esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions  
Based on the review results of 49 selected articles regarding AR-supported professional training, we provided tentative answers to the four research questions presented. First, the publication of AR-supported professional training has witnessed an overall upward trend since 2001, with design cases and empirical studies being the dominant research types before and after 2015. However, most studies were implemented in the contexts of engineering and health and medicine. Second, the advancement of AR technology has led to increased portability of its computing devices, yet the intuition of human-computer interaction suffered a slight setback, with artificial approaches (e.g., scanner, mouse, and keyboard) outweighing natural approaches (e.g., motion trackers) in terms of input in the last five years. Third, two widely used pedagogies for AR-supported instruction turned out to be trial-and-error and experiential learning, which are effective approaches for practice-oriented professional training. Moreover, content delivery has emerged as the main instructional function of AR for professional training in recent years. Lastly,  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Moderator** & **K** & \(g\) & **95\% CI** & \(Q_{B}\) & \(p\)-Value** \\ \hline Scaffolding & & & & 2.551 & 0.279 \\ No scaffolding & 8 & 0.346 & \([-0.876,1.568]\) & & & \\ Computer & 3 & 0.520 & \([-0.120,1.160]\) & & & \\ Manual & 4 & \(-0.079\) & \([-0.485,0.327]\) & & & \\ \hline Input & & & 12.080 ** & 0.007 \\ Controller & 4 & \(-0.996\) & \([-2.557,0.564]\) & & & \\ Scanner & 3 & 1.748 & \([0.421,3.075]\) & & & \\ Haptic sensor & 4 & 0.749 & \([0.104,1.395]\) & & & \\ Mixed & 4 & \(-0.0749\) & \([-0.485,0.327]\) & & & \\ \hline Computing devices & & & & 11.721 ** & 0.003 \\ Desktop/laptop & 8 & \(-0.547\) & \([-1.414,0.320]\) & & & \\ Mobile devices & 4 & 1.666 & \([0.742,2.590]\) & & & \\ Not mentioned & 3 & 0.520 & \([-0.120,1.160]\) & & & \\ \hline Output & & & 14.609 ** & 0.001 \\ Monitor-based & 7 & \(-0.755\) & \([-1.636,0.126]\) & & & \\ Video see-through & 5 & 1.506 & \([0.752,2.259]\) & & & \\ Not mentioned & 3 & 0.520 & \([-0.120,1.160]\) & & & \\ \hline Media representation & & & & 19.417 *** & 0.000 \\
3D object & 6 & \(-0.539\) & \([-1.640,0.562]\) & & & \\ Text & 3 & 1.748 & \([0.421,3.075]\) & & & \\ Video & 4 & \(-0.079\) & \([-0.485,0.327]\) & & & \\ Mixed & 2 & 1.204 & \([0.625,1.783]\) & & & \\ \hline \end{tabular}  
* \({}^{**}\)\(p<0.01\); *** \(p<0.001\).  
\end{table}
Table 3: _Cont._the meta-analysis results indicated that AR had an overall small effect on professional training outcomes, and the factors of discipline, pedagogy, instructional function, input, output, computing devices, and media representation moderate the effectiveness to varying degrees. Informed by the review results, practical implications and future research agenda are discussed below.
