esa-sceva/satcom-chunk-collection huggingface dataset:
# Overview of the Research Status of Intelligent Water Conservancy Technology System
## 4 Application Status
### Water Conservancy Project  
The intelligent application of hydraulic engineering covers intelligent monitoring, intelligent inspection and digital modeling. The intelligent monitoring method of hydraulic engineering based on digital twins [89], through the construction of the digital twin model of hydraulic engineering construction for simulation and analysis, determines the accurate emergency plan on the basis of the minimum emergency risk, and its advantage lies in the realization of the automatic monitoring and emergency response of the intelligent monitoring system for hydraulic engineering safety. The digital twin hydraulic engineering safety monitoring system [90] realizes monitoring data acquisition and feedback by connecting virtual entities, improves real-time monitoring, early warning and decision support capabilities, realizes real-time monitoring, analysis and prediction of hydraulic engineering operations, provides accurate decisions, and supports intelligent operation and reliable maintenance. In terms of intelligent inspection, BIM and knowledge map-based methods have innovatively solved past inspection problems, established an intelligent perspective inspection BIM coding model and knowledge map, and improved the level of hidden danger detection, monitoring and early warning [91]. As for digital modeling, there are some problems in the application, such as complex data preparation steps and model processing. One study [92] proposes to use photographic equipment to take 360\({}^{\circ}\) rotating photos of the reservoir site, modify and design the acquired data through 3D model tools, design model attributes and events combined with digital twin construction tools, and finally build an intelligent water conservancy digital twin model, which simplifies the steps of data preparation and improves the accuracy of the reservoir model. It also supports simple processing and virtual combination of models. In general, the application of digital twin technology provides important support for the intelligent development of water conservancy projects. Through the integration of GIS data, the two-way linkage between the system interface and digital display scene is realized, which promotes intelligent development [93].
# Multivariate Relationship in Big Data Collection of Ocean Observing System
## 2 Materials and Method
### The Two-Steps Quality Check Routine  
As Emodnet is the result of a large data collection task, the presence of incorrect, noisy, or unreliable samples cannot be excluded. Indeed, the application of the model to the original dataset produced some outlier outputs, for which our model was drastically deficient. The number of these anomalous values was significantly smaller than the number of good-quality predictions (about 4.6%); however, it caused a significant rise in the total error among the test set. The analysis of this preliminary study (not shown) suggested that the model did not fail these predictions because of some intrinsic problems in the training, but because of the presence of the aforementioned anomalous data contained in the _EMODnet_ dataset. Even if the number of outlines was small, the possibility that they introduce a bias on the prediction's capability of the model cannot be excluded (e.g., learning information originating from anomalous measurements could potentially have drastically incised the relations inferred from all the valid measurements).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \(\mathbf{NO_{3}^{-}}\) & \(\mathbf{PO_{4}^{3-}}\) & \(\mathbf{Si(OH)_{4}}\) & \(\mathbf{A_{T}}\) & \(\mathbf{Chl-a}\) & \(\mathbf{NH_{4}}^{+}\) \\ \hline Epochs & 50.000 & 50.000 & 50.000 & 50.000 & 25.000 & 50.000 \\ \hline lr & 0.005 & 0.005 & 0.005 & 0.001 & 0.005 & 0.005 \\ \hline \end{tabular}
\end{table}
Table 1: Epoch and learning rate used for the training phase.  
To overcome the potential issue represented by anomalous data, we propose a two-step quality check routine for the removal of incorrect data, which is summarized through the flowchart in Figure 1. First, the model is trained on the whole dataset. Thereafter, the subsets with anomalous data are identified and removed, both from the training and testing set by looking at values that fell inside the 0.1% of the highest error prediction and checking whether they belong to any specific and circumscribable subset (e.g., same sampling cruise, date or provider). The last criterion was applied only if at least the 25% of the samples of the subset were classified as outliers. Subsequently, the model is trained among the cleaned dataset and the process is repeated until no more predictions are cataloged as unreliable.  
The final dimensions of the training datasets are reported in Table 2.
