esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Analysis of the Influence of Process Parameters on the AE Signal  
The influence of the main AWJC process parameters on the AE signal, based on the developed mathematical models of \(AE_{RMS}\), was analyzed.  
Figure 8a illustrates the relationship between the \(AE_{RMS}\) measured at the cutting head and the process parameters. Essentially, this is the jet energy lost due to friction with the internal components of the cutting head (focusing tube). The \(AE_{RMS}\) value increases with the rise in the water pressure and abrasive mass flow rate. However, variations in the feed rate and material thickness do not affect the \(AE_{RMS}\) measured at the cutting head.  
For the AE signal measured at the CFRP workpiece, the influence of the process parameters is analyzed and presented graphically in Figure 8b. This signal is correlated with the direct jet energy involved in the cutting (erosion) of the part. Increasing the water pressure, feed rate, and material thickness results in an increase in the \(AE_{RMS}\), while increasing the abrasive mass flow rate leads to a decrease in the value of the \(AE_{RMS}\).
# An Improved Jacobi-Based Detector for Massive MIMO Systems
## 3 Proposed Algorithm for MIMO Detection  
In this section, an improved Jacobi algorithm for MIMO detection is proposed in uplink massive MIMO systems. Specifically, the proposed algorithm is divided into two stages. In the first stage, the SD method is applied towards an efficient searching direction in the Jacobi algorithm. In the second stage, the whole-correction method is utilized to further improve the convergence rate of the algorithm. Then, a fast convergence of the proposed algorithm is proved following an analysis of the complexity of the algorithm.
# Real-Time Control System for Model Railway Based on SIMIS W Interlocking System
## 1 Introduction
A control system consisting only of PLCs offers limited options for creating and controlling visualization. A protocol-based control system such as OpenDCC does not resemble the internal structure of railway signalling systems. By combining PLC and PC platforms, it is possible to use almost unlimited possibilities to create visualization. At the same time, the PLC represents the execution part of the interlocking systems. This study outlines the design and implementation of the SIMIS W model signalling system tailored for model railways. The central objective is to facilitate real-time data transmission while ensuring the signalling system's capacity to manage this data effectively, thereby delivering prompt responses. Additionally, the interlocking system is augmented by an operator workstation derived from the ILTIS-N system, also developed by Siemens Mobility. The primary contribution of this paper lies in presenting an innovative methodology for constructing analogous simulation devices that are conducive to educational initiatives centred on signalling systems. This work has the potential to inspire the creation of similar simulation tools aimed at training, which would be advantageous for both academic institutions and the professional development programs of railway personnel.
## 3 Results  
From 2015 to 2019, 833,318 well-babies within the Dutch population were eligible for the newborn hearing screening, and 830,696 (99.7%) participated. A total of 0.3% of this population (2835 children) did not pass the hearing screening. These children were referred to a speech and hearing center for further audiological diagnostics. Diagnostic results are known for 94.6% of these children (2682 children). Reasons for missing data on the diagnostic results of the 153 children are: missing reports from the speech and hearing center (57.5%), not visiting the speech and hearing center for diagnostic testing (32.7%), no parental permission for using the diagnostic results (7.2%), diagnostic testing was not possible because of severe medical problems (1.3%) and children who passed away (1.3%). From the 2682 children with complete diagnostic results, 23.7% are diagnosed with bilateral hearing loss of at least 40 dB and 14.4% with unilateral hearing loss. This leads to a prevalence rate within well-babies of 0.46 per 1000 for unilateral hearing loss and 0.76 for bilateral hearing loss. An overall prevalence rate of 1.23 per 1000 children was detected for a permanent hearing loss in one or both ears. In 61.9% of the children, no permanent hearing loss was found, although temporary conductive hearing losses or hearing losses just below 40 dB were often present. An overview of these results is presented in Figure 1. In Table 2, the results are presented for each year separately.
