esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### HNS Propagation Characteristics
Figure 5: (**a**) The change in the HNS layer thickness for the input variables (\(v_{c}\) and \(\rho_{HNS}\)), and (**b**) the schematic of the HNS layer thickness. Figure 6b shows the maximum propagation length of the HNS interface in the downstream direction. The maximum propagation length linearly increases with time and is significantly affected by the current velocity. As shown in Figure 6c, however, very similar tendencies are found for different densities because the advection and diffusion effects become more dominant than the buoyancy effect. The propagation velocity \(v_{p}\) is slightly greater than \(v_{c}\). For example, in the case of \(v_{c}\) = 1.5 m/s, \(v_{p}\) is approximately 1.6 m/s. In addition, \(v_{p}\) varies with HNS density. When \(v_{c}\) is 0.5 m/s, \(v_{p}\) is 0.56, 0.60 and 0.65 m/s with respect to the HNS density of 700, 800, and 900 kg/m\({}^{3}\), respectively. The reason is as follows. The dissolved HNS floats with vertical-directional momentum because of the buoyancy-driven force. The vertical-directional momentum of dissolved HNS will change to the streamwise-directional momentum at the sea surface region. Therefore, floating HNS with a density of 700 kg/m\({}^{3}\) propagates faster than that with a density of 900 kg/m\({}^{3}\). This effect is valid even in the case of \(v_{c}\) = 1.5 m/s but is not significant at higher-velocity conditions. This is because the vertical direction momentum component of the dissolved HNS decreases as \(v_{c}\) increases. Figure 7 represents the change in the averaged HNS mass fraction at sea level for different current velocity and HNS density. The averaged HNS mass fraction at the sea level, \(\phi_{s-l}\), is defined as the area-averaged mass fraction of the HNS at sea level (\(y=0\)) as follows:  
\[\phi_{s-l}=\left.\frac{1}{A}\int\int\phi\left(\overset{\ast}{x}\right)dA\right| _{t=1000\,s} \tag{14}\]  
Figure 6: (**a**) The movement of the interface from 50 to 100 s for different current velocities with \(\rho_{\text{hns}}\) = 700 kg/m\({}^{3}\), (**b**) the maximum \(x\) location of HNS interface in the case of \(\rho_{\text{hns}}\) = 700 kg/m\({}^{3}\), and (**c**) the averaged HNS propagation velocity for the input variables (\(v_{c}\) and \(\rho_{\text{hns}}\)). where \(\phi(x)\) represents the HNS mass fraction at sea-level and \(A\) is the area at the sea surface. As \(v_{c}\) increases, \(\phi_{s-l}\) decreases proportionally, since spilled HNS is not stagnant but is actively propagated by advection. From 1 to 1.5 m/s \(v_{c}\), the averaged HNS mass fraction at sea level increased for less dense HNS because buoyancy is a significant driving force for floating HNS at sea level. However, when \(v_{c}\) is 0.5 m/s, \(\phi_{s-l}\) is estimated to be about 0.021 regardless of HNS density. This is because the spilled HNS with a density of 700 kg/m\({}^{3}\) propagates faster than that with a density 900 kg/m\({}^{3}\) due to the buoyancy force, and it makes the HNS layer less dense. Therefore, the \(\phi_{s-l}\) values become similar regardless of HNS density at a \(v_{c}\) of 0.5 m/s.
## 3 Materials and Methods
### Test Methods
#### 3.4.4 Temperature Shrinkage Test  
Eighteen specimens (three compaction methods, two aggregates) were cured for 28 days under standard conditions and then completely dried to eliminate the effect of moisture loss on dry shrinkage. This complete dry was selected in agreement with other studies in which testing dried cement-based materials was required [33; 34; 35]. Strain gauges were pasted on the central side surfaces of these specimens. Temperature shrinkage strain was recorded at the temperatures \(-15\)\({}^{\circ}\)C, \(-5\)\({}^{\circ}\)C, \(5\)\({}^{\circ}\)C, \(15\)\({}^{\circ}\)C, \(25\)\({}^{\circ}\)C, \(35\)\({}^{\circ}\)C and \(45\)\({}^{\circ}\)C, respectively. The temperature changed at a speed of 1 \({}^{\circ}\)C/min.  
The temperature shrinkage coefficient was calculated using Equation (3).  
\[\beta_{ij}=\frac{\epsilon_{i}-\epsilon_{j}}{t_{i}-t_{j}} \tag{3}\]  
where \(\epsilon_{i}\) and \(\epsilon_{j}\) were the strain values of the \(i\)th and \(j\)th measurement; and \(\mu\epsilon\); \(t_{i}\) and \(t_{j}\) were the temperature of the \(i\)th and \(j\)th measurements, \({}^{\circ}\)C.
