esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The methodology for evaluating the sustainability of concrete described and applied in this study provided a rank for the studied group of concretes. The effects of durability, material mechanical properties, and ecological and economic aspects were included. The analysis brought the following major conclusions:  
1. The sustainability assessment of volcanic pumice concrete mixture indicated that VPP is a suitable SCM for civil engineering infrastructures, considering eco-friendly aspects. The mixture containing 65% of Portland Cement Type II-V, 10% of Silica fume, and 25% of Volcanic Pumice Pozzolan showed the best values in terms of sustainability, without considering cost.
2. The highest value, considering cost, was assigned to the mixture containing 55% of Portland Cement Type II-V, 20% of Class F fly Ash, and 25% of Volcanic Pumice Pozzolan.
3. The pumice mixtures significantly over performed other SCM-based mixtures on the basis of the evaluated sustainability indicator.
4. The rank of the mixtures was different at different concrete maturity, but the trend was very similar.
5. There was no correlation of strength and the diffusion parameter with sustainability indicators.
6. The present study shows that the \(k_{\text{SB}}\) and \(k_{\text{SB,C}}\) sustainability indexes are suitable for quantifying the level of sustainability. Therefore, this study might help the industry to tackle sustainability issues in civil engineering in a rather simple way.  
It should be added that the advantage of this procedure is the possibility to consider the various degradation effects, or their combinations, of concrete mixtures containing different SCMs, either with the use of mathematical modelling of service life or with the help of experimental measurements on specific samples. This is an evaluation of the material, which can be used especially at the beginning of the process of designing a concrete structure, when, among other variables, the type of concrete is chosen. At the same time, the sustainability indicator can be viewed as an evaluation tool that can help to choose materials with regard to the issue of carbon footprint.  
**Author Contributions:** Conceptualization, P.L. and P.K.; methodology, B.T. and P.G.; validation, K.H., P.L. and P.K.; formal analysis, K.H.; investigation, P.G.; resources, K.H.; data curation, B.T.; writing--original draft preparation, P.L.; writing--review and editing, P.K., K.H., B.T., and P.G.; visualization, P.L.; supervision, B.T.; project administration, P.K.; funding acquisition, K.H. All authors have read and agreed to the published version of the manuscript.  
Financial support from VSB-Technical University of Ostrava by means of the Czech Ministry of Education, Youth, and Sports through the Institutional support for the conceptual development of science, research, and innovation for the year 2021 is gratefully acknowledged.  
The authors declare no conflict of interest.
## 1 Introduction
TDLs incorporate a thin (typically 50-250 \(\upmu\)m) disk-shaped gain material used in reflection in the laser cavity and pumped by high-power fiber-coupled diodes in a multi-pass configuration that enables a high pump absorption efficiency to be achieved despite the low single-pass absorption of the disk. Beside DPSSLs based on bulk gain media and TDLs, ultrafast dielectric waveguide lasers constitute another promising technology for future compact frequency comb generation with laser diodes pumping. These lasers combine advantages of ion-doped solid-state and semiconductor lasers, with the potential to achieve high output powers with excellent beam quality and wafer-scale production capability with a high level of integration. However, these high power waveguides have hardly been modelocked so far and have thus not demonstrated any frequency comb operation yet. Therefore, such lasers will not be considered in this review. The content of this paper is organized as follows. In Section 2, we review the different requirements and steps involved in the CEO-stabilization of a frequency comb by self-referencing, which covers nonlinear interferometry for CEO detection, nonlinear spectral broadening of the modelocked laser to a coherent octave-spanning spectrum, and some important aspects of a CEO stabilization loop. In Section 3, we review the main achievements on frequency combs produced by DPSSLs based on a bulk gain material, and in Section 4 for diode-pumped TDLs. Finally, a conclusion is presented in Section 5.
