esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Evolution of Network Technologies
Currently, initiatives are starting to form and research projects are set up in an attempt to begin the design, development, and experimentation on the required network infrastructure to support this new mobile network generation. According to recent research [6], China has already set up two working works. Similarly, Japan has also invested $2 billion (two billion US dollars) in order to support research activities for 6G. Europe has also approved the research of 6G under the Horizon 2020 plan. North America has also begun working on the initiative called \"Next G\", mainly at a university level.  
Users can share data on the go with their smart devices using mobile communication and the mobile internet (MI). MI has millions of connected smart devices and has revolutionized various industries such as logistics, education, healthcare, and transportation in order to maintain quality of service (QoS) and quality of experience (QoE) for diverse customers [7]. It also enables device-to-device (D2D) communication and invented the term \"Internet of Things\" (IoT), which allows low-power devices and/or equipment to execute specific functions in the area in which they are installed [8]. Mobile devices can share prime or vital information in situations when a millisecond delay could harm a human life via D2D communication. The current cellular network architecture is not adequate for such sensitive data sharing, which has a latency of more than 20 milliseconds, due to poor data rates and significant delays.  
However, for most smart applications, this delay for D2D connections is excessively long. Similarly, the term \"tactile internet\" (TI) was coined in early 2014 to characterize the ability to monitor and, as a result, act over the internet [9]. As a result, TI is expected to offer up several new opportunities and applications that will improve the quality of life and work. According to market research, the worldwide market might be worth up to $20 trillion, accounting for at least 20% of global GDP today [10]. Even though the term Internet of Things was established in 1995 it has recently faced significant popularity. Moving on, the TI is considered to be the evolution of IoT. Hereinafter, the key characteristics of each internet era are summarized [11]:  
* Mobile internet: suited for static or streaming content, video with limited resolution, web browsing
* Internet of Things (IoT): machine-to-machine (M2M) communication, billions of interconnected smart devices, low rate, latency, secure and reliable
* Tactile internet: human-to-machine communication (H2M), ultra-low latency, ultra-high availability, end-to-end security
## 2 Materials and Methods  
### GO and PGO Characterization  
Graphene oxide (batch #GOB067) was kindly supplied by Graphene (San Sebastian, Spain). A complete characterization of the material was reported by Fusco and co-authors [21] and a brief elemental analysis performed with a LECO CHNS\(-\)932 analyzer (LECO Corporation, St. Joseph, MI, USA) showed average values of 59.40 \(\pm\) 0.10% C, 1.40 \(\pm\) 0.10% H, 0.07 \(\pm\) 0.02% N, \(<\) 36.6% O, and 2.50 \(\pm\) 0.10% S. Raman spectroscopy performed with an inVia Raman Microscope (Renishaw, Wotton-under-Edge, UK) revealed the two characteristic D and G bands of GO, with their maxima located at \(\sim\)1350 and \(\sim\)1600 cm\({}^{-1}\), respectively. Lateral dimension was evaluated by laser diffraction in the GO slurry in the range from 6000 to 30,000 nm, with an average of 15,100 \(\pm\) 400 nm. X-ray diffraction (XRD) analysis was performed on a GO dry film, revealing flakes with an average thickness of six layers. GO was firstly synthesized in acidic media (H\({}_{2}\)SO\({}_{4}\)) and then dispersed in water. To remove acidic residues due to the production process, purification was performed by repeated washings in distilled water, each followed by a centrifugation step at 4800\(\times\)\(g\). After purification, only around 2% of the starting S remained in the purified GO (PGO), mostly as SO\({}_{4}\)\({}^{2-}\)[22]. The acidity of the materials used in this study was verified with a HI5521 pH meter equipped with a HI1131B electrode (Hanna Instruments Italia s.r.l., Padua, Italy); data are reported in Table 1.
