esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper explores the multi-objective flexible job shop scheduling problem, taking into account transportation time and energy consumption. We establish a multi-objective mathematical model for a flexible job shop with transportation constraints, where the optimization objectives are maximizing the maximum completion time and minimizing total energy consumption. To address the characteristics of the MOFJSP problem, we propose an enhanced multi-objective wolf pack algorithm as a solution approach. The improvements include designing a coding scheme, introducing a mixed initialization strategy, incorporating crossover and mutation operators, and applying a non-dominated sorting method. Moreover, we extend the traditional algorithm to solve for the optimal Pareto solution set, making it more relevant to real production scenarios. To evaluate the importance of each index, we combine the AHP with the gray relational decision analysis method. This approach allows us to compare the significance of different factors using AHP and quantify their respective weights. Subsequently, we employ gray relational analysis for decision analysis. By combining qualitative and quantitative methods, we can obtain the optimal processing scheme from the Pareto solution set under the current weight settings. This enables enterprises to select the optimal scheduling strategy from a variety of solutions. To verify the algorithm's performance, we compare the proposed algorithm with the non-dominated sorting genetic algorithm. The comparison results demonstrate the superior performance of the proposed algorithm in terms of solving performance and solution distribution. It provides a better decision-making basis for flexible job shop scheduling.  
However, this method can only be applied to static flexible job shop scheduling problems. When dealing with dynamic flexible job shop scheduling problems, such as machine failures or changes in workpiece quantities, the data needs to be reprocessed and the calculation process is complicated. Therefore, in the future research, the solution method of the algorithm and the accuracy and efficiency of the algorithm will be further improved, so that it can be more in line with actual production workshop processing.  
Conceptualization, J.L. and H.L.; Data curation, H.L. and P.H.; Funding acquisition, J.L., L.X., K.H. and S.L.; Methodology, J.L.; Writing--original draft, J.L. and H.L.; Writing--review and editing, L.X. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key R&D Program of China [grant number 2018 YFB1701205]; and the Technology Innovation Leading Program of Shanxi Province [grant number 2020 QFY03-08].  
Not applicable.  
The authors declare no conflict of interest.
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 3 Selection of Solvent Systems Used in PCL Electrospraying
### Solution Properties for Tailoring Characteristics of Electrosprayed PCL Particles  
The fission and solidification processes of electrospray droplets are also closely related to the physical and chemical properties of the working solutions. Therefore, the morphology, size, and size distribution of obtained particles from electrospraying are affected and determined by the properties of the working solution. Thus, understanding of the relationships between the solution properties and the structure, morphology of the electrospray makes it possible to select the suitable solvent systems for the electrospraying process.
# Model Updating Using Measurements from Sensors Installed in Arbitrary Positions and Directions
## 5 Conclusions  
A systematic approach using modal identification results in model updating was proposed. The mode shape obtained by modal identification and the mode shape of the model updating process did not coincide even for the same mode. A method constructing transform matrices considering cases where the measurement is done by acceleration, velocity, and displacement sensors and cases where measurement is achieved by strain sensors was proposed to remedy this disagreement among these mode shapes. The constructed transform matrices were applied when the mode shape residual was  
Figure 8: Error ratio in model updating as a result of considering uncertainties in sensor position and direction and measurement noise: (**a**) without measurement error; (**b**) with standard deviation of \(1\times 10^{-6}\) m/m in measurement error; (**c**) with standard deviation of \(2\times 10^{-6}\) m/m in measurement error.  
used as the objective function and for the mode pairing in the model updating process. The feasibility of the proposed approach was demonstrated through numerical examples involving strain or acceleration measurement with/without installation and measurement errors. The proposed approach allows the sensors to be disposed at locations representing appropriately the behavior of interest since the layout and direction of the sensors are not limited by the meshing of the model.  
Conceptualization, K.C. and J.-R.C.; Methodology, K.C.; Software, K.C.; Validation, K.C.; writing--original draft preparation, K.C.; Writing--review and editing, J.-R.C. and Y.-H.P.; Project administration, Y.-H.P.; Funding acquisition, Y.-H.P.  
