esa-sceva/satcom-chunk-collection huggingface dataset:
# Combining Virtual Reality and Organizational Neuroscience for Leadership Assessment
###### Abstract  
In this article, we introduce three-dimensional Serious Games (3 DSGs) under an evidence-centered design (ECD) framework and use an organizational neuroscience-based eye-tracking measure to capture implicit behavioral signals associated with leadership skills. While ECD is a well-established framework used in the design and development of assessments, it has rarely been utilized in organizational research. The study proposes a novel 3 DSG combined with organizational neuroscience methods as a promising tool to assess and recognize leadership-related behavioral patterns that manifest during complex and realistic social situations. We offer a research protocol for assessing task- and relationship-oriented leadership skills that uses ECD, eye-tracking measures, and machine learning. Seamlessly embedding biological measures into 3 DSGs enables objective assessment methods that are based on machine learning techniques to achieve high ecological validity. We conclude by describing a future research agenda for the combined use of 3 DSGs and organizational neuroscience methods for leadership and human resources.  
leadership style; evidence-centered design; serious game; decision-making behaviors; visual attention; assessment method; machine learning +
Footnote †: journal: Applied Sciences  
tasks and roles, organizing employees and resources efficiently, and monitoring progress within the team [5; 6]. Conversely, relationship-oriented leadership (ROL) refers to leaders fostering positive relationships, good communication, motivation, collaboration, and teamwork among team members. The main behaviors in ROL relate to supporting people's needs and motivating them, facilitating effective communication, and recognizing teamwork [7].  
Conventional TOL and ROL assessment in organizational leadership research has relied on self-reported measures, which present numerous limitations and biases with regard to ecological validity and response veracity [8; 9; 10]. Specifically, self-reported measures and laboratory settings are often decontextualized from real situations and are not conducive to the display of real-life behavioral responses [10]. Experimental situations can result in anxiety and social desirability bias in the participant to provide a positive image, which results in a negative effect on the veracity of the responses [8; 9]. In addition, in the traditional approach, each self-reported scale corresponds to measuring one skill or construct, thereby limiting our ability to capture more than one variable at a time. To overcome these limitations, we propose an assessment method that is based on non-verbal cues and combines the use of a three-dimensional serious game and biological implicit measures used in organizational behavior research.
## 3 Multimetric Map-Aware Routing Protocol (MMMR)  
### Motivation  
VANET nodes are vehicles that move along roads, potentially at a high speed. These vehicles follow transit rules, respect the direction of the streets, traffic lights and also the presence of buildings and other vehicles. The vehicle density in VANETs constantly changes depending on the area and the time of the day, so it is difficult to establish and maintain end-to-end communication paths between sources and destinations, as is traditionally done in MANETs. Several routing protocols based on geographic information have specially been proposed for VANETs. Nonetheless, a proper data forwarding mechanism is still needed to cope with the special constraints of VANETs, e.g., the high nodes' speed, the dynamic network topology and the variable nodes' density.  
We propose a new routing protocol for VANETs in urban scenarios that we call the multimetric map-aware routing protocol (MMMR). MMMR seeks to improve the decision of the next forwarding node based on four routing metrics, which are the distance to destination, the vehicles' density, the vehicles' trajectory and the available bandwidth. We weight the four metrics to finally obtain a multimetric value for every neighbor node that is a candidate as the next forwarding node. The scheme is self-configured and able to adapt to the changing vehicles' density in real time.  
In our proposal, we can distinguish five processes: map-aware capability, use of a local buffer, signaling, the evaluation of metrics and the forwarding decision. We explain each one in the following.
