esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Test Site Description  
The study area is located at the northern extent of the mainland of the Northwest Territories, Canada (see Figure 1a). The region is part of the Canadian Arctic and lies entirely north of the tree-line along the coastal tundra plains of the Southern Arctic Ecozone [32]. The climate here is characterized cold winters, followed by short and cool summers. The mean annual air temperature at the climate station Tuktoyaktuk is \(-1\)\({}^{\circ}\)C (1971-2000), and the mean air temperature between October and April is below \(-10\)\({}^{\circ}\)C. The average precipitation is about 150 mm [33]. The ground surface is characterized by the presence of continuous permafrost and its thickness is estimated to be up to 600 m [34]. Therefore, the soils of the Tuktoyaktuk Peninsula are Cryosols, with an active layer thickness of several centimeters to decimeters. The soils developed on glacial deposits of Pleistocene to Holocene age [32], and current morphodynamics are dominated by periglacial processes. The land surface is therefore characterized by low-lying and flat coastal plains, rolling hills, thermokast lakesand pingos, and extensive networks of high- and low-centered ice wedges [33]. The ground surface is also characterized by tundra vegetation, with upland tundra usually composed of short herbaceous vegetation and shrubs (dwarf shrubs up to tall shrubs). The wetland vegetation (grasses, sedges or rushes) is frequently at or near water bodies, e.g., at drained lakes, or in the flat and low-lying intertidal zone. Depending on the coastal currents, the beach zone is characterized by fine sandy material, mixed sediments dominated by gravel, pebble or cobble and driftwood accumulations [33].  
Figure 1: Location of the test site: Tuktoyaktuk (Northwest Territories, Canada) and RGB composites of remote sensing imagery: (**a**) elevation and slope of intermediate TanDEM-X DEM, coverage of TerraSAR-X, Radasat-2, ALOS and ALOS-2 imagery, locations of in situ field work in 2010 and 2012, locations of land cover reference samples derived from high resolution ortho-photos, extent indicator of the subsequent sub-figures (red rectangle); (**b**) RGB false-color composite of sigma nought HV intensities [dB] of X-Band (TerraSAR-X, 2011), C-Band (Radarsat-2, 2011) and L-Band (ALOS, 2010); (**c**) RGB false-color composite of Kennaugh Matrix Elements K2, K1 and K3 of quad-polarized Radarsat-2 data (see Section 2.2.2 Polarimetric SAR Data and Decompositions for more details on the Kennaugh Matrix Elements); (**d**) Landsat TM (2011) true-color composite of red, green and blue surface reflectance. RGB channels are stretched linearly between 1 and 99% of the data range.
# Convergence Check Phase-Field Scheme for Modelling of Brittle and Ductile Fractures
## 5 Numerical Examples
### Brittle Fractures  
5.1.1 Single-Edge Notched Tensile Test  
The first verification problem is a notched plate loaded in tension. Linear elastic material behaviour was adopted with [PERSON]'s modulus \(E=210\) GPa and [PERSON]'s ratio \(\
u=0.3\). The fracture parameters required for phase-field calculations are the critical fracture energy density \(G_{\rm c}=2.7\) N/mm and the length scale parameter \(l=0.0075\) mm. Geometry and boundary conditions are presented in Figure 4.  
Figure 3: Extrapolation-based cycle-skipping scheme.  
The model was discretised by 18,868 quadrilateral finite elements in a single layer, with refinement in the expected fracture processing zone (FPZ). Vertical displacement loading \(v=0.0085\) mm was imposed in 100 increments. The results obtained by two approaches are compared in Figure 5a. As may be observed, the CCPF provides identical results to the RCTRL scheme. Figure 5b presents the distribution of the phase-field variable. It is visible that the correct crack path has been obtained, which is additionally confirmed by the force-displacement curve.  
Furthermore, the numerical efficiency of both approaches was compared. Both simulations were conducted with the parallelisation option available in ABAQUS, where four CPUs were utilised. The RCTRL scheme exhibits a wall clock time of 4442 s while the CCPF scheme completes the simulation in 2752 s, which is 38% faster. An additional efficiency comparison of the two frameworks was achieved by comparing iterations per loading increments, as displayed in Figure 6.  
Figure 4: Geometry and boundary conditions of a single-edge notched tensile test.  
Figure 5: Single-edge notched tensile test: (**a**) force-displacement diagram, (**b**) crack pattern.  