# The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey
## 5 Fog Computing Security Challenges
### Device Level Security Challenges
#### 5.3.1 Confidentiality
One approach to preserve privacy is to create fake tasks by fog client and send them to other nodes; in this way, the real tasks are hiddenbehind fake ones. However, this solution is inefficient in terms of cost and energy consumption. Therefore, an effective approach is to design a solution that divides the application in a way that ensures the usage of distributed resources minimizes the disclosing private information [98]. * **Location privacy:** The term location privacy denotes the privacy of the location of fog clients in fog computing. When a fog client divests the task to fog nodes, it assumes that those nodes are located nearby, and other nodes are distant, though this is not always the case. Moreover, the fog client may use several fog services at different locations, there are fair chances that the path trajectory may be disclosed to fog nodes. The location privacy is at risk as long as the fog client is attached to a person or device [98]. One way to hide the location of the fog clients is to obfuscate the fog client identity, so that even the fog node knows that client is nearby and still unable to locate it. [PERSON] et al. [144] proposed several techniques to obfuscate the identity, one of which is to employ a trustworthy third party which may generate false identities for each fog client. In real scenarios, it is not necessary for a fog client to choose the nearest fog node, but even if it does so, the client may undergo some criteria such as reputation, latency or load balance to reach that node which may utilize more resources than usual. This can lead the node to have an idea of clients' location but not in a precise manner. [PERSON] et al. [145] proposed a method to preserve the privacy of client's location in such situations.
## 2 Materials and Methods
### Design of the Proposed Capacitive Sensors
#### 2.4.1 Sensor Design MT  
This sensor is made up of two concentric plates, where one of the plates is circular with radius \(a\) and the other plate has the shape of a circular crown with inner and outer radius \(b\) and \(c\), respectively. Figure 2 shows the proposed diagram for the MT sensor, accompanied by the distribution of electrical charges on each of the plates and electric field lines, in order to establish the conditions and key elements for estimating the capacitance value.  
For the process to estimate the capacitance value for the MT sensor model, it begins with Equation (1), which corresponds to Gauss' Law [23], which defines the mathematical expression that allows calculating the electric field (\(E\)), by defining a Gaussian surface radius (\(r\)) containing the total charge of the capacitor (\(q\)) uniformly distributed over the surface area (\(A\)) and electrical permittivity \(\varepsilon(\varepsilon=\varepsilon_{o}\varepsilon_{r})\).  
\[EA=\frac{q}{\varepsilon}=\frac{q}{\varepsilon_{o}\varepsilon_{r}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Plastic Type** & **Electrical Permittivity (\(\varepsilon_{r}\))** \\ \hline Polyethylene & 2.3 \\ Polystyrene & 2.6 \\ Polypropylene & 2.2 to 2.6 \\ PVC & 2.9 \\ Polyethylene Terephthalate PET & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electrical permittivity values for some types of plastic.  
Figure 2: Diagram of the MT sensor, illustrating the concentric plates and electric field lines. The dielectric distance (\(d\)) and charge distribution are fundamental parameters for calculating the capacitance.  
where  
* \(\varepsilon_{o}\) : Electrical permittivity in a vacuum (\(8.85\times 10^{-12}\)[ C\({}^{2}\)/Nm\({}^{2}\)])
* \(\varepsilon_{r}\) : Relative permittivity of the dielectric between the plates of the capacitor.  
Solving for the electric field (\(E\)) and replacing the area of the circumference (\(A=\pi r^{2}\)) in expression (1) results in Equation (2):  
\[E=\frac{q}{\varepsilon A}=\frac{q}{\varepsilon(\pi r^{2})} \tag{2}\]  
Subsequently, we proceed to define the mathematical expression that allows us to calculate the value of the voltage (\(V\)) existing between points \(a\) and \(b\). The result of this process is described in Equation (3):  
\[V=-\int_{b}^{a}Ed_{r}=-\int_{b}^{a}\frac{qd_{r}}{\varepsilon(\pi r^{2})}=- \frac{q}{\pi\varepsilon}\int_{b}^{a}\frac{d_{r}}{r^{2}}V=\frac{q}{\pi \varepsilon}\left[\frac{1}{a}-\frac{1}{b}\right]=\frac{q}{\pi\varepsilon} \left[\frac{b-a}{ab}\right] \tag{3}\]  
Finally, the value of the capacitance for the MT sensor (\(C_{MT}\)) is given by the relationship between the total charge (\(q\)) and the voltage (\(V\)), as presented in Equation (4):  
\[C_{MT}=\frac{q}{V}=\pi\varepsilon\left[\frac{ab}{b-a}\right]=\pi\varepsilon_{ o}\varepsilon_{r}\left[\frac{ab}{b-a}\right] \tag{4}\]
## 4 Experimental Design
### Experiment Implementation
#### 4.2.2 Nodes and Mobility  
The ns-3 provides helper classes, which work like interfaces and simplify the overall configuration process of a simulation. Algorithm 2 shows the NodeContainer helper class, which allows us to create and store groups of nodes that can be batch-configured. Instead of configuring each node individually, configurations can be applied to a container and all nodes within will receive that configuration.  
