esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 1 Introduction
Then, an enhanced algorithm based on SPEA2 (ESPEA) is proposed to solve the problem with a high efficiency. Specifically, this paper has the following contributions:  
1. The pickup vehicle scheduling problem with mixed steel storage (PVSP-MSS) is investigated in this paper. The PVSP-MSS is formulated as a multi-objective mixed-integer linear programming model, aiming at simultaneously optimizing the makespan of pickup vehicles and the makespan of the steel logistics park. By solving this problem, the optimal yard allocation and loading sequence for vehicles can be obtained. 2. The ESPEA is proposed to solve the PVSP-MSS with a high efficiency. In the proposed algorithm, a cooperative initialization strategy is first proposed to provide an initial solution for scheduling optimization. Then, insertion decoding is designed to optimize the quality of the solution by utilizing the idle time in the yard. Moreover, local search technology is proposed in the ESPEA to improve the solution quality by swapping pickup operations on critical paths in steel logistics parks. 3. The experiments are executed based on the data from a real steel logistics park. The results show that compared with other algorithms, the proposed algorithm significantly reduces both the makespan of each pickup vehicle and the makespan of the steel logistics park. The rest of this paper is organized as follows. Section 2 introduce the framework of scheduling based on IIoT and the data features of pickup vehicles. An optimization model is formulated in Section 3. Section 4 proposes an ESPEA to solve the proposed problem. Section 5 discusses the experiment results, and the conclusion is given in Section 6.
# Review of Flexible Piezoresistive Strain Sensors in Civil Structural Health Monitoring
## 2 Piezoresistive Mechanisms
### Crack Propagation  
Crack-based flexible strain sensors rely on the reconstructible disconnection and reconnection of cracks [43; 44; 45]. For strain sensors with thin nanomaterial films coated on flexible substrates, nano/microcracks generate at the stress-concentrated areas to release stress [23]. The quick departure of adjacent crack junctions caused by the applied strain considerably confines the conduction of electrons through the film, inducing a substantial change in the sensor resistivity [42; 46]. Therefore, the crack propagation principle is advantageous to develop ultra-high-sensitive and flexible strain sensors [47; 48; 49]. Moreover, the sensing performance and working range are significantly influenced by parameters such as the length, width, depth and density of cracks [44; 50].  
According to recent studies, microcracks were generated in thin CNT-, silver nanoparticle-, gold nanowire-, graphene- and other conductive-filler-based films coated on flexible substrates [51; 52; 53]. Figure 5 depicts the process of microcrack generation and development in a graphene-polydimethylsiloxane (PDMS) composite film under cyclic stretching and releasing strain. It is shown that the density and size of microcracks increased with the stretching and then recovered to their initial states after the release of the thin films. In addition, microcrack edges were reconnected upon the release of the applied strain, ensuring complete recovery of the electrical resistance [54].  
In summary, the four categories of piezoresistive mechanisms explained above are common in flexible piezoresistive strain sensors. Different mechanisms predominate the electrical conductivity of strain sensors at various strain levels or concentrations of conductive fillers, and they work together to form a complete conductivity system during the overall strain range of flexible sensors. In addition, the above mechanisms can be regarded as extrinsic conductive mechanisms because the conductivity of the composite is attributed to the additional carbonaceous or noncarbonaceous conductive fillers rather than the polymer substrates [22]. The conductivity can be tuned by changing the content of the fillers, leading to different piezoresistive mechanisms.  
Figure 4: (**a**) percolation network of the randomly assigned graphene flakes [23] (Copyright 2019, Elsevier Ltd.); (**b**) percolation network of silver nanowires with low filler density [42] (Copyright 2016, WILEY-VCH).
## 4 Experiment and Comparison  
### Use Cases for Evaluation  
To verify whether the proposed BPMN model applies to multiple scenarios of SCs and check the effectiveness of the reuse mechanism in improving the development efficiency in practical applications, we extracted five representative contracts from the literature for evaluation. These contracts have certain complexity, involve multiple stakeholders, and are widely used as use cases in related research. However, the most important aspect is that the selected contracts permit reuse in multiple fields, which proves that our tools embody the idea of reuse.  