## 1 Introduction
These DSM-based path planning studies only separated obstacle information but did not consider surface roughness information in the planning map. The transportation environment of open-pit mines is unstructured, and the Hybrid A* algorithm (HA* algorithm) is a heuristic algorithm widely used in real-time vehicle path planning for unstructured environments [30, 31, 32, 33, 34]. This algorithm adds the non-holonomic constraints into the heuristic functions and plans the path in the Voronoi field map. Nevertheless, in the planning process, the regular Hybrid A* algorithm only considers the large obstacles. However, it ignores the little pit or roughness of the surface, which may cause an additional cost for large vehicles. There are a lot of open spaces in the cutting zone of open-pit mines without any obstacles. In this scenario, the path planner considers the road surface as a passable area without obstacles, thus planning a path directly to the loading position which may result in extra tire cost because of the ground roughness. In order to reduce the extra cost, this paper proposes a high-precision planning map construction method based on DSM and an improved Hybrid A* algorithm. This planning map contains both surface obstacles and roughness information. Firstly, the DSM is divided into an obstacle point set and surface roughness point set with different terrain features. Secondly, the obstacle point set is calculated as obstacle cost map (OCM) according to generalized Voronoi diagram (GVD). Then the sliding window standard deviation method is used to process the surface roughness point set and generate the roughness cost map (RCM) without obstacle. Finally, the two maps are integrated into the cost map (C-MAP) for path planning, which contains the information of obstacles and roughness on the road. With this C-MAP, we modified the Hybrid A* algorithm by adding the contact cost between the tire and ground into the heuristic function, so that the path can be planned on the smooth road surface. The main contributions of this paper are as follows:  
1. We propose a truck path planning method based on high-precision digital map for the final transportation section of open-pit mines, which can effectively reduce the driving cost of trucks. 2. We design a planning map that contains obstacle information and roughness information on the road surface and its construction method. 3. We improve the Hybrid A* algorithm to incorporate the roughness information into the cost estimation function of node extension. The structure of this paper is as follows: Section 2 describes the modified Hybrid A* algorithm in detail, including planning map building method and algorithm improvement. In Section 3, we evaluate the algorithm performance in different real open-pit cutting zone scenes and analyze the algorithm robustness through a thousand random tests with different terrain resolution maps. Conclusions and future work are presented in Sections 4 and 5.
## 4 Proposed Method for Sensitivity Analysis of SORM-Based Estimates  
### SORM-Based Determination of the Sensitivity of the Failure Probability  
Sensitivity analysis has been a key domain in engineering practice and research, particularly in the design optimization area. The dependence of the reliability on the design parameters or random variables can be evaluated by performing a sensitivity analysis. Furthermore, the sensitivity analysis is integral to utilize an efficient gradient-based optimization algorithm in the RBDO. The sensitivity analysis in the RBDO framework using the SORM is the focus of this study. The sensitivity of [PERSON]'s failure probability with respect to a design parameter \(\mathbf{d}\) can be derived by applying a chain rule to Equation (10):  
\[\begin{array}{ll}\
abla_{\mathbf{d}}P_{f\text{SORM}}&=\
abla_{ \mathbf{d}}\bigg{(}\Phi(-\beta)\prod\limits_{i=1}^{n-1}\frac{1}{\sqrt{1+\beta \kappa_{i}}}\bigg{)}=-\varphi  
\[\
abla_{\mathbf{d}}P_{f\text{SORM - i}} =\
abla_{\mathbf{d}}\left(\Phi(-\beta)\prod\limits_{i=1}^{n-1} \frac{1}{\sqrt{1+\psi(\beta)\kappa_{i}}}\right)=-\varphi(\beta)\cdot\
abla_{ \mathbf{d}}\beta(\mathbf{d},\mathbf{x}*)\cdot\left(\prod\limits_{i=1}^{n-1} \frac{1}{\sqrt{1+\psi(\beta)\kappa_{i}}}\right) \tag{15}\] \[+\Phi(-\beta)\cdot\left(\sum\limits_{j=1}^{n-1}\left(-\frac{1}{2} \left(\frac{1}{\sqrt{1+\psi(\beta)\kappa_{j}}}\right)(-\beta+\psi(\beta))\cdot \psi(\beta)\kappa_{j}\cdot\begin{matrix}\prod\limits_{m\in\{1,\cdots,n-1\}}& \frac{1}{\sqrt{1+\psi(\beta)\kappa_{m}}}\\ \backslash\{j\}\end{matrix}\right)\right)\cdot\
abla_{\mathbf{d}}\beta( \mathbf{d},\mathbf{x}*)\]  
where \(\psi(\beta)=\varphi(\beta)/\Phi(-\beta)\). Note that Equations (14) and (15) are derived assuming that curvatures \(\kappa_{i}\) are nearly independent of the design variable. To realize the sensitivity analysis, the gradients of the reliability index at \(\mathbf{x}^{\star}\) need to be computed. The CS derivative approximation is applied to compute the gradients.
