esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The dimension detection of high-speed railway track slabs is one of the most important tasks before the track slabs delivery. Based on the characteristics of a 3D scanner which can acquire a large amount of measurement data continuously and rapidly in a short time, this paper uses the integration of 3D scanner and the intelligent robot to detect the China Railway Track System (CRTSIII) track slab supporting block plane, then the dense and accurate supporting block plane point cloud data is obtained, and the point cloud data is registered with the established model. An improved Random Sample Consensus (RANSAC) plane fitting algorithm is also proposed to extract the data of supporting block plane point cloud in this paper. The detection method is verified and the quality analysis of the detection results is assessed by a lot of real point cloud data obtained on site. The results show that the method can meet the quality control of CRTSIII finished track slab and the detection standard. Compared with the traditional detection methods, the detection method proposed in this paper can complete the detection of a track slab in 7 min, which greatly improves the detection efficiency, and has better reliability. The method has wide application prospects in the field of railway component detection.  
high-speed railway CRTSIII track slab; 3D scanning technology; point cloud; robot; rapid detection of the supporting block plane; improved RANSAC plane fitting algorithm +
Footnote †: journal: Applied Sciences  
1 Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University,  
Chengdu 611756, China  
China Railway 22 nd Bureau Group Co., Ltd., Beijing 100043, China  
Center for Geodetic Data Processing, National Administration of Survey, Mapping and Geoinformation, Xi'an 710054, China  
State-province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety, Chengdu 610031, China  
Correspondence: [EMAIL_ADDRESS]
# Tire Model with Temperature Effects for Formula SAE Vehicle
## 3 Thermal Model
### Comparison Results  
The simulation results obtained from the considered thermal models were compared with measurement data for various maneuvers. The initial values of coefficients were taken from [18; 25] and further tuned using constrained parameter optimization.  
To tune parameters, a non-linear least square method was applied to obtain a suitable approximation of temperature effect and was based on the error from the measurement defined as:  
\[e_{\text{meas}}=\sqrt{\frac{\sum(T_{\text{model}}-T_{\text{measurement}})^{2}}{ \sum T_{\text{model}}^{2}}} \tag{21}\]  
It was checked that the fitting parameters provide a good estimation for different maneuvers. For example, the parameters were first optimized for lateral tests and later checked again for acceleration/braking tests. Then an average was taken for the tuning parameters to cover all the experimental tests and represent the whole driving envelope. The comparisons between the measured temperature, the temperature obtained from the Kelly and Sharp model, and the proposed thermal model are shown in Figure 7.  
The accumulated error for the compared thermal models is summarized in Table 2.  
The results of the proposed thermal model and the compared models are close to the measurement data for all conducted tests. The proposed thermal model provides the smallest error for pure cornering. For pure braking the Sorniotti model shows the best performance and for pure acceleration the smallest error can be achieved using the Kelly and Sharp model. However, the conclusions regarding adaptability of the models to different normal loads cannot be extracted from Table 2. Since the Kelly and Sharp model considers the contact patch area depending on the normal load and air pressure, the  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Sorniotti Model** & **Kelly and Sharp Model** & **Proposed Model** \\ \hline Pure acceleration & 5.2\% & 4.7\% & 5.5\% \\ Pure braking & 2.5\% & 10\% & 2.9\% \\ Pure cornering & 3.2\% & 3.0\% & 2.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accumulated errors for compared thermal models.  
Figure 7: Temperature change in pure braking test: (**a**) results of the Kelly and Sharp model; (**b**) results of the proposed model.  
model can adapt to different normal loads and the maximum error is limited to \(\sim\)6%. Therefore, the models were also checked using measurement data for different load conditions. It was found that although the Sorniotti model has a good fit to the measured data, the error increases when the load condition is changed (Figure 8).
## 2 Numerical Calculation Method
### Computational Domain and Boundary Conditions  
The computational domain of the tunnel aerodynamics consists of regions outside and inside of the tunnel. The size of the outside region is determined by the fact that the disturbance at the boundary should not have a significant impact on the MPW emitted from the tunnel. The numerical results show that the boundary of the outside region has almost no effect on the MPW when the diameter of which exceeds 300 m. Therefore, the diameter of the outside region is taken as \(D=300\) m. Both the boundary disturbance and the smooth acceleration of the train should be taken into consideration for the length of the outside region, and a length of \(L=550\) m is taken.  
