esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Dedicated Heuristic  
For testing the performance of the proposed dedicated heuristic method, we generated a benchmark composed of several sets of instances with different problem sizes by using the real data obtained from the manufacturing environment of the plant. Accordingly, there are 10 groups of benchmark problems of different sizes, varying from 60 to 150 jobs.  
Table 9 provides for each instance the tardiness found, denoted by \(T_{DH}\), as well as the execution time (CPUs column) to reach the best value. The column denoted by \(T_{real}\) recalls the real results obtained by the planner. Finally, the gap between both solutions is calculated in the column (gap). The last row represents the average values. The values denoted in bold indicate that the heuristic reaches the optimal value for the considered instances, meaning that the solution found by the heuristic is equal to the one obtained by the exact method \"CP\". For instances up to 80 tasks, the results obtained with the MILP and CP models are not provided since the solver ran out of memory before providing any initial solution.  
According to Table 9, if we compare the results of the dedicated heuristic against the real results obtained by the planner, we see that the tardiness obtained by the heuristic is significantly lower than that obtained by the planner, with an average gap of 39%. On average, the dedicated heuristic provides a better solution overall for all the tested instances  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{Instance Characteristics} \\ \hline
**N** & **O** & **M** & **U** & \(T_{real}\) & \(T_{DH}\) & \(CPU_{DH}\) & **Gap** \\ \hline
60 & 148 & 19 & 5 & 30,120 & **22**,**695** & 4 & 25\% \\
70 & 160 & 19 & 6 & 48,215 & **27**,**458** & 4 & 43\% \\
80 & 189 & 19 & 7 & 68,743 & **38**,**548** & 6 & 44\% \\
90 & 210 & 19 & 9 & 80,471 & 49,895 & 8 & 38\% \\
100 & 260 & 19 & 9 & 94,875 & 58,951 & 8 & 38\% \\
110 & 298 & 19 & 11 & 100,458 & 64,251 & 8 & 36\% \\
120 & 352 & 19 & 12 & 124,524 & 70,589 & 12 & 43\% \\
130 & 397 & 19 & 15 & 150,427 & 86,758 & 12 & 42\% \\
140 & 410 & 19 & 16 & 159,751 & 89,827 & 12 & 44\% \\
150 & 480 & 19 & 20 & 180,058 & 118,745 & 19 & 34\% \\
105 & 290 & 19 & 11 & 103,764 & 62,772 & 9 & 39\% \\ \hline \hline \end{tabular} Optimal values in bold.  
\end{table}
Table 9: Main characteristics of the considered large-sized instances and comparison of \(MILP_{RC}\) and \(CP_{RC}\) models.  
within a reasonable time compared to the real solution proposed by the planner, which proves the efficiency of the dedicated heuristic.
## 5 Results  
Comparison of mean down- and upload rates prior to any significance testing indicated that interference reduced the mean download throughput from 11.09 \(\pm\) 5.85 Mbps to 1.66 \(\pm\) 1.12 Mbps (Figure 8). Similarly, interference reduced mean upload from 14.96 \(\pm\) 3.85 Mbps to 2.24 \(\pm\) 1.82 Mbps.  
To evaluate the validity of our approach of using internet-based throughput measurements we performed additional measurements using the same setup from Figure 5 but this time we used a laptop (Lenovo V330, Lenovo Group Limited, Hong Kong, China) as the client device and a local desktop computer from the laboratory as the throughput server.  
We followed the approach from [22] and performed 180 measurements of throughput for each scenario using _iPerf_ application. _iPerf_ is a tool used by network professionals for measuring maximum achievable data rates on IP networks. It was configured to use the default TCP window size of 64 KB. You can find more information on [[https://iperf.fr/](https://iperf.fr/)]([https://iperf.fr/](https://iperf.fr/)) (accessed on 26 March 2021). Since the measurements were performed only on the local wireless link, it allowed us to eliminate any potential variability due to a wider network ecosystem and internet-based throughput test.  
