esa-sceva/satcom-chunk-collection huggingface dataset:
# Working State of ECC Link Slabs Used in Continuous Bridge Decks
## 6 Conclusions  
Structural working state theory and attached methods can effectively reveal some unseen features from the experimental and expanded data. The working states of three experimental ECC link slab models were investigated to achieve the following conclusions:  
1. Advanced mathematical modeling technique based on the GSED for characterizing the working states of ECC link slabs was used successfully and structural working state leaps could be identified by the M-K criterion. Meanwhile, these leap features were verified by the strain/displacement-based working state modes, as well as the development of structural strain fields and internal forces. The working state leaps complying with the natural course from quantitative change to qualitative change indicate the essential evolution of structural performance governed by the material attributes.
2. It was revealed that Type I and Type II ECC link slabs working through the whole loading process had three different working stages with two critical points, i.e., the yielding point and the initial structural failure point. The yielding point is a transition of structural working state between being elastic and elastic-plastic. The ECC link slabs working in the elastic-plastic stage could still withstand increasing loads despite the growth of cracks. The initial structural failure point is the origin of structural performance deterioration and ECC link slabs rapidly became closer to the ultimate failure from this point on.
3. Type II and Type III ECC link slabs benefitted more than Type I ECC link slab from the FRP reinforcements. The remarkable rise in yielding load (166.67%) was obtained from ECC link slab incorporating CFRP bars and the structural toughness index was raised by 78.79% owing to the CFT grid reinforcement.
4. In summary, the investigation on ECC link slabs' working states explores a new way to structural analysis and the information involved in working state can make important contributions to the improvement of structural design.  
Figure 14: Internal forces of the mid cross section of Type II ECC link slab: (**a**) the axial force and (**b**) the bending moment.  
**Author Contributions:** Formal analysis, [PERSON] and J.S. ([PERSON]); Investigation, [PERSON], [PERSON] ([PERSON]) and [PERSON]; Methodology, [PERSON] ([PERSON]); Resources, [PERSON] and W.W.; Writing--original draft, [PERSON]; Writing--review and editing, [PERSON] ([PERSON]) and [PERSON].  
**Funding:** This research was funded by National Natural Science Foundation of China, grant number 51608069.  
**Acknowledgments:** The authors would like to express their gratitude to [PERSON] for carrying out the excellent experiment on bridge deck models and detailing the test data. And many thanks to all members of the HIT 504 office for their useful suggestions.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Highly Sensitive SPAD-Based Receiver for Dimming Control in LiFi Networks +
## 3 Single-Carrier Transmission Schemes with Dimming Control  
### On-Off Keying (OOK)--Analogue Dimming  
VLC commonly utilizes OOK modulation, a widely used method where \"ON\" and \"OFF\" pulses represent binary bits \"1\" and \"0\", respectively. This method adjusts the brightness of an LED by modulating the forward current through the LED, allowing for dimming by reducing the current. In the case of equiprobable input conditions, the OOK signal transmitted can be expressed as  
\[x(t)=2\gamma\overline{P}_{\rm TX,max}d\,{\rm rect}(\frac{t}{T_{\rm s}}), \tag{8}\]  
where \(d\in\{0,1\}\) is the information bit, rect(.) is defined as one for \(0<x\leq 1\) and zero otherwise, and \(T_{\rm s}\) is the symbol time.
# IMU-Aided High-Frequency Lidar Odometry for Autonomous Driving
## 3 The Proposed Approach
### Pre-Processing Step 2: Intra-Frame Correction  
As mentioned in Section 3.1, it usually takes 100 ms for the lidar to spin a full circle. During this time, the vehicle also keeps moving. Some of these data are received at the previous moment \(t^{\text{L}}_{\text{k}}-t_{j}\,(t_{j}\in(0,100)\,(\text{ms}))\) and their coordinates are represented in the coordinate system corresponding to the moment \(t^{\text{L}}_{\text{k}}-t_{j}\). These points can be denoted as \(\mathbf{X}^{\text{B}}_{(\text{i},\text{b},\text{k}-\text{j})}\). If we want to represent these points in the coordinate system corresponding to time \(t^{\text{L}}_{\text{k}}\), we need to transform the point \(\mathbf{X}^{\text{B}}_{(\text{i},\text{b},\text{k}-\text{j})}\) to \(\mathbf{X}^{\text{B}}_{(\text{i},\text{b},\text{k})}\). This transformation is usually called \"correction\" or \"rectification\" of the lidar scan.  
