esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Optimal Allocation of Irrigation Water Diversion (Model I)
#### 3.2.3 Results of Optimal Allocation of Water Resources (Model I)  
According to the optimized allocation results of irrigation water diversion, the total irrigation water diversion for the current years and the future years reaches the maximum allowable limit of 145 million m\({}^{3}\) in the irrigation area. This indicates that the irrigation efficiency in the area is constrained by the total available water resources. If return flow can be fully utilized, the irrigation efficiency could be improved.  
Figure 9 shows the net irrigation quota for each crop during different periods. The irrigation quota is influenced by both crop water demand and effective precipitation. The crops with irrigation quotas from largest to smallest are corn, forest fruits, flax, melon, potato, spring wheat, and goji berry. In the long term, the current irrigation quota is the highest, while the net irrigation water demand in both the short and long term gradually decreases. This is due to an increase in future precipitation and a decrease in reference evapotranspiration, leading to a reduced water demand for crop irrigation.  
Figure 10 presents the optimization results of crop water allocation across different periods. Crop water allocation is influenced by the planting area and irrigation quota of each crop, resulting in significant variation among different crops. Forest fruit and corn require the most water due to their extensive planting areas and high irrigation demands.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Crop Type**} & \multicolumn{2}{c}{**Current**} & \multirow{2}{*}{**Short-Term**} & \multirow{2}{*}{**Long-Term**} \\ \cline{2-2} \cline{4-4}  & **Before Optimization** & & & \\ \hline Spring wheat & 22.6\% & 12.2\% & 11.7\% & 11.5\% \\ Corn & 30.0\% & 18.8\% & 18.0\% & 17.8\% \\ Flax & 5.0\% & 1.8\% & 5.6\% & 7.2\% \\ Potato & 5.3\% & 11.9\% & 11.5\% & 11.3\% \\ Melon & 5.6\% & 6.5\% & 6.2\% & 6.1\% \\ Goji berry & 11.2\% & 19.2\% & 18.4\% & 18.1\% \\ Forest fruits & 20.4\% & 29.7\% & 28.5\% & 28.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: Planting structure optimization results of different crops in different periods (Model I).  
Figure 9: Net irrigation quota of different crops in different periods.  
Consequently, the water allocation for these crops is substantial. In contrast, flax and melon have relatively low water allocations due to their smaller planting areas. Although goji berry has a large planting area, its water allocation remains relatively low because it has the lowest irrigation quota among all crops.
# Corrosion Fatigue Assessment of Bridge Cables Based on Equivalent Initial Flaw Size Model
## 4 Corrosion Fatigue Lifespan Assessment of Parallel Wire Cable  
A cable lifespan assessment model, which takes into account the corrosion fatigue effect, is proposed based on the series-parallel fiber bundle model. Using the Runyang Suspension Bridge as a case study, the measured stress time history spectrum is employed to conduct parameterized lifespan analysis of the cable. The analysis considered various factors, including traffic loads, initial defects, temperature, and humidity, and their effects on the cable lifespan.
# Research on Large Divergence Angle Laser Ranging System +
## 3 Design of Large-Divergence Transmitter and Wide-Field-of-View (WFOV) Receiver Systems  
### Selection of Laser Source  
The requirements for the weight of spaceborne payloads are extremely strict, involving various aspects such as launch vehicle capability, fuel efficiency, maneuverability, structural strength, thermal control systems, scientific instruments, redundancy design, mission objectives, as well as international competition and cooperation. To meet these demands, aerospace engineers must perform comprehensive optimization and trade-offs in areas such as design, material selection, manufacturing processes, and mission planning. Reducing payload weight not only lowers mission costs but also enhances the success rate and scientific returns of the mission, making it one of the key factors for the success of aerospace missions. Therefore, choosing erbium glass lasers with high peak energy and low volume and weight as the ranging light source is critical.  
