esa-sceva/satcom-chunk-collection huggingface dataset:
# Enhancing Cloud Security--Proactive Threat Monitoring and Detection Using a SIEM-Based Approach
## 3 Materials and Methods  
This section outlines the approach taken to conduct our study and the resources used in the process. It provides insight into the experimental design, security event collection procedures, and employed techniques.  
The first step in the SIEM's functional process typically involves the data collection phase. At this stage, the SIEM system gathers data from various sources, such as logs, events, and other security-related information from across the network and information systems. These data are then aggregated and normalized to create a standardized dataset that can be further analyzed for security insights and alerts. Before the data collection phase, it is crucial to have a solid infrastructure in place. To obtain this infrastructure, we successfully deployed a virtual network (Vnet) within the Microsoft Azure public cloud environment. This virtual network establishes the foundation infrastructure to support the SIEM system.  
A virtual network in a cloud environment is an abstraction of a physical network. It is a logically isolated section of the cloud provider's network infrastructure and allows one to define their own IP address ranges, subnets, security groups, etc. [34]. It still exists physically in the sense that it relies on the underlying hardware and internet connections of the cloud service provider to transmit data and signals. Hence, in the subsequent sections, when we refer to a virtual network or virtual machines (VMs), we are referring to the way these resources are abstracted and provisioned, not that they do not physically exist. The implemented Vnet consisted of three network subnets, facilitating the deployment of diverse Azure cloud resources such as VMs, firewalls, and gateways. For illustrative purposes, this study employed Microsoft Azure as a public cloud environment and adopts the Azure Well-Architected Framework recommendations for deploying solutions on a cloud-based infrastructure.  
Through the subsequent subsections, we provide in-depth insights into the deployed cloud instances, highlighting their roles, functionalities, and contributions to the overall functionality and security of the implemented virtual network. We also provide a detailed account of how we integrated other solutions into this deployed infrastructure. Figure 1 depicts the overall deployment architectural model.
## 5 IoRT Systems Based on Space-Borne NTN Networks
### Enhanced Coverage and Availability
Simulation results demonstrate the algorithm's improvement in resource utilization and coverage performance. Additionally, the findings highlight the significance of service demand distribution in gateway placement decisions. A comprehensive modeling framework for IoT-over-satellite access systems is introduced in [93]. This framework considers a multitude of factors, including satellite orbit, uplink interference from terrestrial IoT devices, atmospheric conditions, gas absorption, and the probability of line-of-sight. The efficacy of the system is evaluated based on the uplink SINR and satellite link time availability during a pass. Focusing on satellites in low-earth orbit, the study employs actual orbital parameters ranging from 300 to 800 km. Additionally, a numerical model for optimizing antenna beamwidth is proposed to maximize link availability while minimizing terrestrial interference and enhancing the IoT signal. The authors of [94] introduce a Ka-band multibeam satellite industrial IoT framework to overcome the limitations of conventional ground-based IIoT, particularly its limited coverage range. By employing NOMA within each beam, they aim to optimize transmission rates for enhanced QoS. Resource allocation is maximized by optimizing beam power to align theoretical transmission rates with service demands. Moreover, the study proposes a satellite-ground integrated IIoT solution, utilizing terrestrial cellular networks to extend satellite coverage in obstructed areas while incorporating transmission cost reduction strategies. Simulation results validate the benefits of NOMA in satellite IIoT. Table 5 summarizes the research on enhancing the coverage and availability of IoRT systems based on space-based NTN networks. These advancements include the development of beam-hopping-based satellite systems for IoT, solar-powered satellite access points, algorithms for optimal gateway placement, modeling frameworks for maximizing link availability, and novel multibeam satellite architectures. These innovations have the potential to significantly expand the reach of IoRT applications to remote and underserved areas, paving the way for a more interconnected and globally accessible IoT ecosystem.