## Appendix B Basic Algorithm in Pseudocode
### Greedy Algorithm  
```
1:whilemax(LSEE_0) > target precision
2: Finding which group of measurement would result in the most benefit for the point with the lowest
3: LSEE
4: N2 = N;
5: Calculation of LSM and LSEE from covariance matrix
6: PP = N2*P;
7: Normal_equation = [A*PP*A, B; B', zeros(4,4)]:
8: EX_0 = inv(Normal_equation);
9: LSEE = evaluation (Ex)
10: In the cycle, the number of repetitions of every group of measurement is increased by 1 and LSEE is calculated
11: fork = 1:number of groups in measurement_group
12: indexing k-th group of measurement in matrixes N, A,...
13: index(k);
14: calculation of LSM and LSEE from covariance matrix
15: N2(index) = N2(index)+1;
16: PP = N2*P
17: Normal_equation = [A*PP*A, B; B', zeros(4,4)]
18: EX = inv(Normal_equation);
19: LSEE = evaluation (Ex)
20: vector of differences of LSEE for each point
21: LSEE_difference=LSEE - LSEE_0
22: differences for each group of measurement increasing
23: LSEE_difference_group(k,:)=LSEE_difference
24: endfor
25: Evaluation of which group of measurement had the biggest benefit for point with worse LSEE
26: finding the worst point with index i
27: i = max_K_point_index (LSEE_0);
28: finding group of measurement with maximal benefit for worst point i, index-ing this group by i
29: i = max_benefit_index (LSEE_difference_group(,:i));
21: Increasing the number of repetitions with the biggest benefit
30: N(i2) = N(i2)+1;
31:endwhile
```  
**Algorithm 1** Greedy algorithm
# A Real-Time Detection of Pilot Workload Using Low-Interference Devices
## 4 Experimental Setup
### Spectral Clustering of Physiological Features  
Individuals exhibit differences in their physiological responses even when faced with the same situation [91; 98; 99]. This study employs spectral clustering to separate features with significant differences, laying the groundwork for subsequent differentiated model training.  
The spectral clustering module is characterized by constructing a similarity graph and utilizing its eigendecomposition to identify and differentiate individual physiological variations effectively, which provides a foundation for personalized driver workload assessment. The basic framework of spectral clustering can be described as follows:  
First, a weighted undirected graph is established as follows:  
\[G=(v,\varepsilon,\omega),\]  
where  
\(v\) is the node set, representing the set of data points for individual physiological characteristics;  
\(\varepsilon\) is the edge set, representing the similarity between data points;  
\(\omega\) is the similarity matrix, where each element \(\omega\)ij denotes the similarity between nodes \(i\) and \(j\).  
The similarity matrix \(\omega\) is constructed using the radial basis function (RBF) kernel to measure the similarity between two nodes, with an expression as follows:  
\[\omega_{ij}=\exp\left(-\frac{\left\|x_{i}-x_{j}\right\|^{2}}{2\sigma^{2}} \right),\]  
involving \(\sigma\), a predetermined scale parameter that controls the rate of similarity decay.  
Based on the similarity matrix W, we further construct the graph Laplacian matrix L. Dimensionality reduction is achieved by solving for the eigenvalues and eigenvectors of L. Subsequently, clustering is performed using the K-means algorithm in the reduced-dimensional space. Utilizing the aforementioned methods, spectral clustering algorithms can effectively cluster the physiological characteristics data of pilots.  
The number of clusters is determined based on the Calinski-Harabasz index, with the optimal ratio of inter-cluster separation to intra-cluster compactness achieved with four categories, as shown in Figure 7. In this way, all physiological characteristic data are clustered into four categories. Subsequently, distinct soft voting ensemble learning classifiers will be trained based on these four categories of features.
