esa-sceva/satcom-chunk-collection huggingface dataset:
# Immersive Safe Oceans Technology: Developing Virtual Onboard Training Episodes for Maritime Safety
## 4 Immersive Safe Oceans Technology with Virtual Maritime P Episodes
### MarSEVR  
Description: The MarSEVR (Maritime Safety Education with VR Technology) was developed at Turku University of Applied Sciences and in close cooperation with Aboa Mare maritime specialists to practice situational awareness and decision making by replicating a ship bridge environment. In previous studies, we presented in detail how this replication was developed and how to use virtual training technology in maritime safety training [18]. At this moment, we are focusing on usability and user experience studies before creating different training scenarios with various difficulty levels.  
Training Objectives: Our first training scenario was designed to be a watch change and collision avoidance situation, where the learning goals are for the trainee to practice paying attention to the equipment's status and settings and to take actions when required for collision avoidance.  
Experiences: This training episode consists of tutorial and command bridge stages. All controllers to be used in the command bridge stage are first introduced. At the moment, trainees must use VR controllers which are especially challenging for unexperienced users without any gaming experience. Therefore, in this MarSEVR training episode, we minimized the interaction methods by using only a trackpad button which is highlighted in all interactions. In addition, interactive objects are also highlighted to help users familiarize themselves in this immersive training environment (Figure 3). Based on these first experiences, it seems that high resolution with Varjo VR devices will be needed (e.g., recognizing an approaching fishing ship on the horizon). In addition, these devices will also provide eye tracking information to be used in human behavior observation. To increase the usability and to avoid the use of VR controllers, for example, the use of a Leap Motion sensors would offer finger recognition.
# Kinematic Tripod (K3P): A New Kinematic Algorithm for Gait Pattern Generation
## 4 The K3P Algorithm
### Propelling Forward the Body of the Robot  
The position and orientation of the robot's body frame \(B\), as well as their corresponding derivatives, with respect to the world reference frame \(W\), describe the desired trajectory of the robot. This target trajectory can be expressed by the state vector:  
\[{}^{B}\mathbf{X}_{W,k}=[\mathbf{r},\mathbf{q},\dot{\mathbf{r}},\dot{\mathbf{q }}]^{T}\]  
where \(\mathbf{r}=(x,y,z)^{T}\) corresponds to the three-dimensional coordinates of \(B\), while vector \(\mathbf{q}=(\theta,\phi,\psi)^{T}\) contains the three Euler angles that define the orientation of the robot's body, both with respect to the world frame \(W\).  
K3P works at a fixed rate, so after every time step \(\Delta_{t}\), the desired position \({}^{D}\mathbf{X}_{W,k}\) for the CoM of the robot is determined by the commanded velocity vector \(\dot{\mathbf{r}}_{d}=(\dot{\mathbf{r}},\ \dot{\mathbf{q}})^{T}\) and the current position of the robot (see line 7 of Algorithm 1). This is only carried out if the robot is moving \(\eta=0\), otherwise \({}^{D}\mathbf{X}_{W,k}\) remains the same. The \([\dot{\mathbf{r}}]_{z}\) component is updated to manage any elevation changes of the terrain. To determine \([\dot{\mathbf{r}}]_{z}\) with respect to \(B\), the difference between the average height of the leg tips \(SP\) of the supporting tripod (see lines 1 and 3 of Algorithm 1), and the commanded clearance height is divided by \(\Delta_{t}\).  
The spatial difference between \({}^{B}\mathbf{X}_{W,k}\) and \({}^{D}\mathbf{X}_{W,k}\) defines an error metric (line 10 of Algorithm 1), which is fed to a PD controller; the result is a control command \(\mathbf{u}\) to propel \(B\) in the direction encoded in \(\mathbf{v}_{c}\) (line 11 of Algorithm 1). Diagonal matrices \(\mathbf{K}_{p}\) and \(\mathbf{K}_{d}\) contain the proportional and derivative gains.
# A 5G-Based VR Application for Efficient Port Management
## 2 Smart Ports' Demand Analysis  
### G Features and Application Scenarios  
The International Telecommunication Union (ITU) has defined the three major application scenarios of 5G as enhanced mobile broadband (eMBB), massive Machine Type Communications (mMTC), and ultra-reliable low latency communications (uRLLC). eMBB mainly provides much higher bandwidth than that of 4G. mMTC is mainly to meet the communication needs of a large number of Internet of Things (IoT) nodes, and is oriented to application scenarios that target sensing and data collection. uRLLC is based on 5G technology with low latency (1 ms) and high reliability (99.999%) characteristics, mainly for the special application needs of vertical industries such as smart ports.  
