esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Response Prediction of Spar Platform in Deep Water Using an Artificial Neural Network
## 3 Results
### Heave Response  
The prediction of the heave response of the spar hull using the ANN model was compared with the FEM results for a wave height of 9.65 m and a 9.94 s wave period (Figure 8). The figure shows that the training data of the ANN model results follow the same trends as the FEM results.  
The statistical heave responses for a wave height of 9.65 m and a wave period of 9.94 s are reported in Table 4, as predicted by both the ANN and FEM approaches. The results show that the ANN approach is suitable for predicting the heave response.  
The performance of the ANN approach was assessed by the heave response predictions of a spar platform using newly selected environmental force and mechanical parameters and comparing these predictions with the FEM results (Figure 9). The 3000 s heave response time history was predicted for a wave height of 6.65 m and a wave period of 8.25 s. The heave response oscillated about the mean location, alternating between smaller and greater amplitudes and repeating the same trend over time. The fluctuations narrowed by around 20% as they progressed from wide to narrow.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Heave Response (m)** & **Max** & **Min** & **Mean** & **Standard Deviation** \\ \hline Resp-Pred & 0.574 & \(-\)0.575 & 0.033 & 0.222 \\ FEM & 0.675 & \(-\)0.668 & 0.033 & 0.250 \\ Difference (\%) & 14.96 & 13.92 & 0.480 & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Statistical heave responses of ANN and FEM at a wave height of 9.65 m and a period of 9.94 s.  
Figure 8: The heave response time history predicted by the training data of ANN and FEM results for wave height of 9.65 m and wave period of 9.94 s.
###### Abstract  
Reinforced bar corrosion induced by chloride ingression is one of the most significant threats to the durability of concrete structures in marine environments. The concrete cover thickness, compressive strength, chloride diffusion coefficient, and surface defects of reinforced concrete in the Jiaozhou Bay sea-crossing railway bridge were measured. The temperature and relative humidity in the concrete and the loading applied onto the reinforced concrete were monitored. Based on the DuraCrete model, a revised model for the service life prediction of concrete structures was established, considering the effects of temperature and loading on the chloride diffusion coefficient. Further, the reliability indexes of the reinforced concrete box girder, pier, and platform, located in the marine and land sections, in relation to service lives lasting various numbers of years, were calculated. The measured and calculated results show that the mean cover thicknesses of concrete piers in the marine and land sections are 52 mm and 36 mm, respectively, and the corresponding standard deviations are 5.21 mm and 3.18 mm, respectively. The mean compressive strengths of concrete in the marine and land sections are 56 MPa and 46 MPa, respectively. The corresponding standard deviations are 2.45 MPa and 2.67 MPa, respectively. The reliability indexes of the reinforced concrete box girder and platform in the marine section, under the condition of a service life of 100 years, are 1.81 and 1.76, respectively. When the corrosion-resistant reinforced bar was used in the pier structure in the marine section, its reliability index increased to 2.01. Furthermore, the reliability index of the reinforced concrete damaged by salt fog in the land section was 1.71.  
reinforced concrete; sea-crossing railway bridge; service life prediction; reliability index
# Improved Calculation of Load and Resistance Factors Based on Third-Moment Method
## 6 Conclusions  
In this paper, a simplified third-moment method for determining the load and resistance factors is proposed. The following conclusions can be drawn:  
1. The iteration calculation used in the existing 3M methods is cancelled. A new 3M method for the calculation of load and resistance factors is proposed with acceptable accuracy and applicable range. Different from other 3M methods, there is no mathematical limitation in the calculation process.
2. To investigate the accuracy of the proposed method, an example from ASCE 7-10 (C2.3.6) is considered. Comparing the results of four different methods with MC simulation (100,000 times), it can be concluded that the proposed method is considered accurate enough.
3. In order to verify the application of the new method, \(\beta_{T}\) = 1.0-4.5 is adopted. The result shows that the reliability index calculated by the proposed method is closest to the \(\beta_{T}\), while the ASCE method and Mori method are much greater than the \(\beta_{T}\).