# Genetic Variation Affects the Anti-Melanogenic Efficacy of _Platycodon grandiflora_ Flowers
## 3 Results and Discussion
### Molecular Docking Analysis of P. grandiflora Saponins as Tyrosine Inhibitors  
The molecular docking approach is a pivotal tool in analyzing the interactions between target proteins and small molecules, greatly contributing to the discovery of high-quality enzyme inhibitors that have progressed to clinical trials [36]. To elucidate the TYR inhibitory mechanisms of _P. grandiflora_, we analyzed the interaction sites and patterns between major saponins and TYR using the structure-based molecular docking approach. Initially, we  
Figure 2: Effect of genetic variation on saponin biosynthesis in _Platycodon grandiflora_ flower. (**A**) The levels of triterpenoid saponins in BuOH fractions were assessed using HPLC. (**B**) Transcription levels of genes involved in triterpenoid saponin biosynthetic pathway. The transcription level of each gene is expressed relative to that of the purple flower (PF). Error bars represent the standard error of three independent experiments. Statistical significance was assessed using Student’s _t_-test, where *_p_ < 0.05, **_p_ < 0.01, and ***_p_ < 0.005. WF, white flower; PF_BU, BuOH fraction of purple-flower extract; WF_BU, BuOH fraction of white-flower extract; SS, squalene synthase; SE, squalene epoxidase; B-AS, \(\beta\)-amyrin synthase; A280, \(\beta\)-amyrin 28-oxidase.  
employed blind molecular docking to identify the binding sites of selected saponins on TYR. Subsequently, using a grid centered around the TYR residues identified in the blind docking, defined molecular docking was performed to predict the binding energies of PE, PD3, PD, PcA, and PgD to _Bacillus megaterium_ TYR (TyrBm). Our findings revealed that PD3 demonstrated the highest binding affinity to tyrosinase among the tested saponins, with a binding energy of \(-\)9.39 \(\pm\) 0.11 kcal/mol, followed by PD (\(-\)8.76 \(\pm\) 2.22 kcal/mol), PE (\(-\)4.90 \(\pm\) 1.85 kcal/mol), PcA (\(-\)5.86 \(\pm\) 0.01 kcal/mol), and PgD (\(-\)6.30 \(\pm\) 0.84 kcal/mol) (Table 1). In TyrBm, Val 218 functions as a gatekeeper residue, modulating the accessibility of the active site entrance. Substitution of this Val with Phe results in decreased TYR activity [37], suggesting that Val 218 plays a regulatory role in substrate binding of TyrBm. In _P. grandiflora_, all tested saponins, except for PE, are engaged in hydrophobic interactions with the Val 218 residue (Table 1), indicating that the TYR inhibitory activity of _P. grandiflora_ saponins is likely mediated by inhibiting substrate binding.
## 2 Literature Review
### Fundamental Concepts
#### 2.1.1 Q-Learning  
[PERSON] (1989) proposed a model-free learning technique called Q-learning to solve the Bellman equation deterministically when the state probability and reward system are known ([PERSON], 1989). Q-learning is an off-policy temporal-difference control algorithm that enables the direct approximation of the Bellman equation. Q-learning has been mostly used for solving unicast routing RL problems in networks. This is because the technique was proven to converge to the optimum action values with one as the probability if, and only if, all actions are continuously sampled in all of the states. In Q-learning, the quality of selecting action \(a\in A\) when in a state \(s\in S\) is based on the calculated state-action-value function called Q-value. The Q-values give the measure of accumulated rewards an agent gets from all pairs of state-action. The estimate of the state-action-value function \(Q(s,a)\) that an agent used to learn the best action in a particular state is achieved by storing the Q-values \(Q(s_{t},a_{t})\) of pairs of state-action with the iterative update rule in Equation (4).  
\[Q(s_{t},a_{t})=(1-\alpha)Q(s_{t},a_{t})+\alpha\bigg{[}R_{t+1}+\gamma*max\{Q(s_ {t+1},a)\}\bigg{]} \tag{4}\]  
where \(0<\alpha\leq 1\) represent the learning rate and \(0\leq\gamma\leq 1\) represent the discount factor. The learning rate gives the degree to which the later learned Q-value impacts the former Q-value. The nearer the magnitude of \(\alpha\) is to one, the more the effect of the later calculated Q-value on the former one. If \(\alpha\) is one, then the later computed Q-value replaces the former Q-value totally. The discount factor manages the agent's appreciation for hereafter rewards with respect to the recent reward. If \(\gamma\) is 1, both the recent reward and the hereafter reward are regarded equally.  
Q-learning, as a model-free off-policy RL technique, is appropriate for learning optimal routing rules in WSNs because of its simplicity and non-requirement for any knowledge of the underlying transition and reward mechanism. However, Q-learning suffers the following drawbacks if it is used in learning optimal routing rules in WSNs.  