With high speed, multiple connections, and low latency, the 5G network can contribute to the development of the digital economy and is an important foundation for promoting the implementation of \"a Community of Shared Future for Markind\" strategy [21; 22; 23]. The 5G network will be extremely conducive to creating a cross-industry integrated ecosystem. The 5G network is rapidly gaining popularity in smart transportation, smart manufacturing, smart education, autonomous driving, and so on. Among these, the typical applications of enhanced mobile broadband are 4K/8K/3D high-definition video, VR, augmented reality (AR), etc. The ultra-low latency and ultra-high bandwidth of 5G solve a series of users' dizziness and unclear frame problems.  
As an extension of the 4G technology, 5G has the advantages of higher speed, larger capacity, and lower delay, and is an important carrier to support the interconnection of everything. 5G technology comprehensively uses key technologies such as large-scale antennas, ultra-dense networking, flexible duplex, full-duplex, and spectrum sharing. In addition, 5G also uses network slicing [24] that allows the creation of a logically independent network on general physical information infrastructure.  
5G network technology can support the mixed bearing of industrial control signals with millisecond-level delay and multi-channel high-definition camera video data. 5G network technology can give full play to the differentiated performance advantages in the construction and operation of smart terminals, breaking through the limitations of traditional methods. What is more, 5G networks significantly improve the operating efficiency of automated terminals and inject innovation power into the transformation of traditional artificial terminals.
###### Abstract
* One of the primary approaches to obtain a robust learning algorithm that is more robust to outliers is to replace the traditional loss function of the performance measure MSE with another robust function, to improve performance in the presence of outliers. In this approach, robustness against outliers has been satisfied by minimizing the effect of significant training errors due to outliers. * The lack of accurate machine-learning analysis to achieve adequate performance. * The computational complexity of challenging problems in optimizing QoS measures. The research contributions have been summarized as follows:
* A novel DL algorithm has been proposed to estimate the performance of V2X using robust M-estimator loss functions instead of the standard MSE loss function. * A new comparison of the M-estimator approach and the conventional MSE loss function has been applied in terms of RMSE and MAPE, and under different sets of outliers on V2X traffic datasets used to verify the efficiency of the proposed method. * Finally, the results of the simulation-based tests show that:  
* The robust M-estimator loss functions have the best performance in all cases and outperform the conventional MSE loss function when data is clean or contains Gaussian noise or outliers. * When using noise-free data, the robust Fair loss function performs well and has the best performance compared to its peers. * When using data corrupted by Gaussian noise, the robust Cauchy loss function shows the best performance compared to the others. * Even on training data contaminated with outliers, the robust Fair loss function performs better than its competitors. Outline of the article: The article is structured accordingly: the related literature review is presented in Section 2; the proposed work is explained in Section 3; the robust learning and outliers are introduced in Section 4; the basic concepts of M-estimator loss function are defined in Section 5; V2X simulation environment is introduced in Section 6; Deep Neural Network Learning is presented in Section 7; our theoretical results are illustrated in Section 8; and finally, in Section 9 we conclude.
