esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Parameter Sensitivity Analysis and Convergence Analysis  
There are three parameters in the proposed KBDSP, i.e., \(\alpha\), \(\beta\) and \(\gamma\). The parameter \(\alpha\) is used to balance the similarity-preserving term \(\left\|K-Z^{T}KZ\right\|_{F}^{2}\), parameter \(\beta\) is used to control the term \(\left\|Z-B\right\|_{F}^{2}\), and parameter \(\gamma\) is used to control the block diagonal representation term \(\left\|B\right\|_{\begin{subarray}{c}\mathbf{k}\end{subarray}}\). The Yale and JAFFE datasets are selected for the evaluation of KBDSP. As shown in Figures 4 and 5, the NMI versus different values of parameters \(\alpha,\ \beta\) and \(\gamma\) are shown. Parameters \(\alpha,\ \beta\) and \(\gamma\) take values in set \(\left\{1\times 10^{-5},\ 1\times 10^{-4},\ 1\times 10^{-3},\ 1\times 10^{-2},\ 0.1,\ 1 \right\},\)\(\left\{1\times 10^{-5},\ 1\times 10^{-3},\ 0.1,\ 1\right\},\) and \(\left\{1\times 10^{-2},\ 1\times 10^{-1},\ 1,\ 10,\ 30,\ 50\right\},\) respectively. From Figures 4 and 5, it can be seen that KBDSP performs well for a wide range of \(\alpha,\ \beta\) and \(\gamma\) on the two datasets, and KBDSP is not very sensitive to these parameters. Moreover, when parameters \(\alpha,\ \beta\) and \(\gamma\) are set to 0.01, 0.001, and 0.1, respectively, the clustering performance of KBDSP is better than other comparison methods. Therefore, we fix \(\alpha=0.01\), \(\beta=0.001\) and \(\gamma=0.1\) for KBDSP in all experiments of this paper.  
For convergence analysis, we show the empirical convergence curves of the KBDSP algorithm on four datasets in Figure 6. The four datasets are YALE, ORL, JAFFE, and TR45, respectively. It is observed that our method converges in objective value within a few iterations and reaches a steady state with more iterations, which indicates fast convergence and efficiency of KBDSP.  
Figure 3: ACC of Yale dataset with Gaussian noise.  
Figure 2: Example images of the Yale dataset corrupted by Gaussian noise.  
Figure 4: The NMI versus different values of parameters on the Yale datasets.  
Figure 5: The NMI versus different values of parameters on the JAFFE datasets.
## 3 Analysis of AI Techniques in SVAD
### Learning
#### 3.1.2 Unsupervised Learning  
UL groups data by identifying hidden patterns or intrinsic structures. Data input is necessary, but there are no predetermined output variables. There is neither labeled input data nor a training technique, in contrast to SL. As a result, it operates independently, and its performance could be more measurable. Although some researchers use the UL model's pre-existing labeled data to verify its results, this is only sometimes possible in practice. To conduct an external evaluation, specialists may need to analyze the results manually.  
UL is mostly used for reducing dimensionality and clustering. UL is used in dimensionality reduction to find the dataset's linked features so that redundant data can be removed to reduce noise. Using clustering techniques, the clustering problem allows for the possibility of a sample belonging to more than one cluster or just one. Market research and object identification are common applications [35].  
One proposed approach in UL is that of [36]. They provided a technique for detecting anomalies in surveillance missions, including UAV-acquired footage. They combined an unsupervised classification technique called One-Class Support Vector Machine (OCSVM) with a deep feature extraction technique utilizing a pre-trained CNN. Their quantitative findings demonstrated that their proposed strategy produces positive outcomes for the dataset studied. The authors in [37] extended their previous work by using mobile cameras to assist UAVs when acquiring videos. They added two feature extraction methods, the Histogram of Oriented Gradients (HOG) and HOG3D. They used the same UL method, which was OCSVM [38]. They obtained good results based on the used video-obtained datasets. There are many techniques under UL; PCA [39] and GANs [40] are examples of them.
## 6 Shear Strength of the Beams Tested in Lab
### Shear Strength of the Beams before Retrofitting  
The shear strength of the RC beams with steel shear reinforcement before retrofitting selected in SS6 was predicted by Equation (10) in Eurocode 2 [70] assuming the partial factors are equal to one. The material mechanical characteristics, the beam concrete geometries and the reinforcement configurations are given in Tables 13 and 14 for each beam.  
