esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Topology Optimization Method  
As mentioned previously, an improved crank arm design was developed from the conventional crank design detailed earlier. The topology optimization was performed using software such as Autodesk Inventor 2025\({}^{\text{\textregistered}}\) and Autodesk Inventor Nastran 2025\({}^{\text{\textregistered}}\). In order to obtain a first approach, the Shape Generator tool from Autodesk Inventor was used. In the Shape Generator settings, [PERSON]'s modulus was defined as 72 GPa, and the applied load was set to 1300 N at an angle of 45\({}^{\text{\textregistered}}\), 65 mm from the crank arm's cross-sectional plane (yellow arrow in Figure 2a), as specified in the NP EN ISO 4210-8:2019 [15] standard for adults and youth leisure bicycles. Furthermore, two cylinders from the standard shape were preserved, as marked with black arrows in Figure 2a, and 206,691 tetrahedral elements were used to perform the Finite Element Analysis (FEA).  
Figure 2b shows the result of the Shape Generator analysis, achieving a weight reduction of 51% in comparison with the standard model. The authors considered the mechanical properties presented in Table 2. These properties were obtained in previous work [9] under the same conditions as those to be examined in this work: an additively manufactured aluminum alloy, AlSi10 Mg, in two conditions: as-built and heat-treated.  
Using these mechanical properties and the data obtained from the Shape Generator tool, a new crank arm was designed through FEA in Autodesk Inventor Nastran. The FEA model was developed using a linear static analysis with 308,648 tetrahedral elements, resulting in a final shape that achieved a 26% weight reduction compared to the standard form. Figure 3 shows the FEA results for the final topology analyzed.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Al** & **Si** & **Mg** & **Mn** & **Cu** & **Fe** & **Zn** & **Ti** & **Others** \\ \hline Bal. & 0.4–0.8 & 0.8–1.2 & 0–0.15 & 0.15–0.4 & 0–0.7 & 0–0.25 & 0–0.15 & 0–0.15 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Nominal chemical composition for AA 6061-T6 (wt.%) [14].  
Figure 1: Crank with a simple shape, with a weight of 335 g.  
Observing the FEA results (Figure 3b), it can be concluded that the maximum Von Mises stress value was 203.51 MPa, which is clearly below the yield stress listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Series** & \(\sigma_{\text{UTS}}\) **(MPa)** & \(\sigma_{\text{TS}}\) **(MPa)** & \(\varepsilon\) **f (\%)** & **E (GPa)** \\ \hline As-build & 435 & 250 & 4.1 & 72 \\ \hline HT & 380 & 210 & 5.6 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of the tested aluminum alloy [9].  
Figure 3: (**a**) FEA results and (**b**) final topology.  
Figure 2: Topology optimization, (**a**) standard shape, and (**b**) result from the Shape Generator.
## 4 QoS Aware Multipath Routing Protocols for WMSNs
### [PERSON] et al. proposed a directional geographic multipath routing protocol DGR [46]. The main idea of DGR can be explained with Figure 8. When a node \(j\) receives the broadcast probe from the upstream source node \(i\), node \(j\) converts its absolute coordinates \((x_{j}^{0},y_{j}^{0})\) in the global coordinate system to the virtual coordinates \((x_{j}^{v},y_{j}^{v})\) in the virtual coordinate system. The virtual coordinate system is chosen with \(i\) located at the origin and the x-axis connecting \(i\) to the sink \(s\). To control direction of the path, the source node \(i\) also specifies a deviation angle \(\alpha\). Accordingly, node \(j\) calculates its mapping coordinates \((x_{j^{\prime}}^{v},y_{j^{\prime}}^{v})\) by shifting its virtual coordinates in a clockwise direction if \(\alpha\) is positive and counter clockwise direction if negative. Node \(j\) falls in the forwarding candidate set only if the mapping coordinates lands in the shaded region, from which \(R\) is the transmission range, \(L_{t}\) is the optimal mapping location, and \(D\) is the threshold. To compete for the role of nearby, candidates set up timers such that the candidate whose mapping coordinates with smaller distance \(\Delta D\) to \(L_{t}\) will get smaller timer. The node, say node \(j\), whose timer expires first sends a reply message REP to the upstream node \(i\). Upon receiving the REP, \(i\) responds with a confirm message SEL. Candidates that overhear either the REP or the SEL message will cancel their timers. To ensure path disjointness, the winner candidate \(j\) will not participate in the establishment of any other paths for the same source node. Node \(j\) repeats the above procedure by sending its own probe message with an adjusted deviation angle so that the path will gradually converge toward the sink. To establish multiple paths, the source node initiates a series of probe messages, each with a different initial deviation angle. DGR also proposes a video delivery scheme. For each video frame, the source node first broadcasts the entire frame to its one-hop neighbors. Only neighbors that are on the selected paths will then transmit along the respective path a set of video packets specified by the video source.  