## 3 Results  
Between January and February 2023, 112 participants were screened, and 34 met the eligibility criteria, entered the study, and were allocated to the mild (17) and severe (17) FHP groups. Four of the participants were excluded because they could not complete the pillow assessment for sensor device or time-related reasons. As a result, the data from these four participants were not included in the analysis. Table 2 shows the distribution of the participants based on the severity of FHP and the similarity between the groups regarding the baseline demographic and clinical characteristics. In the supine position, Pillow D (\(p\) = 0.011) provided better head comfort in participants with mild FHP, while Pillow C (\(p\) = 0.045) and Pillow E (\(p\) = 0.046) provided better head support. There was no difference between the groups in the rest of the support and comfort parameters of the head and shoulder region (\(p\) > 0.05). In the side-lying position, the support and comfort values provided by the pillows did not differ between the groups separated according to the FHP degree (\(p\) > 0.05). In the prone position, the support values provided to the shoulder-upper back region were higher for the A (\(p\) = 0.040) and D (\(p\) = 0.022) pillows for individuals with a mild FHP group. There was no difference between the groups in the rest of the support and comfort parameters of the head and shoulder region (\(p\) > 0.05) (Table 3). When the comfort and support parameters of pillow materials were compared among all participants, Pillow B and Pillow E provided higher head comfort (\(p<0.001\)), while Pillow E also provided higher shoulder support (\(p=0.044\)) in the supine position. In other parameters, there was no difference between the pillows (\(p>0.05\)). In the side-lying position, Pillow B provided higher head comfort (\(p<0.001\)) and Pillow A (\(p=0.003\)) higher head support. There was no difference between the pillows in other parameters (\(p>0.05\)). In the prone position, Pillow B and Pillow E provided higher head comfort (\(p<0.001\)), while Pillow E also provided higher shoulder support (\(p=0.002\)). There was no difference between the pillows in other parameters (\(p>0.05\)) (Table 4). Pairwise comparisons of pillows with the help of post-hoc tests are also shown in Figure 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Features** & **FHP\textless{} 2.8 cm (n = 15)** & **FHP \(\geq\) 2.8 cm (n = 15)** & \(p\) \\ \hline Gender (n(\%)) & 11 & 8 & 0.450  \\ \hline BMI & 23.99 \(\pm\) 3.12 & 22.84 \(\pm\) 3.08 & 0.714  \\ \hline Physical Activity Level (IPAQ) & 1491 \(\pm\) 1007 & 1626 \(\pm\) 675 & 0.279  \\ \hline Pittsburgh Sleep Quality Index & 5.67 \(\pm\) 2.80 & 5.62 \(\pm\) 2.10 & 0.959  \\ \hline Head Anterior Tilt (cm) & 1.70 \(\pm\) 0.77 & 4.01 \(\pm\) 0.70 & \textgreater{}0.00  \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Demographic, physical, and psychosocial characteristics of individuals with FHP.
# NFV-Enabled Efficient Renewable and Non-Renewable Energy Management: Requirements and Algorithms
## 9 Evaluation
### Analysis for Small-Scale Scenarios
#### 9.2.2 Comparison between OptTs and FastTs  
To evaluate the difference between strategies, the criterion of approximation ratio (\(\rho\)) has been adopted [28]. This parameter estimates how many times bigger the approximate solution is compared to the exact result, and it is typically defined as the ratio between the suboptimal and optimal solutions [28]. However, because \(P_{RES}\) and \(P_{LACK}\) may be zero, its definition has been adapted to the conditions of the strategies, as shown below:  
\[\rho=1-\frac{1}{T_{s}^{k}}\sum_{i=1}^{T_{s}^{k}}\frac{|OptTs_{i}-FastTs_{i}|}{ disOptTs_{i}} \tag{35}\]  
\[disOptTs_{i}=\left\{\begin{array}{ll}1&\quad\text{if }OptTs_{i}=OptTs_{0},\\ |OptTs_{i}-OptTs_{0}|&\quad\text{if }OptTs_{i}\
eq OptTs_{0}.\end{array}\right. \tag{36}\]  
where, the first term in Equation (35) represents the exact solution, while the second term corresponds to the mean absolute error of all time-shifting values, except for \(T_{s}^{k}=0\). As shown in Equation (35), each absolute error \(i\) is weighted to the \(disOptTs_{i}\) parameter (Equation (36)), which corresponds to the maximum distance between the optimal value (\(OptTs_{i}\)) and the initial conditions of the system (\(OptTs_{0}\), when \(T_{s}^{k}=0\)) in order to obtain the proportional error of each time-shifting \(i\). The \(\rho\) parameter ranges from 0 (no improvements in the system) to 1 (when FastTs and OptTs produce the same results); an intermediate value (\(0<\rho<1\)) represents the similarity or closeness factor to the optimal solution (\(\text{FastTs}=\texttt{OptTs}\times\rho\)). For a better understanding of the computation of the \(\rho\) factor, Figure 17 presents an example for metric \(AR\) of _Case A_. The computations of the absolute errors that correspond to \(T_{s}^{k}=\{1,\cdots,3\}\) and the resulting \(\rho_{AR}\) are presented in Equation (37).  
\[\rho_{AR}=1-\frac{1}{3}\times\left(\frac{66.7-55.6}{66.7-33.3}+\frac{100-88.9} {100-33.3}+\frac{100-88.9}{100-33.3}\right)=0.78 \tag{37}\]  
Figure 16: Combinations processed and computational resources used to execute OptTs and FastTs within the scope of simulation. Parameters: According to the Case B in Table 5, with \(numPart=2\) for FastTs.  