There are two common ways to rectify the distorted point clouds. One utilizes the information of IMU [16; 27]. We make some improvements in this method which is described in Section 3.4.1. The other one uses the previous registration results [15], which is presented in Section 3.4.2. Furthermore, we also propose an interesting two-step correction procedure which does not rely on the IMU in Section 3.4.3.
# Detection of High-Temperature Gas Leaks in Pipelines Using Schlieren Visualization
## 4 Conclusions  
This study employed Schlieren optics to visualize leaks in pipes carrying gases at temperatures exceeding those of the surrounding environment. Two experimental setups were constructed to simulate gas leaks. The first setup involved a PTFE tube with a 25 mm outer diameter and a 2 mm diameter hole, while the second setup used a 100 mm outer diameter pipe covered with insulation and an aluminum jacket. The Schlieren system employed a single-mirror off-axis configuration with a 150 mm diameter parabolic mirror. The temperature of the leaking air was varied between 20 \({}^{\circ}\)C and 100 \({}^{\circ}\)C, while the ambient temperature was maintained at 14 \({}^{\circ}\)C. To quantify the leaks, the leakage areas observed in the flow visualization images were analyzed, and the coefficient of variation was calculated, representing the standard deviation of pixel brightness values in the defined regions.  
In the PTFE tube, leaks were discernible when the temperature difference exceeded 34 z\({}^{\circ}\)C, with a coefficient of variation reaching 0.1 or higher. On the other hand, in the second experimental setup, where the pipe was covered with insulation and a jacket, distinct leak flows were more challenging to detect due to the increased insulation thickness and gap size, with all experiments exhibiting a coefficient of variation below 0.1. This study underscores the complexity of the research and the efficacy of the Schlieren optical system and flow visualization technique in detecting leaks in high-temperature pipes. These findings are expected to significantly improve the safety and efficiency of high-temperature gas pipelines, with potential applications across a wide range of industrial sectors.  
Conceptualization, T.-J.P. and D.-W.O.; methodology, T.-J.P., K.-Y.K. and D.-W.O.; software, K.-Y.K.; validation, K.-Y.K. and D.-W.O.; writing--original draft preparation, T.-J.P. and D.-W.O.; writing--review and editing, T.-J.P. and D.-W.O.; project administration, T.-J.P.; funding acquisition, T.-J.P. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Korean government, Ministry of Trade, Industry & Energy (MOTIE) and the Korea Institute of Energy Technology Evaluation & Planning (KETEP), and Ministry of Science and ICT (MSIT), for support (No. 20224B10100060 and No. RS-2022-00144000).  
**Informed Consent Statement**: Not applicable.  
Data Availability Statement: The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation
### Results
#### 5.3.3 Embedding Cost  
As for the average embedding cost, Figure 9 shows that the proposed ILP-P can perform the embedding process with lower cost compared to the ILP-A, due to the fact that ILP-A maps the selected decomposition based on the clustering selection. As for the multiple-large scenario, both ILP-A and ILP-P achieve almost the same cost because the possible clustering of nodes in this scenario is very high. Thus, the process of clustering nodes is very helpful for both ILP-A and DSBM to reduce the embedding cost.  
Figure 8: Average acceptance ratio for service requests vs. (**a**) the number of nodes and edges; (**b**) the number of paths and edges.  
Figure 7: Average acceptance ratio for: (**a**) Simple-Small, (**b**) Simple-Large, (**c**) Multiple-Small, and (**d**) Multiple-Large scenarios. The shaded background behind each curve represents the 95% confidence interval on the reported average values.  
Figures 9 and 10 show that the proposed ILP-P and DcPSM can perform the embedding process with lower cost compared to DSBM. More specifically, ILP-P reduces the embedding cost by 4% compared to ILP-A in the small-scale networks for all request types, while in the long simulation runs, the average embedding cost of DSBM is 297% and 295% higher than the proposed ILP-P and DcPSM, respectively.
