esa-sceva/satcom-chunk-collection huggingface dataset:
# A Coupled Darcy-Forchheimer Flow Model in Fractured Porous Media
## 1 Introduction
(1997) [17] derived Forchheimer's Law from the theoretical perspective through the homogeneous and perturbation methods, respectively, and proved its existence and uniqueness. Numerically, [PERSON] et al. (1993) [18] and [PERSON] et al. (2017) [19] obtained the approximate solution of seepage by using different methods, such as the finite difference method and the multipoint flux approximation method. The forms of those solutions are consistent with [PERSON]'s law. For Darcy flows in the fractures and pores of the rock, the main difficulty of the dual media simulation method is the coupling problem between n-dimensional (\(n=2\), \(3\)) pore media flows and \(n-1\)-dimensional fractures [20; 21; 22]. In recent years, many scholars have conducted in-depth studies on this problem. For example, [PERSON] et al. (2014) [23] proposed a calculation method for the total permeability tensor of fracture-pore dual media based on the dual media theory. However, there are few reports on a Forchheimer flow simulation with dual media seepage considering fractures under high permeability pressure. The main difficulty of this problem lies in solving the nonlinear system after fracture discretization. Among them, [PERSON] et al. (2008) [24] adopted the interface model to deal with fractures, added nonlinear transfer conditions to the contact, and simplified the problem into a nonlinear system problem of (\(n-1\))-dimension by using region decomposition technology. [PERSON] et al. (2019) [25] adopted the multi-grid method to calculate the nonlinear system problem and discussed the robustness of the nonlinear system for the dual media Darcy-Forchheimer flow. The results showed that the algorithm was heavily dependent on mesh size and the Forchheimer coefficient. So far, there is still no good method to solve the double medium Darcy-Forchheimer flow problem [26; 27]. During the long-term operation of tunnels under high water heads, water gushing is a commonly existing engineering geological disaster that needs to be highly paid attention to [28]. The Darcy flow is usually used to analyze seepage conditions in the tunnel rock mass. However, under a high water head, the Darcy flow would result in a large deviation from the field test [29]. Therefore, it is necessary to analyze the seepage field of the tunnel surrounding the rock by using the Darcy-Forchheimer coupled flow method in the fractured rock mass. Thus, based on previous studies, a Darcy-Forchheimer coupled flow method in the fractured rock mass is proposed based on the finite volume method, taking into account the Forchheimer flow in fractured media and the Darcy-Forchheimer coupled flow in porous media. Based on this method, the nonlinear seepage law of the tunnel surrounding rock is studied, which provides some theoretical and technical support for the waterproofing design of a fractured tunnel.
## 5 Results and Discussion
### Original Scheme of Forced Ventilation
#### 5.1.2 CO Concentration Field  
The distribution of CO mass concentration in the central axial plane within 30 min of ventilation after blasting is shown in Figure 10. It can be seen that in the early stage of ventilation, the CO near the excavation face cannot be moved to the connecting roadway in time due to the eddy zone in this area. The connecting roadway is located 678 m away from the left excavation face as shown in Figure 2a. With the increase in ventilation time, CO is continuously diluted and moving towards the connecting roadway. The maximum mass concentration of CO in the cavern decreases and the air volume containing CO expands continuously. The CO migration velocity in the middle of the cavern is faster and near the wall surface, it is slower, showing a U-shaped distribution in the cavern.  
Figure 8: Distribution of airflow field near the working face: (**a**) Velocity vector at z = 5.5 m; (**b**) Velocity vector at y = 18 m.  
Figure 9: Air velocity distribution along the cavern.  
