esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
The conventional switch layout (shown in Figure 3) is for the C-switch in the UK rail network, for which the design speed is 25 mph (40.248 km/h) according to NR60 design of Network Rail [19]. Figure 2: Overall structure of the paper. Other railway S&C layouts, such as a stub switch layout, are also used in the rail network [20; 21]. In the stub switch configuration (shown in Figure 4), the switch toe and heel positions are swapped. One of the advantages of a stub switch is that it reduces the number of rails and a single switch can be used for more than two routes. But in stub switch arrangements it is difficult to control track alignments because of the discontinuity. Also, this type of system needs more flexible rails and is thus not used in main lines. The different switch systems are listed in Table 1 along with their actuation mechanism and type, including mechanical, electromechanical, hydraulic, and electrohydraulic. \begin{table}
\begin{tabular}{c c c} \hline
**Actuation Mechanism** & **Actuation Type** & **Reference** \\ \hline Mechanical & Manual & [22] \\ \hline  & HW & [23; 24] \\  & P80 & [25; 26] \\  & Style 63 & [27; 28] \\  & S700K & [29; 30] \\  & NTS-type & [31; 32] \\  & SURELOCK & [33] \\  & High-performance switch system (HPSS) & [34] \\  & REPOINT & [20; 35; 36] \\ \hline  & L700H, L710H & [37] \\ Hydraulic/electrohydraulic & Clamplock & [19; 38] \\  & AH90 & [39] \\ \hline \end{tabular}
\end{table} 
Table 1: Different switch systems and their actuation mechanism and type. Figure 4: Stub-type railway track S&C layout. Figure 3: Conventional railway S&C layout.
# Aircraft Track Anomaly Detection Based on MOD-Bi-LSTM
## 2 Algorithm Description
### Bi-LSTM for Track Outliers Detection
#### 2.3.3 SVDD Evaluates Threshold  
To solve the adaptive problem of detection threshold of aircraft abnormal behavior, the difference sequence could be obtained by taking the difference between the predicted sequence value in this paper and the real sequence value. Based on the difference sequence, a Support Vector Domain Description (SVDD) classifier [34; 35] is achieved to judge flight anomaly data. The main idea of SVDD classifier design is divided into several steps. Firstly, the difference sequence is mapped to high-dimensional space by nonlinear mapping. Then, the smallest hypersphere containing all or most of the difference sequence samples is found in the high-dimensional space. Finally, the obtained hypersphere functions as the discrimination boundary for anomaly detection (the discrimination rule is: if the sample point of the difference sequence falls into the hypersphere in the high-dimensional space, the sample point is discriminated as a normal point; if the sample point of the difference sequence falls outside the hypersphere in high-dimensional space, judge the sample point as an abnormal point).  
Based on the difference sequence between the predicted sequence and the real sequence, the radius \(r\) and the center \(a\) of the hypersphere can be obtained by SVDD solution, thus obtaining the classifier.
# An Accuracy-Aware Energy-Efficient Multipath Routing Algorithm for WSNs
## 5 Complexity Analysis  
### Message Complexity  
Suppose each node has \(\sigma\) neighbors. In the network initialization phase, each node exchanges information with its neighboring nodes once. Therefore, a total of \(2\sigma\) messages are required for a node to complete the initialization. Then, the message complexity is \(O(\sigma)\). In the path discovery phase, a node also needs to send \(\sigma\) messages to its neighbors, and the messages will be forwarded to maximum \(\zeta\) hops. Then, the message complexity is \(O(\sigma\zeta)\). After the destination node obtains the optimal paths, it will send back a response along each path in the multipath set. Then, the message complexity is \(O(1)\). Based on the above analysis, the message complexity is \(O(\sigma+\sigma\zeta)=O(\sigma\zeta)\).