## Appendix A Details on the Algorithm for Processing the Measured Spectra  
The algorithm attributes the intensity changes across the wavelength range to the absorption changes of three chromophores: HbO, HbR, and CCO. The algorithm uses a least-square fitting to back-calculate the concentrations. We used the extinction coefficient \(\epsilon\) for each chromophore, differential pathlength factor (DPF), delivery-pickup fiber distance \(d\), and intensity change at each wavelength \(I_{0}(\lambda)/I_{t}(\lambda)\):  
\[\begin{bmatrix}\Delta[HbO]\\ \Delta[HbR]\\ \Delta[CCO]\end{bmatrix}=\frac{1}{DPF\times d}\times\begin{bmatrix}\epsilon_{ HBO}(\lambda_{1})&\epsilon_{HBR}(\lambda_{1})&\epsilon_{CCO}(\lambda_{1})\\ \epsilon_{HBO}(\lambda_{2})&\epsilon_{HBR}(\lambda_{2})&\epsilon_{CCO}( \lambda_{2})\\ \vdots&\vdots&\vdots\\ \epsilon_{HBO}(\lambda_{n})&\epsilon_{HBR}(\lambda_{n})&\epsilon_{CCO}( \lambda_{n})\end{bmatrix}^{-1}\begin{bmatrix}log(I_{0}(\lambda_{1})/I_{t}( \lambda_{1}))\\ log(I_{0}(\lambda_{2})/I_{t}(\lambda_{2}))\\ \vdots\\ log(I_{0}(\lambda_{n})/I_{t}(\lambda_{n}))\end{bmatrix}\]  
For the studies reported in this paper, 47 wavelengths from 759 nm to 897 nm were used. The parameters for the above formula were selected as follows: the specific extinction coefficient was provided by [PERSON] et al. (2018), the DPF was estimated to be 6.26 for an adult's forehead ([PERSON] et al., 2018), and the delivery-pickup fiber distance d was 3 cm for the attention test or the breath holding test, while it was 2 cm for the blood pressure test.
# Mode Shape-Based Damage Detection Method (MSDI): Experimental Validation
## 3 Results and Discussion  
On the basis of experimental research, we performed a sensitivity analysis of the MSDI method, considering the effect of the damage level, the effect of the damage zone size, and ultimately the effect of multiple damages.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
## 4 A Comparison of Sparse Signal Recovery Algorithms  
In this section, the properties of different algorithms are revealed and the factors that affect the estimated accuracy and computational complexity are also presented. Further, the estimated error and complexity are compared for three kinds of algorithms.
## 3 Methodology
### Hierarchical and Optimized Clustering for Superpixels
#### 3.3.3 Fast Clustering for Superpixels Based on Nearest Neighbor Graph  
The clustering of superpixels based on RAG takes a bottom-up way and a hierarchical merge strategy. During the above process, the merging as the minimum of edge weight involves in repeated update and storage of RAG, the computation of which is too expensive. Therefore, this paper combines NNG with RAG in order to get the required results with higher efficiency and less computation.  
From Figure 10, it is obvious that one superpixel node may correspond to several adjacent nodes and edges, and these edges have no direction so that the RAG is a scalar graph. The NNG keeps nodes the same as the RAG, while scalar edges are converted to vector ones in the NNG. Figure 12 shows the conversion process between RAG and NNG.  
Figure 11: The updated RAG.  
Figure 10: An example of RAG: (**a**) The sketch map of superpixels distribution; (**b**) The corresponding RAG.  
In the NNG, all adjacent superpixels are connected by scalar edges, and the length of each edge reflects the regional dissimilarity of the adjacent superpixels. Compare weights of all scalar edges, preserve the one with minimum weight, and specify the direction from the current node to the adjacent one with minimum weight. As one node corresponds to no more than one edge, the total number of edges is no more than the number of nodes. For example, in Figure 12, node \(V_{1}\), \(V_{2}\) and \(V_{7}\) respectively correspond to edge \(E_{2}\), \(E_{6}\) and \(E_{14}\); node \(V_{3}\leftrightarrow V_{4}\), \(V_{5}\leftrightarrow V_{6}\) and \(V_{8}\leftrightarrow V_{9}\) respectively share edge \(E_{7}\), \(E_{11}\) and \(E_{15}\). In comparison of RAG and NNG, it can be concluded that the number of edges in NNG is less than the one in RAG although they have the same number of nodes, and edges of NNG are vector. An important characteristic of NNG is that for any NNG, there exists at least one bidirectional edge which connects the two adjacent superpixels with the minimal regional dissimilarity. Therefore, the clustering of superpixels could be achieved by searching all bidirectional edges in NNG.  
