esa-sceva/satcom-chunk-collection huggingface dataset:
# Generative AI-Enabled Energy-Efficient Mobile Augmented Reality in Multi-Access Edge Computing
## 4 Problem Formulation and Solution
### Discussion for Practical Considerations  
As a practical consideration, we conduct experiments to analyze the SR effect on the MAR system in terms of the latency and object detection accuracy. We implement all modules, including our proposed framework, on the MEC side using Python and PyTorch. Specifically, to establish a practical MAR and SR environment, we implement the object detection module using the OpenCV library and the ultralytics YOLOv8 model [29], while the SR module is implemented using the EDSR model [30]. The client node sends object detection requests to the proposed framework, and after the MEC server node processes the object detection tasks based on the SR decision from our framework, the client displays the results using the OpenCV library. The overview of our implementation is shown in Figure 4.  
To evaluate the effect of SR on object detection, our implementation collects three metrics from the object detection results using ultralytics YOLOv8 built-in methods: image preprocessing time, inference time, and confidence score. We compare the original low-resolution video case to the video with the SR case through the three metrics above. Image preprocessing time and inference time are used to analyze the impact on the object detection task's latency regarding whether SR is applied or not, and confidence scores are used to analyze the impact on the object detection task's accuracy. The higher the confidence score, the higher the accuracy. The comparison results about performance metrics are shown in Table 3. As shown in Table 3, the task execution time increases when SR is applied, and the confidence score also increases. That is, applying SR in the MAR system causes improvement in terms of accuracy but degeneration in terms of service latency. Also, depending on the trade-off between the latency decrement and the accuracy increment, users' satisfaction with MAR applications such as QoS may vary. Therefore, the satisfaction model design in our framework is effective in a practical MAR environment.  
Figure 4: The result of object detection on implemented framework.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Image Preprocessing Time** & **Inference Time** & **Confidence Score** \\ \hline Original video & Avg: 0.67 ms, Std: 0.95 ms & Avg: 1.53 ms, Std: 0.175 ms & Avg: 76.21, Std: 7.57 \\ \hline Video with SR & Avg: 7.72 ms, Std: 0.479 ms & Avg: 1.81 ms, Std: 0.1 ms & Avg: 88.47, Std: 9.12 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The performance metrics’ comparison of YOLO implementation.
## 4 Experimental Results
### Dataset  
We adopt multi-shot enrolment including more than one image to evaluate our method on the following three benchmark datasets.  
1. ORL dataset [60]: this dataset comes from Olivetti Research Laboratory formerly named American Telephone and Telegraph Company. This dataset is composed of 10 different face images of each 40 face subjects, which includes different illuminations, expressions, and poses. In addition, we randomly select five face images of each subject for enrollment, and other face images are applied to test the performance of bio-key generation during the reconstruction stage.
2. Extended YaleB dataset [61]: this dataset includes 2332 face images of 38 subjects, and it is captured under 64 different lighting conditions. Hence, the face image of each user has 64 different illuminations. We randomly choose 10 face images of each subject in the enrollment stage, and the rest images are used for testing.
3. CMU-PIE dataset [62]: the CMU-PIE dataset contains 41,368 face images of 68 subjects, including larger variations in illuminations, poses, and expressions. In this experiment, we utilize five different poses (p05, p07, p09, p27, p29) and illuminations to validate our scheme. We follow the same partition strategy with the Extended YaleB dataset in training and testing images.
## Abbreviations  
### Notation  
\(a\) = wave propagation speed (m s\({}^{-1}\))  
\(A\) = cross sectional area (m\({}^{2}\))  
\(\mathrm{a_{rel}}\) = relative opening/closing (/)  
\(C\) = minor loss coefficient (-)  
\(C_{N}\) = coefficient defined in the negative equation of the Method of Characteristics (m\({}^{3}\) s\({}^{-1}\))  
\(C_{P}\) = coefficient defined in the positive equation of the Method of Characteristics (m\({}^{3}\) s\({}^{-1}\))  
\(D\) = diameter (m)  
\(g\) = gravity acceleration (m s\({}^{-2}\))  
\(H\) = head (m)  
\(H^{*}\) = absolute pressure head (m)  
\(L\) = length (m)  
\(m\) = mass (kg)  
\(m\) = mass discharge (kg s\({}^{-1}\))  
\(n\) = polytropic exponent (-)  
\(n_{T}\) = transient turbine revolving speed (%)  
\(p^{*}\) = absolute pressure (Pa)  
\(p\) = gauge pressure (Pa)  
\(Q\) = discharge (m\({}^{3}\) s\({}^{-1}\))  
\(R\) = specific gas constant (J kg\({}^{-1}\) k\({}^{-1}\))  
\(T\) = temperature (k)  
\(V\) = volume (m\({}^{3}\))  
\(Z\) = elevation (m a.s.l. for the object, mWC for fluid)  
\(\Delta t\) = time step (s)  
\(\lambda\) = friction loss coefficient (-)  
\(\rho\) = density of water (kg m\({}^{-3}\))
## 3 Bispectrum Analysis of EEG Signals for an Emotion Recognition System
### Back-Propagation Neural Networks  
BPNN is one of the ANN classifiers constructed by the multi-layer perceptron (MLP) architecture. BPNN provides a mechanism to update the weights and biases of each neuron's connection by propagating and minimizing the error in each iteration (epoch). The BPNN used in this work has three layers of architecture: one input layer, one hidden layer and one output layer. The number of neurons in the input layer was equal to the dimension of the feature vector, while the number of hidden neurons amounted to half of the input neurons. Because the classification for arousal and valence were conducted separately, each emotion was divided into high and low class, resulting in only one neuron in the output layer. The Nguyen-Widrow method [22] was used to initialize the weight and bias of each neuron.  