This research was funded by the Korea Institute of Civil Engineering and Building Technology, grant from a Strategic Research Project (Smart Monitoring System for Concrete Structures Using FRP Nerve Sensor).  
The authors declare no conflict of interest.
# UAV Cluster Behavior Modeling Based on Spatial-Temporal Hybrid Petri Net
## 1 Introduction
However, most of the current UAV modeling research has only studied a single physical object. There is a lack of studies regarding the characteristics of UAV clusters, and a lack of research work on the formal verification of the practical application of scenario modeling. With the popularization of UAV deployment, the cooperation of multiple UAVs in the cluster has become a prominent problem, which makes it necessary to investigate an integrated modeling method. In order to study the characteristics of the UAV cluster, based on the requirements of intelligent war, this paper studied the model construction method of a heterogeneous UAV cluster, represented by the maneuvering behavior using spatial-temporal Petri nets, and defined the interaction events to solve the integrated modeling problem. The UPPALL tool was used to verify the modeling method. The contributions of this paper are two-fold, as follows. 1. A modeling method based on spatial-temporal hybrid Petri nets was proposed. Its high expressiveness allows one to effectively model the complex behavior of the UAV cluster. 2. By introducing the labeling mechanism, an external event can be specified to enrich the expressiveness of the model.
# Experimental Investigation into the Thermal Performance of Personal Cooling Mechanisms
## 4 Discussion
### Limitations and Future Considerations  
The conclusions drawn from this study are based exclusively on thermal perception under specific environmental conditions of 35 \({}^{\circ}\)C of air temperature, 40% relative humidity, and an air velocity of 0.4 m/s, which simulate a hot dry environment. This setup was selected to compare the results obtained from this testing method with those defined in the ASTM F2371-24 and ASTM F2300-22 standards. Under these conditions, a favourable relative humidity gradient was established between the manikin's skin and the surrounding air in the hot-dry environment. Future research will implement the same in-house testing protocol under varying relative humidity levels to validate further cooling vests' performance in different hot environmental scenarios.
# Fatigue Resistance Characterization of Warm Asphalt Rubber by Multiple Approaches
## 2 Materials and Methods  
### Sample Preparation and Lab-Simulated Aging  
#### 2.1.1 Binder  
Asphalt with a penetration grade of 60/70 (Pen60/70, 2017, Shell, Singapore) was selected as the raw bitumen to produce AR and WAR binders. AR binder was prepared by blending 18% of 40-mesh crumb rubber by the total weight of base asphalt and crumb rubber with base asphalt at 176 \({}^{\circ}\)C and 4000 r/min for 1 h using a high shear mixer. WAR binders were produced with three WMA additives with different working mechanisms, namely Sasobit (organic additive, Sasol-Wax Co. Ltd., Hamburg, Germany), Evotherm-DAT (chemical additive, MeadWestvaco Co. Ltd., Richmond, VA, USA), and Asphalt-min (foaming additive, Eurovia GmbH Co. Ltd., Berlin, Germany). Table 1 shows the physical properties of the selected WMA additives. The prepared samples with different WMA additives were labeled as ARE (with Evotherm-DAT), ARS (with Sasobit), and ARA (with Asphalt-min), respectively. Right after the blending process of AR binder, the WAR binders were produced by adding WMA additives via high shear mixing at 160 \({}^{\circ}\)C for 10 min. The material dosage and preparing procedure were determined based on both preliminary test results and recommendations of WMA manufacturers [20,22]. Before the fatigue tests, AR and WAR binders were lab-simulated aged by both standard rolling thin film oven (RTFO) and pressure aging vessel (PAV) processes [23,24].
