esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Water Vapor Transmission Rate (WVTR) Testing  
Materials included a 40 mL septum vial, a desiccant (2-5 mm silica beads), fume hood, a balance showing uncertainty to four decimal places (\(\pm\)0.0001 g), adhesive silicone or other water-resistant sealant, and the testing material: a sheet of PLA with a width of 0.0304 cm. The scale used was a New Classic MF model MS105 DU, produced by Mettler Toledo. The following methodology is a modified version of ASTM Standard E96: Standard Test Methods for Water Vapor Transmission of Materials [43]. PLA was cut from a sheet to completely cover the surface area of the opening of the 40 mL septum vial. The septum vial was weighed, and the initial weight was recorded. The silica beads were added to the vial and filled to within 1/2 inch of the top of the vial. The weight of the vial with the additional silica beads was measured. The PLA sample was then attached to the top of the vial using the silicone sealant. A continuous layer of sealant was applied to the edges of the top of the vial, and the sample was pressed to the sealant and allowed to dry. The vial was weighed again for the initial testing weight and placed in the test chamber. After initial weighing, the sample was weighed once every day for the following six days. The weight of the vial was plotted over time. WVTR was calculated using Equation (4):  
\[WVTR=\frac{G}{t\ \times\ A} \tag{4}\]  
where \(G\) is the weight change in grams, \(t\) is time in days, and \(A\) is the test area of the vial mouth in meters squared. \(WVTR\) is measured in gd\({}^{-1}\)m\({}^{-2}\).
## 5 Performance Evaluation
As the complexity of the calculations increased due to the large number of potential solutions, the execution times of the algorithms were significantly increased as well. The execution times of the algorithms were also affected by the number of sensor nodes and not as much by the k-coverage points and the degree of coverage which they demand. Another factor which undoubtedly affects the execution time is the sampling step which, nevertheless, contributes to the improvement of the algorithm's results. What is considered as an issue of outmost importance is to define the optimal sampling step, since it was observed that after a certain point the further decrease of the sampling step led to a great increase of the time without the respective improvement of the area coverage. As far as the simpler cases are concerned, the execution of the GA algorithm demanded less time compared to the PSO algorithm; yet, as the difficulty of the experiments increased, the GA algorithm needed more time and a larger size of population so as to achieve a result comparable to the one that was calculated with the use of the PSO algorithm. The results derived from a group of 30 executions of the algorithm for each case and in each one of them, the standard deviation was rather small, depending on the complexity of the problem. Based on these small standard deviation values, it was concluded that the algorithm's results, having used both methods, present a high repeatability. Furthermore, the results derived were close enough to the ideal coverage percentage value. Even though some cases where the problem needed k-coverage of certain points and the existence of overlaps within the sensor nodes--which is not calculated in the ideal area coverage--were inevitable, the results of the optimal solution were very close to the ideal optimal values. It is, thus, observed that through the use of the GA as well as the PSO algorithm a high degree of accuracy concerning the results can be achieved. The examination of the last three case studies showed that both the proposed algorithms are more advantageous compared to the grid-based algorithm proposed in [30] as far as the average time is concerned, but not as good as the Voronoi-based algorithm proposed in [31]. This is reasonable because the construction of the Voronoi diagram is a less complicated approach than the sampling methodology. Nevertheless, both of the algorithms proposed were found to be superior compared to the older two approaches in all of the case studies examined in terms of the area coverage ratio. The superiority of the proposed algorithms over the algorithms proposed in [30] and [31] is statistically significant with a confidence level higher than 99%, since the p-value is always very close to 0.
# Synthesis of Star Nanoparticles and Their Applications for Bioactive Compound Encapsulation
## 2 SNP Preparation Method
### Ball Milling  
Ball milling is one of the reliable techniques to produce nanoparticles without the aid of additives or the requirement of a final dehydration step to recover the nanoparticles. Over the years, the ball milling process has been explored for the nanoreduction of different polymeric materials including starch [49], cellulose [50] and protein-based polymers [51] for  
Figure 6: Synthesis of SNPs through sacrificial template method and TEM micrograph of pea, mung, bean, potato, and corn hollow SNPs [48].  
different applications. This process can be conducted in dry (without solvents) or wet (with solvents) conditions. The milling occurs due to mechanical friction between the grinding media, such as balls of the same or various sizes, and the ground material. The container and the grinding balls are typically made of the same, super hard material (zirconia, corundum, or stainless steel). Mechanical energy is provided to the system by the rotary motion of the bowl, as in the case of a planetary ball mill. Wet ball milling is also considered to be a green chemistry approach, as it does not require high temperatures (energy saving) and consumes minimal quantities of solvents [52]. A schematic representation of the ball milling method is shown in Figure 7.  
