esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
On 22 May 2021, an earthquake (98.3\({}^{\circ}\) E and 34.59\({}^{\circ}\) N) struck Maduo town in Qinghai province, occurring along a relatively obscure secondary fault within the block. We utilized 105 archived Sentinel-1A/B acquisitions to investigate the coseismic deformation and the evolution of postseismic displacements in both the temporal and spatial domains, as well as the associated dynamic mechanisms of the 2021 Maduo earthquake. The interference fringes and coseismic deformation revealed that the primary feature of this event was the rupture along a left-lateral strike-slip fault. The released seismic moment was close to 1.88 \(\times\) 10\({}^{20}\) N-m, which is equivalent to an Mw 7.45 event. Simultaneously, the maximum coseismic slip reached approximately 4 m along the fault plane. The evolution of postseismic displacements in both the temporal and spatial domains over 450 days following the mainshock was further analyzed to explore the underlying physical mechanisms. Generally, the patterns of coseismic slip and afterslip were similar, although the postseismic displacements decayed rapidly over time. The modeled afterslip downdip of the coseismic rupture (at depths of 15-40 km) effectively explains the postseismic deformation, with a released moment estimated at 4.57 \(\times\) 10\({}^{19}\) N-m (corresponding to Mw 7.04). Additionally, we found that regions with high coseismic slip tend to exhibit weak seismicity, and that afterslip and aftershocks are likely driven by each other. Finally, we estimated the Coulomb Failure Stress changes (ACFS) triggered by both coseismic rupture and asesismic slip resulting from this event. The co- and postseismic ACFS show similar patterns, but the magnitude of the postseismic \(\Delta\)CFS is much lower (\(\leq\)0.01 MPa). We found that ACFS notably increased on the Yushu segment of the Garze-Yushu-Xianshuine Fault (GYXF), as well as the Maqin-Maqu and Tuosuo Lake sections of the East Kunlun Fault (EKF). Therefore, we infer that these fault segments may have a higher potential seismic risk and should be carefully monitored in the future.  
Maduo earthquake; InSAR observations; afterslip; Coulomb Failure Stress changes +
Footnote †: journal: Applied Sciences
## 4 Flight Test Results and Discussion
### Random Forest Analysis  
In addition to the statistical analysis in previous subsections, a machine learning analysis, specifically the random forest algorithm, was implemented to analyze the flight test data. The algorithm is an ensemble of multi-decision trees with the advantages of versatile use, easy-to-understand hyperparameters, and avoiding overfit with enough trees compared with other machine learning techniques. It is also one of the most-used algorithms due to its simplicity and diversity that can be used for both classification and regression tasks. The regressor model of the random forest algorithm was used to analyze the latency time, and the classifier model of the random forest algorithm was used to analyze the detection warning.  
The complete flight test data was used for both analyses, with 75% chosen as the training data and 25% as the testing data. The random forest algorithm from the SKLEARN module version 1.0.2 of Python programming was used.
## 5 Bonded Anchor Tests
### Anchor Installation  
These types of experiments and measurements are very sensitive and highly dependent on the boundary conditions. It is well known that the cleaning and moisture of the hole during the installation can significantly affect the anchors' performance [52]. Thus, in this study, all known and relevant influence factors were carefully taken into account to minimize the scatter as much as possible. Therefore, concrete specimens (geometrical details provided in Section 5.3) were stored inside the laboratory to ensure the same temperature history with a minimum of variations for all specimens. The anchors were installed at a concrete age of 7 months and were loaded consistently always 24 h after the installation, even though the manufacturer specifies a minimum curing time at that ambient temperature of only 6-12 h. This type of thermoset polymers are never fully cured in practice and, thus, are also assessed in a partly cured state according to current guidelines. Undoubtedly, post-curing affects the tests. The significance of such post-curing phenomena are being currently assessed in a follow-up study.  
