esa-sceva/satcom-chunk-collection huggingface dataset:
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Failure Modes of Railway S&C Systems  
The failure modes of railway S&C systems may arise from many sources. In an attempt to organize and classify these diverse failures, this paper proposes to classify them according to their source and  
Figure 8: Mechanical lock.  
nature into mechanical failures, electrical failures, construction failures, and functional failures, as shown in Figure 9. Each of these failure types can be found in one or more different S&C subsystems, i.e., in the actuators, sensors and controllers, detection and locking parts, and/or in the permanent way (the rails and other elements which support the vehicle). In addition, these S&C system failures can be caused by a change of the environmental conditions and/or human error. Any fault during the maintenance process such as a lack of lubrication, improper tampering, or loose fasteners can lead to failure of the S&C system [48; 49]. The environmental conditions, such as snow, rain, or obstruction on the track, have an important role in the failure of some components of S&C systems.  
In the following sections, a brief description of some of these S&C system failures is presented according to their location in the different aforementioned S&C subsystems. It should be noted that the two major subsystems of an S&C system are the permanent way and the point machine. In the present paper, major emphasis has been given to point machine failures when reviewing the existing FDD methods for railway S&C systems. However, a brief description of possible permanent way failures is presented.
# Experimental Study of Perfobond Rib Shear Connector under Lateral Force
## 3 Test Apparatus and Procedures  
### Test Apparatus and Specim Details  
The push-out tests are divided into 13 groups and each group consists of either two or three identical specimens, as in Table 1. There are three types of specimens, namely, BH-UN, BH-PT, and BH-PP. BH-UN specimens are tested to study the influence of the effects of rib holes, transverse reinforcement bars, and rib thickness. Thus, there are no external transverse forces on those BH-UN specimens. BH-PT and BH-PP specimens are designed to study the influence of transverse tensile and compressive forces, respectively. Thus, hydraulic lacks are placed within BH-PT specimens and post-tensioned prestressing tendons are placed within BH-PP specimens to examine the influences of transverse tensile and compressive forces. Detailed properties and dimensions of those three types of specimens are provided in Table 1, Figures 1-3. All the specimens have three rib holes and three transverse reinforcement bars on each side. Those test specimens are all designed to exclude the influence of the concrete components under the shear connection by leaving a gap under the rib plate, as shown in Figures 1-3. The concrete strength of all the specimens is 55 MPa. The reinforcement is HRB 400 and the rib plate is Q345 qE.  
In addition, as the concrete at the bottom of the steel rib could affect the resistance of the shear connection, a gap was left under the steel rib in the BH-UN and BH-PP specimens, as shown in Figures 1 and 3. BH-PT specimens already had space for the transverse force, so the gap is not necessary, as in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **Rib Holes** & **Transverse Rebar** & **Rib Thickness** & **Transverse** & **Major Failure Mode** \\
**Specimen Type** & **Specimens No.** & **Diameter** & **Diameter** & **(mm)** & **Stress (MPa)** & **Major Failure Mode** \\  & & **(mm)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline BH-UN & BH-1 (1/2/3) & 60 & 16 & 20 & 0 & Rebar failure \\ BH-UN & BH-2 (1/2/3) & 60 & 20 & 20 & 0 & Rebar failure \\ BH-UN & BH-3 (1/2/3) & 75 & 16 & 20 & 0 & Concrete dowed failure \\ BH-UN & BH-4 (1/2/3) & 75 & 20 & 20 & 0 & Rebar failure \\ BH-UN & BH-5 (1/2/3) & 75 & 25 & 20 & 0 & Rebar failure \\ BH-PT & BH-6A & 75 & 25 & 20 & 3 & Concrete crack failure \\ BH-PT & BH-6(1/2) & 75 & 25 & 20 & 2 & Concrete crack failure \\ BH-PT & BH-6P & 75 & 25 & 20 & 0 & Rebar failure \\ BH-PT & BH-7(1/2) & 75 & 25 & 20 & 1 & Rebar failure \\ BH-PP & BH-8 (1/2/3) & 75 & 25 & 20 & \(-2\)* & Rebar failure \\ BH-UN & BH-9 (1/2/3) & 75 & 25 & 24 & 0 & Rebar failure \\ BH-UN & BH-10 (1/2/3) & 90 & 25 & 20 & 0 & Rib failure \\ BH-UN & BH-11 (1/2/3) & 90 & 28 & 20 & 0 & Rib failure \\ \hline \hline \end{tabular}
*: minus sign represents compressive forces.  