In order to investigate the air quality at the breathing height of field workers in the cavern, the CO mass concentration distribution at a height of 1.6 m from the ground was studied as shown in Figure 11. The CO near the excavation face and the maximum CO concentration in the cavern decrease with the ventilation time, and the distribution span of CO air mass increases gradually. Ventilation time has a great impact on construction ventilation effect. Although the \"Safety Operation Regulations for Tunnel Drilling and Blasting\" stipulates that the ventilation time after blasting should not be less than 15 min [36], the specific ventilation time is not clearly defined in the code and the industry usually takes 30 min into account. If the ventilation time is too short, the air volume and ventilation equipment configuration capability need to be increased, resulting in an increase in ventilation costs; if it is too long, it will affect the subsequent working procedures. It can be seen from Figure 11 that the CO only moves to the vicinity of the connecting roadway after 30 min of forced ventilation, and its concentration value is much higher than the safety standard value of 30 mg/m\({}^{3}\).  
Figure 11: CO mass concentration distribution at breathing height.  
Figure 10: CO mass concentration distribution in the central axial plane. (**a**) Ventilation for 5 min; (**b**) Ventilation for 10 min; (**c**) Ventilation for 15 min; (**d**) Ventilation for 20 min; (**e**) Ventilation for 25 min; (**f**) Ventilation for 30 min.
## 3 Results
### Force Characteristics Results of Initial Support
#### 3.2.2 Impact of Soil Bulk Density on the Force Exerted on the Initial Support  
To study the influence of the soil unit weight (\(\gamma\)) on the initial support stress characteristics in the combined method, the soil unit weight was varied in the calculation model as 18, 19, 20, 21, 22, 23, and 24 kN/m. The resulting initial support stresses were obtained for different soil unit weights.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Safety Control Indicators** & **Early Warning Value** & **Control Value** \\ \hline Tunnel Horizontal Displacement & \textless{}10 mm & \textless{}20 mm \\ Tunnel Vertical Displacement & \textless{}10 mm & \textless{}20 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Urban rail transit structural safety control index values.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**H (m)** & **M\({}_{\text{max}}\) (kN\textcdot{m}\)**) & **-M\({}_{\text{max}}\) (kN\texttextcdot{m}\)**) & **N\({}_{\text{max}}\) (kN)** \\ \hline
24 & 131.35 & 138.74 & 854.12 \\
26 & 146.05 & 154.30 & 951.33 \\
28 & 149.36 & 158.61 & 981.30 \\
30 & 154.97 & 165.93 & 1026.27 \\
32 & 166.96 & 176.88 & 1102.92 \\
34 & 171.87 & 180.66 & 1133.06 \\
36 & 179.23 & 1 As evident from the initial support stress variation curve in Table 4 and Figure 6b, both the maximum bending moment and axial force of the initial support section are linearly and positively correlated with the soil weight, with their values increasing as the weight increases. Moreover, as the unit weight parameter increases, the growth rate of the maximum axial force curve gradually outpaces that of the maximum bending moment curve. This suggests that, in this project, the influence of the unit weight on the axial force was more pronounced than that its influence on the bending moment.
###### Abstract  
The exploitation of the spatial and spectral characteristics of hyperspectral remote sensing images (HRSIs) for the high-precision classification of earth observation targets is crucial. Convolutional neural networks (CNNs) have good classification performance and are widely used neural networks. Herein, a morphological processing (MP)-based HRSI classification method and a 3D-2D CNN are proposed to improve HRSI classification accuracy. Principal component analysis is performed to reduce the dimensionality of the HRSI cube, and MP is implemented to extract the spectral-spatial features of the low-dimensional HRSI cube. The extracted features are concatenated with the low-dimensional HRSI cube, and the designed 3D-2D CNN framework completes the classification task. Residual connections and an attention mechanism are added to the CNN structure to prevent gradient vanishing, and the scale of the control parameters of the model structure is optimized to guarantee the model's feature extraction ability. The CNN structure uses multiscale convolution, involving depthwise separable convolution, which can effectively reduce the amount of parameter calculation. Two classic datasets (Indian Pines and Pavia University) and a self-made dataset (My Dataset) are used to compare the performance of this method with existing classification techniques. The proposed method effectively improved classification accuracy despite its short classification time.