1. A large number of iterations are required to learn the optimal routing path; this leads to the degradation of the convergence speed and routing performance.
2. It is very sensitive to parameter settings; for example, changes in the learning rate affect the routing performance.
## 3 Control of the Inverter
### Inner Current Control Loops  
To enforce a sliding mode regime, the switches of the boost inverter can be operated using two different commutation techniques which have in common the complementarity between the states of the high-side and low-side switches of each boost cell, i.e., when one of them is turned on, the other is turned off. A brief description of features distinguishing these techniques can be summarized below:  
* The single surface sliding mode control (SS-SMC) produces two circuit structures and is obtained when the high-side switch of one boost cell is turned on and turned off simultaneously with the low-side switch of the other boost cell and the same for the other switches. In that case, it is possible to reduce the number of control signals. One control signal \(u_{1}\) is sufficient to describe the circuit behavior. The corresponding model is obtained replacing \(u_{2}\) by \(1-u_{1}\) in the previous model. The use of this coupled operation of the switches in the control law allows to use only the measurement of the output voltage and both inductor currents to ensure the desired behavior. Furthermore, only one hysteresis comparator enforces the sliding regime tracking the inner reference given by an outer controller which in turn enforces a pure sine-waveform behavior in the output voltage. Although optimal in terms of implementation and computational cost, this commutation method is limited to guarantee a single control objective: provide a high quality output voltage. * The double surface sliding mode control (DS-SMC) produces four circuit structures and is obtained when the switches of one boost cell commutate completely independent of the switches of the other cell. This signifies that control of the cells is independent although the cells are interconnected through the load and share the connection to the input DC voltage. This is the common way to configure the control loops although it requires measurement of both capacitor voltages and both inductor currents. In addition, one hysteresis comparator is required per boost cell to track the reference given by the outer compensator operating on the capacitor voltage error. Although two  
Figure 4: Circuit structures of the boost inverter operating in DS-SMC. (**a**) Cell 1 state 0, (**b**) cell 1 state 1, (**c**) cell 2 state 0 and (**d**) cell 2 state 1. DC biased pure sine-waves are normally used as references, as discussed in the previous section, enriching their harmonic content allows considerably reducing the voltage stress of the switches. Therefore, this control approach is adopted to develop the contribution of this study. Consider the following sliding surfaces  
\[S(x)=\left[ \begin{array}{c}S_{1}(x)\\ S_{2}(x)\end{array} \right]=\left[ \begin{array}{c}i_{1}(t)-i_{1e}(t)\\ i_{2}(t)-i_{2e}(t)\end{array} \right] \tag{15}\]  
where \(i_{1e}(t)\) and \(i_{2e}(t)\) are given current signals.
## 2 Experimental
### Characterizations  
The surface morphology of the membranes was characterized via scanning electron microscopy (SEM, S-4800, Hitachi, Tokyo, Japan), and the elemental composition was analyzed via energy-dispersive X-ray spectroscopy (EDS, Hitachi Regulus 8100, Tokyo, Japan). The surface morphology of the membrane was characterized via scanning electron microscopy (SEM, S-4800, Hitachi, Tokyo, Japan).  
Japan). The surface element composition was characterized using X-ray photoelectron spectroscopy (XPS, Escalab 250 Xi, Thermo Fisher, Waltham, MA, USA). A laser confocal microscope (Nikon Eclipse Ti, Tokyo, Japan) was used to analyze the emulsion droplets and the droplet size in water with an accuracy of \(\pm\)2%. The hydrogel was characterized via Fourier transform infrared spectroscopy (ATR-FTIR) on a Nicolet 6700 (Thermo Fisher, Waltham, MA, USA) instrument to detect its functional groups. The pore size of the membrane was determined using the bubble-point method with a membrane pore size analyzer (PSMA-20, GaoQ Functional Materials Co., Ltd., Nanjing, China). The contact angles (CA) of 10 \(\mu\)L droplets were measured using a drop shape analyzer (DSA 30s, Kruss, Hamburg, Germany). After examining the separation performance of all developed membranes, the membrane modified with a plasma exposure time of 4 min and the hydrogel in a monomer mass ratio of 2:3 AM/NaA was chosen for conducting all aforementioned characterizations. Each characterization was repeated 5 times for reliability.  