## 2 Related Work and Motivation
As indicated in these works, an improvement in the reliability, responsiveness and accuracy of the sensors behavior can be achieved in WSNs if they incorporate collaborative algorithms. Nevertheless, they require a significant amount of resources in terms of CPU performance, memory, wireless communication bandwidth and battery consumption. In order to solve this problem, a fuzzy rule-based collaborative approach (D-FLER) has been presented in [19]. This system incorporates distributed and embedded collaborative mechanisms of reasoning on the observed data and taking decisions or actions in a coordinated manner. Following this idea, D-FLER uses two types of inputs: individual observations (sensor readings of the current node) and neighborhood observations (fuzzified sensor data from the neighboring nodes). However, D-FLER presents some limitations, derived from the need to:  
* Transmit a significant amount of data to neighbor nodes. * Process a significant amount of data. These features cause a decrease in the lifetime of the batteries. To improve the above-mentioned problem, in this work we propose a new scheme that fuse:  
* Individual observations. * The knowledge that was obtained from their neighbor nodes. To the best of our knowledge, the collaborative approach for WSNs presented in this paper is the first one that proposes the use of a FRBS to define the collaborative knowledge. This approach allows the users to define the collaboration among sensors by means of a specific KB (variables, fuzzy sets and rules), which presents significant advantages:  
* The collaborative scheme may deal with uncertainty and imprecision. * It is possible to separate control or modeling knowledge from collaborative knowledge, using interpretable rules in both cases. * The collaborative approach may support sensor failures and communication errors because, in this case, the collaborative sensor would infer a proportional value to the number of failuresbetween control or modeling knowledge (local knowledge without collaborative scheme) and the collaborative scheme. Some scenarios that could benefit from the proposed approach are real-world applications with complex control or modeling in which sensor inferences may be affected by measures or inferences of neighboring sensors, e.g., event detections (fire detection, alarms), environmental monitoring (water quality, pest detection), industrial process control, robotics, _etc._
# Remote-Controlled Method with Force and Visual Assists Based on Time to Collision for Mobile Robot
## 2 Modeling  
### System Configuration  
Figure 1 shows the system configuration of remote-controlled robot with visual and force feedback. This system consists of a monitor, a control device, and a mobile robot. The laser range finder (LRF) and visual sensor have been installed on the mobile robot. The LRF can detect a wide range of high-precision environments around the mobile robot. Two PCs were used to control the mobile robot, monitor, and control device. The user datagram protocol (UDP), which is a communication protocol across the Internet, was used to connect the two PCs. The environmental information was measured by the LRF and the visual sensor and sent from the PC of the mobile robot to the PC at the operator's side. The translational and angular velocity commands (\(v^{cmd}\), \(\omega^{cmd}\)) were sent from the PC at the operator's side to the mobile robot's PC.  
The mobile robot used is an i-Cart mini robot [36]. The mobile robot moves according to the velocity commands from the operator. To measure the environmental information, the LRF produced by HOKUYO AUTOMATIC CO., LTD. was used [37]. The control device consists of two linear motors, one for translational velocity and the other for angular velocity, as shown in Figure 2. The operator can operate the control device by grabbing knobs of each linear motor. The operator uses this remote-controlled system by looking at the monitor and feeling the tactile information as translational and angular force commands (\(f_{v}^{cmd},f_{w}^{cmd}\)).
## 4 Cases Study
### Portugal Arch Bridge Case Study  
The second case study looks at a single-span bridge in Portugal's Guarda community, specifically, its open spandrel deck arch, which measures 24.00 m long and 4.65 m in height (Figure 17). This bridge spans the Cro River and carries regional route 324(ER). Each travel lane is 2.53 m wide, and the other is 2.51 m wide, while the safety strips are 0.45 m wide and 0.51 m wide, and the walkways are 1 m wide on both sides of the road. Initially constructed in 1940, the bridge underwent restoration in 2010. In 2016, 1766 vehicles traversed it daily, with 5% of them being hefty. Inspecting these spots allows us to pinpoint the vulnerable zones. Finite element analysis indicates that the entire structure is supported since the bending moments within the supports are less than those at the midspan. As a result, the midspan section's reliability was used to determine the bridge's overall dependability.
