esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Development and Validation
### Indicator Screening Based on LASSO Regression  
The data were centrally normalized to eliminate the effect of different index magnitudes [40]. Using Lasso regression, the selected gender, height, weight, back object pressure, back peak object pressure, back peak contact pressure, back peak strength, back contact area, back contact pressure, back strength, hip object pressure, hip peak contact pressure, hip peak strength, hip contact area, hip contact pressure, hip strength, and another 17 indicators were used for parameter estimation and variable selection, with the help of Statuses 15 software. The LASSO regression coefficients are shown in Table 2.  
Through the parameter estimation results of Lasso regression, gender, height and weight were selected according to the model optimum, four variables of back indexes, back peak object pressure, back peak contact pressure, back contact area and back contact pressure; two variables of hip indexes, hip contact area and hip strength, and eight variables that were not relevant to the prediction of comfort were excluded.
# GPS/BDS Medium/Long-Range RTK Constrained with Topospheric Delay Parameters from NWP Model
## 1 Introduction
As a result, the ZTD derived from NWP models is still not accurate enough for high-precision GNSS real time positioning applications without the estimation of the residual tropospheric delay. In this study, we focus on developing and testing a new GPS/BDS RTK processing algorithm constrained with the NWP model to improve the GPS/BDS double difference precise positioning over medium/long-range baselines. tropospheric delay parameters, which are derived from the medium-range NWP model from the Shanghai Meteorological Bureau (SMB), are applied to GPS/BDS RTK. The precise tropospheric delay is computed using GPS observation with GAMIT (GNSS at MIT) software, which is one of the most famous GPS precise data processing software packages [34]. The quality of tropospheric delay parameters retrieved from this medium-range NWP model is assessed by comparison with the result of GAMIT. The performance of GPS RTK and GPS/BDS RTK making use of the NWP-derived tropospheric delay parameters is evaluated in terms of both initialization time and positioning accuracy. Descriptions for multiple-GNSS and the tropospheric delay estimation methods are presented in the \"Introduction\" section. In this section, we also investigate the significance and challenge of estimating the tropospheric delay using the NWP model. The NWP data collection and theoretical foundation of the new RTK algorithm constrained with the NWP model proposed in this study can be found in \"Materials and Methods\" section. The quality of the tropospheric delay derived from the NWP model used in this study is assessed in \"Results\" section. To examine the performance of the NWP-constrained RTK, compared with that of the standard RTK, experiments are carried out based on GPS and BDS observation over medium/long-range baselines. Finally, a discussion and conclusions are presented based on our findings.
# A Survey on Penetration Path Planning in Automated Penetration Testing
## 5 Planning Methods in Penetration Path Planning
### Reinforcement Learning
#### 5.2.3 Hierarchical Deep Reinforcement Learning  
Traditional DRL algorithms view the state space as a vast, flat expanse, leading to long paths from start to finish for agents. This can cause issues like weak rewards and high latency. Common exploration methods like greediness, curiosity, and action noise are limited, especially in sparse reward settings, and do not encourage broader state-space exploration.  
Hierarchical Deep Reinforcement Learning (HDRL) addresses these challenges by decomposing complex tasks into a sequence of smaller, more manageable subtasks [120, 121]. Each subtask is formulated as an individual RL problem, allowing HDRL to efficiently navigate the larger task space and enhance the overall learning process. It effectively addresses issues of sparse rewards and high-dimensional state spaces, and also streamlines decision-making in complex action spaces.  
In 2023, [PERSON] et al. [122] proposed a hierarchical DRL method that simplifies penetration path planning by dividing the PT process into the inter-host lateral movement process and the single-host penetration process. Obtaining host permissions as a sub-goal effectively alleviates the issues of state space explosion, large-scale action space, and sparse rewards for the agent. [PERSON] et al. [123] proposed an HDRL model that integrates the prior knowledge of PT experts to implement penetration path planning more efficiently. The model is divided into two layers: the upper layer selects the type of PT action, while the lower layer selects the specific action. Actions are divided into three categories, each processed by different agents.