At the feed-forward part of the training phase, for each epoch, the input vectors were presented one at a time to the input layer, and their values were passed to the hidden layer. In the hidden layer, the value received by each neuron was multiplied by its weight and added with the bias. The activation function used was a sigmoid function, \(f(x)=1/(1-e^{x})\). On the output layer, the value received by the neuron was multiplied by its weight and added with the bias. The activation function at the output layer was also a sigmoid function, resulting in the output value of the ANN.  
At the back-propagation part of the algorithm, the calculated error between the values in the output layer (the predicted emotion) and the target (the actual emotion) was propagated back to adjust the weights and the biases for the neurons in the output and the hidden layer [23]. The learning process was carried on until the preferred minimum error was achieved; in this case, we used root mean sum squared error (RMSSE), or until it reached the maximum number of epochs.  
At the testing stage, the testing data part was fed-forward to the input layer and sent to the hidden layer and up to the output layer using the weights and the biases calculated and obtained from the learning stage. The testing stage was similar to the feed-forward part of the learning stage,but the error between the predicted emotion and the stimulated emotion was calculated to produce the recognition rate of the automatic emotion recognition system.
# Reconfigurable Formation Control of Multi-Agents Using Virtual Linkage Approach
## 4 Simulation and Results
### Parameters Analysis
#### 4.3.1 Number of Agents  
In order to analyze the performance of a different number of agents, five agents are required to perform the same task as in Section 4.1: aligning in a line formation with a uniform distance of 0.1 m and moving around a circle with a radius of 1 m in 10 s. With the consideration of only changing the number of agents, a virtual linkage which also consists of two virtual links is designed:  
\[\text{virtual linkage\_B}\,=\,\left\{\begin{array}{l}\text{virtual link1}\,=\,\{\text{agent1, agent2, agent3}\}\\ \text{virtual link2}\,=\,\{\text{agent3, agent4, agent5}\}\end{array}\right. \tag{16}\]  
Similar to Section 4.1.1, each virtual link then holds a length of 0.2 m (See Figure 18) and has the following points:  
\[\begin{array}{l}{}^{v1}p_{1}=[0,0],{}^{v1}p_{2}=[0.1,0],{}^{v1}p_{3}=[0.2,0],\\ {}^{v2}p_{3}=[0,0],{}^{v2}p_{4}=[0.1,0],{}^{v2}p_{5}=[0.2,0]\end{array} \tag{17}\]  
The trajectories of the individual agents and snapshots during the simulation for 10 s are plotted in Figures 19 and 20. These results indicate that the proposed virtual linkage approach is able to be applied to groups with different numbers of agents.  
The influence of the agents' number is analyzed by comparing the two simulations conducted in Sections 4.1 and 4.3.1. As can be seen from Figure 21, each virtual link server needs to communicate with five agents when nine agents are involved in the formation moving simulation (See Section 4.1). In the simulation presented in this section, the communication burden has been reduced, as each virtual link server needs to communicate with three agents.  
Therefore, the influence of the agents' number can be concluded. With the growing number of agents, the largest bandwidth of the communication equipment in the system increases.  
Figure 18: Predefined virtual linkage used in these two simulations.  
Figure 19: Snapshots of the formation moving.  
Figure 20: Trajectory of individual agents.