## 2 Material and Data Collection
### DenseNet Neural Network Model  
In this paper, the DenseNet network model, which is mainly composed of DenseBlock and Transition layers, was used as the main training network (shown in Figure 4). In each DenseBlock, the feature map size of each layer is consistent, and BN + ReLU + 1 \(\times\) 1 Conv + BN + ReLU + 3 \(\times\) 3 Conv is used as a nonlinear combination function for operation, in which the included 1 \(\times\) 1 convolution can reduce the operation parameters and improve the operation efficiency. The transition layer connects two adjacent DenseBlocks to reduce the size of the feature map and compress the model. The transition layer consists of has the structure BN + ReLU + 1 \(\times\) 1 Conv + 2 \(\times\) 2 AvgPooling. For data sets entered into the DenseNet network, the input picture size was set to 224 \(\times\) 224 and training was started after passing through a 7 \(\times\) 7 convolution layer with a step size of 2, followed by a 3 \(\times\) 3 MaxPooling layer with a step size of 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Convolutional Neural Network** & **Characteristic** \\ \hline \multirow{4}{*}{AlexNet} & AlexNet applied ReLU as the activation function of a convolutional neural network in CNN for the first time and proposed that the deeper network structure uses the stacked convolutional layer to extract image features, and uses Dropout and data enhancement data-augmentation to suppress overfitting [28]. \\ \cline{2-2}  & VGG realizes image recognition through the set convolution layer, pooling layer, and full connection layer, and the whole network \\ \cline{2-2}  & uniformly uses the same size of convolution kernel size (3 \(\times\) 3) and maximum pooling size (2 \(\times\) 2), which can improve the performance by deepening the network [29]. \\ \hline \multirow{4}{*}{ResNet} & ResNet adds a direct channel to different convolution layers of the network to allow the original input information to be transmitted directly to the later layers. This approach greatly reduces the problem of gradient vanishing [30]. \\ \hline \multirow{4}{*}{DenseNet} & DenseNet is a dense convolutional neural network with a deeper number, which connects all layers under the premise of ensuring the maximum information transmission between layers in the network. A 1 \(\times\) 1 convolution kernel was also inserted to reduce the feature map and parameters [31]. \\ \cline{2-2}  & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Four types of networks were used in the experiment.  
Figure 4: DenseNet network structure diagram.
###### Abstract  
The present study examined the volatile profiles of _Panax ginseng_ (Korean ginseng) and _Panax quinquefolium_ (American ginseng) grown for different cultivation years by using HS-SPME/GC-MS and determined the key discriminant volatile compounds by chemometric analysis including principal component analysis (PCA), hierarchical cluster analysis (HCA), and partial least squares-discrimination analysis (PLS-DA). Fifty-six compounds, including forty terpenes, eight alcohols, one alkane, one ketone, and one furan, were identified in the ginseng roots. The chemometric results identified two major clusters of American ginseng and Korean ginseng cultivars with distinct volatile compositions. The volatile compounds in fresh white ginseng roots were affected by the species, but the influence of different cultivation ages was ambiguous. The major volatile components of ginseng roots are terpenes, including monoterpenes and sesquiterpenes. In particular, panaginesne, ginsense, \(\alpha\)-isocomene, and cryophyllene were predominant in Korean ginseng cultivars, whereas \(\beta\)-farmesene levels were higher in American ginseng. The difference in volatile patterns between Panax ginseng and Panax quinquefolium could be attributed to the composition of sesquiterpenes such as \(\beta\)-panaginsene, ginsinsene, cryophyllene, and \(\beta\)-farmesene.  
age, and biological factors [8]. It has been reported that the characteristics and qualities of various plant food materials could be derived from their volatile compounds [9; 10]. However, there is a lack of information on whether the volatile composition of white ginseng root is versatile for _Panax_ species, cultivars, and cultivation years.  
In previous studies, analytical methods based on solid-phase extraction techniques have been used to isolate volatile compounds due to trace amounts of volatile compounds in ginseng roots, which often results in drawbacks such as the possibility of sample contamination and the low extraction yield of analytes during the extraction step [11; 12]. Headspace solid-phase microextraction (HS-SPME) is a rapid, simple, solvent-free technique. It is considered an appropriate tool for sample preparation in volatile compound analysis of numerous food samples [13]. Although a few reports determined volatile components generated from ginseng fruits or red ginseng juice by analyzing HS-SPME coupled with gas chromatography mass spectrometry (GC/MS) [14; 15], the volatile profile of fresh white ginseng roots by HS-SPME/GC-MS has not been investigated.  
Therefore, the objectives of this study were to determine the volatile components of four different white ginseng roots grown for 3-6 years, using HS-SPME/GC-MS, and to examine the influence of species, cultivars, and cultivation year on the volatile composition of white ginseng roots using chemometric analysis.