## 2 Mechanism of SPPTMD and Numerical Model of Structure-SPPTMD System  
### Mechanism of the SP  
Since the SPPTMD consists of an SP and a motion limiter, it is necessary to understand the mechanism of an SP. The SP, which is a mass suspended by a spring, has two vibration modes, i.e., a radial vibration mode and a swing vibration mode (Figure 1 [51]). The circular frequencies of the two vibration modes are given as follows:  
\[\omega_{s}=\sqrt{\frac{k_{s}}{m_{d}}}, \tag{1}\]  
\[\omega_{p}=\sqrt{\frac{g}{l_{o}}}, \tag{2}\]  
where \(\omega_{s}\) is the circular frequency of the radial vibration, \(k_{s}\) is the stiffness of the spring, \(m_{d}\) is the mass of the mass block, \(\omega_{p}\) is the circular frequency of the swing vibration, \(g=9.8\) m/s\({}^{2}\) is the acceleration of gravity, and \(l_{o}\) is the length of the spring under gravity:  
\[l_{o}=\frac{g}{(2\pi f)^{2}}, \tag{3}\]  
where \(f\) is the frequency of the main structure.  
A previous study found that when the ratio of \(\omega_{s}\) to \(\omega_{p}\) is close to 2, energy is transferred between the two vibration modes, a phenomenon called internal resonance [46]. The SP relies on internal resonance to transfer the kinetic energy absorbed by the damper from the swing mode to the vibration mode, thereby increasing the vibration absorption capacity of the damper.
# Research and Conceptual Design of Sensor Fusion for Object Detection in Dense Smoke Environments
## 1 Introduction
### Principle of 3D Position Measurement Using Ultrasonic Sensors: 3D Ultrasonic Sensor Module
#### 2.1.1 Eliminate Measurement Errors Due to Temperature Changes  
Note that the \(c_{t}\) is a function of \(T\) in Equation (1), and the temperature of dense smoke is generally very high and non-uniform or has a temperature gradient, as described in the introduction. Therefore, the three-dimensional position of an object derived by Equation (2) cannot be accurately measured in a dense smoke environment.  
Figure 4: 3D measurement configuration using ultrasonic sensors: 3D ultrasonic sensor module.  
To avoid inaccuracies due to the temperature effects of ultrasonic sensors, we define the coefficient \(\kappa\) of the TOF ratio [36] as  
\[\begin{array}{c}\kappa_{1}=\frac{\Lambda_{1}}{\Lambda_{2}}=\frac{d_{1}}{d_{2}} \\ \kappa_{3}=\frac{d_{3}}{\Lambda_{2}}=\frac{d_{3}}{d_{2}}\\ \kappa_{4}=\frac{\Lambda_{4}}{d_{2}}=\frac{d_{4}}{d_{2}}\end{array} \tag{3}\]  
At this point, the measured \(\kappa_{1}\), \(\kappa_{2}\), and \(\kappa_{3}\) are related to the unknown position (\(p_{x}\), \(p_{y}\), \(p_{z}\)) of the object, as shown below  
\[\begin{array}{c}\left(\kappa_{1}^{2}-1\right)\left(p_{x}^{2}+p_{y}^{2}+p_{z} ^{2}\right)=-2 ap_{x}+a^{2}\\ \left(\kappa_{3}^{2}-1\right)\left(p_{x}^{2}+p_{y}^{2}+p_{z}^{2}\right)=-2 bp_{ y}+b^{2}\\ \left(\kappa_{4}^{2}-1\right)\left(p_{x}^{2}+p_{y}^{2}+p_{z}^{2}\right)=2 bp_{ y}+b^{2}\end{array} \tag{4}\]  
Thus, the temperature independent (\(p_{x}\), \(p_{y}\), \(p_{z}\)) can be derived as  
\[\begin{array}{c}p_{x}=\frac{4\Phi^{2}\alpha\beta-2(\beta+1)\alpha^{2}}{-4 \alpha(\beta+1)}\\ p_{y}=\frac{b(1-\beta)}{2(\beta+1)}\\ p_{z}=\left\{\begin{array}{l}\sqrt{\frac{-2 ap_{x}+a^{2}-\left(\kappa_{1}^{2 }-1\right)\left(p_{x}^{2}+p_{y}^{2}\right)}{\kappa_{1}^{2}-1}},\text{ if }\kappa_{1}\
eq 1\\ \sqrt{\frac{-2 bp_{y}+b^{2}-\left(\kappa_{3}^{2}-1\right)\left(p_{x}^{2}+p_{y} ^{2}\right)}{\kappa_{3}^{2}-1}},\text{ if }\kappa_{3}\
eq 1\\ \sqrt{\frac{2 bp_{y}+b^{2}-\left(\kappa_{4}^{2}-1\right)\left(p_{x}^{2}+p_{y} ^{2}\right)}{\kappa_{4}^{2}-1}},\text{ if }\kappa_{4}\
eq 1\end{array}\right.\end{array} \tag{5}\]  
where \(\alpha\) and \(\beta\) are defined as  
\[\alpha=\frac{\kappa_{1}^{2}-1}{\kappa_{3}^{2}-1},\text{ }\beta=\frac{\kappa_{3}^{2}-1}{ \kappa_{4}^{2}-1} \tag{6}\]
