esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Evaluation of Process Parameters  
#### 3.2.1 Effect on Contamination of the Coupling Glass  
Figure 8 shows the contamination index distributions in the laser transmission region after W10, under all experimental conditions except the reference case. The contamination index in each domain was quantified and is presented in Figure 9. The symbols denote the median for each domain, and the error bar denotes the first and third quartile of the measured data.  
Figure 8: Contamination index distribution according to the cylinder gas nozzle (\(D_{nozzle}\)) and shielding gas flow rate.  
Figure 9: Contamination according to \(D_{nazzle}\) and shielding gas flow rate: (**a**) \(\mathrm{D_{nozzle}}\) of 4 mm diameter, (**b**) \(\mathrm{D_{nozzle}}\) of 6 mm diameter, and (**c**) \(\mathrm{D_{nozzle}}\) of 8 mm diameter.  
As shown qualitatively in Figure 8, the higher the flow rate, the smaller the contamination index. By increasing the shielding gas flow rate, the pressure difference across the aperture of the protective system increases, and the adhesion of particles intruding into the protective system decreases, which is the first protective mechanism explained in Section 2.1. When the flow rate was 2.0 L/min or more, a lower-than-5.2% contamination index was exhibited for all domains, and the difference in contamination index according to the change in D\({}_{\text{nozzle}}\) was less than 2.0%.  
When the flow rate was 0.5 L/min, which was the lowest rate in the experiment, the distribution of contamination was influenced by D\({}_{\text{nozzle}}\) because the horizontal shielding gas flow affects the trajectory of the future particles inside the chamber. This is the second mechanism explained in Sec. 2.1. When the D\({}_{\text{nozzle}}\) is smaller, the flow rate at the center is higher and the time trajectory is more deflected. Thus, the median contamination indexes at domain 1 for the D4_F0.5 and D6_F0.5 cases were 23.3% and 30.7%, respectively, while for the reference case it was 41.2%. On the other hand, the contamination indexes of domains 2 and 3 for the D4_F0.5 case had an asymmetric distribution in the direction of the shielding gas flow, rather than a normal distribution. Their medians were 34.3% and 30.2% for domain 2 and domain 3, respectively, which were greater than that of domain 1. For the case D8_F0.5, the contamination index was normally distributed, and the mean contamination index of domain 1 was 34.5%.  
As the flow rate increased to 1.0 L/min, the contamination indexes drastically decreased compared with those for the cases with a shielding gas flow of 0.5 L/min. The median contamination indices for D6_F1.0 and D8_F1.0 were approximately 12% for all domains; moreover, the contamination index for D4_F1.0 was 7.3% on average, which confirms that a higher flow rate at the center can reduce the contamination at a flow rate of 1.0 L/min. Additionally, differences between the indices for domains decreased. For example, the mean contamination index for domain 1 for the case D8_D1.0 was only 1.7% lower than that of domain 3.
# Applying Game Theory to Teach the PERT Scheduling Method
## 5 Analysis of Results
### Quantitative Analysis
#### 5.2.2 Quantitative Analysis Using the Wilcoxon Test and t-Student Test  
Figure 3 shows the averages of correctly answered responses for each category. In all categories, a higher percentage of correct responses was obtained in the post-test, confirmed in the analyses below.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**ID**} & \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Questions**} & \multicolumn{2}{c}{**Correct Answers (\%)**} & \multirow{2}{*}{**Difference**} & \multirow{2}{*}{**\(p\)-Value**} \\ \cline{5-6}  & & & **Pre-Test** & & & \\ \hline I & Identification of activities & 1, 2, 3, 4, 7, 14 & 72.7 & 80.8 & 8.1 & 0.0032 \\ II & Duration of activities & 5, 15, 18, 19, 20, 22 & 52.0 & 68.2 & 16.2 & 0.0102 \\ III & Critical Path & 9, 10, 11, 12, 13, 17, 21 & 70.1 & 86.6 & 16.5 & 0.2100 * \\ IV & Variability and Decision & 6, 8, 16, 23, 24, 25, 26 & 60.2 & 81.0 & 20.8 & 0.0737 * \\  & Total & 1–26 & 63.9 & 79.5 & 15.6 & 0.4833 * \\ \hline \hline \end{tabular}  
*: normally distributed.  