* **Voting and Election Contract**[21]: This case is one of the earliest applications of SCs and reflects the idea of decentralization. Therefore, this application is taken as one of our cases. The sponsor of a poll can register both the voters and the candidates, and, within a specific time, the voters can choose a candidate to vote for. After the polls close, everyone can view the results.
* **Voting Result Notification Contract**[22]: The contract involves the originator, voter, and candidate who is registered. When the number of votes reaches a specified number, messages are sent to other contracts.
* **Crowdfunding Contract**[23]: The contract covers the crowdfunding originator and shareholders. The user becomes a crowdfunding shareholder when money is transferred from a user's account to a crowdfunding project. A crowdfunding project is established if it is successfully raised within the specified time, and the shareholders  
Figure 11: Flight delay insurance SC.  
in the project have the right to vote on all proposals. All money will be refunded unless the fund is raised successfully.
* **Supply Chain Contract**[24]: The contract involves buyers, manufacturers, intermediaries, carriers, and suppliers. The buyer places an order with the manufacturer, who sets delivery dates and compensation for overdue payments. Manufacturers contact intermediaries, who identify suppliers and carriers. After receiving the raw materials from the supplier, the manufacturer produces goods. After the manufacturer has delivered the goods to the buyer, the compensation clause will be enforced if the delivery time is exceeded.
* **Buying and Selling Contract**[25]: The contract involves buyers and sellers. Sellers can register goods for buyers to view and purchase. After the buyer buys the goods, the seller will determine the delivery time and overdue compensation. The indemnity clause will be applied if the delivery time exceeds the agreed time.
# rSEM: System-Entropy-Measure-Guided Routing Algorithm for Industrial Wireless Sensor Networks
## 5 Conclusions  
This paper introduces the system entropy measure into the problem of routing and proposes a high-power performance rSEM algorithm. In a two-hop cluster network architecture, the cluster head selection and cluster member selection are carried out based on the principle of the minimum system entropy measure. The experiments show that the power consumption of the network generated by the rSEM can reach the same magnitude as [PERSON]'s algorithm. Moreover, the delay performance of the rSEM algorithm is very close to that of the LEACH algorithm. The rSEM algorithm provides an optimization idea for the routing design of the next-generation industrial network from the perspective of the network topology and improves the comprehensive performance of the network. Therefore, the rSEM provides a solution for 5G industrial networks with \"time-delay and power consumption dual sensitivity\".  
Conceptualization, K.N., C.D. and X.X.; methodology, K.N., C.D. and X.X.; software, X.X.; validation, X.X. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key Program of the National Natural Science Foundation of China of funder grant number 92067202, the National Natural Science Foundation of China of funder grant number 62071058, and the Key Laboratory of Universal Wireless Communications (BUPT), Ministry of Education, China (No. KFKT-2022104).  
Not applicable.  
The authors declare no conflict of interest.
## 4 Energy Consumption Analysis
### Final Energy Consumption  
The final energy requirements for both building types can be obtained by subtracting the energy production from the energy consumption. The energy consumption reduction rate was higher in elementary schools than in large offices because 617.5 kW of solar power was produced due to the large rooftop area of elementary schools compared to 402.8 kW produced in large offices. In addition, while the difference in energy production due to the active element by region is not large, the difference in energy consumption by the passive element is large, so the regional variation in the final energy consumption occurs. As a result, the final energy consumption in Busan, the southern region, is significantly reduced compared to Seoul in the central region, and the smaller the energy consumption, the higher the probability of achieving a zero-energy building (Figure 7).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multicolumn{2}{c}{**Type**} & \multicolumn{2}{c}{**Large Office**} & \multicolumn{2}{c}{**Primary School**} \\ \cline{2-6}  & **Location** & **Seoul** & **Busan** & **Seoul** & **Busan** \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Maximum energy \\ production \\ \end{tabular} } & Energy Production [k/h] & 600,004,585 & 747,771,046 & 762,676,316 & 914,300,619 \\ \cline{2-6}  & Orientation [\({}^{\circ}\)] & 90 & 90 & 100 & 100 \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Minimum energy \\ production \\ \end{tabular} } & Energy Production [k/h] & 434,560,713 & 506,904,344 & 646,663,522 & 745,810,532 \\ \cline{2-6}  & Orientation [\({}^{\circ}\)] & 230 & 250 & 260 & 260 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Energy production according to the orientation (year sum, kJ/h).  