# Cascaded Regression-Based Segmentation of Cardiac CT under Probabilistic Correspondences
## 3 Results
### Comparison with Other Methods  
[PERSON] et al.'s method uses a deep, fully convolutional neural network for ventricle segmentation in cardiac MRI [18]. This method evaluates the surface distance of 30 cases on the LV's endocardial and epicardium surfaces. The endocardial surface error was 1.73 \(\pm\) 0.35 mm, and the epicardium surface error was 1.65 \(\pm\) 0.31 mm. [PERSON] et al.'s method was evaluated only on the ventricle model. Although the repeatability is not considered in the evaluation metric calculation, the proposed method shows a comparable accuracy of 1.88 \(\pm\) 0.37 mm. [PERSON] et al.'s method uses a 3D active appearance model and produced an error of 2.75 \(\pm\) 0.86 mm for endocardial contours and 2.63 \(\pm\) 0.76 mm for epicardium contours, which were evaluated on 359 slices of cardiac MRI [6]. Compared with this result, our method shows a better accuracy of 1.88 \(\pm\) 0.37 mm in LV SSD. [PERSON] et al.'s method performed the segmentation of four-chamber model by using artificially enlarged training set and Active Shape Model-based segmentation in 25 subjects. This method produced 1.77 \(\pm\) 0.36 mm in LV, and 2.44 \(\pm\) 0.85 mm in LA, although the enlargement method of training set is used [38]. Our method shows a better result in LA, compared with this method. [PERSON] et al. evaluated whole heart segmentation under deep learning (DL) based-method and multi atlas segmentation (MAS) frameworks [36]. This evaluation result shows mean SSD of 2.12 \(\pm\) 5.13 mm and mean DC of 87.20% \(\pm\) 8.70%. Range of DC can be different from that of the proposed method because the characteristics of the two datasets are different. In this Open-Access Grand Challenge, the active appearance-based method is not utilized. If the current research includes whole heart components, we can also apply to a challenge like this.
# Improved Safety Analysis Integration in a Systems Engineering Approach
## 5 Improved System Behavior Based on DFT Recommendation
### Step 3: Automatic Generation of DFT from SysML Diagrams and Redundancy Profile
#### 5.1.1 Fault Trees  
A fault tree is composed of gates and events and it can be static or dynamic. The static fault tree is used when the order of the failure events is not important. Yet, the dynamic fault tree is used when the order of the failure events has an impact on the top event. A fault tree is composed by at least one event and we define three different types of events: the top undesired event, the intermediate events, and the leaf events. In addition, two types of logic gates are defined which are the static and dynamic gates and they aim to describe the propagation of failures leading to the top event. The static gates like \"AND\", \"OR\", and \"VOTING\" are used in the construction of the SFTs. The dynamic gates [32] such as \"SPARE\", \"PAND\", \"FDEP\", and \"SEQUENCE\" are used in addition to the static gates in the construction of the DFTs. In this work, we use the dynamic fault trees in order to model redundancy obtained in step two.
## 4 Analysis of the Numerical Simulation and Experimental Results
### Experimental Results and Simulation Analysis
#### 4.2.2 Simulation Verification  
In the positive coverage income of roadside RSU, the Warshell algorithm is used to verify the reliability of the model. In the simulation space of 40 \(\times\) 1000 m\({}^{2}\), 20, 40, 60, and 80 vehicle nodes are randomly distributed. In their respective networks, the vehicle nodes are randomly selected as intelligent connected vehicles. As the number of vehicles gradually increases, the permeability also increases accordingly. At this time, the permeability when the respective networks begin to form connectivity is 24%, 28%, 48%, and 82%, as shown in Figure 7b, which is very similar to the value of node a when the model begins to form connectivity in Section 3.2.1, as shown in Figure 7a.  
