esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions and Future Work  
This study proposes an autonomous driving sensor big data-based C-ITS environment to improve the safety of autonomous vehicles. Unlike the current C-ITS environment, which highly depends on the infrastructure, a structure that utilizes big data for autonomous vehicles and connected cars was proposed and implemented based on the Hadoop ecosystem. The implemented Hadoop ecosystem-based LDM linkage platform collects images, sensors, and LiDAR data from ROS in charge of controlling autonomous vehicles. It delivers them to Kafka, a distributed messaging system that guarantees fast latency. Messages delivered from the vehicle system to the platform through Kafka are stored in MariaDB for structured data and HDFS for unstructured big data. The platform analyzes and processes the loaded data using Spark and visualizes the results with Zeppelin. The data processed in the platform transmit a message to the central LDM through Kafka for vehicle services. As the proposed platform is constructed using the Hadoop ecosystem, which can be distributed, it is possible to expand if the vehicle big data are increased and to ensure stability and fault tolerance.  
Additional studies that can be conducted based on this study are as follows.  
1. For a systematic management of the vehicle big data, metadata of MariaDB and HDFS are created and constructed. As the big data accumulate, it is difficult to obtain the location and type of the existing data, so it is crucial to optimize the data management. Therefore, in future work, metadata storage using NoSQL should be investigated.
2. In order to utilize LDM, a real-time system can be configured, and a process of transmitting and receiving messages conforming to the standard can be performed. It can also be linked with research on security enhancement of the message transmission and reception processes.  
Conceptualization, A.Y. and C.M.; methodology, A.Y. and C.M.; software, A.Y., S.S. and J.L.; validation, A.Y. and S.S.; data curation, A.Y. and S.S.; writing--original draft preparation, A.Y.; writing--review and editing, A.Y., S.S., J.L. and C.M.; visualization, A.Y.; supervision, C.M.; project administration, C.M.; All authors have read and agreed to the published version of the manuscript.  
This paper was supported by the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korea Government (MOTIE) (N0002428, The Competency Development Program for Industry Specialist).  
The authors declare no conflict of interest.
## 4 Computational Results
### Experimental Results for Larger Instances
Regarding the density of the frontiers, Table 11 shows the results obtained. From these results, it can be noticed that, in most of the cases, the MA-RK v2 produced lower values for the maximum distances (more compactness). However, the MA-RK v1 algorithm produced better values for the average distances. Finally, Table 12 reports the values obtained for the set coverage metric. The first column refers to the name of the instance, and the rest show comparisons in coverage between the algorithms. Again, MA-RK v2 performed better than MA-RK v1, by dominating the entire front provided by MA-RK v1. This confirms that the selective version of the MA-RK clearly dominates. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Instance** & **MA-RK** & **MA-RK** \\
**Name** & **v1** & **v2** \\ \hline FNO3 & 0.680387 & 0.836718 \\ FNO4 & 0.752206 & 0.648306 \\ FNO5 & 0.397265 & 0.823822 \\ FNO6 & 0.642281 & 0.929850 \\ FNO7 & 0.472473 & 0.646997 \\ FNO8 & 0.43841 & 0.847476 \\ FNO9 & 0.527335 & 0.836342 \\ FNO10 & 0.390825 & 0.959196 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Hypervolume for the rest of the instances. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Instance** & **MA-RK v1** & **MA-RK v2** \\ \hline FNO3 & 0.579687 & 0.308544 & 0.503147 & 0.389821 \\ FNO4 & 0.418041 & 0.256448 & 0.643338 & 0.344496 \\ FNO5 & 0.483586 & 0.166288 & 0.742506 & 0.168127 \\ FNO6 & 0.516558 & 0.298917 & 0.483949 & 0.107271 \\ FNO7 & 0.342773 & 0.179116 & 0.628917 & 0.176940 \\ FNO8 & 0.371050 & 0.212086 & 0.386249 & 0.248088 \\ FNO9 & 0.606253 & 0.161301 & 0.574856 & 0.179261 \\ FNO10 & 0.387176 & 0.187895 & 0.221685 & 0.0603717 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Maximum and average \(k\)-distances for the large-size instances. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Instance** & **MA-RK** & **MA-RK** \\
**Name** & **v1** & **v2** \\ \hline FNO3 & 0.262 & 0.817 \\ FNO4 & 0.768 & 1.087 \\ FNO5 & 6.415 & 6.586 \\ FNO6 & 9.919 & 11.837 \\ FNO7 & 62.262 & 70.162 \\ FNO8 & 47.039 & 47.819 \\ FNO9 & 48.816 & 54.017 \\ FNO10 & 96.541 & 97.29 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Elapsed CPU time for the rest of the instances. In summary, we conclude that, even when both metaheuristics provide good results in a reasonable computational time, the MA-RK v2 consistently outperforms the non-selective MA-RK version.