4. The sensitivity of load in the new method is investigated (with snow load Sn/Dn = 0.5-2.5 and dead load Ln/Dn = 0.5-2.5). The result shows that the proposed method is the most accurate among all methods examined in this paper.
5. The influence of distribution type of variables is verified by nine cases. For \(\beta_{T}\) = 1.0-2.0, the reliability index calculated by the new method is very consistent with the target reliability. With the increase in the reliability index, the calculation error increases. Thus, further research is expected.  
Conceptualization, P.L. and J.W.; methodology, J.W.; software, X.Y.; validation, J.W. and X.Y.; formal analysis, X.Y.; investigation, P.L.; resources, W.Z.; data curation, J.W. and X.Y.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y.; visualization, W.Z.; supervision, J.W.; project administration, P.L. and W.Z.; funding acquisition, J.W. and P.L. All authors have read and agreed to the published version of the manuscript.  
This research work was funded by the Open Foundation of National Engineering Laboratory for High Speed Railway Construction (Grant No. HSR202011) and the Basic Research on Science and technology program of Shenzhen (Grant No. JCYJ20170818143541342 and JCYJ20180305123935198).  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
# Secure Cooperative Routing in Wireless Sensor Networks
###### Abstract  
In wireless sensor networks (WSNs), sensor nodes are randomly distributed to transmit sensed data packets to the base station periodically. These sensor nodes, because of constrained battery power and storage space, cannot utilize conventional security measures. The widely held challenging issues for the network layer of WSNs are the packet-dropping attacks, mainly sinkhole and wormhole attacks, which focus on the routing pattern of the protocol. This thesis presents an improved version of the second level of the guard to the system, intrusion detection systems (IDSs), to limit the hostile impact of these attacks in a Low Energy Adaptive Clustering Hierarchy (LEACH) environment. The proposed system named multipath intrusion detection system (MIDS) integrates an IDs with ad hoc on-demand Multipath Distance Vector (AOMDV) protocol. The IDS agent uses the number of packets transmitted and received to calculate intrusion ratio (IR), which helps to mitigate sinkhole attacks and from AOMDV protocol round trip time (RTT) is computed by taking the difference between route request and route reply time to mitigate wormhole attack. MATLAB simulation results show that this cooperative model is an effective technique due to the higher packet delivery ratio (PDR), throughput, and detection accuracy. The proposed MIDS algorithm is proven to be more efficient when compared with an existing LEACH-based IDS system and MS-LEACH in terms of overall energy consumption, lifetime, and throughput of the network.
## 5 Discussion
Using a 60 deg inclined orbit, it is estimated that full coverage of Scotland every two weeks could be achieved using eight spacecraft in repeating ground-track orbits, positioned to avoid any overlap in coverage. Such a system is estimated to provide an average revisit time across Scotland of 3-7 days, depending on the latitude. In the simulations performed, 97% of the areas of interest have a maximum revisit time of less than 14 days and it is expected that this could be extended to the full region with more precise orbit modelling. The ground tracks of the satellite constellation would pass over various land masses across the globe. This presents a collaboration opportunity where research, development and costs could be shared with other countries in exchange for access to the system. The orbit characteristics recommended are designed to cover Scotland but could be adapted for better coverage across various countries. Land masses at a similar latitude to Scotland (56\({}^{\circ}\)) could be subject to a comparable coverage pattern and rate of revisit (Figure 8), provided that the satellites are capable of handling larger quantities of data. For example, Lithuania is at the same latitude as Glasgow (55\({}^{\circ}\)), allowing for similar results without changes to the existing orbit path. Due to the circular orbit selected, areas in the southern hemisphere of approximately \(-\)56\({}^{\circ}\) latitude (Figure 8) are subject to a similar coverage pattern and rate of revisit. However, this area is almost exclusively ocean. The use of an elliptical orbit could allow for longer dwell times in the northern hemisphere, reducing this impact, though this would require significant fuel for orbit maintenance.