## 2 Materials and Methods
### Dust Used in Tests  
In order to maintain the possibility of comparing the effects of filtration with the use of filter bags operating in different industrial dust collectors, the same dust was used in laboratory tests of all bags. It was fine dust from the sanding of bent beech wood furniture panels. The bulk density of this dust was 177.8 kg\(\cdot\)m\({}^{-3}\). The particle-size distribution of the dust was also determined by the sieve analysis method (Figure 4). The mass arithmetic mean particle size of 91 \(\upmu\)m was determined based on the particle-size distribution. Standard deviation of the distribution was of 39.8 \(\upmu\)m.
# Transcriptional Analysis of Masson Pine
## 3 Results
### Energy Metabolism under Elevated CO2 Stress  
Figure 2 shows the energy metabolic pathways and the general pattern of the relative changes in the related gene expression patterns in Masson pine under high CO2 concentration conditions. Notably, in the transcriptome data, the pathways involved in energy metabolism were polarized. After Masson pine had been exposed to CO2 stress, in the photosynthesis pathways, including the photoreactions and Calvin cycle (Figure 2A, black arrow), the relative expression of each gene in the metabolic pathway except for _GAPD_ (glyceraldehyde-3-phosphate dehydrogenase) showed a downward trend (Figure 2B). GAPDH catalyzes 3-phosphoglyceraldehyde (PGAL) to 1,3-diphosphoglycerate (1,3-DPG) in the Calvin cycle and 1,3-DPG to dihydroxy-acetone phosphate (DHAP) in the Embden-Meyerhof-Parnas (EMP) pathway (Figure 2A, orange arrow). Since _GAPD_ plays a role in these two metabolic pathways, the change in its expression patterns may be the result of superposition. Moreover, the gene expression patterns involved in both photosynthesis and other metabolic pathways (such as _RPI_ (Ribulose  
Figure 1: Differential gene expression in seedlings under high CO2 stress. (**A**) Venn diagram of differentially expressed genes. (**B**) Statistical map of differentially expressed genes between different comparisons. Red and blue represent up- and down-regulated expression, respectively. (**C**–**E**) The top 20 pathways in the KEGG enrichment analysis of CK compared with the 6 h, 12 h and 24 h treatments, respectively. CK: 0 h or control check group.  
Phosphate Isomerase) or _RPE_ (Ribulose Phosphate Epimerase) decreased more slowly than those involved only in photosynthesis (such as _RCA_ (Rubisco Activase) or _FBP_ (Fructose-1,6-diphosphate)). On the other hand, the relative expression levels of genes in other pathways involved in energy anabolism, including the EMP pathway, the pentose phosphate (PPP) pathway (Figure 2A, blue arrow) and TCA (Figure 2A, green arrow), generally increased (Figure 2B). Among them, the expression rate of _OGDC_ (\(\alpha\)-ketoglutate dehydrogenase) increased the fastest and was 4.8, 6.09 and 6.18 times that of CK at 6 h, 12 h and 24 h, respectively. In general, the omics data revealed that energy metabolism was strongly enhanced for contributing to elevated CO\({}_{2}\) tolerance in Masson pine, except for the light reaction and Calvin cycle.
# Model Test on Grouting Properties of Alluvial Filler Soil
## 7 Engineering Characteristic Analysis of Grouting Soil
### Observation and Analysis of Slurry Diffusion
#### 7.4.2 Analysis of Slurry Diffusion  
Based on the observation of the whole section excavation and the results of the indoor test, the characteristics of slurry diffusion were obtained and illustrated as follows:  
(1) In the sand-gravel and clay-filling zones, the slurry diffusion was dominated by the split-filling; In the sandy gravel zone, the slurry spread mainly by infiltration. In the clay and silty sand filling zone, the slurry diffusion was dominated by the split compaction method, as shown in Figure 15.  
(2) Slurry splitting generally occurs along the interface of different media and compacts on the interface of different media, as shown in Figure 16a;  
(3) The splitting channel of the cement slurry is unobstructed and does not close with the end of grouting; the widths of the slurry splitting channel were 2.0-6.0 cm, as shown in Figure 16b.  
(4) The slurry veins were mainly fractured horizontally, with widths between 0.3-6.0 cm. There were fewer grouting veins in the vertical direction than in the horizontal direction, as shown in Figure 17. There are two possible reasons for this phenomenon. On the one hand, due to the shallow burial depth (only 1.6 m) of the grouting pipe, the vertical stress of the  
Figure 16: Slurry filling of different soil.  