# A Bandwidth-Efficient Dissemination Scheme of Non-Safety Information in Urban VANETs +
## 4 Parameter Tuning and Evaluation through Simulations
### Results
#### 4.3.3 Comparative Evaluation of the Store-Carry-Forward Strategies
The fixed interval strategy, configured with a 10-s retransmission period, also shows very similar results. However, increasing the period for any of these two strategies would not imply better results. The store-carry-forward mechanism would cause less retransmissions at the cost of reaching fewer vehicles. Regarding the other metrics, the fixed interval approach achieves a higher coverage at the cost of a large number of duplicates, as it almost doubles the speed-adaptive overhead. The map polling option, on the other hand, caused a slightly lower number of messages than the speed-adaptive one, but its coverage was also slightly lower. Therefore, we choose the speed-adaptive strategy, as it is the approach that achieves the best compromise on the different metrics.
## 3 Results  
Comparing the stems boundary conditions and mesh sizing published in the literature is complicated because of the wide variety of studies that focus on different subjects and the limited access to the drawings of stem manufacturers (see Table 1). From this Table, the Exeter prosthesis [29; 48] was selected because it presented complete information about dimensions.  
The first step was to determine and validate the boundary conditions indicated in Section 2.1 and used to reproduce the work of [PERSON] et al. [29]. As a result, 0.11% error was obtained between the stress reported by [PERSON] et al. [29] (247.8 MPa) and the results obtained from this work (247.52 MPa). The stresses location were consistent in the same area for both studies, showing the reliability of the results obtained from this work.  
The multi-objective analysis (equivalent stress and strain of von Mises) in the sixteen implants was assessed under the same conditions, and their results are shown in Appendix A. According to Appendix A, the maximum von Mises stress and strain were located in the medial part of the stem between the M1 point and the reference line (top surface of the concrete block, Figure 4a). Activity 1 shows the maximum stress and deformation in the medial neck section of the hip implant around M8 in Figure 4a).  
The lower results obtained from the 16 points created in Section 2.3.2 were used to generate the new geometry for the hybrid profile. The trapezoidal cross-section geometry presented the most consistent behavior for both stresses and strains (Appendix B) and was used as a base for the proposal. The sixteen chosen reference points of Table 5 were overlapped and re-parametrized to obtain a hybrid stem geometry (Figure 4c).  
The hybrid proposal underwent the same analysis process described previously, and it is compared with the best stem (reference stem, P2-T) of Appendix A in Table 6. The hybrid profile produced lower stress results when the applied load had an angle between 7\({}^{\circ}\)-13\({}^{\circ}\) (activities 2-4 and 6). While for activities 1 and 5 (20\({}^{\circ}\) and 16\({}^{\circ}\)), P2-C produced the lower stress results. The hybrid profile presents the best results for all the activities in terms of deformation. The maximum stress on the hybrid profile occurred in the medial neck area  
Figure 5: Boundary conditions considered as per ASTM F2996-13 standards.
## 5 Numerical Examples
### Nonstationary Wind Fields
#### 5.2.2 Nonstationary Wind Fields with Time-Varying Coherence  
Based on the given time-varying coherence function, the deterministic phase \(\theta_{jk}(\omega,t)\) in the simulation formulation can be calculated by the aforementioned two schemes. The typical time-varying phases \(\theta_{12}(\omega,t)\) obtained by two schemes are shown in Figure 9. Obviously, two phases are the periodic function of \(\omega\). The amplitude and period of the separated phase are twice the phase, using the classical formulation.  
Substituting the phases to the simulation formulation, the time-varying coherent nonstationary wind fluctuation samples can be generated. Figure 10 displays three typical sample time histories and compares the samples based on the above two phases. The similar laws can be observed. The higher the phase participation of the sample, the greater the error caused by the classical phase formula. In addition, it can be also found that the influence of the phase on the time-varying coherent nonstationary simulation is less than the time-invariant coherent nonstationary simulation.  
