esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Proposed IoT Sensor's Outlier Detection Model  
The sensor malfunction diagnosis for IoTs has unique challenges, multi-agent distributed deep reinforcement learning-based sensor outlier detection is a machine learning technique that can be utilised for IoTs sensor malfunction diagnosis. Within the IoT module, the heterogeneous sensors will be connected to the Outlier Detection Module (ODM) and the microcontroller. The data produced by the sensors are sent to both ODM and microcontroller. The ODM is equipped with a PICAXE-08M2 microcontroller and the sensor outputs are connected to its IO ports.  
The microcontroller will be monitoring the data from the sensors while executing the multi-agent deep reinforcement learning-based algorithm. The schematic of ODM module and IoTs modules main processing unit is shown below in Figure 6. The ODM ensemble, the heterogeneous sensors connected within the individual IoT module and the dynamics of each pair of heterogeneous sensors was modelled.  
The multi-agent deep reinforcement learning uses multi-agents in learning and transfers learning between the agents [50; 51]. Consider a set of heterogeneous sensors connected to a time-invariant IoT system. Even though the sensors within the IoT systems are diverse (in operation characteristics), all the responses from the sensors depend on the common physical system and a linear relationship exists between the physical parameters measured by these sensors. This relationship is utilised to evaluate the \"optimal fitness\" of the sensor measurements. In this method the agents (allocated for each sensor) share information to the dependent agents, to analyse the patterns.  
Each individual agent within the ODM trains itself from the inputs, which are the outputs shared by other agents to check whether their outputs are consistent and have a linear relationship, as shown in Figure 7.  
When the agents notice any inconsistencies in the linear relationships between the sensors (within individual IoT module), the ODM will identify the sensor outlier, fault or error. The pseudo-code of the implemented algorithm is shown below in Algorithm 1.  
Figure 6: Outlier detection model for IoT sensors.  
Figure 7: Learning process structure of an agent.  
```
Inputs: Sensor 1 data, Dependent sensor data (Sensor 2, Sensor 3,..., Sensor \(n\)) Result: Sensor anomalies detection in an IoT module  Initialise the agent memory based on the previous patterns; whileTRUEdo  analyse the current and memory patterns;  Check the main pattern and individual patterns of fellow sensors; ifdetects anomaliesthen  check which sensor/s affected;  check for noise/sensor failure/type of errors;  share the anomaly information with other agents to get informed about the failure/s; else  update and initialise the agent memory;  end if  fellow agents verify the shared anomalies to confirm the error report;  send alert for corresponding sensor to repair/replace;  update the agent memory;  end while
```  
**Algorithm 1**Multi-agent distributed deep reinforcement learning-based outlier detection.
# The Impact of Gravity on Different Height Systems: A Case Study on Mt. Medvednica
###### Abstract  
This study examines the influence of gravity on different height systems by integrating Global Navigation Satellite Systems (GNSS), leveling, and gravimetric measurements. Although the theoretical influence of gravity on height systems is well known, empirical studies that quantify these effects along steep terrain are rare--particularly within the Croatian reference systems. Geometric leveling, recognized for its precision in geodesy, was employed alongside gravimetric data to analyze the relationship between gravity variations and height differences. The research was conducted along Slieme Road on Mt. Medvednica, Croatia, where altitude-dependent gravity effects were systematically investigated along an elevation profile with a height difference of about 650 m. GNSS measurements provided positional coordinates referenced to the Croatian Terrestrial Reference System 1996 (HTRS96) (EPSG:4888), while leveling and gravimetric data were analyzed within the Croatian Height Reference System 1971 (HVRS71) (EPSG:5610) and Croatian Gravimetric Reference System 2003 (HGRS03), respectively. The results demonstrate that differences between geometric and normal-orthometric heights become more pronounced at higher elevations but remain at the millimeter level. Notably, the impact of gravity is evident in normal and orthometric heights, with differences from geometric heights reaching up to 3.7 cm at the highest points. Additionally, a comparison between normal and orthometric heights reveals that at the beginning of the leveling line, the difference is around 4 mm. However, as the elevation increases, this difference grows, reaching over 1 cm at the end of the leveling line. The study also confirms the theoretical correlation between the geoid-quasigeoid height difference and terrain elevation, with increasing differences observed at higher altitudes. To examine the consistency of different height determination methods, two approaches were applied: one based on adjustment within the geopotential system, and the other involving direct adjustment in the desired height system, with specific height corrections applied. The results confirmed that the height differences between the two methods were 0, to the tenth of a millimeter, indicating that both methods provided identical results. These findings contribute to a deeper understanding of geodetic height systems and the role of gravity in height determination.  