## 3 Cluster Formation
### Failure Management  
The inherent capability constraints of sensor nodes, harsh physical environment and unattended nature of deployment make WSNs vulnerable to failure. In addition, the expectation that WSNs will operate autonomously for a long time necessitates providing fault-tolerant techniques in order to guarantee the network performance and network QoS. The sources of faults in sensor networks may be node-failure due to depletion of battery or destruction by an external event (permanent failure); or it may be link-failure due to environmental condition or medium congestion (transient failure). To overcome the faults, fault-management approaches are applied into networks in three phases: _monitoring, fault detection_ and _failure recovery_. In cluster-based schemes, a faulty CH entails isolation of a part of the network; thus, fault-management at CH level is of great importance. In this respect, most of fault-management techniques address the faulty-CH issue. Several investigations propose CH redundancy [58; 59; 61] (described in Section 2.2.2) as a simple solution for the failure of CHs. However, although CH redundancy is proper mechanism for failure recovery, monitoring and fault detection phases are two other determinant stages that greatly affect the efficiency and latency of dealing with the faults; hence, it is essential to consider fault management as a platform. In addition, the occurrence of fault at other hierarchical levels of cluster is an issue that cannot be addressed only through CH-redundancy. Therefore, several investigations [90; 91; 92; 93; 94; 95], which provide fault-management platform for permanent and transient faults in cluster-based schemes, are surveyed in this section.
# Failure Detection for Semantic Segmentation on Road Scenes Using Deep Learning
## 3 Experimental Results
### Experimental Results on the Challenging Dataset
#### 3.6.2 Qualitative Results  
This section presents examples of mispredicted HMG dataset images among the results classified by mIoUNet.  
False-Negative Image in the HMG Dataset  
The first column of Figure 14 depicts the input images, and the second column of Figure 14 depicts the segmentation maps. In the case of false-negative images (Figure 14), many images were taken during the day, and the degree of light dispersion was large within one image. For example, images with shadows and images with lights shining on a tunnel were classified.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Loss Function** & **Failure Detection Accuracy (\%)** & **mIoU Prediction Accuracy (\%)** \\ \hline Mean squared error & 79.8 & 88.21 \\ Modified loss function & 83.3 & 90.51 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Failure detection and mIoU prediction accuracy for the different loss function for the Hyundai Motor Group (HMG) dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Threshold MIoU** & **Accuracy (\%)** & **Precision** & **Recall** & **F1-Score** \\ \hline
0.6 & 75.46 & 0.775 & 0.806 & 0.791 \\
0.7 & 83.33 & 0.707 & 0.829 & 0.764 \\
0.8 & 87.5 & 0.125 & 0.333 & 0.182 \\ \hline \hline \end{tabular}
\end{table}
Table 17: Failure detection results of the selected network structure for the Hyundai Motor Group (HMG) dataset.  
False-Positive Image in the HMG Dataset  
The first column of Figure 15 presents the input images, and the second column of Figure 15 presents the segmentation maps. In the case of false-positive images, many images were taken at night, and the images with high light dispersion were detected. The difference from the false-negative image is that many images were detected when the edge information, such as road markers or lanes, was not adequately visible or was in the road area. To summarize the results, the proposed model has a problem in that the mIoU cannot be accurately predicted when the light change is considerable in the vertical image of the road. However, the qualitative evaluation results reveal that using additional information, such as edges, can increase prediction accuracy.  
Figure 14: Examples of false-negative images on the Hyundai Motor Group (HMG) dataset.
## 4 Analysis, Results, and Discussion
### Displacement Analysis
#### 4.1.3 Model Validation  
In order to validate the accuracy of the model calculation results, monitoring data were obtained for the portal section of the Guanyin Mountain Tunnel, namely, the cumulative  
Figure 11: Change curve of monitoring point of tunnel section: (**a**) the left tunnel; and (**b**) the right tunnel.  
subsidence curves for the vault of the tunnel YK29+268 and ZK29+283 and the surface subsidence curves for the portal section, as shown in Figures 12 and 13, respectively. The surface monitoring points were arranged horizontally at 4 m intervals, with monitoring points ZP1 to ZP3 located above the left tunnel and YP4 to YP7 above the right tunnel.  
Figure 12: Cumulative subsidence temporal curve of the vault at the tunnel portal section.  
Figure 13: Surface subsidence temporal curve at tunnel portal section: (**a**) the left tunnel; and (**b**) the right tunnel.  