###### Abstract  
Synthetic Aperture RADAR Interferometry (InSAR) provides a unique tool for the quantitative measurement of the Earth's surface deformations induced by a variety of natural (such as volcanic eruptions, landslides and earthquakes) and anthropogenic (e.g., ground-water extraction in highly-urbanized areas, deterioration of buildings and public facilities) processes. In this framework, use of InSAR technology makes it possible the long-term monitoring of surface deformations and the analysis of relevant geodynamic phenomena. This review paper provides readers with a general overview of the InSAR principles and the recent development of the advanced multi-track InSAR combination methodologies, which allow to discriminate the 3-D components of deformation processes and to follow their temporal evolution. The increasing availability of SAR data collected by complementary illumination angles and from different RADAR instruments, which operate in various bands of the microwave spectrum (X-, L- and C-band), makes the use of multi-track/multi-satellite InSAR techniques very promising for the characterization of deformation patterns. A few case studies will be presented, with a particular focus on the recently proposed multi-track InSAR method known as the Minimum Acceleration (MinA) combination approach. The presented results evidence the validity and the relevance of the investigated InSAR approaches for geospatial analyses.  
Synthetic Aperture RADAR Interferometry techniques; deformation; geodesy; multi-track; and satellite constellations +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Numerical Investigation
#### 2.2.4 Simulated Loading Procedure  
The loading procedure is carried out in the analysis is of two methods. In the first method, the helical pile is subjected to individual simulation under either pure uplift load or pure lateral load. The first method consists of three phases in staged construction. In the first phase, the initial stresses were activated, that is geostatic stress for soil, generating the \(K_{o}\) consolidation where \(K_{o}=1-sin\phi\). In the second phase the pile geometry and interfaces are activated. The third phase involves load application; either uplift or lateral load is applied separately by means of displacement control and the ultimate uplift and lateral capacity are found. The failure criterion to determine the ultimate uplift capacity (\(Q_{u}\)) was based on the load taken by the pile to displace the pile head by 10% of the equivalent helical plate diameter (\(D=40\) mm) (i.e., 4 mm) [19]. The same procedure is followed to determine ultimate lateral capacity (\(Q_{l}\)), where the failure criteria is defined as the half of the load that causes the pile head to displace 25 mm from its original position [7].  
The second method of loading consists of four phases in which combined loading is applied. The first two phases are the same as in the previous method. In the third phase, the point load is applied by means of a percentage of the obtained uplift capacity, say 25% and 50%. In the fourth phase, the uplift load is kept constant, and the lateral load is applied as displacement control. The same kind of approach is found in previous literature [19; 35; 36].
# Low-Light Image Enhancement Algorithm Based on Deep Learning and Retinex Theory
## 3 Research Process  
### Dataset  
In this study, the low-light dataset was chosen for training and testing the network model. The dataset was collected by [PERSON] et al. under real natural conditions by altering the camera's sensitivity and exposure time. It consists of 500 pairs of low-light and normal-light images with a resolution of 400 \(\times\) 600 pixels and stored in a portable network graphics format.
# Mobility Management Based on Beam-Level Measurement Report in 5G Massive MIMO Cellular Networks
## 2 System Model
### Beam Shaping Model for Massive MIMO Networks
#### 2.1.1 Element Radiation Pattern  
The element radiation pattern \(A_{E}(\cdot)\) is described as  
\[A_{E}(\theta,\phi)=G_{\rm E,max}-\min[-\{A_{E,V}(\theta)+A_{E,H}(\phi)\},A_{m }], \tag{2}\]  
where \(G_{\rm E,max}=8\) dBi is the maximum directional gain of antenna elements. \(A_{E,V}(\cdot)\) and \(A_{E,H}(\cdot)\) are vertical and horizontal element patterns, respectively. Furthermore, \(A_{m}=30\) dB is a front-back ratio. In particular, the vertical element pattern is obtained as  
\[A_{E,V}(\theta)=-\min\left[12\left(\frac{\phi-90^{\circ}}{\theta_{\rm 3 dB}} \right)^{2},SLA_{v}\right], \tag{3}\]  
where \(\theta_{\rm 3 dB}=65^{\circ}\) is the vertical 3 dB beamwidth, and \(SLA_{v}=30\) dB is the side-lobe level limit. In addition, the horizontal element pattern is obtained as  
\[A_{E,H}(\phi)=-\min\left[12\left(\frac{\phi-90^{\circ}}{\phi_{\rm 3 dB}} \right)^{2},A_{m}\right], \tag{4}\]  
where \(\phi_{\rm 3 dB}=65^{\circ}\) is the horizontal 3 dB beamwidth.