The sliding mesh and dynamic layering are used to simulate the driving process of trains in tunnels. The entire computational domain consists of four parts, the moving domain around the train, deforming domains before and after the train, and the residual fixed domain, as shown in Figure 2. The moving domain includes trains moving forward at the same speed, while the deforming domain deforms as the train moves to maintain the boundary of the stationary computational domain. The _Interface_ boundary condition is used for data exchange between two adjacent domains.  
When the speed of the train increases directly from zero to the specified running speed, abnormal compression waves will be generated. To eliminate the effects of the sudden start on the compression wave, a polynomial velocity curve is used when the train accelerates [34]:  
\[v(t)=\frac{a}{20}t^{5}-\frac{at_{a}}{8}t^{4}+\frac{at_{a}^{2}}{12}t^{3} \tag{7}\]  
where \(a=120\,v_{\mathrm{t}}/t_{\mathrm{a}}{}^{5}\), \(t_{\mathrm{a}}\) denotes the acceleration time of the train, and \(v_{\mathrm{t}}\) represents the train speed. When the train moves forward, for any control volume in the moving domain, the governing equation in integral form can be expressed as:  
\[\frac{d}{dt}\int_{CV}\rho\phi dV+\int_{CS}\rho\big{(}\mathbf{u}-\mathbf{u}_{\mathrm{g}} \big{)}\mathbf{\cdot}ndA=\int_{CS}\Gamma\mathbf{\
abla}\mathbf{\phi}\mathbf{\cdot}ndA+\int_{CV} S_{\Phi}dV \tag{8}\]  
where \(CS\) is boundary of the control volume \(CV\), \(\mathbf{u}_{\mathrm{g}}\) is the velocity vector of the dynamic mesh, and \(\mathbf{n}\) is the normal vector of the surface element \(dA\).  
_Pressure-Inlet_ is applied to the boundary of the region outside the tunnel entrance, while that of the region outside the tunnel exit is set as _Pressure-Outlet_. The _Non-Reflecting_ condition in the _Acoustic Wave Model_ is activated to eliminate disturbances from the boundary reflection.  
Figure 1: Schematic diagram of the geometric model: (**a**) Three views of the train nose; (**b**) Tunnel section; (**c**) Sketch of the hood.  
Figure 2: Computational domain and boundary conditions.
# Low-Resolution Precoding for Multi-Antenna Downlink Channels and OFDM +
## 2 System Model
### Linear MMSE Precoding  
To describe the linear MMSE precoder, consider the channel from base station antenna \(n\) to UE \(k\):  
\[\mathbf{h}_{kn}=(h_{kn}[0],\ldots,h_{kn}[L-1],\underbrace{0,\ldots,0}_{(T_{F}-L)\ \text{zeros}})^{\text{T}} \tag{6}\]  
and denote its DFT as \(\hat{\mathbf{h}}_{kn}=(\hat{h}_{kn}[0],\ldots,\hat{h}_{kn}[T_{F}-1])^{\text{T}}\). The channel of subcarrier \(m\) is the \(K\times N\) matrix \(\hat{\mathbf{H}}[m]\) with entries \(\hat{h}_{kn}[m]\) for \(k=1,\ldots,K\), \(n=1,\ldots,N\). The linear MMSE precoder (or Wiener filter) for subcarrier \(m\) is  
\[P[m]\hat{\mathbf{H}}[m]^{\text{t}}\left(P[m]\hat{\mathbf{H}}[m]\hat{\mathbf{H}}[m]^{\text{t }}+\sigma^{2}\mathbf{I}\right)^{-1} \tag{7}\]  
where \(P[m]=\text{E}[|\hat{u}_{k}[m]|^{2}]\) is the same for all \(k\), \(\hat{\mathbf{H}}[m]^{\text{t}}\) is the Hermitian of \(\hat{\mathbf{H}}[m]\), and \(\mathbf{I}\) is the \(K\times K\) identity matrix. The precoder multiplies \(\hat{\mathbf{u}}[m]\) by (7) for all subcarriers \(m\), and performs \(N\) IDFTs to compute the resulting \(\mathbf{x}[0],\ldots,\mathbf{x}[T_{F}-1]\). We remark that ZF precoding is the same as (7) but with \(\sigma^{2}=0\), where \(\hat{\mathbf{H}}[m]\hat{\mathbf{H}}[m]^{\text{t}}\) is usually invertible if \(N\) is much larger than \(K\).
# Detection of Forest Windstorm Damages with Multitemporal SAR Data--A Case Study: Finland
## 1 Introduction
### Background and Objectives of the Study
The overall goals of this study were to develop methods to localize the forest windstorm damages, assess the severity and area of damaged forests and quantify the uncertainties in forest damage prediction when using space-borne SAR data. The detailed objectives were:  
1. to study the potential of Sentinel-1 SAR data in localizing the forest windstorm damages;
2. to assess the accuracy of the developed methods; and
3. to assess the time lag from the damage to the damage detection and a ready product.