It can be seen from Figures 13 and 14 that the actual monitoring data of the tunnel and the deformation curve of the numerical model have similar regularity. The maximum subsidence value of the left tunnel vault was 18-20 mm and the maximum ground subsidence value was 14.6 mm, and the measured data were about 4 mm larger than the simulated value; the maximum subsidence value of the right tunnel arch is 22-24 mm and the maximum subsidence value of the ground surface is 16.2 mm, and the measured data are about 2-3 mm larger than the simulated value. This is due to factors such as rheological properties and water-rich conditions not being considered; the measured values were slightly larger than the simulated values and the deformation had a small fluctuating nature. As shown in Figure 13, the maximum subsidence difference in the ground surface occurs in the early stage of excavation and the maximum subsidence point is on the right side directly above the left tunnel and on the left side directly above the right tunnel, which is the same as the results of the model. In conclusion, it shows that the model conforms to the actual project and the calculation results are accurate.  
Figure 14: Distribution of different plastic zones: (**a**) distribution of shear plastic zone on the slope surface; (**b**) distribution of shear plastic zone of the surrounding rock; (**c**) distribution of tension plastic zone on the slope surface; and (**d**) distribution of tension plastic zone of the surrounding rock.
## 2 Research Object and Methods
### Numerical Setups
#### 2.4.2 Boundary Conditions and Turbulence Model of the CFD
The actual formulation for a two-equation model is  
\[\frac{\partial(\rho k)}{\partial t}+\frac{\partial(\rho\overline{U_{j}}k)}{ \partial x_{j}}=P_{k}-\rho\frac{k^{3/2}}{\min(L_{t},C_{DES}\Delta_{\max})}+ \frac{\partial}{\partial x_{j}}\bigg{(}(\mu+\frac{\mu_{t}}{\sigma_{k}})\frac {\partial k}{\partial x_{j}}\bigg{)} \tag{8}\]In this case, the length scale used in the computation of the dissipation rate in the equation for the turbulent kinetic energy is replaced by the local mesh. As the grid is refined below the following limit, the DES-limiter is activated and switches the model from RANS to LES mode. \[ \begin{array}{l}C_{DES}\Delta_{\max}>L_{t}\to RANS\\ C_{DES}\Delta_{\max}<L_{t}\to LES\\ \Delta_{\max}=\max(\Delta_{x},\Delta_{y},\Delta_{z})\\ L_{t}=k^{3/2}/\varepsilon=(\sqrt{k})/\beta^{*}\omega\end{array}  \tag{9}\]  
where \(x\), \(y\), \(z\) means three directions of the spatial coordinate system for the local computational cell. This model allows the user to avoid the high computing costs of covering the wall boundary layers in LES mode. In order to avoid the limitation of grid refinement inside attached boundary layers, the dissipation term in the \(k\)-equation is thereby re-formulated as follows:  
\[E_{DES}=\rho\frac{k^{3/2}}{\min(L_{t},C_{DES}\Delta)}=\rho\frac{k^{3/2}}{L_{t} \text{min}(1,C_{DES}\Delta/L_{t})}=\rho\frac{k^{3/2}}{L_{t}}\text{max}(1,\frac {L_{t}}{C_{DES}\Delta}) \tag{10}\]  
The numerical formulation is also switched between an upwind biased and a central difference scheme in the RANS and DES regions, respectively. The default value of the software is adopted in the calculation. The inlet boundary is set with total pressure; the outlet boundary condition is selected for mass flow; the working medium is 25 \({}^{\circ}\)clear water; the convergence accuracy is set to 10\({}^{4}\). An appropriate time step is important to perform unsteady flow field calculations. Courant number is always used as a criterion to judge if the time step satisfies the periodic numerical simulation, which is defined by Equation (11) [27]. The time step for the unsteady simulation is set to the value equivalent impeller rotational step of 3. Data from 10 full rotations are stored after the calculation convergence and used as the sound source starting information file. \[C_{o}=v\frac{\Delta t}{L}<100 \tag{11}\]  
where \(L\) means the smallest size of the grid cell, \(v\) is the biggest velocity of the main flow, and \(\Delta t\) is the time step. Taking pump operation at 6750 r/min as an example, the maximum \(v\) is approximately 25 m/s base on the impeller size, and \(L\) is more than 0.1 mm according to grid information. The time step set as \(2.469\times 10^{-5}\) s. Then, the calculated maximum \(C_{\text{o}}\) is 6.17, which satisfies the time-step independence.