## 3 Learning Model
### Encoding the Geometry  
At present, many experts and scholars have conducted a lot of research on vehicle-situational-awareness coding. The environment that was designed for this experiment is two lanes, and there are two types of vehicles. We needed to redesign a vehicle-situational-awareness and coding scheme that is suitable for this experimental environment. First, as shown in Figure 2, we established a Cartesian coordinate system with the receiver's position as the origin. The unit of the distance is such that all objects can be accurately encoded. For the receiver, the closer a vehicle in the first lane is to the receiver, the more light the vehicle blocks, and the more susceptible is the result. Not only that, although some large trucks are not very close to the receiver, they will also greatly affect the beam of the receiver. Therefore, for the above phenomena, we propose the following encoding and sorting methods to improve the accuracy of the results.  
A feature (\(\mathbf{v}\)) of the vehicle can be defined as a vector, which could be described by the following formula:  
\[v=[\boldsymbol{r},\boldsymbol{t}_{1},\boldsymbol{t}_{2},c_{1},c_{2}], \tag{5}\]  
where \(\boldsymbol{t}_{1}\) and \(\boldsymbol{t}_{2}\) represent trucks in the first and second lanes, respectively; \(c_{1}\) and \(c_{2}\) are low-height cars in the first and second lanes, respectively; and \(\boldsymbol{r}\) is the vehicle's position coordinates. The serial number of the car in the first lane is generated in the following way. On the x-axis of the Cartesian coordinate system, the order of trucks along the x-axis is defined as \([x_{1},x_{2},\cdots x_{n}]\), and so \(\boldsymbol{t}_{1}\) is composed of all the trucks located in the first lane:  
\[\begin{array}{c}\boldsymbol{t}_{1}=\big{[}x_{i_{1}},y_{i_{1}},x_{i_{2}},y_{ i_{2}},\cdots x_{i_{N}},y_{i_{N}}\big{]},\\ \big{|}x_{i_{1}}|<\big{|}x_{i_{2}}|<\cdots<\big{|}x_{i_{N}}\big{|},\end{array} \tag{6}\]  
where \(N\) is the maximum value of one type of vehicle in each lane. This maximum value was limited by us in order to keep the dimensionality of the feature unchanged in different scenarios. Provided that the number of trucks in the actual environment is \(n>N\), when encoding, we will remove the redundant trucks with long distances to the feature point. In the same way, if the number of trucks in the actual environment is \(n<N\), we will add some virtual trucks to the spare places. These spare places are generally far from the feature point. In this way, the vehicles in all lanes will be accurately encoded.
###### Abstract
Multiple wind direction simulations on one urban block or building are well studied in the literature; however, to our knowledge, there are no published papers with the goal of presenting the results synthesized from multiple simulations that account for conditions throughout the year. The method described in our paper addresses this issue. Assessment of air quality should address all possible wind directions and every velocity for every day throughout the year. This could be interpreted as a task requiring hundreds of simulations for each variation. Because this is a time-consuming task, some simplification is required regarding the number of analyses. To overcome the issues related to the previously stated problems, we propose a novel method that can identify areas with the most stagnant air in an urban block environment directly on the 3D mesh. This method presents the results in a form where stagnant air areas are highlighted from data that represent the wind conditions from most of the year. The paper is structured as follows: Section 2 presents related work and the literature review on the CFD simulations used in the context of urban airflow. In Section 3, the overview of the framework for the proposed method is outlined. In the same section, the procedures of the method are described in detail. Section 4 presents the results of the method described in this paper in respect of 8 blocks with different urban morphologies. The discussion of the results is included in the same section. Section 5 describes the main findings, suggestions for further research, and some limitations of the study.
## 3 Visible-Light and Thermal Infrared Confidence Measures
### Visible-Light Confidence Measures  
The average or mean grey level value of visible-light image \(I_{V}\) is the basis for establishing the confidence intervals in the visible-light spectrum. In order to improve the invariability from the camera settings, the color image \(I_{V}\) is transformed into a grey level image \(I_{G}\).  