The result of Equation (37) shows that the heuristic solution is similar to the optimal solution in a factor equal to 0.78 (78% similarity), or that FastTs can produce a solution that is within \(\sim\)1.3 times the optimal result. The \(\rho\) factors for small-scale scenarios are summarized in Table 8 and reveal that FastTs produces not only near-optimal or optimal solutions, but also a stable performance. Even in the worst case (i.e., \(\rho_{P_{LACK}}=0.71\)), the heuristic solution is within only \(\sim\)1.4 times the optimal result. In addition, for random scenarios we observe that the best results are achieved for _Case D_. Therefore, the conditions of this case study are taken as the baseline for the evaluation of FastTs for larger \(N\) values.
# Calculation of the Center of Mass Position of Each Link of Multibody Biped Robots
## 2 First Validated Theoretical Formulation
### Proposed Coefficients  
In following, four novel coefficients (\(\alpha_{i}\), \(\beta_{i}\), \(\gamma_{i}\), \(\delta_{i}\)) are introduced  
\[-(M_{Y0i}+M_{Y1i}+M_{Y2i})/(F_{Z0i}+F_{Z1i})=\alpha_{i} \tag{10}\]  
\[(F_{X0i}+F_{X1i})/(F_{Z0i}+F_{Z1i})=\beta_{i} \tag{11}\]  
Figure 2: Two examples of balance configurations; scheme A and scheme B. \(m_{2}\) is the total CoM. \(m_{u}\) and \(m_{w}\) are the CoMs of the two links.  
\[\left\{\begin{array}{c}\left(M_{X0i}+M_{X1i}+M_{X2i}\right)/\left(F_{Z0i}+F_{Z1i }\right)\\ +[\cdot(F_{Z1i}-F_{Z0i})/(F_{Z0i}+F_{Z1i})]=\gamma_{i}\\ \\ \left(F_{Y0i}+F_{Y1i}\right)/(F_{Z0i}+F_{Z1i})=\delta_{i}\end{array}\right. \tag{12}\]  
Rewriting Equations (8) and (9) (for \(I=A\) or \(I=B\))  
\[X_{2i} =\alpha_{i}+\beta_{i}\cdot Z_{2i} \tag{14}\] \[Y_{2i} =\gamma_{i}+\delta_{i}\cdot Z_{2i} \tag{15}\]  
Using the parameters \(m_{u}\), \(m_{w}\), \(r_{u}\), \(l_{u}\), \(r_{w}\) and \(l_{w}\), the Equations (16) and (17) can be obtained. Thus, the Equations (14), (16) and (17) can be seen as a system composed of six equations in six unknown variables (for \(i=A\) and \(i=B\)) \(X_{2A}\), \(Z_{2A}\), \(X_{2B}\), \(Z_{2B}\), \(r_{w}\), \(l_{w}\); the relation between \(m_{w}\) and \(m_{u}\) is given by the Equation (18). \(\theta_{t}\) is fixed by the user (\(\theta_{t}=0\) in \(i=A\)), in a way that does not allow to tilt the platform.  
\[X_{2i} =\left[m_{u}\cdot r_{u}+m_{w}\cdot(l_{w}\cdot\sin\theta_{t}+r_{w} \cdot\cos\theta_{t})\right]/m_{2} \tag{16}\] \[Z_{2i} =\left[m_{u}\cdot l_{u}+m_{w}\cdot(U+l_{w}\cdot\cos\theta_{t}-r_{ w}\cdot\sin\theta_{t})\right]/m_{2}\] (17) \[m_{w} =m_{2}-m_{u} \tag{18}\]  
Solving the equations system constituted by (14), (16) and (17), the positions of the center of mass are calculated in both the configurations A and B (for \(i=A\) and \(i=B\)). It must be underlined that only \(X\) as a function of \(Z\) has been considered, but the same result can be obtained considering \(Y\) as a function of \(Z\).