# Expansion Lemma--Variations and Applications to Polynomial-Time Preprocessing
## 2 Preliminaries
### Graph Parameters  
We introduce different graph parameters that we will use in this paper. A subset \(S\subseteq V(G)\) is a _vertex cover_ of \(G\) if for every edge \(uv\in E(G)\), \(\{u,v\}\cap S\
eq\emptyset\). It follows that for an undirected graph \(G\), if \(S\) is a vertex cover, then \(V(G)\setminus S\) is an independent set (and vice versa). A subset \(S\subseteq V(G)\) is called a _cluster vertex deletion set_ of \(G\) if every connected component of \(G-S\) is a clique. A subset \(S\subseteq V(G)\) is a _feedback vertex set_ of \(G\) if \(G-S\) has no cycle.  
The following graph parameter requires a more non-trivial definition.  
**Definition 3** (Pathwidth [16; 17; 18]).: _A path decomposition of graph \(G\) is a pair \((T,\chi)\) in which \(T=(V_{T},E_{T})\) is a path and \(\chi=\{\chi_{i}\mid i\in V_{T}\}\) is a family of subsets of \(V(G)\) called bags, such that_  
1. \(\cup_{i\in V_{T}}\chi_{i}=V(G)\)_._
2. _for each edge_ \(uv\in E(G)\)_, there is_ \(i\in V_{T}\) _such that_ \(u,v\in\chi_{i}\)_._
3. _for each vertex_ \(u\in V(G)\)_, the set of nodes_ \(\{i\in V_{T}\mid u\in\chi_{i}\}\) _induces a connected subgraph of_ \(T\)_._  
The value \(\max_{i\in V_{T}}\{|\chi_{i}|-1\}\) is called the _width_ of the path decomposition \((T,\chi)\). The _pathwidth_ of a graph is the minimum width taken over all possible tree decompositions of \(G\). It is not very hard to understand that the pathwidth of a path (or a linear forest) is \(1\). Similarly, the pathwidth of a cycle is \(2\), and the pathwidth of a caterpillar is also \(1\). Moreover, the pathwidth of a clique with \(n\) vertices is \(n-1\). On an informal note, the pathwidth of a graph is a measure of how close a graph is to a path.  
We begin the technical part of the survey with a structure and a lemma that could be considered a precursor to the expansion lemma.
# Privacy-Preserving Authentication Based on PUF for VANETs
## 3 Preliminaries
### Threat Model  
The proposed scheme's initial phase and registration phase occur within a secure channel. However, it is important to note that the security of the communication between vehicles and other entities, including RSUs and other vehicles, cannot be fully guaranteed. A crucial aspect to consider is that, despite adhering to strict protocols, RSUs often exhibit curiosity towards sensitive vehicle information, such as travel routes and speeds. Furthermore, all algorithms of this scheme are discussed in a CA domain, where each domain has a limited geographical coverage, typically corresponding to a single city. Consequently, it is assumed that all entities participating in this scheme maintain synchronous time. And the identities of all RSUs are publicly accessible. The adversary model assumes the following:  
Figure 1: System model.  
Firstly, it is presumed that an attacker possesses the capability to intercept, manipulate, delete, and replay any information transmitted over unsecured public channels. This encompasses all forms of communication that lack robust encryption or protection.  
Secondly, the system's entities are vulnerable to physical attacks, making it likely that their secret parameters could be stolen. Additionally, apart from executing the protocol with integrity, RSUs might attempt to decipher the privacy of individual vehicles by analyzing legitimately received messages.