The project employs a passive Q-switched Er-glass laser; passive Q-switching technology is simple in principle, easy to operate, reliable in system performance, and low in cost. It is particularly suitable for various miniaturized laser systems and, therefore, has been widely used [4]. The laser used in this project has an emission wavelength of 1535 nm, as the light energy at this wavelength is strongly absorbed by water. When it enters the human eye, it is almost completely absorbed by the cornea and does not reach the retina, thus posing no harm to the eyes. The core parameters of the Er-glass laser adopted in the project are shown in Table 1. The appearance of the laser is shown in Figure 2.  
Figure 2: Outline drawing of Er-glass laser.
# Effect of Physical Exercise on the Release of Micorparticles with Angiogenic Potential
## 5 Production Mechanisms of MPs  
It is well known that MPs can be released upon activation or apoptotic stimuli; however, most cells constitutively shed MPs by ectocytosis and significant concentrations of these vesicles can be detected in the plasma [57]. The common mechanism for the production of MPs is represented by a change in the asymmetry of the phospholipids composing the plasma membrane. The lipid bilayer of normal cells presents an asymmetrical composition of phospholipids [58]. PS and phosphatidylethanolamine (PE) are located in the internal layer of the plasma membrane whereas the external layer is composed of phosphatidylinositol (PI), phosphatidylcoline (PC), sphingomyelin (SM), and other glycolipids. In order to allow MPs formation, this precise distribution is disrupted. Such changes in membrane architecture are controlled by three enzymes: flippase, flippase, and scramblase [59]. Flippase is involved in the maintenance of the phospholipid asymmetry between the membrane layers. Flippase controls the ATP-dependent translocation of PS to the external layer in response to cellular activation. Finally, scramblase is responsible for the membrane randomization allowing phospholipids to flow down their concentration gradients. In resting cells, only flippase is active, contributing to the internalization of negatively charged phospholipids (e.g., PS and PE) and to the maintenance of the physiological membrane asymmetry. Nevertheless, various events providing cellular activation areable to increase intracellular calcium concentration, causing the flippase inhibition and the activation of floppage, scrambase, and other calcium-sensitive enzymes such as calpain and gelsolin, resulting in MPs release [60].  
An important mediator of apoptotic MPs production is Rho-associated kinase (ROCK I), a protein kinase activated via caspase-mediated cleavage. ROCK I may allow the phosphorylation of myosin light-chains, triggering cytoskeletal rearrangement and formation of apoptotic MPs through the coupling of actin-myosin filaments to the plasma membrane [61].
## 3 Results and Discussion
### In Situ SM Measuring Techniques and Uncertainty  
Each sensor used for SM measurement has inherent uncertainties that can influence the accuracy of the collected data. These uncertainties, when propagated through the training and validation processes of ML models, can impact the reliability of the model outputs. Therefore, it is crucial to identify measurement techniques that minimize uncertainty while also evaluating their efficiency in terms of time and cost.  
We found that 80 out of 144 papers explicitly mentioned the in situ SM measurement techniques used to train and validate ML models with RS data. Figure 8 illustrates the frequency of each technique employed in these 80 studies. The oven-drying/gravimetric method was the most frequently used SM measurement technique (30% of the studies), followed by TDR (22.5%), FDR (5%), Theta Probe (5%), and Stevens HydraProbe (5%). These measurement techniques encounter challenges such as inconsistent performance across varying soil types, discrepancies between laboratory and field calibrations compared to manufacturers' specifications, and limitations in the accuracy of calibration equations [214]. While the gravimetric (oven-drying) method is the most accurate, its destructive nature and inability to provide continuous measurements limit its utility for long-term monitoring [30; 215]. Among modern techniques, neutron probes offer high accuracy with a short response time (1-2 min); however, they are less sensitive to SSM (<30 cm), have limited portability, and incur high setup costs [216; 217]. Commercially available TDR, FDR, and capacitance sensors provide an accuracy range of \(\pm\)1-3% [218]. The advantages, disadvantages, and uncertainties associated with various SM measurement techniques are comprehensively documented in [PERSON] et al. [219], [PERSON] et al. [220], and [PERSON] et al. [214]. Future studies should select SM measurement techniques based on their accuracy, response time, and associated costs to optimize the training and validation of ML models.
