esa-sceva/satcom-chunk-collection huggingface dataset:
# Proposal of an Automated Mission Manager for Cooperative Autonomous Underwater Vehicles
## 4 Description of the Missions and Tasks Register and Reporter (MTRR)
### Activities Performed by the MTRR
#### 4.2.2 Receiving and Recording Mission Status Reports  
Figure 15 shows the functional overview of how a mission status report sent by a vehicle is processed within the SWARMs architecture. It starts with the vehicle publishing the corresponding mission status part to a specific DDS topic (step 1). The Task Reporter component, which is subscribed to that topic, receives the published data (step 2), processes it, and sends the generated report to the MTRR (step 3). The MTRR then processes the report, stores the relevant information into the SWARMs repository through the Semantic Query component (step 4), notifies the MMT (step 5), and if there are pending tasks in the vehicle plan, publishes the next one to the specific DDS topic through the Publish/Subscription Manager (step 6).  
Figure 14: Sequence diagram for the _startMission_ procedure performed by the MTRR.  
In summary, the reception of a new mission status report in the MTRR has three main responsibilities:  
1. To extract and store the status report in the ontology using the Semantic Query component.
2. Notifying the MMT if there is any important status update, like the vehicle being unable to fulfill the mission.
3. If the assignment mode was set to wait until task completion for requesting the next task in the mission plan, then the assignment of the tasks to the vehicles besides the first one is performed also when receiving a mission status report with a status of \(COMPETED\).  
The first part is straightforward: it is just the extraction of the status from the report and its storage into the ontology using the interface provided by the Semantic Query.  
The second part is also straightforward: if the received status is of interest for the MMT, then the MTRR will send a notification to it.  
The third part is also very simple, as depicted in Figure 16. If the task status report indicates that the task has been completed, the next task for the vehicle, if any, is extracted from the vehicle plan and published through the P/S Manager.  
Figure 16: Flow diagram for next task assignment when received a \(COMPETED\) task status.  
Figure 15: Functional overview of the processing of a mission status report from a vehicle.  
Figure 17 shows the sequence diagram for the full procedure performed by the MTRR when receiving a new task status report.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 3 Atmospheric Influence on Grapevine Development
### The Role of Radiation  
Solar radiation is also a crucial factor in viticulture. The synthesis and accumulation of sugar, phenolic, and many aromatic compounds during maturation are indeed favoured by high solar radiation levels [68]. Regions with relatively low solar radiation normally adjust the training systems and canopy density to maximize the sun-exposed leaf area. Although more exposed leaves generally favour photosynthesis and stomatal conductance, water demand also increases [69], and other problems may also arise, such as leaf and cluster sunburn. On the contrary, less exposed clusters result in lower berry temperatures, generally leading to lower sugar contents and lower anthocyanin concentrations [70].  
Additionally, shading due to high canopy density may significantly decrease bud fertility [71], thus negatively affecting the yield potential in the subsequent season.  
In the Mediterranean-type climatic regions, such as in Southern Europe, vineyards are already typically exposed to high radiation levels, high air temperatures, and soil water deficits, which can impact grapevine productivity. Under these circumstances, grapevine leaves often show temporary photoinhibition, chlorosis, and necrosis, thus leading to low intrinsic water use efficiency and excessive exposure of grape clusters [72]. Hence, low vigour tends to be connected to reduced berry weight, sugar content, and yields. Still, other organoleptic properties of berries, such as flavour and aroma attributes, are frequently inhibited by excessive solar radiance and severe dryness or, as in the case of tannins, may be exacerbated in concentration and/or altered in molecular structure. These conditions may lead to unbalanced wines, with undesirable high alcoholic content and low acidity [73], with low commercial value. However, other studies [74; 75; 76] showed that vineyards exposed to relatively high levels of sunlight, including UV-B radiation, produce berries of high quality for winemaking by inducing synthesis of polyphenols and monoterenes as photo-protectors.