Let us firstly consider the left confidence interval at the top of Figure 1. A low average of \(I_{G}\) means that the image is captured under poor lighting conditions, making any object (including humans) hard to distinguish from the rest of the scene, just as shown in Figure 2a. The thermal infrared equivalent of this frame is shown in Figure 3b. On the other hand, consider the right confidence interval at the top of Figure 1. A very high grey level mean value denotes that it is snowing or the environment is under fog conditions (as depicted in Figure 2c). Regardless, we can be in a situation where a lighting source directly pointing toward the camera is blinding it. Therefore, the visible camera is unable to distinguish anything in the scene. Lastly, an intermediate grey level mean indicates that the lighting conditions in the scene are adequate and that humans are easily distinguished. This is depicted as the central confidence interval at the top of Figure 1. An example of this situation is shown in Figure 2b.  
Figure 2: Different confidence values for the visible spectrum. (**a**) Visible-light image with a low confidence value in night conditions; (**b**) visible-light image with a high confidence value; (**c**) visible-light image with a low confidence value in fog conditions.  
Equation (1) shows how the reliability is established in the visible spectrum (denoted as \(C_{V}\)), with thresholds \(\zeta_{VL}\) and \(\zeta_{VH}\) fixed experimentally, since the grey level values of the elements in the scene determine the conditions where the visual spectrum is trustworthy and also where it is not reliable.  
\[C_{V}=\left\{\begin{array}{ll}\text{HIGH},&\text{if }\zeta_{VL}<\overline{T_{G}}< \zeta_{VH}\\ \\ \text{LOW},&\text{otherwise}\end{array}\right. \tag{1}\]
# Marine Internet for Internetworking in Oceans: A Tutorial
## 2 Overview of Marine Internet
### Typical Applications
#### 2.2.4. Fundamental for Smart Ocean  
A smart ocean environment consists of physical environment, marine sensors and infrastructure for data management as well as data transport systems [24]. Collecting and delivering data effectively and timely is crucial to provide information for problem solving, decision making and forecasting as well as to support offshore engineering and design [25]. Smart ocean aims to sense and understand the ocean based on marine information collection and transmission systems that integrate devices and activities for sharing marine resources (e.g., marine renewable energy) and coordination of marine activities (e.g., environment monitoring). To this end, many data need to be collected, which are often multi-dimensional, multi-granularity and across time and space. Such massive data need to be processed, integrated and shared effectively, which requires the support of marine Internet. Similarly, various instruments distributed in oceans need effective remote control, and different parts involved in a cooperation need high-speed communication. All these practices are inseparable from effective data transmission supported by marine Internet.
# Fire Detection and Geo-Localization Using UAV's Aerial Images and Yolo-Based Models
## 6 Geo-Localization
### Calibration Process
\begin{table}
\begin{tabular}{c c c c c} \hline
**Camera** & **Focal Length** & **Principal Point** & **Intrinsic Matrix** & **Radial Distortion** \\ \hline \multirow{2}{*}{Camera parameters 1} & \multirow{2}{*}{[932.1819, 929.1559]} & \multirow{2}{*}{[338.5335, 246.8962]} & \multirow{2}{*}{[923.1819 & 0 & 0] \\  & & & 0 & 29.1559 & 0 \\  & & & [338.5335 & 246.8962 & 1] \\ \hline \multirow{2}{*}{Camera parameters 2} & \multirow{2}{*}{[1.1652 \(\times\) 10\({}^{3}\), 1.1760 \(\times\) 10\({}^{3}\)]} & \multirow{2}{*}{[355.8581, 330.2815]} & \multirow{2}{*}{[1.1652 \(\times\) 10\({}^{3}\), 0 & 0] \\  & & & 0 & 1.1760 \(\times\) 10\({}^{3}\) & 0 \\  & & & 335.8881 & 330.2815 & 1 \\ \hline \end{tabular}
\end{table} 
Table 2: Camera parameters. Figure 5: Calibration process. The perspective either centered on the camera or the pattern was designated as camera centric or pattern centric, respectively. This input choice governed the presentation of the camera extrinsic parameters. In our scenario, the calibration pattern was in motion while the camera remained stationary. This perspective offered insights into the inter-camera separation distance, the relative positions of the cameras, and the distance between the camera and the calibration images. This distance information contributed to assessing the accuracy of our calibration procedure. The re-projection errors served as a qualitative indicator of the accuracy. Such errors represent the disparity between a pattern's key point (corner points) detected in a calibration image and the corresponding world point projected onto the same image. The calibration application presented an informative display of the average re-projection error within each calibration image. When the overall mean re-projection error surpassed an acceptable threshold, a crucial measure for mitigating this was to exclude the images exhibiting the highest error and then proceed with the recalibration. Re-projection errors are influenced by camera resolution and lenses. Notably, a higher resolution combined with wider lenses can lead to increased errors, and conversely, narrower lenses with lower resolution can help minimize them. Typically, a mean re-projection error of less than one pixel is deemed satisfactory. Figure 6 below illustrates both the mean re-projection error per image in pixels and the overall mean error of the selected images.