e 2025 15 5680 +
Footnote †: journal: Applied Sciences  
Article
## 3 Problem Definition and Network Model  
### Problem Definition  
We define the satellite routing problem as an UMCF problem; it involves a graph with undirected edges and capacities assigned to those edges, and each satellite has a maximum of four neighboring satellite nodes that communicate with each other through ISL. The number of neighboring nodes for each satellite depends on the number of visible nodes for that satellite in the current time step. Additionally, there is a set of commodities, each with a source node, a destination node, and a demand value. In order to describe the satellite routing problem as an UMCF problem, we treat the traffic forwarding requests made by each ground station as commodities, which include three attributes: source ground station, destination ground station, and traffic demand. The goal is to find a unique path for each commodity to route their traffic demand from the source node to the destination node, without exceeding the capacity limit on the link when passing through the path. This problem has been extensively studied in the field of network optimization.  
The UMCF at hand is known to be NP-hard, which means that finding optimal solutions for instances with a large number of nodes and commodities is extremely challenging. However, there are heuristics and approximation algorithms available that can still provide high-quality solutions for such instances. In this paper, we will focus on the GA.
# Helicopter Takeoff and Landing Point Location in Cities for Emergency Services
## 3 Data Processing Using ArcGIS and Methods  
### Data Preparation  
1. Terrain data  
We logged into the Geospatial Data Cloud website ([[http://www.gscloud.cn](http://www.gscloud.cn)]([http://www.gscloud.cn](http://www.gscloud.cn)), accessed on 22 January 2021) to obtain the elevation information of Nanjing City, and the data accuracy is 30 m. The latitude range is 31.14-32.37, the longitude range is 118.22-119.14, and the city covers an area of 6587 square kilometers.  
We logged into the Meteorological Data Network of China ([[http://data.cma.cn](http://data.cma.cn)]([http://data.cma.cn](http://data.cma.cn)), accessed on 22 January 2021) to obtain the geographic information of weather stations in Nanjing and surrounding cities. See Table 2. With all these weather stations, the whole area of Nanjing could be covered. Using the data from these stations, a whole picture of the weather situation in the city could be depicted.  
As severe winter weather prevails in the middle and lower reaches of the Yangtze River, in order to obtain better location results to meet the actual needs, the meteorological data in the winter afternoon with mist or intermittent showers, which is the worst-case scenario, are selected. The following is the meteorological data reported by Nanjing Weather Station for every 3 h on 24 January 2022, see Table 3 (9999 represents no observation data).  
\begin{table}
\begin{tabular}{c c c c} \hline
**Item** & **Core Parameter** & **Tech Requirement** & **Remarks** \\ \hline Site evenness & Slope/\({}^{\circ}\) & \(\leq\)5 & surface rock and hard soil \\ Area of temporary stop area & F/m & \(\leq\)0.5 & equivalent area for square \\ Visibility & v/m & \(\geq\)1600 & visual flight requirement \\  & H \(\geq\) 3D & \(\geq\)41 & theoretical value \\ Safe clearance of obstacles & H \(\leq\) 10 & \(\geq\)50 & actual operating value \\  & H \(>\) 10 & \(\geq\)80 & \\ \hline \end{tabular}
\end{table}
Table 1: Technical requirements for helicopter take-off and landing point.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Code** & **Name** & **Longitude/\({}^{\circ}\)** & **Latitude/\({}^{\circ}\)** & **Elevation/m** \\ \hline
58238 & Nanjing & 118.90 & 31.93 & 36.4 \\
58235 & Luhe & 118.85 & 32.37 & 12.3 \\
58237 & Pukou & 118.58 & 32.07 & 46.6 \\
58339 & Gaochun & 118.90 & 31.33 & 20.9 \\
58340 & Lishui & 119.03 & 31.65 & 27.0 \\
58242 & Yizhen & 119.17 & 32.30 & 23.5 \\
58344 & Jurong & 119.20 & 31.97 & 35.0 \\
58331 & Hexian & 118.37 & 31.73 & 23.5 \\
58336 & Ma’anshan & 118.57 & 31.70 & 81.2 \\ \hline \end{tabular}
\end{table}
Table 2: Weather station data.