# Time Domain Investigation of Hybrid Intelligent Controllers Fed Five-Phase PMBLDC Motor Drive
## 4 Simulation and Discussion
### ANFIS-Controller-Driven Drive
#### 4.5.2 Step or Gradual Application and Removal of Load
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Load** & **Characteristics** & **Time** & **Overshoot** & **Peak Time (s)** & **Rise** & **Settling** & **Steady** \\
**Variation** & & **Interval (s)** & **(\%)** & **(\%)** & **Time (s)** & **Time (s)** & **State Error** \\ \hline \multirow{4}{*}{Step load variation} & Starting & 0–1 & \(-\)1.07 & 1 & 0.2046 & 0.5742 & 37 rpm \\  & Loading & 1–1.2 & \(-\)2.214 & 1.2 & 0.1599 & 1.1952 & 31 rpm \\  & Unloading & 3.3-3.5 & \(-\)1 & 3.5 & 0.1599 & 3.4957 & 13 rpm \\  & Load removal & 3.5–5 & \(-\)0.857 & 5 & 1.1858 & 4.957 & 9 rpm \\  & Starting & 0–1 & \(-\)1.07 & 1 & 0.2046 & 0.5742 & 37 rpm \\ Linear & Gradual increases of load & 1–2.5 & \(-\)2.42 & 2.5 & 1.097 & 2.422 & 16 rpm \\  & Constant Max Load & 2.5–3.5 & \(-\)1.07 & 3.5 & 0.7919 & 3.4720 & 12 rpm \\  & Gradual decrease in load & 3.5–4.5 & \(-\)0.928 & 4.5 & 0.8014 & 4.477 & 10 rpm \\  & No Load & 4.5–5 & \(-\)0.922 & 5 & 0.3996 & 4.988 & 9 rpm \\ \hline \hline \end{tabular}
\end{table} 
Table 5: ANFIS Controller Characteristics. Figure 9: Transient behavior of five-phase PMBLDC motor with ANFIS controller. to their respective final values as the load is removed. The jerk is a perturbation window, which is equal to the step change in speed during load removal. Figure 10 shows the torque speed characteristics of different controllers fed into the PMBLDC drive. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**At the Time of**} & \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**PI Controller**} & \multicolumn{4}{c}{**Fuzzy Controller**} & \multicolumn{2}{c}{**Fuzzy-PI Controller**} & \multicolumn{2}{c}{**ANFIS Controller**} \\  & & **Step** & **Linear** & & & & **Linear** & & **Step** & **Linear** & \\  & **Load** & **Load** & & & **Step Load** & & **Load** & **Load** & **Load** & **Load** & **Load** \\ \hline \multirow{2}{*}{Starting} & Peak torque (Nm) & 7.0 & 7.0 & 12.43 & 12.43 & 10 & 10.68 & 12.11 & 12.12 \\  & Speed at peak & 254 & 256 & 247 & 264 & 220 & 205 & 126 & 127 \\  & torque (rpm) & \multirow{2}{*}{Maximum speed} & \multirow{2}{*}{1708} & \multirow{2}{*}{1710} & \multirow{2}{*}{1398} & \multirow{2}{*}{1399} & \multirow{2}{*}{1471} & \multirow{2}{*}{-} & \multirow{2}{*}{1390} & \multirow{2}{*}{1390} \\  & Maximum speed & & & & & & & & \\  & attained & & & & & & & & & \\  & jerks (rpm) & \multirow{2}{*}{1708–1321} & \multirow{2}{*}{-} & \multirow{2}{*}{-} & 1398–1396 & \multirow{2}{*}{-} & 1347–1399 & \multirow{2}{*}{-} & 1383–1387 & \multirow{2}{*}{-} \\  & jerks (rpm) & (387 rpm) & & (2 rpm) & & (52 rpm) & & (4 rpm) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Speed torque comparison of different controllers. Figure 10: Speed torque characteristics with different controllers.
