esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Serum Collection  
On the first WBV session (5 day after T0), the participant was acclimated in the lab for 30 min before baseline blood collection. Blood samples were drawn into vacutainers from the antecubital forearm vein using a 20-gauge needle (without additives) for measurement of creatine kinase (CK), total serum testosterone, growth hormone and cortisol concentration. After the baseline measurement, a WBV exercise session was performed. Measurements were repeated immediately after intervention (Post), 1 and 2 h after the end of the intervention, and for CK was also repeated 12 and 24 h after the end of the intervention. Blood was collected into serum-separator tubes (Greiner Bio-One), allowed to clot for 30 min at room temperature, and then centrifuged at 2000\(\times\)\(g\) for 10 min. Sera were then harvested in aseptic conditions and frozen at \(-\)80 \({}^{\circ}\)C for storage. Serum samples were allowed to thaw in ice and then mixed thoroughly before assaying. Enzyme-linked immunosorbent assays (ELISA) for testosterone (cat#ab108666; Abcam, Cambridge, UK), cortisol (cat#ab108665; Abcam) and growth hormone (GH, cat#ab190811; Abcam), and the Reflotron assay for creatine kinase (cat#11126695; Roche, Basel, Switzerland) were carried out following the manufacturer's instructions. The sensitivities of these assays were 0.07 ng/mL, 2.44 ng/mL, 1.4 pg/mL, 0.7 pg/mL, and 24.4 U/L, respectively. The intra- and inter-assay variability coefficients for the ELISA were 5.8% and 10.5% for testosterone, 9% and 9.8% for cortisol, 3% and 1.6% for GH. To calculate sample concentrations in ELISA, the optical density (OD) of the standards was zero-corrected for blank value and then regressed against their concentration using a 4-parameter logistic (4-PL) model. Once the standard curve had been obtained, the unknown sample concentration was calculated by substituting their zero-corrected OD value in the standard curve equation. For Reflotron assays, the concentration was automatically determined by the Reflotron Plus device.
## 7 Conclusions  
This paper clarifies the stability condition and theoretical performance of the steady-state fourth-order tracking filter, i.e., the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter that tracks the velocity, acceleration, and jerk of moving objects. First, the stability conditions of the filter were derived using the Jury stability criterion. Then, the tracking and smoothing performance indices of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter were analytically derived in the closed form. The optimal smoothing performance of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter was analyzed and compared with that of the conventional \(\alpha\)-\(\beta\) and \(\alpha\)-\(\beta\)-\(\gamma\) filters. The results indicated a relatively large deterioration in the smoothing performance of the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter. This indicated that although it can track more complicated motion than the conventional low-order filter, its tracking accuracy is poor for tracking simple motion such as constant-velocity motion. However, the numerical simulation assuming a maneuvering target showed the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is ideal for tracking complicated motion including large jerk and snap.  
Because the \(\alpha\)-\(\beta\)-\(\gamma\)-\(\delta\) filter is one-dimensional tracking filter, this paper assumed the tracking problem along \(x\)-axis only. Extensions to our performance analysis to 2- and 3-dimensional problems are important future tasks. Furthermore, an experimental study to validate the filters in real-world environments is the focus of our future work.  
[PERSON] designed this research; [PERSON] derived and calculated main results of this paper including the numerical analyses and simulations; [PERSON] and [PERSON] wrote the paper; [PERSON] approved the final manuscript.  
This research was supported by the JSPSKKENHI Grant No. 16K16093.  
The authors declare no conflict of interest.
# A Secure and Lightweight Group Mobility Authentication Scheme for 6 LoWPAN Networks
## 1 Introduction
### Contribution  
The following summarizes our contribution:  
* Secure and Lightweight Group Mobility Authentication Scheme (SL_GAS) proposed using secret parameters in the registration phase to ensure that the session keys are confidential and robust, providing a robust foundation for secure communication against attacks such as replay attacks and ensuring that each session's parameters are unique.
* SL_GAS proposed a ticket during the initial group authentication phase for all mobile nodes and the group leader, enhancing the group authentication process's security and enforcing access control in dynamic and resource-constrained environments. It provides a secure and efficient way to manage authenticated sessions, prevent attacks, such as DoS and key leakage, and provide PFS and untraceability. Additionally, it reduces the overhead associated with authentication, provides continuity and resumption of sessions, and optimizes resource utilization that does not require key redistribution.