# Precipitation Estimation Using FY-4B/AGRI Satellite Data Based on Random Forest
## 2 Materials and Methods
### Materials
#### 2.1.2 GPM IMERG Product  
The Integrated Multi-satellite Retrieval for GPM (IMERG) is the latest generation of multi-satellite fusion retrieval precipitation data designed specifically for the Global Precipitation Measurement Mission (GPM) [26]. IMERG is based on the mutual calibration and inversion of microwave (PMW), infrared (IR) and other precipitation observations with a high spatial and temporal resolution for GPM satellite constellation. Thus, it is the best global algorithm currently available. IMERG offers three products: Early Run (ER), Late Run (LR), and Final Run (FR), covering the latitude range of 90\({}^{\circ}\)N to 90\({}^{\circ}\)S. Among them, FR is released from GPM IMERG after a delay of approximately 3.5 months, using monthly precipitation analysis from ground observation stations for its calibration, which is mainly applied in scientific research [27]. Research shows GPM IMERG FR product can accurately capture the precipitation distribution characteristics of Chinese mainland on the whole [28]. In this study, FR product is used as reference data for precipitation retrieval, with a spatial resolution of 0.1\({}^{\circ}\) and a temporal resolution of 30 min.
# Vulnerability Analysis of a Multilayer Logistics Network against Cascading Failure
## 3 Topological Properties  
### Scale-Free  
Using the formula defined by Equation (11), the degree cumulation distribution of the networks is calculated, as shown in Figure 3. The degree distributions of the four networks are different from each other. The maritime network (Figure 3a) fits more with the exponential distribution. In addition, the effect of fitting with a power function is more obvious than the exponential function in the air cargo network (Figure 3b) and the organization network (Figure 3c). The trade network in Figure 3d also seems to obey the power law distribution, while it does not have a good fit. Overall, it is suggested that among the four single networks, the scale-free properties are similar to \(G_{b}>G_{c}>G_{d}>G_{a}\). The fitting curves for the degree cumulation distribution of the multilayer network are shown in Figure 4. The multilayer network still has the scale-free property (\(y=1094.474x^{-0.405}\), \(R^{2}=0.918\)). The global logistics hub city network is in the stage of complex and orderly structure, forming an overall large-scale network structure and gradually tending to mature. The polarization effect is a reflection of the scale-free network, thus affecting the uneven distribution of the centrality of logistics hub cities in the global space and prompting the phenomenon of geographical concentration of the links between logistics hub cities.  
Figure 3: Fitting curves for the degree cumulation distribution of the subnetwork. (**b,c**) are consistent with a power-law behavior.
# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 4 Discussion
### Different Drivers of Greening in North and South Parts of Eastern China  
Greening trends are observed throughout EC (Figure 4), however the main drivers of these trends are likely different in the north and the south. In the northern part, cropland production has increased significantly from 1987 to 2010 [10] in order to feed the large increase in Chinese population (from 1.08 to 1.34 billion). Increases in agricultural productivity come from increased cropland area [10; 74; 75] and increased irrigation from water diversion projects moving water from the south to the North China Plain [22; 73]. In addition, increased fertilization by nitrogen and phosphorus application [76] and lengthening of the growing period due to warmer temperatures with climate change [77] could provide more nutrients and a longer period of optimal growth conditions.  
In the southern part, tree canopy coverage has increased by \(9.27\pm 4.93\%\) in 2016 from \(30.51\pm 13.90\%\) in 2003 [50]. Over the last century, widespread deforestation and forest degradation occurred due to conversion to cropland, infrastructure development, mining, fire, and urbanization. During the 21 st century, afforestation and reforestation increased significantly in the south part of EC [4], due to governmental policy. Climate conditions over southern EC are suitable for forest growth and expansion to increase carbon uptake with protection provided by land management policies [9].
# Reliability Improved Cooperative Communication over Wireless Sensor Networks
## 3 System Model and Problem Statement  
### Network Model  
The network model is the same with Refs. [28; 31; 33], the network includes \(n\) homogenous sensor nodes. The studies area is a circular areas, its network radius is \(R\) (in meters), per-hop transfer distance \(r\) (in meters), and one sink node (base station) is in the center. Its density of nodes in the network is \(\rho\). The network data collection strategy is the same as that of the NCCC algorithm proposed in [31]. The NCCC algorithm is a cluster-based data collection approach, and each cluster is assumed to be composed of \(N\) sensor nodes.  