\end{table}
Table 1: Specim dimensions and transverse forces.  
Figure 1: Dimensions of push-out specimens and perforated rib: BH-UN (unit: mm).  
Figure 2: Dimensions of push-out specimens and perforated rib: BH-PT (unit: mm).
# Lightweight RT-DETR with Attentional Up-Downsampling Pyramid Network
## 5 Discussion
### Limitations  
Despite the promising performance of the Lightweight RT-DETR with AUDPN, several limitations remain:  
1. Real-time Performance: Although the model has an FPS of up to 57.2 in the training environment, its FPS will be significantly reduced when deployed on resource-limited devices such as industrial control computers without GPU acceleration. Therefore, if you want to achieve fast and accurate inference, we recommend deploying it on devices with GPU acceleration.  
Figure 13: Fall Pose Detection and Gesture Recognition Results Under Significant Occlusion or Lighting Effects.  
Task Specificity: The current model is primarily optimized for gesture recognition and fall detection. Its generalization to other object-detection tasks still requires further validation. One limitation of the proposed system is its reliance on commonly used gestures, which, while reducing the cognitive burden on elderly users, may lead to potential gesture misclassification in environments where similar gestures are frequently performed. For example, the \"OK\" gesture might be misinterpreted in certain social settings where it is commonly used. However, as previously discussed, requiring elderly users to learn custom gestures is impractical, making the adoption of familiar gestures the most viable solution. Future research should explore advanced disambiguation strategies, such as integrating hand keypoint tracking or multi-modal inputs (e.g., combining voice commands with gestures) to improve recognition accuracy in these scenarios.
3. Cost Considerations: An important limitation is the potential high cost associated with building and deploying intelligent companion robots. In the context of China's healthcare system--where cost sensitivity is critical--this could impede widespread adoption despite the model's technical advantages. The intelligent companion robot used in this paper costs about 10,000 RMB. However, the recommended high-configuration version costs around 20,000 RMB.
# Enhancement of RWSN Lifetime via Firework Clustering Algorithm Validated by ANN
## 3 The Framework of the Present Study
### Cluster Head (CH) Selection  
As each sensor node keeps limited energy, the cluster heads are required to gather needed information of the sensor nodes and then send it forward. The remaining energy of the sensor node should be kept in mind while selecting the cluster head. The selection process of cluster head (CH) is given below.  
1. The center of gravity is determined according to the coordinates of each sensor node. The center of gravity (\(X_{c}\), \(Y_{c}\)) must be fulfilled, based on the least square and minimum distance of sensor node given in the region. The formula below can evaluate the concrete calculation method. \[(X_{c},Y_{c})=\text{min}{\sum_{i=1}^{N}}[(x-x_{i})^{2}+(y-y_{i})^{2}]\] (15) (\(x_{i}\), \(y_{i}\)) represent coordinates of each sensor node.
2. Node's coordinates can calculate the distance between the sensor node and center of gravity. \[d=\sqrt{(x_{c-}x_{i})^{2}+\ (yc-y_{j})^{2}}\] (16)
3. The remaining average energy of all nodes evaluated in each cluster.
4. If the remaining energy of a node is higher than the other nodes of the average leftover energy, the sensor node will be elected as the head. Otherwise, next node can be chosen.