# A Hybridized Flower Pollination Algorithm and Its Application on Microgrid Operations Planning
## 1 Introduction
The popular algorithms are representative examples, e.g., Genetics-algorithms (GA) [26,27], Particles swarm algorithm optimizations (PSO) [28], Artificial bees colony-algorithm (ABC) [29], Ants colony optimization (ACO) [30], Cats swarms optimization algorithm (CSO) [31], Differential evolution (DE) [32], Bats algorithm (BA) [33], Flowers pollination algorithm (FPA) [34], Sine-Cosines algorithm (SCA) [35], etc. Flowers pollination algorithm (FPA) is one of the advanced meta-heuristic algorithms with effective intelligent optimization, with a few simple parameters that have attracted the attention of many science and engineering researchers in recent years [34,36]. Nevertheless, it has suffered from slow convergence, falling in the optimal local trap, and insufficient accuracy when dealing with complicated problems like the operations planning problem. Hybridizing two algorithms is one of the suggested ways of dealing with these issues, for example of hybrid methods, e.g., Bats algorithm with Artificial bee colony (BA-ABC) [37], hybridized Particles swarm optimization with Artificial bee colony optimization (PSO-ABC) [38], and hybridized Grey wolf optimization with Flower pollination algorithm (GWO-FPA) [39]. This study proposes a Hybridizing FPA with SCA (namely HSFPA) to enhance the flowers pollination algorithm for dealing with the complicated problem of microgrid operations planning. Strategies selected from the SCA method, e.g., adapting hop size, diversifying local search, and diverging agents, are used as the specific description of the proposed method in combining advantages to avoid the FPA algorithm drawbacks. The HSFPA is implemented to enhance diversity agents, increase convergence and prevent local optimal traps. The suggested method's performance efficiency is verified using the selected benchmark functions in CEC2017 [40] and the microgrid operations planning test [41]. The suggested approach contributions are highlighted as follows:  
* Suggesting strategies to enhance the flowers pollination algorithm (HSFPA) based on hybridizing FPA with SCA for the power microgrid operations planning;Evaluating the suggested method's performance by testing the selected benchmark functions in CEC2017 and comparing the proposed method's results with the other algorithms in the literature;
* Establishing the multi-object approach for the suggested method (MHSFPA) based on the Pareto optimality for planning the grid operations with generation resources. The rest of the paper is arranged as follows: Section 2 presents the microgrid operations planning statement and reviews the FPA and SCA. Section 3 proposes an HSFPA by hybridizing FPA with SCA; and uses the critical test function to verify the viability of the proposed algorithm. Section 4 describes the HSFPA for tackling the operations planning issue. The ending is discussed as the conclusions in Section 5.