## 3 Geometric Model, Computational Mesh, and Physical Parameter
### Model for the Jack-Up Platform-Seabed Foundation Dynamics
The governing equation for the dynamics of the jack-up platform is degraded from the Biot dynamic equation when the porosity of materials and the pore pressure are ignored. When simulating the dynamic response of the jack-up platform and its seabed foundation under the wave loads, the applied boundary conditions are as follows:  
(1) The displacement in the \(x\) direction on the left and right lateral sides of the seabed foundation is zero, and the displacement in the \(y\) direction on the front and back lateral sides of the seabed foundation is also zero:  
\[u_{x=0\text{m}}=u_{x=24\text{6m}}=0 \tag{3}\]  
\[u_{y=0\text{m}}=u_{y=100\text{m}}=0 \tag{4}\]  
Figure 4: Yield surface of the Mohr-Coulomb soil model in the principal stress space and \(\pi\) plane. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\rho\) (kg/m\({}^{3}\)) & \(C\) (kPa) & \(\varphi\) & \(E\) (MPa) & \(\
u\) & \(k\) (m/s) & \(n\) & \(\text{d}_{50}\) (mm) & \(S_{\pi}\) \\ \hline
2000 & 40 & 36.5 & 120 & 0.3 & 0.0001 & 0.3893 & 0.44 & 1.0 \\ \hline \hline \end{tabular} Note: \(\rho\) is soil density, \(C\) is cohesion, \(\varphi\) is internal friction angle, \(E\) is elastic modulus, \(\
u\) is [PERSON]’s ratio, \(k\) is soil permeability, \(n\) is porosity, \(d_{50}\) is the mean diameter of soil particles, \(S_{\pi}\) is saturation.  
\end{table} 
Table 3: Physical property parameters of seabed foundation soil. (2) The bottom of the seabed foundation is fixed with displacements in the \(x,y\) and \(z\) directions, and the bottom of the seabed is an impervious boundary. \[u_{z=0\text{m}}=v_{z=0\text{m}}=w_{z=0\text{m}}=0\quad\frac{\partial p}{\partial z }=0 \tag{5}\]  
(3) Hydrodynamic boundary: at each time step, the wave impact (including hydrostatic pressure and hydrodynamic pressure) determined by the OlaFlow model is imposed to the outer surface of the jack-up platform and the surface of the seabed foundation. (4) A node-to-node tie contact is set on the contact surface between the four pile legs of the jack-up platform and its surrounding seabed soil. It means that the deformation of these pile legs and the seabed foundation is considered to be continuous at their contact surfaces.
# A Depth-Based Hybrid Approach for Safe Flight Corridor Generation in Memoryless Planning
## 6 Implementations and Results  
### Simulation  
We test our proposed navigation planning system, which we now call **FSD planner**--**Free **S**pace **D**etection planner for brevity, on a simulation system (Our simulation system source code is available online at [[https://github.com/thethaibinh/agile_flight/tree/fsd](https://github.com/thethaibinh/agile_flight/tree/fsd)]([https://github.com/thethaibinh/agile_flight/tree/fsd](https://github.com/thethaibinh/agile_flight/tree/fsd)), accessed on 1 June 2023) from DodgeDrone Challenge 2022 [38]. The challenge is to navigate a quadrotor through the obstacle-dense area to a 65 m far ahead goal. For the convenience of auto evaluation, we modify the scenario to be a quadrotor flying through a 15 m route to a goal with obstacles along the route. The flight area is a 3D bounding box with sizes of [0, 15, \(-\)5, 5, 0, 10] (meters) in a frame system with the axis \(Ox\) and \(Oy\) parallel to the ground and \(Oz\) pointing upwards, in which the robot is at the origin, and the goal is at [17, 0, 5]. Obstacles are solid spherical balls of different diameters ranging from 0.1 to 4.0 m, stationary, and randomly generated inside the bounding box. There are three levels of challenge based on the density of balls in space: Easy, Medium, and Hard, with 29, 51, and 67 balls in the test box, respectively. Figure 11 capture a typical scenario with additional visualization of point cloud ground truth. All obstacles of the easy scenario are already included in the medium scenario, and the hard scenario has all the medium obstacles in its configuration. A trial will be labeled successful when the robot reaches the goal's position before the timeout and does not collide with any obstacle.  