# Software Fault Localization Based on SALSA Algorithm
## 4 Experimental Settings  
### Experimental Subjects  
In the experiments of this paper, the proposed method was evaluated for effectiveness on Defects4j. Defects4j is a mature real fault dataset used for testing experiments and has been widely applied in software testing research. It contains six Java open-source projects, including JFreefigure, Google Closure Compiler, Apache Commons Lang, Apache Commons Math, Joda-Time, and Mockito. All buggy versions of the benchmark programs can be downloaded from the Defects4j repository. For each bug, Defects4J provides the buggy program, the minimally changed fixed program, the failing tests, and the modified source files. Note that this experiment used 348 real bugs selected from Defects4j, excluding the Mockito project, as well as nine other bugs, because the program errors were not located in methods or constructors, and some buggy projects could not obtain execution tracking through the tracer. Table 4 displays specific information about the Defects4J dataset used. The first column shows the topic IDs that will be used in the remaining text; the second column shows the full names of the projects; the third column shows the number of defects for each project; the forth column shows the latest version's LoC (Lines of Code) for each project in Defects4J; the fifth column shows the number of test cases each project has.
# Intelligent Edge-Cloud Framework for Water Quality Monitoring in Water Distribution System
## 2 Edge Intelligent with IoT
### Deep Learning in Edge Computing  
Typically, IoT devices generate significant amounts of data and send it to the cloud for processing. These data include multimedia information like photos, sounds, videos, and structured data like vibration, temperature, and humidity. There are numerous developed solutions for analysing structured data and then operating IoT devices autonomously [28]. Deep learning has been successful in many fields, including multiple visual tasks, speech recognition, natural language processing and industrial applications [29], this is because of its capacity to learn complicated models, and diversity of architectures [30]. A deep learning network is often composed of numerous layers. These layers will process the input data. Each layer processes the previous layer's intermediate features and then generates new features. Finally, the extracted features generated by the final deep learning network layer will be recognised as the output by a classifier. Deep learning networks consider the layers closest to the input data to be lower levels, while the others are higher layers [28; 31].  
In this work, we have used an MLP model to detect and classify water contaminant levels for data that include water quality parameters. The developed model contains several layers that are completely connected. The model was trained using time series data for more than 50,000 samples. The MLP model was used to classify the data into five different classes representing the various levels of water contamination. Deep learning increases prediction accuracy for IoT services by extracting features through numerous  
Figure 2: Standard edge-cloud architecture.  
layers rather than traditional complex pre-processing. However, with increased accuracy, communication performance will be the bottleneck. The amount of data needed to train deep-learning models is huge. Such a large volume of network traffic can cause various changes to the network in terms of increasing transmission delay, reducing the packet delivery ratio, increasing energy consumption due to high transmission rate, and may pose security risks; therefore, typical cloud-based solutions for deep learning approaches on IoT may not be ideal [32].  
As illustrated in Figure 3, we present an edge computing topology for IoT deep learning applications. The structure is composed of two layers and also a normal edge computing structure. Edge servers are placed at the edge to process gathered data and the cloud server is used to train the deep learning network [33]. Applying deep learning entirely at the edge will reduce network traffic. However, the capacity of edge servers is limited compared to cloud servers. It is impossible to process infinite tasks in edge servers. Dedicating the entire work of deep learning to the edge will bring additional computing overhead to the servers. Therefore, we can only deploy part of the deep learning network into edge servers. The issue is deciding how to partition each deep learning network.
# Development of Rockburst Research: A Comprehensive Review
## 2 Rockburst Definition and Types  
### Rockburst Definition  
Many journal papers and government publications have defined rockburst. Table 1 provides a timeline of rockburst definitions based on the literature review. There were controversies in early academic circles, mainly including the following three points:  
* Whether rockburst occurs only in hard brittle rock mass.
* Whether static failure, such as spalling and splicing, is rockburst.
* Whether pressure bump can be attributed to rockburst.  
With the accumulation of engineering data and the deepening of research, the above three controversies have basically reached a consensus:  
* Rockburst failure is a dynamic instability phenomenon, which is essentially different from static failure. Simple static failure does not belong to rockburst, but the precursory phenomenon of rockburst may be static failure.