* SL_GAS used aggregated MAC with a tag approach that employs an improved authentication efficiency and integrity verification while minimizing computational overhead, unlike existing schemes that rely on plaintext MAC transmission.
* We formally analyze SL_GAS, considering Dolev-Yao (DY) [14] and Canetti-Krawczyk (CK) and extended CK [15] intruder adversary models, and informally analyze utilizing SVO logic and the Scyther tool (v1.1.3, [PERSON], Saarbrucken, Germany, and Oxford, UK). SL_GAS can provide additional security properties and the ability to withstand various protocol attacks.
* Performance evaluations have demonstrated that SL_GAS handover authentication has an advantage concerning signal cost, transmission delay, and computation overhead.
## 4 Modeling and Implementations of Software-Defined Vehicular Networks for V2X
### Modeling and Implementations of SDN for Vehicular Networks
The SDN controller conveys routing policies to UEs (Vehicles) by implementing ITS’s goals set up on the cloud. ITS scenarios in future VANETS require quality of service (QoS)s and efficient utilization of network resources for enabling autonomous driving. The authors in [11; 14] addressed the challenge of efficient resource utilization. In [11], the system on which the problem is associated use the fog(edge) computing technology, thus the SDN-based fog network is evaluated to propose location-aware services with less communication latency. To this end, the system analysis centers on the deployment of the SDN to support hybrid mode (central-based and distributed-based configuration of SDN controller) and on the configuration of control plane (SDN controller) in distributed mode with both the base station (BS) and RSU. Considering the outputs of the SDN-based fog computing, the authors in [11] propose a model that combines edge(fog) computing services for allowing heterogeneous communication access for V2V, V2I, and V2N. The authors in [14] provide a system analysis that centers of the topology deployment of SDN controller and the possibility to model communication nodes (vehicles) as an SDN switch using the open-source simulation tool known as Mininet-WiFi [14]. The proposed model offers efficient utilization of network resource after modeling the vehicle as a node using Mininet-WiFi. Taking mobile edge computing step further, the authors in [10] investigate the possibility of deploying VANET's application with low-latency and high-reliability communication delay in software-defined mobile edge computing. The system analysis provided by the authors in [10] takes into consideration
# Laser Transmission Characteristics of Seawater for Underwater Wireless Optical Communication
## 2 Method
### Simulation Settings  
As mentioned above, the photon weight, transmission length, and scattering direction are determined by the optical properties of water. In this study, we focused on the three typical water types since they represent the global marine waters since the IOPs are obtained in the real sea [40; 41], which are shown in Table 2 and Figure 1. The single scattering albedo (SSA) denoted by \(\omega_{0}\) indicates the scattering strength in a certain water type. According to Table 2, it is evident that clear water is absorption-dominant (\(\omega_{0}\) is below 0.5), coastal water has comparable absorption and scattering (\(\omega_{0}\) is slightly higher than 0.5), and turbid harbor water is scattering-dominant (\(\omega_{0}\) is higher than 0.5). When the scattering effect is strong in a particular water body, the transmitted laser beam will become significantly divergent. Moreover, there are differences in the SPFs of these three typical waters, with forward distinctions influencing the change in the scattering angle.  
For the convenience of comparison, we utilized the attenuation length (\(=c\cdot r\), \(c\) is the attenuation coefficient of a water body) instead of the geometrical distance. The attenuation lengths of the coastal and turbid harbor water types ranged from 1 to 35. For clear water, the maximum attenuation length was set to 25. Effective results could not be produced in clear water when the attenuation length exceeded 25 due to the relatively high absorption (smaller \(\omega_{0}\) value) and computational constraints.  