This approach ensured that the characterization focused on the most promising candidate, aligning with the study's objective to identify high-performing hydrogel membranes.  
The degree of grafting was calculated as the percentage weight increase of the membrane after grafting, according to Equation (1) [56]:  
\[GR=\frac{m_{1}-m_{0}}{m_{0}}\times 100\% \tag{1}\]  
where \(m_{1}\) is the weight of the grafted membrane, and \(m_{0}\) is the weight of the membrane.
# Structuring Reference Architectures for the Industrial Internet of Things
## 5 Considered Reference Architectures
### Arrowhead Framework  
The EU Arrowhead Project ([[http://www.arrowhead.eu/](http://www.arrowhead.eu/)]([http://www.arrowhead.eu/](http://www.arrowhead.eu/))) started in 2013 and is in the form of the succeeding project Productive4.0 still ongoing. The resulting framework [34] consists, similar to FIWARE, of several central roles (service registry, orchestrator, authorization). The open-source components form a SOA-based architecture in five example domains: Production (process and manufacturing), Smart Buildings and infrastructures, E-Mobility, Energy production and Virtual Markets of Energy.
## 5 Discussion  
In this study, a simulation model was developed for the fatigue life evaluation of the spiral level gear of tractors. Although this simulation model has strengths, in terms of repeatability and reproducibility, it is essential to confirm whether the simulation model shows a level similar to that of the actual fatigue life evaluation results. Thus, the simulated fatigue life prediction results of spiral level gears were compared with previous studies based on ALT. Previous studies have evaluated the fatigue life of spiral level gears through ALT based on constant load conditions, not dynamic load conditions. In this study, the simulation model was compared under the two load condition cases (Case 1: Torque = 1128 Nm, Speed = 559 rpm; Case 2: Torque = 621 Nm, Speed = 482 rpm) performed in previous studies [41]. Figure 11a,b show the fatigue life obtained by our developed simulation model and that obtained from the previous study (i.e., based on ALT) under cases 1 and 2, respectively. Table 8 shows the fatigue life results of simulations and the previous study under the two load cases. In load case 1, the fatigue life of the simulation was 197 h, while the fatigue life of samples 1, 2, and 3, and their average obtained in the previous study were 273, 157, 184, and 184 h, respectively. In load case 2, the fatigue life of the simulation was 43,315 h, while the fatigue life of samples 1, 2, and 3, and their average obtained in the previous study were 26,330, 58,453, 33,702, and 39,495 h, respectively. There was a slight difference between the fatigue life obtained through simulation and the fatigue life obtained in the previous study. These differences are related to the differences between samples in the previous study. It has been reported that the fatigue life obtained in the previous study differed by up to 222% between samples, which was judged to be due to environmental conditions and test environments during gear processing. Nevertheless, the fatigue life obtained by the developed simulation model under the two load cases were distributed within the minimum and maximum fatigue life ranges of the previous ALT-based study. In particular, the differences between the average fatigue life of the three samples and that of the developed simulation model was not significant. However, verification of the simulation model considering dynamic load conditions is still insufficient. In order to verify whether the simulation model reflects dynamic field load conditions well, an actual life test based on dynamic field load conditions is required. Studies improving the accuracy of and verifying the developed simulation model will be addressed in future work.  
Figure 10: Fatigue life evaluation for major agricultural operations: (**a**) fatigue life ratio based on service life and (**b**) relative fatigue life based on subsoiler tillage.
## 3 Background
### Lstm  
LSTM enhances RNNs' memory, crucial for handling sequential financial data. LSTM units, integrated into RNNs, have three gates: input gate (i), forget gate (f), and output gate (o). These gates use sigmoid functions to write, delete, and read information, addressing long-term dependencies and preserving data patterns. In the LSTM architecture illustrated in Figure 3, three gates play pivotal roles:  
1. Input Gate (i): This gate facilitates the addition of new information to the cell state.
2. Forget Gate (f): The forget gate selectively discards information that is no longer relevant or required by the model.
3. Output Gate (o): Responsible for choosing the information to be presented as the output.  
Each of these gates operates utilizing sigmoid functions, transforming values into a range from zero to one. This mechanism empowers LSTMs to adeptly write, delete, and read information from their memory, rendering them exceptionally skilled at handling  
Figure 3: LSTM architecture.  
