esa-sceva/satcom-chunk-collection huggingface dataset:
# How Will Rwandan Land Use/Land Cover Change under High Population Pressure and Changing Climate?
## 2 Materials and Methods
### Data Sources  
The LULC data used in this study was obtained from the Rwanda LULC product generated by the Regional Centre for Mapping of Resources for Development (RCMRD) in 2015 [23]. The data spans four periods (1990, 2000, 2010, and 2015), and has a spatial resolution of 30 \(\times\) 30 m\({}^{2}\), and an average inspection accuracy of 77.50% for all LULC types. The original data contains 14 types of LULC. These 14 types were reclassified to obtain the following land use types: forest, grassland, cropland, wetland, water, urban land, and unused land. The reclassification rules are shown in Table 1. The digital elevation model (DEM) downloaded from the United States Geological Survey (USGS) EarthExplorer [24] database has a 30 \(\times\) 30 m\({}^{2}\) spatial resolution and was selected to derive the topographic factor. Precipitation data was taken from the Climate Hazards Group InfraRed Precipitation  
Figure 1: Location of the study area.  
with Station data (CHIRPS), which has been successfully used for scientific research in East Africa [25; 26]. CHIRPS data for the period 2000-2015 was used; the spatial resolution of the data was 0.05 \(\times\) 0.05\({}^{\circ}\). The soil data, which had a spatial resolution of 250 m, was sourced from the Africa Soil Information Service (AfSIS) [27]. The soil data used in this study included soil texture (percentage of sand, silt, and clay) and soil organic carbon content. Population data were obtained from WorldPop [28]. The resolution of the population data was 1 arc-second (about 30 m at the equator). The vector data for Rwanda Waterways was provided by the National Institute of Statistics of Rwanda (NISR); it was published in 2015 and covers the main waterways in Rwanda [29]. The vector point data for major cities was based on the coordinates (longitude and latitude) of 15 major cities in Rwanda, published by the RCMRD in 2008 [30]. The road data for Rwanda were downloaded from the website of the African Development Bank Group [31]. The data includes national roads in Rwanda, which are further classified as paved and unpaved according to surface conditions.
## 3 Testing Results  
### Macroscopic Failure Patterns  
Figure 3 exhibits the relationship of stress and strain of two typical specimens with different moisture conditions. It can be found that the uniaxial compressive strength (UCS) of the saturated specimen is obviously lower than the dried one. Table 1 shows the UCS of each sample based on the test results. Due to the misoperation of the load cell of MTS, the failure of DM3 occurred before the start of test. Therefore, its mechanical and AE data failed to collect. The range of the dried samples' UCS is among 67.06 to 74.57 MPa, with an average value of 70.16 MPa. In comparison, the range of the saturated samples' UCS is between 61.33 and 69.03 MPa; the average value is 61.25 MPa. The softening efficient (UCS\({}_{\text{Sat}}\)/UCS\({}_{\text{dri}}\)) can be obtained as 0.87, which obviously indicates a moisture-induced softening of rock.  
Figure 4 shows the macroscopic failures of two representative samples following the test. It clearly indicates that dried samples' failure models are mostly shear failure. In addition, the failure model of saturated samples is the combination of both shear and tensile failures. Therefore, the number of tensile cracks increased during the test of saturated rock, is due to the effect of pore water.  
Figure 1: Experimental setup of uniaxial compression tests.  
Figure 2: Typical acoustic emission (AE) waveform and its spectrum.
# Attention Transfer Reinforcement Learning for Test Case Prioritization in Continuous Integration
## 5 Experimental Design
### Evaluation Metric  
In this paper, NAPFD is used as the evaluation metric, which has been discussed in detail previously. Its calculation method is shown in Equation (2). APFD was initially proposed to measure the effectiveness of TCP techniques. It evaluates the quality of the prioritization by the ranks of fault-revealing test cases in the final sorted order. The earlier the fault-revealing test cases appear, the higher the value. This indicates more effective prioritization. However, APFD assumes that all test cases can be executed, which is not suitable for situations where only a subset of test cases is selected for execution. NAPFD, an extension of the APFD metric, includes the proportion of faults detected by the executed subset of test cases, making it suitable for scenarios where only a portion of the test cases can be executed.  