Once the nodes are created, they must be assigned a position within the scenario, which can be done using the MobilityHelper class. Algorithm 3 shows examples of two position allocators, Grid and RandomRectangle. The Grid allocator places the nodes in a grid in which the center coincides with the center of the scenario, separated by DeltaX and DeltaY units from each other. The RandomRectangle creates a bounded area within which the nodes are randomly positioned. Other position allocators such as List allow the user to define a list of positions, and let the simulator pick the positions sequentially for each node in order. During our experiments, we have made extensive use of the RandomRectangle and List position allocators.  
```
1. NodeContainer walkerNodes;
2. walkerNodes.Create (65);
3. NodeContainer vehicleNodes;
4. vehicleNodes.Create (25);
```  
**Algorithm 2** Node Creation  
```
1. MobilityHelper mobility; //Grid
2. mobility.SetPositionAllocator( \"ns3::GridPositionAllocator\", \"MinX\", DoubleValue (0.0), \"MinY\", DoubleValue (0.0), \"DeltaX\", DoubleValue (distance), \"DeltaY\", DoubleValue (distance), \"GridWidth\", UntegerValue (10), \"LayoutType\",StringValue(\"RowFirst\") ); //Random Rectangle
3. mobility.SetPositionAllocator(\"ns3:: RandomRectanglePositionAllocator\", \"X\",StringValue(\"ns3::UniformRandomVariable[Min=-250.0]\"), \"Y\",StringValue(\"ns3::UniformRandomVariable[Min=-250.0]\"));
```  
**Algorithm 3** Position allocation  
Finally, the nodes must be assigned mobility models. The ns-3 has a comprehensive list of these models, including random walk, random waypoint, random direction, Gauss-Markov, etc. Moreover, user-made models can easily be imported into the simulations to fit a specific scenario. Algorithm 4 shows two models, the Constant Position, which is self-explanatory, and the Random Walk 2D based on Time and on Distance. In the Random Walk 2D model based on Time, the nodes move for time t (in seconds) and then randomly change direction and speed. For the Random Walk 2D based on distance, nodes move distance d and then change direction and speed randomly. If we want the nodes to follow a predefined path, we can assign direction, speed, and waypoints manually, and modify these values through triggers at predefined times.  
Once the position allocator and mobility models have been set, they must be assigned to a node, or NodeContainer. Only one allocator and mobility model can be assigned to a single node, but any number of nodes can have any combination of allocator and mobility model during a single simulation.