All the installation steps regarding the preparation, drilling, and cleaning hole were carefully carried out following the product specific guidelines by the same two researchers in concrete cylinders  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Product** & **Young’s Modulus** & **Tensile Strength** & **Poisson’s Ratio** \\  & **[MPa]** & **[MPa]** & **[-]** \\ \hline P1 & 6816.7 & 64.2 & 0.36 \\ P2 & 5580.2 & 13.9 & 0.33 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties experimentally obtained for the two adhesive products post-cured at room temperature (23\({}^{\circ}\)) for 24 h.  
after reaching thermal equilibrium. Even though high quality drill bits with \"PGM\" certificate were used for drilling the holes, always a new drill bit is used for each set of tests (e.g., sustained load, or loading rate sensitivity tests). The installation and testing procedure were carried out in the temperature controlled room with a constant temperature (\(T\)) of \(23\;\pm\;0.5\)\({}^{\circ}\)C and relative humidity (\(RH\)) of 50% with a variations of \(\pm 3\)%.
## 3 Settings for Prediction Accuracy Tests
### Types of Input Variables  
The input variables to compare the prediction accuracy of the RSM-computed and ANN-computed surrogate models can be classified into two different groups. The ones belonging to the first group are the control points of the Bezier curve that affect the shape of the baseline wind turbine airfoil. These input variables will be called the geometric variables in the rest of the paper. The other variables belonging to the second group do not have an influence on the shape of the baseline airfoil. Rather, they determine the operating conditions of a wind turbine airfoil. Although there are several parameters affecting the wind turbine airfoil's operating conditions such as tip speed ratio and solidity, the Reynolds number and angle of attack are the ones being dealt with in this study. These variables will be referred to as the operational variables.  
With these types of input variables, two types of prediction accuracy tests for the surrogate models are implemented. The difference between these two types of tests is the inclusion of operational variables in creating the datasets. In more detail, in the first type of prediction accuracy tests where all the input variables are geometric variables (no operational variables are included), the aerodynamic performance of a redesigned wind turbine airfoil from its baseline shape is estimated using the surrogate models at a fixed Reynolds number (\(\text{Re}=10^{6}\)) and angle of attack (AOA = 5\({}^{\circ}\)), which implies no change of the wind turbine airfoil's operating conditions. However, in the second type of prediction accuracy tests, two operational input variables, the Reynolds number and angle of attack, are set as variables besides geometric variables so that the performance of the redesigned wind turbine airfoil is predicted with the change in its operating conditions. The purpose of considering geometrical and operational parameters simultaneously in the second type of prediction accuracy tests is to make the output value more intricately related to the input variables and to check how this complex association affects the prediction accuracy of the RSM-computed and ANN-computed surrogate models. For clarification, the former prediction type of accuracy test will be referred to as \"prediction accuracy test with geometric variables\", while the latter one will be called \"prediction accuracy test with geometric and operational variables\". The reason for investigating these two types of prediction accuracy tests is to make datasets with different complexities and to test the prediction accuracy change of the RSM-computed and ANN-computed surrogate models depending on the complexity of the datasets.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 3 Implementation of MBSS Technique Using SDR Technology
### Impulsive Noise Reduction  
Due to the nature of the devices used in this work, the addition of a classical noise (flat spectrum) is disturbed by the apparition of spikes in the PSD estimation. These spikes are called impulsive noise and, in general, are more significant with the SDR devices than those observed by a commercial spectrum analyzer. Eliminating this type of noise is necessary to improve the results obtained by the proposed MBSS technique. Many methods have been proposed to mitigate this noise, even novel techniques such as compressed sensing [41] or a recursive least square method based on the state space variant [42].  
In this research work, an impulsive noise elimination module is proposed, which is undoubtedly a novel proposal to mitigate abrupt noise changes in the PSD signal that is processed by the MBSS technique. The operation of this module is shown in Figure 5. The elimination of impulsive noise along every frame is done through the approximation and detail coefficients issued from an MRA [9] acting over the PSD estimate.  
In the next sections, the operation of each submodule that shapes the impulsive noise elimination module is described in detail.
## 4 Compensation with Unbalanced Voltages
### Calculation of the NSCC Compensator Reactances with Unbalanced Voltages  
According to Equation (32), to determine the values of reactances \(X_{aP}\), \(X_{bF}\) y \(X_{cF}\), it is necessary to determine the negative-sequence current \(I_{zF-}\) consumed by the NSCC compensator. From the values of \(I_{zF-}\) and \(V_{z+}\), we will determine the reactive power \(Q_{zF+}^{-}\).  