In the part of the information transmission deficit test, the improved Kmeans clustering algorithm is used to verify the reliability of the model. To verify the model curve with a traffic density of 60, as shown in the red curve in Figure 8a, 60 vehicle nodes are randomly selected in the simulation space of 40 \(\times\) 1000 m\({}^{2}\) and 12, 24, 36, and 48 vehicle nodes are randomly selected as intelligent connected vehicles. The clustering algorithm is used to simulate the loss caused by information transmission between nodes, which are 1.773, 7.945, 22.54, and 48.82, respectively. As shown in Figure 8b, the simulation results are similar to the model values.  
Figure 6: Variation of model slices at high and low densities.
providing scalability and cost reduction. In particular, the latest studies report non-linear frequency up-conversion in quasi-phase-matched (QPM) Si-based WGs [27; 28; 29; 30; 31; 32; 33; 34; 35], particularly second-harmonic generation (SHG) and sum-frequency generation (SFG) [36; 37]. Recent experiments with Si-based devices reported non-linear generation with output powers above the nW range [38; 39; 40]. In other recent studies, silicon nitride (Si\({}_{3}\)N\({}_{4}\)) was used for SHG [41], although with an efficiency of about two orders of magnitude lower than in traditional non-linear crystals. It is well known that Si\({}_{3}\)N\({}_{4}\) is a centrosymmetric material, therefore lacking any bulk second-order non-linearity; it is nevertheless observed in Si\({}_{3}\)N\({}_{4}\) or Si\({}_{x}\)N\({}_{y}\) thin films [42; 43; 44]. Another well-known possibility with Si\({}_{3}\)N\({}_{4}\)is to strain silicon WGs, thus enhancing the non-linear response [31; 45; 46], but in this case the non-linearity is mostly due to the silicon itself [47; 48]. To date, an extremely small \(\chi^{(2)}\) coefficient in integrated platforms is attributed to the presence of an asymmetric dipole potential at the interface between the two materials (i.e., Si\({}_{3}\)N\({}_{4}\) and SiO\({}_{2}\)). In this way, it is possible to break the center-symmetry of the two materials in a limited spatial region [41]. In previous studies, large propagation losses were reported [49]. This was explained by poor optical confinement of guided modes, due to a WG core thickness that was too small. In particular, the large strain occurring between the core and the cladding layers limits the thickness value [40; 50].  
One of the targets of this study is the evaluation of the optical properties of waveguiding structures in different spectral regions. This is the first step to enabling non-linear processes. Indeed, to host these processes, a platform is needed that would be capable of guiding a wider range of wavelengths. The following steps are a WG design optimizing phase matching and using opportune materials to enhance the desired non-linear processes.  
In the present study, we improve optical confinement by testing non-stoichiometric Si\({}_{x}\)N\({}_{y}\)/SiO\({}_{2}\) multilayer WGs (hereafter, we refer to the layer material as SiN). These devices were fabricated and provided by the Bruno Kessler Foundation (FBK) and the University of Trento. The presence of intermediate, thin SiO\({}_{2}\) layers was used to reduce the tensile stress fraction of the overall stress acting on the WG. Thus, a WG core with a total SiN thickness larger than the critical one could be obtained. Our study is divided into two parts: In the first part, we perform a finite element modeling (FEM) of multilayer structure WGs focused on waveguiding behavior in the telecom range to host non-linear optical processes; in the second part, we carry out an initial optical characterization of WGs built according to the theoretical modeling.
## 4 Experiments and Discussions  
### Synthetic Data Experiments  
FECG extraction is mainly affected by three factors: the energy ratio of each noise in the mixed signal, the FECG and MECG amplitude ratio (FMAR), and the FECG and MECG heart rate ratio (FMHRR).
# Low-Cost Dual-Frequency GNSS Receivers and Antennas for Surveying in Urban Areas
## 2 Materials and Methods
### Study Area  
The study area was selected near the building of the Faculty of Civil and Geodetic Engineering of the University of Ljubljana (UL FGG), which offers both open-sky and urban conditions for conducting the observations. Five measuring stations were selected with positions shown in Figure 2. Station 1 (ST 1) is located on the rooftop of UL FGG and has open-sky conditions, while station 1A (ST 1A) is surrounded by trees (10 m) and buildings (Figure 3). GNSS observations from these two stations are used to verify the observation quality in open-sky and urban areas.  