# Digital Twin-Based Automated Guided Vehicle Scheduling: A Solution for Its Charging Problems
## 1 Introduction
[17] believe that with the characteristics of virtual reality data interaction and fusion, digital twin technology can improve shop-floor performance in the scheduling decision-making process. [PERSON] et al. [18] proposed a digital twin-based job shop scheduling method. Compared to traditional scheduling methods, it can obtain more accurate data for rescheduling and timely response to dynamic events. [PERSON] et al. [19] proposed a DT-enhanced dynamic scheduling method for machine task assignment. The results show that this method can reduce manufacturing time and delays. In general, the application of digital twin in manufacturing brings a chance to promote scheduling technology. On the one hand, by establishing a digital twin workshop, the working status and task progress of the physical workshop can be mapped to the virtual workshop to track and improve the production process [20]. On the other hand, the digital twin system collects a large amount of data from physical workshops and virtual workshops, which provides sufficient data support for workshop production [21]. Hence, the digital twin based dynamic scheduling system can meet the needs of real-time scheduling and strong predictability. To utilize the advantage of the digital twin technology and solve the problems mentioned above, a digital twin-based dynamic AGV scheduling (DTDAS) method is proposed in this paper. A dynamic energy consumption model is first established to describe the latest energy consumption characteristics of AGV. The prediction method for AGV availability based on the current scheduling plan and dynamic energy consumption models is then proposed. Thirdly, a digital twin-based AGV scheduling system is constructed. The main contribution of this paper includes: (1) A proposal for an overall framework of digital twin-based dynamic AGV scheduling; (2) Exploration of the methods for building an AGV dynamic energy consumption model, and digital twin-based AGV availability prediction; (3) The building of a mathematical model of initial scheduling and charging scheduling. The proposed methods are validated by taking a batch of orders in the discrete workshop as a case study. The structure of the article is arranged as follows. Section 2 formulates the problem of AGV scheduling. Section 3 presents the principle of the proposed DTDAS. Section 4 gives a case study to verify the proposed method. The conclusion of this article is laid down in Section 5.
# Digitalizing Industrialized Construction Projects: Status Quo and Future Development
## 5 Research Gaps and Future Directions
### Operation and Maintenance Phase  
The prevailing trajectory in the application of digital technologies within the operation and maintenance phase of industrialized construction is to realize intelligent operation and maintenance management alongside sustainability imperatives. On one front, researchers are leveraging BIM, digital twins, and sensors to facilitate the real-time monitoring, assessment, and prognostication of structural health conditions. On another front, the amalgamation of monitoring data with digital models facilitates comprehensive building sustainability management initiatives. These include energy efficiency analyses, noise source identification, and maintenance decision-making. Prospective directions for future research can be delineated as follows:  
* Existing digital twin models cater to specific building types, facilitating structural condition monitoring and predictive maintenance endeavors [95]. Future efforts should further improve the comprehensiveness of model data, aggregating information about earthquake magnitudes and the number of buildings that collapsed in accidents. Such a resource will aid in creating a multifunctional digital twin system adaptable to a wide range of industrialized construction projects.
* Present operation and maintenance practices prioritize predictive maintenance strategies geared towards enhancing the overall living experience. However, these approaches often overlook the nuanced aspects of occupants' daily lives, such as commuting experiences and the rational allocation of building spaces. Future interventions could simulate user experiences and solicit feedback on living spaces, thereby enhancing occupant satisfaction and well-being.
# Hyperspectral Image Super-Resolution Based on Spatial Group Sparsity Regularization Unmixing
## 5 Results and Analysis  
We present and analyze the performance of the proposed HSI super-resolution method in this section. Due to space limitations, not all results are presented. First, we give a visual result of our unmixing and super-resolution method. Then, examples of visual comparison results of images in the \"Harvard\" and \"UNIVERSITY OF PAVIA\" data sets are shown. Finally, we present the tables of objective image quality metrics of all the compared methods.