## 1 Introduction
Overall, this paper includes the following four aspects: the first part constructs the dynamic model of the WIPR system, decouples the multi-coupled state variables, and facilitates the subsequent controller design; the second part establishes the kinematic trajectory tracking controller of the system and solves to obtain the desired speed of the dynamic control system. In the third part, an optimization model of the WIPR system combining nonlinear disturbance observer and hierarchical sliding mode control is designed, and the convergence of the nonlinear disturbance observer and the stability of the improved hierarchical sliding mode controller is demonstrated. The fourth part constructs the simulation model using the MATLAB/Simulink platform and conducts numerical simulation comparison experiments. The contributions of this paper are as follows:  
1. A wheeled inverted pendulum robot with a transport platform is envisioned for use in warehouses or other application scenarios to move goods. 2. The convergence law of hierarchical sliding mode control is improved to mitigate the jitter phenomenon of the sliding mode control system, and an adaptive function is introduced to minimize the system jitter. 3. By combining a nonlinear disturbance observer and hierarchical sliding mode control to estimate unknown external disturbances as input compensation, the system is made to control more accurately.
## 4 Discussion  
Compared to both fully actuated and fully underactuated, the limited underactuated tail joint yields a favorable performance (e.g., speed and energy efficiency of locomotion)  
Figure 10: Energy efficiency of the fishbot as a function of the frequency of oscillations \(f\) (**a**) and maximum angle of the limited underactuation \(\delta\) (**b**), and in relation to the speed of the robot featuring a fully actuated (\(\delta=0^{\circ}\)) joint of the tail fin (**c**).  
of the fishbot. However, the effect of the limited underactuated joint is profound at lower frequencies of oscillations and tends to somehow diminish as the frequency increases. In order not to exceed the technical specifications of the angular speed of servo motors (\(600^{\circ}/\mathrm{s}\), as detailed in Section 3.1), we could not experiment with higher frequencies of oscillations. However, from the data visualized in Figures 9 and 10, we may suppose that there is a threshold frequency at which the limited underactuated tail joint will no longer be performant regardless of the angle of the underactuation.  
Within the whole range of frequencies, the behavior of the performance increases with the increase in the maximum angle of underactuation \(\delta\) for values of up to \(10.5^{\circ}\). However, for \(\delta=180^{\circ}\) (fully underactuated joint), the performance decreases sharply, suggesting that, beyond the advantage of providing naturally plausible undulatory gaits of the fishbot, underactuation might not necessarily result in a better speed and (or) energy efficiency of the robot. We could conclude that a limited underactuated joint is superior to both fully actuated and fully underactuated ones in terms of the speed and energy efficiency of locomotion.  
As Figure 8 suggests, the optimal angle of underactuation that results in maximum speed is different depending on the frequency of undulation. At higher frequencies, a lower angle of limited underactuation should be used to reach optimal performance. This phenomenon could be the result of complex forces applied to the limited underactuation joint. Consequently, the optimal performance of the fish robot should be represented as a function of both the frequency of undulation \(f\) and the maximum angle of limited underactuation \(\delta\).  
And, finally, it should be noted that the optimal angle of underactuation that results in the maximum speed is different from that of the maximum energy efficiency (\(\delta=10.5^{\circ}\) and \(\delta=6.3^{\circ}\), respectively). The drawback of the proposed implementation of the limited underactuation via an angular limiter implies that it could not be adjusted in real time. This might suggest the feasibility of considering in the future a different implementation of the angular limiter that would allow the robot to adjust the degree of underactuation \"on the fly\" depending on the task (maximum speed or maximum energy efficiency) that it is currently facing.