\(I_{zF-}\) is given by Equation (44), where \(I_{zL-}\) is the negative-sequence current that consumes the load, and \(I_{zP-}\) is the negative-sequence current consumed by the SVC compensator. As the reactances in each phase of the SVC compensator are equal, the value of \(I_{zP-}\) is obtained from Equation (45).  
\[\underline{I_{zF-}}=\underline{I_{zL-}}+\underline{I_{zP-}} \tag{44}\]  
\[\underline{I_{zP-}}=\frac{V_{z-}}{\underline{X_{zP}}} \tag{45}\]  
Considering the value \(I_{zF-}\) in each phase obtained from Equation (44), the reactive power \(Q_{zF+}^{-}\) is determined from Equation (46).  
\[Q_{zF+}^{-}=V_{z+}I_{zF-}sin(\alpha_{z+}-\beta_{zF-}) \tag{46}\]  
Substituting the values of Equations (44) and (46) into Equation (32), the values of reactances \(X_{aP}\), \(X_{bF}\) and \(X_{cF}\) are calculated from (47). For negative values of \(X_{zF}\), we use capacitors, and for positive values, we use coils.  
\[X_{zF}=-2\frac{Q_{zF+}^{-}}{I_{zF-2}}\,z=\{a,b,\,c\} \tag{47}\]  
With balanced voltages, Equations (32) and (47) are identical as it is true that \(Q_{zF+}^{-}=Q_{zL+}^{-}\) and \(\tilde{I}_{zF-}=\tilde{I}_{zL-}\).
## 1 Introduction
### Literature Overview on the Use of Ferrite-Assisted Synchronous Reluctance Motors in Traction Drives
Firstly, the mass and dimensions of SHMs exceed those of WRSMs due to each rotor tooth covering roughly one pole pitch, leaving approximately half of the pole pitches unused for torque production. Secondly, SHMs necessitate a higher inverter power rating compared to WRSMs. Several studies [28; 29; 30; 31] have explored multi-pole SHMs that feature an excitation winding on the stator and rare-earth magnets housed in the rotor slots. These SHMs, equipped with rare-earth magnets, offer advantages over those without permanent magnets. The use of rare-earth magnets enhances the utilization of the rotor surface, while keeping the weight and dimensions comparable to conventional PMSMs. In traction drives with a wide constant power speed range (CPSR), SHMs with rare-earth magnets excel by exhibiting higher inverter utilization and lower costs for the inverter. The excitation winding enables the adjustment of the excitation flux across a wide range of speeds, utilizing the excitation winding current as an additional control signal to enhance SHM performance optimization. However, a notable drawback of SHMs with rare-earth magnets lies in their dependency on a limited number of rare-earth element suppliers, leading to high material costs. Moreover, the depth of the rotor slots in these SHMs is significantly smaller compared to those without magnets, compromising the saliency of the rotor. Consequently, although rare-earth magnets provide substantial additional torque, the primary torque generated by the interaction between the field of the excitation winding, modulated by rotor stacks, and the field of the stator winding is diminished. To enhance the characteristics of SHMs with magnets, an alternative approach involves utilizing cost-effective ferrite magnets in their construction. Ferrite magnets are considerably less expensive than rare-earth magnets and are produced in numerous countries worldwide [10]. The application of ferrite magnets in SHMs is exemplified in [26], where they are employed as under-car generators for railway passenger cars. The use of SHMwith ferrite magnets, compared with those without magnets, offers such advantages as reducing the weight and dimensions of the machine, as well as minimizing power losses. Research on synchronous homopolar motors featuring ferrite magnets (for brevity in this paper, we call them ferrite-assisted synchronous homopolar motors, FaSHMs) is relatively infrequent. For example, in [14], the characteristics of a FaSHM are compared with the characteristics of an SMH without magnets in the drive of a subway train bogie.