###### Abstract  
A systematic review and meta-analysis were performed to determine if heart rate variability-guided training (HRV-g), compared to predefined training (PT), maximizes the further improvement of endurance physiological and performance markers in healthy individuals. This analysis included randomized controlled trials assessing the effects of HRV-g vs. PT on endurance physiological and performance markers in untrained, physically active, and well-trained subjects. Eight articles qualified for inclusion. HRV-g training significantly improved maximum oxygen uptake (VO\({}_{2}\)max) (MD = 2.84, CI: 1.41, 4.27; \(p<\) 0.0001), maximum aerobic power or speed (WMax) (SMD = 0.66, 95% CI 0.33, 0.98; \(p<\) 0.0001), aerobic performance (SMD = 0.71, CI 0.16, 1.25; \(p=\) 0.01) and power or speed at ventilatory thresholds (VT) VT1 (SMD = 0.62, CI 0.04, 1.20; \(p=\) 0.04) and VT2 (SMD = 0.81, CI 0.41, 1.22; \(p<\) 0.0001). However, HRV-g did not show significant differences in VO\({}_{2}\)max (MD = 0.96, CI -1.11, 3.03; \(p=\) 0.36), WMax (SMD = 0.06, CI -0.26, 0.38; \(p=\) 0.72), or aerobic performance (SMD = 0.14, CI -0.22, 0.51; \(p=\) 0.45) in power or speed at VT1 (SMD = 0.27, 95% CI -0.16, 0.70; \(p=\) 0.22) or VT2 (SMD = 0.18, 95% CI -0.20, 0.57; \(p=\) 0.35), when compared to PT. Although HRV-based training periodization improved both physiological variables and aerobic performance, this method did not provide significant benefit over PT.  
autonomic nervous system; cardiac autonomic regulation; cardiorespiratory fitness; daily training; endurance +
Footnote †: journal: Applied Soft. 2020, 10, 8532; doi:10.3390/app10238532
## 5 Conclusions  
A missing well logs prediction method based on the hybrid BO-HKELM algorithm is proposed in this research by using the common petrophysical data from a field of the Ordos Basin in China. The ELM, KELM (RBF), and HKELM algorithms are used to assess the precision and generalizability of the obtained model. The LightGBM algorithm is applied in conjunction with the original model (HKELM) to show that modeling errors decrease as  
Figure 8: (**a**) BO-HKELM, (**b**) HKELM, (**c**) KELM, (**d**) ELM. The cross plot of measured RHOB versus the predicted one using BO-HKELM, HKELM, KELM, and ELM at testing phases in Well A2.  
Figure 9: Local enlarged display of prediction results in Well A2.  
the number of estimator model inputs increase; however, the trend is only minimal after four input variables. Thus, it can be concluded that the variables DT, NPHI, CAL, and GR are the appropriate modeling inputs. In comparison to the other models studied in this research, the BO-HKELM model has the highest accuracy (R-square: 0.9029) and the lowest error (RMSE: 0.0767 and MAE: 0.0613). In addition, the BO-HKELM model outperforms other models in terms of fitting different local logging curve features, and it is better at rebuilding local curve characteristics when the reservoir curve exhibits a local mutation. The reason is that the BO-HKELM model incorporates a hybrid kernel function and uses a Bayesian algorithm to optimize the parameter weights, which may extract the spatial properties of the log curve more effectively and improve the model's predictive ability. Therefore, it can be confidently stated that the BO-HKELM model has higher precision and is much better suited for missing well logs prediction, compared to the other models studied in this research.  
Conceptualization, L.Q. and Y.C.; methodology, L.Q.; software, L.Q.; validation, L.Q., Y.C. and Z.J.; formal analysis, H.S.; investigation, K.X.; resources, K.X.; data curation, K.X.; writing--original draft preparation, L.Q.; writing--review and editing, L.Q.; visualization, H.S.; supervision, Z.J.; project administration, Z.J.; funding acquisition, K.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Key Research and Development Program of China (2016 YFC0600201), the Academic and technical leader Training Program of Jiangxi Province (20204 BCJ23027) and the Joint Innovation Fund of State Key Laboratory of Nuclear Resources and Environment (2022 NRE-LH-18).  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the confidentiality requirements of the data provider.  
China petrochemical corporation (Sinopec) is gratefully acknowledged for providing useful data and valuable support.  
The authors declare no conflict of interest.
## 3 Methodology
### Analysis Methods
#### 3.2.2 Modeling Hard-Taskers' Usage Patterns (RQ2)  
For the second research question, we adopted a statistical modeling approach to explore relevant usage patterns of hard-taskers, who show a relatively high proportion of non-normal tasks with cognitive and physical task loads (i.e., _math_task_, _shake_task_, and _picture_task_). Specifically, we conducted a multiple linear regression analysis, which builds a linear model of predicting the proportion of the hard task uses (_prop_hard_task_) based on the alarm-set and alarm-dismiss usage variables of each user. We used the above twelve independent usage variables, including eight alarm-set variables and four alarm-dismiss variables from Table 1.  