Measurements were taken during time window of 180 s in the absence and in the presence of interference. Interference from the wireless audio transmission reduced the mean throughput rates from 49.38 \(\pm\) 1.55 Mbps to 6.51 \(\pm\) 3.54 Mbps or 86%. This aligns to the previous drop of 85%. This suggests that focusing only on differences between data rates in the presence or absence of interference to determine whether the interference from wireless transmission could be a dominant factor in degrading IEEE 802.11b/g/n network throughput was optimal strategy and allowed us to confidently use our initial data and results to continue with further analysis.  
Figure 8: Mean throughput in the absence or presence of interference, based on download (**a**) and upload (**b**). Mean values are shown atop each bar.
# Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
## 3 Methods
### Inclusion and Exclusion Criteria  
This study's inclusion and exclusion criteria set the boundaries and scope for the scholarly publications that were included in the final review. These criteria were determined after the establishment and refinement of the research topic and the objectives. A variety of factors were considered in formulating the eligibility criteria to define a practical scope of analysis, enhance the study's focus, and ensure the inclusion of only recent articles. First, the researcher included only articles that addressed AR in academic contexts (formal education), where outcomes could be measured. Other related technologies, such as VR and MR, which address similar benefits, limitations, and challenges in academic settings, were not included in order to narrow the scope of the review and enhance the focus of the analysis. Similarly, articles published before 2009 were excluded from this study to allow a review of studies published in the past decade (i.e., from 2009 through 2019). This allowed studies to be included that could be considered recent, thereby reporting various fundamental advancements in technology. Only peer-reviewed published studies were included in this review because they were judged to have more credibility than studies that were not reviewed or published. Only articles published in English were considered for inclusion. Although there are software programs that can translate articles to English, the researcher was skeptical about the quality and outcomes of the translation, which informed the decision to exclude non-English articles. Only original articles, not duplicates, were included in this study. To determine the originality of a publication, the researcher investigated the authors, publication dates, and journals where the studies were published. For instance, Google Scholar provided the results for most of the duplicates in this study. Using the search criteria that included the author, publication date, and journal, the researcher was able to identify original works. For example, [31] had an exact duplicate published by [32],  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Inclusion Criteria** & **Exclusion Criteria** \\ \hline Addressed AR in an academic setting & Addressed related concepts (e.g., VR and MR) \\ Published from 2009 through 2019 & Published before 2009 \\ Written in English & Non-English publication \\ Peer-reviewed journal & Not a peer-reviewed journal \\ Published study & Unpublished study (e.g., proposal and ongoing project) \\ Original publication & Duplicate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Inclusion and exclusion criteria.  
which was retrieved from Google Scholar. After examination, the researcher determined that the study was originally published by [31].
# GNSS Spoofing Detection Based on Wavelets and Machine Learning
## 3 Methodology
### Features Used for Classification
The features extracted from images were image processing and texture analysis features. In this research, all of these features were extracted for three channels (red, green and blue) because RGB images were used for the analysis. The image processing and texture analysis features were as follows [52, 53, 54, 55, 56]:  
* Mean histogram value is the mean value of all pixel intensities in a given color channel (e.g., red, green, blue). It is called mean histogram value because the mean is calculated by using a histogram as the distribution. It provides information about the overall brightness of the image--lighter images have larger average values, while darker images have smaller average values. * The standard deviation of the histogram measures the dispersion of pixel intensity values from the mean. A large standard deviation indicates images with high contrast or large differences in brightness between pixels, while a small deviation indicates a more even distribution of brightness. * The mean gradient magnitude represents the average strength of the edges of the image, i.e., how quickly the intensity changes between adjacent pixels. Higher values indicate images with pronounced edges and details. * The standard deviation of the gradient shows the variation in the strength of the edges. If there is a large variation, it means that the image has a variety of textures, from smooth areas to areas with pronounced details. The following features are Gray Level Co-occurrence Matrix (GLCM) texture features. The GLCM considers the relationships between neighboring pixels, providing rich texture information. * Contrast (C) measures the difference between the intensity of adjacent pixels. High contrast indicates coarser textures, while low contrast indicates smoother or more uniform textures. * Energy represents the uniformity of texture. Higher energy values indicate that the image has regular or repeating patterns, while lower values indicate more random pixel arrangements. * Homogeneity measures the similarity of intensity between adjacent pixels. High homogeneity indicates smooth textures without large differences in intensity.