Based on all the above analysis, superpixels could be clustered hierarchically and efficiently by combination of RAG and NNG.
# Optimization of Pickup Vehicle Scheduling for Steel Logistics Park with Mixed Storage
## 5 Numerical Experiments
### Performance Analysis via an Algorithm Comparison  
In order to better illustrate the performance of the proposed ESPEA, the performances of other excellent multi-objective algorithms are compared with that of the proposed algorithm. These comparison algorithms include ROMA/D [24], MOEA/D [25], SPEA2 [19], NSGAII [26] and NSGAIII [27], all of which have been proven to have an excellent performance. In order to fairly compare the solving ability of different algorithms, all algorithms used the same cooperative initialization strategy and genetic operators and all algorithms were run 10 independent times. These algorithms' parameter settings are shown in Table 4, and the results of \(HV\) are given in Table 5.  
As shown in Table 5, it can be seen that the ESPEA is very competitive, obtaining the optimal values of the average \(HV\) and the best \(HV\) for different problem scales. It is worth noting that as the number of vehicles increases, the advantage of the ESPEA becomes increasingly prominent. This is because the local search technology based on critical paths in the ESPEA is more targeted than other local search technologies. This local search technology of swapping critical operations not only reduces the computational cost, but also optimizes the overall scheduling time. Additionally, the insertion decoding method adopted in the ESPEA effectively utilizes the idle time in the yard and improves the logistics efficiency. As the problem scale increases, the advantages of the local search based on critical paths and insertion decoding become more apparent.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**SPEA2**} & \multicolumn{2}{c}{**ESPEA-1**} & \multicolumn{2}{c}{**ESPEA-2**} & \multicolumn{2}{c}{**ESPEA-3**} \\ \cline{2-10}  & **aver** & **best** & **aver\_gap** & **best\_gap** & **aver\_gap** & **best\_gap** & **aver\_gap** & **best\_gap** \\ \hline
1 & 0.54297521 & 0.557345 & 3.62\% & 3.06\% & 4.79\% & 3.06\% & **4.82\%** & **3.06\%** \\
2 & 0.36817818 & 0.409204 & 14.87\% & 8.46\% & 17.06\% & 10.03\% & **19.94\%** & **11.89\%** \\
3 & 0.35772439 & 0.380317 & 21.37\% & 20.68\% & 26.66\% & 23.26\% & **29.43\%** & **26.36\%** \\
4 & 0.36172758 & 0.400679 & 17.41\% & 12.94\% & 22.50\% & 16.72\% & **25.56\%** & **19.06\%** \\
5 & 0.33120254 & 0.35396 & 26.70\% & 24.87\% & 29.18\% & 24.62\% & **33.40\%** & **30.81\%** \\
6 & 0.25820991 & 0.296639 & 37.35\% & 24.16\% & 41.45\% & 25.86\% & **47.79\%** & **31.36\%** \\
7 & 0.22072806 & 0.248489 & 48.40\% & 35.68\% & 53.32\% & 41.78\% & **57.34\%** & **44.66\%** \\
8 & 0.20389379 & 0.225313 & 57.08\% & 49.95\% & 59.15\% & 47.52\% & **64.47\%** & **57.68\%** \\
9 & 0.21106804 & 0.224812 & 56.37\% & 49.36\% & 59.92\% & 52.98\% & **64.26\%** & 58.99\% \\
10 & 0.21625091 & 0.235104 & 53.35\% & 47.19\% & 56.03\% & 48.36\% & **61.94\%** & **52.91\%** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Values of evaluation metrics for ESPEA variants.