Additionally, this paper uses the TTF (test to fail) value as another evaluation metric in Equation (8):  
\[\text{TTF}=\text{rank}(t_{\text{fail},1}) \tag{8}\]  
where TTF represents the Test to Fail value, which indicates the rank of the first fault-revealing test case in the prioritized test suite, and \(\text{rank}(t_{\text{fail},1})\), which denotes the rank of the first fault-revealing test case \(t_{\text{fail},1}\) in the prioritized test suite. The TTF value is computed by identifying the first test case in the prioritized test suite that detects a fault and recording its rank within the ordered test suite. A smaller TTF value reflects better prioritization, as the first fault-revealing test case appears earlier. In contrast, a larger TTF value suggests less effective prioritization. This definition ensures that TTF serves as an effective metric for assessing the efficiency of test case prioritization in detecting faults as early as possible.  
To verify the effectiveness of the prioritization, recall is also an important metric. Recall measures the proportion of detected faults (i.e., fault-revealing test cases ranked in the first half of the total execution time) to the total number of faults in Equation (9):  
\[\text{Recall}=\frac{|\text{Detected Faults}|}{|\text{Total Faults}|} \tag{9}\]  
where Recall measures the proportion of detected faults to the total number of faults, indicating the effectiveness of the test case prioritization, the numerator \(|\text{Detected Faults}|\) represents the number of fault-revealing test cases ranked in the first half of the total execution time, and the denominator \(|\text{Total Faults}|\) denotes the total number of faults present in the test suite. A higher recall value indicates that more faults are detected within the prioritized test cases, signifying better prioritization performance. By analyzing these three metrics together, we gain a more balanced and robust assessment of prioritization performance, accounting for both early detection and overall fault coverage.
###### Abstract  
High-precision and rapid detection of apparent defects in subway tunnel linings is crucial for ensuring the structural integrity of tunnels and the safety of train operations. However, current methods often do not adequately account for the spatial characteristics of these defects and perform poorly in detecting and extracting small-scale defects, which limits the accuracy of detection and geometric parameter extraction. To address these challenges, this paper proposes an efficient algorithm for detecting and extracting apparent defects in subway tunnels. Firstly, YOLOv8 was selected as the foundational architecture due to its comprehensive performance. The coordinate attention module and Bottleneck Transformer 3 were then integrated into the model's backbone to enhance the focus on defect-prone areas and improve the learning of feature relationships between defects and other infrastructure. Subsequently, a high-resolution detection layer was added to the model's head to further improve sensitivity to subtle defects. Additionally, a low-quality crack dataset was created using an open access dataset, and transfer learning combined with Real-ESRGAN was employed to enhance the detail and resolution of fine cracks. The results of the field experiments demonstrate that the proposed model significantly improves detection accuracy in high-incidence areas and for small-scale defects, achieving a mean average precision (mAP) of 87% in detecting cracks, leakage, exfoliation, and related infrastructure defects. Furthermore, the crack enhancement techniques substantially improve the representation of fine-crack details, increasing feature extraction accuracy by a factor of four. The findings of this paper could provide crucial technical support for the automated operation and maintenance of metro tunnels.  
subway tunnel; defect inspection; deep learning; image super-resolution; transfer learning 2024 14 7824. [[https://doi.org/10.3390/app14177824](https://doi.org/10.3390/app14177824)]([https://doi.org/10.3390/app14177824](https://doi.org/10.3390/app14177824))
## 5 Conclusions  
Radiomics features reported as stable were analyzed for reproducibility using the RIDER lung dataset, with 29 of 43 features found to be reproducible to changes in the feature extraction toolkits when intensity threshold was applied, maintaining stable rank ordering (_rs_ > 0.8), and are recommended for use for biomarker analysis. We found that 18/43 reported features were common in the RIDER and HN1 datasets, suggesting they may be agnostic to disease site. Useful radiomics features should be selected based on reproducibility. This study identified a set of features that meet this requirement and validated the methodology for evaluating reproducibility between datasets.  
