esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Due to the distance having no influence on the spectral range of azimuth signals, the spectral reconstruction method can effectively handle imaging issues in close-range scenarios. The problem of spectral reconstruction for azimuth non-uniformly sampled signals can be formulated as an inverse problem in mathematics, and the solution to this problem may be ill-posed. Therefore, we employ the Tikhonov regularization method for solving the problem of spectral reconstruction. The Tikhonov regularization method is an approach that, overall, minimizes the norm of the solution and residual norm based on the selected regularization parameter [18]. It has been widely applied in solving ill-posed problems [19; 20]. To tackle the problem of non-uniform sampling in the azimuth of THz SAR, a regularization-based algorithm in this paper is proposed for reconstructing the spectrum of non-uniformly sampled signals. It transforms the spectrum reconstruction problem into a linear equation system and solves it using Tikhonov regularization. The proposed algorithm achieves a fast transformation of SAR non-uniformly sampled signals from the spatial domain to wavenumber domain in azimuth, thereby establishing an efficient image reconstruction algorithm suitable for non-uniformly sampled SAR. Considering the characteristics of SAR echo signals as two-dimensional matrices and unknown noise, a regularization parameter selection method based on L-curve criterion is established. Moreover, we utilize a Fourier series to analyze the error in spectrum reconstruction, demonstratingthat the accuracy of estimating the signal spectrum for finite-length approximate band-limited signals depends on the high-frequency components beyond the signal bandwidth and the signal noise. The simulation and experimental results validate that the proposed algorithm achieves a more accurate reconstruction of the non-uniformly sampled signals compared to the azimuth resampling algorithms, resulting in improved imaging quality. Additionally, it offers higher computational efficiency compared to BPA, making it more suitable for compensating the significant azimuth motion errors in THz SAR applications. This article is organized as follows. In Section 2, a spectral reconstruction model for non-uniformly sampled signals is established. In Section 3, the Tikhonov regularization method is used to solve the model and the regularization parameter is determined using the L-curve criterion. In Section 4, the proposed method is applied to the problem of THz SAR with non-uniform sampling, and a corresponding algorithm flowchart is provided. In Section 5, simulations and experiments are conducted using a 0.3 THz stepped-frequency radar system. Finally, a conclusion is drawn in Section 6.
## 3 Probabilistic Topology Control Algorithm
### Communication Network Architecture of SWARMs Project  
The architecture of the communication network used in the SWARMs project can be seen in Figure 1. In SWARMs project, the communication network has been divided into five different categories: (1) overwater RF wireless communication network; (2) satellite communication network; (3) cabled communication network; (4) acoustic MF communication network; (5) acoustic HF communication network. In this paper, we mainly focus on the acoustic MF communication network. Based on the architecture of the communication network, many use cases have been proposed in the SWARMs project; for instance, corrosion prevention in offshore installation, monitoring of chemical pollution, detection, inspection and traction of plumes, berm building, and seabed mapping, which can all be found at [[http://swarms.eu/usecases.html](http://swarms.eu/usecases.html)]([http://swarms.eu/usecases.html](http://swarms.eu/usecases.html)).  
In this paper, the topology control algorithm is designed for the detection, inspection and traction of plumes (see Figure 2). In this use case AUVs display two different kinds of movement pattern: (1) all AUVs in network move in a group with the same movement pattern as plumes; (2) in the interior of network, the AUVs move freely in the area where the plumes exist; moreover, for guaranteeing this area can be covered by the AUVs' transmission area, the movement of AUVs should be able to guarantee that the AUVs are approximately uniformly distributed in the area where the plumes exist. These two kinds of movement are different. Considering the first kind of movement, since the movement of plumes is random, the AUVs must be able to detect the movements of plumes and trace them; in most cases, the movement of plumes under the water can be regarded as a group mobility problem and many mobility models can be used to describe this kind of movement, such as the reference point group mobility model [28], the nomadic community mobility model [29], the reference velocity group mobility model [30], etc. Consequently the first kind of movement of AUVs is similar to the movement of plumes. However, concerning the second movement, which is the inner-network movement, the AUVs can move in the area where the plumes exists freely; moreover, for guaranteeing this area can be covered by AUVs' transmission area, the AUVs should be uniformly distributed in this area.