Node generates one data packet at probability \(\lambda\). Considering sensor nodes is static, the energy of a common node is limited, and the energy of the sink is infinite [3; 20; 29]. In this paper, because the energy consumption for data transmission occupies a large part of the total energy consumption, only the energy consumption of data transmission is considered in this paper; the related studies are shown in References [29; 30]. Although the total energy consumption of nodes includes the energy consumptions of small computations, sensing, and data encryption, the energy consumptions of these processes do not affect the effectiveness of the RICC scheme. The reasons are as follows: First, those energy consumptions are small compared to the energy consumption for data transmission; thus, most studies compute only the energy consumption for data transmission, such as References [3; 6; 24; 28; 31]. Second, the main innovation of the RICC scheme is to use the remaining energy of nodes in non-hotspot areas to increase the transmission power and thereby improve the communication reliability. Thus, the effectiveness of the RICC scheme is not affected by the remaining energy in the non-hotspot areas. Energy is left in non-hotspot areas because the amount of data relayed in these areas is smaller, so the energy consumption is smaller. The energy consumptions for computations and sensing of the nodes are equal; thus, the energy left in non-hotspot areas is not reduced. However, the energy consumption for data encryption is larger in hotspots areas, which causes more energy to be left in non-hotspot areas. Thus, the performance of the RICC scheme can be improved effectively.
## 1 Introduction
In particular, the Unites Nations Convention on the Law of the Sea (UNCLOS) and the International Civil Aviation Organization (ICAO) define those international waters and airspace as beginning at a distance of 12 nautical miles from the coast; additionally, UNCLOS defines an exclusive economic zone (EEZ) where each sovereign state has exclusive rights regarding marine activities, which may stretch up to 200 nautical miles [8; 9]. Thus, interference levels from 5G NR networks deployed in the coastline areas need to be calculated to understand whether 5G NR rollout would cause harmful interference. This type of scenario is pretty challenging due to the fact that aggregate interference from the technologies such as cellular networks have a long-term impact because the networks are gradually getting deployed, slowly increasing external noise near the deployed areas. Apart from that, studies to assess the impact of an entire 5G NR network should take into account the varying nature of a 5G NR network, in particular the power control of user equipment, network loading factors, TDD factors, beamforming of base stations, etc. It is important to note that it is nearly impossible to measure aggregate interference levels in field tests before the deployment of the networks since field tests would involve only a few numbers of interfering devices which will not have any significant impact on \begin{table}
\begin{tabular}{c c c} \hline \hline
**Low Bands** & **Mid Bands** & **High Bands** \\ \hline  & 1427–1518 MHz & \\  & 1710–2025 MHz & \\
450–470 MHz & 2110–2200 MHz & 24,250–27,500 MHz \\
470–608 MHz & 2300–2400 MHz & 37,000–43,500 MHz \\
614–698 MHz & 2500–2690 MHz & 45,500–47,000 MHz \\
694–960 MHz & 3400–3600 MHz & 47,200–48,200 MHz \\  & 3600–3700 MHz & 66,000–71,000 MHz \\  & 4800–4990 MHz & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of the bands currently identified for IMT. victim receivers. Therefore, computer simulations are the only possible way to estimate the aggregate interference level from the cellular systems deployed in large areas. The objectives of this research were achieved by doing simulations of aggregate interference from the 5G NR networks deployed in the cities near the coastline. For example, in this study, the Far Eastern region of Russia was chosen. This example is good for several reasons; first, it has some big cities located near the coastline, and second, the clutter height of buildings in this area is not high compared to countries such as China, for example, and thus the aeronautical and maritime stations are effectively less shielded from the interference of 5G NR networks deployed inside urban areas. Therefore, one of the variants of the worst-case scenario is considered.
## 2 Anisotropic Damage Model
### Stiffness Degradation Model  
Based on the theory of continuous damage mechanics [42], the stress-strain relationship of the damaged zone can be established by introducing the degenerated elastic stiffness coefficient \(C_{ijkl}\) as follows:  
\[\sigma_{ij}=C_{ijkl}\epsilon_{kl}. \tag{10}\]  
Under the assumption of isotropic damage, the degenerated stiffness coefficient \(C_{ijkl}\) is expressed as  
\[C_{ijkl}=(1-D)C_{ijkl}^{0}, \tag{11}\]  
where \(C_{ijkl}^{0}=\lambda\delta_{ij}\delta_{kl}+\mu\left(\delta_{ik}\delta_{jl}+ \delta_{il}\delta_{jk}\right)\) is the initial elastic stiffness coefficient for isotropic materials, \(\lambda=\frac{vE}{(1+v)(1-2v)}\) and \(\mu=\frac{E}{2(1+v)}\) are [PERSON]'s constants, and \(E\) and \(v\) are [PERSON]'s modulus and [PERSON]'s ratio, respectively. \(D\) is the damage variable reflecting the damage state of the material. \(D\) = 0 corresponds to the undamaged state, \(D\) = 1 corresponds to the complete damage (fracture or failure) state, and 0 < \(D\) < 1 corresponds to the different degrees of damage.  