# Design and Research of Thermoelectric Generator Simulation System for Boiler Flue Gas Waste Heat
## 3 Experimental Research on TEG Simulation System
### Design of cold end module
During the experiment, the thermoelectric plate is coated with thermal conductive silicone grease. The cold end and the hot end modules are connected by wires to form a TEG module, which is called a single-layer generation module. The thermal radiation from the hot end becomes severe when the single-layer TEG module is exposed to high temperatures. This leads to a swift temperature increase at the cold end, resulting in a decline in the thermoelectric conversion efficiency. Based on the above disadvantages, the paper design a double-layer TEG module, with the hot end in the middle and the cold end on both sides. The structure is more compact, as shown in Figure 7. This structure can reduce the influence of thermal radiation and can better ensure the temperature difference and power generation efficiency. (4) Design of DC-DC module  
The design of DC-DC module is one of the key technologies of thermoelectric conversion in TEG system, and its performance directly affects the normal operation and efficiency of TEG system. Its function is to convert a fixed direct current into another fixed or adjustable voltage direct current by the action of a power electronic switch [30]. It can use the pulse width modulation (PWM) mechanism to control the conduction element, change the duty cycle, and keep the output voltage fixed within a limited range of input voltage and load current [31; 32]. Due to the wide range of output voltage of the TEG module, and the load and the battery have a fixed voltage rating value. In order to meet the requirements, a Buck-Boost converter is designed, as shown in Figure 8. It has the advantages of good stability and low loss.
## 2 Related Work
### Group Signature (GS)-Based  
The rationale behind GS-based is that the group administrator is responsible for generating the signature on the behalf of the whole group or members. Many researchers [18; 19; 20; 21; 22] have proposed a GS-based scheme to tackle the vulnerabilities arising from the PKI-based scheme in a vehicular network. However, the two main limitations of this approach are (i) the huge size of the certificate revocation list (CRL) owing to the growing number of revoked vehicles; and (ii) the low efficiency of the system, since two pairing operations are included.
## 2 Materials and Methods
### Inclusion and Exclusion Criteria  
The inclusion criteria comprised of patients with the following characteristics: (1) reading and signing the written informed consent document; (2) patients aged \(\geq\) 25 years who underwent dental implant treatment; (3) individuals suffering from peri-implantitis, i.e., peri-implant BOP at \(\geq\)30% sites, PD \(\geq\) 4 mm, and CBL \(\geq\) 3 mm [24]; (4) individuals without peri-implantitis, i.e., BOP of \(<\)30% sites, PD of \(<\)4 mm, and CBL of \(<\)3 mm [24]; (5) self-reported tobacco consumers: subjects who have been smoking a minimum of one cigarette/day over the past 2 years [25]; (6) never smokers: persons that stated to have never utilized tobacco in any form [26].  
The exclusion criteria consisted of individuals with the following characteristics: (1) individuals who refused to participate and/or sign the written consent document; (2) individuals consuming smokeless tobacco products and/or area nut; (3) dual smoker, i.e., persons smoking tobacco along with other nicotinic products including waterpipe, bidi, and electronic cigarettes; (4) individuals who consumed alcohol habitually; (5) completely edentulous individuals; (6) individuals with maligned dentition; (7) patients suffering from medical conditions such as diabetes mellitus, renal and hepatic disorders, cardiovascular diseases, and patients suffering from the HIV infection; (8) gestation and/or nursing individuals; (9) individuals who reported to have utilized steroids, NSAIDs, probiotics, antibiotics and bisphosphonates over the past 3 months.
## 5 System Model
### THz Channel Model
#### 5.1.1 Free-Space Path Loss (FSPL)  
The line-of-sight (LOS) free-space path loss can be expressed using the Friis equation [5]:  
\[h_{p}=\frac{c\sqrt{G_{TX}G_{RX}}}{4\pi df}, \tag{4}\]  
where \(d\) represents the distance between the transmitter and the receiver, \(f\) is the carrier frequency, and \(G_{TX}\) and \(G_{RX}\) signify the gain of the transmitter and receiver antenna, respectively.