Pros and Cons. DGR achieves fast and reliable video delivery by using multipath routing and FEC. Owing to its stateless geographic routing, the protocol also provides good scalability. However in times of node failure, the route discovery procedure and the path recovery time is too long. And the lack of consideration for inter-path interference can greatly increase the need of retransmission which in return will result in increased energy consumption. Finally, a more practical scenario needs to be considered instead of allowing only one active video source at any time.
## 1 Introduction
However, \"ongoing\" or \"online\" measurements and monitoring have become increasingly popular, reducing the need for offline inspections and interventions in power systems. These measurements were put into practice for a case study of cast-resin transformers [12; 13; 14]. Furthermore, the efficiency and reliability of transformers in power plants can be gradually increased. Another benefit is that it can reduce maintenance costs, improving the efficiency of equipment and maintenance procedures, helping to manage the increased risk of aging transformers [15; 16; 17]. This research proposes an online measurement method for partial discharge detection and location in case-resin transformers using frequency and acoustic sensors. Our method of detection and diagnosis of PD identifies the location and assesses the potential risk of PD. This method can assess the position of PD in order to correct it as soon as possible, preventing further damage.
## 6 Engineering Application
### Roadway Evaluation Result  
The evaluation grades of eight roadways are shown in Table 9, and the evaluation results are shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Goal Layer** & **Criterion Layer** & **Criterion Layer** & **Indicator Layer** & **Index Layer** & **AHP-Weight** & **EW-Calculated Weights G** & **Calculated Weight W** \\ \hline \multirow{8}{*}{\
\begin{tabular}{} \end{tabular} } & \multirow{8}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{8}{*}{0.26} & Stable rock thickness \(\text{U}_{11}/\text{m}\) & 0.46 & 0.12 & 0.09 & 0.11 \\ \cline{3-6}  & & & Geological condition \(\text{U}_{12}\) & 0.28 & 0.07 & 0.10 & 0.09 \\ \cline{3-6}  & & & Cross-section shape of roadway \(\text{U}_{13}\) & 0.09 & 0.02 & 0.07 & 0.04 \\ \cline{3-6}  & & & Angle between maximum horizontal principal stress and roadway \(\text{U}_{14}/^{\text{c}}\) & 0.18 & 0.05 & 0.09 & 0.07 \\ \cline{3-6}  & & & The amount of displacement of roof & & & & \\ \cline{3-6}  & & & strata \(\text{U}_{21}/\text{m}\) & 0.22 & 0.10 & 0.08 & 0.09 \\ \cline{3-6}  & & & Visible fracture development & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \cline{3-6}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Index weight. It can be found from Figure 3 that there is a deviation between roadway number 3 and roadway number 5 in the evaluation of the roadway number. The remaining lane numbers are basically consistent with the actual results. This shows that the established AHP-EW combined weight matter element extension model can be used to evaluate the  
