esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Integrated Multi-Criteria Decision-Making Model for Resilient Supplier Selection
## 4 Case Study
### Discussion  
A food manufacturing company with the financial problem caused by the COVID-19 pandemic was selected to be assessed by the proposed model. In this case, 10 suppliers of the company were evaluated based on seven resilience criteria which were Quality (C1), Lead Time (C2), Cost (C3), Flexibility (C4), Visibility (C5), Responsiveness (C6) and Financial Stability (C7). Based on the proposed model, the results were generated and analyzed. In Table 10, GRA was applied to identify the importance level of each resilience criterion. The outcomes show that C3 which is Cost has the highest grey relational grade of 0.8667 at the 1 st rank among the criteria, followed by C7, C5, C6, C4, C2 and lastly C1 with the lowest grade of 0.5133. The higher the grey relational grade, the more crucial is  
criteria weights were calculated. In this study, C3 is the best criterion whereas C1 is the worst criterion. From Table 11, the greatest weight is C3 with a value of 0.4225 whereas the smallest weight is C1 with a value of 0.0367. The weights were further applied for the evaluation of suppliers. To ensure the result consistency, a consistency test was carried out where Table 12 indicates that the consistency ratio generated is 0.09 which is close to 0. This implies that the result is consistent and the study could proceed to the next phase. Else, the process could be reversed to the scoring stage to improve the data collected. In this case, the criteria weights were carried to the supplier ranking phase by applying TOPSIS. Table 16 shows the outcomes of the supplier evaluation process. From the outcomes, Supplier 4 (S4) has the highest closeness coefficient which is 0.7512. This means that S4 has the nearest distance to the PIS and the furthest distance from the NIS which is the most ideal supplier for the case company. In contrast, Supplier 1 (S1) has the lowest closeness coefficient of 0.2222 which implies that S1 has the furthest distance from PIS and nearest distance to NIS. The suppliers' rank starts from S4 with the best performance, followed by S3, S5, S10, S2, S7, S9, S6, S8 and lastly S1 showing the worst performance in the evaluation. Therefore, S4 is the most prioritized supplier for the case study. By applying the proposed resilience criteria and the validated technique of GRA-BWM-TOPSIS, the best resilient supplier is identified to improve the SC resilience of the company.
# Multi-Objective Task Scheduling Optimization in Spatial Crowdsourcing
## 2 Related Work  
Recently, the spatial crowdsourcing field has gained much attention in both research and industry communities, e.g., Gigwalk and TaskRabbit. There are many surveys in the SC area (e.g., [27; 28; 29; 30]) that mention the main taxonomy, challenges, and general techniques of task assignment in SC. A general example of SC is Gmission, which was developed by [20]. Gmission is a typical SC platform that has location-dependent tasks. This means that the system assigns spatial tasks to nearby workers. Thus, the workers should go to a particular location to complete the assigned tasks. This system allows individuals to register on the platform through the interface model to post tasks. After this, they can collect answers to their tasks, which are submitted in a specific format by the worker after executing them. The system can check whether the worker has reached the location of their assigned tasks by tracing the worker's location upon their submitted answer.  
In this section, we review the most common research works that have studied the task assignment problem in SC. The task assignment problem is formulated as either the task matching problem [3; 4; 6; 8; 9; 12; 31; 32; 33] or the task scheduling problem [2; 7; 10; 11], depending on the objective to be achieved. In contrast, there are limited studies that have highlighted the MOO in SC. We display our related works regarding three aspects commensurate with our research aspects as follows: task matching in SC, task scheduling in SC, and the binary-objective optimization problem in SC.