## 4 Results and Discussions
### Characterization of Polypropolee Nanocomposite
#### 4.1.3 TEM Analysis  
Transmission electron microscopy (TEM) is a critical tool for studying the internal microstructure of materials at the nanoscale. TEM images (Figure 6a,b) of graphene flakes and conducting polymer composites provide detailed morphological insights. In Figure 6a, the TEM image reveals the presence of wrinkles, ripples, and scrolls in the graphene powder, indicating the presence of few-layered graphene sheets. Figure 6b shows a TEM micrograph of PP-GNP 1 wt%, where soft domains (SDs) appear white and complex parts (HDs) appear dark.  
The TEM analysis of thermoplastic polypropylene reinforced with graphene systems (Figure 6b) demonstrates the nanoscale dispersion of nanoparticles within the polymer matrix, prepared using various methods of nanocomponent incorporation, including solution-based synthesis processes. TEM provides direct insights into these nanocomposite systems' morphology, confirming the nanofillers' effective dispersion in the polypropylene matrix.  
Figure 5: FESEM analysis of **(a)** PP, **(b)** GNP, **(c)** GNP/PP composite, **(d,e)** MWCNT with different magnifications.
## 2 Materials and Methods
### Machine Learning Method--Decision Tree Regression  
Decision tree regression (DTR) is a nonparametric supervised learning method used for predicting continuous outcomes by recursively partitioning the feature space. Unlike parametric approaches such as response surface methodology (RSM), which rely on predefined polynomial functions, DTR adaptively splits data into subgroups to minimize prediction error. In this study, DTR is employed to model and correct the residuals of the discrepancies between experimental measurements (\(R_{axym}\)) and RSM-predicted values (\(R_{arsm}\)). Since RSM may not fully capture complex nonlinear relationships in the data, DTR enhances predictive accuracy by learning the underlying patterns in these residuals.  
The splitting process in DTR is governed by impurity reduction, typically measured using mean squared error (\(MSE\)). At each node, the algorithm evaluates potential splits across all features and thresholds, selecting the one that maximizes the decrease in \(MSE\). The \(MSE\) for a given node is calculated as the average squared difference between the observed values and their mean within that node (Equation (4)):  
\[MSE=\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\overline{y})^{2} \tag{4}\]  
where \(n\) represents the number of samples in the node, \(y_{i}\) denotes the actual response value, and \(\overline{y}\) is the mean response of the node. The quality of a split is quantified by the reduction in \(MSE\) (\(\Delta MSE\)), computed as the difference between the parent node's \(MSE\) and the weighted \(MSE\) of its child nodes (Equation (5)):  
\[\Delta MSE=MSE_{parent}-(w_{L}\cdot MSE_{L}+w_{R}\cdot MSE_{R}) \tag{5}\]  
where \(MSE_{parent}\) is the \(MSE\) before the split, \(MSE_{L}\) and \(MSE_{R}\) are the \(MSE\) values for the left and right child nodes, and \(w_{L}\) and \(w_{R}\) are the proportions of samples in the left and right nodes.  
To prevent overfitting, tree growth is constrained by stopping criteria such as the maximum number of splits (MaxNumSplits), minimum samples per leaf node, or a minimum improvement in impurity. These parameters control the model's complexity, balancing bias and variance. By integrating DTR-corrected residuals with RSM predictions (Equation (6)),  
\[R_{alhybrid}=R_{arsm}+Residual, \tag{6}\]  
the hybrid model achieves superior accuracy, particularly in capturing nonlinearities that the RSM alone might miss. This approach combines the interpretability of RSM with the flexibility of machine learning, offering a robust framework for predictive modeling.