For this analysis, all the variables were measured for each of the 211,273 users. We found several independent variables that were correlated. For example, the two highest correlations were observed between _avg_daily_alarm_ and _sd_daily_alarm_, and between _avg_freq_snooze_ and _sd_freq_snooze_. We excluded _avg_daily_alarm_ and _sd_freq_snooze_ to make all the pairs of the independent variables have a Pearson correlation value less than 0.7, and the variance inflation factor (VIF) for each independent variable was lower than the benchmark of 10 for multicollinearity [27]. The highest value was 1.757. Therefore, our regression models were safe from the multicollinearity effect.
## 4 Experimental Analyses
### Verification Analysis
It can be seen that the variation amplitude and trend of the air pressure inside the car is basically consistent with the measured variation amplitude and trend of the air pressure inside the car. It verifies the reliability and accuracy of the proposed method. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No. Parameter** & **1** & **2** & **3** \\ \hline Maximum running velocity \(v\) (m/s) & 7 & 10 & 10 \\ Relative height of the departure floor \(H_{s}\)(m) & 217 & 217 & 110 \\ Air pressure of the departure floor \(P_{s}\)(hPa) & 980.4 & 980.4 & 992.2 \\ Relative height of the destination floor \(H_{e}\)(m) & 0 & 0 & 0 \\ Air pressure of the destination floor \(P_{e}\)(hPa) & 1004.0 & 1004.0 & 1003.3 \\ Elevator running total time consuming (s) & 38 & 32 & 22 \\ Whether there is ear pressing & yes & yes & yes \\ \hline \multirow{3}{*}{First ear pressing} & Air pressure \(P_{1}\)(hPa) & 992.8 & 992.9 & 1001.5 \\  & Air pressure difference to departure floor \(\Delta P_{1}\)(hPa) & 914.6 & 923.7 & 930.0 \\  & Duration \(t_{1}\)(s) & 3.4 & 3.9 & 3.1 \\ \hline \multirow{3}{*}{Second ear pressing} & Air pressure \(P_{2}\)(hPa) & 1002.1 & 1001.9 & - \\  & Air pressure difference to departure floor \(\Delta P_{2}\)(hPa) & 1847.3 & 1823.6 & - \\ \cline{1-1}  & Duration \(t_{2}\)(s) & 3.5 & 4.0 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental data of air pressure change inside the car.
# Aircraft Structural Stress Prediction Based on Multilayer Perceptron Neural Network
## 3 Dataset Creation and Model Variable Definition  
### Dataset Creation  
Finite element computational data for 300 flight conditions were selected for the wing rib experiencing the highest stress load to establish the stress dataset for this component. The dataset was divided into training, validation, and test sets in an 8:1:1 ratio. To ensure  
Figure 4: Nodal stress vs. angle of attack and inflow velocity at node No. 18428.  
Figure 3: Simulation Stress Contours at Various Angles of Attack and Inflow Velocities.  
the model can effectively learn the impact of variations in angle of attack and inflow velocity on stress magnitude and distribution, the training and test sets primarily consist of data from angles of attack ranging from 1 to 8 degrees. Additionally, to evaluate the model's predictive performance under flight conditions not included in the training and validation sets, as well as to address high-load scenarios, data for angles of attack of 9 and 10 degrees were reserved for the test set.  
The grid node coordinates of the finite element model of the wing rib, along with the angle of attack and inflow velocity, were used as input parameters for the neural network, while the stress magnitude served as the output. The model evaluation function was employed to compare the predicted stress values with the actual results, thereby assessing the overall performance of the neural network model.
# Formation Control Technology of Fixed-Wing UAV Swarm Based on Distributed Ad Hoc Network
## 2 Problem Formulation  
This paper focuses on two critical problems of formation switching and formation obstacle avoidance in a distributed ad hoc network for UAV swarm formation control.  