According to Figure 6, the CCPF seeks less iterations through increments, especially at the onset of a brittle fracture, demonstrating better stability of the CCPF scheme compared to the RCTRL scheme. Considering the smaller number of degrees of freedom for the same discretisation and less iterations required per increment, the CCPF framework is computationally more efficient, providing faster numerical solutions.
## 5 Adaptive Data Transmission Algorithm Based on Reinforcement Learning
### RL-ADTA Packet Design  
The data transmission algorithm between the nodes is finally realized by transferring the packets containing the control and application information. RL-ADTA does not generate special requirements for the lower layer protocol, nor does it design data packets separately to transfer status or control information between nodes. The packet defined by RL-ADTA contains both the service information and the node status information. The underwater network nodes update the environment status by monitoring the packets sent by the neighbor nodes. When the relay node is the current node, the optimal data forwarding strategy is obtained according to RL-ADTA, and the packet is repackaged and forwarded to the next node. The packet structure of RL-ADTA is shown in Figure 6. The data package contains two parts: the fields in the 'data head' are related to underwater communication, while the fields in the 'data content' are related to monitoring applications.  
The underwater monitoring application forms the monitoring data into data packets during every period T. The 'data ID' is the ID of the current period data, and the 'destination address' is the ID of the destination sink node. The 'node ID', 'V value','residual energy' of the current node, the 'timestamp' of sending data, and the 'next forward' representing the relay node ID are all included in the data header. The fields 'task type' and 'threshold'  
Figure 6: RL-ADTA packet structure. Figure (**a**) shows the composition of RL-ADTA package. Figure (**b**) shows the constituent fields of the ‘data content’.  
in the data content are determined by the type of monitoring data collected. Additionally, (p, d, q),'correction point', {correct\({}_{1},\cdots,\)correct\({}_{\text{num}}\)} and 'num' are the edge prediction parameters, correction time-point, correction data, and their quantities, respectively.
# Assessing Cyber Challenges of Maritime Navigation
## 3 Findings
### Cyber Trends and Challenges
#### 3.3.1 Types of Cyberattack  
There may be nothing new about the need for ships to deliver cargo or patrol their country's coasts. However, the threats they are increasingly likely to encounter, invisible to any telescope, might place the maritime sector in uncharted waters [74]. According to [PERSON] et al. [75], current threat implications of maritime-based cyberattacks include business disruption, financial loss, damage to reputation, damage to goods and environment, incident response cost, and fines or legal issues. For example, attackers could manipulate passenger lists, perform illegal activities (transports), breach sensitive cargo transports, cause engines failures, shut down vessels, or otherwise manipulate onboard control systems [74; 76]. [PERSON] et al. [45] categorized the risks associated with an attack into three dependent factors: threats (who is attacking), vulnerabilities (the weaknesses they are attacking), and impacts (what the attack does).  
There is a variety of methods that exist for those who seek to target the shipping industry [74; 77]:  
* [noitemsep,topsep=0 pt]
* Extortion/ransomware for allowing the vessel/port to restore operations;
* Digital piracy by shutting down the vessel/port;
* Espionage for gaining sensitive information that can be used by the competition;
* Defamation/litigation by causing ISPS Code noncompliance/delaying the vessel/causing disruption;
* Subversion of the supply chain;
* Terrorism;
* (H)Activism for conveying a message.  
In the Danish Cyber and Information Security Strategy for the maritime sector, 2019-2022 [73], it is assessed that the general cyber threat is directed against maritime commercial businesses and does not currently pose a direct threat to maritime operations. The strategy on one side considers that the threat from cyber espionage and cybercriminals against the maritime sector is very high. In contrast, on the other side, it evaluates the threat from destructive cyberattacks, cyber activism, and cyber terrorism as low.  
In the maritime domain, a rise in spear-phishing of vessels at sea has been noted. The BIMCO survey [78] presented several incidents where malicious software was introduced to ship systems unintentionally, often by third parties, to check or even update specific bridge equipment. Although the malware significantly degraded functionality of the onboard computer system, no essential vessel control system had been impacted [79]. Consequently, BIMCO's survey [78] shows that maritime companies are increasingly not only assessing their own systems and work practices in a bid to limit the likelihood of an attack but are also assessing the risk introduced across their supply chain. Respondents have also noted that a company's staff was its greatest cyber vulnerability; therefore, many cybersecurity measures remain firmly focused on reducing human error.