## 3 Result
### Explainability
For the b6 band, the main changes occur at the reflectance values of 0.26 and 0.31. Regarding b7 band, significant changes in SHAP contributions are observed around the reflectance values of 0.30 and 0.33. The b3 band interacted differently, with medium-to-high reflectance values (above 0.15) negatively affecting the \(\mathrm{F}\mathrm{v}^{\prime}/\mathrm{F}\mathrm{m}^{\prime}\) prediction, while low reflectance values (below 0.15) positively affected the model. Bands in the shortwave infrared (SWIR) region, particularly b11 and b12, showed a complex relationship with model predictions, as their SHAP values fluctuate between positive and negative depending on reflectance levels, however, very low reflectance values were associated with more positive contributions (below \(\sim\)0.18 for both bands). Finally, band b5 showed that reflectance values between \(\sim\)0.18 and \(\sim\)0.26 contributed negatively to the prediction, low ones (below \(\sim\)0.80) slightly improved it, and high ones had minimal influence. Figure 7: SHAP plot showing the importance of the features (Sentinel-2 bands reflectance values) in contributing to Random Forest predictions. Each point represents an individual data instance, with its horizontal position indicating the contribution of a specific feature to the deviation from the model’s average prediction. The color of the points indicates the value of the feature.
## Materials and Methods
### Preparation of Cellular Extracts and Western Blot Analysis  
HaCaT cells were seeded into cell chambers in a standard medium. Then, cells were serum-deprived for 4 h and, after this time, they were exposed or not to simulated microgravity for adaptation or recovery experiments. At the end of the experimental time, the cells were washed twice with ice-cold PBS (Phosphate-buffered saline, Sigma-Aldrich, Saint Louis, MO, USA) and cell pellets were lysed with RIPA buffer (Sigma-Aldrich). A mix of protease inhibitors (Complete-Mini Protease Inhibitor Cocktail Tablets; Roche, Mannheim, Germany) was added just before use. Then, cellular extracts were centrifuged at 8000\(\times\)\(g\) for 10 min. The protein content of supernatant fractions was determined by using the Bradford assay. For immunoblot analysis, cellular extracts (40 ug of protein for each sample) were separated on SDS-PAGE gels with a concentration of acrylamide specific for the proteins studied. Proteins were blotted onto nitrocellulose membranes (Bio-Rad Laboratories, Hercules, CA, USA) and probed with mouse anti-E-cadherin C-terminal end monoclonal antibody (BD Transduction LaboratoriesTM, San Diego, CA, USA, Cat# 610181);  
Then, the membranes were incubated with the appropriate HRP-conjugated secondary antibody (sheep anti-mouse IgG ECL antibody, GE Healthcare, Little Chalfont, UK, Cat# NA9310) for 1 h at room temperature. Immunocomplexes were detected with an enhanced chemiluminescence kit (Western Bright ECL HRP Substrate, Advansta Inc., Menlo Park, CA, USA) according to the manufacturer's instructions. All Western blot images were acquired and analyzed through Imaging Fluor S densitometer (Biorad-Hercules). Each experiment was performed three times.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 2 Virtual Inertia and Current Control Techniques  
### Concept of Virtual Inertia  
Virtual inertia is a combination of control algorithms, ESS, and power electronics that emulates inertia similar to that of a conventional synchronous generator with a large rotating mass and damper windings. The virtual inertia control algorithm presents RES units and/or ESS as a dispatchable energy source with the capability of responding to frequency changes in the power system. There are numerous methods of implementing virtual inertia that are discussed in the literature. Approaches such as synchronous power controllers discussed in [9] use a complete electromechanical model of a synchronous generator to generate a set of reference voltages. The corresponding current references for the inverter are then generated using the concept of virtual admittance. A current controller is finally used to inject the reference current. Other topologies like the synchronverter [8] and Ise labs topology [15] operate the inverters as voltage sources instead of current sources. A review of these various topologies was discussed in detail in [16; 17; 18].  