## 3 Hybrid Weight Assignment Model
### Hybrid Model
#### 3.3.1 Time Ordered Weighted Averaging Operator  
In order to introduce the impact of the time dimension on the evaluation indicators, the time ordered weighted averaging (TOWA) operator [43] has been presented on the basis of the ordered weighted averaging (OWA) [42], the induced ordered weighted averaging (IOWA) [44], the ordered weighted geometric (OWG) [45], and the induced ordered weighted geometric averaging (IOWGA) [46]. The mathematical calculation model is:  
\[F(\langle t_{1},a_{1}\rangle,\langle t_{2},a_{2}\rangle,\cdots,\langle t_{k},a_ {k}\rangle,\cdots,\langle t_{s},a_{s}\rangle)=\sum\limits_{k=1}^{s}w_{k}b_{k} \tag{8}\]  
where \(\langle t_{k},a_{k}\rangle\) is the basic pair of TOWA operator; \(t_{k}\) is the time induced component and \(a_{k}\) is the data component (\(k=1,2,\cdots,s\)); \(w_{k}\) is the weight of the same element at the \(k\)th moment in the time induced component, \(w_{k}\in[0,1]\); and \(\sum_{k=1}^{s}w_{k}=1\); \(b_{k}\) is the data component of the TOWA pair corresponding to the \(k\)th moment in the time induced component of the same element; and \(F\) is the \(n\) dimensional TOWA operator.  
The essence of the TOWA operator is to perform weighted summation of the data components (\(a_{1},a_{2},\cdots,a_{s}\)) of the same element corresponding to the time induced components sorted in a certain time sequence.
# Durability Investigation of Fiber-Reinforced Functionally Graded Concretes in Cold Regions
## 2 Test Methods and Proportioning Model
### Shrinkage Performance Tests  
The latest research results and engineering tracking survey results show that more than 60% of premature failure of concrete projects is caused by shrinkage cracking [24; 25; 26]. Once the concrete cracks, it dramatically increases its permeability. Thus, concrete shrinkage is often unavoidable, and jeopardizes the structure's durability and safety. The shrinkage includes drying shrinkage and autogenous shrinkage. As the capillary pores of ordinary concrete contain sufficient moisture coupled with large pore size, the shrinkage tension caused by drying is not enormous, making the resulting self-shrinkage small--only about 1/10 of the dry shrinkage. Therefore, compared to the dry shrinkage of concrete, self-shrinkage is often overlooked [27]. However, the composite anti-freezing and shrinkage-reducing polycarboxylic acid water reducers were prepared for high-performance concrete in this project. The relatively low water-cement ratio and the incorporation of mineral admixtures increased the proportion of self-shrinkage in the total shrinkage, and the self-shrinkage also merits attention.
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 3 Methods
### Segmentation-Based Observation Units
#### 3.2.2 Segmentation Algorithm  
We decided to test whether an algorithm implemented by us could be fine-tuned just for the damage detection with SAR data. The directed tree algorithm by [PERSON] and [PERSON] [44] was modified to use SAR data (see also [45; 46]). In this simple test, we used only the difference of the intensities of two scenes, one before and one after the damage and separately VV and VH polarizations.  
The algorithm utilizes an edge gradient calculated from an edge image. Any edge operator can be used to construct the edge image. Simplified steps of the segmentation algorithm are as follows. (1) Plateau points are calculated using the inverted edge image and a selected sensitivity (threshold) parameter \(e\). (2) For each pixel \((i,j)\) that is not a plateau point, a parent is determined. The parent of \((i,j)\), \(P(i,j)\), is the neighbor that gives the highest positive value of the inverted edge gradient among the neighbors of \((i,j)\). Ties are resolved arbitrarily. If no such neighbor exists, \((i,j)\) has no parent and is therefore a root node. The parents of each plateau points are thus determined. All points on a uni-modal plateau will belong to the same directed tree with one point on the plateau that will be called a root. (3) Once the parent of each point is determined, the points can be labeled by the directed trees (segment) they belong to. The root pixels are first labeled. (4) Once the roots have been labeled, the label of each pixel is determined by tracing of the chains to link the pixels to their root pixels (for details, see [44]). The final segmentation result does not depend on the processing order of the image.
## 3 Results and Discussions
### Effect of Groove on Cavity Surface
The interaction between these reverse vortices enhances the energy dissipation and leads to better performance. Furthermore, the better matching degree between the  
Figure 13: The distributions of pressure in leakage passage along axial direction under different groove on the cavity surface. circle groove profile and the outline of the local small vortex for circle groove is observed. The energy dissipation caused by the small vortex near the stationary surface of the circle groove is higher than that of the rectangle groove. Therefore, the circle groove achieves the best performance. Under the design condition, the relative leakage of stator1 and rotor, power and efficiency are \(6.34\times 10^{-3}\), \(8.53\times 10^{-3}\), \(3.463\) MW and \(86.61\%\) respectively. With an increase in pressure ratio, the variation trends of these four parameters are the same as those in Section 3.1. Below the design point, the relative leakage increases rapidly with an increase in pressure ratio. After the pressure ratio exceeds the design value, the relative leakage is almost unchanged. As the pressure ratio increases, the power increases while the efficiency increases firstly and then decreases.