# ViT-Based Classification and Self-Supervised 3D Human Mesh Generation from NIR Single-Pixel Imaging
## 7 Conclusions  
This work introduces a novel methodology that leverages NIR-SPI imaging to generate 3D human models through point cloud generation and integration of ViT classification in a self-supervised learning framework. The approach has demonstrated strong performance in creating accurate 3D reconstructions; however, accurately localizing the hands remains challenging because of the inherently low contrast of the NIR-SPI images. Despite this limitation, both qualitative and quantitative assessments (see Table 3) indicate that the detection of core people retains high accuracy in the estimation of 3D poses, highlighting the potential of the method for single-image modeling of the human body--even at low resolutions (Figure 8). The experimental results show that our model achieves a V2V error of 36.1 mm, placing it between IPNET (28.2 mm) and VIBE (57.29 mm). Although our method does not produce the lowest V2V error, it significantly outperforms competing approaches in the mean per-joint position error (MPJPE), achieving a minimum deviation of approximately 39 mm. This suggests that, although our model may exhibit lower precision in vertex placement relative to IPNET, it demonstrates greater accuracy in estimating joint positions. PTF, which reports the second-lowest MPJPE (41.1 mm), also performs well, but our model maintains a slight advantage. These findings are highly pertinent to application domains such as motion capture and rescue operations, where precise joint localization is essential to achieve high-fidelity performance and realism. The relatively higher V2V error indicates potential areas for refinement, such as enhanced vertex placement strategies or the integration of more detailed surface geometry features. However, the method achieves a well-calibrated trade-off between vertex-level and joint-level accuracy, offering a robust and versatile solution for a wide range of human body estimation and tracking applications. Furthermore, it represents a significant advancement in the expressive reconstruction of body, hand, and facial features using NIR-SPI imaging, particularly under low-resolution conditions (see Tables 4 and 5). Future research will aim to improve the accuracy at the vertex level while preserving or further improving the precision of the joint position. Although laboratory results for the NIR-SPI system are promising, real-world outdoor deployment introduces additional complexities, such as variable weather conditions, background clutter, and dynamic lighting. To overcome these challenges, future work will focus on adaptive calibration strategies, real-time environmental compensation algorithms, and advanced noise filtering techniques. Further efforts will involve integrating machine learning-driven correction mechanisms and implementing hardware-level optimizations to ensure robust and reliable system performance in diverse outdoor environments.
## 5 Developing the Models for Predicting UCS
### Kelm-Guo  
Although the KELM model with the kernel parameters \(C\) and \(\gamma\) of \(2^{0}\) show a good predictive performance, it is not practical to manually test every possible parameter combination due to the large range of \([2^{-2},2^{9}]\). Therefore, the modified KELM with the new kernel parameters was obtained by using the GWO algorithm, and a reasonable comparison  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Model No.**} & \multirow{2}{*}{**Neurons of Hidden Layer**} & \multicolumn{2}{c}{**RMSE**} & \multicolumn{2}{c}{**R\({}^{2}\)**} \\ \cline{3-6}  & & **Training** & **Testing** & **Training** & **Testing** \\ \hline
1 & 20 & 28.7283 & 27.4770 & 0.6786 & 0.7050 \\
2 & 30 & 24.0466 & 21.9013 & 0.7480 & 0.8126 \\
3 & 40 & 22.5454 & 21.5436 & 0.8021 & 0.8186 \\
4 & 50 & 22.1986 & 21.7206 & 0.8081 & 0.8157 \\
**5** & **60** & **22.3844** & **21.3123** & **0.8049** & **0.8225** \\
6 & 70 & 19.6458 & 22.7176 & 0.8497 & 0.7983 \\
7 & 80 & 19.9072 & 25.3655 & 0.8457 & 0.7486 \\
8 & 90 & 17.6883 & 31.2900 & 0.8782 & 0.7683 \\
9 & 100 & 18.7275 & 25.3018 & 0.8634 & 0.7499 \\
10 & 110 & 17.5671 & 23.6957 & 0.8798 & 0.7806 \\
11 & 120 & 18.8266 & 27.1939 & 0.8620 & 0.7110 \\
12 & 130 & 18.6357 & 34.9914 & 0.8648 & 0.5216 \\
13 & 140 & 16.8519 & 34.6346 & 0.8894 & 0.5313 \\
14 & 150 & 17.1542 & 34.7546 & 0.8714 & 0.5285 \\ \hline \hline \end{tabular} Note: Line in bold represents the better solution.  