Figure 8: Estimated correlation functions by time-invariant coherent nonstationary wind fluctuation samples (1000 samples): (**a**) \(R_{21,21}(t,\tau)\); (**b**) \(R_{1,21}(t,\tau)\).  
Figure 9: Time-varying phases obtained by two schemes: (**a**) separated phase; (**b**) classical formulation.  
The ensemble auto-/cross-correlation functions can be estimated by 1000 simulated samples, based on Equation (30). The target correlation functions are calculated by Equation (32). Figure 11 verifies the estimated correlation functions and the targets when the time lag \(\tau=0\), 16, and 32 s. Although the autocorrelation function is affected by the phase (see Equation (33)), the impact is small and can be ignored (see Figure 11). However, the cross-correlation function based on the classical phase formula is still seriously deviated from the target. Therefore, the classical phase formula cannot be directly used in the nonstationary simulation.  
Figure 10: Simulated time-varying coherent nonstationary wind fluctuation samples: (**a**) Point 1; (**b**) Point 21; (**c**) Point 41.
## 4 Experimental Results  
### Experimental Setup  
The performance evaluation results of the proposed weighted mean-based action recognition model are described. The learning and testing of the model proceed with HAR benchmark dataset UCF-101.  
UCF-101 consists of 13,220 video clips and 101 actionable classes taken on YouTube. The actionable categories are divided into (1) human-object interaction, (2) body-motion only, (3) human-human interaction, (4) playing musical instruments, and (5) sports. Video is a relatively challenging dataset, filmed at various illuminations, poses of people, and viewpoints of cameras. According to the train-test list of provided datasets, 9.5K datasets are divided into learning and 3.8K datasets are divided into tests, and 20% of the learning datasets are used as validation datasets. In addition, the train-test list consists of three scenarios with random order of video data, so the average of three performance evaluations was selected as the final result.  
The proposed system was trained at the workstation, and performance evaluations were conducted at the workstation and NVIDIA Jetson NANO. At the workstation, tests were conducted to compare and evaluate the accuracy of the proposed method with the existing HAR models, and at Jetson NANO, the tests were performed to evaluate usability based on throughput of low-cost embedded systems. The model of the proposed system is  
Figure 4: Process for generating weighted mean-based spatiotemporal features.  
built in the Python environment, Keras, and the system environment of the workstation consists of Intel i9-10900X CPU, NVIDIA Titan RTX (24 GB) GPU, and 128 GB of main memory. The system environment of Jetson NANO consists of Quad-core ARM A57 CPU, 128-core Maxwell GPU, and 4 GB of main memory, as shown in Figure 5.
## 3 Methodology
### Defining the Data Defender
#### 3.1.1 Access Control  
The defender's first role is to manage data access by accepting or rejecting data access requests. Since this paper considers construction projects that utilize CDEs, access control is implemented on the data stored in a CDE. As mentioned earlier, CDEs are widely used for storing, viewing, and exchanging data due to the increasing use of BIM technologies in construction projects. The CDE utilized by the project may either be cloud-based or hosted on the in-house servers of a project. Companies from different sectors increasingly rely on cloud computing technologies for storage due to their advantages, such as no upfront cost, location flexibility for employees, and pay-as-you-go pricing models [34]. This transition also applies to the construction sector, with the increasing number of off-the-shelf cloud platforms that make cloud implementation more effortless. Some examples of cloud platforms offered by leading construction software development companies are Autodesk Construction Cloud, Graphisoft BIMcloud, and Trimble Connect.  
This paper assumes the use of one of the cloud-based platforms (e.g., Autodesk Construction Cloud) offered by a construction software development company, considering their widespread use in the industry, especially by small and medium-sized enterprises (SMEs), which constitute more than 95% of construction supply chains [35]. The CDE platform selection in construction projects depends on the software packages used for various tasks, such as design authoring, clash detection, structural analysis, energy analysis, and reality capture. The platform developed by the software company commonly used in the project is prioritized for improved interoperability. This paper assumes a generic cloud platform without selecting a specific one.  
The access control mechanism of the cloud platform that handles accepting and rejecting data access requests is considered a part of the data defender in this paper. There are three major access control models widely used in computer systems: role-based access control (RBAC) (i.e., non-discretionary access control), mandatory access control (MAC), and discretionary access control (DAC) [36]. In this paper, RBAC is assumed to be implemented in the cloud platform with the least privilege design principle in mind, similar to Autodesk Construction Cloud [37]. Therefore, individual users and user groups are given privileges based on their roles in the project. The BIM manager of the project is assumed to function as the administrator of the cloud platform, responsible for assigning roles and defining privileges for each role. It should be noted that assigning roles is not a one-time task but rather an ongoing process in a typical construction project due to the changing roles and new employees joining the project.