Figure 6: Energy production of each building type according to the location.  
Summarizing the results of this chapter, elementary schools in Seoul and Busan are at least 2% to 11% larger than large offices, and energy production is about 24% to 26% higher in elementary schools than large offices. As a result, the final energy consumption of elementary schools in Seoul is 7% higher than that of large offices, but in Busan, energy consumption of elementary schools is 7% lower than that of large offices (Table 8).
## 1 Introduction
### The Arylaminoethanol Group of \(\beta\)-Blockers
#### 2.2.3. Labetalol  
From the chemical point of view the substance is related to salicamide, bearing in the basic part of the aminoethanol moiety 4-phenylbutan-2-yl group instead of branched groups such as propan-2-yl (isopropyl) or 2-methylpropan-2-yl (_tert_-butyl). Labetalol is thus capable of simultaneous blockage of \(\alpha\)- and \(\beta\)-receptors. As a result of this combined adrenolytic activity, labetalol is used as a mixture of four stereoisomers for the indication of hypertension. Recent investigations substantiated the suitability of this drug also in controlling hypertensive crisis in patients with hypertensive disorders of pregnancy (HDP), similarly to hydralazine [37].  
In regard to the stereochemistry of the isomers of labetalol, the (\(SR\))-isomer exerts \(\alpha\)-lytic activity, whereas the (\(RR\))-form blocks only \(\beta\)-receptors [38]. The (\(SS\))- and (\(RS\))-isomers are inactive. Despite exhibiting higher ISA activity in clinical tests, the usage of the pure (\(RR\))-isomer (dilevalol) had to be discontinued due to occurrence of hepatitis during the therapy. Since in the racemic form of the drug no hepatotoxicity was encountered, the question remains whether the other enantiomer shows hepatotoxicity properties. This clearly indicates that the employment of pure enantiomeric form of a drug is not always of advantage.
## 4 HMBAC Design and Implementation  
First, we formally define the HMBAC model (Section 4.1) and, on the basis of it, we describe an HMBAC architecture design (Section 4.2), based on the underlying building blocks. The architecture is eventually translated into _Janus_, an actual HMBAC implementation (Section 4.3).
# Progressive Pruning of Light Dehaze Networks for Static Scenes
## 6 Conclusions  
Our study on the progressive pruning of Light DeHaze Networks has yielded significant insights and advancements in efficient image dehazing. Through our progressive pruning approach, we have demonstrated the potential for substantial reduction in computational complexity while maintaining high performance, especially in fixed camera scenarios. Our research reveals that fine-tuning models for specific scenes significantly enhances their dehazing performance on those particular environments. We observed that channels within the network exhibit non-uniform importance, with some consistently showing higher significance across various images and models. This finding led to the development of progressive pruning strategies that consider layer-wise sensitivity, optimizing the trade-off between model size and dehazing quality. Notably, pruned models showed remarkable resilience in fixed camera environments, indicating their suitability for deployment in resource-constrained, static monitoring systems. This pruning method, while resulting in some performance trade-offs, enables significant model compression, opening up new possibilities for real-time dehazing applications on devices with limited computational resources. Our approach represents a significant step towards making high-quality image dehazing more accessible and efficient, particularly in fixed camera setups. Looking forward, this research paves the way for further advancements in efficient dehazing algorithms. Future work could explore more sophisticated pruning techniques, potentially incorporating dynamic pruning methods that adapt to changing environmental conditions. Additionally, investigating the generalization capabilities of pruned models to diverse hazy scenarios could further enhance their practical applicability. In conclusion, our progressive pruning approach has the potential to improve various real-world applications, from traffic monitoring to outdoor surveillance systems, by providing clearer visibility in hazy conditions with reduced computational overhead. This advancement brings us closer to widespread deployment of effective dehazing solutions in resource-constrained environments.  