5. After completion of one energy cycle and wireless charging, vehicle visits all cluster heads (CH), a round of reception and data transmission is completed. \(T\) represents each series of data transmission cycles in the network. \(T\) can be evaluated by using the formula. \[T=\sum_{i=0}^{n-1}\frac{\sqrt{(x_{i}+1-x_{i})^{2}\ +\ (y_{i}+1-y_{i})^{2}}}{V}\] (17) where \(V\) represents the moving velocity of wireless charging vehicle.
## 3 Empirical Gramians
### State-Space Empirical Gramians  
Gramian-based controllability and observability analysis originates in linear system theory [50], which investigates linear (time-invariant) systems,  
\[\dot{x}(t) = Ax(t)+Bu(t),\] \[y(t) = Cx(t). \tag{3}\]  
An obvious approach for nonlinear systems is a linearization at a steady-state [51], but this may obfuscate the original transient dynamics [52; 53]. Alternatively, the nonlinear balancing for control affine systems from [54], based on controllability and observability energy functions, could be used. Yet practically, the associated nonlinear system Gramians require solutions to a Hamilton-Jacobi partial differential equation (nonlinear controllability Gramian) and a nonlinear Lyapunov equation (nonlinear observability Gramian) or a nonlinear Sylvester equation (nonlinear cross Gramian), which is currently not feasible for large-scale systems. A compromise between linearized and nonlinear Gramians are empirical Gramians [2; 7].  
Empirical Gramians are computed by systematically averaging system Gramians obtained from numerical simulations over locations in an operating region near a steady-state. An operating region is defined in this context by sets of perturbations for inputs/controls and the steady-state. Originally in [2], these perturbation sets are constructed by the Cartesian product of sets of directions (standard unit vectors), rotations (orthogonal matrices) and scales (positive scalars) for the input and steady-state respectively. In the empirical Gramian framework, the rotations are limited to the set of the unit matrix and negative unit matrix, as suggested in [2]. This constraint on the rotation entails many numerical simplifications and reduces the perturbation sets to directions (standard unit vectors) and scales (non-zero scalars):  
\[E_{u} = \{e^{m}\in\mathbb{R}^{M}:m=1\ldots M,e_{i}^{m}=\delta_{im}\},\] \[S_{u} = \{c_{k}\in\mathbb{R}:k=1\ldots K,c_{k}\
eq 0\},\] \[E_{x} = \{e^{j}\in\mathbb{R}^{N}:j=1\ldots N,e_{i}^{j}=\delta_{ij}\},\] \[S_{x} = \{d_{l}\in\mathbb{R}:l=1\ldots L,d_{l}\
eq 0\}.\]  
Yet, only single input and state components can be perturbed at a time in this manner, which is often practically sufficient.  
The original empirical Gramians use a centering of the trajectories around the temporal average and solely use impulse input type controls \(u(t)=\delta(t)\)[2]. The related empirical covariance matrices center the trajectories around a steady-state and allow arbitrary step functions \(u(t)=\sum_{k}v_{k}\chi_{[t_{k},t_{k+1})}(t)\), \(v_{k}\in\mathbb{R}\), \(t_{k}\in R_{\geq 0}\), \(t_{k+1}>t_{k}\)[35; 55]. The empirical Gramian framework allows to compute either as well as further centering variants (Section 5.4). In the following, empirical Gramians and empirical covariance matrices will be jointly referred to by the term \"empirical Gramian\".
# Research on Grouting Dynamic Monitoring Based on Borehole-Tunnel Joint Resistivity Method
## 3 Resistivity Monitoring
### Introduction to Resistivity Monitoring System  
The coal mine working face resistivity monitoring system is widely employed for dynamic monitoring of electrical properties in coal seam strata. As illustrated in Figure 2, the system operates as follows: Electrodes and cables are embedded in the roadways on both sides of the fully mechanized mining face, with the monitoring host unit positioned at the entrance of the two roadways. The host unit is connected via optical transceivers to the surface control server, enabling remote control and data acquisition through specialized software (ZDJ11). Upon transmission to the surface server, the data undergo automated inversion and imaging via processing software, with seamless interaction between the acquisition database and the processing system, eliminating the need for manual intervention. This achieves unmanned data collection and preliminary analysis underground [25].  