# Fronthaul Design for Wireless Networks
## 4 Fronthaul Design Study Results
### Individual Links Assessment  
In order to study the performance of MRT, FSO, and FO systems, four different scenarios were considered by taking into account the ranges of values associated with rain and fog intensities [25; 28]; these test scenarios are henceforth denoted as S(r_l/_f_l), S(r_h/_f_l), S(r_l/_f_h), and S(r_h/_f_h) and their respective characteristics are given in Table 7; for the sake of clarity, r_l and r_h refer to low and high rain \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**MRT**} & \multirow{2}{*}{ID} & \(B\) & \(f\) & \(P_{Tx}\) & \(G_{Tx}\) & \(G_{Rx}\) & \(A_{qui}\) & \(S_{Rx}\) & \(N_{f}\) & \(M\) & F.Costs(MRT) & V.Costs(MRT) \\  & & (Mbps) & (GHz) & (dBW) & (dB) & (dB) & (dB) & (dBW) & (dB) & (dBW) & (M-QAM) & (EUR) & (EUR) & (EUR/\(\sqrt{\text{km}}\)) \\ \hline  & 1 & 380 & 13 & 2 & 28 & 28 & 3 & \(-111\) & 4 & 1024 & 55,895 & 11,590 \\ \hline  & 2 & 570 & 42 & 0 & 34 & 34 & 3 & \(-122\) & 4 & 1024 & 85,326 & 5016 \\ \hline  & 3 & 1100 & 42 & 0 & 34 & 34 & 3 & \(-122\) & 4 & 1024 & 95,736 & 9680 \\ \hline \multirow{2}{*}{**FSO**} & \multirow{2}{*}{ID} & \(B\) & \(\lambda\) & \(P_{Tx}\) & \(G_{Tx}\) & \(G_{Rx}\) & \(A_{qui}\) & \(S_{Rx}\) & F.Costs(PSC) & & \\  & & (Mbps) & (nm) & (dBW) & (dB) & (dB) & (dB) & (dBW) & (EUR) & & \\ \hline  & 4 & 100 & 785 & \(-12.2\) & 30 & 0 & 3 & \(-60\) & 29,903 & & \\ \hline  & 5 & 1000 & 1550 & \(-27\) & 30 & 0 & 3 & \(-60\) & 32,353 & & \\ \hline  & 6 & 1000 & 1550 & \(-7\) & 30 & 0 & 3 & \(-60\) & 48,186 & & \\ \hline  & 7 & 10,000 & 1550 & \(-7\) & 30 & 0 & 3 & \(-48\) & 86,654 & & \\ \hline \multirow{2}{*}{**FO**} & \multirow{2}{*}{ID} & \(B\) & \(B\)x\(D\) & \(Tx_{min}\) & \(Rx_{min}\) & \(L\) & \(F_{L}\) & F.Costs(PO) & V.Costs(PO) \\  & & (Mbps) & (Mbps km) & (dBW) & (dBW) & (dBW) & (dB) & (dB/km) & (EUR) & (EUR) & (EUR/km) \\ \hline  & 8 & 10,000 & 80,000 & \(-27.5\) & \(-43\) & 3 & 0.25 & 35,135 & 18,865 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the test equipment. intensity, respectively, whereas f_1 and f h refer to low and high fog intensity, respectively. In addition, the minimum accepted link margins \(M\overline{l}_{min}^{\text{(MRT)}}\), \(M\overline{l}_{min}^{\text{(FSO)}}\), and \(M\overline{l}_{min}^{\text{(FO)}}\) were all set equal to 3 dB [10; 32]. Following the previous test scenario settings, the link design algorithm was run for each scenario considering a set of distances ranging from 0 to 20 km and a required bit rate ranging from 0 to 1.5 Gbps, in order to assess how the most cost-effective solution varied for different link requirements and for different weather conditions. Figures 4-7 present the outputs of the link design algorithm (i.e., the cheapest working solution for the considered distance and required bit rate ranges) for the four test scenarios.
###### Abstract  
We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed edges), and (ii) the agents do not bid on the exact same items but may bid on conflicting items that cannot be both assigned or that require accessing a specific resource with limited capacity. This setting is motivated by real applications such as Earth observation slot allocation, virtual network functions, or multi-agent path finding. We model several directed path allocation problems (vertex-constrained and resource-constrained), investigate several solution methods (qualified as exact or approximate, and utilitarian or fair), and analyze their performances on an orbit slot ownership problem, for realistic requests and constellation configurations.  
path allocation; fairness; constraint optimization; satellite constellation
# A Rapid Verification System for Automatic Emergency Braking Control Algorithm of Passenger Car
## 4 Verification of Passenger Car AEB Control Algorithm
### AEB Control Algorithm
The AEB control model built in Simulink is shown in Figure 9. It mainly includes TTC calculation, AEB braking status decision, and PID acceleration control module.