\end{table}
Table 6: Shapiro–Wilk test for each category and the total of responses.  
Figure 3: Comparison of average scores by category of the pre- and post-tests.  
In other words, the analysis by categories obtained statistically significant differences in the results for each category. According to the above results, the methodology proposed significantly changed the students' post-test scores for the total number of responses and each category. This finding shows the benefits of using Game Theory to teach the PERT method.
# Image Dehazing Algorithm Based on Deep Learning Coupled Local and Global Features
### End-to-End Deep Learning Defogging Approach
Transformer extracts global view features by its core operation of self-attention, and the self-attention mechanism has a clear advantage in capturing long-range dependencies in natural language processing. In contrast to CNN, each hidden unit in each feature learning layer of the transformer involves the global contextual information of the input. Therefore, the transformer architecture has been widely introduced to vision tasks in recent years and has received more and more research and attention. However, the vision transformer ignores local detailed features, and the heavy computational load of the self-attentive mechanism limits the depth of application of vision transformer in image defogging coding and decoding frameworks. [PERSON] et al., published \"Vision Transformers for Single Image Dehazing\" in 2022 using the network structure of vision transformer for haze removal [15]. The network borrowed the network structure of the Swin transformer and U-Net and made some modifications based on them, and then conducted haze removal experiments on the synthetic haze dataset and remote sensing haze dataset, and achieved good results. However, its proposed method of shifted window partitioning with reflection padding consumes a lot of cost and reduces its operational efficiency. Second, the network is less effective in removing non-uniform haze, and its ability to remove real haze is not outstanding. In order to solve the problems that the convolutional neural network-based image defogging algorithm model captures incomplete global feature information and incomplete defogging, a parallel depth coding and decoding structure of a defogging network model is proposed in this paper. The encoder and decoder consist of a CNN branch and a vision transformer branch to capture the local and global features of the haze image, respectively, and a convolutional layer is used to fuse the hybrid features, which covers the global representation while retaining the local features. Finally, the features obtained by the encoder and decoder are fused to obtain richer feature information.
# Space Debris In-Orbit Detection with Commercial LiDAR Sensors
## 4 Conclusions  
Our study aims to demonstrate the application of LiDAR sensors, similar to those used in autonomous vehicles, in the context of space applications. Following this conceptual demonstration, our intention is to validate the technology transfer of LiDAR sensors to address challenges in space debris detection, collision prevention, and debris management. Our mission serves the dual purpose of confirming the capabilities of these sensors and contributing to the information available in the space debris database published by space agencies. This effort is expected to foster growth in the space industry and in the tracking and surveillance sector.  
The proposed LiDAR systems, deployed on a CubeSat or even in constellations of satellites, will bring a set of advantages in the study of the debris flux that make them stand out in the context of space debris detection and collision prevention. Their high precision in distance measurement, superior angular resolution, and all-weather operation make LiDAR-equipped CubeSats well-suited for continuous monitoring of LEO orbits. While LiDAR does not rely on ambient light like optical cameras do, it can operate effectively in various lighting conditions [21; 22; 23]. In addition, their flexibility, cost-effectiveness, and quick-response capabilities offer a dynamic and efficient approach to space debris management.  
Advancements in technology, including the miniaturization of LiDAR components and their low power consumption, have made it feasible to integrate LiDAR on CubeSats, overcoming historical size and weight constraints. These advancements contribute to the adaptability of LiDAR technology for space debris monitoring.  
This work was funded in part by the University of Cadiz under Grant \"Plan Propio 2022-2023\".  
Not applicable.  
Data Availability StatementData are contained within the article.  
The author declares no conflicts of interest.
# Trends of Global Scientific Research on Reclaimed Coal Mine Sites between 2015 and 2020
## 2 Materials and Methods  
The materials used were scientific publications from the Web of Science (WoS) website. In order to obtain as many publications related to coal reclamation sites as possible, the input query words were \"coal AND soil AND reclama\" OR reclaim\".\" The search filter was set to \"topic,\" and the time span was 2015-2020. This search included journal articles and conference proceedings that appeared in the search up until 30 March 2020.  