# Introduction to Reconfiguration
## 2 Specifying and Analyzing Reconfiguration Graphs  
Long before the term reconfiguration was used to categorize problems, sliding-block puzzles, such as the 15-puzzle (Figure 1a) were studied by mathematicians. In the reconfiguration framework, each arrangement of the tiles can be seen as a feasible solution, where a reconfiguration step consists of the movement of a single tile into an adjacent hole. Equivalently, the 15-puzzle can be viewed as a graph problem, where each feasible solution is the placement of distinctly labeled tokens on fifteen of the vertices in a \(4\times 4\) grid graph (corresponding to the placement of the tiles), leaving one vertex uncovered (corresponding to the placement of the hole). For this formulation of the problem, a reconfiguration step consists of moving a token to the uncovered vertex from one of its neighbors in the graph (Figure 1b). Adding further constraints on the feasible solutions gave rise to a variety of source problems with feasible solutions that can be represented as the placement of tokens. In subsequent work, different types of reconfiguration steps were defined by other ways of moving tokens, and eventually to problems for which tokens were not used at all.  
Questions that arise in the context of puzzles form the foundation of questions asked about reconfiguration today, such as determining whether one arrangement can be reached from another (_reachability_) or indeed if all arrangements can be reached from all others (_connectivity_), and if so, finding the shortest sequence of steps possible (_shortest transformation_), a sequence of at most a specified length (_bounded transformation_), or a bound on the number of steps for any pair of arrangements (or, equivalently, on the diameter of the reconfiguration graph, hence _diameter_). In the remainder of this section, we formalize definitions of feasibility and adjacency (Section 2.1) and definitions of problems in the reconfiguration framework (Section 2.2). We will return to sliding-block puzzles at the end of the paper (Section 9) after briefly noting that the answer to the connectivity problem for the 15-puzzle was shown to be negative more than a century ago [3].
# Efficient Traffic Flow Guidance Feedback Strategy Considering Drivers' Disobedience in ITS
## 3 Proposed Approach
### Labeling of Input Datasets  
Our first contribution is based on the support vector machine (SVM) algorithm. SVM is a supervised learning algorithm that can be used for classification and regression issues. Although a feature vector is proposed, the conditions of this research did not provide an opportunity to collect data from drivers in real-life scenarios, and it also limits the possibility of operating a real test on whether drivers will choose to follow or disobey the instructions in a real driving environment. The obedience feature vector (or datasets) is thus generated randomly during the simulation. A supervised learning algorithm in this context means that the training datasets should be labeled. A labeling procedure should thus be added between the processes of dataset generation and SVM training. The data mining techniques, including clustering methods [14; 15], have shown their powerful capability to elaborate and analyze the similarities and differences between the driving behavior characteristics regarding longitudinal driving behavior. The \(k\)-means clustering with \(k\) being 2 is chosen to be applied in this research.  
\(k\)-means is a classical unsupervised learning model. The goal of this iterative algorithm is to minimize intra-class inertia. Its main steps are briefly summarized hereafter:  
1. Choose the \(K\) centers of \(K\) classes \(m_{k}^{(0)}\); \(t=0\)
2. Given the centers \(m_{k}^{(t)}(k=1\dots K)\) by selecting the closest center, assign all the points (input vector) to class \(S_{k}^{(t)}\)
3. Given the points in classes \(S_{k}^{(t)}(k=1\dots K)\), calculate the new centers \(m_{k}^{(t+1)}\) \[m_{k}^{(t+1)}=\frac{1}{|S_{k}^{(t)}|}\sum_{x_{j}\in S_{k}^{(t)}}^{k}x_{j}\] (2)
4. Repeat steps (2) and (3) until convergence: \(\left|m_{k}^{(t+1)}-m_{k}^{(t)}\right|<\epsilon,\forall k,\epsilon>0\)  
In our work, the datasets could be clustered by using the \(k\)-means clustering methods. For example, points with low historical obedience rates (e.g., \(R_{o}=0.3\)) and little distance (e.g., \(d\leq 10\)km) could be labeled as disrespect to the instruction. Based on the pre-labeled centers, all data could be manually labeled by \(y=\{-1,1\}\), where \(-1\) and \(1\) represent the disobedience and obedience of the driver, respectively.