**Definition 1**.: _Formation switch in distributed ad hoc network._  
For \(n\) UAV, given an initial position \(\mathbf{X}_{i}(0)\) of UAV\({}_{i}\), a UAV swarm forms a communication topology in the ad hoc network (as shown in Figure 1). Plan the waypoint \(P_{i}=\{P_{i1},\cdots,P_{ik},\cdots\}\) of the UAV\({}_{i}\) under the dynamic constraints of the maximum turning angle constraint \(\beta_{\max}\) and minimum route length constraint \(d_{s}\), so that the distance between UAV\({}_{i}\) and other UAVs reaches the expected value \(\Delta\mathbf{X}_{jinf}\) within the time \(T\):  
\[\left|\mathbf{X}_{j}(t)-\mathbf{X}_{i}(t)-\Delta\mathbf{X}_{jinf}\right|\to 0 \quad i=1,2,\cdots,n \tag{1}\]  
For \(n\) UAV, given an initial position \(\mathbf{X}\) of UAV\({}_{i}\), intelligently split the UAV swarm into \(N_{c}\) formation; the number of UAVs in the \(p\)th subformation is \(N_{p}\). Plan the waypoint \(P_{i}=\{P_{i1},\cdots,P_{ik},\cdots\}\) of the UAV\({}_{i}\) under the dynamic constraints of the maximum turning angle \(\beta_{\max}\) and the minimum direct flying distance \(d_{s}\), so that the UAVs will not collide through the rectangular obstacle avoidance area \(S\) containing the circular obstacle \(O\), and the distance between UAV\({}_{i}\) the and other UAVs will eventually reach the expected value \(\Delta\mathbf{X}_{jinf}\); namely, reconstructed into the required formation.  
Where \(n=\sum\limits_{p=1}^{N_{c}}N_{p}\), \(O\) is a collection of obstacles, and \(O=\{o_{1},o_{2},\cdots,o_{oN}\}\), \(oN\) is the number of obstacles, each obstacle \(o_{m}\) can be described as a dyadic array \(<R_{om},R_{m}>\); \(R_{om}\) is the center point of the \(m\)th circle; \(R_{m}\) is the radius of the \(m\)th circle; \(S\) is the rectangular obstacle avoidance area described as a ternary array \(<S_{o}\), \(L\), \(W>\), where \(S_{o}\) is the center point of the rectangular obstacle avoidance area; \(L\) is the length of the rectangle; and \(W\) is the width of the rectangle.  
A reasonable formation-switching control method requires that the UAV can perform online formation switching based on the neighboring UAV information, and can meet the UAV dynamic constraints and can handle communication delays and flight anomalies, eliminating track deviation. Formation obstacle avoidance requires that a UAV swarm can effectively avoid obstacles, and can form a desired formation after the avoidance is completed. The UAV studied in this paper was a highly dynamic fixed-wing UAV with a uniform speed. The dynamic constraints were required to satisfy the minimum turning radius constraints and minimum track length constraints. The research space of this paper was the two-dimensional Euclidean horizontal plane.  
Figure 1: UAV swarm formation problem.
# Derivation of New Staggered Compact Schemes with Application to Navier-Stokes Equations
## 2 Problem Formulation and Derivation of New Compact Schemes
### Schemes for Interpolation  
The interpolation scheme can be obtained with a procedure similar to that described for staggered first derivative. The evaluation of the local Hermitian polynomial \(P_{j}\) at node \(x_{j+1/2}\) gives a consistent approximation of the function value, which is based on a fourth-degree polynomial. However, in this  
Figure 2: Graphical representation of new schemes. (**a**) Staggered fourth order Hermitian derivative scheme (Equation (7)). (**b**) Staggered fourth order interpolation scheme (Equation (9)). (**c**) Collocated fourth order second derivative scheme (Equation (11)).  
case the procedure produces a non-symmetric formula, which causes the interpolation scheme to be dissipative. To avoid this circumstance, one can derive a symmetric scheme either by taking the arithmetic mean of the two interpolation formulae given by the polinomials \(P_{j}\) and \(P_{j+1}\) at node \(x_{j+1/2}\), or by evaluating the interpolation \(\widehat{P_{j}}\) based on the reduced set of values \(f_{j+1},f_{j}^{\prime},f_{j+1}^{\prime}\). The two approaches give respectively the schemes 6 SH-D0 and 4 SH-D0 (D0 stands for _interpolation_):  
\[f_{j+1/2}=\frac{1}{128}\left(f_{j-1}+f_{j+2}\right)+\frac{63}{128}\left(f_{j}+ f_{j+1}\right)+\frac{9h}{64}\left(f_{j}^{\prime}-f_{j+1}^{\prime}\right), \tag{8}\]  
\[f_{j+1/2}=\frac{1}{2}\left(f_{j}+f_{j+1}\right)+\frac{h}{8}\left(f_{j}^{\prime }-f_{j+1}^{\prime}\right), \tag{9}\]  
with global truncation errors \((1/1024)h^{6}f^{VI}\) and \((1/384)h^{4}f^{IV}\), when Equation (1) is used for first derivatives at the right hand side of Equations (8) and (9). In the case of interpolation, the third-degree polynomial based on the data \(f_{j},f_{j+1},f_{j}^{\prime},f_{j+1}^{\prime}\) furnishes a fourth-order approximation of the function \(f\) at any point interior to the interval \(\left[x_{j-1},x_{j+1}\right]\). This suggests that Equation (9), which gives a fourth-order formula for \(f_{j+1/2}\), is to be preferred to Equation (8) for consistency to other fourth-order approximations, and will be the baseline choice in the following. Equation (9) has a computational cost of 5N operations and its graphical representation is given in Figure 2b. The reference comparison formula is the classical fourth-order compact interpolation scheme, 4 SC-D0,  
\[\frac{1}{8}f_{j-1/2}+\frac{3}{4}f_{j+1/2}+\frac{1}{8}f_{j+3/2}=\frac{f_{j}+f_{ j+1}}{2}, \tag{10}\]  
which has truncation error \((1/128)h^{4}f^{IV}\).