# Improved 2D Ground Target Tracking in GPS-Based Passive Radar Scenarios
## 7 Conclusions  
This paper presents a GPS-based PR exploiting digital array processing techniques for ground target detection and localization. To cope with problems related to the temporal variation and low power of the received signal at ground level, a processing scheme combining reference signal reconstruction, adaptive filtering techniques, and a two-stage spatial filtering algorithm is implemented. Conventional beamforming techniques are used to increase the echo level of the target prior to the detection stage. A high-resolution DoA estimation technique is applied to estimate the target azimuth and allow the target to be located on the ground, first in local Cartesian space and then in geodetic coordinates.  
Real radar data were recorded using an updated version of IDEPAR demonstrator at the external campus of the University of Alcala, in order to analyse the system detection and localization performance. During the measurement campaign, a cooperative vehicle was used for validation purposes and two additional non-cooperative targets were available in the AoI. The use of a linear array in the surveillance channel increased the target's echo level improving the system detection capabilities and the maximum detection range. The target localization capability was validated along the trajectory of the cooperative targets with an average estimation error of 2.38 m. Non-cooperative targets were also correctly located along their trajectories. The results confirm the possibility of detecting ground targets with GPS-based PR, but at low ranges currently. Further research is necessary to increase the detection range, maintaining the positive characteristics of these radars.  
Conceptualization, M.-P.-A. and D.M.-M.; Methodology, P.G.-d.-H. and N.d.-R.-M.; software, P.G.-d.-H., N.d.-R.-M. and M.-d.-C.B.-O.; validation and formal analysis, M.-d.-C.B.-O.; investigation, P.G.-d.-H. and N.d.-R.-M.; data curation, P.G.-d.-H., N.d.-R.-M.; writing--original draft preparation, all; writing--review and editing, all; project administration, M.-F.J.-A. and N.d.-R.-M.; funding acquisition, M.-P.J.-A. and N.d.-R.-M. All authors have read and agreed to the published version of the manuscript.  
This work is part of the project RTI2018-101979-B-100, funded by MCIN/AEI/10.13039/501100011033/FEDER, EU; the project SBPLY/19/180501/000350 co-funded by both the Community Board of Castilla-La Mancha and the European Union through the European Regional Development Fund (ERDF); and the project CM/JIN/2021-010, funded by the Community of Madrid through the Multiannual Agreement with the University of Alcala in its line of stimulating research by young doctors, within the framework of the V PRICIT (V Regional Plan for Scientific Research and Technological Innovation).  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Novel Relay Selection Scheme Based on Q-Learning in Multi-Hop Wireless Networks
## 2 System Model and Conventional Selection Schemes  
Figure 2 shows the multi-hop system model. The \(S\) and \(D\) represent a source and destination respectively. The \(R_{m}\) means the \(m\)-th relay among the total \(K\) relays. The \(h_{S,D}\), \(h_{S,R_{m}}\), \(h_{R_{m},D}\) and \(h_{R_{m},R_{m}}\) represent a channel coefficient of the source-destination, source-relay, relay-destination and relay-relay node. The total number of nodes including source and destination is \(N(N=K+2)\). The distance between the source and destination node is normalized to 1. The distance between relays is also normalized randomly from zero to 1. However, if the signal goes through a random relay between source and destination, the total sum of the distance between source-relay and relay-destination is greater than 1 and it causes a signal deterioration. So, if the relay is not located on the line-of-sight (LoS), the distance is longer. This deterioration should be reduced as much as possible. Therefore, the goal is to select the most suitable relay among random relays through the Q-Learning in this paper. The signal transmission scheme uses a DF scheme in the OFDM system.