This process is a cost-efficient and environmentally friendly physical processing method that has been proven capable of changing starch properties [55; 56]. In 2019, [PERSON] et al. carried out a study synthesizing water chestnuts SNPs by a physical method of ball milling. SNPs were obtained by reducing the starch size with a planetary ball for 5 h and finally, the average particle size obtained after the milling process was 271 nm [53]. In recent studies, [PERSON] et al. also synthesized SNPs through a ball milling method using a mixture of starches (lotus stem, horse, and water chestnut) to create nanocapsules for bioactive compounds encapsulation. SNPs were obtained in the same way as their previous study with a planetary ball for 5 h. For this study, higher hydrodynamic particle sizes were obtained, being the smallest for the horse chestnut starch with a particle size of 420 nm approximately and with a polydispersity index of 32% what indicated a narrow size distribution [54].
# Information-Theoretic Analysis of a Family of Improper Discrete Constellations
## Appendix B Proof of Theorem 3  
For the proof, we follow the technique used in [31; 34]. Let us denote the standard complex Gaussian density as \(\psi(y)=\frac{1}{\pi}e^{-|y|^{2}}\), and let us define the functions:  
\[g_{0}(y;\text{snr}) =E\left[\psi(y-\sqrt{\text{snr}}V)\right],\] (A6) \[g_{1}(y;\text{snr}) =E\left[V\psi(y-\sqrt{\text{snr}}V)\right],\] (A7)  
where the expectation is taken over \(V\). The conditional mean estimate can be expressed as [31]:  
\[E[V|Y=y]=\frac{g_{1}(y;\text{snr})}{g_{0}(y;\text{snr})},\] (A8)  
and (taking into account that \(E[|V|^{2}]=1\)), the mmse can be calculated as [34]:  
\[\text{mmse}(\text{snr})=1-\int\frac{|g_{1}(y;\text{snr})|^{2}}{g_{0}(y;\text{ snr})}dy,\] (A9)  
where the integral is calculated over the complex field.  
When \(\text{snr}\to 0\), the Gaussian complex density can be expanded as:  
\[\psi(y-\sqrt{\text{snr}}V)=\psi(y)\left[1+(y^{*}V+yV^{*})\sqrt{\text{snr}}+ \frac{1}{2}\left((y^{*}V)^{2}+(yV^{*})^{2}+2|V|^{2}(|y^{2}|-1)\right)\text{ snr}+\mathcal{O}(\text{snr}^{3/2})\right].\] (A10)  
Using this Taylor series expansion, the functions \(g_{0}(y;\text{snr})\) and \(g_{1}(y;\text{snr})\) defined in (A6) and (A7), respectively, can be computed in terms of the moments of \(V\) as:  
\[g_{0}(y;\text{snr}) =\psi(y)\left[1+\frac{1}{2}\left((y^{*})^{2}E[V^{2}]+y^{2}E[V^{2 }]^{*}+2E[|V|^{2}](|y^{2}|-1)\right)\text{snr}\right],\] (A11) \[g_{1}(y;\text{snr}) =\psi(y)\left[\left(y^{*}E[V^{2}]+yE[|V|^{2}]\right)\sqrt{\text{ snr}}+\frac{1}{2}(y^{*})^{2}E[V^{3}]\text{snr}\right],\] (A12)  
where in (A12), we have used \(E[|V|^{2}V^{*}]=0\). We also have the expansion:  
\[\frac{1}{g_{0}(y;\text{snr})}=\frac{1}{\psi(y)}\left[1-\frac{1}{2}\left((y^{* })^{2}E[V^{2}]+y^{2}E[V^{2}]^{*}+2E[|V|^{2}](|y^{2}|-1)\right)\text{snr} \right].\] (A13)  
We are assuming \(|E[V^{2}]|^{2}=1\), and in addition, it is easy to check that \(E[V^{3}]=0\). Therefore, substituting these values in (A12) and (A13), we get:  
\[g_{1}(y;\text{snr})=\psi(y)\left(y^{*}E[V^{2}]+y\right)\sqrt{\text{snr}}\] (A14)and:  
\[\frac{1}{g_{0}(y;\text{snr})}=\frac{1}{\psi(y)}\left[1-\frac{1}{2}\left((y^{*})^{2 }E[V^{2}]+y^{2}E[V^{2}]^{*}+2(|y^{2}|-1)\right)\text{snr}\right], \tag{115}\]  
which only depend on \(E[V^{2}]\).  