Key system specifications include:  
Maximum output voltage: 105 V.  
Maximum output current: 60 mA.  
Frequency range: 1-128 Hz.  
Minimum signal resolution: 5 \(\upmu\)V.  
Recorded parameters: Output current and received voltage signals.  
After the electrode array is deployed, calibration tests, including electrode array integrity verification and electromagnetic noise suppression, are performed to ensure the normal operation of system installation. The data processing software supports automated interaction with the development database to enable real-time data processing, preprocesses the received voltage signals using techniques such as wavelet analysis, and conducts three-dimensional inversion and imaging via the quasi-Gauss-Newton method. The entire system is composed of the independently developed ZDJ11 mine electrical  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Hole No.** & **Azimuth (\({}^{\circ}\))** & **Dip (\({}^{\circ}\))** & **Depth (m)** &
\begin{tabular}{c} **Grout Volume** \\ **(t)** \\ \end{tabular} \\ \hline
1 & 325 & \(-18\) & 150 & 783 \\
2 & 335 & \(-18\) & 174 & 1515 \\
3 & 329 & \(-18\) & 324 & 593 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Grouting parameters.  
prospecting monitoring device, monitoring control software, and data processing and interpretation software.
# Semantic 3D Reconstruction for Robotic Manipulators with an Eye-In-Hand Vision System
## 1 Introduction  
In an unstructured environment, the type and shape of the objects are unpredictable. While, in order to achieve autonomous operations, the robot must be able to use visual sensors, such as lasers or cameras, to get the information about the scene [1; 2; 3]. Therefore, the robot can obtain features and identify relevant objects in the surrounding environment and then plan the motion accordingly. In the process, besides providing the location information of objects, a semantic 3D map can facilitate its decision-making based on actual world processes, such as judging the stability of the scene objects [4; 5; 6], grasping and placing objects by imitating human beings [7], and generating relevant action sequences [8; 9; 10].  
Environmental information is usually collected by different sensors, such as lasers [11], a monocular camera [12], or a depth camera [13], and is then processed through a series of algorithms, such as height estimation [14; 15], target detection, image segmentation, visual odometer, and image stitching to generate an environmental map, which is called simultaneous localization and mapping (SLAM) or structure from motion (SFM). The visual odometer-based method seriously affects the accuracy of the mapping due to the position error caused by the sensors. However, the eye-in-hand vision system is more accurate than the visual odometer. Therefore, it is necessary to make full use of the high accuracy of the robotic manipulator to improve the quality of the 3D reconstruction of the scene [16; 17]. Another problem is that the precision of semantic segmentation is still insufficient, even by the latest method, so it is necessary to find a way to improve the quality of semantic segmentation.  
Therefore, we explore to establish an integrated 3D object semantic reconstruction framework for eye-in-hand manipulators, including RGBD image segmentation, camera pose optimization, and mapstitching. This enables us to achieve the following: (1) combine deep learning with geometric feature methods to perform the semantic segmentation; (2) employ the object point cloud segmentation-based Segment Iterative Closest Point (SICP) method to optimize the camera pose and position; and (3) stitch together a semantic 3D map by data association.  
In summary, the main contributions of this work are:  
* The accuracy of image segmentation and the quality of object modeling are improved with an eye-in-hand manipulator through combining deep learning with geometric methods.
* A high-precision semantic 3D map is established by applying the SICP method to optimize the camera position.  
The paper is organized as follows: related works and the present work are described in Sections 2 and 3, respectively. In Section 4, the experimental results are detailed and presented. The discussion and conclusion are given in Section 5.