# Fatique Crack Growth Studies under Mixed-Mode Loading in AISI 316 Stainless Steel
## 1 Introduction
The CTS specimen, which was first proposed by [PERSON] [17; 18], is the predominant specimen utilized in planar mixed-mode I/II experimental tests [1; 13; 14; 15; 16; 17; 19; 20; 21; 22]. Currently, there are numerous software options available to address the issue of fatigue crack growth, such as ABAQUS [23; 24], FRANC3D [25; 26; 27], ZENCRACK [28; 29], ANSYS [30; 31; 32; 33; 34], and BEASY [35]. Therefore, utilizing numerical simulation with the finite element method (FEM) is a viable method for reducing the time and cost associated with experimental work. [PERSON] et al. [36] conducted a numerical study on the CTS specimen using an older version of ANSYS, which lacked the SMART crack growth feature used in the present study. The SMART crack growth feature in ANSYS APDL 19.2 enables a more efficient and precise simulation of fatigue crack growth by automating the crack creation and growth process and employing advanced algorithms to predict the crack growth path. In contrast, older versions of ANSYS APDL require more manual intervention, leading to a more time-consuming and less accurate modeling and crack growth process. The current state of using numerical simulations to model mixed-mode fatigue crack growth in CTS specimens involves advanced techniques and tools such as the SMART crack growth feature in ANSYS APDL 19.2. This feature allows for more efficient and accurate simulation of fatigue crack growth by automating the process of creating and growing cracks and employing advanced algorithms to predict the crack growth path. This study aims to investigate the use of ANSYS APDL 19.2 software as an effective tool for accurately predicting the crack growth trajectories, stress-intensity factors (SIFs), and associated fatigue life of the compact tension shear specimen, which has been widely used in numerous studies on mixed-mode (I/II) fatigue crack growth. The limitations of utilizing ANSYS smart crack growth include its capability to solely simulate fatigue crack growth for materials exhibiting linear-elastic fracture mechanics and the requirement for significant expertise and experience to use it effectively, which may restrict its accessibility to certain users.
## 5 Analysis of Numerical Results
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Cases** & \multicolumn{4}{c}{**With an Adjacent Tunnel**} & \multicolumn{4}{c}{**Without Adjacent Tunnel**} & \multicolumn{4}{c}{**Influence of Adjacent Tunnel**} & \multicolumn{4}{c}{**Relative Position between**} \\ \hline
**S/m** & \(\mathbf{Y_{A}}\) & \(\mathbf{X_{B}}\) & \(\mathbf{Y_{C}}\) & \(\mathbf{X_{D}}\) & \(\mathbf{Y_{A}}\) & \(\mathbf{X_{B}}\) & \(\mathbf{Y_{C}}\) & \(\mathbf{X_{D}}\) & \(\mathbf{Y_{A}}\) & \(\mathbf{X_{B}}\) & \(\mathbf{Y_{C}}\) & \(\mathbf{X_{D}}\) & **Two Tunnels** \\ \hline
7.48 & \(-\)20.9 & \(-\)11.1 & 20.4 & 6.4 & & & & & \(-\)3.0 & \(-\)0.1 & \(-\)1.1 & \(-\)3.7 & \\ \hline
9.28 & \(-\)22.0 & \(-\)11.2 & 20.6 & 7.8 & & & & & \(-\)1.9 & 0 & \(-\)0.9 & \(-\)2.3 & \\ \hline
12.28 & \(-\)22.9 & \(-\)11.2 & 21.0 & 9.0 & \(-\)23.9 & \(-\)11.2 & 21.5 & 10.1 & \(-\)1.0 & 0 & \(-\)0.5 & \(-\)1.1 & \\ \hline
15.28 & \(-\)23.1 & \(-\)11.2 & 21.4 & 9.6 & & & & \(-\)0.8 & 0 & \(-\)0.1 & \(-\)0.5 & \\ \hline
18.28 & \(-\)23.3 & \(-\)11.2 & 21.5 & 10.1 & & & & \(-\)0.6 & 0 & 0 & 0 \\ \hline \hline \end{tabular} Note: X and Y represent the horizontal displacement and the vertical displacement of the monitoring point, respectively; A, B, C, and D represent the monitoring points. The left tunnel is being excavated, and the right one is the adjacent existing tunnel.  
\end{table} 
Table 8: Deformation of shield segment in different cases (unit: mm).