Substituting (114) and (115) in (109) and evaluating the resulting integral over \(y\), we obtain the result:  
\[\text{mmse}(\text{snr},\kappa)=1-(1+\kappa^{2})\text{snr}+(1-\kappa^{2})\text {snr}^{2}+\mathcal{O}(\text{snr}^{3}). \tag{116}\]  
Finally, by integrating Expression (116), we obtain the expansion around \(\text{snr}=0\) of the mutual information as:  
\[\text{I}(\text{snr},\kappa)=\text{snr}-\frac{(1+\kappa^{2})}{2}\text{snr}^{2} +\frac{(1-\kappa^{2})}{3}\text{snr}^{3}+\mathcal{O}(\text{snr}^{4}). \tag{117}\]  
which completes the proof.
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 2 The Influence of Sea-Surface Turbulence on Laser Beams and Aiming Error
In Figure 2, we can see that the variance in aiming error angle of the uplink caused by light intensity scintillation changes with altitude, and the higher the altitude, the smaller the aiming deviation causedby light intensity scintillation; the closer it is to sea level, the more obvious the effect of atmospheric turbulence on the beam; with an increase in altitude, the atmosphere gradually becomes thinner, caused by turbulence changes in the refractive index of the atmosphere becoming smaller; therefore, the aiming angle caused by the change is also smaller.
# Early Evidence That Soil Dryness Causes Widespread Decline in Grassland Productivity in China
###### Abstract  
The burning of fossil fuels by humans emits large amounts of CO\({}_{2}\) into the atmosphere and strongly affects the Earth's carbon balance, with grassland ecosystems changing from weak carbon sinks that were previously close to equilibrium to core carbon sinks. Chinese grasslands are located in typical arid-semi-arid and semi-arid climatic regions, and drought events in the soil and atmosphere can have strong and irreversible consequences on the function and structure of Chinese grassland ecosystems. Based on this, we investigated the response of the gross primary production (GPP) of Chinese grasslands to land-atmosphere moisture constraints, using GPP data simulated through four terrestrial ecosystem models and introduced copula functions and Bayesian equations. The main results were as follows: (1) Soil moisture trends were not significant, and changes were dominated by interannual variability. The detrended warm-season SM correlated with GPP at 0.48 and 0.63 for the historical and future periods, respectively; thus, soil moisture is the critical water stress that regulates interannual variability in Chinese grassland GPP. (2) The positive correlation between shallow SM (0-50 cm) and GPP was higher (r = 0.62). Shallow-soil moisture is the main soil layer that constrains GPP, and the soil moisture decrease in shallow layers is much more likely to cause GPP decline in Chinese grasslands than that in deep-soil water. (3) The probability of GPP decline in Chinese grasslands caused by drought in shallow soils of 0-20 and 20-50 cm is 32.49% and 27.64%, respectively, which is much higher than the probability of GPP decline in deeper soils. In particular, soil drought was more detrimental to grassland GPP in Xinjang and the Loess Plateau. (4) The probability of soil drought causing GPP decline was higher than that of atmospheric drought during the historical period (1.78-8.19%), but the probability of an atmospheric drought-induced GPP deficit increases significantly in the future and becomes a key factor inhibiting GPP accumulation in some regions (e.g., the Loess Plateau). Our study highlighted the response of grassland ecosystems after the occurrence of soil drought, especially for the shallow-soil-water indicator, which provides important theoretical references for grassland drought disaster emergency prevention and policy formulation.  
Keywords:gross primary productivity; water constraint; Chinese grasslands; soil drought; probabilistic framework +
Footnote †: journal: _Land_ **2023**, 12, 484. [[https://doi.org/10.3390/land12020484](https://doi.org/10.3390/land12020484)]([https://doi.org/10.3390/land12020484](https://doi.org/10.3390/land12020484))
## 3 Proposed System  
### Software Module  
#### 3.1.1 Definition of DataMatrix-ArUco-Hybrid Pattern  
Fiducial markers commonly used for docking, such as ARToolKit, AprilTag, and ArUco, are typically limited to encoding numbers only [33]. However, there are many docking targets in manufacturing factories with similar characteristics, requiring a secondary validation process to confirm the actual string content of target workstations or devices before executing the subsequent action.  
To address this issue, we propose a new 2D fiducial marker pattern called the DataMatrix-ArUco-Hybrid (DAH) pattern, consisting of four inner markers--two ArUco and two Data Matrix (DM)--in a diagonal form. The clockwise order of the inner markers in the DAH pattern is left-top DM (LT-DM-region), right-top ArUco (RT-ArUco), right-bottom DM (RB-DM-region), and left-bottom ArUco (LB-ArUco), as shown in Figure 1.  