# Synchronous Remote Calibration for Electricity Meters: Application and Optimization
## 4 Results and Discussions
### Experimental Scenario Settings
#### 4.3.2 Impact of Server Quantity on Total Execution Time  
To assess the impact of the number of servers on the maximum task completion time, we maintained the parameters for the Central Lab, Detection Points, Total Clients, and Task Number according to the Parameter Settings for Different Scale Scenarios, while setting the Task Types to 10. The total number of servers varied across different scenarios. In the small-scale scenario, the total number of servers was set to 2, 4, 6, 8, and 10. In the medium-scale scenario, the total number of servers was set to 2, 4, 6, 8, and 10. In the large-scale scenario, the total number of servers was adjusted to 3, 6, 9, 12, and 15.  
Figures 9(a), 9(b) and 9(c), respectively, show the impact of the number of servers on the maximum task completion time under three different scale scenarios. From the figures, it can be observed that initially, as the number of servers increases, the task completion time decreases. This occurs because the increase in the number of servers reduces the average workload per server, enabling more efficient handling of tasks at each detection point and leading to better utilization of server resources, thereby reducing the maximum task completion time. However, in later stages, although the number of servers continues to increase, the reduction in the maximum task completion time becomes limited and shows fluctuating changes. This is because the number of clients remains constant; therefore, despite the increase in the number of servers, their impact on the maximum task completion time gradually diminishes. Furthermore, regardless of the scenario or the number of servers, the maximum task completion time for the Hybrid Genetic Algorithm (HGA) is consistently lower than that of similar algorithms, demonstrating its superior performance.  
Figure 9: Impact of task quantity on different scenarios: (**a**) small-scale, (**b**) medium-scale, and (**c**) large-scale.
## 5 Limitations of ANNs  
As described above, ANNs are powerful computing techniques, which are designed to mimic human learning processes by establishing linkages between process input and output data. These techniques have been widely applied with advanced development with their unique advantages, such as no underlying assumption about the distribution of data, arbitrary decision boundary capabilities, universal approximation capabilities, easy adaptation to different types and structures of data, ability to fuzzy output values to enhance classification, and good generalization capabilities. However, ANNs have some disadvantages in common, which need to be considered in practical application:  
* Black box ANNs are black box in nature. Therefore, if the problem is to find the output response to the input such as system identification [96], ANNs can be a good fit. However, if the problem is to specifically identify causal relationship between input and output, ANNs have only limited ability to do it compared with conventional statistical methods.
* Long computing time ANN training needs to iteratively determine network structure and update connection weights. This is a time-consuming process. With a typical personal computer or work station, the BP algorithm will take a lot of memory and may take hours, days and even longer before the network convergesto the optimal point with minimum mean square error. Conventional statistical regression with the same set of data, on the contrary, may generate results in seconds using the same computer.
* Overfitting With too much training time, too many hidden nodes, or too large training data set, the network will overfit the data and have a poor generalization, i.e. high accuracy for training data set but poor interpolation of testing data. This is an important issue being investigated in ANN research and applications as described above.  
Also, SVMs were originally developed to solve binary classification problems. How to effectively extend it for multiclass classification is still an ongoing research issue. Typically, a multiple class classifier can be constructed by combining several binary classifiers. Further, all classes can be considered at once. [PERSON] and [PERSON] [98] gave decomposition implementations for two such \"all-together\" methods. They compared the performance with three methods based on binary classifications: one-against-all, one-against-one, and Directed Acyclic Graph SVM (DAGSVM). The experiments indicated that the one-against-one and DAG methods are more suitable for practical use than the other methods.
# A Simplified Query-Only Attention for Encoder-Based Transformer Models
## 5 Discussion
### Limitation  
While the experimental results showed that the proposed attention was comparable to the traditional attention, it should be noted that the experiments were evaluated only with the EEG conformer and EEG datasets. The temporal relationships in EEG signals are relatively local. Therefore, additional experiments with other types of data and models are required in future work. Moreover, query-only attention cannot be applied to encoder-decoder- or decoder-based models; it can only be applied to encoder-based models.
# Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones
## 3 Problem Description
### Time-Dependent Vehicle Speed  
The real-time traffic speed data for Dalian were collected from the Baidu Maps Traffic Data Platform ([[https://jaotong.baidu.com/congestion/city/urban](https://jaotong.baidu.com/congestion/city/urban)]([https://jaotong.baidu.com/congestion/city/urban](https://jaotong.baidu.com/congestion/city/urban)), accessed on 10 April 2024) over a period of 14 working days starting from 10 April 2024, with a data collection interval of 5 min. The data were analyzed in 2024 and are plotted in Figure 3. In Figure 3, the black and red solid lines, from top to bottom, represent the maximum and minimum speeds observed at different time points over the 14 working days. The smooth blue curve represents the vehicle speed variation trend obtained through polynomial fitting. After comparison, a 9 th-degree polynomial fit was selected, with an \(R^{2}\) value of 0.9908. The  
Figure 2: Detour decision diagram. (**a**) illustrates the vehicle’s Detour decision diagram, while (**b**) illustrates the drone’s Detour decision diagram.  
polynomial function is given by Equation (3), where v(\(T\)) represents the velocity of the vehicle at time \(T\).  
\[\begin{array}{l}v(T)=p_{0}+p_{1}T+p_{2}T^{2}+p_{3}T^{3}+p_{4}T^{4}+p_{5}T^{5}+p _{6}T^{6}+p_{7}T^{7}+p_{8}T^{8}+p_{9}T^{9},\\ p_{0}=30.0416,\ p_{1}=0.14556,\ p_{2}=-0.00152,\ p_{3}=1.80319\text{e}^{-5},\ p_{4}=-1.4603 \text{e}^{-7},\\ p_{5}=7.52005\text{e}^{-10},\ p_{6}=-2.45204\text{e}^{-12},\ p_{7}=4.83586\text{e} ^{-15},\ p_{8}=-5.19437\text{e}^{-18},\ p_{9}=2.31133\text{e}^{-21}.\end{array} \tag{3}\]  
The area between the curve \(v(T)\) and the x-axis corresponds to the distance traveled by the vehicle from \(T_{ki}^{a}\) to \(T_{ki}^{d}+t_{ij}\). Based on \(l_{kij}=\int_{T_{ki}^{d}}^{T_{ki}^{d}+t_{ij}}v(T)dt\), the time \(T_{kj}^{a}\) at which the vehicle reaches node \(j\) and the travel time \(t_{ij}\) from node \(i\) to node \(j\) can be obtained.
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 4 Discussion  
Plant phenotyping is a promising science that connects genomics with plant ecophysiology and agronomy [19]. This work presents an affordable high-throughput phenotyping platform prototype (phenomobile) which allows one to collect a great number of field data with high potential applications, for example, regarding the morphological traits of crops, plant physiological activities, plant diseases, fruit maturity, and plant characterization with regard to, i.e., water stress. This could be a reasonably priced solution to phenotype large numbers of field data with great efficiency and coverage. This phenomobile is a ground-based system and for this reason is allowed to carry heavier sensors and equipment with respect to aerial ones, providing data with a higher resolution and quality, regulating it for different environment and climatic conditions. In detail, the mounted multispectral camera (MAIA), by detecting absorbed and emitted radiation in the visible and near-infrared regions of the electromagnetic spectrum, was able to provide accurate information on the plant vigor and health status in a heterogeneous environment with high sensitivity. In fact, the GDVI measurement performs very well in land characterization, even in an arid environment [20]. The cost of the system is around 25.000 EUR, which is compatible with several applications if compared to professional phenotyping throughput screening systems. The data, being precisely georeferenced, can be elaborated into maps and/or be attributed to micro and macro plots for an extensive experimental acquisition campaign.