# Integrating Pareto Optimization into Dynamic Programming
## 2 Key Operations in Pareto Optimization and Dynamic Programming
### A Simple Example  
In order to ease the reader into the above definitions, in this section, we will have a brief look at an implementation of [PERSON]'s algorithm Gotoh (1995) in ADP. We will later also use this application in experiments evaluating different Pareto operators. The tree grammar rule is described in Figure 1.  
As the second part of the problem description, the scoring functions for Gotoh need to be defined. For this example, we use three algebras MATCH, GAP, and GOTOH, defining match costs, gap costs, and combining both criteria into one; see Table 1. Let us define the costs as: \(s(a,b)=1\) if \(a=b\), otherwise \(s(a,b)=0\), \(\gamma_{open}=-2\) and \(\gamma_{extension}=-1\).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algebra Functions** & **MATCH** & **GAP** & **GOTOH** \\ \hline \(nil(\langle e,e\rangle)\) & 0 & 0 & 0 \\ \(del(\langle a,e\rangle,x)\) & \(x\) & \(\gamma_{open}+x\) & \(\gamma_{open}+x\) \\ \(ints(\langle e,a\rangle,x)\) & \(x\) & \(\gamma_{open}+x\) & \(\gamma_{open}+x\) \\ \(match(\langle a,b\rangle,x)\) & \(x+s(a,b)\) & \(x\) & \(x+s(a,b)\) \\ \(delx(\langle a,e\rangle,x)\) & \(x\) & \(\gamma_{extension}+x\) & \(\gamma_{extension}+x\) \\ \(intsx(\langle e,a\rangle,x)\) & \(x\) & \(\gamma_{extension}+x\) & \(\gamma_{extension}+x\) \\ \hline Choice function & \(\max\) & \(\min\) & \(\max\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation algebras MATCH and GAP. MATCH computes the score \(s(a,b)\) by aligning \(a\) with \(b\), while GAP computes the penalties due to gaps.  
Figure 1: Tree grammar for the Gotoh algorithm for the [PERSON]’s GAP compiler. Symbol \(\epsilon\) stands for an empty character; \(a\) and \(b\) stand for any character in the sequence alphabet.  
If we consider the monocriteria computation \(\mathcal{G}(GOTOH,aaccc,cccaaaa)\), the scoring function Gotoh is maximized, and we get the result:  
\[\left(\begin{array}{cccccccc}-&a&a&a&c&c&c\\ c&c&c&a&a&a&a\end{array}\right)\]  
with a score of \(-1\). Instead of computing the score as a whole, however, we can also use gap and match costs as parts of a Pareto front. In this case, both scoring functions are combined into a new Pareto operator by the ADP mechanism. The Pareto combination \(Gotoh2D=\mathcal{G}(MATCH^{\wedge}GAP,aaccc,cccaaaa)\) with the same parameters returns:  
\[\left\{\left(\begin{array}{cccccccccccc}-&-&-&a&a&a&c&c&c\\ c&c&c&a&a&a&a&-&-\end{array}\right),\left(\begin{array}{cccccccccccc}-&-&a&a&a &c&c&c\\ c&c&c&a&a&a&a&-\end{array}\right),\left(\begin{array}{cccccccccccc}-&a&a&a&c &c&c\\ c&c&c&a&a&a&a\end{array}\right)\right\}\]  
with scores of, respectively, \((3,-7)\), \((2,-5)\) and \((1,-2)\). In Figure 2, the candidate derivation of the first Pareto front element is shown.
# Fault Imaging of Seismic Data Based on a Modified U-Net with Dilated Convolution
## 2 Illustration of Dilated Convolution  
We use a set of pictures to illustrate the principle of dilated convolution. Figure 1a is the conventional convolution kernel, and the dilated convolution is obtained by adding intervals to this basic convolution kernel. Figure 1b corresponds to the convolution of \(3\times 3\), with dilation rate of 2 and interval 1, that is, corresponding to \(7\times 7\) image blocks. It can be understood that the kernel size becomes \(7\times 7\), but only 9 points have parameters, and the rest have parameters of 0. Convolution calculation was performed for the 9 points in Figure 1b and the corresponding pixels in the feature map, and the other positions were skipped. Figure 1b,c are similar, except that the dilation rate is 4, which is equivalent to a \(15\times 15\) convolution kernel. As the convolution kernel becomes larger, the receptive field becomes larger naturally.  