Conceptualization, S.Y.; methodology, S.Y.; software, S.Y.; validation, H.L.; formal analysis, S.Y.; investigation, S.Y. and B.P.; writing--original draft preparation, B.P.; writing--review and editing, S.Y.; visualization, Y.-K.K.; supervision, S.Y.; project administration, S.Y.; funding acquisition, S.Y.; resources, B.P.; data curation, B.P. All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Wonkwang University in 2022.  
Not applicable.  
The data presented in this study are available upon reasonable request from the corresponding author.  
The authors thank the anonymous reviewers and editors for their insightful comments and suggestions.  
The authors declare no conflict of interest.
# A Machine Learning Approach to Determine Oyster Vessel Behavior
###### Abstract  
In this work, we address a multi-class classification task of oyster vessel behaviors determination by classifying them into four different classes: fishing, traveling, poling (exploring) and docked (anchored). The main purpose of this work is to automate the oyster vessel behaviors determination task using machine learning and to explore different techniques to improve the accuracy of the oyster vessel behavior prediction problem. To employ machine learning technique, two important descriptors: speed and net speed, are calculated from the trajectory data, recorded by a satellite communication system (Vessel Management System, VMS) attached to the vessels fishing on the public oyster grounds of Louisiana. We constructed a support vector machine (SVM) based method which employs Radial Basis Function (RBF) as a kernel to accurately predict the behavior of oyster vessels. Several validation and parameter optimization techniques were used to improve the accuracy of the SVM classifier. A total 93% of the trajectory data from a July 2013 to August 2014 dataset consisting of 612,700 samples for which the ground truth can be obtained using rule-based classifier is used for validation and independent testing of our method. The results show that the proposed SVM based method is able to correctly classify 99.99% of 612,700 samples using the 10-fold cross validation. Furthermore, we achieved a precision of 1.00, recall of 1.00, \(F1\)-score of 1.00 and a test accuracy of 99.99%, while performing an independent test using a subset of 93% of the dataset, which consists of 31,418 points.  
o _et al._ Article  
osyter vessel behavior; trajectory data; support vector machine; machine learning
# The Development and Analysis of a Multistage Spraying Method for Liquids in an Ultrasonic Field
## 4 Results and discussion
### Dependence on the Sound pressure Level  
Figure 10 shows a sequence of histograms characterizing the evolution of the disperse composition of the formed droplets (at the outlet of the tubular emitter) depending on the sound pressure level. The liquid supply rate is 5 mL/s.  
Figure 10: Evolution of disperse composition of droplets at different sound pressure levels.  
It follows from the presented histograms that the sound pressure level was the main parameter of the process, which significantly affected the dispersion characteristics of the generated aerosol. At the same time, hydrodynamic parameters (the flow rate of the sprayed liquid) and physical properties of the liquid (will be shown below) had much less influence.  
This makes the proposed technique invariant to the conditions of the process (flow rate and physical properties of the liquid), but at the same time, it can be configured through the selection of ultrasonic exposure modes (sound pressure level). All this ensures adjustability in the disperse characteristics of the generated aerosol. Figure 11 shows the dependence of the median diameter of the formed droplets on the sound pressure level to identify the limits of controllability of the disperse composition of the formed droplets. Each experimental point in Figure 11 was obtained by averaging 10 Sauter diameter measurements (determined using Malvern SprayTech). The experimental data satisfactorily correspond to linear regression. The error bars indicate standard deviation.  
It can be seen that changing the sound pressure level in the range of 160-182 dB enables an adjustment of the median diameter of the formed droplets in the range of 170-34 \(\upmu\)m.
## 3 Results and Analysis
### Vegetation Evaluation Indicator Variations for Different FCIs  
Figure 9a shows the statistics of different forest coverage indices in 2020 for each country in the Lancang-Mekong region. The FCI ranges from 10% to 100% with a 10% interval. The results showed that for each FCI from 20% to 90%, the percentages were relatively low and did not exceed 13% for any of the five countries. Cambodia and Myanmar had higher percentages (37.88% and 35.91%) where the FCI was greater than 90%. On the other hand, Cambodia, Thailand, and Vietnam had notable proportions (17.89%, 27.05% and 20.79%) where the FCI was lower than 10%. Figure 9b shows the difference in FCI between 2020 and 2015, and Figure 9c-e presents statistics on the differences in the annual average LAI, annual maximum FVC, annual cumulative GPP, and KEQI from 2010 to 2020,  
Figure 7: Spatial distribution of KEQI for each country in the Lancang–Mekong region from 2010 to 2020. (**a**) KEQI; (**b**) summary of KEQI statistics.  