## 10 Simulation Results and Discussion
### Influence of Coverage Area  
From Figure 16, in order to verify the coverage area of the TANVEER, BIN-TANVEER, and DPD-TANVEER, we have conducted simulation against the number of nodes and coverage area. The slip of the area is also shown beside the simulation graph. From Figure 13, TANVEER has the highest coverage area among all other sub-sections, including the baseline approach. For example, the L2-ABF works with 500 nodes, and the quantity of nodes with TANVEER is 100 more nodes in order to cover the simulation environment and network volume.  
L2-ABF used an angled approach while TANVEER works with tri-angular vector-based approach, and the DPD mechanism is more robust than the L2-ABF approach. Furthermore, the BIN technique that is novel in nature is only introduced with TANVEER, and the alternate path coped with binary internodes, hence the coverage area from source to sonobuoys was maximum as desired and that is depicted in Figure 16.  
Figure 15: Throughput Analysis of TANVEER.
## Nomenclature  
\(h_{ap}^{*}\)Addendum coefficient  
\(h_{jp}^{*}\)Dedendum coefficient  
\(S_{F}\)Safety Factor  
\(\gamma_{NT}\)Life factor  
\(f_{korr}\)Correction factor  
\(m_{n}\)Normal module  
\(\tau_{\tau/\sigma}\)Ratio between \(\gamma_{f}\) and \(\sigma_{f}\)  
\(x^{*}\)Profile shift coefficient  
\(P_{n}\)Stress exerting on a plane defined by a normal vector n  
\(T_{n}\)Curve determined by \(\tau_{n}\) along the time  
\(\alpha_{Fen}\)Angle between the loaded tooth axis and the applied forces  
\(\alpha_{n}\)Normal pressure angle  
\(\rho_{jp}^{*}\)Root radius factor  
\(\sigma_{F}\)Maximum stress due to pure bending in the fillet region  
\(\sigma_{FP}\)Permissible bending stress in the fillet region  
\(\sigma_{Flim}\)Material strength  
\(\sigma_{FlimMG}\)Material strength measured via MG tests  
\(\sigma_{FlimSTBF}\)Material strength measured via STBF tests  
\(\sigma_{c,max}\)Maximum stress component normal to the critical plane  
\(\sigma_{f}\)Material fatigue limit at symmetrical alternating bending loading  
\(\sigma_{n,max}\)Maximum value assumed by \(\sigma_{n}\)  
\(\sigma_{n,min}\)Minimum value assumed by \(\sigma_{n}\)  
\(\tau_{c,a}\)Alternating tangential stress on the critical plane  
\(\tau_{f}\)Material fatigue limit at symmetrical alternating torsional loading  
\(\tau_{n,a}\)Alternating tangential stress on the plane defined by a normal vector n  
\(\tau_{n,m}\)Average tangential stress on the plane defined by a normal vector n  
\(\phi_{n}\), \(\theta_{n}\)Angles in polar coordinates defining a plane having normal vector n  
\(\overline{\sigma}(t)\)Stress Tensor History  
\(\sigma_{n}\)Stress component normal to the plane defined by a normal vector n  
\(\tau_{n}\)Stress component tangential to the plane defined by a normal vector n  
DOE Design Of Experiment  
DP Damage Parameter  
FEA Finite Element Analysis  
FEM Finite Element Model  
MCC Minimum Circumscribed Circle method  
MG Meshing Gears
## 4 Evaluation
### Lab Testing Results
#### 4.2.1 Reference Charger with High-Level Stack and IEC 61850  
The first reference charger implements the high-level communication stack defined in 15118-2 and IEC 61850-90-8 to enable interactions with the grid operator and data mapping between 15118 and 61850 messages. Figure 9a illustrates the internal architecture of the reference charger with the 61850 capability.  
To our best knowledge, this is the first reference charger that can interact with the operator using the IEC 61850 information model according to IEC 61850-90-8. This reference charger, however, lacks the IEC 61851 basic signaling and PLC communication stack. Instead, it connects with EV by Ethernet. While no real power transfer occurs, this reference charger can demonstrate how our testing system can perform conformance tests of high-level communication and the interactions with the operator.  