# Dynamic Characteristics of Sandstone under Coupled Static-Dynamic Loads after Freeze-Thaw Cycles
## 3 Strength Characteristics of Sandstone under Freeze-Thaw Cycles and Axial Pre-Compression Stresses  
Rock is a non-homogeneous and brittle geo-material consisting of mineral grains, cementation bonds, and various defects. These defects mainly include joints, micro-cracks, pores, and faults. The characteristics of the inherent structure of rock make its physical and mechanical properties susceptible to changes due to external load or environmental actions. Changes in the microstructure under the coupled effects of freeze-thaw processes and pre-compression stress will eventually be expressed by the changes in the rock's macroscopic mechanical properties.  
Figure 6: Configuration of coupled loads experiment.  
When it is subjected to the freeze-thaw process, rock undergoes extension, expansion, and connection of micro-cracks. The freeze-thaw process causes damage in the rock in three ways. First, the pore water freezes and expands by 9-10%, exerting stress on the pore wall. Second, the ice lenses act like wedges which promote crack initiation and propagation. Third, the effect of hydrostatic pressure, in the process of freezing, part of the water is dispersed by the ice, resulting in additional hydrostatic pressure on the rock. From the discussion on pore structure characteristics using NMR technology [2], it was demonstrated that with the increasing number of freeze-thaw cycles, the rock porosity gradually increases, and the bond across the grain boundaries between the individual minerals in the rock matrix gradually decreases, with the introduction of minor flaws and cracks, leading to lower rock strength. With more freeze-thaw cycles applied the strength loss is more obvious.  
In the dynamic impact tests, the dynamic load stress wave generated a tensile wave on the surface of a crack in the rock, which may cause crack propagation. However, when axial static stress is applied to a low degree, the internal micro-cracks may be forced to close and there is no stress wave reflection or transmission across the crack surface, which restrains the damage function and increases the strength of the rock. However, when the axial static pressure reaches crack initiation, internal damage occurs, leading to the generation of a large number of micro-cracks. The new generated micro-cracks form the stress wave reflection interface for dynamic loading again, resulting in a decrease in strength. It clearly demonstrated that both freeze-thaw cycles and pre-compression stress can affect the physical and mechanical properties.
## 5 Discussion  
In recent years, there has been a clear trend toward automation. Individual sub-steps or tasks are increasingly being taken over by new methods and algorithms. More and more different sensor systems are being linked together to obtain better and more detailed information about the condition of the object to be evaluated. In the near future, these multi-sensor systems will evolve into collaborative robots that can be used autonomously or in cooperation with human workers in railway maintenance. The sensor technology employed will become more effective in terms of detection and smaller in design, making it easier to deploy on smaller carrier platforms such as UAVs.  
Optical processes continue to demonstrate their potential in the non-destructive detection of damage, now extending beyond surface-level analysis. Using thermographic cameras or specialized lasers, it is already possible to draw conclusions about the interior condition of an object, thereby obtaining more comprehensive information from the recorded data. The development of laser-based systems toward the use of different methods for distance determination (phase-shift measurement, pulsed time-of-flight measurement) as well as the use of different wavelengths should be further promoted to counteract the current limits and deficits.  
Both in the acquisition of measurement data and in their evaluation and analysis, the methods are evolving from heuristic approaches to the use of artificial intelligence for the semantic interpretation of 3D and 2D data. The application of AI in analyzing and evaluating sensor data from optical measurement systems is becoming increasingly significant. The wide range of potential applications highlights the great promise of this tool. The developments and research mentioned here provide just a brief insight into the possible applications, but it is clear that AI is becoming more established and important in visual inspection technology. It can detect errors in real time and reduce human error, thereby increasing productivity and improving efficiency and safety in the rail industry.  
Manual inspections are gradually being replaced by automated interpretations. Fully autonomous measurement platforms that carry out infrastructure inspections independently of human control would be possible. The use of sensor systems in regular train services could significantly shorten inspection intervals. Another challenge is the cost-effective management of large volumes of data. There are already approaches to reduce big data not only in post-processing but also in real time, minimizing the data to the necessary amount. This significantly reduces storage requirements. However, there is a lack of standardization in data processing, and solutions are often tailored only to specific requirements.