In the simulation of this study, the light source was set to a parallel beam with a beam width of 2 mm and a wavelength of 532 nm. From the simulations, we obtained the critical parameters influencing the performance of the underwater laser communication system, such as the AOA distribution, beam distribution, and power loss. The AOA distribution refers to the variation of the normalized power per unit angle with angles relative to the axes perpendicular to the receiving plane, and beam distribution refers to the variation of the power loss per unit area on the receiving plane with an offset relative to the center of the receiving plane. To obtain the AOA distribution and beam distribution, plenty of receivers with full FOV were set on the whole receiving plane and placed in unequal intervals, which refers to the offsets used in subsequent analysis. Figure 2 exhibits how the photons transmit through the seawater channel and are then received by the receivers on the receiving plane.
# Traffic Offloading in Unlicensed Spectrum for 5G Cellular Network: A Two-Layer Game Approach
## 2 System Model
### System Performance Analysis
Due to concavity of the function analyzed above, balancing the stations and making the station number as close as the optimal value \(n^{*}\) could improve the performance of the whole WiFi system. Therefore, we form a coalition game by encouraging individuals in the WiFi system to form groups in the first layer of CAGT mechanism, and then make a traffic transfer between LAA and WiFi in the second layer of CAGT.
# Stochastic Modeling and Optimal Time-Frequency Estimation of Task-Related HRV
## 3 Results and Discussion
### Regression Analysis
#### 3.3.3 Parameter \(a\)  
The parameter \(a\) corresponds to the power at time zero, with a larger value of \(a\) corresponding to higher power, and it represents the amplitude multiplier that scales the exponential function. We consider the logarithmic transformation of \(a\) in the regression models to avoid positively skewed residuals.  
Only the covariate Age is a significant predictor when considering models with a single explanatory variable. The regression model with only Age as predictor achieves \(R^{2}_{\rm adj}=0.24\). The same \(R^{2}_{\rm adj}\) is obtained with the categorical variable Age, with all Age groups significantly different from the baseline. The strong correlation of the scaling parameter \(a\) with the variable Age is expected, since it is known that the HRV power decreases with age.  
In multivariate models, both Age and Stress play a role, and the best predictive model includes them as categorical variables with \(R^{2}_{\rm adj}=0.27\) (Table 5). According to this model, the value of \(\log(a)\) is affected similarly by being in the age group 30-40 years old, compared to the baseline 20-30 years old, and having an SMBQ value compatible with pre-ED.  
The fact that both Age and Stress are significant predictors of the value of \(\log(a)\) with a similar effect (negative slope and same scale) suggests an analogy between the effect of aging and higher stress levels on the HRV instantaneous power. However, being in the age group over 40 years old has a 3.5 times stronger effect than having an SMBQ value compatible with pre-ED.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coeff. Est.** & **S.E.** & \(p\)**-Value** \\ \hline Age & 5.6738 & 0.4896 & \(<\!\!2\times 10^{-16}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Regression model for parameter \(d\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Coeff. Est.** & **S.E.** & \(p\)**-Value** \\ \hline Gender (M) & \(-2.826\times 10^{-5}\) & \(1.322\times 10^{-5}\) & 0.0353 \\ Age & \(8.156\times 10^{-6}\) & \(7.310\times 10^{-7}\) & \(<\!\!2\times 10^{-16}\) \\ SMBQ & \(-1.175\times 10^{-5}\) & \(5.760\times 10^{-6}\) & 0.0443 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regression model for parameter \(m\).
# A Fiber-Optic Six-Axis Force Sensor Based on a 3-UPU-Compliant Parallel Mechanism
## 3 Structure Design and Detection Principle of Six-Axis Force Sensor  
### Structural Design  
A 3-UPU parallel mechanism is adopted in the six-axis force sensor. The designed structure is shown in Figure 4. It consists of a base platform, three elastic force-measuring branches and a loading platform. The three force-measuring branches are identical and uniformly distributed between the loading platform and the base.  
Each force-measuring branch employs a hybrid rigid-flexible structure, as shown in Figure 5. It consists of a rectangular connecting block, two connecting rods, two semi-arc  
Figure 4: Structure of the six-axis force sensor.  
Figure 3: The calibration curve between the output of fiber optic and the curvature of FEM.  
shaped elastic bodies (featuring a single-sided flexible hinge structure), and two FEMs fixed between the semi-arc-shaped elastic bodies. The two elastic bodies are symmetrically distributed on both sides of the rectangular connecting block by using connecting rods. The material of the force-measuring branches is 7075 aluminum alloy.  