# The Impact of Eclipsing GNSS Satellites on the Precise Point Positioning
## Materials and Methods
### The Multi-GNSS Precise Point Positioning and Its Strategy  
The traditional PPP model adopts Ionosphere-Free (IF) combinations to eliminate the first-order ionospheric delays, which amounts to ignoring the spatial and temporal characteristics of the ionospheric delays. The corresponding observations of dual-frequency IF combinations are presented as follows:  
\[P^{sys,s}_{r,if}=\mathbf{u}^{sys,s}_{r}\Delta\mathbf{x}_{r}+m^{sys,s}_{r}Z_{r}+dt^{sys }_{r}-dt^{sys,s}+G^{sys,s}_{r,if}+b^{sys,s}_{r,if}-b^{sys,s}_{,if}+\varepsilon ^{sys,s}_{P,rf}, \tag{4}\]  
\[L^{sys,s}_{r,if}=\mathbf{u}^{sys,s}_{r}\Delta\mathbf{x}_{r}+m^{sys,s}_{r}Z_{r}+dt^{sys }_{r}-dt^{sys,s}+G^{sys,s}_{r,if}+W^{sys,s}_{r,if}+\lambda^{sys,s}_{if}N^{sys, s}_{r,if}+B^{sys,s}_{r,if}-B^{sys,s}_{if}+\varepsilon^{sys,s}_{Ly,if}, \tag{5}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Constellation** & \(\mathbf{i}\) & \(\mathbf{T}\) & \(\dot{\mathbf{\mu}}\) & **Attitude** \\ \hline GPS & \(55^{\circ}\) & 11 h 58 min & 0.00836\({}^{\circ}\)/s & YS \\ GLO & \(65^{\circ}\) & 11 h 16 min & 0.00888\({}^{\circ}\)/s & YS \\ GAL & \(56^{\circ}\) & 14 h 05 min & 0.00710\({}^{\circ}\)/s & YS \\ BDS-GEO & \(\approx\)\(0^{\circ}\) & 23 h 56 min & 0.00418\({}^{\circ}\)/s & ON \\ BDS-IGSO & \(55^{\circ}\) & 23 h 56 min & 0.00418\({}^{\circ}\)/s & YS/ON \\ BDS-MEO & \(55^{\circ}\) & 12 h 53 min & 0.00776\({}^{\circ}\)/s & YS/ON \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GNSS orbits and attitude mode (_i_: orbital inclination, _T_: orbital period, _j_: average orbital angular velocity, YS: yaw-steering, ON: orbit normal). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Constellation** & **Noon Maneuver** & **Shadow Crossing** & **Post-Shadow Maneuver** \\ \hline GPS-II/IIA & Yes 1, \(|\beta|\leq 2.39^{\circ}\)-\(5.69^{\circ}\)2 & Yes, \(|\beta|\leq 13.5^{\circ}\) & Yes \\ GPS-IIR & Yes, \(|\beta|\leq 2.39^{\circ}\) & Yes, \(|\beta|\leq 2.39^{\circ}\)3 & No \\ GPS-IIF & Yes, \(|\beta|\leq 4.34^{\circ}\) & Yes, \(|\beta|\leq 13.5^{\circ}\) & No \\ GLO-M & Yes, \(|\beta|\leq 2.03\) & Yes, \(|\beta|\leq 14.2^{\circ}\) & No \\ GAL-IOV & Yes, \(|\beta|\leq 2.0^{\circ}\) & Yes, \(|\beta|\leq 2.0^{\circ}\) & No \\ GAL-FOC & Yes, \(|\beta|\leq 4.1^{\circ}\) & Yes, \(|\beta|\leq 4.1^{\circ}\) & No \\ BDS-GEO & ON 4 & ON & ON \\ BDS-IGSO/MEO & ON, \(|\beta|\leq 4.0^{\circ}\) & ON, \(|\beta|\leq 4.0^{\circ}\) & ON, \(|\beta|\leq 4.0^{\circ}\) \\ \hline \hline \end{tabular}  