* Pressure bump has the characteristics of high intensity, long lag time and large influence range, but its essence is the same as rockburst and thus it can be generalized as rockburst.  
\begin{table}
\begin{tabular}{c c} \hline
**Researchers (Year)** & **Rockburst Definition and Its Description** \\ \[PERSON] (1965) [5] & * Rockburst is an uncontrolled disruption of rock associated with a violent release of energy. \\  & * Rockburst is a sudden rock failure characterized by the breaking up and expulsion of rock from its surroundings, accompanied by a violent release of energy. \\  & * Rockburst is a phenomenon that occurs when a hard and complete rock mass that has accumulated a large amount of strain energy has been excavated, the initial stress of the rock mass exceeds the elastic limit of the rock mass and the accumulated strain energy is suddenly released, accompanied by loud noises and lenticular rock fragments flying out. \\ [PERSON] et al. (1994) [7] & * Rockburst is a sudden and violent expulsion of rock from the surrounding rock mass. \\ [PERSON] et al. (1996) [8] & * Rockburst is a seismic event that is associated with damage to a mine opening. \\  & * Rockburst is a violent failure in hard (brittle) and massive rock masses of Class II * (* uniaxial compressive strength (UCS) test on Class II type) when subjected to high stress. \\ [PERSON] et al. (2003) [10] & * Rockburst can be regarded as a large seismic event or a small seismic event. \\ [PERSON] et al. (2007) [11] & * Rockburst is the phenomenon of nonlinear dynamics with the instantaneous release of energy along the free surface of rock excavation. \\ \hline \end{tabular}
\end{table}
Table 1: Rockburst definitions.  
At present, although there is no internationally recognized definition of rockburst, the definition is basically the same in terms of failure phenomenon and failure mechanism.
## 3 Results  
### DPSC Characteristics After Passaging  
FACS analysis was conducted to verify the surface antigens of the DPSCs using CD29, CD34, CD44, CD45, and CD90 antibodies. At 3P, CD34 (recognized as the endothelial and hematopoietic cell marker) and CD45 (as a leucocyte marker) were minimally expressed in the cell population, whereas CD29, CD44, and CD90, which are widely expressed in DPSCs [12; 14], were expressed in these cells (Figures 2 and S2 and Table 1). A modest reduction in the proportion of CD90-positive cells was observed after 26P (Figure 2 and Table 1). The cell and nucleus areas and nuclear volume of DPSCs increased with the increasing number of passages (Figure 3). As is consistent with the previous study conducted by other researchers utilizing fibroblast (WI-38 cells) [38], an increase in the number of stress fibers was observed in the senescent DPSCs (26P) (Figure 4).
## 3 Results and Discussion
### Simulation of Sorption Data  
Table 6 summarizes the parameters obtained from the logistic equation fitting and for the Bohart-Adams, [PERSON], and Yoon-Nelson models (also see Supplementary Materials, Figure S1). As shown in Figures 2 and 4, the logistic equation accurately describes the experimental data. When the Logistic equation parameters are used to predict the empirical model parameters, it can be concluded that the Bohart-Adams models do not correctly represent the experimental data, especially regarding the experimental adsorption capacity and the adsorption capacity determined by the model. The relative errors were higher than 60% in almost all runs (except for runs 12 and 13). In contrast, the [PERSON] and [PERSON] models showed a better prediction of the experimental data. Thus, these two models can be used confidently to predict the breakthrough curves for the same adsorbent/adsorbate  
Figure 4: Experimental breakthrough curve for different initial P concentrations in wastewater [\(Q\) = 5 mL/min; \(h\) = 30 cm; \(20\pm 1\)\({}^{\circ}\)C] and fitting with Logistic Equation (7).  
system on large-scale columns. The rate constants of the different models (\(k_{hu}\), \(kth\), and \(k_{ym}\)) increased with superficial velocity and decreased with increasing bed height. In addition, for the same bed height and superficial velocity (Runs 2 and 12), these constants decreased for higher feed concentrations. Regarding \(\tau_{ym}\) found by the Yoon-Nelson model adjustment, these values are quite close to those found experimentally (\(t_{50\%}\)). As expected, a lower \(\tau_{ym}\) is observed for higher superficial velocities (Runs 7, 8, and 9) since the adsorbent saturates faster under these conditions.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 3 Methodology
### Machine Learning Algorithms
#### 3.2.6. Bagged Decision Trees  
As far as the Bagged Decision Tree algorithm is concerned, Bagging, or bootstrap aggregating, is an ensemble learning method that combines multiple decision trees to reduce variance and improve the predictive accuracy of regression models [53].  