Conceptualization, H.M.T.T., E.M.D., C.P.S., A.N., V.P. and P.M.E.; methodology, H.M.T.T., H.Y.C.W., E.M.D., C.P.S., A.N. and P.M.E.; software, H.M.T.T. and H.Y.C.W.; validation, H.M.T.T., H.Y.C.W. and H.S.; formal analysis, H.M.T.T. and H.Y.C.W.; investigation, V.P., A.J.V., B.K.S., S.P.P., D.D., M.M. and R.G.I.; resources, A.N., V.P., P.M.E. and D.D.; data curation, H.M.T.T. and H.Y.C.W.; writing--original draft preparation, H.M.T.T., H.Y.C.W. and P.M.E.; writing--review and editing, H.M.T.T. and P.M.E.; visualization, V.P., A.J.V., B.K.S., S.P.P., D.D., M.M. and R.G.I.; supervision, E.M.D., C.P.S., A.N., V.P. and P.M.E.; project administration, H.M.T.T., V.P. and P.M.E.; funding acquisition, H.M.T.T., A.N., V.P. and P.M.E. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the DBT/Wellcome Trust India Alliance Early Career Fellowship [Grant number: IA/E/18/1/504306] awarded to [PERSON]. [PERSON] acknowledges the support from the University of Surrey IAS fellowship for external academics in 2022-23. [PERSON] acknowledges PhD funding from Alliance Medical Ltd.  
Ethical review and approval were waived for this study due to its use of solely publicly available datasets.  
Informed Consent Statement: Informed consent was not applicable for this study.  
Data Availability Statement: This study used two publicly available datasets: RIDER and HN1. The RIDER dataset is available from the Cancer Imaging Archive: [[https://wiki.cancerimagingarchive](https://wiki.cancerimagingarchive)]([https://wiki.cancerimagingarchive](https://wiki.cancerimagingarchive)). net/display/Public/RIDER+Lung+CT (accessed on 17 June 2023). Details of HN1 are given in reference [1]. The MATLAB code used is from the toolbox of Vallieres: [[https://github.com/mvallieres/radiomics/tree/master/TextureToolbox](https://github.com/mvallieres/radiomics/tree/master/TextureToolbox)]([https://github.com/mvallieres/radiomics/tree/master/TextureToolbox](https://github.com/mvallieres/radiomics/tree/master/TextureToolbox)) (accessed on 17 June 2023). The functions used were: prepareVolume.m, equalQuantization.m, getGLCM.m, getGLSZM.m, getGLRLM.m, getNGTDM.m, and getGlobalTextures.m. The MATLAB user code is given in Appendix A. The Pyradiomics parameter file used for the feature extraction is given in Appendix B.  
The authors have no relevant conflicts of interest to disclose.
## 3 Path-Tracking Control Strategy
### CG Generalized Force Calculation  
First, the vehicle discrete dynamics model is obtained by using the zero-order hold method from (19)-(20):  
\[X^{k+1}=A_{d}^{*}X^{k}+B_{d}^{*}F_{G}^{k}, \tag{32}\]  
\[Y^{k}=C_{d}^{*}X^{k}, \tag{33}\]  
where \(A_{d}^{*}\), \(B_{d}^{*}\), and \(C_{d}^{*}\) are the discrete state matrix, discrete input matrix, and discrete output matrix, respectively.  