Two different values for the inclination angle of the concrete compressive strut (\(\theta\)) were selected:  
1. A fixed value of \(\theta\) equal to 45\({}^{\circ}\)
2. A variable value of \(\theta\) smaller than 45\({}^{\circ}\) calculated by the variable inclination angle method in SS5.1.  
These two values of \(\theta\) permit consideration of the minimum (cot0 = 1) and the maximum (1 \(\leq\) cot0 \(\leq\) 2.5) transverse steel contribution to the shear resistance (VRd,s) in Equation (10). In fact, if the inclination angle of the cracks (\(\theta\)) is smaller than 45\({}^{\circ}\), the number of internal stirrups crossing the crack and the resulting shear carried by the stirrups may be higher, up to the maximum value defined by cot0 = 2.5.  
The experimental shear strength (VRd,exp) of the selected beams is given by the authors (SS6) that tested these beams.  
In Figure 10, each point gives information about the predicted and the experimental shear strength values for the beams considering the two angles \(\theta\). The line \"m\" indicates the perfect correspondence between the predicted and the measured shear strength values.  
The data points are below the straight line \"m\" when the fixed inclination angle is used (Figure 10a) and so this prediction underrates the real shear strength of the beam before retrofitting. The data points are closer to the straight line \"m\" when the variable inclination angle is used (Figure 10b) and therefore the predictions of the shear strength are more realistic. In Figure 10b, there are some points above the line \"m\" for which the predicted shear strength using the variable angle is a little greater than the experimental one but the application of the partial factors may assure safe design.  
The statistical values of the ratio \(\mathrm{V_{Rd,exp}}/\mathrm{V_{Rd}}\) for each beam in Figure 10a,b, are given in Table 5.  
The prediction of the beam strength using the fixed value of \(\theta\) equal to 45\({}^{\circ}\) gives, on average, values equal to 47% of the experimental ones. The inclination angle of the shear cracks in a beam before retrofitting is usually smaller than 45\({}^{\circ}\) and so the number of internal stirrups crossing the crack is greater. The steel reinforcement plastic strains increase the shear cracks width and therefore the concrete contribution (aggregate interlock, dowel effect, etc.) can be much reduced. For that reason, it may be neglected in Equation (10).
# The Influence of Immersive and Collaborative Virtual Environments in Improving Spatial Skills
## 2 Literature Review  
### Spatial Skills and STEM Education  
Spatial ability is generally considered to be the ability to visualize and mentally manipulate relationships between objects and space. Spatial tests were originally used in the study of psychology to predict which individuals would be successful in mechanical vocations [31]. Today, spatial testing and spatial training remain very popular as many researchers believe that improving spatial skills can help students to succeed in STEM [30]. A meta-analysis of training studies concluded that including spatial training in education could lead to increased participation in STEM [32]. While spatial training often leads to improved performance on spatial tests, the evidence that spatial training enhances long-term STEM achievement remains outstanding. A review concluded that spatial training alone is unlikely to raise STEM scores substantially, and rigorous studies are still needed in order to establish a causal effect between spatial training and STEM outcomes [33].  
The most common way to assess spatial ability is by using psychometric tests, which are considered to measure \"fundamental\" spatial skills [34; 35]. Two spatial tests are used in this study and are discussed in more detail in Section 3.2 of this paper. Existing spatial tests have many limitations. Multiple researchers have pointed out that spatial skill is not a singular skill [31; 36]; thus, no single test can be considered to measure spatial skill on its own. Spatial ability is really an umbrella term that describes many different possibly related but distinct skills, and different tests may assess different specific spatial skills.  
Spatial tests typically portray 3D shapes as 2D black and white line drawings, and the way some of the shapes are drawn may lead to confusion for test takers and introduce additional difficulty into spatial tests that is not related to the 3D mental manipulation of shapes [37]. For this reason, [PERSON] and [PERSON] advocated for using more advanced visualization technologies to present spatial problems [38]. This recommendation is relevant not only for spatial testing, but also for spatial training applications such as the one described in this paper.