The above model applies to tension damage. For compression shear damage, the damage parallel to the failure surface is usually larger, and the damage perpendicular to the failure surface is usually smaller and can be ignored. This will lead to obvious weakening of the shear stress parallel to the failure surface, while the normal stress vertical to the failure surface is basically unaffected. Therefore, it is not necessary to weaken the stiffness in all directions, but only in the direction parallel to the failure surface.  
Assuming the unit normal tensor and tangent tensor of the failure surface are \(n\) and \(t\) respectively, then the shear stress on the failure surface is  
\[\tau_{\alpha}=\frac{1}{2}\beta_{ij}\sigma_{ij}. \tag{12}\]  
where \(\beta_{ij}=n_{i}t_{j}+t_{i}n_{j}\)  
Substituting Equation (10) into Equation (12) and letting \(D\) = 0, yields  
\[\tau_{\alpha}=\mu\beta_{kl}\epsilon_{kl}. \tag{13}\]  
Hence, the contribution of shear stress on the failure surface to the stress tensor is  
\[\sigma_{ij}^{\tau}=\tau_{\alpha}\beta_{ij}=C_{ijkl}^{\tau}\epsilon_{kl}. \tag{14}\]  
where \(C_{ijkl}^{\tau}=\mu\beta_{ij}\beta_{kl}\) is the elastic stiffness coefficient contributing to the shear stress of the failure surface. Assuming the angle between the normal vector of the failure plane and the positive X-axis is \(\alpha\), then \(C_{ijkl}^{\tau}\) is as follows:  
\[C_{ijkl}^{\tau}=\begin{bmatrix}\mu(\sin 2\alpha)^{2}&-\mu(\sin 2\alpha)^{2}&0&- \mu\sin 2\alpha\cos 2\alpha&0&0\\ -\mu(\sin 2\alpha)^{2}&\mu(\sin 2\alpha)^{2}&0&\mu\sin 2\alpha\cos 2\alpha&0&0\\ 0&0&0&0&0&0\\ -\mu\sin 2\alpha\cos 2\alpha&\mu\sin 2\alpha\cos 2\alpha&0&\mu(\cos 2\alpha)^{2}&0&0\\ 0&0&0&0&0&0\\ \end{bmatrix}, \tag{15}\]  
Hence, the stiffness degradation model considering anisotropic damage can be written as follows:  
\[C_{ijkl}=C_{ijkl}^{0}-DC_{ijkl}^{\tau}. \tag{16}\]
## 5 Conclusions  
Concrete pavement noises are collected by an on-board sound intensity system (OBSI) and statistical analysis results show that there are different correlations between pavement surface texture characteristics and the noise value.  
The roughness of concrete pavement has a significant influence on the noise level. The corresponding noise level increases with the increase in the international roughness index (IRI).  
The macrotexture of concrete pavement is closely related to the noise level. The noise level of transverse groove concrete pavement is lower than that of longitudinal groove concrete pavement.  
The microtexture of concrete pavement has no significant influence on the tire/pavement noise level. In other words, pavement noise can be reduced by choosing superior pavement texture while ensuring vehicle safety.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Sample Mean** & **Standard Deviation** & **N** \\ \hline SFC & 57.8 & 7.30 & 92 \\ OBSI/dB(A) & 100 & 2.27 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical description of side-way force coefficient (SFC) and OBSI.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Statistical Test Parameters** & **SFC** & **OBSI/dB(A)** \\ \hline \multirow{4}{*}{SFC} & Pearson correlation coefficient & 1 & 0.136 \\  & Significant (bilateral) & & 0.196 \\  & Sum of squared & 4847.652 & 204.803 \\  & Covariance & 53.271 & 2.251 \\  & N & 92 & 92 \\ \hline \multirow{4}{*}{OBSI/dB(A)} & Pearson correlation coefficient & 0.136 & 1 \\  & Significant (bilateral) & 0.196 & \\ \cline{1-1}  & Sum of squared & 204.803 & 466.972 \\ \cline{1-1}  & Covariance & 2.251 & 5.132 \\ \cline{1-1}  & N & 92 & 92 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Correlation analysis between SFC and noise.  