# Proactive Mission Planning of Unmanned Aerial Vehicle Fleets Used in Offshore Wind Farm Maintenance
## 4 Case Study  
### Wind Turbine Service  
Consider the set of turbines \(T=\{T_{10},T_{28},T_{50},T_{52},T_{68},T_{90}\}\) from the example discussed in Section 3.1, which has been extended by an additional WT \(T_{47}\). Due to difficult weather conditions, the time for the maintenance and repair mission was reduced to 7 h. There are three service teams, whose parameters are listed in Table 3 (all teams have the same competencies). For these data, the answer to the following question is sought: Do the available vessels and teams \(W_{1}\), \(W_{2}\), and \(W_{3}\) allow for the servicing of a given set of \(T=\{T_{10},T_{28},T_{50},T_{52},T_{68},T_{90},T_{47}\}\) in the assumed time horizon \(H=420\) (7 h)? And, if so, what is the service plan that guarantees minimal cost \(C\)?  
The purpose of the relevant \(CP_{AP}\) analysis problem (66) implemented in the IBM ILOG environment is to determine a proactive mission plan. The permissible solution obtained during the 300 s calculations guarantees the service of the set of turbines within 466 min (see Figure 5). Despite the fact that the time obtained is the shortest among those obtained for the same data, it does not meet the accepted expectations, i.e., it is not an acceptable solution (service should be finished in the assumed time horizon \(H=420\) min).  
Because of the lack of an acceptable solution, an attempt was made to determine the composition of the ship's crew (set \(W\)) guaranteeing the service of \(T\) WTs in the assumed time horizon \(H\). In other words, the answer to the following question is sought: Does there exist a set of teams \(W\) guaranteeing the service of a given set of \(T\) WTs in the assumed time horizon \(H\)?  
It was assumed that the set \(W\) may consist of one to five service teams with the parameters specified in Table 3. The considered problem was implemented in the IBM ILOG environment. The obtained solutions are presented in Table 4.  
It is easy to see that the solutions enabling WT service within 7 h require the use of at least four teams (time \(MD\leq 420\)). Thus, among the two possible solutions, the plan assuming the use of four service teams has the lowest cost: \(C=1817\). Therefore, this plan was selected for implementation (Figure 6).
###### Abstract  
Periodic narrowband signals and white noise are the main interferences in online detection and localization of cable partial discharge (PD), however, existing research has always focused on the white noise suppression only, which is not in line with the actual scene. A novel de-noising method for effectively extracting random PD pulse from complex and strong interferences is proposed in this paper and applied to PD localization. Firstly, an improved adaptive variational mode decomposition (AVMD) is used to decompose periodic narrowband interference, white noise, and PD signal into different intrinsic mode. According to the characteristic that the power of intrinsic mode component of periodic narrowband interference in the discrete Fourier transformation (DFT) power spectrum is much larger than that of PD and white noise, the periodic narrowband is removed out. In order to effectively filter out white noise, a scale adaptive wavelet packet decomposition method based on correlation coefficient is proposed, which decomposes the signal into high, middle, and low-frequency components. The components with low frequency, small amplitude are removed out as the white noise interference according to the threshold method, and the residual is the de-noising PD signal. Experimental results show that the proposed method can robustly suppress the interference of periodic narrowband signal and white noise, and effectively preserve the essential characteristics of the real PD signal. In the multi-sensor travelling wave based localization system of cable PD source using time-varying kurtosis, accurate estimation of first arrival time of PD pulse can be achieved by the de-noising results.  
arrival time estimation; cable partial discharge; complex noise; correlation coefficient; de-noising 2020 10, 1641; doi:10.3390/app10051641
## 3 Methods
### Modeling Vertical PAR with a Radiative Transfer Model  
In this study, we also compared the measured PAR and modeled PAR values to simulate PAR in the vertical direction. According to the light transfer function in the vegetation canopy [58], the vertical PAR at different heights impacts the LAI through light absorption, reflectance, and transmittance. In the vertical direction, incident PAR for direct light follows the Beer-Lambert law:  
\[PAR(z)_{dir}=PAR_{0}\times e^{\tau_{0}} \tag{8}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **TMK** & **DHS** \\ \hline \multicolumn{3}{c}{**Input parameters**} \\ \hline \hline \multicolumn{3}{c}{Overstory LAI} & 5.4 & 2.6 \\ X (leaf angle distribution in spherical distribution) & 1 & 1 \\ Daytime zenith angle (mean) & 31 & 45 \\ \hline \multicolumn{3}{c}{**Output results**} \\ \hline \hline \multicolumn{3}{c}{Direct PAR \(\tau_{0}\) \%} & 4.2 & 15.9 \\ Diffuse PAR \(\tau_{0}\) \% & 1.6 & 9.3 \\ Understory simulation \% & 5.6 & 25.2 \\ Understory in situ \% & 8.2 & 27.5 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Understory photosynthetically active radiation (PAR) simulation: parameter value inputs and results.  