Figure 15: Slurry filling of different soil.  
soil is small, and the slurry is easier to split on the plane perpendicular to the vertical stress (i.e., the horizontal plane). On the other hand, the soil used in the test is taken from an alluvial stratum with a layered structure, and the soil samples are layered in a model test container. Thus, the slurry is easier to split between layers (i.e., in the horizontal direction).
## 1 Introduction  
Entomopathogenice fungi are eukaryotic, filamentous, heterotrophic, and saprophy microorganisms. These fungi are essential to balance the cycle of decomposition of organic matter in the environment and show specific pathogenicity for insects without causing damage to human health [1, 2].  
In this way, entomopathogenice fungi are used in biological pest control strategies because they cause infection in all insect development stages with a broad spectrum of action. Their mechanism of action is associated with enzymes secreted in the extracellular medium during their infection and development in the insect's host body, directly influencing its ability to absorb nutrients, pathogenicity, and virulence. _Beauveria bassiana_, _Metarhizium anisopliae_, and _Isaria funosorsose_ are widely used in biological control programs and mycosonictides development owing to their easy handling, knowledge about their metabolism,and interaction with insects of different orders [3; 4]. The challenges of entomopathogenic fungi application to pest control include reduced shelf life and low viability when exposed to high temperatures, ultraviolet radiation, and humidity [5; 6; 7].  
Thus, synthesizing silver nanoparticles (AgNPs) from fungal extracellular enzymes is an alternative to applying these microorganisms and obtaining nanoparticles with insecticidal action. This method consists of reducing silver metal salts using entomopathogenic fungi extracts, such as AgNPs synthesized with _1. funsoroscea_ that showed insecticide activity in _Aedes aegypti_ and _Culex quinquefasciatus_ (Diptera: Culicidae) [8] and AgNPs synthesized with _B. Bassiana C M. anisopliae_ extract with insecticidal action on the mustard aphid (_Lipaphis ergsimi_) (Homoptera: Aphididae) and _Culex pipiens_ (Diptera: Culicidae) [9]. Thus, AgNPs with entomopathogenic fungus extract can produce an innovative insecticidal product using a green production process.  
In the synthesis of AgNPs with fungus, the formation of nanoparticles occurs by the reaction between silver ions and secondary metabolites produced by the fungi [10; 11]; however, there is a difference in the potential for the synthesis of different species isolates of fungi. Not all fucane to promote the reduced action of silver ions [12]. Therefore, it is necessary to select the most promising fungi for later application to the target insect.  
The present work aims to evaluate pathogenicity, virulence, biomass production, and the potential to synthesize AgNPs using isolates of entomopathogenic fungi.
## 3 The Proposed Method
### The DSP Algorithm
#### 3.2.1 The Differentiable Pruning Criterion  
As shown in Figure 3, given a weight matrix \(\mathbf{W}\in\mathbb{R}^{nsd}\), we employ an importance factor \(I\in\mathbb{R}^{d}\) to guide the pruning of \(\mathbf{W}\), where \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}\) is a one-hot vector, and \(l_{k}=0\) means the \(k\)-th dimension is to be pruned and \(l_{k}=1\) means the \(k\)-th dimension is to be kept:  
\[\mathbf{W}_{pruned}=\mathbf{W}\odot\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}} }=[I_1}1\mathbf{w}_{1},\cdots,I_{d}\mathbf{w}_{d}] \tag{5}\]  
However, since values of the importance factor \(\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ \mathbf{\mathbf{\mathbf{\mathbf{\mathbf{ }}}}}}}}}}}}}}}}}}}\) is discrete, the gradient can not be calculated directly, which makes it hard to optimize with the back propagation algorithm. Considering the sigmoid activation is a special case of the softmax activation:  
\[\text{Sigmoid}(x)=\frac{1}{1+\exp(-x)}=\frac{\exp(x)}{\exp(x)+\exp(0)}=\text{ Softmax}([x,0]) \tag{6}\]  
Figure 2: (**a**) is four human-designed encoder architectures. (**b**) is our topology-fused encoder search space. (**c**) is three human-designed decoder architectures. (**d**) is our topology-fused decoder search space.  
we propose a Gumbel-Sigmoid activation on top of the Gumbel-Softmax [24] and relax the importance factor \(I\) into soften one-hot with Gumbel-Sigmoid:  
\[\mathbf{I}=\text{Gumbel-Sigmoid}(\boldsymbol{\beta})=\frac{\exp((log\boldsymbol {\beta}+G_{1})/\lambda)}{\exp((log\boldsymbol{\beta}+G_{1})/\lambda)+\exp(G_{ 2}/\lambda)} \tag{7}\]  
where \(\boldsymbol{\beta}\) is the corresponding pruning parameter, \(G_{1}\) and \(G_{2}\) are two independent Gumbel random variants, and \(\lambda\) is a temperature close to zero.  