A simple implementation of the virtual inertia control algorithm is shown in Figure 1. Since this topology does not implement any electromechanical models within the controller, the implementation is quite straightforward [16; 17]. The controller continuously measures the frequency of the system typically using a phase-locked loop (PLL) and generates power references, \(P_{VI}\), based on the change in frequency (\(\Delta f\)) and the rate of change of frequency (ROCOF) (\(\frac{d(\Delta f)}{dt}\)) as:  
\[P_{VI}=(K_{drop}\ \Delta f)+K_{inertia}\frac{d(\Delta f)}{dt} \tag{1}\]where \(K_{droop}\) is the damping constant similar to a frequency droop and \(K_{inertia}\) is the inertia constant, which tries to arrest the change in frequency. The calculated reference power is then injected/absorbed to/from the system using a current-controlled voltage source inverter (CC-VSI), which uses a current control loop, which are compared in this paper.
###### Abstract
As reported in previous work [20], the application of this procedure to GCEs leads to the formation of a homogenous and perfectly adherent layer four times less resistive than the original surface. Since the interesting results were obtained with paracetamol, the effect of the same activation process was applied to the voltammetric detection of DCF. The electrochemical behavior of DCF was studied on both GCE and screen-printed carbon electrodes (SPCEs) after the anodic activation. The surface of the SPCEs was analyzed by Scanning Electron Microscopy (SEM) and Energy Dispersive Spectrometry (EDS). On both the activated carbon-based electrodes, the voltammetric procedure (Differential Pulse Voltammetry, DPV) for the determination of DCF was optimized by a Design of Experimental (DoE) approach, and the linearity range of the response as well as the calibration and limit parameters (limits of detection-LoD; limit of quantification-LoQ) were defined. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electrode 1** & \
\begin{tabular}{c} **Linear Range** \\ **(\(\mu\)mol L\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Detection Limit** \\ **(\(\mu\)mol L\({}^{-1}\)/\(\mu\)g L\({}^{-1}\))** \\ \end{tabular} & **Reference** \\ \hline EPPGE & 0.01–1 & 0.0062/1.8 & [8] \\ MWCNTs/Cu(OH)\({}_{2}\)NP/ILNC/GCE & 0.18–119 & 0.04/11.8 & [9] \\ VFMCNITPE & 5–600 & 2.0/592.3 & [10] \\ IL/CUTFE & 0.5–300 & 0.2/59.2 & [11] \\ MWCNT-IL1 CCE & 0.05–50 & 0.018/5.3 & [12] \\ IL/CNITPE & 0.3–750 & 0.09/26.7 & [13] \\ MWCNTs/PGE & 0.047–12.95 & 0.017/5.0 & [14] \\ Cu/CTS/MWCNTs/GCE & 0.3–200 & 0.021/6.2 & [15] \\ Silica NPs-CPE & 0.1–500 & 0.046/13.6 & [16] \\ SPCE-CeO\({}_{2}\) NP & 0.4–26 & 0.4/118.5 & [17] \\ SPCE/MWCNTs-COOH & 0.0001–0.020 & 0.00003/0.01 & [18; 19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Analytical performance of electrochemical methods previously developed for the quantification of diclofenac using carbon-based electrodes.
# Fatigue Crack and Residual Life Prediction Based on an Adaptive Dynamic Bayesian Network
## 3 Fatigue Crack Propagation Experiment Verification
### Single Adaptive Variable DBN Model
#### 3.1.1 Data Analysis  
The crack propagation a-N curves [34] for the three specimens are shown in Figure 8. According to ASTM E399, the stress intensity factor, \(K\), for three-point bending specimens is calculated using  
\[K=\frac{3 PS}{2 BW^{2}}\sqrt{a}\frac{1.99-\xi(1-\xi)\big{(}2.15-3.93\xi+2.7\xi^{2} \big{)}}{(1+2\xi)(1-\xi)^{\frac{3}{2}}} \tag{21}\]  
where \(\xi=a/W\) with \(a\) as the crack length; \(P\) is the load amplitude. Letting \(K=K_{\text{IC}}\), the critical crack lengths, \(a_{C}\), of the three specimens were calculated as 20.6, 26.2, and 36.3 mm, respectively. And the measured values of \(a_{C}\) are 20.5, 25.9, and 36 mm. It can be seen that the measured critical crack lengths in Figure 8 are highly consistent with those calculated theoretically.  
In addition, fractographs were taken of the fracture sections of the three specimens and are shown in Figure 9a-c. The areas indicating the stages of steady crack propagation and sudden specimen fracture can be well distinguished from the figures, and the regions with the distance of \(a_{\text{c}}\) from the lower edge of the specimens exactly locate the transition between these two stages.  