The MPC algorithm calculates the CG generalized forces of each module required for path tracking: the CG lateral force and CG yaw moment. In order to further reduce the hinge force, it is considered a part of the cost function. The problem can be arranged as follows:  
\[J(X,F_{G})=\left\|Y^{N_{c}}-Y_{d}\right\|_{Q_{Y}}^{2}+\sum_{k=0}^{N_{c}-1} \left(\left\|Y^{k}-Y_{d}\right\|_{Q_{Y}}^{2}+\left\|F_{G}^{k}\right\|_{Q_{F_{G} }}^{2}+\left\|F_{h}^{k}\right\|_{Q_{F_{h}}}^{2}\right), \tag{34}\]  
\[\min J(X,F_{G}). \tag{35}\]  
\[s.t.\ X^{k+1}=A_{d}^{*}X^{k}+B_{d}^{*}F_{G}^{k}. \tag{36}\]  
\[Y^{k}=C_{d}^{*}X^{k}. \tag{37}\]  
\[F_{h}^{k}=J^{*}P^{*}X^{k}+J^{*}Q^{*}F_{G}^{k}. \tag{38}\]  
\[F_{G}^{\min}<F_{G}^{k}<F_{G}^{\max}, \tag{39}\]  
where \(N_{c}\) is the control horizon, and the cost function (34) includes the path-tracking error, the control input, and the hinge force term. The semi-positive definite matrices \(Q_{Y}\), \(Q_{F_{G}}\), and \(Q_{F_{h}}\) are the weight matrix of the path-tracking error, the control input, and the hinge force term, respectively. The path-tracking target state is obtained from Equation (30). The batch solution method can transform the above MPC problems into quadratic programming (QP) problems [31; 32]. Therefore, the results can be quickly obtained by the onboard ECU. After calculating the optimal control sequence, the first element in the sequence is taken as the system input for the current control step, which is the CG lateral force and CG yaw moment of each module:  
\[F_{G}^{1}=\begin{bmatrix}F_{y,d}^{1}&M_{z,d}^{1}&F_{y,d}^{2}&M_{z,d}^{2}&F_{y,d}^{3}&M_{z,d}^{3}&F_{y,d}^{4}&M_{z,d}^{4}\end{bmatrix}^{T}. \tag{40}\]  
To ensure the constant longitudinal speed of the vehicle, the CG longitudinal force of each module is set to zero. Therefore, the CG generalized forces of the \(i^{th}\) module required for the path tracking can be expressed as  
\[F_{Desired}^{i}=\begin{bmatrix}0&F_{y,d}^{i}&M_{z,d}^{i}\end{bmatrix}^{T},\ (i=1,2,3,4). \tag{41}\]
###### Abstract  
Statistical body shape models are widely used in 3D pose estimation due to their low-dimensional parameters representation. However, it is difficult to avoid self-intersection between body parts accurately. Motivated by this fact, we proposed a novel self-intersection penalty term for statistical body shape models applied in 3D pose estimation. To avoid the trouble of computing self-intersection for complex surfaces like the body meshes, the gradient of our proposed self-intersection penalty term is manually derived from the perspective of geometry. First, the self-intersection penalty term is defined as the volume of the self-intersection region. To calculate the partial derivatives with respect to the coordinates of the vertices, we employed detection rays to divide vertices of statistical body shape models into different groups depending on whether the vertex is in the region of self-intersection. Second, the partial derivatives could be easily derived by the normal vectors of neighboring triangles of the vertices. Finally, this penalty term could be applied in gradient-based optimization algorithms to remove the self-intersection of triangular meshes without using any approximation. Qualitative and quantitative evaluations were conducted to demonstrate the effectiveness and generality of our proposed method compared with previous approaches. The experimental results show that our proposed penalty term can avoid self-intersection to exclude unreasonable predictions and improves the accuracy of 3D pose estimation indirectly. Further more, the proposed method could be employed universally in triangular mesh based 3D reconstruction.  