The Bagged Decision Tree (BDT) algorithm was implemented using MATLAB's _fitensemble_ function. A total of 100 decision trees were used as weak learners, with bagging employed to aggregate predictions and reduce variance. Configured for regression tasks, the model leveraged default MATLAB parameters for tree depth and leaf size, ensuring robust and unbiased predictions across the dataset.  
Bootstrapped Data:  
For each iteration \(t=1,2,\ldots,T\) (where \(T\) is the number of iterations), randomly sample the training data with replacements to create bootstrapped datasets \(Dt\).  
Decision Tree Training:  
Train a decision tree on each bootstrapped dataset \(Dt\). Each tree is denoted as \(ht(x)\) and aims to capture different patterns or noise in the data.  
For each tree \(t\), the decision tree is constructed by recursively partitioning the data based on feature splits that minimize impurity or reduce the Mean Squared Error (MSE).  
Predictions:  
For a given input instance \(x_{i}\), the predictions are made using all the trained decision trees:  
\[\hat{y_{i}}=\frac{1}{T}\sum_{i=1}^{T}h_{t}(x_{i}) \tag{10}\]  
where \(\hat{y_{i}}\) represents the predicted output for instance \(x_{i}\), which is the average prediction across all decision trees.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 2 SINS/GPS Fusion Model  
### System State Model  
Based on the error model of INS, a more accurate dynamic model of systematic error is constructed to show that it is suitable for Kalman filter. For the sake of simplification, some relatively insignificant terms have been ignored in the process of linearization [22]. The psi-angle error models of INS can be expressed as [23]  
\[\left\{\begin{array}{l}\delta\dot{p}=-\phi_{ne}\times\delta p+\delta s\\ \delta\dot{s}=-(2\phi_{ie}+\phi_{ne})\times\delta s-\delta\theta\times\tau+\zeta \\ \delta\dot{\theta}=-(\phi_{ie}+\phi_{ne})\times\delta\theta+\sigma\end{array}\right. \tag{1}\]  
in which \(\delta p\), \(\delta s\), and \(\delta\theta\) represent the position, speed, and orientation error vectors, respectively. \(\phi_{ne}\) denotes the rate of navigation frame with respect to earth, and \(\phi_{ie}\) denotes the rate of earth with respect to inertial frame. \(\tau\) indicates the specific force vector. The systematic error of GPS/INS integrated system is achieved through expansions from both the accelerometer bias error vector \(\zeta\) and the gyro drift error vector \(\sigma\).  
Compared with the INS/GPS integrated system, resemble precision of system state estimation is proposed on the basis of increasingly wide utilization of error states for the EKF. Nevertheless, if a total states model of UKF is utilized, every output data of system strapdown processing which contains each sampling point will be obliged to be repeated, and then the additional computational cost will be increased greatly. Hence, the error states of INS/GPS integrated system can be utilized and expressed as follows [24].  
\[\left\{\begin{array}{l}X_{Nav}=\left[\delta P_{N},\delta P_{E},\delta P_{D}, \delta V_{N},\delta V_{E},\delta V_{D},\delta\delta_{N},\delta\theta_{E},\delta \theta_{D}\right]^{\mathrm{T}}\\ X_{Acc}=\left[\xi_{bx},\xi_{by},\xi_{bz},\xi_{fx},\xi_{fy},\xi_{fz}\right]^{ \mathrm{T}}\\ X_{Gyro}=\left[\sigma_{bx},\sigma_{by},\sigma_{bz},\sigma_{fx},\sigma_{fy}, \sigma_{fz}\right]^{\mathrm{T}}\\ X_{Ant}=\left[\delta l_{bx},\delta l_{by},\delta l_{bz}\right]\end{array}\right. \tag{2}\]  
in which \(\delta P_{N}\), \(\delta P_{E}\), \(\delta P_{D}\) denote the three-dimension position error; \(\delta V_{N}\), \(\delta V_{E}\), \(\delta V_{D}\) denote the velocity error; and \(\delta\theta_{N}\), \(\delta\theta_{E}\), \(\delta\theta_{D}\), denote orientation error, respectively. The nine navigation error vectors are represented by the north-east-down (NED) navigation frame. \(\zeta_{bx}\), \(\zeta_{by}\), \(\zeta_{bz}\), \(\zeta_{fx}\), \(\zeta_{fy}\), \(\zeta_{fz}\) denote accelerometer error vectors; and \(\sigma_{bx}\), \(\sigma_{by}\), \(\sigma_{bz}\), \(\sigma_{fx}\), \(\sigma_{fy}\), \(\sigma_{fz}\) denote gyro sensor errors vector. \(\delta l_{bx}\), \(\delta l_{by}\), \(\delta l_{bz}\) denote lever arm errors.