###### Abstract  
This study demonstrates a three-dimensional numerical simulation of earth pressure balance (EPB) shield tunnelling using a coupled discrete element method (DEM) and a finite difference method (FDM). The analysis adopted the actual size of a spoke-type EPB shield tunnel boring machine (TBM) consisting of a cutter head with cutting tools, working chamber, screw conveyor, and shield. For the coupled model to reproduce the in situ ground condition, the ground formation was generated partially using the DEM (for the limited domain influenced by excavation), with the rest of the domain being composed of FDM grids. In the DEM domain, contact parameters of particles were calibrated via a series of large-scale triaxial test analyses. The model simulated tunnelling as the TBM operational conditions were controlled. The penetration rate and the rotational speed of the screw conveyor were automatically adjusted as the TBM advanced to prevent the generation of excessive or insufficient torque, thrust force, or chamber pressure. Accordingly, these parameters were maintained consistently around their set operational ranges during excavation. The simulation results show that the proposed numerical model based on DEM-FDM coupling could reasonably simulate EPB driving while considering the TBM operational conditions.  
coupled numerical analysis; discrete element method; earth pressure balance shield; finite difference method; tunnel boring machine; operational condition +
Footnote †: journal: selected
## 3 Results and Discussion
### Ventilation Rate Determinants  
#### 3.3.1 Ventilation Rates vs. Indoor Ozone Concentrations  
The effects of ventilation rates on indoor ozone concentrations were investigated for each of the wood-based panels and synthetic fibers based on a standard room using Equation (8) and are summarized in Figure 7.  
Figure 7: _Cont._  
As shown in Figure 7a,b, the effect of ventilation rates on indoor ozone concentrations varies from material to material. Based on Equation (8), both the deposition velocity and the VOC emissions play a role in affecting the indoor ozone concentration, although the former was much more crucial than the latter. For wood-based panels, the deposition velocities of green-core OSB and lauan plywood were similar, while those of paint-free pine were significantly lower. As a result, the indoor environment was more tolerant toward (outdoor) ozone pollution when OSB or plywood was used, as an ACH of less than approximately 5.1 or 4.4 h\({}^{-1}\), respectively, resulted in an indoor ozone concentration below 74.7 ppb (160 ug\(\cdot\)m\({}^{-3}\)) (see [43]) when the outdoor ozone concentration was 100 ppb. As a comparison, when pine is used, the ACH should be less than 1.1 h\({}^{-1}\) to maintain a relatively low level of indoor ozone. When outdoor ozone is extremely high at 300 ppb, little to no ventilation should be provided if the pine covers a considerable area of indoor surfaces. However, to keep the indoor ozone concentration at a \"safe\" level, the maximum ACH would still be reasonably close to 0.5 h\({}^{-1}\), which is the common minimum ventilation rate requirement for residential buildings worldwide [57; 58; 59] when either OSB or plywood is the dominant indoor building material.  
A similar trend for ventilation rates on indoor ozone concentrations was observed for the scenarios using synthetic fibers, as shown in Figure 7c,d. When the ozone was at 100 ppb, all the cases resulted in an ozone concentration below 74.7 ppb (160 ug\(\cdot\)m\({}^{-3}\)) when the ventilation rates were less than approximately 2.7 h\({}^{-1}\). Flannel and weaved leather are more prone to ozone deposition than micro lining and carpet. Accordingly, the indoor environment has a superior capacity for depleting outdoor ozone via ventilation when flannel or leather is present in large quantities. Even when the outdoor  
Figure 7: The effect of ventilation rates on indoor ozone concentrations by considering ozone deposition on indoor materials. (**a**) On wood-based panels; outdoor ozone concentration = 100 ppb. (**b**) On wood-based panels; outdoor ozone concentration = 300 ppb. (**c**) On synthetic fibers; outdoor ozone concentration = 100 ppb. (**d**) On synthetic fibers; outdoor ozone concentration = 300 ppb.  
ozone concentration is as high as 300 ppb, by ventilating at around 0.5 h\({}^{-1}\), the indoor environment of the standard room should still be able to endure ozone pollution without further eliminating measures.
# Study of Information Dissemination in Hypernetworks with Adjustable Clustering Coefficient
## 4 Simulation Results and Analysis
### Summary of the Experimental Results  
To present the experimental results more clearly, we have tabulated the comparison results of various parameters as shown in Table 1.  
The tabulated data reveal that the simulation and theoretical predictions are consistent. Additionally, it is noteworthy that while the clustering coefficient has no effect on the density of informed nodes in the steady state, it does impact the density of informed nodes before the steady state is reached. This means that the clustering coefficient affects the rate of information propagation, with higher clustering coefficients leading to a slower information spread. Importantly, this conclusion cannot be derived from the theoretical analysis alone.