Figure 2: Unfolded recurrent neural network.  
long-term dependencies and preserving crucial patterns in data. Crucially, LSTMs address the challenge of the vanishing gradient, ensuring that gradient values remain steep enough during training. This characteristic significantly reduces training times and markedly enhances accuracy, establishing LSTMs as a foundational technology in the domain of sequence prediction, especially for intricate datasets prevalent in financial markets.
## 3 Results
### Effect of JGTW on the LPS-Induced MAPK Activation and NF-\(\kappa\)B Signaling  
To test whether this anti-inflammatory response of JGTW was mediated by the MAPK-responsive mechanism, the levels of p38 and ERK phosphorylation were evaluated in JGTW-treated and LPS-stimulated RAW264.7 cells. As presented in Figure 7A, RAW264.7 cells treated with LPS showed a significant increase in the phosphorylation of p38 and ERK, which was reduced by pretreatment of JGTW dose-dependently. We also examined the phosphorylation of JNK; however, the suppression of JNK phosphorylation by JGTW treatment was less significant compared to those of p38 and ERK (data not shown).  
Figure 5: Effect of JGTW on the expression of adhesion molecules in BEAS-2B cells. RT-PCR was performed to analyze the expressions of ICAM-1 and VCAM-1 after 48 h IT (50 ng/mL) treatment with or without JGTW pretreatment in BEAS-2B cells. The representative photographs of ICAM-1 (**A**) or VCAM-1 (**B**) expressions, and band intensities of ICAM-1/GAPDH (**C**) or VCAM-1/GAPDH (**D**). The values are presented as mean \(\pm\) SEM. \({}^{\#}\)_p_ < 0.01 versus vehicle-treated cells; and \({}^{**}\)_p_ < 0.01 versus IT-treated cells.  
Figure 6: Effect of JGTW on the expression of iNOS and NO production in LPS-stimulated RAW264.7 cells. Cells were pretreated with JGTW (6.25 to 100 \(\mathrm{\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro \SIUnitSymbolMicro\SIUnitSymbolMicro\SIUnitSymbolMicro\SI  
It is well known that iNOS expression is mediated by the transcription factor NF-kB. Consequently, the effect of JGTW on the phosphorylation of NF-kB subunit p65 and IxB-\(\alpha\) was investigated. LPS-stimulated RAW264.7 cells showed a marked increase in phosphorylated NF-kB subunit p65 and IxB-\(\alpha\); however, pretreatment with JGTW attenuated the increase of NF-kB and IxB phosphorylation (Figure 7B).
# Lineal Energy of Proton in Silicon by a Microdosimetry Simulation
###### Abstract  
Single event upset, or Single Event Effect (SEE) is increasingly important as semiconductor devices are entering into nano-meter scale. The Linear Energy Transfer (LET) concept is commonly used to estimate the rate of SEE. The SEE, however, should be related to energy deposition of each stochastic event, but not LET which is a non-stochastic quantity. Instead, microdosimetry, which uses a lineal calculation of energy lost per step for each specific track, should be used to replace LET to predict microelectronic failure from SEEs. Monte Carlo simulation is used for the demonstration, and there are several parameters needed to optimise for SEE simulation, such as the target size, physical models and scoring techniques. We also show the thickness of the sensitive volume, which also correspond to the size of a device, will change the spectra of lineal energy. With a more comprehensive Monte Carlo simulation performed in this work, we also show and explain the differences in our results and the reported results such as those from [PERSON] et al. which are commonly used in semiconductor industry for the prediction of SEE in devices.  
single event effect; Monte Carlo simulation; microdosimetry; linear energy transfer; lineal energy Article
## 4 Discussion
### Effects of the Charging Angle \(\theta\)  
The effects of the charging angle \(\theta\) on the number of required chargers are explored in this section. The charging angle \(\theta\) is changed from 40 to 360 degrees. Since LMD can only be applied to the omnidirectional chargers, PBAD is compared with RPRO only in this section. Figure 11 shows that the number of required chargers and the charging angle \(\theta\) are positive correlated. When the charging angle \(\theta\) is set to 360 degrees, the deployment results of PBAD can be compared with the results of LMD in Section 3.2. The PBAD deploys 13 chargers less than LMD.