To locate the DM candidates in an image quickly, we defined a homocentric square surrounding the original DM as the DAH pattern finder pattern, as shown in the green area in Figure 1b. The region enclosed by the largest outer square in the homocentric square is called a DM region, which is represented by the blue area in Figure 1a. Each valid DAH pattern contains at most two DM regions, with each DM region containing at most one DM. The two DM regions in a DAH pattern are distinguished by the first symbol in the encoded content. We defined the first symbol of the LT-DM-region content as % and the first symbol of the RB-DM-region content as \(\mathbf{\kappa}\).  
To determine the suitable RT-ArUco and LB-ArUco IDs in the generation process, we developed a rule to map the input production information to a valid number in the chosen ArUco dictionary. First, we use the CRC32 hash algorithm to map the input string content to an integer \(N\). Assuming the RT-ArUco ID is \(a\) and the LB-ArUco ID is \(b\), if the chosen dictionary's maximum number is \(K\), then the inner ArUco IDs \(a\) and \(b\) are, respectively, defined as the remainder of \(N\) to \(K\) and the remainder of \(N\) to \(K-1\); a calculation example is illustrated in Figure 1b.  
By calculating the two ArUco IDs, all inner markers in a DAH pattern can be defined, which can help the detection algorithm find the correct ArUco ID to combine with a DM region candidate to form a valid DAH pattern.  
Figure 1: (**a**) The area definition in the DAH pattern. (**b**) An example of a DAH pattern: The desired encoded content of this DAH pattern is “DAH-pattern”; the corresponding decimal number obtained using the CRC32 is 1166492279. The chosen ArUco dictionary is TAG36h10, with the chosen value of \(K\) being 2023. The RT-ArUco ID is defined as 134, while the LB-ArUco ID is defined as 479. The LT-DM-region content is defined as “%DAH-pattern”, while the RB-DM region is defined as “%DAH-pattern”.
## 4 Equations of Motion  
The equations of motion associated with the present finite element model are derived using the [PERSON]'s principle, which is defined as [52]  
\[\int_{0}^{T}\left[\delta K-\left(\delta U+\delta V\right)\right]dt=0, \tag{17}\]  
where \(\delta K\) represents the virtual kinetic energy, \(\delta U\) is the virtual strain energy, and \(\delta V\) is the virtual potential energy due to external loads.  
From [PERSON]'s principle, the mass matrix, the stiffness matrix, and the load vector can be obtained straightforwardly using the virtual kinetic energy, the virtual strain energy, and the virtual potential energy, respectively. However, for the present study, it is sufficient to compute the stiffness and mass matrices to define the finite element model.  
For the displacement field considered here, the virtual kinetic energy is given by  
\[\delta K = \int_{A}\bigg{\{}I_{0}(\delta\dot{\mathbf{u}}\cdot\dot{\mathbf{u }})\ +\ I_{1}\bigg{[}\frac{h}{2}\left(\dot{\mathbf{u}}\cdot\delta\dot{\mathbf{\varphi }}+\dot{\mathbf{\varphi}}\cdot\delta\dot{\mathbf{u}}\right)\bigg{]}+I_{2} \bigg{[}\frac{h}{2}\left(\dot{\mathbf{u}}\cdot\delta\dot{\mathbf{\Psi}}\mathbf{ n}+\frac{h}{2}\,\dot{\mathbf{\varphi}}\cdot\delta\dot{\mathbf{\varphi}}+\dot{ \mathbf{\Psi}}\mathbf{n}\cdot\delta\dot{\mathbf{u}}\right)\bigg{]}+ \tag{18}\] \[+\ I_{3}\bigg{[}\frac{h^{2}}{4}\left(\dot{\mathbf{\varphi}}\cdot \delta\dot{\mathbf{\Psi}}\mathbf{n}+\dot{\mathbf{\Psi}}\mathbf{n}\cdot\delta \dot{\mathbf{\varphi}}\right)\bigg{]}+\ I_{4}\bigg{[}\frac{h^{2}}{4}\left(\dot {\mathbf{\Psi}}\delta\dot{\mathbf{\Psi}}||\mathbf{n}||^{2}\right)\bigg{]} \bigg{\}}dA,\]And the virtual strain energy is given by  
\[\delta U=\int_{A}\left[A^{ijkl}\delta\varepsilon_{ij}^{(0)}\varepsilon_{kl}^{(0)}+ B^{ijkl}\left(\delta\varepsilon_{ij}^{(1)}\varepsilon_{kl}^{(0)}+\delta \varepsilon_{ij}^{(0)}\varepsilon_{kl}^{(1)}\right)+D^{ijkl}\delta\varepsilon_ {ij}^{(1)}\varepsilon_{kl}^{(1)}\right]dA. \tag{19}\]  
In Equation (18), \(||\mathbf{n}||\) is the norm of the normal vector \(\mathbf{\underline{n}}\), the dot indicates the partial derivative with respect time, and the mass inertias are defined as  
\[\{I_{0},I_{1},I_{2},I_{3},I_{4}\}=\int_{-1}^{1}\biggl{\{}1,\xi^{3},\left(\xi^{ 3}\right)^{2},\left(\xi^{3}\right)^{3},\left(\xi^{3}\right)^{4}\biggr{\}} \rho\left(\xi^{3}\right)Jd\xi^{3},\]  
where \(\rho\left(\xi^{3}\right)\) is the mass density.  