To ensure that the FEMs can effectively detect the deformation of the elastic bodies, soft silicone adhesive is used to fix the FEMs onto the elastic bodies. The adhesive offers high strength, high-temperature resistance, and excellent waterproof properties.
###### Abstract  
Gross primary productivity (GPP) is an important parameter that represents the productivity of vegetation and responses to various ecological environments. The Greater Khingan Mountain (GKM) is one of the most important state-owned forest bases, and boreal forests, including the largest primeval cold-temperature bright coniferous forest in China, are widely distributed in the GKM. This study aimed to reveal spatiotemporal vegetation variations in the GKM on the basis of GPP products that were generated by the Global LAnd Surface Satellite (GLASS) program from 1982 to 2015. First, we explored the spatiotemporal distribution of vegetation across the GKM. Then we analyzed the relationships between GPP variation and driving factors, including meteorological elements, growing season length (GSL), and Fraction of Photosynthetically Active Radiation (FPAR), to investigate the dominant factor for GPP dynamics. Results demonstrated that (1) the spatial distribution of accumulated GPP (AG) in spring, summer, autumn, and the growing season varied due to three main reasons: understory vegetation, altitude, and land cover; (2) interannual AG in summer, autumn, and the growing season significantly increased at the regional scale during the past 34 years under climate warming and drying; (3) interannual changes of accumulated GPP in the growing season (AGG) at the pixel scale displayed a rapid expansion in areas with a significant increasing trend (\(p<0.05\)) during the period of 1982-2015 and this trend was caused by the natural forest protection project launched in 1998; and finally, (4) an analysis of driving factors showed that daily sunshine duration in summer was the most important factor for GPP in the GKM and this is different from previous studies, which reported that the GSL plays a crucial role in other areas.  
b  
boresian forest; GPP; spatiotemporal distribution and variation; meteorological factors; phenological parameters 1
# A Review of Distribution Grid Consumption Strategies Containing Distributed Photovoltaics
## 3 Distribution Grid Consumption Strategy with Distributed PV
### Optimization of Distribution Grid Energy Storage with Distributed Photovoltaics
#### 3.2.2 Energy Storage Optimization for Distributed PV
Further market expansion: with the further decline in the cost of energy storage and continued policy support, the market size of PV energy storage systems is expected to expand further. 3. Intelligent management enhancement: through integration with technologies such as Internet of Things, big data analysis and artificial intelligence, the management of PV energy storage systems will become more intelligent and improve energy utilization efficiency. 4. Distributed power generation and micro-grid development: PV energy storage will play an important role in the construction of distributed power generation and micro-grid, enhancing the stability and flexibility of the grid. 5. Significant environmental benefits: with the popularization of PV energy storage technology, it can reduce dependence on fossil fuels, help reduce greenhouse gas emissions, and promote environmental protection. 6. Convergence of electric vehicle charging network: the combination of PV energy storage systems and electric vehicle charging infrastructure will be one of the future development trends, realizing the complementarity of green transportation and energy.
# Point-Line-Aware Heterogeneous Graph Attention Network for Visual SLAM System
## 2 SLAM System Framework  
The framework of the SLAM system proposed in this paper is shown in Figure 1. Firstly, a new image is input into the PL-Net network to detect key points and line segments, and the corresponding descriptors are obtained through the point-line-aware attention module to enhance the feature expressiveness for both points and lines in images. Then, the point and line features of the two images are transferred to the point-line heterogeneous graphs, which are constructed by using the point and line features as the vertices and connecting a vertex to its neighbors within a fixed radius. Secondly, the attention network HAGNN obtains the enhanced features and inputs them into the GIPOT to generate point-line matching results and calculate the pose of the current frame. Finally, by reprojecting the features in the local map to the current frame, the projection error is calculated for the backend processing of SLAM to complete the map.