# A Two-Stage Co-Evolution Multi-Objective Evolutionary Algorithm for UAV Trajectory Planning
## 4 Experiments and Analysis
### MOUTP Simulation Experiments Based on Classical DE Algorithm
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{\#} & \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Metrics**} \\ \cline{3-6}  & & **HV** & **IGD** & **FP (\%)** & \(T_{average}\) **(s)** \\ \hline \multirow{4}{*}{Test Instance 1} & DD-CMOEA & \(9.8750\times 10^{-3}\) (\(7.45\times 10^{-3}\)) & \(7.1922\times 10^{-1}\) (\(4.36\times 10^{-2}\)) & 100\% & 20 \\ \cline{2-6}  & cDADSEA & \(3.5523\times 10^{-3}\) (\(3.65\times 10^{-3}\)) & \(7.6204\times 10^{-1}\) (\(2.69\times 10^{-2}\)) & 94.2\% & 25 \\ \cline{2-6}  & TSCEA & \(1.7388\times 10^{-2}\) (\(4.57\times 10^{-2}\)) & \(6.9665\times 10^{-1}\) (\(1.68\times 10^{-1}\)) & 100\% & 27 \\ \cline{2-6}  & DPCPRA & \(2.9169\times 10^{-3}\) (\(4.53\times 10^{-3}\)) & \(7.7373\times 10^{-1}\) (\(3.46\times 10^{-2}\)) & 100\% & 31 \\ \hline \multirow{4}{*}{Test Instance 2} & DD-CMOEA & \(2.2770\times 10^{-1}\) (\(3.87\times 10^{-2}\)) & \(2.5567\times 10^{-1}\) (\(2.63\times 10^{-2}\)) & 100\% & 35 \\ \cline{2-6}  & cDADSEA & \(2.3768\times 10^{-1}\) (\(7.65\times 10^{-3}\)) & \(2.9148\times 10^{-1}\) (\(3.01\times 10^{-2}\)) & 86\% & 30 \\ \cline{2-6}  & TSCEA & \(2.4992\times 10^{-1}\) (\(5.30\times 10^{-3}\)) & \(2.4768\times 10^{-1}\) (\(1.86\times 10^{-2}\)) & 100\% & 32 \\ \cline{2-6}  & DPCPRA & \(2.5206\times 10^{-1}\) (\(5.45\times 10^{-3}\)) & \(3.1852\times 10^{-1}\) (\(8.95\times 10^{-2}\)) & 100\% & 40 \\ \hline \multirow{4}{*}{Test Instance 3} & DD-CMOEA & \(2.0949\times 10^{-1}\) (\(7.92\times 10^{-3}\)) & \(3.4199\times 10^{-1}\) (\(1.48\times 10^{-2}\)) & 100\% & 32 \\ \cline{2-6}  & cDADSEA & \(1.5448\times 10^{-1}\) (\(8.48\times 10^{-2}\)) & \(4.5464\times 10^{-1}\) (\(1.75\times 10^{-1}\)) & 75\% & 40 \\ \cline{1-1} \cline{2-6}  & TSCEA & \(2.1166\times 10^{-1}\) (\(1.99\times 10^{-2}\)) & \(3.2324\times 10^{-1}\) (\(7.55\times 10^{-2}\)) & 100\% & 37 \\ \cline{1-1} \cline{2-6}  & DPCPRA & \(1.1034\times 10^{-1}\) (\(9.26\times 10^{-2}\)) & \(5.3951\times 10^{-1}\) (\(1.91\times 10^{-1}\)) & 95\% & 58 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metrics of COOEAs for different instances. Figure 10 shows the average planning time and the shortest planning time for each CMOEA in different test instances. From Figure 10, it can be seen that although the TSCEA-based MOUTP was not the shortest in terms of the average planning time and shortest planning time in different test instances, the average planning time and the shortest planning time of MOUTP based on the TSCEA were more stable in each test instance. By combining the HV, IGD, and PF values of the TSCEA above, it can be concluded that the MOUTP planning system based on the TSCEA is more stable in its operation and able to provide an effective method for solving the key trajectory points.