## 2 Model and Experimental Research
### Dust Settler Segment Experimental Tests of Air Filter Multicyclone
#### 2.5.2. Research Methodology and Conditions  
The dust removal efficiency tests for single cyclones of the \(S_{MT}\) segment were carried out on the stand, the diagram of which is shown in Figure 20. The tested segment, being the fifth part of the MT multi-cyclone, includes 21 D-40 cyclones arranged in seven rows and three columns in the same way as in the original multi-cyclone. In the first stage of research, the segment settling chamber has the shape of a multi-cyclone settling chamber. On the other hand, in stage II of the tests, the sedimentation tank is divided into four suction channels--as is shown in variant D. Directly at the outlet of the segment's sedimentation tank, in the suction line, there is a safety filter that stops the dust separated by the cyclones. The cyclone outlet nozzles of the segment (purified air) are connected by a common chamber, in which there is a measuring filter cartridge designed to retain the \(m_{ZA}\) dust mass carried along with the exhaust air from the cyclones. Figure 20: Test stand diagram of the air filter multi-cyclone segment: 1—partitions of dust settling channels, 2—dust settler, 3—cyclone, 4—compressed air stream rotameter, 5—dust dispenser, 6—dust chamber, 7—measuring filter, 8—absolute filter of the suction stream, 9—measuring orifice of the suction stream, 10—measuring orifice of the main stream, 11—suction fans, 12—device for measuring temperature, pressure and humidity of the ambient air, 13—analytical balance. The test dust was dosed to the inlet stub of the tested cyclone, which ended with a dust chamber where it was mixed with the inlet air stream \(Q_{0}\). The separation efficiency was determined successively for the extreme single cyclones (No. I, II,... VII) of the left column (L) of the multi-cyclone segment under the following conditions:  
* air outlet stream from a single cyclone \(Q_{Gc}=34\) m\({}^{3}\)/h, resulting from the number of cyclones used in the filter multi-cyclone and the air demand at the rotational speed \(n_{N}=2000\) obr/min in vehicle engine;
* air stream sutioned from a single cyclone \(Q_{Sc}=2.72\) m\({}^{3}\)/h for the degree of the ejection dust extraction from the settling tank \(m_{0}=8\%\);
* dust concentration in the air \(s=1\) g/m\({}^{3}\);
* measurement time--time of uniform dust dosing to the cyclone \(t_{pom}=3\) min;
* PTC-D test dust, being a domestic replacement for AC-fine dust. For each cyclone from the right column, starting from cyclone No. I up to cyclone No.
## 4 Experiments and Result Analyses
### Experimental Results  
#### 4.4.1 Overall Performance  
In Figure 12, the first column is the wheel image, the second to fifth columns are the detect maps of SegNet, Unet, DeepLab v3+, and the proposed network, and the sixth column is the ground truth. The detect maps of SegNet and DeepLab v3+, have some white dots outside marked by white boxes, indicating that SegNet and DeepLab v3+ are overfitting and some normal pixels are detected as defect pixels. In the detect maps of Unet and the proposed network, such white dots are gradually reduced. Furthermore, the area of defects detected in the proposed network is gradually reduced compared with that in Unet, indicating that the proposed network can effectively reduce the false detection rate of defects.  
As shown in Figure 13, the proposed network also has better detect effects on the three defect datasets (tile, fabric, and weld) than the other networks, indicating that it is suitable for different detection scenarios.  
Figure 14 shows the PR curves of SegNet, Unet, DeepLab v3+, and the proposed network on the four defect datasets. On the three defect datasets (tile, fabric, and weld), the PR curves of the proposed network are significantly better than those of the other networks. On the weld defect dataset, when the recall is small, the PR curve of the proposed network is near the PR curves of Unet and DeepLab v3+, but when the recall is large, the PR curve of the proposed network is better than those of Unet and DeepLab v3+, and the AP of the proposed network is greater than those of Unet and DeepLab v3+, indicating that the proposed network is also better than the other networks on the weld defect dataset.  