## 3 Method Description
### Decoder Layer  
The objective of the decoding layer is to determine the boundaries of the head and tail entities based on the head-tail entity association classification labels and decode entity-relation triplets from the relation label classification matrix. The specific algorithm (Algorithm 1) used is as follows:  
The entity-relation triplet extraction algorithm locates potential entity-relation coordinates based on the relation label classification matrix. For each relation matrix, if the current matrix cell indicates the start tag of a head-tail entity pair, the corresponding end tag of the entity is identified. Based on the head and tail entity start and end coordinates \(h_{b}\), \(h_{e}\), \(t_{b}\), and \(t_{e}\), the complete range of the head and tail entities can be determined. Subsequently, the extracted head entity, relation, and tail entity are formed into a triplet and added to the set \(S\). Finally, a collection of all entity-relation triplets is generated and returned.  
```
Input: Original text \(T\), predicted relation label classification matrix \(M\), label mapping TagId Output: Entity-relation triplet list \(S\)
1 Initialize entity-relation triplet set \(S\); \(relations,heads,tails\leftarrow\text{get\_indices}(M)//\) Find indices where \(M>0\)
2 for\(i\gets 1\)to len(\(relations\))do
3\(r_{i}\gets relations[i],h_{b}\gets heads[i],t_{b}\gets tails[i]\); // Get the current relation index and the starting position indices of the entities if\(M[r_{i}][h_{b}][t_{b}]==\text{TagId}[\text{`HB-TB'}]\)and\(i+1<len(relations)\)then // Check if the current position is the starting tag of head and tail entities for relation \(r_{i}\) (HB-TB) \(t_{e}\gets tails[i+1]\); if\(M[r_{i}][h_{b}][t_{e}]==\text{TagId}[\text{`HB-TE'}]\)then  Find \(M[r_{i}][h_{e}][t_{e}]\) (HE-TE) and add \((H,R,T)\) to \(S\); // Check if the current position is the ending tag of head and tail entities for relation \(r_{i}\) (HE-TE), and extract the triplet based on the identified label positions, then add it to set \(S\) if\(M[r_{i}][h_{b}][t_{b}]==\text{TagId}[\text{`HB-TS'}]\)and\(i+1<len(relations)\)then  Find \(M[r_{i}][h_{e}][t_{e}]\) (HE-TS) and add \((H,R,T)\) to \(S\); if\(M[r_{i}][h_{b}][t_{b}]==\text{TagId}[\text{`HS-TB'}]\)and\(i+1<len(relations)\)then  Find \(M[r_{i}][h_{e}][t_{e}]\) (HS-TE) and add \((H,R,T)\) to \(S\); return\(S\);
```  
**Algorithm 1**Entity-Relation Triplet Decoding Algorithm  
In taking the relation matrix \"Controls weed\" in Figure 4 as an example, through combining \"HB-TE\" and \"HE-TE\", observing \"HB\" and \"HE\" allows the head entity \"Cyhalofop butyl\" to be identified. Through combining \"HB-TB\" and \"HB-TE\", observing \"TB\" and \"TE\" allows the tail entity \"Barnyard grass\" to be identified. Similarly, through combining \"HB-TS\" and \"HE-TS\" labels, the head entity \"Cyhalofop butyl\" and the tail single-character entity \"Polygonum\" can be decoded. Based on this decoding process, the triplets (\"Cyhalofop butyl\", \"Controls weed\", \"Barnyard grass\") and (\"Cyhalofop butyl\", \"Controls weed\", \"Polygonum\") are ultimately extracted.