\end{table}
Table 2: Performance evaluation of ELM models with different number of neurons in the hidden layer.  
Figure 5: Comparison of the KELM models using various performance indicators in the training and testing phase: (**a**) RMSE; (**b**) R\({}^{2}\).  
between the KELM and novel hybrid KELM-GWO could be conducted. It is interesting to note that only the numbers of grey wolves should be tuned in the GWO algorithm to optimize the other models [77]. To this purpose, the populations of grey wolves were considered to be equal to 25, 50, 75, 100, 150, and 200 for 600 iterations in this study. Meanwhile, the MSE is used as the stopping condition to select the optimal population, as shown in Figure 6. As can be seen in this figure, the numbers of populations have no effect on the fitness values after 500 iterations. The ideal KELM-GWO model with the lowest MSE was reached at the population of 75, where it has the best kernel parameters of \(C=254.63\) and \(\gamma=0.52\), respectively. Therefore, the final KELM-GWO model considering \(C=254.63\) and \(\gamma=0.52\) was used for predicting UCS of rock samples.
# Efficient IoT-Based Formal Model for Vehicle-Life Interaction in VANETs Using VDM-SL
## 2 Related Work
### Emergency Vehicle's Route Clearance  
In the modern age, a lot of accidents occur every day due to rapid motorizing. It is a big challenge to identify the accident location to provide emergency services, such as an ambulance or first aid. If there is a lot of traffic, rescue services cannot get to their target on time, which cause the loss of lives. Recently, the authors suggested a visual emergency vehicle priority system for VANETs based on sensors [24]. The auditory sensors connected to RSUs detect the existence of an emergency vehicle as well as measure the distance by using various distance measurement algorithms from the road intersection in the proposed scheme. The information is submitted to the traffic control center, which will change the signals of traffic to get emergency services out of the congested traffic lane as quickly as possible. The authors suggested a signaling scheme to clear the way for an ambulance or other emergency services [9], in which the green light is turned on for the emergency vehicle's path and does not turn red before the emergency vehicle exits the route. In [25], the authors proposed an automatic accident detection prototype model using IoT and VANET Network deployed in the vehicle with the help of sensors that detect accidents as well as emergency levels. The proposed model supports delivering medical aid at the correct time after detecting the accident. The main objective of the internet of things and VANETs is to prioritize emergency vehicles. The vehicular sensor networks (VSNs) concept in an IoT-based vehicular paradigm, focusing on the security aspects and, authors discussed the VSN's design features for reliability, relevant communication technologies, robustness, and their security concerns [26].  
Advanced technology collaboration can help to decrease fatalities, save lives, and handle emergency situations. Drones, IoT devices, the ad hoc network, the SAR team,and the disaster center unit are all part of the planned network architecture in [27]. Their proposed scenario is appropriate for all stages of emergencies. Drones with IoT capabilities play a critical role in disaster preparedness. Sensors on the drone board, for example, are utilized to gather physical data such as water level, vibration, and displacement in a disaster region, as well as transmit data to the disaster center unit. The camera is also used to make forecasts of the impacted region. The network connectivity, i.e., latency, load, and throughput, is used to evaluate performance.