Having in mind current research activities on coal reclamation sites, they were split into 6 categories:  
1. C, N, and SOM;
2. Physical;
3. Biological;
4. Chemical;
5. Technology;
6. Review and Metadata.  
Each publication from the search query was analyzed, and accredited with one point in total. If the research was precisely focused on only one of the categories, that category received a full 1 point. If the research was split between two categories, each of the categories received 0.5 points if they were equally distributed throughout the research, or an uneven fraction of the point if they were not. Thus, several categories could have equally or unequally distributed points, but the sum of all fractions of all 6 categories for each single publication had to be one.  
The category C, N, and SOM comprises all the research that deals with soil organic matter, soil carbon (both organic and inorganic), and macronutrients, mainly nitrogen. If the topic of the research deals with nutrients in greater detail, it can be commonly interrelated with the Chemical section. The Physical category encompasses soil physical and hydrological research, including bulk density, soil structure, soil texture, porosity, water retention and transport, compaction, etc. The Biological category deals with topics of soil micro- and macro-fauna and its activities, as well as vegetation and its establishment. The Chemical category is related to mine soil's chemical characteristics and its changes, acidification, element toxicity, etc. The category Technology has been introduced in order to fill the gap for the research that is related to the introduction and use of new technologies, models, statistical methods, and equipment, and their research, development, calibration,etc. The Review and Metadata category deals with reviews, state-of-the-art papers, and processing metadata.  
The results with distributed category points are presented in a tabular form, together with references and the country a particular study originates from, as well as in the form of graphs showing the trends in the observed period.
## 3 Compositional Data on Date Seeds and Date Seed Coffee Products
### Macronutrients
#### 3.1.4 Fat  
[PERSON] et al. described in detail the chemical composition of two studied date cultivars, _Allig_ and _Deglet Nour_[11]. The fat content corresponds to about one-eighth of the whole date seed, with 10.2% fat content in _Deglet Nour_ and 12.7% in _Allig_. Lower fat content was measured for the cultivars _Degla-Baidha_ and _Tafezouine_, with around a 5.5% fat total [1; 30].  
Date seeds typically contain between 5 and 13% fat, with the distribution of fatty acids being influenced by the date variety [31], growing conditions, and extraction technique used. The oil content of the Tunisian cultivars (_Allig_ and _Deglet Nour_) is comparatively high. Hexane is widely used as a solvent and the Soxhet method is commonly employed for extracting date seed lipids. Gas chromatography (GC) was performed to determine the detailed fatty acid composition, although non-conventional methods such as UAE and SC-CO\({}_{2}\) can be used as environmentally friendly alternatives [1; 39].  
An overview of the oil contents and fatty acid composition of almost 20 different important date varieties is provided in Table 2. About 92% to 99% of the fatty acid content is distributed among the most important acids, including: oleic, linoleic, palmitic, myristic, lauric, and minor stearic [11; 23]. A similar fatty acid composition can be found in commonly used olive oil or other edible oils [40]. The oxidative stability of date seed oil is high and comparable to that of olive oil [11]. The tested date seeds have the highest concentration of oleic acid. Date seed oil has similar characteristics to edible vegetable oil; thus, it can be considered safe for human consumption [6].  
Table 2 summarizes the investigated fatty acid contents. Where specified, the date variety is mentioned. Bigger differences are indicated for the lauric acid content in a roasted date seed sample, although the other results are quite homogeneous. The difference between fatty acid concentrations is linked to the date varieties, roasting processes, and pollination [2]. Slight increases and decreases in the fatty acid content may occur after the roasting processes of the date seed powder [24]. The roasted samples contain values that are two to three times higher than those found in the comparison group, which only contains unprocessed date seeds [41]. Most varieties have a comparatively low proportion of fat and protein, which amounts to only 5-10% of the total composition. The fatty acid composition may vary slightly between cultivars, but they exhibit good oxidative stability and a high content of unsaturated acids.
# Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference
## 3 Solar Interference Assessment Methodology
### Using Online Solar Hits to Assess Radar Axis Tilt  
The potential for radar axis tilt is assessed here following the ideas presented in [PERSON] et al. (2011) and [PERSON] et al. (2016), whereby online solar hits are analysed as a function of solar azimuth. For a visual depiction of these bias sources, see [PERSON] et al. (2011), which presents them well. In this case, the tilt is modelled using a simple sine approximation, where only the impact of the tilt on elevation offsets is considered, as the impact on azimuth is significantly smaller for low-elevation angle scanning data at small degrees of tilt. This allows a three-parameter model of the offset  
\[E_{\phi}=\mathrm{I.}\cos(\mathrm{D}+\phi)+y_{0} \tag{9}\]  
to be fit to the observed azimuthal distribution of elevation offsets (difference between radar elevation and solar elevation; a function of the pointing biases and position of the radar beam within the solar disk) using least squares regression, for which the three parameters that define the tilt are the orientation of the tilt, declination (D); the magnitude of the tilt, inclination (I); and a fixed elevation offset parameter (\(y_{0}\)) such that the tilt varies between \(y_{0}+I\) and \(y0-I\) depending on the solar azimuth (\(\phi\)). As per [PERSON] et al. (2016), the power of the observed interference is omitted from the analysis, which thus relies on only the positional information.  
This approach provides an alternative estimate of the fixed elevation offset (\(y_{0}\)) to the bullseye approach outlined in the previous section. Where there is no observable tilt, these two estimates will be near equivalent due to the expected symmetry of the solar observation. Where they are not equivalent, it provides a strong indication that tilt is impacting the radar system and the bullseye results are being biased by asymmetric observations.
## 3 Formation Cooperative Search and Transformation Strategy
### Formation Control Scheme
The AUV formation achieves an adaptive search through the forbidden search mechanism and the adjustment of sensor detection distance, and performs formation transformation under the constraint of Equation (1) to adapt to the change in environment and operational requirements. When any single robot in the formation detects an obstacle, this robot is set as the leader and the rest of the robots switch to the follower state. The system switches from the formation cooperative search state to the distributed tracking state when the target is detected, and the cooperative capture of the target is realized through the path-planning algorithm. The advantage of this formation control method is that each AUV can be considered a virtual pilot, and the other AUVs choose their own movement according to the virtual  
Figure 4: Formation change scheme when crossing array-type obstacles. (**a**) Formation I (**b**) Formation II (**c**) Formation III (**d**) Formation IV. pilot. By replacing the virtual leader when the leader breaks down, the whole formation can continue to operate without going into a paralyzed state. When a robot fails, the robot can still navigate according to the path that was planned at the previous moment, and other robots can reach the designated target position according to the original plan, with a strong fault tolerance.
## 6 Clustering, Virtual Backbone and Minimum Connected Dominating Set
### Pruning-Based Algorithms  
Besides constructive algorithms, there also exist other algorithms which are based on pruning procedures to approximate MCDS. As indicated by its name, a pruning-based algorithm gradually reduces a candidate CDS according to some greedy criteria, and the left-over set after the reduction is the approximate MCDS.  
[PERSON] and [PERSON] [100] proposed a two step localized algorithm to construct a CDS in a network. In the first step, each node in the network gets information of its two-hop neighbors via the exchange of beacons. Every node that has two neighbors that are not within the radio range \(r\) of each other marks itself as a dominator. The second step is a pruning process, which takes out some redundant nodes from the dominator set generated in the first step. There exist two rules to reduce the size of the CDS generated in the first step. A node \(u\) can be removed from the CDS if its closed neighborhood is a subset of that of another node \(v\) in the CDS, or the open neighborhood of \(u\) is a subset of the union of the open neighborhood of another two nodes \(v\) and \(w\), which are in the CDS. The open neighborhood of a node \(u\) is defined as the set of all the nodes that direct link to \(u\). The closed neighborhood of \(u\) is the union of its open neighborhood and \(u\).  