* Yes means that this type of satellites will experience the specified maneuver while No is the opposite. 2 The angle sun limits for GPS II/IIA satellites are computed according to the averaged JPL reprocessing yaw-rate solutions. 3 The shadow crossing for GPS IIR satellites is equivalent to the midnight maneuver. 4 The BDS GEO satellites always use the orbit normal mode.  
\end{table} 
Table 2: The average threshold value for different periods of GNSS attitude mode.
## 2 Numerical Details and Layouts for Analysis
### Grid Topology to Simulate  
Due to existence of sharp edges and offset fins, triangular, boundary layers and tetrahedral meshes are employed to obtain a suitable mesh model to achieve flow field and heat transfer characteristics of the proposed heatsink. A zoomed-in view of the mesh in the fluid is shown in Figure 5. As it can be seen in Figure 5, close to the solid fin and in the fluid, the boundary layer mesh is used. This mesh is used when an intense gradient exists in the model. In this model, in the studied flow, there is a relatively intense velocity gradient that quickly ended at \(U=0\) at the boundary.  
By moving away from the fluid and solid interface, anisotropic prismatic elements in the shape of hexahedral are used for simulating the fluid and the solid. As a result, a mesh which is finer in the water domain than the solid domain is created. Also, all sharp angles are identified and meshed by finer elements (Figure 5).  
Table 4 lists the different geometric parameters (which are used in Comsol multiphysics) of the mesh configuration. This table shows that the finest and least fine mesh belongs to wetted walls and IGBT module, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sign** & **Description** & **Value** \\ \hline \(H\) & Heatsink height & 10 mm \\ \(H_{c}\) & Channel height & 6 mm \\ \(H_{t}\) & Rib height & 4 mm \\ \(W\) & Heatsink width & 36 mm \\ \(L\) & Heatsink length & 60 mm \\ \(W_{f}\) & Fin width & 0.86 mm \\ \(W_{c}\) & Channel width & 1.14 mm \\ \(L_{f}\) & Fin length & 10 mm \\ \(A_{1}\) & Curve angle & 40\({}^{o}\) \\ \(A_{2}\) & Curve angle & 100\({}^{o}\) \\ \(A_{3}\) & Curve angle & 40\({}^{o}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Dimensions of the heatsink.  
Figure 5: Mesh configuration to simulate the flow in the heatsink (**a**) Adjacent to boundary layers, (**b**) In sharp edges.
## 3 Experiments and Results
### Performance Analysis  
The device classification model trained by current shapelets is assessed to evaluate the efficacy of the proposed framework. There are five test scenarios in this analysis. Classification accuracies are calculated for the applied learning algorithms considering variations in length of shapelets \(m\), beginning point of subsequences \(p\) and peak value of sequence index \(i_{max}\). The test scenarios are explained in the following.