## 3 Feature-Selection Method and Application
### ReliefF  
ReliefF is a feature-weighting algorithm proposed by [PERSON] in 1992. Because it is limited to the classification of two kinds of data, it has not been applied too much. Hence, [PERSON] expanded it in 1994. ReliefF feature selection is proposed to deal with more complex multi-category situations [25]. This method is simple and relatively efficient in execution. This feature-selection method uses correlations between the calculation characteristics and each fault category by randomly selecting any sample \(Y_{m}\) from its training sample set and through the sample set of the same category as \(Y_{m}\), taking \(K\) of the same category as \(Y_{m}\). The neighboring sample \(H\) can be called near-hits. Similarly, find \(k\)-nearest neighbor samples \(M\) from the sample set and which are different from the \(Y_{m}\) category, which can be called near-misses. This study sorts the weights of the features from high into low to facilitate the research. However, a limitation of this algorithm is that it cannot effectively remove redundant features. the steps of ReliefF are listed as follows:  
* Set feature set \(F=\{F_{1},F_{2},\ldots,F_{N}\}\), sample category, \(Y=\{Y_{1},Y_{2},\ldots,Y_{N}\}\), sampling times \(Z\), the number of neighbor samples \(K\), the threshold \(r\) of feature weights and the initial weight of each feature are zero;
* Randomly select any sample \(Y_{m}\) from all the sample types of \(Y\);
* Extract \(K\) adjacent samples of the same category as the sample \(Y_{m}\);
* \(K\) Near-misses are also found from the sample set sums different from the \(Y_{m}\) category;
* Calculate the weight of each feature in (4);
* Repeat sampling to determine whether the number of sampling times \(Z\) was reached. If not, return to _Step 2_ and repeat until the maximum number of sampling times is reached;
* Sorting features according to feature weights from large to small is mean the importance of the selected features;
* Calculate the sum of the feature weights of the first \(n\) items (expressed as \(W\_all\))  
Figure 3: Classifications based on relevancy and redundancy.  
Step 9 If \(W\_all<r\) then \(n+1\) and repeat _Step 8_ until \(W\_all>r\) stops, where \(r\) is 90% of the sum of all feature weights;  
Step 10 Output the feature.  
\[\begin{array}{l}W(N)=W(N)-\sum\limits_{j=1}^{K}\frac{diff(N,Y,H_{j})}{ZK}\\ +\sum\limits_{C\&class}(Y)\left[\frac{P(C)}{1-p(class(R))}\sum\limits_{j=1}^{ K}diff(N,Y,H_{j}(C))\right]\\ /(ZK)\end{array} \tag{4}\]  
Among them, \(N\) is the number of features, \(K\) is the nearest neighbor sample taken by samples of different categories, \(Y\) is the sample category, \(Z\) is the number of samplings and k-nearest neighbors \(H_{j}(j=1,2,\ldots K)\) in the same sample set of \(Y_{m}\) can be found, \(y_{1}\) and \(y_{2}\) in \(diff(N,y_{1},y_{2})\) can expressed as the difference from feature \(N\).
## 4 Experimental Results and Discussions
### Experiment Comparison of OBBO and ROBBO  
In order to state clearly the contribution of opposite paths, ROBBO algorithm is introduced in this paper. Unlike the previously defined opposite paths, uniformly generated random paths are employed as opposite paths. As shown from Table 3, the experiment results of OBBO and ROBBO are rearranged and analyzed with software Statistical Product and Service Solutions (SPSS) 14.0 version (SPSS Inc., Chicago, IL, USA) and significance is assumed at 0.05.  
As seen from Table 3, we observe that their mainly difference is characterized by three aspects, which are computation time, utilization rate of opposite paths and population diversity. When compared with ROBBO, OBBO has higher utilization rate of opposite paths and worse population diversity, in the statistical sense. According to the definition of opposite path in [28], it is created based on the original path in a limited and controlled manner, though its object is to maximize the proximity between the adjacent vertices. At this time, there has been an appreciable increase in the probability of the opposite path selected as offspring in next iteration. In contrast, the opposite path in ROBBO proposed in this paper is sampled by uniformly distribution over the whole search space, and then ROBBO has better population diversity inevitably.  
Seen from the evaluation criterion of best solution found, no significant differences (\(p>0.05\)) are observed between OBBO and ROBBO. In other words, the opposite path from [28] has the same contribution with random generated paths. Such being the case, it is futile to attempt to apply the opposite path from [28], although it is a most plausible story. Maybe the ROBBO algorithm is a good choice to instead OBBO. It is another interesting and valuable conclusion in this paper.