# An Effective Surrogate Ensemble Modeling Method for Satellite Coverage Traffic Volume Prediction
## 4 Experimental Results
### Accuracy and Robustness
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline
**Performance** & **Surrogate** & **North** & **Western** & **Caribbean** & **South** & **Middle East** & **East Asia** & **Oceania** & \multicolumn{2}{c}{**Global Domain**} \\
**Metric** & **Model** & **America** & **Europe** & **Sea** & **America** & **Russia** & **Middle East** & **East Asia** & **Oceania** & \multicolumn{2}{c}{**Domain**} \\ \hline  & PRS & 0.6425 & 0.7029 & 0.5712 & 0.6067 & 0.7023 & 0.4787 & 0.7968 & 0.6469 & 0.6760 \\  & OK & 0.9622 & 0.9918 & 0.9877 & **0.9873** & 0.9918 & 0.9507 & 0.9869 & **0.9816** & 0.9837 \\ R\({}^{2}\) & **RBF** & **0.9671** & 0.9931 & 0.9884 & 0.9836 & 0.9872 & 0.9532 & **0.9890** & 0.9795 & 0.9830 \\  & PWTA & 0.9667 & **0.9937** & **0.9892** & 0.9866 & **0.9920** & **0.9541** & 0.9885 & 0.9814 & **0.9846** \\  & WTA & 0.9470 & 0.9688 & 0.9612 & 0.9584 & 0.9729 & 0.9184 & 0.9771 & 0.9556 & 0.9626 \\  & BestGMSE & **0.9671** & 0.9931 & 0.9884 & 0.9836 & 0.9872 & 0.9532 & **0.9890** & **0.9816** & 0.9833 \\ \hline  & PRS & 0.2700 & 0.2548 & 0.3131 & 0.4378 & 0.3245 & 0.2602 & 0.2515 & 0.3593 & 0.3169 \\  & OK & 0.0878 & 0.0423 & 0.0530 & **0.0786** & 0.0539 & 0.0800 & 0.0639 & **0.0819** & 0.0711 \\  & RBF & **0.0819** & 0.0388 & 0.0515 & 0.0895 & 0.0673 & 0.0779 & **0.0585** & 0.0867 & 0.0727 \\  & PWTA & 0.0824 & **0.0372** & **0.0496** & 0.0808 & **0.0532** & **0.0772** & 0.0598 & 0.0824 & **0.0691** \\  & WTA & 0.1039 & 0.0825 & 0.0942 & 0.1424 & 0.0978 & 0.1029 & 0.0844 & 0.1274 & 0.1076 \\  & BestGMSE & **0.0819** & 0.0388 & 0.0515 & 0.0895 & 0.0673 & 0.0779 & **0.0585** & **0.0819** & 0.0719 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparisons of R\({}^{2}\) and NRMSE for different surrogates. In summary, compared with surrogate ensemble modeling globally, the proposed sub-domain modeling method has better accuracy and robustness. Moreover, the overall performance of PWTA which adaptively selects contributing surrogates for different sub-domains is better than not only the candidate surrogates but also the other two surrogate ensemble modeling methods (WTA and BestGMSE). These suggest that the method proposed in this paper can better capture the local characteristics of the objective function in each sub-domain with different SCTV features.
###### Abstract  
GPS-based, satellite-to-satellite tracking observations have been extensively used to elaborate the long-scale features of the Earth's gravity field from dedicated satellite gravity missions. We proposed compiling a satellite gravity field model from Gravity Field and Steady-State Ocean Circulation Explorer (GOCE) satellite accelerations directly estimated from the onboard GPS data using the point-wise acceleration approach, known as the carrier phase differentiation method. First, we composed the phase accelerations from the onboard carrier phase observations based on the sixth-order seven-point differentiator, which can eliminate the carrier phase ambiguity for Low Earth Orbiter (LEO). Next, the three-dimensional (3D) accelerations of the GOCE satellite were estimated from the derived phase accelerations as well as GPS satellite ephemeris and precise clock products. Finally, a global gravity field model up to the degree and order (d/o) 130 was compiled from the 71 days and nearly 2.5 years of 3D satellite accelerations. We also recovered three gravity field models up to d/o 130 from the accelerations derived by differentiating the kinematic orbits of European Space Agency (ESA), Graz, and School of Geodesy and Geomatics (SGG), which was the orbit differentiation method. We analyzed the accuracies of the derived accelerations and the recovered gravity field models based on the carrier phase differentiation method and orbit differentiation method in time, frequency, and spatial domain. The results showed that the carrier phase derived acceleration observations had better accuracy than those derived from kinematic orbits. The accuracy of the recovered gravity field model based on the carrier phase differentiation method using 2.5 years observations was higher than that of the orbit differentiation solutions for degrees greater than 70, and worse than Graz-orbit solution for degrees less than 70. The cumulative geoid height errors of carrier phase, ESA-orbit, and Graz-orbit solutions up to degree and order 130 were 17.70 cm, 21.43 cm, and 22.11 cm, respectively.  
