then, several dense layers are connected and the classification or decision is ensured by a soft-max activation function.  
Figure 13: Fitness course during genetic algorithm.  
As the performance of the CNN is strongly affected by its structure, we decided to include several well-known architectures for testing. Namely, Net1 and Net2 are the simplest architectures. Both of these were adapted from [41]. In addition to these networks, the following more complex and widely accepted topologies were selected: LeNet-5 [37; 38], AlexNet [39], VGG-16 net [40] and MobileNet [42]. Note that more advanced CNNs were not included into this selection because of the strong need for computational efficiency. More recent CNNs are generally much more computationally demanding.
# Reliability of NAND Flash Memories: Planar Cells and Emerging Issues in 3D Devices
## 2 The NAND Memory Array
### Array Operation
#### 2.2.2 Program  
NAND Flash cells adopt channel Fowler-Nordheim (FN) tunneling to store electrons onto their floating gates [39]. To this aim, the selected cell is first connected to the external circuitry by biasing the unselected WLs to a pass voltage and turning on the DSL, while the BL is grounded and the SSL is left off. A high voltage is then applied to the desired WL, resulting in tunnel electron injection from the inverted substrate to the floating gate. In MLCs or TLCs, fine placement of the \(V_{T}\) levels is needed because of the reduced distance between adjacent distributions (see Figure 2). This is achieved via an _incremental step-pulse programming_ (ISPP) technique [40], in which fast pulses of increasing amplitude are used to inject a constant, controlled amount of charge onto the floating gate at each step, interleaved with _program verify_ phases in which \(V_{T}\) is monitored. Several refinements are applied to this basic concept in 8- or 16-level cells [38].  
Since the program operation is controlled by the WL bias, it acts on a page of the array. However, not all cells must be programmed at the same level (or programmed at all), which means that a program-inhibit strategy must be implemented for those cells. Inhibiting programming means reducing the gate-channel bias of such cells, thereby lowering their tunnel oxide field responsible for the electron injection. In NAND arrays, this is achieved via a _self-boosting_ procedure [36], where the inhibited strings are first floated and their potential is temporarily raised via capacitive coupling to the WLs, that are raised at the passing voltage [38]. Program throughput in excess of 50 MB s\({}^{-1}\) has been reported [10].
## 3 Numerical Validation and Experiments
### Initial Deformation of Panel Components
The skewed drilling points would be joined in the subsequent assembly process, which seriously affects the product quality. Thus, the deformation prediction of stiffened aircraft panel components has a practical significance in aeronautical engineering. It can be observed from Figures 4b, 5b and 6 that clearances \(\Delta_{\mathrm{w}}\) of the regions between stringers and skin have the similar trend with the displacements \(w\) of deformed shell on sections \(\beta=\beta_{p}\). The major portion of clearance is the contribution from the skin deformation in the direction of surface normal. Absolute maximum deviations between predicted and measured clearances related to region I and II are 0.247 mm and 0.260 mm. Mean absolute deviations are 0.1127 mm and 0.1125 mm, respectively. It can be confirmed from the previous results that clearances provided by the proposed method and experimental approach have a good agreement.
## 2 Materials and Methods
### Analysis of Endotoxin and Allergens  
After sampling, all samples were kept in a cold box, transported to the laboratory within the same day and filters were stored at \(-20\)\({}^{\circ}\)C for 1-3 months to await measurement.  
GF filters were extracted in 5 mL of PFW plus Tween-20 (0.05% \(v/v\)) via shaking for 1 h at room temperature on a reciprocating shaker (Promax 1020, Heidolph Instruments GMBH & CO KG, Schwabach, Germany). The extracts were centrifuged for 10 min at 1000\(\times\)\(g\) (2094 r.p.m.). Undiluted and serial dilutions were analyzed using Kinetic-QCL LAL assay (Lonza Walkersville, MD USA) and interpreted against a 5-point (concentration range 0.005-50 EU mL\({}^{-1}\)) standard curve of Escherichia coli (CSE). All procedures were carried out using pyrogen-free reagents and all glassware was baked at 180 \({}^{\circ}\)C for 4 h to remove endotoxin. To obtain information about possible enhancement or inhibition reactions of the LAL assay, a replicate of each sample was spiked with an endotoxin standard (CSE, 50 EU/mL) (5 EU/mL final activity). The recovery of spiked samples was in the range of 50 to 200%; otherwise, the measurement was not included or repeated. The limit of detection (LOD) was 0.005 EU/mL\({}^{-1}\). The concentrations are given as ng/m\({}^{3}\).  
MCE filters were extracted via submerging in 1 mL phosphate saline buffer (PBS plus 0.01% Tween 20) in a 15 mL centrifuge tube, vortexing at room temperature and shaking for 2 h. The eluate was collected by compressing the filter in a plastic syringe; 1% of human serum albumin was added for protein stabilization and it was stored at \(-20\)\({}^{\circ}\)C until assay. ELISA kits for Mus m 1, Rat n 1, Can f 1, Fel d 1 and Equ c 4 were used following the protocols of analysis provided by the manufacturer (Indoor Biotechnologies Ltd., Manchester, UK) with reading of the plates at 405 nm optical density. Results are expressed in ng/m\({}^{3}\).  
Limits of detection (LOD) were 0.097 and 0.024 ng/filter for rat allergen and mouse allergen, respectively. A concentration equal to half the LOD was attributed to samples below this limit, while two samples with very high concentrations (>25 ng/filter) were attributed a value of 25 ng/filter for statistical analysis.