# Evaluation of Ride Comfort under Vortex-Induced Vibration of Long-Span Bridge
## 3 Ride Comfort Criteria
### Calculation of Overall Vibration Total Value  
Using the frequency weighting function provided in Table 2, each \(a_{ij}\) A undergoes filtering to derive the time history signal \(a_{\omega-ij}(t)\) representing the acceleration of human body vibrations. Subsequently, the second root mean square value (\(RMS_{ij}\)) of \(a_{\omega-ij}(t)\) is calculated as follows:  
\[RMS_{ij}=\left\{\frac{1}{T}\int\limits_{0}^{T}\left[a_{\omega-ij}(t)\right]^{2 }dt\right\}^{1/2}, \tag{34}\]  
Then, the axis weighting coefficients of the vibration components in each direction are combined using a weighted summation to calculate the Overall Vibration Total Value (\(OVTV\)), which represents the root mean square value of the total weighted acceleration.  
\[OVTV=\left\{\sum\left(M_{ij}RMS_{ij}\right)^{2}\right\}^{1/2}, \tag{35}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Location** & **Axis** & **Frequency Weighting Function** & **Axis Weighting Coefficients \(k\)** \\ \hline \multirow{4}{*}{Seat} & \(x_{s}\) & \(W_{k}\) & 1.0 \\  & \(y_{s}\) & \(W_{d}\) & 1.0 \\  & \(z_{s}\) & \(W_{k}\) & 1.0 \\  & \(r_{x}\) & \(W_{e}\) & 0.20 m/rad \\  & \(r_{y}\) & \(W_{e}\) & 0.40 m/rad \\  & \(r_{z}\) & \(W_{e}\) & 0.63 m/rad \\ \hline \multirow{4}{*}{Backrest} & \(x_{b}\) & \(W_{c}\) & 0.8 \\  & \(y_{b}\) & \(W_{d}\) & 0.5 \\  & \(z_{b}\) & \(W_{d}\) & 0.4 \\ \hline \multirow{4}{*}{Floor} & \(x_{f}\) & \(W_{k}\) & 0.25 \\  & \(y_{f}\) & \(W_{k}\) & 0.25 \\ \cline{1-1}  & \(z_{f}\) & \(W_{k}\) & 0.4 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Vibration component weighting function and axis weighting coefficient.  
After the \(OVTV\) is calculated through Equation (35), the drivers' ride comfort is assessed according to the criteria of different discomfort levels in Table 3 as recommended by ISO 2631-1 [14].
###### Abstract  
Device-to-device (D2D) multicast communication can greatly improve the spectrum utilization in a content delivery scenario. However, the co-channel interference and power consumption brought by D2D bring new challenges. All the D2D multicast groups expect to achieve a higher system capacity with less extra energy cost. In this paper, we investigate the uplink resource allocation issue when D2D multicast groups share the resources with other cellular uses (CUs), while guaranteeing a certain level of quality of service (QoS) to CUs and D2D users. Firstly we address a flexible tradeoff framework in which the system power consumption and the system capacity (i.e., the number of admitted D2D links) are assigned with different weight factors so that these two objectives are jointly considered. Then we propose an efficient resource optimization scheme, which comprises sub-channel allocation and signal-to-interference- plus-noise ratio (SINR) assignment. Numerical results validate the effectiveness of the proposed framework, and demonstrate the advantages in dealing with the proposed multi-objective optimization problem.  
cellular network; multiple device-to-device (D2D) communication; uplink resource sharing; sub-channel allocation +
Footnote †: journal: Applied  
Article