Earth's gravity field; GPS; GOCE; point-wise acceleration approach 2019 11 1420 13390/rs11121420
# Computational Investigations and Control of Shock Interference +
###### Abstract  
Computational fluid dynamics (CFD) has aided the development, design, and analysis of hypersonic airbreathing propulsion technologies, such as scramjets. The complex flow field in a scramjet isolator has been the subject of intense interest and study for several decades. Many features of this flow field also occur in supersonic wind-tunnel nozzles and diffusers. Computational analysis of these topics has frequently provided immense insight into the actual functionality and performance. Research presented in this work supports scientific investigation and understanding of a less-researched topic, which is shock-shock interference and interaction with the boundary layer in supersonic internal flows, as well as the passive control of its adverse effects to prevent the onset of unstart in a scramjet isolator. This computational investigation is conducted on a backpressured isolator and a modified three-dimensional shock-tube to represent a scramjet isolator with ram effects provided by high-pressure gas and high-speed flow provided by a supersonic inflow. Computational results for the backpressured isolator have been validated against available measured time-averaged wall pressure data. The modified shock-tube provided an opportunity to study the shock-shock interference and shock-boundary-layer interaction effects that would occur in a scramjet isolator or a ram-accelerator when the high-speed flow from the inlet interacted with the shock produced due to the combustor pressure traveling and meeting in the isolator. An assessment of wall cooling effects on these phenomena is presented for both the backpressured isolator and the modified shock-tube.  
high-speed propulsion; shock interference; control; shock-boundary-layer interaction +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# An Efficient GNSS Coordinate Recognition Algorithm for Epidemic Management
## 6 Simulation Result
### Discussion
```
1.\(R:=\text{RectOffPy}(P)\);
2.\(x_{min}:=R[0]\); \(x_{max}:=R[1]\); \(\text{ymin}:=\text{R}[2]\); \(\text{ymax}:=\text{R}[3]\);
3.\(a:=(x_{max}-x_{min})\times(y_{max}-y_{min})\);
4.\(t_{a}:=\) [SELECT count(*) FROM 'G' WHERE 'x' \(\geq x_{min}\) AND 'x' \(\leq x_{max}\) AND 'y' \(\geq y_{min}\) AND 'y' \(\leq y_{max}\)];
5.\(t_{1}:=t_{a}/n_{\text{db}}\);
6.\(t_{2}:=a/t_{1}\);
7.\(sl:=\text{pow}(t_{2},0.5)\);
8. return (\(sl\));
```  
**Algorithm 7**SqrSide(polygon \(P\), point set \(G\), number \(n_{\text{db}}\))Therefore, based on the above discussion, the performance of sqr is far superior to that of the other methods that were discussed in this work. Moreover, it is an efficient GNSS coordinate recognition algorithm.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 4 Design
### Estimating Direction of Arrival by the MUSIC Algorithm  
The smartphone receives the acoustic signals at sampling point \(n\): \(x[t_{n}]=A(\vec{\theta})S(t_{n})+\sigma(t_{n})\)[35]. Due to the BPF we leverage, the noise \(\sigma(t_{n})\) is eliminated. The UCA-RB-MUSIC algorithm utilizes the beamformer \(F_{r}^{\rm H}\) to make the transformation from element space to beamspace:  
\[y[t_{n}]=F_{r}^{\rm H}x[t_{n}]=B(\vec{\theta})S(t_{n}). \tag{25}\]  
Thus, the corresponding beamspace covariance matrix is:  
\[R_{y}=E[y[t_{n}]y[t_{n}]^{\rm H}]. \tag{26}\]  
The next step is the eigenvalue decomposition of covariance matrix \(R=Re\{R_{y}\}\), and we introduce the signal-subspace and noise-subspace. We use \(U_{n}=(v_{2},\cdots,v_{M^{\prime}})\) to represent noise eigenvectors of noise-subspace listed in descending order.  
Eventually, the MUSIC spectrum has this form:  
\[P(\vec{\theta})=\frac{1}{B^{\rm T}(\vec{\theta})U_{n}U_{n}^{\rm H}B(\vec{ \theta})}\propto\frac{1}{v^{H}(\phi)J(\beta)(W\!L_{n}U_{n}^{\rm L}W^{\rm H})J( \beta)v(\phi)}. \tag{27}\]We can estimate that the direction of the target signal depends on the 2D search for peaks in the spectrum \(P(\vec{\theta})=P(\beta,\phi)\). The elevation angle affects the spectrum through the parameter \(\beta=\frac{2\pi f_{a}}{c}r\sin\theta\). The azimuth angle \(\phi(\in 0,2\pi)\) signifies that our system of indoor direction finding is omni-directional.