Keywords:statistical body shape model; self-intersection penalty term; 3D pose estimation
## 2 Fault Analysis and Feature Extraction
### Single-Phase Ground Fault Simulation and Analysis
#### 2.1.2 Arc Ground Fault  
Arc ground fault is a common and harmful fault that is difficult to detect due to its complex mechanism and difficulty in extracting fault characteristics. In order to address these problems, this paper analyzes the transient electrical volume when simulating arc generation and arc grounding fault, and proposes an identification basis.  
(1.) Mayr arc model  
Mayr arc is selected for research in this paper, and the Mayr arc simulation model is built through MATLAB/simulation combined with the arc, as highlighted in Equation (8):  
\[\frac{1}{g}\bigg{(}\frac{dg}{dt}\bigg{)}=\frac{1}{\tau_{M}}\bigg{(}\frac{ei}{p _{loss}}-1\bigg{)}=\frac{1}{\tau_{M}}\bigg{(}\frac{ei}{e_{0}i}-1\bigg{)} \tag{8}\]  
In the formula, the expression of each parameter is given in Equation (9):  
\[\left\{\begin{array}{c}g=g_{0}e^{\frac{Q_{0}}{Q_{0}}}\\ \tau=f\Big{(}g,\frac{dg}{dt},p_{loss}\Big{)}=\frac{g\cdot\frac{dg}{dt}}{p_{ loss}}\\ \tau_{M}=g\cdot\frac{Q_{0}}{g}\cdot\frac{1}{p_{loss}}=\frac{Q_{0}}{p_{loss}}\\ p_{loss}=p_{cd}+p_{d1}+p_{fs}\end{array}\right. \tag{9}\]  
where \(p_{loss}\) is the total arc energy loss, \(p_{cd}\) is transmitted and distributed power, \(p_{d1}\) is convective emission power, \(p_{fs}\) is radiation emission power;\(g\) is a constant; and \(Q_{0}\) is heat per arc column.  
(2.) Simulation of Arc ground failure  
Arc earthed failure not only has a great impact on the normal operation of the line, but also reduces the insulation level of the whole line. Figures 3 and 4 show the arc current voltage waveform, respectively.  
The voltage waveform of the arc has serious distortion and presents nonlinear characteristics, as can be seen in Figures 4 and 5. The arc starting voltage rises rapidly and then falls off immediately. The voltage crosses zero after a period of constant voltage, then turns to arc extinguishing voltage. The current waveform can be regarded as sine wave. When the arc current is close to zero, the current changes slowly and exhibits a \"resting zero\" phenomenon. After the \"resting zero\", the current changes faster than a sine wave, demonstrating the effectiveness of the model.  
Figure 3: Arc voltage waveform.  
The zero-sequence currents under fault and non-fault conditions are shown in Figure 5. As can be seen in Figure 6, when an arc-earthed failure occurs, the suppression of arc coils is effective. The phase of the first-half wave of zero-sequence current of the fault line in the transient process is opposite to that of the non-fault line, that is, the phase difference is \(180^{\circ}\). In the steady-state process, the zero-sequence current has no phase difference; the fault's zero-sequence current has a large amplitude, so it is a good indicator of the arc grounding fault.
# Borehole Trajectory Optimization Design Based on the SAC Algorithm with a Self-Attention Mechanism
## 2 Materials and Methods  
The SAC algorithm [8] consists of three core parts: Firstly, with the help of the maximum entropy framework, it fully stimulates the potential of agents to explore unknown environments while improving the stability of the model; Secondly, the offline strategy update mechanism is used to effectively improve the efficiency of the algorithm by repeatedly mining and using historical collected data; and Thirdly, the work is carried out based on the Actor-Critic (AC) architecture, in which the strategy network and the value network operate independently of each other. Among them, the policy network, as an Actor, is responsible for generating the probability distribution of actions and guiding agent decision-making. The value network acts as a Critic, used to evaluate the Q-value of the state-action pair under the current strategy, so as to feedback and optimize the strategy. The two complement each other and jointly ensure the efficient operation of the algorithm.