# On the Use of the Multi-Agent Environment for Mobility Applications
## 2 Environment as a Spatio-Temporal Reference
### Space-Time Environment for Planning in Mobility Applications
The multi-agent system (MAS) we describe in this section comprises vehicle agents, passenger agents, interface agents and planner agents. The multi-agent environment is explicitly modelled as a space-time network and used by the agents to plan their routes. The MAS simulates a distributed version of the so-called \"insertion heuristic\". Insertion heuristics is a method of inserting individual travellers into vehicle routes. Each traveller is inserted into the route of the vehicle with the minimum marginal cost (the cost can refer to the detour incurred, for example). This method is the fastest known heuristic since there is no reconsideration of previous insertion decisions. In heuristics and multi-agent methods in the literature, the hierarchical objective of minimizing the number of vehicles mobilised is considered to take priority over the overall costs (including the distance travelled by all vehicles). Most of the heuristics are based on a two-phase approach: minimizing the number of vehicles followed by minimizing the distance travelled [16]. The model we propose in this section aims at minimizing the number of vehicles used in priority while maintaining the use of a \"pure\" insertion heuristic, i.e., without any additional improvement to meet the response time requirements. To this end, our heuristic encourages vehicle agents to cover a maximum spatio-temporal area of the transportation network, avoiding the mobilisation of a new vehicle if a new traveller appears in an uncovered area. A space-time pair \(<i,t>\)--with \(i\) a node and \(t\) a time--is said to be \"covered\" by a vehicle agent \(v\) if \(v\) can be in \(i\) at \(t\). The \"vehicle action zone\" is the set of space-time nodes that the vehicle agent covers. In the context of online VRPTW, maximizing the action zones of the vehicle agents gives them a maximum chance to satisfy the demand of a future (unknown) traveller. By modelling the spatio-temporal action zones of the vehicle agents, we propose a new method to compute the price of inserting the traveller into a vehicle's route. This proposal is a kind of regret insertion heuristic. Regret insertion heuristics, instead of choosing the vehicle with the minimum marginal cost, choose the vehicle and traveller with the greatest \"regret\". Regret is a measure of the potential price to be paid if a given traveller were not immediately inserted into the route of a given vehicle. There are several methods for calculating regrets, such as the sum of the differences between all available prices and the minimum price [17].
# SNR-Based Receiver-Type Decision Using Deep Learning for Multiple-Input Multiple-Output Detection
## 1 Introduction
However, to the best of the authors' knowledge, most existing studies on MIMO detection with deep learning have not accounted for this issue. To resolve this problem, in this paper, we focus on predicting channel conditions where large burst errors occur and adopting an appropriate receiver to overcome these poor channel conditions. Since complex relationships between the receiver type and various system parameters, such as signal-to-noise ratio (SNR), modulation order, antenna configuration, and MIMO channel characteristics including singular values, cannot be modeled by conventional mathematical approaches, we utilize deep learning, which specializes in modeling complex relationships [17]. We introduce a receiver-type decision method based on the channel conditions by utilizing deep learning. Based on the channel condition, the proposed method adopts the optimal nonlinear receiver for MIMO channels where large burst errors are expected while mainly using the linear receiver. For this, we establish a criterion and threshold for using both types of receivers based on the ratio of symbol error probabilities. Moreover, we present input features for training the DNN by analyzing the SNR gain and describe the operation of the proposed deep learning-based receiver in two phases. Consequently, we can achieve a compromise between the average error performance and reduced computational complexity. In the simulation results, we demonstrate that the proposed receiver chainachieves the same diversity order as the optimal nonlinear receiver, while maintaining low computational complexity, by alternatively using the two types of receivers. The contributions of this paper are summarized as follows:  
* We analyze the average SNR of the linear receiver to predict poor conditioned MIMO channels and present the criterion and threshold of the function for the receiver-type decision. * We introduce the input features of the DNN and propose the DNN structure to model the complex relationship of the presented function. * We present the operation of the deep learning-based proposed receiver in two phases for practical implementation and analyze the computational complexity of the proposed receiver.
# Radar Voxel Fusion for 3D Object Detection
## 3 Methodology
### Network Architecture  
This paper proposes the RadarVoxelFusionNet (RVF-Net) whose architecture is based on VoxelNet [10] due to its empirically proven performance and straightforward network architecture. While other architectures in the state of the art provide higher detection scores, the application to a non-overengineered network from the literature is preferable for investigating the effect of a new data fusion method. Recently, [PERSON] [29] proposed a shift from model-centric to data-centric approaches for machine learning development.  
An overview of the network architecture is shown in Figure 1. The input point cloud is partitioned into a 3D voxel grid. Non-empty voxel cells are used as the input data to the network. The data are split into the features of the input points and the corresponding coordinates. The input features are processed by voxel feature encoding (VFE) layers composed of fully connected and max-pooling operations for the points inside each voxel. The pooling is used to aggregate one single feature per voxel. In the global feature generation, the voxel features are processed by sparse 3D submanifold convolutions to efficiently handle the sparse voxel grid input. The \(z\) dimension is merged with the feature dimension to create a sparse feature tensor in the form of a 2D grid. The sparse tensor is converted to a dense 2D grid and processed with standard 2D convolutions to generate features in a BEV representation. These features are the basis for the detection output heads.  