# Improving GNSS PPP Convergence: The Case of Atmospheric-Constrained, Multi-GNSS PPP-AR
## 2 Datasets Used and Data Processing Details  
To investigate the impact of atmospheric parameter constraints in both dual- and triple-frequency multi-GNSS PPP, 40 globally distributed MGEX GNSS receiver stations were arbitrarily selected for PPP processing. Figure 1 shows the global distribution of stations.  
Observations from these stations were processed from DOY 253 to 259 in 2018. The stations were randomly selected based on homogeneity of receiver and antenna types to limit the effects of equipment hardware delays impacting the results. Javad (San Jose, CA, USA) and Trimble (Sunnyvale, CA, USA) receiver-antenna combinations were considered.  
Shown in Table 1 are the processing parameters and settings used in the generation of results discussed in subsequent sections. The YorkU GNSS PPP engine, developed by the GNSS research team in York University (Toronto, Canada), was employed in processing the observations [30,31]. GBM precise orbits and clocks from GeoForschungsZentrum (GFZ, Potsdam, Germany) were used because of the availability of orbits and clocks for all available GNSS constellations.
# Traffic Incident Detection Based on Dynamic Graph Embedding in Vehicular Edge Computing
## 3 Traffic Incident Detection Based on Dynamic Graph Embedding
### Dynamic Graph Construction  
Given two time series \(x\) and \(y\), we can calculate their Pearson correlation coefficient [28], which is formulated as \(R(x,y)\cdot|R(x,y)|=0\) indicates that there is no correlation between them. For each time interval \(t\in[0,T]\), the correlation coefficient matrix of the dynamic graph \(G_{t}\) is formulated as \(\mathcal{R}=\{R(G_{t})|t\in[0,T]\}\). The dynamic graph can be constructed by utilizing the correlations.  
To measure the information of graphs, we calculated the graph entropy based on the information entropy [29]. Given an event \(x\), the information of the event can be formulated as \(h(x)=-log_{2}p(x)\) where \(p(x)\) indicates the probability of the event \(x\). The information entropy is described as the probability of the event times the information of the event, which is denoted as \(E(x)=-p(x)log_{2}p(x)\). For the multiple independent events \(x_{i}\in X\), the information entropy is formulated as \(E(X)=-\sum_{i=1}^{N}p(x_{i})log_{2}p(x_{i})\) where \(X\) indicates the set of the events \(x_{i}\) and \(N\) indicates the number of the events. To calculate the graph entropy, we first calculate the entropy of the vertex in the graph, which is calculated by using the weight of the edge.  
**Definition 1** (Vertex entropy).: _Given a graph \(G=(V,E)\) where \(V\) indicates the set of the vertices and \(E\) indicates the set of edges. The entropy of the vertex \(v_{i}\) is calculated based on the correlation between the vertices \(v_{i}\) and \(v_{j}\), which is formulated as \(e(v_{i})=-\sum_{j=0,j\
eq i}^{N}R(v_{i},v_{j})log_{2}R(v_{i},v_{j})\) where \(N\) indicates the number of vertices._  
Then, we have to measure the information of the graph based on the vertex entropy. The vertex entropy measures the information of edges in the graphs. Supposing the vertex entropy \(e(v_{i})\) is high, the vertex \(v_{i}\) has a high correlation with the other vertices. It indicates that the corresponding time series plays an essential role in anomaly detection. To measure the graph entropy, we have to summarize the entropy of each vertex. The definition of graph entropy is as follows.  
**Definition 2** (Graph entropy).: _The graph entropy is calculated by summing the entropy of all vertices, which is formulated as \(e(G)=\sum_{i=0}^{N}e(v_{i})\). The dynamic graph entropy is described as the graph entropy \(e(G)\) at the time interval \(t\in[0,T]\), which is formulated as \(\mathcal{E}=\{e(G_{t})|t\in[0,T]\}\)._