## 4 Experimental Test
Other factors contributing to the localization error of both methods include the calibration error of the camera intrinsic parameters, and the positioning errors of the landmarks and grid corners due to the printing inaccuracy of the landmark pattern and manual measurement inaccuracy. In addition, to test the speed of the proposed single-camera trillateration algorithm, we implemented a compiled standalone executable (exe file) of the algorithm on a laptop PC with a 1.66 GHz Intel Core 2 CPU, when processing the experimental data. The average estimation speed was about 0.06762 s. In our implementation, the main time-consuming steps were to solve Equations (5) and (6) by using the Newton-Raphson method to search for the solutions of the associated least-squares optimization problems in order to obtain an optimal camera pose estimation.
## 2 Multiple Objective-Driven Energy-Saving Control Model of a Single Train
### Methodology
#### 3.3.1 Modeling
The remaining number of passengers for a given service time is defined as \(|\overline{\lambda}\times\Delta T-Q|\). During vacation time, when \(\overline{\lambda}\times\Delta T>Q\), the train arrival interval should be shortened to reduce the number of waiting passengers. The dwell time correspondingly extends and the number of passengers on board should increase. When \(\overline{\lambda}\times\Delta T<Q\), the arrival interval is extended to reduce the traction energy consumption of the train. The optimal passenger waiting time is taken as the control objective. The function is established by Equation (46). \[ \begin{array}{l}Y_{1}=\sum\limits_{i=1}^{I}\sum\limits_{j=1}^{I}\Delta T\\ s.t.\left\{\begin{array}{l}\widetilde{\lambda}d_{j}^{i}+\widetilde{\lambda} (T_{j}^{i}+\Delta t_{j}^{i}-T_{j-1}^{i}-\Delta t_{j-1}^{i})<1\\ T_{j}^{i}-T_{j}^{i}\geq T_{aq}\end{array}\right.\end{array}  \tag{46}\]  
_Step 2_: Traction energy consumption of multiple trains  
The multi-train cooperative model is not a single objective optimization problem, but a multi-objective optimization problem. The main optimization objectives are described as follows:  
* Passenger waiting time  
Passenger waiting time is naturally formed when passengers arrive at the platform through the gate and wait for the train to arrive. As known in Section 3.3, \(\Delta T_{j}^{i}\) is defined as the vacation time of the queuing model. The passenger waiting time can be expressed as follows. \[Y_{1}=\sum\limits_{i=1}^{I}\sum\limits_{j=1}^{I}\Delta T \tag{47}\]
* Traction energy consumption  
According to a single train energy-saving control model, a multi-train traction energy consumption control function is calculated by Equation (48). \[Y_{2}=\sum\limits_{1}^{I}E_{j}=\sum\limits_{1}^{I}\sum\limits_{1}^{I-1}E^{3} \tag{48}\]Combined with the weight summation method, the energy-saving cooperative control method for multiple trains is constructed by the train operation safety, equipment working status and other factors or constraint conditions, which can be expressed as follows. \[ \begin{split}\min& Y=\theta Y_{1}+\omega Y_{2}\\ s.t.&\left\{\begin{array}{l}\Delta t_{j}^{i}\in(0,6 0)\\ t_{1}^{1}+t_{i}^{2}+t_{i}^{3}+t_{i}^{4}+t_{B}\leq T_{j}^{i}+\Delta t_{i}\\ v_{\min}\leq v_{gt}\leq v_{\max}\\ T_{j}^{i}-T_{j-1}^{i}\geq T_{aq}\\ d_{\min}\leq d_{j}^{i}\leq d_{\max}\\ T_{aq}\leq\Delta T\leq(\Delta t_{q})_{\max}\\ \widetilde{\lambda}d_{j}^{i}+\widetilde{\lambda}(T_{j}^{i}+\Delta t_{j}^{i}- T_{j-1}^{i}-\Delta t_{j-1}^{i})<1\end{array}\right.\end{split}  \tag{49}\]