## 3 Bimodal-Information-Based Human Targets Recognition Method
### Suspected Target Detection Based on Multispectral Feature Information
According to the spectral curves captured from the green camouflage, vegetation, and bare soil, the following 8 spectral indexes were taken into consideration, and they are calculated as shown in Table 2. Where \(NDVI\) stands for Normalized Difference Vegetation Index, \(NDGI\) (Normalized Difference Green Index), \(NGBDI\) (Normalized Green-Blue Difference Index), \(PSRI\) (Plant Senescence Reflectance Index), SIPI (Structure Insensitive Pigment Index), \(mNDVI\) (modified red edge Normalized Difference Vegetation Index), \(MSR\) (Modified Simple Ratio Index), \(EVI\) (Enhanced Vegetation Index), \(R\) means reflectivity, \(R_{blue}\) (band1), \(R_{green}\) (band2), \(R_{rd}\) (band3), \(R_{710}\) (band4), \(R_{nit}\) and \(R_{840}\) (band5). The statistical results of the 6 spectral indices over 3 ground objects based on numerous data are shown in Figure 7b. It can be noticed that \(NDVI\), \(mNDVI\), \(MDR\), \(PSRI\), and \(SIPI\) could separate vegetation from the ground subjects effectively. We chose \(NDVI\) to set the upper bound and \(PSRI\) to set the lower bound that could achieve the best effect of separating vegetation. Then, bare soil should be separated from the remaining area, \(EVI\) and \(NGBDI\) were chosen to set a reasonable threshold to exclude the bare soil area. Band 6 was selected to filter the human subjects according to the band reflectivity figure and combined with the size of the target area that noise spot could be excluded out. Figure 7: Two kinds of features for 3 typical subjects. (**a**) Reflectivity of six bands, (**b**) Spectral indexes. Decision free construction for suspected target detection  
The DT is exploited as the target classification and identification model for automatic recognition and positioning in this study. As a method of extracting species information, DT classification has the characteristics of convenience and high efficiency, which is widely used in the classification of below three species [34]. Vegetation index and band reflectivity were selected as spectral variables to classify by threshold method. Based on the spectral index, band reflectance, and target area, we constructed a DT and set reasonable thresholds to complete target identification as shown in Figure 8. Specifically, the NDVI threshold of 0.75 comes from the maximization of interclass variance (Otsu) and statistical experience based on a large number of measured experiments.
# Adaptive Navigation in Collaborative Robots: A Reinforcement Learning and Sensor Fusion Approach
## 3 Methodology
### The Initial Bearing Formula  
The Initial Bearing Formula, also known as the Forward Azimuth Formula, calculates the direction or bearing from one geographical point to another on the Earth's surface. It uses the latitudes and longitudes of the two points to determine the angle relative to true north [33]. This bearing is crucial in navigation and mapping for determining the shortest path or direction to follow over the Earth's curved surface. The result is typically expressed in degrees, ranging from 0 to 360.  
Mathematically: (\(\phi_{1}\), \(\lambda_{1}\)): latitude and longitude of the starting point. (\(\phi_{2}\), \(\lambda_{2}\)): latitude and longitude of the destination point.  
\[\theta=atan2(sin(\Delta\lambda)\times cos(\phi_{2})\times cos(\phi_{1})\times sin (\phi_{2})-sin(\phi_{1})\times cos(\phi_{2})\times cos(\Delta\lambda)) \tag{4}\]  
where \(\theta\) is the initial bearing angle.
## 3 Results
### Sensing Properties Evaluation
Once the detected gas is eliminated, the adsorbed molecules will desorb from the active site. A more challenging desorption process results in a poorer recovery property. It's important to note that desorption is the reverse process of adsorption. In this study, all the adsorption processes are exothermic, meaning that desorption is an endothermic process. The energy barrier required for desorption corresponds to the absolute value of the adsorption energy. Drawing on the [PERSON] expression derived from transition state theory, the relative desorption time can be estimated using the following formula [61; 62]:  
\[\tau=A^{-1}e^{(-E_{a}/k_{B}T)} \tag{7}\]  
where \(A\) and \(E_{a}\) denote the apparent frequency and adsorption energy. From the previous research, \(A\) is approximately \(10^{12}\) s\({}^{-1}\)[61; 62]. The evaluation of desorption times from 300 K to 700 K is shown in Figure 8.  