The charger consists of the 15118 server, the 61850 server and a conversion engine between them. On the ISO/IEC 15118 side, the charger implements the SECC as defined in 15118 Part 2. On the IEC 61850 side, an IEC 61850 server is running in the charger. Between 15118 and 61850, there exists a 15118/61850 converter. This converter translates the IEC 15118 messages encoded in EXI to the IEC 61850-90-8 information model represented in MMS (Manufacturing Message Specification) messages, and _vice versa_.  
The reference charger is developed in Windows 7 in C and C++, deployed in a laptop computer with CPU i5-4210U 1.7 2.4 GHz, RAM 4 GB and HDD 120 GB. The communication between 15118 and 61850 is implemented by shared memory. We based our implementation of the 15118 server on OpenV2G Version 0.9.2. We used PIS10 Stack Version 2 to implement the 61850 server.  
Figure 9: Architecture of the reference chargers. (**a**) Reference charger with high-level stack and IEC 61850; (**b**) Reference charger with full ISO/IEC 15118 stack.
# Weather Radar in Complex Orography
## 2 Practical Applications, Needs and Requirements
### Operational Applications in the Swiss Alps
Both radars provided sufficient information to assess the severity of the thunderstorm thanks to the high vertical resolution of the scan strategy, which is discussed later on in Section 4.6. This demonstrates that, despite the shielding by terrain, one radar can act as backup, should the other be out of operation. Similar plots for a different hail cell and a discussion on the different types of hail data can be found in [75; 98]. The hydrometeor product could trigger new applications in the future, for instance in the automation of human observations (METAR, SYNOP). Figure 5: **(a)** Automatic tracking, extrapolation and (**b**) severity ranking of thunderstorms [94]. All the thunderstorm cells (polygons) are shown in panel (**a**), whereas only cells above a certain severity are marked in panel (**b**). The number in the cells indicates the maximum cell vertically integrated liquid (VIL) water, which is one of the parameters used to compute cell severity. For this time step, there is one cell just slightly above the severity threshold (white ellipse, VIL 12 kg/m\({}^{2}\)), one moderate cell (green), one severe cell (yellow) and one very severe cell (red, VIL 51 kg/m\({}^{2}\)). The uncertainty of the cell position forecast is represented by the increasing size of the extrapolated ellipses. The underlying colour in (**a**) and (**b**) shows the precipitation rate (QPE). The data refer to 20 June 2021, 1400 UTC. The geographic domain corresponds to that of Figure 1.
## 4 Results and Discussion
### Empirical Prediction of the Lift Force for Semi-Submerged Cylinder in Oscillatory Flow
The recalculated lift force and measured force in an oscillatory flow are shown in Figure 9. The blue dashed and red solid line respectively represents the measured and calculated force. The calculated forces are in a good agreement with the measured forces. Therefore, it is feasible and promising to use the prediction formula described in Equation (18) to calculate the hydrodynamic force in the CF direction in an oscillatory flow. Such a good agreement using this novel empirical formula in Equation (18) also provides a potential explanation of the mechanism of the fluctuating lift force with frequency twice of the oscillatory flow, which is worthwhile for further study through flow visualization and CFD. Figure 8: The sum of the squared difference \(J_{1}(\varphi)\) between the predicted and measured fluctuating force versus phase difference under KC = 18.8 at \(\beta\) = 11,364 (A = 0.75 m, T = 5.5 s). Moreover, for computational fluid dynamics, this problem is a typical two-phase flow problem, which is not simple to solve. However, this empirical formula can greatly avoid the complicated and time-consuming hydrodynamic calculation problem and will be very efficient and convenient for engineer to calculate the force on semi-submerged cylinder, such as the floating collar of fish farming. However, in real engineering project, the semi-submerged cylinder will not have a constant vertical displacement, which is likely to result in a different lift force. This work mainly focused on the static semi-submerged status of the cylinder, which will also provide a preliminary estimation of the lift force and basic references for further investigation. More systematic experiments will be carried out to determine the influence of vertical and horizontal vibration and even the submerged depths on hydrodynamic load in near future.