Figure 3: Test of the evaluation results. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Loadway Roof Fall Risk**} & \multirow{2}{*}{**U11**} & \multirow{2}{*}{**U12**} & \multirow{2}{*}{**U13**} & \multirow{2}{*}{**U14**} & \multirow{2}{*}{**U21**} & \multirow{2}{*}{**U22**} & \multirow{2}{*}{**U23**} & \multirow{2}{*}{**U24**} & \multirow{2}{*}{**U25**} & \multirow{2}{*}{**U31**} & \multirow{2}{*}{**U32**} & \multirow{2}{*}{**U33**} \\ \cline{2-2} \cline{6-11} \multicolumn{1}{c}{**Level**} & & & & & & & & & & & \\ \hline I (Very Low Risk) & \(-0.33\) & \(-0.40\) & \(0.07\) & \(0.00\) & \(0.23\) & \(0.00\) & \(0.08\) & \(-0.30\) & \(-0.24\) & \(-0.24\) & \(-0.17\) & \(-0.05\) \\ \hline II (Low Risk) & \(0.00\) & \(-0.20\) & \(-0.07\) & \(0.00\) & \(-0.16\) & \(0.00\) & \(-0.36\) & \(0.19\) & \(0.48\) & \(0.32\) & \(0.17\) & \(0.02\) \\ \hline III (Intermediate Risk) & \(0.00\) & \(0.16\) & \(-0.65\) & \(-0.50\) & \(-0.58\) & \(-0.50\) & \(-0.68\) & \(-0.14\) & \(-0.26\) & \(-0.26\) & \(-0.60\) & \(-0.47\) \\ \hline IV (High Risk) & \(-0.33\) & \(-0.14\) & \(-0.76\) & \(-0.67\) & \(-0.72\) & \(-0.67\) & \(-0.79\) & \(-0.43\) & \(-0.51\) & \(-0.51\) & \(-0.80\) & \(-0.65\) \\ \hline V (Very High risk) & \(-0.50\) & \(-0.37\) & \(-0.82\) & \(-0.75\) & \(-0.79\) & \(-0.75\) & \(-0.84\) & \(-0.57\) & \(-0.63\) & \(-0.63\) & \(-0.90\) & \(-0.74\) \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Roadway No. 8 roof fall index correlation degree.
## Materials and Methods
### Remote Sensing Datasets  
#### 2.2.1 Sentinel-1A Dataset  
Sentinel-1 is a C-band (5.4 GHz) imaging radar mission consisting of two polar orbiting satellites. Sentinel-1A was first launched on 3 April 2014 followed by Sentinel-1B two years later. From 2 March 2015 to 27 November 2016, 47 scenes of Level 1 Sentinel-1A (S1A) Ground Range Detected (GRD) products were acquired in interferometric wide swath (IW) mode, with a swath width of 250 km, a ground resolution of 5 \(\times\) 20 m\({}^{2}\), dual polarization (VV-HV), and an incident angle ranges from 31 to 46\({}^{\circ}\). All of the observations are on descending orbits, which pass over the study site at around 19:00 UTC. The acquired images were first radiometrically corrected to retrieve the backscattering coefficients and then processed by a 7 \(\times\) 7 refined Lee filter to suppress the speckle noise. When considering the processing efficiency, the images were resampled to a 1-km pixel spacing. Image registration was the last step.