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 5 The Protocol Details
### CHs Election
When player \(i\) chooses \(D\) as its strategy, the extra cost \(e_{i}\) can be calculated by:  
\[e_{i}=v_{i}-\frac{q}{Ech_{i}} \tag{25}\]  
where \(v_{i}\) is the payoff when node \(i\) chooses the strategy \(ND\) while there exists at least one CH; \(Ech_{i}\) is the amount of energy consumed by player \(i\) to transmit the packet with length \(q\) to BS when it acts as the CH, and \(Ech_{i}\) can be expressed as follows:  
\[Ech_{i}=q\zeta Nb_{i}+qE_{DA}(Nb_{i}+1)+q\varepsilon_{m}d_{i}^{4} \tag{26}\]  
where \(q\) is data packet length; \(\zeta\) is the energy consumed by the transceiver circuitry; \(Nb_{i}\) is the number of neighbor nodes of player \(i\); \(E_{DA}\) is the energy consumed for aggregating a one-bit data packet; \(\varepsilon_{m}\) is the amplifier characteristic constant regard to two-ray ground reflection model and \(d_{i}\) is the distance between player \(i\) and BS. After the \(CEG\) is finished, \(ch_{k}\) can get the absolute equilibrium probability \(p_{i}{}^{m}\) to be the CH in the next round for any member node \(i\). In fact, sensor nodes usually are unevenly distributed in the sensing field which results in uneven clustering. Some clusters may have more member nodes joining the \(CEG\) than other clusters so that the nodes in different clusters may have different levels of absolute equilibrium probability. To avoid this case, a probability transformation mechanism is adopted to transform absolute equilibrium probability into relative equilibrium probability. For any member node \(i\), its relative equilibrium probability \(p_{i}{}^{r}\) to be CH in the next round can be expressed as follows:  
\[p_{i}{}^{r}=p_{i}{}^{m}-\sum_{j=1}^{n_{k}}p_{j}{}^{m}/n_{k}+p_{ept} \tag{27}\]  
where \(p_{i}{}^{m}\) is the absolute equilibrium probability of being a CH for node \(i\); \(n_{k}\) is the number of active member nodes in the cluster to which node \(i\) belongs and \(p_{ept}\) is the expected probability calculated by Equation (22). After this transformation is finished, sensor nodes in different clusters have the same average probability to be CH. Since each sensor node decides whether to be a CH based on its corresponding probability to be a CH, this random CHs selection mechanism may cause an uneven distribution of clusters. To avoid this case, a sensor node which successfully self-selected as the CH acts as CH tentatively and will further run for the final CH role. A tentative CH will firstly broadcast a tentative CH election message (including node ID, node status and residual energy) within communication radius \(R\). Then it can know the neighbor heads by receiving this kind of messages from other heads. A tentative CH which holds more energy than its neighbors will be the final CH, and an election message including node ID and node status will be broadcasted within radius \(R\). A tentative CH which has received the final CH election message will broadcast a quitting CH election message to abdicate the chance to its neighbor heads.
## 4 Performance Prediction
### SNR Dependency  
The effect of low SNR is further analyzed by Monte Carlo simulations. The simulations (parameters are listed in Table 1) start with generating an artificial noise-like SAR scene on which the antenna pattern in elevation is applied to. After adding white noise, the pointing is estimated as described in the previous section using the correlation method. The performance for various SNR values is shown in Figure 1.  
Figure 1: Performance estimation for the pointing retrieval algorithm w.r.t. additional white noise (in terms of SNR values).  
As expected the standard deviation of the pointing estimation decreases with higher SNR (right scale, +-sign). It is always below 0.5 mdeg for the given parameters. The residual pointing error (left scale, \(\blacktriangle\)-sign) also decreases and saturates at 5 dB SNR to about 0.52 mdeg.
## 2 Materials and Methods
### Analysis Method
#### 2.3.2 Partial Correlation between NEP IAV and Climatic Variables  
We adopted a partial correlation method to analyze the correlations between IAV of NEP and climatic variables. The analysis could enable an investigation of the correlation between NEP and individual climate variables while eliminating the influence of other climate variables. However, before conducting the partial correlation analysis, NEP and climatic variables were detrended [2; 28].