## 2 The Modeling of the Hydraulic Quadruped Robot
### Kinematic Modeling
Since the forelegs of the hydraulic quadruped robot are elbow joints and the hind legs are knee joints [32; 33], the joint variables \(\theta_{i2}\) and \(\theta_{i3}\) obtained based on inverse kinematic are different. The joint angles of the hydraulic quadruped robots are as Equation (5). \[\left\{ \begin{array}{l}\theta_{i1}=\text{arctan}\Big{(}\frac{0p_{i2}}{ \partial p_{i2}}\Big{)}\\ \theta_{\beta}=\text{arccos}\frac{m^{2}+{\theta_{i}}{p_{i2}}^{2}-{l_{2}}^{2}-{ l_{3}}^{2}}{2{l_{3}}}\\ \theta_{i2}=\text{arccos}\frac{({l_{2}}+{l_{3}}{c_{3}})m+{l_{3}}{s_{3}}^{0}p_ {i2}}{{l_{2}}^{2}+{l_{3}}^{2}+2{l_{2}}{l_{3}}{s_{3}}}\end{array} \right. \tag{6}\]  
where \(m\) is defined as:  
\[m=\sqrt{{\theta_{i2}}^{2}+{{{}^{0}}}{p_{i2}}^{2}}-{l_{1}} \tag{7}\]  
Obviously, for RF and LF (\(i=1,2\)) of a hydraulic quadruped robot, \(\theta_{i2}\) and \(\theta_{i3}\) are positive. On the contrary, for LH and RH (\(i=3,4\)) of a hydraulic quadruped robot, \(\theta_{i2}\) and \(\theta_{i3}\) are negative. If given the coordinate of a foot in the torso coordinate frame \(\{\text{O}_{\text{b}}\}\), the joint angles of quadruped robot legs could be solved by Equations (3) and (6). Jacobian matrix is a mapping of foot velocity and joint velocity, which can be derived from Equations (1) and (3). \[\mathbf{J}=\left[\begin{array}{ccc}0&{l_{2}}{c_{2}}+{l_{3}}{c_{23}}&{l_{3}}{c_ {23}}\\ {c_{1}}({l_{1}}+{l_{2}}{c_{2}}+{l_{3}}{c_{23}})&-{s_{1}}({l_{2}}{s_{2
## 2 Technical Background
### Machine Learning Models
#### 2.3.2 CAT Boost Regression  
The CAT algorithm was recently developed by Yandex, and it is perhaps one of the newest regression algorithms available nowadays. As the name suggests, CAT is based on decision trees [40] and gradient boosting [41]. The major features of the algorithm are its capacity to handle categorical variables and ordered boosting. The first refers to the ability not needing to perform any feature transformation before the training stage, which is generally mandatory for other algorithms. To do so, during the training stage, the CAT algorithm uses efficient modified target-based statistics [42]. The second major feature refers to the sequential construction of a set of base predictors so that each subsequent decision tree has a decreased loss [42]. Moreover, unlike traditional gradient boosting algorithms, CAT Boost grows oblivious trees as base predictors, i.e., trees with all nodes at the same level testing the same predictor with the same condition. Therefore, such characteristics make the CAT computationally efficient and less prone to overfitting [41].
## 3 Energy-Efficient Optimal Power Allocation
### Energy-Efficient Optimal Power Allocation for Real-Time Applications  
For real-time applications, which are sensitive to delay, such as voice and video, outage capacity (OC) is more appropriate to be considered as the performance metric, which is defined as the maximum constant rate that can be maintained over fading states with a given outage probability [38]. That is to say, the EE of the satellite user for real-time applications is the ratio of the product of the constant OC and the non-outage probability to the average power consumption. In this section, we propose two energy-efficient optimal power allocation schemes under the AIP constraint, which comply with ATP or PTP constraints, respectively.
## 4 Convolutional Neural Network (CNN): Modeling and Training
### Data Augmentation and Evaluation Metrics  
When a sufficient amount of data is not available to carry out the training and testing of model, an alternative is to use the data augmentation technique [13]. It consists of generating new data by using simple techniques such as rotation, reflection, displacement and zoom on the original images, thereby increasing the set of initial samples and avoiding over-fitting. This technique contributes to the model being used in cases where it is not possible to obtain a database large enough for training and testing, such as the case here presented: simulation of a full car model in CST MWS, which requires a long time to obtain the radiation pattern data.  
All machine learning models minimize or maximize a 'loss function' during the learning, which represents how good a predictive model is at predicting the expected value.  
The mean absolute error (MAE), along with MSE, is the most commonly used regression loss function. MAE corresponds to the average of the absolute values of the individual prediction errors in all instances of the test set. The error is defined as the sum of absolute differences between the target values and the predicted values for the instance:  
\[\text{MAE}=\frac{\sum_{i=1}^{n}|y_{i}-y_{i}^{p}|}{n} \tag{1}\]