## 1 Introduction
This representation effectively integrates the dependencies between operations and the status of each workstation in the panel block assembly line, providing crucial information for generating optimal scheduling decisions. Subsequently, we utilize a Graph Isomorphism Network (GIN) to encode and embed the nodes in the disjunctive graph, enabling efficient computation of the policy network. Based on this approach, we design a policy network capable of handling instances of panel block assembly line scheduling of any size, effectively facilitating the generalization from model training to model deployment without the need for retraining. Finally, we employ the Proximal Policy Optimization (PPO) algorithm [17] to train our policy network. Extensive experiments are conducted using real shipyard data and publicly available benchmark datasets, demonstrating that our proposed method outperforms existing heuristic algorithms, metaheuristic algorithms, and reinforcement learning algorithms in terms of algorithmic performance and computa  
Figure 1: Panel block. tion time. Furthermore, our method exhibits remarkable generalization capabilities when applied to larger-scale instances. The main contributions of this paper are as follows:  
1. We introduce an end-to-end reinforcement learning approach to learn scheduling rules, overcoming limitations such as poor model generalization. This method can effectively solve instances of any scale without the need for retraining;
2. We present an MDP model for the panel block assembly line scheduling problem, providing a comprehensive definition of states, actions, and rewards within this MDP framework. The algorithms utilized for model training are also elaborated upon;
3. We propose a graph embedding method that employs disjunctive graphs to represent the state information of the panel block assembly line. This approach directly extracts scheduling features from the disjunctive graph, marking the first instance of combining DRL with disjunctive graphs to address the scheduling problem in shipbuilding's panel block assembly lines. The remaining sections of the paper are described as follows: Section 2 provides a comprehensive summary of the current research status in the relevant field. Section 3 presents the mathematical model for the scheduling problem and describes the background information on the technologies related to our research. Section 4 elucidates our research methodology, including the establishment of the MDP model, parameterized policy network, and the training process of the algorithm. Section 5 presents the experimental procedure and discusses the results obtained. Finally, in Section 6, we present the conclusions and outline future work for our research.
## 5 Performance Evaluation
### Imperceptibility and Payload Capacity
It can be seen from Figures 7-10 that the extracted image watermarks are very similar to the original image watermarks when resisting against low-pass filter with cutoff frequency 4 kHz, amplitude scaling by 0.8 and 1.2, MP3 compression with 128 kbps and 64 kbps, re-sampling, re-quantization, echo addition with a delay of 50 ms and a decay of 5%, noise corruption in 30 dB and 35 dB. The extracted image watermarks shown in Figures 7d, 8d, 9d and 10d are relatively obscure when suffering attack from noise corruption in 20 dB, but with the decrease of white noise, they become more and more clear, as shown in Figure 7e,f Figure 8e,f Figure 9e,f and Figure 10e,f. The average BER values of the extracted watermarks under different attacks are listed in Table 2. It can be seen that this proposed algorithm has excellent robustness against low-pass filter with cutoff frequency four kilohertz, amplitude scaling by 0.8, amplitude scaling by 1.2, MP3 compression with 128 kbps, MP3 compression with 64 kbps, re-sampling, re-quantization, echo addition with a delay of 50 ms and a decay of five percent, noise corruption in 30 dB and noise corruption in 35 dB, so it is far superior to the algorithms proposed in papers [4; 16; 25]. In particular, when this algorithm resists low-pass filter, BER is only 0.01%, which is much better than 21.975% in paper [16], 6.93% in paper [19], 28.250% in paper [25], 0.12% in paper [12] and 0.39% in paper [4]. The robustness is slightly inferior to that in paper [19] when resisting re-quantization, and also slightly inferior to that in paper [12] when resisting noise corruption in 20 dB. When suffering attack from noise corruption, the quality of the extracted watermark is slightly poor, which is mainly because the algorithm is achieved by comparing two sets of TDC obtained from the fourth level detail coefficient. When the additional noise is very loud, the fourth level wavelet coefficient will be affected by noise, thus reducing the accuracy of the extracted watermark. As the noise becomes smaller, the quality of watermark is significantly improved under the noise attack of 30 and 35 dB.
# Damage Identification of Stay Cables Based on a Small Amount of Deflection Monitoring Data
## 2 Theory Related to the Reduction in Measurement Points  
Taking a single-pylon cable-stayed bridge as an example, its span arrangement is 232 m + 75.4 m + 34 m + 28.6 m, and the total length of the bridge is 370 m. The structure is shown in Figure 1. The main span (232 m) of the river side of the bridge is a three-wayprestressed concrete girder with an open box section and a height of 2.2 m. The bank side span is a closed box girder and a solid girder segment. The first span (75.4 m) is a box section, and the second span (34 m) and the third span (28.6 m) are solid sections. The full width of the girder is 23.5 m, and the width of the bridge deck is 16 m. The cables of the whole bridge are made of galvanized low relaxation high strength parallel steel wire with a diameter of 7 mm. The cable is divided into seven types; the standard tensile strength of the cable is 1670 Mpa and the elastic modulus is 2.06 \(\times\) 10\({}^{5}\) Mpa. The cables are arranged in an asymmetric form. There are 52 pairs of cables on the upstream and downstream sides. The main girder of the box is made of concrete marked C50. Concrete marked C40 is used for the girder of the solid section and the tower.  