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 2 Methodology
### Keyword Co-Occurrence Analysis  
The co-occurrence analysis shows that the keywords that appear more than 5 times in the literature and the trends of flood sensors in the past two decades were categorized into seven categories (Figure 2). They are: (1) soil moisture: hydraulic modeling, hydrological modeling, flood extent; (2) model: river, uncertainty, optimal selection, flood monitoring; (3) data fusion: deep learning, convolutional neural network, flood forecasting, digital twin, short-term memory; (4) satellite data: SAR, image processing, multispectral index (MSI), multispectral satellite images, urban flooding; (5) wireless sensor networks: network life cycle, disaster management, genetic algorithms, AI; (6) water surface extent: flood impacts, disaster risk management, hazard warnings, flood sensors, benefit-cost analyses; and (7) land surface water index: coverage, enhanced vegetation index (EVI), Landsat, moderate-resolution imaging spectroradiometer (MODIS). Consistent with the analysis of keyword bursts given by CiteSpace (Figure 3), we found that most of the studies used remote sensing techniques to detect flood-prone areas [42], soil moisture [43], vegetation cover [44], and normalized difference vegetation index (NDVI) [45]. The application of wireless sensor networks in flood monitoring and early warning occurred probably around 2004 [46] and rapidly became a research hotspot between 2018-2021. The key terms of flood prediction, flood impact assessment, machine learning, and Landsat in the field of flood sensor applications have appeared in many literatures since 2020 [47; 48; 49]. The emergence of deep learning and synthetic aperture radar symbolizes a great advancement in machine learning algorithms, as well as physical sensor technologies, which will likely be the keywords of flood sensors in the future.  
Figure 2: Co-occurrence of keywords about flood sensors in WOS.
## 6 AI-Based Approaches for Autonomous UAV Swarm Systems
### AI-Enhanced Data Processing for Swarm UAV Applications
#### 6.1.3 Collaborative Environmental Monitoring via AI-Integrated UAV Swarms  
In the UAV-WSN-IoT system, UAV devices are equipped with sensors, cameras, or specialized collection devices to be able to perform aerial surveillance, record images, or collect different types of data [26]. The outstanding features of the system are widely applied in many different monitoring fields, especially applications that require data collection from large, inaccessible areas or require continuous monitoring [139; 140].  
A prominent application is described in [141]. UAVs in the WSN-IoT collaborative system are equipped with many different sensors to determine water quality, such as PH, temperature, turbidity, Dissolved Oxygen (DO), and carbon dioxide. All sensors are integrated and monitored wirelessly by a universal asynchronous transceiver (UART) on the ground station. The telemetry system is designed to be able to send and receive data at a distance of up to 1 km in real time. The UAV-WSN system is also a cost-effective solution and suitable for large-area monitoring applications. In [142], the study describes the UAV participating in animal monitoring activities as a Sink node to collect important time-sensitive information from sensor devices to directly observe and track animals. In addition, this system also allows for detecting the location of endangered species in large-scale wildlife areas or tracking animal movements without any attached devices. Design strategies using UAVs to deploy wireless sensor networks are also applied to post-disaster monitoring [143]. They can be deployed to monitor and assess areas affected by disasters or support the search for victims.  
Recently, the use of artificial intelligence (AI) in UAV-WSN networks has improved the efficiency of monitoring applications. AI and machine learning algorithms enable distributed data processing, improve hierarchical multi-protocol networking aspects, and control high-level UAVs constrained by WSNs. The Federated Learning-Based UAV-WSN-IoT system described in [144] leverages the lightweight Dense-MobileNet model to achieve learning from the features of haze images captured by UAVs. The results of the study demonstrate the ability to accurately and timely predict real-time and future air quality indexes.