In practical application, when the same dilation rate is used for all convolutional layers, a problem called grid effect will appear. Since the convolution calculation points on the feature map are discontinuous, for example, if we repeatedly accumulate \(3\times 3\) kernel of dilation rate 2, this problem will occur.  
The blue square in Figure 2 is the convolution calculation points participating in the calculation, and the depth of the color represents the calculation times. As can be seen, since the dilation rates of the three convolutions are consistent, the calculation points of the convolution will show a grid expansion outward, while some points will not become calculation points. Such kernel discontinuities, that is, not all pixels are used for calculations, will result in the loss of continuity of information, which is very detrimental for tasks such as pixel-level dense prediction. The solution is to discontinuously use dilated convolution with the same dilation rate, but this is not comprehensive enough. If the dilation rate is multiple, such as 2,4,8, then the problem still exists. Therefore, the best way is to set the dilation rate of continuous dilated convolution as \"jagged\", such as 1,2,3, respectively, so that the distribution of convolution calculation points will become like Figure 3 without discontinuity.  
Figure 1: The dilated convolution with dilation rate of 1, 2 and 4, respectively. (**a**) dilation rate = 1; (**b**) dilation rate = 2; (**c**) dilation rate = 4.
# Sea Ice Detection Based on Differential Delay-Doppler Maps from UK TechDemoSat-1
## 2 Theory and Method
### GNSS-R Observables
#### 2.2.2 Proposed GNSS-R Observables  
In this paper, an observable called dDDM observable is proposed for distinguishing sea ice from water. Details of the proposed observable are provided as follows.  
Two adjacent normalized DDM observables can be expressed as \(\overline{DDM_{i}}(\tau,f)\) and \(\overline{DDM_{i+1}}(\tau,f)\) respectively. Then the dDDM of these two adjacent normalized DDMs can be simply formulated as,  
\[\text{DDM}_{D}(\tau,f)=\overline{DDM_{i}}(\tau,f)-\overline{DDM_{i+1}}(\tau,f), \tag{4}\]  
where \(\text{DDM}_{D}(\tau,f)\) denotes the dDDM. The dDDM can be normalized using Equation (1) and the normalized dDDM is denoted as \(\overline{\text{DDM}_{D}}(\tau,f)\).  
Considering that the power value of \(\text{DDM}_{D}(\tau,f)\) can be either positive or negative, two new observables modified from the observable \(Weighted-Area\) (Equation (2)) and \(Area\) (Equation (3)) (\(Weighted-Area\) and \(Area\) are math type variables) respectively are defined as,  
\[PS=\underset{|\overline{DDM_{D}}(\tau,f)|>DDM_{T}}{\sum}\overline{DDM_{D}}( \tau,f)\cdot d\tau\cdot df, \tag{5}\]  
\[PN=\underset{|\overline{DDM_{D}}(\tau,f)|>DDM_{T}}{\sum}\overline{\frac{DDM_{ D}}{\left|\overline{DDM_{D}}(\tau,f)\right|}}d\tau\cdot df, \tag{6}\]  
where \(d\tau\) and \(df\) are delay and Doppler shift resolutions for \(\text{DDM}_{D}(\tau,f)\) respectively. If setting \(d\tau\cdot df\) = 1, \(PS\) represents the power summation and \(PN\) depicts abstract pixel number of the grids with absolute values (\(|\overline{\text{DDM}_{D}}(\tau,f)|\)) greater than a preset threshold (\(DDM_{T}\)).