Figure 8: Three-point bending fatigue \(a\)-\(N\) curve.  
Figure 9: Fractographs of the three specimens with dimensions of (**a**) 120 \(\times\) 30 \(\times\) 15 mm\({}^{3}\), (**b**) 160 \(\times\) 40 \(\times\) 15 mm\({}^{3}\) and (**c**) 240 \(\times\) 60 \(\times\) 15 mm\({}^{3}\).  
Paris' law was used as the state equation for particle filtering. Based on the experimental data, the parameters of Paris' law were obtained based on data fitting. Specifically, the value of \(m\) in Paris' law is approximately 3.34, and C ranges from \(5\times 10^{-11}\) to \(7\times 10^{-11}\). Within the context of the DBN, the logarithmic of parameter \(C\) is treated as a variable subject to uncertainty. For particle filtering purposes, it is initially represented by a set of uniformly distributed particles. These particles are then subject to passive, adaptive updates in sync with the revisions of the DBN model. To verify the correction ability of the method, observation was started after several cycles and DBN tracking instead of starting from scratch.
# Predicting Putting Development Using Machine Learning Methods Based on RIOCHTrack Data
## 3 Results and Discussion
### Relationship between Input Variables and Putting  
The matrix scatter plot consists of three parts: an upper triangle, a lower triangle, and a diagonal. Each block in the upper triangle represents the correlation coefficients of two variables in the same row and column as the block. The calculation formula is as follows:  
\[\rho_{X,Y}=\frac{cov(X,Y)}{\sigma_{X}\sigma_{Y}} \tag{10}\]  
where \(\rho_{X,Y}\) is the Pearson correlation coefficient, \(cov(X,Y)\) is the covariance of two variables, and \(\sigma_{X}\sigma_{Y}\) is the standard deviation of the variable. The absolute value of the correlation coefficient indicates the strength of the correlation between two variables. A higher absolute value suggests a stronger correlation between the variables. The lower triangle represents the scatter plot of each pair of variables, and the added line represents a linear correlation trend. Diagonal lines are the histogram that represents the distribution of variables. Figure 9  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Models** & **Hyperparameters** & **Values** & **Mean R\({}^{2}\)** \\ \hline SVR & C: & 1000 & 0.9152 \\  & \(\gamma\) & 5 & \\  & Kernel type & rbf & \\  & epsilon & 1 & \\ RF & max\_depth & None & 0.9765 \\  & min\_samples\_leaf & 1 & \\  & min\_samples\_split & 2 & \\  & n\_estimators & 300 & \\ Extra-trees & max\_depth & 200 & 0.9743 \\  & min\_samples\_leaf & 1 & \\  & min\_samples\_split & 2 & \\  & n\_estimators & 50 & \\  & learning\_rate & 0.03 & 0.9800 \\  & n\_estimators & 250 & \\  & max\_depth & 5 & \\ ANN & activation & relu & 0.9580 \\  & hidden\_layer\_sizes & (50, 50, 100) & \\  & learning rate & 0.0001 & \\  & optimizer & lbfgs & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyperparameters and mean R\({}^{2}\) of different methods based on 5-fold cross-validation.  
shows that axle load has the greatest impact on putting, with a correlation coefficient of 0.73. The impact of temperature is not significant, possibly because the temperature must exceed a certain threshold to have a significant impact on rutting. Therefore, the properties of the lower layer mixture have a greater impact on rutting.  
Figure 8: Hyperparameter search graph. (**a**) SVR; (**b**) ANN; (**c**) RF; (**d**) GBDT; (**e**) Extra Trees.
# A Review on the Prospects of Mobile Manipulators for Smart Maintenance of Railway Track
## 6 Benefits of Using a Mobile Manipulator for Railway Track Maintenance
### Asset utilization  
Currently, many railway asset management companies use state-of-the-art railway track monitoring devices. New measurement train (NMT) is a track monitoring train deployed by Network Rail Limited. This is very costly to operate and needs many crews per shift to collect, analyze and verify recorded data [41]. Because of the size (Figure 4a), NMTs are unable to locate defects in a short range, which is essential for the planning of repair tasks. Besides, these track monitoring trains can only perform inspection. Moreover, other trains are not allowed to use the same track during the inspection, which increases track possession and reduces asset utilization. Human intervention is required for instant inspection, defect verification, and performing repair tasks. The forecasted increased demand for railway usage requires more frequent passenger and freight trains. More frequent track maintenance will be needed with this increased track usage. This is a dilemma and challenge for the railway track maintenance companies to ensure the optimum utilization of the assets. A fleet of mobile manipulators can assist humans in frequent and instant inspection and repair tasks, reducing track downtime and possession.