# Sea Purslane as an Emerging Food Crop: Nutritional and Biological Studies
## 3 Results and Discussion
### Mineral and Heavy Metal Composition
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Composition**} & _H. portulacoides_ & _Sarccornia_ & _Salicornia_ & _Salicornia_ \\  & **Present Study** & _pereunis alpini_ & _ramossissima_ & _bigelovii_ **Tor** \\  & & **[4]** & **[4]** & **[43]** \\ \hline Na (mg/100 g) & 8190.18 \(\pm\) 35.10 & 6430 \(\pm\) 90 & 8990 \(\pm\) 50 & 8618 \(\pm\) 613 \\ K (mg/100 g) & 1433.64 \(\pm\) 4.07 & 1030 \(\pm\) 10 & 892 \(\pm\) 23 & 1520 \(\pm\) 69 \\ Ca (mg/100 g) & 764.77 \(\pm\) 2.86 & 263 \(\pm\) 1 & 486 \(\pm\) 5 & 535 \(\pm\) 17 \\ Mg (mg/100 g) & 306.06 \(\pm\) 1.32 & 703 \(\pm\) 4 & 943 \(\pm\) 8 & 1019 \(\pm\) 52 \\ P (mg/100 g) & 183.93 \(\pm\) 2.68 & - & - & 155 \(\pm\) 9 \\ Fe (mg/100 g) & 9.92 \(\pm\) 0.67 & 128 \(\pm\) 5 & 153 \(\pm\) 2 & 8.64 \(\pm\) 0 \\ Mn (mg/100 g) & 6.87 \(\pm\) 0.16 & 6.52 \(\pm\) 0.03 & 20.4 \(\pm\) 0.4 & - \\ Zn (mg/100 g) & 2.93 \(\pm\) 0.22 & 2.52 \(\pm\) 0.01 & 6.87 \(\pm\) 0.01 & 3.5 \(\pm\) 0.12 \\ Cu (mg/100 g) & 0.94 \(\pm\) 0.08 & - & - & 0.79 \(\pm\) 0.12 \\ I (mg/100 g) & 0.05 \(\pm\) 0.02 & - & - & - \\ Cd (mg/100 g) & 89.02 \(\pm\) 0.47 & 19 \(\pm\) 0.00 & nd & 8.63 \(\pm\) 0.00 \\ Pb (mg/100 g) & 17.91 \(\pm\) 0.55 & 131 \(\pm\) 2 & 145 \(\pm\) 2 & 17.27 \(\pm\) 8.64 \\ Hg (mg/100 g) & 6.98 \(\pm\) 0.10 & - & - & - \\ Cr (mg/100 g) & - & 492 \(\pm\) 11 & 524 \(\pm\) 5 & - \\ \hline \hline \end{tabular} nd: not detected; - not presented. Adapted from [4; 48; 54].  
\end{table} 
Table 3: Mineral composition (dry matter) of _Halimione portulacoides_ and other halophyte plants. the mercury concentration of above-ground tissues can be 174 to 545-fold times lower than that of the roots [10]. In fact, the content of mercury in _H. portulacoides_ leaves (1.53 \(\upmu\)g/100 g of raw matter) is around 33 times lower than the limit values defined for fishery products such as fish (50 \(\upmu\)g/100 g) [56]. Moreover, the content of mercury in _H. portulacoides_ leaves (under study) collected in Figueira da Foz (Portugal) is lower than the values reported for the same plant tissues (6-14 \(\upmu\)g/100 g) collected in Laranjo Bay salt marsh (Ria de Aveiro, Portugal), during April 2003 and April 2004 [10]. The higher concentration of mercury in sea purslane leaves of Ria Formosa can be attributed to its high level of mercury contamination, being one the most mercury-contaminated systems in Europe [10]. The content of heavy metals (cadmium, lead, and mercury) found in _H. portulacoides_ leaves was much lower than the values that could be considered dangerous to human health, making the leaves a safe product.
## 3 Chassis Strains for 3-HP Production
### Yeasts
#### 3.3.1 Saccharomyces cerevisiae  
A report showed that _S. cerevisiae_ could grow in the presence of 25 g/L of 3-HP with half of its \(\upmu_{\max}\). In another study, an _S. cerevisiae_ strain, which could tolerate 50 g/L of 3-HP at pH 3.5, was obtained by ALE. Further, a genome-wide analysis indicated that high concentrations of 3-HP are converted to 3-HPA via aldehyde dehydrogenase, which in turn leads to cytotoxicity. To compensate for this, _S. cerevisiae_ used a glutathione-dependent formaldehyde detoxification mechanism to achieve higher tolerance to 3-HP [59]. [PERSON]'s team reported an engineered _S. cerevisiae_ with an introduced \(\beta\)-alanine pathway, which produced a 3-HP titer of 13.7 g/L with a 0.14 Cmol/Cmol yield from glucose in controlled fed-batch fermentation [60]. Subsequently, another team examined the effect of changing the culture parameters on the microbial production performance based on the strains mentioned above, and the highest 3-HP titer reached 25 g/L with a yield of 25.6% Cmol/Cmol under P-limited conditions [45]. In addition, the oxaloacetate pathway for 3-HP biosynthesis was also developed in _S. cerevisiae_, and a dephosphorylation regulation strategy was developed to increase the ATP supply. As a result, the engineered strain produced 18.1 g/L of 3-HP in a 5 L bioreactor [26]. Currently, the production advantages exhibited by _S. cerevisiae_ are not obvious compared to other 3-HP producing strains.