We train a separate model for use in the simulation by synthetic data also saved from the same simulator. We flew a couple of trials by the planner in our previous work [37] and captured synthetic depth images along the routes. We then labeled them and trained the model using the method mentioned in Section 4.4. The classical nested feedback loops [39] will be integrated as a low-level controller who receives position guidance commands in  
Figure 11: Scenarios in the auto evaluation.  
Figure 10: Illustration of the solution for finding \(p\) inside the bounding box with respect to \(g\).  
a receding horizon manner. We limit the maximum desired velocity to 1 m/s, mitigating control uncertainty because we only focus on SFC evaluation. Figure 11(a) describes an overview of the simulation system, mapping out the planner architecture sketched in Figure 1. All member components in the simulation system are software and will be run on the desktop environment. Figure 11(b),c visualizes predicted SFCs in the current depth frame and corresponding local point cloud.  
We will run the simulation flight 1000 times for each scenario on an Intel(r) CoreTMi7-11800H laptop equipped with an NVIDIA RTX3070 8 GB GPU.
# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction
### Model-Based Methods  
In [33], a hybrid model is established covering inverter and permanent magnet synchronous generators to diagnose inverter open circuit fault by analyzing the residual signal of the generator stator current through the model. In [34], the use of heat flux sensors to monitor the condition of a wind power converter, mainly for the failure and aging of electronic power devices, i.e., insulated-gate bipolar transistors (IGBTs) in the converter, where models are developed for the implementation of condition monitoring is proposed. In [35], a generic mathematical model of a two-level converter with open-switch faults is described. The impact of open-switch fault on the current control system of isotropic permanent-magnet synchronous generator (PMSG) was investigated.  
The method based on the analytical model has the advantages of being unaffected by the load, not requiring new hardware such as sensors and fast diagnosis, but its accuracy depends heavily on the accuracy of the system model and parameter values estimated.  
The wind turbine power converter is a multivariable system involving strong coupling and nonlinearity, and its physical parameters change dynamically in operation. Therefore, the fault detection and diagnosis method based on analytical models is limited in practical applications.
## Materials and Methods  
### Materials  
This research aimed to examine the influence of the addition of fine lightweight aggregates, as well as the partial replacement of cement with fly ash, on the characteristics of hardened foamed concrete. Several mixes with different compositions were cast and examined. Ordinary Portland cement (CEM I 52.5R), according to EN 197-1 and provided by Heidelberg Cement (Heidelberg, Germany), was used. Class F fly ash (FA) (Baumineral, Germany) was used to partially replace the cement, by 25 wt%. Table 1 presents the physical and chemical properties of the binder used, and Figure 1 presents the particle size distributions of the binder used. Two types of fine lightweight aggregate were used. Expanded perlite, with a loose bulk density of 80 kg/m\({}^{3}\), was crushed and particles with sizes between 0.1 and 0.3 mm were selected. The loose bulk density of the fine perlite was 120 kg/\({}^{3}\), though its specific gravity was 2.38. In addition, fine expanded glass (Liaver with size fraction 0.1-0.3 mm) had a particle density of 0.80 g/cm\({}^{3}\). The ratio of water/binder (w/b) was 0.40 for all mixtures, while the paste:foam ratio was fixed at 1:2. A foaming agent, produced by Sika Germany (Lightcrete 400), was used to produce the foam. A tailor-made polycarboxylate superplasticizer (Sika--Visocortex) without a de-foaming agent, which was compatible with the foaming agent, was also used to ensure a workable concrete slump flow diameter higher than 500 mm. The reason for selecting this consistency class lies in the fact that foamed concrete cannot be vibrated, due to the probability of destroying its foam bubbles. It should therefore have a high filling ability, in order to produce homogeneous samples without big voids. To enhance the stability and homogeneity of the mixture, a chemical stabilizer (a viscosity enhancing admixture, Sika ST3) was used. Six different mixes, with a density of 500 kg/m\({}^{3}\), were designed and prepared. The proportions of all the mixes are shown in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Material** & **CaO** & **SiO\({}_{2}\)** & **Al\({}_{2}\)O\({}_{3}\)** & **Fe\({}_{2}\)O\({}_{3}\)** & **MgO** & **Na\({}_{2}\)O** & **K\({}_{2}\)O** & **SO\({}_{3}\)** & **Density** & **Surface Area** (cm\({}^{2}\)/g)** \\ \hline CEM I 52.5R & 66.2 & 20.6 & 3.3 & 4.9 & 1.3 & 0.1 & 0.43 & 2.8 & 3.15 & 3860 \\ Fly ash & 4.8 & 47.9 & 21.0 & 4.6 & 1.4 & 0.7 & 1.1 & 0.8 & 2.27 & 2930 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Chemical and physical properties of the binder used.