## 5 Discussions
### Weathering and Sedimentary Characteristics in Provenance Area
As shown in Figure 9, the samples of Well XC1 and Well D2 have a large Th/U ratio, which is significantly different from the samples of Well D1, indicating that the source rocks of Well XC1 and Well D2 lost more U elements in the process of weathering or the sedimentary cycle, and experienced stronger chemical weathering intensity or sedimentary recycling. In addition to the Th/U ratio, the ratio of Rb/Sr is also a trace element index commonly used to judge the weathering and sedimentary characteristics of provenance areas [19; 20]. During weathering, feldspars are transformed into clay minerals, and Rb\({}^{+}\) is more likely to remain in the exchange position of clay than Sr\({}^{2+}\). The ratio of Rb/Sr is often used to distinguish chemical weathering or sedimentary recycling. Previous studies on Rb/Sr ratio show that the ratio of Rb/Sr in the elastic sediments significantly increases to greater than 0.5 with weathering and diagenesis [14; 19]. In this study, the Rb/Sr ratio of all samples is greater than 0.5, indicating that the source rocks have been subjected to strong chemical weathering or sedimentary recycling. As shown in Figure 9, the ratio of Rb/Sr of samples from Well D2 (0.8-2.2, with an average of 1.3) and Well XC1 (0.7-1.0, with an average of 0.8) is significantly greater than in those from Well D1 (0.5-0.8, with an average of 0.6), indicating that the source rocks of the samples from Well XC1 and Well D2 experienced stronger chemical weathering or sedimentary recycling. As a result, the provenance area of Well XC1 and Well D2 is determined to be the southern Sanwei Mountain provenance area, with strong weathering caused by strong tectonic activity, while the provenance area of Well D1 is indicated as the northern Beishan provenance area, with relatively weak tectonic activity and moderate weathering.
# Propagation Attenuation Maps Based on Parabolic Equation Method +
## Abbreviations  
* 2D two-dimensional
* 3D three-dimensional
* 5G fifth-generation
* BMS battlefield management system
* CDF cumulative distribution function
* DSA dynamic spectrum access
* DTED digital terrain elevation data
* FDM finite-difference method
* FEM finite-element method
* G-REM global radio environment map
* IDW inverse distance weighting
* ISO International Organization for Standardization
* L-REM local radio environment map
* LTE Long Term Evolution Advance
* MANET mobile ad-hoc network
* MIMO multiple-input-multiple-output
* NR New Radio
* OSI open systems interconnection
* PAM propagation attenuation map
* PEM parabolic equation method
* REM radio environment map
* RF radio-frequency
* RF-REM radio-frequency radio environment map
* RRM radio resource management
## 3 Numerical Example
### Damage Localization of Suspension Bridge Hangers  
Based on the trained CNN-LSTM model in Section 3.4, the damage location of the test set is predicted. To intuitively display the performance of the proposed method, the number of samples for which the CNN-LSTM model correctly locates the damage in the test set is shown in Table 4.  
Figure 9 presents the accuracy of the damage localization of hangers under various damage scenarios. \"All\" in the figure denotes the accuracy of the entire test set, i.e., the average value of scenarios 1-15.  
The following can be drawn from Table 4 and Figure 9.  
1. The accuracy of damage localization of the hangers in the test set reaches 94.0%, showing that the proposed CNN-LSTM can effectively complete the task of damage localization.
2. Comparing the recognition results of different damage scenarios, the accuracy of the CNN-LSTM model in damage localization is between 58.0 and 100%. The accuracy in scenarios 1 and 2 is relatively low, i.e., 58 and 76%, respectively, and the accuracy of the other scenarios remains above 94%. The comparison results show that except for scenarios 1 and 2, the CNN-LSTM-based damage identification method has higher accuracy in locating the damage of hangers.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline Scenarios & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 \\ \hline No. of samples correctly identified & 58 & 76 & 95 & 98 & 96 & 97 & 100 & 94 \\ \hline Scenarios & 9 & 10 & 11 & 12 & 13 & 14 & 15 & \(\Sigma\) \\ \hline No. of samples correctly identified & 100 & 100 & 96 & 100 & 100 & 100 & 100 & 1410 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test results of CNN-LSTM on damage localization of the hangers.  
Figure 9: Accuracy of CNN-LSTM for damage localization under different scenarios.  