# Discrete-Time Position Control for Autonomous Underwater Vehicle under Noisy Conditions
## 4 Simulation  
All simulations are performed in Matlab, adopting the fourth-order Runge-Kutta approach. The fixed-step size is set to 0.01 s and the control schemes are simulated for 100 s. In these simulations, some typical sinusoidal, ramp, and step input commands are used to verify the effectiveness of the designed discrete-time controller. In this paper, the hydrodynamic parameters of [18] are used (see Table 2). For simplicity, \(\mathbf{H_{o}}\) is set to the identity matrix and \(\psi_{c}=0\).  
The reference positions are as follows:  
\[\left\{\begin{array}{ll}x_{d}=2\sin(\frac{\pi}{20}t)\\ y_{d}=0.4t+1\\ z_{d}=5\end{array}\right. \tag{48}\]  
and the start location of the AUV is at the origin of the coordinate system, imposed with a position error in the beginning. Then, in Equation (48) the sinusoidal, ramp, and step commands are provided for the controller. The control variables \(\mathbf{K}_{p}=diag\{19,8,3\}\) and \(\mathbf{K}_{d}=diag\{37,15,4\}\) are heuristically defined. First, \(\mathbf{K}_{p}\) is chosen, taking into account the reference position. Based on the results of the simulations, \(\mathbf{K}_{p}\) is adjusted to improve the system behavior. Second, fixing \(\mathbf{K}_{p}\), \(\mathbf{K}_{d}\) is adjusted to improve the system behavior based on the results of the simulations.
## 2 Problem Statement
### Problem Formulation
Considering that the UAV cache placement strategy is only related to content provider pricing and has nothing to do with UAV location, user pairing, or transmission power, the optimization problem P1 is partitioned into two parts for optimization. To tackle the problem of UAV cache placement strategy, a Stackelberg game algorithm is used to solve the optimal content provider file pricing and UAV cache placement strategy. The combined optimization problem of UAV location deployment, NOMA user pairing, and user power allocation is further decomposed into three subproblems, which are solved by the Lagrange multiplier method, a simulated annealing algorithm, and a particle swarm optimization algorithm, respectively.
###### Abstract  
This paper introduces a nonlinear dynamic inversion control algorithm designed to address unknown disturbances and actuator saturation issues in unmanned aerial vehicle (UAV) attitude control. The algorithm is based on a combination of finite-time disturbance observer and anti-saturation auxiliary system, which ensures the rapid convergence of attitude tracking error. Firstly, based on the Newton-Euler equations, this paper establishes a model of the attitude system for quadrotor UAVs, and this paper eliminates the small-angle flight assumption. Secondly, considering the actuator saturation problem, an anti-saturation auxiliary control system is designed to shorten the time when the control volume is in the saturation interval and achieve finite-time convergence of the attitude error. And then, to improve the robustness of the controller, this paper proposes a disturbance observer based on the finite-time stability theory, which achieves a continuous smooth output of the observation results by introducing a hyperbolic tangent function in the observer, so that the observation error can be converged to zero in a finite time. Finally, it is demonstrated by Simulink simulation that the attitude error and the observation error converge quickly to zero.  
finite-time control; attitude control; finite-time disturbance observer; actuator saturation; anti-saturation
# Performance Evaluation of Carbon Black Nano-Particle Reinforced Asphalt Mixture
## 2 Experimental Work
### Tests Performed
#### 2.4.2 Storage Stability  
The storage stability test was performed as per BS EN 13399 (2017). This technique was used to study CBNPs-modified asphalt binder's storage stability at high temperatures. CBNPs-modified asphalt binder was poured into a glass tube of 25 mm diameter and 140 mm height. The tube was placed vertically in an oven for 48 h at 163 \({}^{\circ}\)C and then it was cooled in refrigerator at \(-7\,^{\circ}\)C for 4 h. The tube was finally cut in three equal sections and, after melting the samples, a softening point test was performed for top and bottom portions. A difference of softening point less than 2.5 \({}^{\circ}\)C is a widely used criterion for considering the blend to be stable at high temperatures [5].