**Author Contributions:** Conceptualization, D.W. and B.L.; Methodology, Z.Z.; Validation, F.H.; Formal analysis and investigation, X.Z. and M.Z.; Resources and Data Curation, L.L.; Writing--Original Draft Preparation and Writing--Review and Editing, D.W. and B.L.; Supervision, Q.W.  
**Funding:** The research work reported in this paper was supported by the National Natural Science Foundation of China (Grant No. 51408287), Gansu DOT Research Project (2011-08), the Program for Changjiang Scholars and Innovative Research Team in University (IRT1139), the Young Scholars Science Foundation of Lanzhou Jiaotong University (2011031), and the Science and Technology Support Foundation of Lanzhou Jiaotong University (ZC2012004).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Bridge Displacements Monitoring Method Based on Pixel Sequence
## 4 Experimental Results and Analysis
### Structure Displacement Extraction  
As mentioned earlier, the edge image obtained by the Gaussian difference (DoG) method represents the relative position of the structure in the image. Since the structural deformation characteristics are also relative displacements, there is no need for correction of the structure information image extracted by the Gaussian difference method during the processing.  
Through eroding and dilating, the gaps and spikes in the image are removed. Subsequently, it scans the pixels within the target area, processing them halfway down the vertical pixel column of the image. This scan is conducted from top to bottom, deleting the first encountered white pixel along with its adjacent white pixels. Next, it performs a second scan in the same pixel column, retaining the first detected white pixel and its adjacent white pixels. This process yields the edge contour line of the lower edge of the structure.  
Achieving successful extraction of structural geometric edge features with the proposed method necessitates selecting the same baseline for each condition at identical scales, thereby ensuring that the edge extraction results from the Gaussian difference method, where the edge occupies more than one pixel, do not affect the results. Consider using an algorithm to peel off the target pixels. The specific peeling method entails preserving the first pixel in each column from top to bottom. Since dilation and erosion operations have been performed on the preliminary image, the resulting image in the peeling algorithm is continuous. This acts as the reference point for each condition, as depicted in Figure 20:  
Figure 19: The result of the DoG operator.  
Figure 20: Pixel extraction result.
## 2 Fundamentals of SAR for Antenna Sizing and Transmitting Power Consideration
### Bandwidth  
Radar frequencies are designated by the International Telecommunication Union (ITU). However, bandwidth selection for the frequencies used is another critical parameter in SAR systems that affects the achievable resolution and imaging capabilities. The bandwidth can vary based on mission objectives and available technology. C-band SAR, such as in Sentinel-1, uses a moderate bandwidth of 25 MHz well-suited for a wide range of applications, including monitoring land and sea [14; 15; 16; 17]. X-band SAR, like TerraSAR-X, uses a wider bandwidth of 150 MHz to achieve high-resolution imaging, making it suitable for detailed observations of the Earth's surface [6; 7]. RADARSAT-2's wider bandwidth (300 MHz) enables it to provide high-resolution imaging and versatile polarimetric modes [10; 11]. COSMO-SkyMed uses a wide bandwidth (350 MHz) to offer high-resolution imagery and versatility for various applications, including defense and security [9]. L-band SAR, like ALOS-2, uses a similar bandwidth (350 MHz) to achieve moderate- to high-resolution imaging, suitable for vegetation monitoring and disaster management [18; 19]. ENVISAT's bandwidth (50 MHz) is chosen to balance resolution and versatility for a variety of Earth observation applications. RADARSAT-1 uses a moderate bandwidth (25 MHz) for a variety of Earth observation applications, including agriculture and forestry [6; 11]. Bandwidth selection depends on a combination of factors, including the desired resolution, coverage area, and the mission's specific objectives. Wider bandwidths generally allow for higher resolution but may require more complex technology and come with trade-offs in terms of system complexity and data management. Narrower bandwidths are often chosen for applications that prioritize wide area coverage and lower-resolution monitoring.