## 5 Experimental Evaluation  
RDMA presents asymmetric performance characteristics on either sides (i.e., client and server) [14]; however, the performance of the client side was investigated. All experiments were compiled with the gcc version 4.4.7 with 50 s warmup and 25 s measuring time. Experiments were run on a small cluster with 2 sockets AMD Opteron 6276 (Bulldozer) 2.3 GHz equipped with 20 Gbps DDR ConnectX Mellanox on PCI-E 2.0. The network topology is a direct connection with an Infiniscale-III Mellanox switch. Each machine has four NUMA nodes connecting to two sockets. Libibverbs (a part of OFED) was exploited to manage RDMA resources. Moreover, a single polling thread per machine was considered to handle requests. To compare RDMA paradigms with traditional counterparts, socket-based approach was investigated as well. Furthermore, the implementation of experiments are publicly available ([[https://github.com/mashemat/Communication](https://github.com/mashemat/Communication)]([https://github.com/mashemat/Communication](https://github.com/mashemat/Communication))). In each communication paradigm, throughput, latency, and scalability were investigated.  
To measure the variation in the throughput of RDMA operations, four client processes reside on one host connected to one server, performing consecutive RDMA operations. This experiment was repeated 100 times and each time a client was executed for 50 s. The throughput of each client was measured, and then they were coalesced to find the total value. Figure 5 shows the results of this experiment. As can be seen, the variance between the observed throughputs is negligible, with only a few outliers.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Connection Type** & **READ** & **WRITE** & **FAA/CAS** & **SEND** \\ \hline RC & ✓ & ✓ & ✓ & ✓ \\ UC & ✗ & ✓ & ✗ & ✓ \\ UD & ✗ & ✗ & ✗ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Supported operations in each connection type.
# A Survey on Parameters Affecting MANET Performance
## 3 Simulation in MANETs
### Attacks on MANETs' Routing Protocols  
The MANET's environment is dynamic and nodes continuously join and leave. An attacker could easily take a critical location in the network to block data packets from being delivered to the destination node. Moreover, a malicious node might produce a high-power signal that covers a wide range of network nodes to introduce itself as the best routing path to forward the packet between the source node and the destination node [32]. This malicious node would then block the data packets from being forwarded to the destination node. Such malicious activity leads to increasing the loss of important data packets, and it is reducing the network's overall throughput. MANETs suffer from malicious activities where malicious nodes tend to impact the routing protocol mechanism. The direct impact of the attacks on routing protocols is to degrade the MANET's performance. To disrupt the MANET routing protocol, attackers tend to use several techniques such as follows:  
1. Routing table overflow attack: In this attack, the attacking node tends to crowd the network by advertising several non-existing nodes to overflow the routing table [33].
# Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot
###### Abstract  
The railway industry forecasts growth in passenger and freight traffic over the next 30 years. This places additional demands on rolling stock depot facilities, many of which were designed and built before the modern age of information technology. This paper explores the potential of improving the efficiency and effectiveness of rolling stock maintenance management to meet the challenges of the near future, by utilising advanced computing techniques. The objective of the work is to create optimised maintenance plans for a fleet of trains, considering optimal use of resources. As a \"glue\" for joining up functions and operations, a generic Depot and Vehicle ontology (called the Virtual Depot) is introduced. The ontology captures the structures, relationships, and attributes of objects in the Depot (rolling stock, sensors, depot assets, tools, resources, and staff). The ontology is populated with example company and fleet-specific knowledge using an automated knowledge acquisition method. This paper describes the systematic method for the creation of a Virtual Depot. Two particular aspects are discussed in detail--knowledge acquisition of fleet-specific information obtained from a manufacturer's Vehicle Maintenance Instruction manuals and the construction of a short-term scheduling process within the Virtual Depot. Our evaluation considers the integrative aspects of the method, demonstrating how the ontological structure and its acquired specific information informs and benefits the scheduling process, in particular with respect to schedule optimisation. Results from an initial case study show there is significant potential to optimise short-term maintenance schedules, and the ability to automatically consider resource availability in short-term scheduling is demonstrated.  
PM scheduling; rolling stock PM scheduling; MIP preventive maintenance scheduling; preventive maintenance scheduling optimisation; MIP rolling stock PM scheduling; knowledge graph; ontology; information extraction; rolling stock maintenance