Station 2 (ST 2) and station 4 (ST 4) were 1-5 m from nearby buildings, which is often a situation in land-surveying tasks. Station 3 (ST 3) was more open to the horizon from the south side than the previous two stations (Figure 3). The observations of these three stations are used to evaluate the positioning performance of low-cost GNSS equipment in urban areas.  
Figure 1: Low-cost GNSS receiver simpleRTK2B V1 (in front), Survey calibrated low-cost antenna (left), and Javad RingAnt-G3T geodetic antenna (right).
## 8 Influence of Temperature on Thermal Deformation of Bearing  
As already mentioned, the aim of the research is to verify the sparse literature reports on the influence of the temperature in the bearing on its resistance to motion. The thesis about the change in resistance to motion due to temperature change results from the fact that thermal deformation of the bearing elements can cause a change in the clearance between the balls and raceways. If such a clearance is reduced, the consequence will be an increase in the resistance, while if it is increased, the resistance will decrease, and in an extreme case, skidding of the balls may occur.  
The simulation tests were reduced to the estimation of thermal deformations \(\delta_{(i)T}\) and \(\delta_{(o)T}\) at the points of contact of the ball with the raceways. Figure 12a shows an example of obtained results.  
Figure 11: Temperatures in the steady state for two values of the quotient of the area with free and forced convection: (**a**) for the inner ring, (**b**) for the outer ring, for six variants of heat sources V1–V6; Tir2, Tor2—for the quotient 2, Tir1, Tor1—for the quotient 1.  
Figure 10: Examples of temperature courses on the inner and outer rings for two variants of convection: for the area quotient with free and forced convection 2 and 1; ir2, or2—inner and outer ring for the area quotient 2, ir1, or1—for the area quotient 1.  
Figure 12a shows the effect in the form of thermal deformations in the radial direction of the outer ring. Based on the simulation results as shown in Figure 12a, a graph of such deformations as a function of the axial coordinate was developed (Figure 12b). This diagram shows both thermal and mechanical deformations (caused by preload and centrifugal force). Figure 12c shows the contact loads between the ball and the outer raceway corresponding to thermal and mechanical deformations.  
If, in the unloaded state and with the bearing stationary, the distance between the contact points of the ball and the raceways was equal to the ball diameter, then in the example case as in Figure 12b, this distance changed up to 10 \(\upmu\)m for thermal deformations and 0.25 \(\upmu\)m for mechanical deformations (caused by preload and centrifugal forces). These deformations are the source of contact forces, which for thermal effects amounted to approx. 231N (Figure 12c) and for mechanical effects to approx. 61N. These forces will be a source of resistance to movement in the bearing.  
Also note the substantially different positioning of the contact force vector for thermal and mechanical effects. The values and directions of action of these contact loads vary significantly.  
The examples in Figure 12 show how important it is to design the bearing arrangement due to heat convection to the environment. The constructor of the bearing arrangement has tools for shaping the heat flows by selecting the surface of the bearing seat and shaft, but must have knowledge about the thermal state.
## 3 Proposed Algorithm of Protection Scheme  
The algorithm supported by the Wigner distribution function and alienation coefficient is proposed to be implemented in two steps for the identification and classification of faults on the power system incorporated with a solar PV plant. The first step involves the detection of the investigated faults. The second step involves the classification of faults using the number of faulty phases identified in the detection stage. A fault index (FI) is introduced for the detection of faults on the SE-penetrated power network. This FI is obtained by multiplication of the WD-index and ALN-index. The threshold was set to a value of \(2\times 10^{8}\), which was decided by testing the algorithm on 25 datasets of each type of fault. This dataset was prepared by the variations in parameters, such as fault incidence angle, fault impedance, fault location, superimposing noise on current signals, etc. FI is considered as a key factor for the discrimination of the faulty and healthy phases. A higher FI magnitude compared to the threshold value indicates the faulty phase and a lower magnitude indicates the healthy phase. The number of faulty phases was used to identify the fault type. A ground fault index (GFI) was introduced to discriminate the double phase fault and double phases to the ground fault. This algorithm used the waveform pattern deviations; hence, the normalization of parameters is not required. The performance of the algorithm will be affected by the power network configuration and penetration level of the SE. However, the algorithm can be used in different network configuration and SE penetration levels by changing the threshold magnitude. The proposed algorithm is detailed in Figure 3. The WD-index, ALN-index, FI and GFI are described below.