## 5 Results
### Experiments in Different Scenarios  
The above experiments are all carried out in Scenario 1 to test the robustness of our proposed method in different scenarios, we present the processed results of the multi-layer medium scenario (Scenario 2) and the actual ruin scenario (Scenario 3), respectively. Figure 18 shows the target detection result in Scenario 2, where the actual ranges of the two targets are 8.2 m and 15.0 m, respectively. From the range-slow-time matrix obtained after step A represented in Figure 18a, it can be seen that due to the multiple scattering of electromagnetic waves between the media, strong clutter is exhibited within 0-2 m. The respiration signals of the two targets are weak, especially the target at 8 m, which cannot be observed. After the feature extraction of CNN, the output TPOIs are shown in Figure 18b. The output target ranges are 8.24 m and 14.94 m, respectively. The range errors are 4 cm and 6 cm, respectively.  
Figure 16: Results of Experiment 5.2: (**a**) range-slow-time matrix obtained after step A; (**b**) TPOIs extracted by CNN.  
Figure 17: Results of Experiment 5.3: (**a**) range-slow-time matrix obtained after step A; (**b**) TPOIs extracted by CNN.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Experiment** & **Actual Range** & **Extracted Range** \\ \hline Experiment 5.1 & 5.00 m, and 6.00 m & 4.94 m, and 6.07 m \\ Experiment 5.2 & 5.50 m, and 6.00 m & 5.50 m, and 6.06 m \\ Experiment 5.3 & 5.70 m, and 6.00 m & 5.88 m \\ \hline \hline \end{tabular}
\end{table}
Table 5: Extracted results for experiments with different target intervals.  
Figure 19 shows the target detection result in Scenario 3, where the actual ranges of the two targets are 2.0 m and 4.2 m, respectively. From the range-slow-time matrix represented in Figure 19a, it can be seen that although the two targets are in close range to the radar, due to the energy attenuation and multipath scattering of various obstacles, there is a lot of clutter in the matrix. In addition, the respiration signal extends in the range dimension. After the feature extraction of CNN, the output TPOIs are shown in Figure 19b. The output target ranges are 2.18 m and 4.43 m, respectively, which are consistent with the actual situation.
# Reasonable Support Parameters for Loess Tunnel Under Different Water Contents: A Case Study
## 4 Numerical Simulation
### Modeling  
Due to the unique structure of loess, the stress-strain behavior of materials throughout the stress process is characterized using the Bilinear Strain-Hardening/Softening Model, and the soil is not deformable [1]. The elastic constitutive model was used for the primary support and the bolts. On-site observations indicate that the tunnel structure is covered by 50 m of overlying soil. Additionally, the tunnel is situated 44 m away from both the left and right boundaries, and the distance from the lower boundary to the base of the tunnel is 34 m. These dimensions are designed to mitigate the impact of boundary effects. Fixed boundaries are applied on the left and right sides, as well as the front and back sides, while the bottom of the model is constrained. The top of the model is left free for deformation. The model does not include secondary lining during the construction process. The surrounding rock is Q\({}_{2}\) silty clay. In the modeling process, the steel rib is equivalent to shotcrete. The excavation approach is in accordance with the real-world conditions at the site. Figure 10 provides an overview of the model, and for the purpose of this paper, the simulation results of the middle section have been chosen for analysis to validate the model's rationality.  
The support parameters of the numerical model are consistent with the design parameters of Fengshouling Tunnel, as listed in Table 7.  
The primary support structures consist of shotcrete and a steel rib. When conducting numerical simulations, the support structure is treated as a unified entity, with the modulus of elasticity and density of the steel rib converted to match those of the shotcrete. The calculation equations are presented in Equations (4) and (5), and the physical and mechanical parameters of the support structure post-conversion are detailed in Table 7 in this manuscript.  
\[E=E_{\text{h}}+\frac{A_{g}\times E_{g}}{A} \tag{4}\]  
\[\gamma=\gamma_{\text{h}}+\frac{A_{g}}{A}(\gamma_{\text{g}}-\gamma_{\text{h}}) \tag{5}\]  
where \(E\) is the equivalent elasticity modulus of the primary support, \(E_{g}\) is the elasticity modulus of the steel rib, \(A_{g}\) is the cross-sectional area of the steel frame, \(E_{\text{h}}\) is the modulus of elasticity of the shotcrete, \(A\) is the total cross-sectional area, \(\gamma\) is the equivalent gravity of the initial support, \(\gamma_{\text{g}}\) is the gravity of the steel frame, and \(\gamma_{\text{h}}\) is the gravity of the shotcrete.