## 4 Results and Discussion
### Seismic Sounding and Profiles
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{3}{c}{**Longitudinal Lines**} \\ \hline \multicolumn{3}{c}{**Lines**} & **Profile** & \multicolumn{2}{c}{**Start of Profile**} & \multicolumn{2}{c}{**End of Profile**} \\ \hline \multirow{3}{*}{L1} & 1 st profile & 910,930.55 & 1,449,795.66 & 910,926.8 & 1,449,555.46 \\ \cline{2-6}  & 2 nd profile & 910,934.16 & 1,449,544.78 & 910,932.89 & 1,449,311.97 \\ \cline{2-6}  & 3 rd profile & 910,931.3 & 1,449,298.61 & 910,904.2 & 1,449,056.47 \\ \hline \multirow{3}{*}{L2} & 1 st profile & 910,953.03 & 144,449,639 & 910,949.97 & 1,449,406.84 \\ \cline{2-6}  & 2 nd profile & 910,949.84 & 1,449,381.27 & 910,927.41 & 1,449,152.69 \\ \hline \multirow{3}{*}{L4} & 1 st profile & 910,920.59 & 1,449,815.5 & 910,924.06 & 1,449,558.62 \\ \cline{2-6}  & 2 nd profile & 910,911.86 & 1,449,544.46 & 910,921.15 & 1,449,312.39 \\ \cline{2-6}  & 3 rd profile & 910,916.56 & 1,449,252.8 & 910,901.6 & 1,449,022.78 \\ \hline \multirow{3}{*}{L5} & 1 st profile & 910,846.66 & 1,449,833.82 & 910,849.63 & 1,448,602.29 \\ \cline{2-6}  & 2 nd profile & 910,847.75 & 1,449,576.22 & 910,844.48 & 1,449,341.64 \\ \hline \multirow{3}{*}{L7} & 1 st profile & 910,745.75 & 1,449,844.45 & 910,747.71 & 1,449,612.96 \\ \cline{2-6}  & 2 nd profile & 910,747.57 & 1,449,596.55 & 910,746.33 & 1,449,363.16 \\ \cline{1-1} \cline{2-6}  & 3 rd profile & 910,748.12 & 1,449,253.09 & 910,749.59 & 1,449,066.67 \\ \hline \multicolumn{3}{c}{L9} & 1 st profile & 910,663.18 & 1,449,519.53 & 910,664.54 & 1,449,746.14 \\ \hline \multicolumn{3}{c}{Number of profiles realized} & \multicolumn{2}{c}{14} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Coordinates of the longitudinal seismic profiles.
# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 3 System Model and Problem Formulation
### Problem Formulation  
The objective of our work is to determine optimal service placement and request scheduling decisions in order to maximize the total utility of all users while taking into account both storage and computation resource constraints in MEC systems. For a given service placement profile \(\mathbf{x}\), and request scheduling profile \(\mathbf{y}\), the optimization problem of joint service placement and request scheduling (JSPRS) can be expressed as follows.  