With such a relaxation, \(I\) becomes soften one-hot, and the gradient descent can be directly applied to optimize \(I\) since the loss function is differentiable to \(\beta\). In other words, the structured pruning is equivalent to learning the pruning parameter \(\beta\). Compared with conventional magnitude-aware criterion, a stronger correlation between our pruning criterion and model performance can be built. And we adopt a bi-level optimization manner to optimize \(\beta\)[23]. An additional sparse loss \(\mathcal{L}_{sparse}\) is employed to evaluate the sparsity of the pruned matrix during the bi-level optimization, and it can be calculated by counting the remaining parameter size of W after pruning.
## Appendix C Derivation of Ionic Concentration Correlations
### Flux-Charge Correlations  
The last terms in Equations (62), (67) and (71) account for the correlations between the ionic flux (positive of negative) and the charge density fluctuations. Starting with the last term in Equation (62), and using Equations (39), (40) and (42), we obtain  
\[-\frac{D\kappa^{2}}{2 ze}\Bigg{[}\frac{i\mathbf{k}_{1}\cdot\langle \delta^{\sharp}_{+}(\mathbf{k}_{1},\omega_{1})\delta\hat{\rho}_{e}(\mathbf{k}_{ 2},\omega_{2})\rangle}{(i\omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_{2}^{2})}+\frac {i\mathbf{k}_{2}\cdot\langle\delta^{\sharp}_{+}(\mathbf{k}_{2},\omega_{2}) \delta\hat{\rho}_{e}(\mathbf{k}_{1},\omega_{1})\rangle}{(i\omega_{1}+Dk_{1}^{ 2})(i\omega_{2}+Dk_{2}^{2})}\Bigg{]}= \tag{75}\] \[\Bigg{\{}\frac{\mathbf{k}_{1}\cdot\mathbf{k}_{2}\kappa^{2}cD^{2} \delta(\omega_{1}+\omega_{2})\delta(\mathbf{k}_{1}+\mathbf{k}_{2})}{(i \omega_{1}+Dk_{1}^{2})(i\omega_{2}+Dk_{2}^{2})[i\omega_{1}+D(k_{1}^{2}+\kappa ^{2})]}+\] \[\frac{\mathbf{k}_{1}\cdot\mathbf{k}_{2}\kappa^{2}cD^{2}cD\delta( \omega_{1}+\omega_{2})\delta(\mathbf{k}_{1}+\mathbf{k}_{2})}{(i\omega_{1}+Dk _{1}^{2})(i\omega_{2}+Dk_{2}^{2})[i\omega_{1}+D(k_{1}^{2}+\kappa^{2})]}\Bigg{\}}.\]  
The right-hand side above can be inverse-transformed following the procedure outlined in Appendix B above. The result is  
\[-\frac{c(ze)^{2}}{4\pi\epsilon\epsilon\varrho_{b}R_{\text{\tiny{$\rm{}}}}T} \frac{e^{-\frac{\kappa}{\sqrt{2}}r}}{r}. \tag{76}\]  
Note that (76) is identical to the first term in the right-hand side of Equation (74) but with an opposite sign. This is also true for the cases of negative-negative and positivenegative concentration fluctuations. They always cancel, and summing the results from Equations (A10), (A11) and (A13) leads to  
\[\langle\delta c_{+}(\mathbf{r}_{1},t)\delta c_{+}(\mathbf{r}_{1},t)\rangle=c \bigg{[}\delta(\mathbf{r}_{1}-\mathbf{r}_{2})-\frac{c(ze)^{2}}{4\pi\epsilon \epsilon_{0}k_{B}T}\frac{e^{-\kappa r}}{r}\bigg{]}, \tag{14}\]  
which is equivalent to Equation (63). Equations (68) and (72) can be derived via the same mathematical procedure to read  
\[\langle\delta c_{-}(\mathbf{r}_{1},t)\delta c_{-}(\mathbf{r}_{1},t)\rangle=c \bigg{[}\delta(\mathbf{r}_{1}-\mathbf{r}_{2})-\frac{c(ze)^{2}}{4\pi\epsilon \epsilon_{0}k_{B}T}\frac{e^{-\kappa r}}{r}\bigg{]} \tag{15}\]  
and  
\[\langle\delta c_{+}(\mathbf{r}_{1},t)\delta c_{-}(\mathbf{r}_{1},t)\rangle=c \bigg{[}\delta(\mathbf{r}_{1}-\mathbf{r}_{2})+\frac{c(ze)^{2}}{4\pi\epsilon \epsilon_{0}k_{B}T}\frac{e^{-\kappa r}}{r}\bigg{]}. \tag{16}\]  
The signs' electrostatic contributions to the correlations are positive for the same-charge cases and negative for the positive-negative case. This obviously reflects the fact that same-charge ions repel, while oppositely charged ions attract each other.