where \(PAR_{0}\) is the incident \(PAR\) at the top of the canopy, \(z\) is the length of light transmission through the canopy, and the \(\tau_{b}\) is the transmitted ratio, which is calculated as:  
\[\tau_{b}=\exp(-K_{b}\times LAI) \tag{9}\]  
where \(K_{b}\) is the extinction coefficient, which is affected by the zenith angle \(\phi\)[79]  
\[K_{b}=\frac{\sqrt{x^{2}+\tan^{2}\phi}}{x+1.774(x+1.182)^{-0.733}} \tag{10}\]  
\[\tau_{d}=2\int_{0}^{\pi/2}\tau_{b}(\phi)\sin\phi\cos\phi d\phi \tag{11}\]  
Therefore, \(PAR(z)\) is the understory incident PAR, which can be expressed as follows:  
\[PAR(z)=PAR_{0}\times e^{\tau_{b}}+PAR_{skylight}\times e^{\tau_{d}} \tag{12}\]  
To test the proportional impact of the LAI in different sub-ecosystems on the GPP estimation in dense forests, we used the discrete anisotropic radiative transfer (DART [80,81]) model to simulate APAR based on clear sky conditions in the tree, shrub, and grass layers under different vertical LAI distributions.
# Analysis of Wind Speed Characteristics Along a High-Speed Railway
## 2 Materials and Methods
### Meteorological Department Wind Speed Observation  
In addition to the short-term measurements from the four railway stations (RSs) detailed in Section 2.2, this study utilizes surface weather data from the Jiangsu Provincial Meteorological Bureau's ground monitoring system. Ground meteorological monitoring employs automatic weather stations (WSs) equipped with conventional mechanical anemometers, which have a measurement accuracy of 0.1 m/s and are calibrated annually. Quality control checks were conducted on the recorded data, with significant errors removed.  
The meteorological stations selected for this study are stations 58,237, 58,242, 58,352, and 58,356. Station 58,237, the nearest to K1005, is located on the bank near Dashengguan Bridge, with a hill to the northwest. Station 58,242, closest to K1066, is situated on the north side of the mountain, contrasting with K1066's valley location. Station 58,352, although some distance from K1245, is supplemented by Taihu Lake, located to the west, as a closer monitoring point. Station 58,356, nearest to K1256, is positioned in a relatively flat region. Wind speed data from these meteorological stations, spanning the entire year of 2020, were analyzed alongside HSR data. Figure 1 shows the locations of the selected stations and Table 1 provides relevant information on each station.  
Figure 1: Station location along the Beijing-Shanghai HSR.
## 2 Materials and Methods
### Statistical Analysis  
The collected data underwent thorough statistical analysis to evaluate the significance of the results and identify potential correlations or patterns. Rigorous testing and performance evaluation were conducted to validate the effectiveness of the novel device. The experimental setup, which included simulated rainwater ingress scenarios, was carefully explained. To assess the device's accuracy, sensitivity, and reliability, specific evaluation metrics were defined. The results obtained from the testing phase were presented and discussed, highlighting the device's precise detection of rainwater ingress. To ensure the accuracy and reliability of the experimental setup and measurements, comprehensive validation procedures were implemented. Quality control measures, including regular instrument calibration and periodic checks, were undertaken to maintain data integrity and minimize potential errors or biases. Data collection procedures were established to capture relevant measurements during the experiments. This involved setting up data loggers or real-time monitoring systems to record sensor readings. The experimental procedures were meticulously designed to simulate various scenarios and conditions encountered in closed channels. Variables such as flow rates, channel dimensions, and moisture levels were carefully controlled. The experiments were conducted systematically, following a predetermined protocol, to ensure consistency and repeatability throughout the study. The pipe slope, inclination, flow of water/sewage, and duration of water/sewage flow (time) were analyzed using Spearman correlation and multiple regression techniques with PAST (PAleontological STatistics), software version 4.23.