Figure 6: Work function of Ag-WS\({}_{2}\) monolayer before and after CO, NO\({}_{2}\), and SO\({}_{2}\) adsorption. Figure 7: Assumed work function-based gas sensor of Ag-WS\({}_{2}\) using HSGFET structure. At a temperature of 300 K, the desorption time of NO\({}_{2}\) is the longest, extending beyond 10\({}^{11}\) s. In contrast, the desorption times of CO and SO\({}_{2}\) are significantly shorter, approximately 2 s and 0.39 s, respectively. This implies that the Ag-WS\({}_{2}\) monolayer is well-suited for detecting CO or SO\({}_{2}\) at room temperature. However, in environments containing NO\({}_{2}\), there exists the possibility of NO\({}_{2}\) molecules poisoning the Ag sites. To prevent this phenomenon, elevating the temperature becomes necessary. Around 500 K, the desorption time of NO\({}_{2}\) is reduced to about 100 s. This reduction indicates that the Ag-WS\({}_{2}\) monolayer is less susceptible to poisoning at this temperature or even higher temperatures. Therefore, the Ag-WS\({}_{2}\) monolayer proves more suitable for operation at elevated working temperatures when detecting NO\({}_{2}\). Taking into account selectivity, if the working temperature is raised to about 500 K, CO and SO\({}_{2}\) are more likely to desorb from the Ag sites. Thus, the Ag-WS\({}_{2}\) monolayer exhibits favorable selectivity for NO\({}_{2}\) detection at higher working temperatures. On the other hand, when operating at room temperature and detecting target gases containing only CO and SO\({}_{2}\), the Ag-WS\({}_{2}\) monolayer's heightened sensitivity to SO\({}_{2}\), owing to the larger change in work function caused by SO\({}_{2}\) adsorption, makes it more selective toward SO\({}_{2}\).
## 3 Result Analysis
### Experimental and Case Studies Analysis
[87] focused on exploring the modeling and verification aspects associated with an intelligent tutoring system that integrated augmented reality, speech recognition, and text-to-speech. Their system targeted both students and teachers. Their outcomes highlighted that their approach resulted in users achieving better learning performance and operating the system more effectively. [PERSON] et al. [88] developed and applied an augmented reality simulation to enhance students' authentic learning experience. Their simulation integrated an intelligent tutoring system to more effectively guide the learning process. Their results showcased that this combination can improve students' immersion and learning motivation, offer them realistic learning experiences, and enhance their learning outcomes. [PERSON] et al. [89] examined the use of a mixed reality application that integrated intelligent tutoring systems and real-time analytics to support both students and teachers. The study highlighted the importance of teachers having real-time access to students' behavior, metacognition, and learning can enhance both theirs and their students' performance and experience. Specifically, by offering teachers real-time analytics, the gap between the learning outcomes across students is decreased as students' learning is improved. Finally, the study commented upon the ability of such systems to capitalize on both human and machine intelligence to support students. In their study, [PERSON] et al. [90] looked into improving users' driving skills through the use of immersive virtual reality simulators that included an intelligent tutoring system. Their findings presented the capabilities of this approach to provide users with realistic, immersive, and interactive experiences to cultivate their skills. Additionally, it was revealed that such simulations can provide experiences that are in line with those of the real world and can be perceived as real by users. [PERSON] et al. [91] examined the use of intelligent tutoring systems and augmented reality environments in workplace training. Specifically, they focused on improving users' assembly and operation knowledge and skills. Their results highlighted the ability of this approach to effectively support learners even in the workplace as its use significantly affected users' learning curve and abilities. [PERSON] et al. [92] examined the combination of intelligent tutoring systems with augmented reality to aid learners' assembly knowledge and skills. Their system offered realistic and hands-on training and learners who used it presented increased test scores and completed tasks faster. Hence, it was concluded that when using intelligent augmented reality tutors, students' learning outcomes are further enhanced when compared to using traditional augmented reality training.