As shown in Table 1, the proposed network achieved better results (i.e., F1 score, AP, and IoU) on the four defect datasets (wheel, tile, fabric, and weld) than SegNet, Unet, and DeepLab v3+. On the wheel defect dataset, the proposed network improved the F1 score, AP, and IoU of DeepLab v3+ by 6.2%, 2.1%, and 8.4%, respectively, indicating that using HRNet as the backbone network, introducing ASPP with sum (superimposing features), and using a body and edge decoupling module and a decoder can help the network extract high-resolution, multi-scale, and edge features. These modules can improve the accuracy of detecting automobile wheel hub defects.  
Figure 12: Wheel hub defect maps detected by different networks. White boxes are the mistakes.  
On the challenging weld defect dataset, the proposed network improves the F1 score, AP, and IoU of DeepLab v3+ by 16.5%, 3.1%, and 18.1%, respectively. The obvious improvement proves the accuracy of the proposed network on other defect detection scenarios.  
Figure 14: Comparison of PR curves and AP on four defect datasets.  
Figure 13: Defect maps detected by different networks on three defect datasets of tile, fabric, and weld.
# IoT-Based Smart Surveillance System for High-Security Areas
## 2 Materials and Methods
### Hardware Components Used SS-HSA
#### 2.3.1 Gravity Microwave Sensor v2.0  
A gravity microwave sensor is based on a microwave signal that can penetrate a dielectric material and detect moving objects easily. Microwaves are also very sensitive to changes associated with boundary interfaces that detect minute variations in the shape and dimension of a moving object. The scattered signals contain information on phase and magnitude. The attenuation information is also used to determine the structure of the moving object. Using the detection of moving objects via microwaves, the GMS can track the location of a moving object behind an obstruction, which can also assist in the search process after a disaster, allowing the search-and-evaluation process to be carried out efficiently.
## 1 Introduction
This resource optimisation is critical because 5G is intended to be a major enabler and a leading infrastructure provider in the information and communication technology sector by supporting a wide range of upcoming services with varying requirements. Given the network's rising complexity and the introduction of innovative use cases, such as autonomous cars, industrial automation, virtual reality, e-health and various intelligent applications, ML is projected to be critical in making the 5G vision a reality [6; 7]. Given the relation between 5G and ML, this article focuses on one of the most critical parameters in the 5G system, which is the selection of numerology or'subcarrier spacing'. Moreover, 5G numerology is a method of assigning the subcarriers in 5G frequency bands to different service types. These subcarriers are used for transmitting data and controlling information, as well as for synchronisation and signalling. Furthermore, 5G subcarrier spacing works by dividing a single carrier into multiple carriers, which are then transmitted at different frequencies. This way, we can achieve high speeds with little power consumption and an enhanced coverage area. We can also increase system capacity through this method, which helps to reduce the interference between neighbouring cells. Therefore, 5G networks must be able to choose the optimal subcarrier spacing that can be realised using ML to achieve the benefits. This finding emphasises the importance of acknowledging the ML model characteristics implemented into the wireless system. The contributions of this study are as follows:  
* A concise review of the recent ML algorithms used in the 5G network;
* Development of ML model training in RStudio to observe ML model accuracy based on the hyperparameter changes;
* A description of the working principles and hyperparameter tuning of four ML models: k nearest neighbour (KNN), classification and regression trees (CART), RF and SVM;
* Performance evaluation of the effects of hyperparameter tuning for all four ML models studied;
* An observation of the hyperparameter tuning effects on the cross-validation accuracy for the subcarrier spacing prediction models in the 5G system;* The effects of the number of neighbours, maximum tree depths, number of randomly selected predictors and cost values on the accuracy of KNN, CART, RF and SVM, respectively. The remainder of the paper is structured as follows. Section 2 presents the related work on ML in 5G networks. Section 3 discusses the possibility of tuning the ML model hyperparameters to predict the subcarrier spacing in the 5G network. Section 4 focuses on the performance comparison between the tuned ML models. The acronyms and notations used in this paper are shown in Table 1.