# Comprehensive Comparison of VNE Solutions Based on Different Coordination Approaches
## 2 VNE Problem Decomposition and Modeling
### Problem Description
#### 2.1.5 Execution of the Embedding Functions  
As aforementioned, VNE can be decomposed into two discrete, yet interdependent, subproblems. The computational demands of the virtual nodes should be mapped on the available resources of the physical nodes (Virtual Node Mapping--VNM), whereas the networking demands of the virtual links interconnecting those virtual nodes should be embedded onto the residual resources of the physical paths (Virtual Link Mapping--VLM).  
Under specific VNE requirements, such as QoS assurance or VN survivability, a physical resource can be accordingly divided for hosting a number of virtual resources. Furthermore, specifically modified algorithms have been documented with the functionality to split a virtual link and map its fractions over several substrate paths (SPs). This technique is called path-splitting (PS) and is utilized with the purpose to achieve resource redundancy and demand distribution [18, 22].  
The VNM and VLM embedding subproblems can be summarized in the following two functions as suggested in [26].  
\[f_{i}:N^{i} \to N_{S} \tag{4}\] \[g_{i}:L^{i} \to\text{SP}\subseteq\text{SN} \tag{5}\]  
The function \(f_{i}\), defined in (4), represents the virtual node embedding, whereas the function \(g_{i}\), defined in (5), represents the virtual link mapping. The combined solution of both functions form a complete embedding of the \(i\)th VN \(\in G^{V}\), notated by \(M(G^{V})=(f_{i},g_{i})\).
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 3 Fusion of Improved A* and DWA Algorithm
### Optimizing Evaluation Function  
Once a set of feasible trajectories is obtained, each trajectory is evaluated to identify the optimal one, and its corresponding velocity is selected as the driving speed. The DWA evaluation function before optimization is  
\[G(v,\omega)=\alpha\cdot T(v,\omega)+\beta\cdot V(v,w)+\gamma\cdot D(v,\omega) \tag{18}\]  
where \(T(v,\omega)\) is the objective cost function, \(V(v,w)\) is the speed cost function, \(D  
\(F(v,\omega)\) is the deviation distance evaluation function, specifically  
\[F(v,\omega)=\left\{\begin{array}{l}0\;if\;avg\_dev\leq d_{\max}\\ \lambda\times(avg\_dev-d_{\max})\;avg\_dev>d_{\max}\end{array}\right. \tag{22}\]  
where \(\lambda\) is the weight coefficient, \(d_{\max}\) is the maximum allowable deviation \(avg\_dev\) represents the average deviation of the trajectory from the global path. The calculation method is as follows:  
1. For each trajectory generated by DWA, the Euclidean distance from each point on the trajectory to the nearest point in the global path is calculated.
2. The minimum distance of all points is accumulated, then divided by the number of points on the trajectory, and the average deviation is obtained.
# A Comparative Study of the Pullout Strength of Geostraps and Geogrids in Reinforced Soil
## 2 Materials and Methods
### Numerical Simulation  
The stabilisation of unpaved roads using geogrid reinforcement has been studied [36]. On the same line of action, a physical problem of geostrap-reinforced unpaved road is solved using the Finite Element Method (FEM) in ABAQUS software. An elastoplastic constitutive model for the subgrade soil was made. In addition to elastoplastic models, Mohr-Coulomb, viscoplastic, and hypoplastic models can be used for different types of soil. Finite Element Analysis (FEA) theories state that local variation in cell sizes should be minimal, as large aspect ratios can result in the magnification of errors and can cause low degrees of accuracy. Therefore, the aspect ratio in this study was maintained to be 1:1 or 1. The unpaved test section was 500 mm long, 1000 mm wide, and 500 mm deep. An additional surcharge of 17 kN m\({}^{-2}\) was added to compare it with the geogrid-reinforced subgrade. The material parameters used in the modelling are mentioned in Table 3. A C3D8R element type was used for the geostrap and subgrade, an 8-node linear brick.