According to the design drawings and relevant data of the bridge, the three-dimensional finite element reference model of the cable-stayed bridge is established by ANSYS. Among them, beam188 beam element is used to simulate the main beam, solid65 solid element is used to simulate the pylon, and link10 element is used to simulate the stay cable. Corresponding constraints are imposed on the bottom of the main beam (corresponding position of pier), and the second stage dead load is equivalent to a uniform load on the bridge panel, ignoring the influence of pier on the deformation of the main beam.  
After the cable is damaged, its stiffness decreases, which can be simulated by reducing its section area or elastic modulus [19]. The method of reducing the elastic modulus is adopted here. Assume that the damage index of cable \(i\) is \(\tau_{i}\). Then,  
\[\tau_{i}=\frac{E_{di}}{E_{ui}} \tag{1}\]  
where \(E_{di}\) is the elastic modulus of cable \(i\) after damage, and \(E_{ui}\) is the elastic modulus of cable \(i\) without damage.
###### Abstract  
In this work, a Nonlinear Higher Order Extended State Observer (NHOESO) is presented to replace the Linear Extended State Observer (LESO) used in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. In the NHOESO, the standard LESO is completed with a two-term smooth nonlinear function with saturation-like characteristics. The proposed novel NHOESO enables precise observation of the generalized disturbances with higher-order derivatives. The stability of the NHOESO is examined with the aid of the Lyapunov method. A simulation of an uncertain nonlinear Single-Input-Single-Output (SISO) system with time-varying external disturbances confirms that the proposed NHOESO copes well with the generalized disturbance, which is not true for other ESOs.  
generalized disturbance; Lyapunov method; state estimator; model uncertainty; nonlinear systems; active disturbance rejection control
# Construction Progress and Aviation Flight Test of BDSBAS
###### Abstract
In October 2017, at the meeting of the ICAO DS2 working group, BDSBAS was included in the DFMC SBAS SARPs as one of the main service providers of SBAS [29]. In November 2018, the first BDSBAS GEO satellite was launched, and the system integration and testing work officially began. In July 2020, all of the three GEO satellites began to operate in orbit [30], and the \"final\" PRN codes were successfully applied for from the GPS PRN Coordination Office (PRNCO) of the US Air Force in August [31]. At present, BDSBAS has completed the preliminary construction and is carrying out performance testing, and the first phase of BDSBAS will provide the SF service to augment the GPS satellites. We introduce the system composition and work flow of BDSBAS. At Caihu Airport in Shenyang, China, the general aviation aircraft was used to carry out the aviation flight test to simulate the take-off and landing operation during the approach phase and the transition flight operation during the enroute phase. The aeronautical performance of the SF service was analyzed and evaluated. The results show that the SF service performance during the flight test can meet the APV-I level requirements. A false alarm event was discovered during the test due to some unknown aviation environmental factors, which reminded us that we need to strengthen the detection and protection capabilities of aviation electromagnetic interference in the development of BDSBAS aviation airborne terminals. This contribution can help the majority of GNSS and SBAS users, as well as related engineering R&D and scientific research personnel, to better understand the latest construction progress of BDSBAS, and provide experience and reference for the upcoming BDSBAS civil aviation airworthiness certification work. In the following sections, the system architecture and operation flow of BDSBAS is presented first. Afterward, the user algorithm of the BDSBAS SF service, including the use of the corrections and degradation parameters and the generation of the protection level is illustrated. Then, the performance evaluation method of the SBAS service is described, including four aspects: positioning accuracy, service availability, continuity, and integrity. Subsequently, the test environment is set up, and the aeronautical flight tests are constructed to evaluate and analysis the performance of the service. Finally, the conclusions are drawn, including BDSBAS' next plan.