# Adaptive PI Control Strategy for Optimal Microgrid Autonomous Operation
## 1 Introduction
### Literature Review  
Although the optimization methodologies are efficient and robust tools for designing PI controllers, they have significant drawbacks, such as a complicated process, a considerable memory demand, and more time in the optimization procedure. As a result, the adaptive PI controller is a strong choice for tuning PI controller parameters online without any optimization methods. This reduces the time spent on the layout and the effort expended on this problem. In addition, several adaptation techniques were used to adjust the PI controller. Adaptive control (ACT) is introduced, depending on an affine projection algorithm with quicker convergence and fewer system complexities than optimization techniques [22]. It also displays some appealing elements, including the coefficient vector. Moreover, this strategy repeatedly approaches accurate data independent of the variables, as described in [23]. A novel ACT utilizing a set-membership engine algorithm outperforming existing strategies is presented [24]. In [25], the least mean (LM) and the square root of exponential (SRE) techniques are presented as unique ACT. The use of an absolute negative error causes this approach to be normalized. LMSRE also offers quick convergence and reliability with minimum error. The proposed enhanced block-sparse adaptive Bayesian algorithm (EBSABA) outperforms conventional adaptive techniques [26]. The EBSABA's advantages include quick convergence, a straightforward technique, and decreased computing complexities. Furthermore, the suggested technique may consider the influence of the actuation mistake, its magnitude signal, and the sparse block method. This is the primary motivation for the authors to use this high-performance technique for live updating the PI controller gains of the DGRs.  
This work adds to that pool by introducing a unique way for an optimum regulation of islanded MGDs based on the EBSABA. This study employs this adaptive technique in a PI controller optimum control strategy with multiple PI controller parameters to improve the efficiency of the off-grid mode process. To verify the proposed technique, several simulations are run to demonstrate the effectiveness of the proposed EBSABA in the system's transient responses over LMSRE-based ACT and specific other optimization strategies, such as the SFO and PSO approaches.
## 2 Materials and Methods
### Sample Preparation and Calibrations  
A five-point external calibration curve was prepared from stock solutions of TMAO, BMAA and DAB. The concentrations of BMAA and DAB in the five solutions were 50, 100, 250, 750 and 1000 \(\upmu\)g/L, while TMAO was present at 5, 10, 25, 75 and 100 \(\upmu\)g/L. IS was added in all the calibration solutions to a final concentration of 250 \(\upmu\)g/L. The peak area ratio (A/A\({}_{\text{IS}}\)) related to the selected fragment ions acquired in tandem MS mode for each compound was plotted against concentrations for calibration purposes. Linearity was assessed using the least-squares regression.  
Mouse plasma and urine samples were collected as previously reported [19]. For the matrix-matched calibration, plasma samples were pooled from ten different animals and split into 100 \(\upmu\)L aliquots. Five aliquots were spiked with both BMAA and DAB at concentrations of 500, 1000, 2500, 7500 and 10,000 \(\upmu\)g/L and TMAO at concentrations of 50, 100, 250, 750 and 1000 \(\upmu\)g/L, respectively; one aliquot was treated as blank. The basal concentration of TMAO, which is a metabolite physiologically present in plasma and urine, was preliminarily estimated by the standard addition method. Plasma aliquots were extracted with 400 \(\upmu\)L of ice-cold acetonitrile added with 10 mM FA and 625 \(\upmu\)g/L of IS, vortexed and centrifuged at 14,000\(\times\)\(g\) and 4 \({}^{\circ}\)C for 10 min. 100 \(\upmu\)L of the supernatant was further diluted 1:1 with water 10 mM FA and injected for the analysis. The same protocol was used for urine samples. The selected analytes were analyzed in their free form, i.e., not bound to proteins, which were removed during the sample preparation.
# Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments
## 1 Introduction
Building on a procedure recently proposed for target metric estimation [20], the mathematical framework for this method initially follows previous studies for consistency [21] but introduces novel elements focused on drone navigation. The method calculates distances from the camera center to two reference points within a relative coordinate system and determines the relative angles between these points. By converting these relative coordinates to world coordinates through a series of rigid transformations, the camera's position in world coordinates is efficiently retrieved. This approach is both fast and accurate, eliminating the need for additional reference points or time-intensive calculations. The method was validated in an outdoor environment to represent a realistic scenario. An accurate Digital Elevation Model (DEM) generated via LiDAR was used in conjunction with 11 aerial images acquired from a multi-rotor UAV.