In Equation (19), the contravariant components \(A^{ijkl},B^{ijkl},D^{ijkl}\) are the effective extensional, extensional-bending coupling, and bending fourth-order stiffness tensor components [47]. Such components are calculated using the following definitions:  
\[A^{ijkl}=\int_{-1}^{1}C^{ijkl}\,Jd\xi^{3},\quad B^{ijkl}=\int_{-1}^{1}\xi^{3}C^ {ijkl}\,Jd\xi^{3},\quad D^{ijkl}=\int_{-1}^{1}\left(\xi^{3}\right)^{2}C^{ijkl} \,Jd\xi^{3}.\]  
The expressions of Equations (18) and (19) are used to determine the mass and stiffness matrices, respectively.
# Effect of Mix Proportion Parameters on Behaviors of Basalt Fiber RPC Based on Box-Behnken Model
## 2 Experimental  
### Materials and Mixture Proportions  
The Portland cement P.II 42.5 produced by Yatai Cement Ltd. (Jilin, China) was used. SF92 silica fume with a mass fraction of 93.3% and specific surface area of 18,100 m\({}^{2}\)/kg was sourced from Si'ao Technology Ltd. (Changchun, China). The chemical compositions of the Portland cement and silica fume are shown in Table 1. The basalt fiber used was 22 mm length and 23 \(\upmu\)m diameter produced by Haining Anjie Composite Material Ltd. (Haining, China) with a tensile strength of 2836 MPa and elastic modulus of 62 GPa as shown in Table 2.  
Quartz sand with three particle sizes was used in this study: 20-40, 40-80 and 80-120 mesh, produced by Zhenxing Quartz Sand factory in Luoyang. A screening test on three kinds of quartz sand was carried out first to divide into two particle levels: Medium sand, which includes particle  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Materials**} & \multicolumn{6}{c}{**Chemical Compositions (\%)**} \\ \cline{2-7}  & \(\mathbf{SiO_{2}}\) & \(\mathbf{Al_{2}O_{3}}\) & \(\mathbf{CaO}\) & \(\mathbf{MgO}\) & \(\mathbf{Fe_{2}O_{3}}\) & \(\mathbf{SO_{3}}\) \\ \hline Cement & 22.6 & 5.6 & 62.7 & 1.7 & 4.3 & 2.5 \\ Silica fume & 93.3 & 0.73 & 0.85 & 1.21 & 0.49 & 1.02 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical compositions of Portland cement and silica fume.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Fiber** \\ **Type** \\ \end{tabular} } & **Length** & **Diameter** & **Linear** & **Tensile** & **Elastic** & **Breaking** & **Strength** \\  & **(mm)** & **(\(\upmu\)m)** & **(tex)** & **(MPa)** & **(GPa)** & **(N/tex)** & **(\%)** \\ \hline Basalt fiber & 22 & 23 & 2392 & 2836 & 62 & 0.69 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Properties of basalt fibers.  
sizes above 0.3 mm and fine sand, which includes particle sizes below 0.3 mm. The mix proportion of medium and fine sand was 1.6:1. The ultrafine quartz powder with a particle size of 400 mesh was added as a micro filler aggregate in this study with its ratio to cement was 0.32. The chemical compositions of the quartz sand is shown in Table 3. The HRWR-Q8011 polycarboxylic superplasticizer with a water reducing rate of 25% was sourced from Qinfen Building Materials Ltd., the mix content of which was 5%. The proportions of mixtures are shown in Table 4.