\[\underset{\mathbf{x},\mathbf{y}}{\text{max.}} \sum_{u\in\mathcal{U}}W_{u}\] (7) s.t. \[\sum_{n\in\mathcal{N}\cup\{l\}}y_{un}=1,\quad\forall u\in \mathcal{U}, \tag{7a}\] \[\sum_{s\in\mathcal{S}}x_{sn}img_{s}\leq R_{n},\quad\forall n\in \mathcal{N},\] (7b) \[\sum_{u\in\mathcal{U}}y_{un}f_{un}\leq F_{n},\quad\forall n\in \mathcal{N},\] (7c) \[y_{un}\leq x_{sn},\quad\forall u\in\mathcal{U},s_{u}\in \mathcal{S},n\in\mathcal{N},\] (7d) \[x_{sn},y_{un}\in\{0,1\}\,,\quad\forall u\in\mathcal{U},s\in \mathcal{S},n\in\mathcal{N}\cup\{l\} \tag{7e}\]  
The constraint in Equation (7a) indicates that each user is scheduled to exactly one computing node, i.e., either a MEC server or the core cloud. The constraint in Equation (7b) guarantees that the total amount of data of services placed in a MEC node do not exceed its storage capacity. The constraint in Equation (7c) guarantees that the computing resources allocated to users at each MEC node do not exceed its computation capacity. The constraint in Equation (7d) states that a MEC node can only serve a user \(u\) if the requested service \(s_{u}\) is already placed on that MEC. In addition, the constraint in Equation (7e) denotes \(\mathbf{x}\), and \(\mathbf{y}\) are binary vectors.
## 2 Health Condition Monitoring
### Epilog  
Condition monitoring could improve the reliability of MSAE and reduce the failure rate to improve the system's overall safety level, reduce the risk of life and property loss, and minimize the maintenance cost. Although many research achievements have been made on the related problems of ship condition monitoring, there are also many problems to be solved as soon as possible. For example, obtaining more comprehensive MSAE condition information, strengthening communication between equipment and systems, and intelligent condition assessment needs to be further improved.
## 1 Introduction
To overcome this shortcoming, we decided to take advantage of the special kind of M2M transformation called _M2M transformation based on drag-and-drop actions_ (hereafter, drag-and-drop transformation or, simply, DnD transformation) [7] by developing a model-based executable library of DnD transformation rules for the extraction of SBVR BV from UML UCM. The distinctive feature of such DnD transformation is that it is selectively triggered by a drag-and-drop action. Here, a drag-and-drop action represents a situation when a user drags a certain model element from the browser of a _source_ model and drops it onto either a _target_ diagram or another element's representation in that diagram to generate a subset of target model elements (such a transformation is called a _partial M2M transformation_). **Main contribution and novelty of the results. ** This paper presents the basic aspects of the approach for the extraction of SBVR BV from UML UCM (hereafter, UML UCM \(\rightarrow\) SBVR BV) using model transformations based on drag-and-drop actions. Compared to our previously developed UML UCM \(\rightarrow\) SBVR BV approach, the presented solution provides the same model transformation capabilities while simultaneously delivering more flexibility and control over the M2M transformations, starting with the model-baseddevelopment of transformations themselves and finishing with the selective execution of the modeled transformations. The main conceptual novelty and contribution of this research is the proposed _library_ of model-based transformation specifications for the UML UCM \(\rightarrow\) SBVR BV approach. It contains a set of transformation specifications based on transformation rules presented in [6]. These specifications are based on the metamodel, which was first introduced in [7] and briefly discussed in this paper as well (Section 3). The developed set of transformations can be executed in the DnD transformation tool implementation [7]. The developed set of the UML UCM \(\rightarrow\) SBVR BV transformation specifications provides a transparent ('white box') view of the actual mappings between UML and SBVR metamodels (the latter being implemented as a UML profile). Moreover, due to the flexibility and universality of the DnD approach itself, the developed set of model transformations could serve as a reference point for the development of corresponding transformations between the models expressed in other modeling notations. The paper is structured as follows: an overview of the latest relevant research is presented in Section 2; Section 3 presents an overview of the concepts and definitions relevant to the research, together with a brief description of the implementation architecture of the developed approach; in Section 4, the specification of model-based transformation rules for extracting SBVR BV from UML UCM is presented, which is then followed by the experiment results in Section 5; the paper concludes with Section 6, where conclusions and insights for future improvements are drawn.