## 4 Discussion
However, the challenge for our governing agent is that it does not know the decision thresholds of user agents. To better understand this challenge, suppose that the governing agent underestimates the mean decision threshold of user agents. In this case, before giving sufficient promotional labels and increasing the utility of the desired behavior in the user agents, the governing agent asks for the costly behavior. As a result, the unprepared user agents do not cooperate with the governing agent. As another result, the promotional activity of the governing agent is delayed by its untimely request, and the emergence of the norm will be postponed. Now suppose another case, where the governing agent overestimates the mean decision threshold of user agents. In this case, the governing agent continues increasing the utility of the desired behavior in the user agents, without realizing when they are ready to cooperate with the governing agent. As such, the governing agent loses time in unnecessary promotional activity and does not ask user agents to perform the desired behavior. This case, as well, results in postponed emergence of the norm. The success of the governing agent, therefore, depends on the proper timing of its activities. Our governing agent's RL algorithm adjusted itself by occasionally making costly requests and checking the response of the user agents. In this way, the RL algorithm achieved higher user agent cooperation rates than a random baseline, as evident in Figure 7.  
The scope of this study is the evolution of the simulated society of the governing and user agents, from a situation where no user agents cooperate with the governing agent till a situation where the desired proportion of user agents voluntarily cooperate with the governing agent and perform the costly behavior that the governing agent requests. As such, our work addresses another question raised by [PERSON] and [PERSON] [12] regarding the early stages of norm creation and ideation. What happens afterwards is beyond the scope of this study. Nevertheless, it is worth noting as an implication that when many user agents voluntarily perform the behavior that the governing agent requests, the society of user agents may develop a tendency to take that behavior for granted and sanction those who do not participate in that behavior [80]. As another implication, the governing agent may introduce new laws to enforce the newly emerged norm [8]. These can be subjects of future works. As another idea for future work, our model can be used in the study of complex systems that involve our case of Principal-Agent setting in conjunction with another phenomenon. For example, in environmental management there is typically a governing agent or entity with demands from users of an environmental resource. Such social interactions can be simulated in our model. The environmental resource, in turn, is subject to the laws of nature. If there exists a model of natural changes in the environmental resource, then by coupling that model with the model described in the present study, it is possible to simulate the changes in that social-ecological system.
# Statistical Power Analysis in Reliability Demonstration Testing: The Probability of Test Success
## 4 Comparison of the Calculation Methods for the Probability of Test Success
### Conclusion of the Comparison  
From these comparisons, it can be concluded that the approximate calculation is a good fit for most applications. If a precise calculation is desired, the general method should be used, as the accuracy can be set as desired via the number of iterations. The approximate method can be used for MLE and sample quantile estimation approximation. Both seem to approximate very well, even for smaller sample sizes. The calculation using the test simulation is plagued by the same disadvantages as the physical test evaluation methods. Therefore, it should be investigated if a bias correction is of use in the individual case. The values of \(P_{\text{bs}}\) of censored EoL tests are best calculated using the general method. However, even the approximate calculation shows very good results. The calculation methods for the SR test shows a very strong advantage when using the exact method instead of the general method. Consequently, the exact method should always be used where an SR test is concerned. For SR tests, the general method and the test simulation method coincide due to the lack of quantile estimation. These findings are summarized in Table 2.  
Figure 11: \(P_{\text{bs}}\) of a censored EoL test for \(R_{\text{r}}=C_{\text{r}}=90\%\), \(s=0.2\), and prior knowledge of \(b=3\), as well as a censoring of \(30\%\).  
Figure 12: \(P_{\text{bs}}\) of a censored EoL test for \(R_{\text{r}}=C_{\text{r}}=90\%\), \(s=0.2\), and prior knowledge of \(b=3\), as well as a variable censoring proportion of the sample size of \(n=40\).