under different FCIs for each country. The results show that the forest coverage decreased in 2020 compared to 2015 for all intervals, particularly in Cambodia; however, in Thailand, there was a trend toward increasingly dense forest coverage (100%) (Figure 9b). Moreover, the difference in the annual average LAI, annual maximum FVC, annual cumulative GPP, and KEQI for Cambodia and Laos were all below 0 for different FCIs. Cambodia showed more significant decreasing trends compared to Laos in these vegetation evaluation indicators, indicating deterioration in the health of the forest ecosystem (Figure 9c-e). For Myanmar, Thailand, and Vietnam, the annual average LAI, annual maximum FVC, annual cumulative GPP, and KEQI changed from increasing trends to decreasing trends as the FCI increased from 0% to 100%. The amplitude of the variation gradually decreased with the increase in FCI, suggesting that the forest ecosystems were more stable when there was a higher FCI. Although the results for the vegetation evaluation indicators in Thailand and Myanmar showed an increasing trend each year, the amplitude of each vegetation evaluation indicator decreased with the increase in FCI.  
Figure 8: Deforestation of two regions in Cambodia monitored by Chinese GF-1 satellite images with true-color composite (red, green, and blue) before 2015 and 2020, respectively. (**a**) 6 December 2013 in region 1; (**b**) 1 June 2021 in region 1; (**c**) 15 January 2015 in region 2; and (**d**) 24 February 2020 in region 2.  
Figure 9: _Cont._
## 2 Materials and Methods
### Materials  
The conditions used for this set of experiments were control unreinforced soil specimens (PNF = 0%) and mixture of soil and 1% and 2% of natural fibres, relative to the mass of soil (PNF of 1% and 2%). Figure 1 illustrates the materials studied.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Condition** & **Designation** & **Number of Specimens Tested** \\ \hline Unreinforced & PNF = 0\% & 3 \\ Reinforced with a percentage of natural fibres of 1\% & PNF = 1\% & 3 \\ Reinforced with a percentage of natural fibres of 2\% & PNF = 2\% & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the experimental programme carried out—RL-CBR tests on unreinforced soil and soil reinforced with a percentage of natural fibres (PNF) of 1% and 2%.  
The soil originates from the Aveiro Lagoon, Portugal, and was collected from a salt pan wall. Considering the Unified Soil Classification System [18], the soil is considered a ML, sandy silt. This soil is mostly formed of fine particles (>0.074 mm), 65.7% precisely. Given the high percentage of fine particles, the Atterberg limits [19] of the soil were evaluated; its liquid limit, \(w_{L}\), is 35%, and its plastic limit, \(w_{P}\), is 25%, resulting in a plasticity index, \(I_{P}\), of 10%. Considering the conditions of modified Proctor tests (method C) [20], the optimum water content, \(w_{opt}\), and the corresponding maximum dry density, \(\gamma_{\text{dmax}}\), were 13.9% and 18.09 kN.m\({}^{-3}\), respectively. The grain size distribution of the soil is illustrated in Figure 2, determined according to [21].  
The reinforcement solution analysed in this study uses natural pine needles collected from stone pines, _Pinus pinea_, in the same region, Figure 1. With 52-60% cellulose, 13-15% lignin, 20% hemicellulose, and 5% of other extractives, this biodegradable material has a maximum tensile strength ranging between 35.8 and 36.94 MPa [22]. The physical characteristics of the natural reinforcement were analysed from a sample of 15 g in mass, concluding that the length of the pine needles varied from 60 to 180 mm, giving an average value of 117 mm; the average width was 0.87 mm, with values ranging between 0.62 and 1.11 mm. The ratio length/width (considering average values) was 134. The fibres were used in their natural state, after cleaning and an air-drying process. Damaged fibres were discarded.