# Error Source Analysis and Correction of GF-3 Polarimetric Data
## 2 Error Model of Polarization Observation  
For the GF-3 satellite, pulses are produced by the signal source, pass through the transmit channel, are then radiated by horizontal (H) and vertical (V) antenna array, and finally irradiate ground objects through the ionosphere. As the polarization transformer, ground objects change the polarization direction of the wave. Electromagnetic waves reflected by ground objects again pass through the ionosphere, are received by the H and V antenna array, pass through the H and V receive channels, and are recorded as raw echoes, which serve as the input of the PolSAR image product generator. To describe this complex process with error sources, a full-link error model for GF-3 PolSAR system is proposed in this study and is shown in Figure 1. Channel imbalances stand for the difference of V channel relative to the H channel, so the H channel is set as \"1\" in Figure 1.  
As shown in Figure 1, error contributions throughout the GF-3 PolSAR system include 5 parts: 1) channel imbalances (\(f_{T}\), \(f_{R}\)) introduced by transmit and receive channel; (2) sampling delay errors (\(t_{T}\), \(t_{R}\)) introduced by transmit and receive channel; (3) crosstalks (\(\delta_{i}\)) and channel imbalances (\(f_{t}\), \(f_{r}\)) caused by antenna array [6]; (4) polarization orientation angle (POA, \(\varphi\)) aroused by satellite attitude [7]; (5) Faraday rotation angle (FR, \(\Omega\)) introduced by ionosphere [15]. Combining the influence of all of the error sources, the relationship between measure scattering matrix (\(M\)) and real scattering matrix (\(S\)) is expressed as Equation (1):  
\[\left[\begin{array}{cc}M_{HH}&M_{HV}\\ M_{VH}&M_{VV}\end{array}\right] = Ae^{\rho\left[\begin{array}{cc}1&0\\ 0&f_{R}\end{array}\right]\left[\begin{array}{cc}1&\delta_{3}\\ \delta_{4}&f_{r}\end{array}\right]\left[\begin{array}{cc}\cos\varphi&\sin \varphi\\ -\sin\varphi&\cos\varphi\end{array}\right]\left[\begin{array}{cc}\cos\Omega& \sin\Omega\\ -\sin\Omega&\cos\Omega\end{array}\right] \tag{1}\] \[\times\left[\begin{array}{cc}S_{HH}(t,\tau)&S_{HV}(t-t_{T}, \tau)\\ S_{VH}(t-t_{R},\tau)&S_{VV}(t-t_{T}-t_{R},\tau)\end{array}\right]\] \[\times\left[\begin{array}{cc}\cos\Omega&\sin\Omega\\ -\sin\Omega&\cos\Omega\end{array}\right]\left[\begin{array}{cc}\cos\varphi& -\sin\varphi\\ \sin\varphi&\cos\varphi\end{array}\right]\left[\begin{array}{cc}1&\delta_{2 }\\ \delta_{1}&f_{t}\end{array}\right]\left[\begin{array}{cc}1&0\\ 0&f_{T}\end{array}\right]\]  
Figure 1: The entire propagation path of electromagnetic wave and the full-link error model for GF-3 PolSAR system.  
where \(A\) and \(\Theta\) are absolute amplitude and phase factors of system, \(M_{pq}\) is the measured signal for the polarization \(pq\), \(S_{pq}\) is the target scattering value for the polarization \(pq\), \(t\) and \(\tau\) mean the time axis of range and azimuth, respectively. Here, \(pq\) means different polarization combinations, where received by \(p\) polarization and transmitted by \(q\) polarization.
## 5 Regional Division and Analysis  
In this section we introduce the regional division of satellite groups according to the network topology in LEO satellite networks. We describe our regional division method, and then discuss how to reduce the computing overhead and network load.