The detection head consists of three parts: The classification head, which outputs a class score for each anchor box; the regression head with seven regression values for the bounding box position (\(x\), \(y\), \(z\)), dimensions (\(w\), \(l\), \(h\)) and the yaw angle \(e_{\theta}\); the direction head, which outputs a complementary classification value for the yaw angle estimation \(c_{dir}\). For more details on the network architecture, we refer to the work of [10] and our open source implementation. The next section focuses on our proposed yaw loss, which is conceptually different from the original VoxelNet implementation.
## 5 Discussion
### Changing Topology  
The dynamic nature of IoT environments necessitates frequent updates to wireless network topology. Changes in topology, such as the addition or removal of nodes, can significantly impact data transmission efficiency and reliability [37]. When IoT devices join or leave a network, additional energy and time are consumed during the transition, exacerbating power inefficiencies.  
Maintaining a stable network topology is essential for minimising the energy consumed during these changes. Research suggests that predictive algorithms capable of estimating node requirements and anticipating changes in network topology can help mitigate energy inefficiencies by reducing the frequency of node transitions. For example, in applications such as smart buildings, the number of required network nodes may vary seasonally, with greater demand in winter than in summer. Therefore, employing predictive models to estimate the necessary network topology under varying conditions can enhance stability and reduce unnecessary energy consumption.
# A Data-Driven Approach to Discovering Process Choreography
## 5 Experimentation
### Experiment Results
In the validation of the discovered process choreography, a value of 1.00 was obtained for all metrics (see Table 4), which indicates that the proposed approach could identify all interactions between participants. On the other hand, in the evaluation of the quality of the collaborative model, values of 0.80 and 1.00 were obtained for the precision and recall metrics, respectively, demonstrating that the collaborative model could reproduce most of the behavior found in the merged event log, without adding behaviors not included in the event log (see Table 6). The process choreography between the participants _Customer_ and _Travel agency_ in both models (see Figure 2a,b) contained interactions through five message flow connectors, as described below. 1. The participant _Travel agency_ sends a message using the task _Make Travel Offer_ of the subtype _send_ and the participant _Customer_ receives the message using the task _Check Travel Offer_ of the subtype _receive_. The interaction is represented by a message flow connector named _offer_, deployed as _message_4_ in the discovered process model (see Figure 2b). 2. The _Customer_ confirms his interest in the travel proposal through the task _Book Travel_ of the subtype _send_, which establishes a communication with the participant _Travel agency_ using the message connector _Travel (message_0)_. The message is received by the company _Travel agency_ in the task _Book Received_ of the subtype _receive_. 3. The next interaction of _Customer_ with the _Travel agency_ is presented through the message flow connector _Payment_ (_message_3), which links the task _Pay Travel_ of the subtype _send_ with the _Payment Received_ of subtype _receive_ contained in the _pool_ of the participant _Travel agency_. 4. The participant _Travel agency_ confirms the travel reservation through a message sent by the task _Confirm Booking_, which is received with the _Booking Confirmed_ of the subtype _receive_ from the participant _Customer_. 5. Finally, the participant _Travel agency_ sends a business document about the paid order, sending it in the message flow connector _Ticket_ (_message_1_) using the task _Order Ticket_ of subtype _send_ and the task (_Ticket Received_ of the subtype _receive_. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**IOBP** & **Precision** & **Recall** & **Generalization** \\ \hline Air quality system & 1 & 0.99 & 0.86 \\ Healthcare & 1 & 1 & 0.67 \\ Travel agency & 0.80 & 1 & 0.93 \\ Purchase order & 1 & 1 & 0.50 \\ Transfer of goods & 1 & 1 & 0.35 \\ Manufacturing process & 0.85 & 0.99 & 0.48 \\ Average & 0.94 & 0.99 & 0.63 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Quality assessment of discovered IOBP models.
# Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming
## 3 Navigation Resource Allocation Algorithm Based on Dynamic Programming
### Algorithm Principle
The performance index function is subject to the constraints specified in Equation (13).