3. When comparing the recognition results of different damage types, the proposed method has little difference in the accuracy of a single damaged hanger, as well as two and three damaged hangers. The accuracy increases from 84.6 to 99.2%, which is an increase of approximately 15%. From the above results, it can be inferred that the accuracy of the proposed model in damage localization is less affected by the number of damaged hangers.
## 1 Introduction
### Outline  
The current scenario of investment, research, and development described above is being traduced in numerous manuscripts and patents published in leading scientific journals, conferences, and intellectual property databases. When referring to vehicular communication, the research includes a very rich literature, but mostly oriented to cover specific aspects of the technology. As an example, this is the case of ad-hoc networks [12], information management systems [13], security [14], and access technologies [15] around vehicle-to-everything (V2X) communication published in 41 review and survey manuscripts between 1997 and 2018 (i.e., secondary research).  
Nonetheless, there is hardly a mention in the bibliography--neither primary research nor secondary research--to other types of communication, such as infrastructure-to-everything (I2X), pedestrian-to-everything (P2X), and their variants. The main reason is that they stand for more emerging markets and do not enjoy yet a standardized definition in the state-of-the-art to be used by the research community (i.e., there is no uniformed consensus about these terms). Moreover, as far as we know, most surveys do not always emphasize on the historical evolution (i.e., they are frequently limited to specific aspects and/or contemporary technologies), do not cover statistical profiles, and do not conduct bibliometric studies with a broad perspective of the context over time.  
For this reason, the research question this paper aimed to examine was: What are the past, current, and future interests in V2X, I2X, and P2X communication? It had four main objectives:  
1. To situate the industrial and scientific progress through the examination of who, when, and about what the research has been done on emerging technologies for smart cities, especially focused on V2X, I2X, and P2X communications;
2. To systematically collect features on the type of vehicular communication, field of study, technologies, and applications to establish a time reference frame on significant characteristics;
3. To undertake a comprehensive bibliographical analysis on the relationship of the publications comparing the most productive countries and organizations along time, as well as to the inference of an emerging technology over other;
4. To review what future milestones lead technologies to more powerful applications on V2X, I2X, and P2X.  
To this end, the present paper is designed as a review article--which is not a primary research article--and structured as follows. The following section provides a taxonomy on V2X, I2X, and P2X communication and their variants. Then, the paper describes the recent attention attracted from governments, academics, and industries around V2X, I2X, and P2X applications. As a result, a summary table about the profile of the most representative applications present in the R&D literature is provided in Appendix A. The next section provides a contextual bibliographic study on the most influential technologies around smart cities. Finally, the paper presents the future trends and emerging technologies to reach the conclusions.
## 3 The Proposed Model
### Bi-Directional Context Using Deep Reinforcement Learning
#### 3.2.2 Conversation Simulation  
The main idea of the proposed model is to simulate a conversation by allowing two chatbots to communicate with each other, as shown in Figure 4. While the pre-trained encoder-decoder allows the model to generate coherent responses with the conversation history, using RL enhances the model's ability to generate responses optimized for long-term goals. We simulate the conversation as follows. At the first step, we obtain an input sentence with conversation history as contextual information \(c_{L}\) from the training dataset and feed it to the first agent. The first agent encodes the inputs into a vector representation and decodes it to generate a response \(s_{t}\) for the next turn. The second agent updates the state of the simulation by combining the conversation history with the output \(s_{t}\). It immediately encodes this new state into a representation and decodes it into a new response, fed back to the first agent and repeated. At the end of the simulation, the right-context \(c_{R}\) is a sequence of \(k\) consecutive utterances \(\{s_{t+1},s_{t+2},\ldots,s_{t+k}\}\) to the right of the generated response \(s_{t}\). The transition probability distribution \(\pi\) is initialized as a pre-trained BERT-based model and represents the policy \(P\):  
\[\pi=p_{Bert2 Bert}(a_{t}|[s_{t},c_{L}]) \tag{9}\]  
where \(s_{t}\) is the current state of the conversation and its left-context \(c_{L}\); we generate a list of candidate responses \(A\) as follows:  
\[A=\{a|a\sim\pi\} \tag{10}\]  
In Reinforcement Learning, the agent and environment interact over a sequence of actions in a conversation. The goal of the agent is to maximize the expected reward from its actions through the interaction [48]:  
\[\text{maximize}\sum_{[a_{t},\ldots,a_{t+k}]\in c_{R}}\pi_{\theta}(a_{t}, \ldots,a_{t+k})r(a_{t},\ldots,a_{t+k}) \tag{11}\]  
where \(a_{t}\) is a generated response in turn \(t\) and \(\theta\) is the set of parameters in the model; \(c_{R}\) is the right-context obtained in the simulation process; and \(r(a_{t},\ldots,a_{t+k})\) is the cumulative discounted reward associated with the sequence of utterances \(a_{t},\ldots,a_{t+k}\). When the simulator reaches the end of the conversation, it estimates the rewards based on the specific goals and computed by  
\[r(a_{t},\ldots,a_{t+k})=\sum_{\tau=t}^{t+k}\gamma^{\tau-t}r(a_{\tau}) \tag{12}\]  
where \(\gamma\) is the discount factor which adjusts the importance of rewards over time in the reinforcement learning algorithm. \(\gamma\) is a real value \(\in[0,1]\) and tells how important future  
Figure 4: Bi-directional context for response generator using deep reinforcement learning. rewards are to the current state. We begin a curriculum learning strategy by simulating the dialogue for \(k\) turns and using the policy gradient method to find parameters that maximize the expected future reward.