[PERSON] _et al._[101] extended this algorithm to make it energy aware. For an arbitrary node \(u\), the remaining energy level of \(u\) is considered when the pruning process is performed. Nodes with lower energy level are preferentially removed.  
[PERSON] _et al._[102] propose another algorithm that uses pruning. This algorithm takes the entire set of nodes \(V\) as the initial candidate CDS, and then greedily removes nodes. In order to determine whether a node can be removed from the current CDS, the connectivity of the remaining nodes is tested. The node can be removed only if the remaining candidate CDS is still connected. This algorithm has high message complexity due to the operations of connectivity tests, especially for large dense networks. However, simulation results show that the cardinality of the CDS it finds is comparable to existing algorithms. [PERSON] [103] also discusses a similar approach in the construction of dominating sets.
# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 4 The Design of the Tracking Algorithm
### Trajectory Acquisition  
As a single turntable cannot calculate the depth of the aircraft and parachutes in space, it is unable to obtain the three-dimensional trajectory of the flight. To deal with this problem, TuSeSy adopts two turntables to position the target, which contributes toward calculating the depth of the target in space.  
Two tracking turntables were previously placed on the positions where two principal optic axes of the cameras intersected at the point where the target was. We obtained the distance \(l\) between the two turntables by GPS. In addition, the elevation angles (\(\beta_{1}\), \(\beta_{2}\)) and the azimuth angles (\(\alpha_{1}\), \(\alpha_{2}\)) could also be accessed from the turntables, respectively. Based on these five parameters, the target's spatial coordinates could easily be calculated with the following equations:  
\[x =\frac{l\sin\alpha_{2}\cos\alpha_{1}}{\sin(\alpha_{1}+\alpha_{2})}, \tag{1}\] \[y =\frac{l\sin\alpha_{2}\sin\alpha_{1}}{\sin(\alpha_{1}+\alpha_{2})},\] (2) \[z =\frac{l\sin\alpha_{2}\tan\beta_{1}}{\sin(\alpha_{1}+\alpha_{2})}. \tag{3}\]  
The elevation angle \(\beta_{2}\) is used to verify whether the measurement error \(e\) is within an acceptable range.  
\[z_{*}=\frac{l\sin\alpha_{2}\tan\beta_{2}}{\sin(\alpha_{1}+\alpha_{2})}, \tag{4}\]  
\[e=|z-z_{*}|. \tag{5}\]  
According to Equations (1)-(3), a series of three-dimensional coordinate values of targets could be obtained, and then the coordinate values of outlier points with large errors could be eliminated based on Equations (4) and (5). As a result, the remaining coordinates could be used for curve fitting by the least squares method to calculate the space trajectory of the target. The target's trajectory measurement principle is shown in Figure 7.
# Graph-Based Modeling in Shop Scheduling Problems: Review and Extensions
## 5 Theory and the Application of Different Types of Graphs
### Clique  
Clique is one of the fundamental concepts of graph theory [28]. To define clique, first we need to introduce a few concepts. Let \(C\) be a subgraph in an undirected graph \(G\). Subgraph \(C\) is _complete_ if each node within \(C\) is connected to every other node. \(C\) is _maximal_ if \(C\) is a subset of \(G\) and is not contained in any other complete graph within \(G\). Finally, \(C\) is a _clique_ if it is a complete maximal subgraph of \(G\)[107].  
The _maximum clique_ is the clique with the most nodes. The clique number, \(\omega(G)\), dictates the number of nodes in the maximum clique within \(G\)[108].  
Consider a random undirected graph in Figure 9. The maximum clique contains nodes {3,4,5,6} which is a 4-vertex clique. This clique is called a clique of size 4. Moreover, {3,4,5}, {3,5,6} are cliques of size 3 (3-vertix).  
The application of cliques to manufacturing/production related problems is mainly for partitioning [109] and simplifying classification [110]. We are particularly interested in its applications to shop scheduling problems where the concept of clique usually is coupled with graph coloring, agreement graph, and the disjunctive graph which are explained respectively in Sections 5.2, 5.4 and 5.14.1.