## 3 Results and Discussion
### Influence of Fluorine Content  
In this section, the condition of maintaining a temperature of 1100 \({}^{\circ}\)C for 4 h was considered. As illustrated in Figure 2a, in the absence of AlF\({}_{3}\), the flaky boehmite precursor retained its original morphology at 1100 \({}^{\circ}\)C, but there were significant changes in particle size and thickness. This could be attributed to the absence of fluoride ions, which led to the lack of gas-phase intermediates, such as Al-O-F, in the sintering environment [13]. Upon the introduction of fluoride ions, the flaky alumina developed fully, as depicted in Figure 2b. No residual morphology of the flaky boehmite was detected under the SEM, and the size of the flaky alumina increased significantly to 10-15 \(\upmu\)m, which is 5-10 times larger than the product in Figure 2a. Consequently, the introduction of fluoride ions played a crucial role in the synthesis of large-sized flaky alumina and reduced the phase transition temperature, enabling the production of large flaky alumina with a high \(\alpha\)-phase at lower temperatures. As depicted in Figure 2c,d, the particle size of flaky alumina diminishes with escalating concentrations of AlF\({}_{3}\), ranging from 1% to 6%, and stabilizes within a specific thickness range. In contrast, the thickness of flaky alumina initially decreases and then increases as the concentration of AlF\({}_{3}\) increases. In conclusion, the optimal diameter-to-thickness ratio for flaky alumina is attained with the addition of 4% AlF\({}_{3}\).  
Select a material with a 2% fluorine ion content and conduct XRD tests at temperatures ranging from 800 \({}^{\circ}\)C to 1300 \({}^{\circ}\)C. The test results are presented in Figure 2d. At 800 \({}^{\circ}\)C, the transition phase Al\({}_{2}\)O\({}_{3}\) can still be observed, but the diffraction peak of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) has begun to emerge. At 900 \({}^{\circ}\)C, the diffraction peak of the transition phase Al\({}_{2}\)O\({}_{3}\) disappears, and the diffraction peak of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) becomes significantly enhanced, indicating that the crystallinity starts to increase. From 900 \({}^{\circ}\)C to 1300 \({}^{\circ}\)C, the diffraction peaks gradually become narrower and taller, suggesting that the crystallinity of \(\alpha\)-Al\({}_{2}\)O\({}_{3}\) is continuously increasing, which aligns with the conventional crystallographic rules. It is found that at 1100 \({}^{\circ}\)C, the large-sized flaky alumina can be completely transformed, presenting a smooth surface. Figure 2e clearly depicts the finished product after calcination at 1100 \({}^{\circ}\)C for 4 h with the addition of 4% AlF\({}_{3}\). It has a particle size of approximately 20 \(\upmu\)m, a thickness of just 183 nm, and a diameter-to-thickness ratio close to 100.
## 3 Methodology
### Data Sampling Methodology  
When incorporating imbalanced data into a model, there is a concern in that while the majority class may have high accuracy, biased results with low accuracy may be obtained for the minority class. To address this issue, several sampling methods have been proposed [28]. Sampling techniques alter the class distribution of imbalanced data to create balanced data, which are typically classified into two categories: undersampling, where instances of the majority class are removed, and oversampling, where instances of the minority class are artificially increased.  
Unlike simple methods that merely replicate instances of the minority class, SMOTE innovatively crafts synthetic samples through interpolation between existing instances of the minority class [29]. This expansion leads to a more diverse and enriched decision space boundary, which is useful for applying ML algorithm models.
## 2 Literature Review
### Digitization and Extraction  
Digital access to cultural heritage materials can be provided through a web interface. Initially, web interfaces did not offer article-level searches, which made it difficult to access relevant data [19]. With an article-level search, one can query a collection based on a particular context, for example, searching for articles in collections related to a specific topic, place, event, or object. The primary purpose of article segmentation is to extract more focused information compared to what is possible with a page-level search. Article segmentation is challenging because of the different layouts, content types, and languages. Many different techniques have been proposed to accomplish this task, such as segmentation based on reading order [36; 37], layout information [19; 38], segmenting close regions [39], redefining the text blocks [40], and a semantic linking of the textual information [41].  
Machine learning approaches have also been used to extract information from historical documents [42; 43; 44; 45]. A number of these approaches rely on image-based solutions to classify and categorize them. Several methods are used for the context-aware querying of historical collections, such as clustering [46; 47], detecting patterns/similarity [48], and linking articles to open data sources such as Wikipedia and DBpedia [49]. Topic modeling [50] and the clustering of news items on a single topic [51; 52], such as politics, sports,  
Figure 2: An overview of general workflow.  
and education, can be used to enrich textual data in newspaper collections. Furthermore, it is now possible to segment historical documents using some recent state-of-the-art machine learning approaches such as PubLayNet [53], DocBank [54], and DocBed [55]. Alternatively, layout analysis models can also be trained on other datasets [56; 57]. Compared to standard OCR results, these models achieve good layout segmentation, but they lack the article-level information that is required to improve searchability in historical collections. Just as text data can be classified according to its characteristics, or content, illustrations can also be classified according to their context [58], location, and features such as color or shape [59], enabling the evaluation of visual content.