# Bilateral Teleoperation System with Integrated Position/Force Impedance Control for Assembly Tasks
## 2 Teleoperation Assembly System  
To achieve remote assembly tasks in hazardous and inaccessible environments, such as assembling printed components into a mammoth structure system in space, the idea of a teleoperation assembly system has been proposed. The aim is to use this system to assemble small, fragmented components into a large system structure.  
Figure 1 shows the proposed teleoperation assembly system. The system includes the local subsystem, which is manipulated by the user, and the remote assembly systems that deal with the remote environment.  
The remote assembly system is composed of a 6-DOF (Degree of Freedom) robot (Universal Inc., Odense, Denmark, robots UR5), and an adaptive gripper with a 50 mm movement range (Robotiq Inc., Levis, QC, Canada, hand_E) to perform assembly tasks. The stereo cameras were fixed beside the worktable to monitor the remote scenarios. A 6-DOF force/torque sensor (Robotiq Inc., FT300) produced by Robotiq Inc. was equipped at the wrist of the arm. This was used to observe and record hand-environment contact forces in teleoperation. The assembled components were placed randomly on the side of the operating table.  
The wireless positioner can significantly improve mobility, allowing a user to control the virtual object in a much more natural way [28]. On the local side, a wearable, high-precision positioner, the HTC VIVE tracker, was selected as the wireless wearable locator for motion tracking. The self-developed data glove [29] integrated with force feedback and bending sensing was applied to manipulate the gripper. The communication between the local side and the remote robot was performed by protocol/internet protocol (TCP/IP). All of the functions were performed on an AMD Core R5-4600 CPU and Windows 10 system. When the operator sends a command to the remote side through the motion-tracking device, the remote robot follows the trajectory of the locator device. After the communication delay, the remote robot is controlled by the commands, and the visual and contact force information are feed back to the operator.
## 3 Indoor Positioning Systems
### Systems Based on Computer Vision
#### 3.5.2 Mobile Cameras  
[PERSON] et al. described that indoor location approaches that use a single type of sensory perception, under certain physical and logical conditions and dispositions, are more susceptible to failure [113]. One way to reduce the inaccuracies or limitations of these approaches is the combination of different sensory perceptions, composing a hybrid arrangement, which despite having a higher cost of implementation, provides more accurate and reliable results. The authors designed an indoor positioning system that combined the camera sensors, inertial sensors, and the Wi-Fi sensor available on smartphones to experience the levels of accuracy and limitations. The authors' strategy was to combine stereo vision with data obtained from inertial sensors and Wi-Fi.  
Some studies that use the passive mobile camera approach used visual odometry (VO) to update the user's position. VO is the process of estimating the movement of an agent (for example, vehicle, human, or robot) using images captured by one or several cameras as input information. VO is not a recent approach, but it has been gradually improved in efficiency and accuracy [109]. An example of VO is presented by [PERSON] et al., where a sequential set of stereo images was subjected to filtering to extract location information, keeping the consumption of computational resources low (low use of processes and memory) [6]. Another approach to indoor navigation adopts the structural perception of the scenario and the possible presence of obstacles through stereo vision [114; 115].