# Windowed Hamming Distance-Based Intrusion Detection for the CAN Bus
## 6 Discussion  
### Identifify Attack Types  
Our method is able to detect four types of attacks. It leverages the changes in the Hamming distance between the car data domain and the benchmark under attack conditions to identify attacks. Specifically, we observe that DoS attacks tend to decrease the Hamming distance, while fuzzy attacks tend to increase the Hamming distance. Therefore, we can differentiate between different types of attacks by comparing the calculated Hamming distance with a threshold. If the Hamming distance is greater than the threshold, it indicates a fuzzy attack. Otherwise, it suggests a DoS attack. Furthermore, to detect the two types of spoofing attacks, we establish specific thresholds as signatures. If the calculated Hamming distance falls within the range defined by one of the thresholds, it indicates the presence of the corresponding attack. Therefore, we can infer the type of attack based on the analysis of the comparison result.
## 3 Results and Analysis
### Indoor Testing  
In order to study GNSS signal behavior with the GPSMap 66 and P10 units in an indoor environment, a test was conducted in the basement of a private residence. GPSMap 66 results are reported below. The P10 only recorded a few GPS SVs periodically, although it was initialized outside, hence there are no results to report. Indoor environments are difficult to fully characterize in term of GNSS signal behavior. An important metric is signal attenuation. Others are the size of the room in which the test occurs, wall materials and windows as these impact multipath characteristics. The 90-min test reported herein took place when up to six Galileo SVs were available. The room size was 4 m \(\times\) 4 m \(\times\) 2.5 m, concrete walls were 1 m above the ground and one outer wall had a 2.5 m \(\times\) 1 m window. The coordinates were transferred from an outdoor position with accuracy of 1 m.  
The GPSMap 66 C/N\({}_{0}\) values measured during the test are shown in Figure 23. All values are below 35 dB-Hz and drop to below 15 dB-Hz. The unit was able to acquire and track many signals for continuous periods of time. The cyclic variations are typical of multipath. The lower Galileo signal values are due to generally lower SV elevations. Again, code measurements were processed in epoch-by-epoch single point mode to evaluate their stability. The combined GPS/Galileo and GPS only solutions are shown in Figures 24 and 25. The RMS coordinate variations are 8.8, 3.7 and 9.7 m in latitude, longitude and height for the combined solution, and 9.8, 5.8 and 16.2 m for GPS only. The GPS only results are slightly poorer, demonstrating the importance of using as many signals as possible (GPS + Galileo in this case) to somewhat mitigate noise and multipath in such a harsh environment. The GPS/Galileo average coordinates agree at the level of 1.5 m with the values transferred from the outdoors. The residuals of the epoch-by-epoch least-squares solutions are shown in Figure 26 and are within a band of 20 m.  
Figure 22: GPS single point and differential (code/carrier phase and code only) accuracy of P10 during vehicular test (28 Jun18).  
Figure 23: GPSMap 66 GPS and Galileo C/N\({}_{0}\) values during indoor test (18 Sep18).  
Figure 24: GPSMap 66 Combined GPS/Galileo epoch-by-epoch single point positions during indoor test.
# Structural Adversarial Variational Auto-Encoder for Attributed Network Embedding
## 3 Model
### Problem Definition  
**Definition 1** (Atributed Network).: _A attributed network is defined as \(\mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{X})\), where \(\mathcal{V}=(v_{1},v_{2},...,v_{N})\) is the set of nodes, and \(\mathcal{E}\) denotes the sets of edges with \(e_{ij}=(v_{i},v_{j})\in\mathcal{E}\). \(\mathbf{X}\) is the feature matrix with \(\mathbf{x_{i}}\in\mathbf{X}\) denoting the feature information associated with node \(v_{i}\). Meanwhile the topology information of the network can be represented by adjacency matrix \(\mathbf{A}\). All notations are summarized in Table 1._  
**Problem 1** (Attributed Network embedding).: _The goal is to project each node \(v_{i}\in V\) to a low-dimension vector \(z_{i}\in\mathbb{R}^{N\times D}\):_  
\[f:V\mapsto Z \tag{1}\]  
_where \(D<<N\), such that embedding matrix \(Z\) should well preserve the topological structure and attribute information of the graph, given an attributed network denoted as \(\mathcal{G}=(\mathcal{V},\mathcal{E},X)\)._