esa-sceva/satcom-chunk-collection huggingface dataset:
# Leaking Gas Source Tracking for Multiple Chemical Parks within An Urban City
## 3 A Large-Region Sensor-Based Source Tracking Model Based on a Fully Connected Network
### Self-Attention Module  
The conception of self-attention is carried out by a popular model, transformer [25]. Although transformer is an outstanding model which was first used in translation, the powerful performance of the self-attention module allows it to be applied to other fields as well. The module can be visualized as shown in Figure 4.  
Where \(x\) represents the raw inputs, and \(\mathbf{W^{Q}}\), \(\mathbf{W^{K}}\), \(\mathbf{W^{V}}\) are the weight matrices of query, key, and value, respectively. Their relations are written as below:  
\[\mathbf{q}=\mathbf{x}\mathbf{W^{Q}}\quad\mathbf{k}=\mathbf{x}\mathbf{W^{K}}\quad\mathbf{v}=\mathbf{x}\mathbf{W^{V}} \tag{7}\]  
\[Attention(\mathbf{q},\mathbf{k})=softmax(\frac{\mathbf{q}\mathbf{k^{T}}}{\sqrt{d_{k}}}) \tag{8}\]  
\[\mathbf{output}=Attention(\mathbf{q},\mathbf{k})\mathbf{v} \tag{9}\]  
Figure 4: The basic components of the self-attention module.  
Self-attention in this task aims to build an inner relation among the concentration data from the 61 sensors. It seems like integrating information from various sensors and paying more attention to some significant sensors will help to improve the performance. The multi-head mechanism is also implemented, as shown in Figure 5, to further improve the module's performance:  
In brief, this structural design allows each attention head to learn features through \((q_{i},k_{i},v_{i})\) mapping to different feature spaces, focusing on various potential leaking sources, and, thus, balancing the biases that may arise from the single-head attention mechanism. The visualization of our main structure with a self-attention module is shown in Figure 6.
## 2 Signal Processing and Height Inversion  
In order to examine the accuracy of the coherent integration optimization model, we use the data obtained by the Institute for Space Studies of Catalonia (IEEC) through an airborne experiment on the Baltic Sea on the 3 December 2015, for verification. The delay difference between direct and reflected signals is calculated using interference processing. The raw IF datasets are processed on the ground with the software receiver, including data acquisition, processing, height retrieval, and precision calculation.  
The aircraft's altitude was about 3 km during the experiment, and the velocity was about 50 m/s. The direct and reflected signals are captured by the 8-element phased array antennas of RHCP (right-handed circular polarization) and LHCP (left HCP) respectively. The radiofrequency (RF) signals received by the antenna elements are filtered, amplified, and down-converted into IF signals. The IF signals are quantized by a comparator, and the quantized signals are connected to a field-programmable gate array (FPGA) through D-type flip-flops whose parallel processing capability is used for sampling. Analogue signals received by an element are quantized into one-bit in-phase components and one-bit quadrature components per sample. The sampling frequency is 80 MHz. Finally, the sampled digital signal is transmitted into the ground receiving station through the PCIe bus [23]. The acquired data is processed by a software-defined receiver. A simplified diagram of data processing and altimetry retrieval is depicted in Figure 1.
# 2-D Cross-Plot Model Analysis Using Integrated Geophysical Methods for Landslides Assessment
## 1 Introduction
These methods are less invasive and provide comprehensive information on a large volume of soil, thus overcoming the point-scale features of geotechnical measurements. Successful geophysical methods depend on the presence of a significant contrast in the physical properties of the different lithological units [31]. Therefore, 2-D resistivity and seismic refraction surveys were utilized to visualize the failure zones as a purpose to identify the subsurface characteristics with the trigged factors for the slope failure for this study. As the continuous work from both methods, a cross-plot analysis with the development of a 2-D cross plot model was introduced based on the integration of resistivity and velocity values in order to enhance the subsurface resolution besides the provided comprehensive subsurface images.
## 2 Design of Structure and Main Parameters
### Overall Structure and Working Principle  
A commercial six-rotor agricultural UAV (model: 3 WDM6-10, Lockheed Co. Ltd., Wuhan, China) was selected as the seeding platform. After removing the original spraying module, we added the aerial seeding system. The whole hardware structure with the air-assisted centralized metering device is shown in Figure 1. The whole system is composed of three main modules: aircraft, seed supply module, and seed distributing and guiding module. The seed supply module is used to get seeds from the seed box at a preset seed flow rate and deliver them to the distributor via airflow from the fan. The seeds are accelerated under the airflow and divided into several rows by the distributor. In each row, seeds proceed to move through the hose and hard tube, and then they drop from the seeding tube toward the field.  
Removing the aircraft platform, the remaining seeding system can be seen as a scaled-down version derived from a traditional air-feeding seeding system on a ground planter designed by [PERSON] et al. [14]. However, the whole system was redesigned to match the aircraft's structure and operating characteristics. In particular, a new seed feeding component was built to overcome the problems of serious air backflow and difficult seed feeding after miniaturization. The aircraft wheelbase (the distance between two motors at opposite corners) is about 1.2 m, so the width of the seeding device was designed to be about 1.8 m with seven seed outlets. Correspondingly, the working width of the aerial seeding system was 2.1 m. The nominal maximum takeoff weight of the aircraft was 20 kg, and the nominal maximum working speed was 8 m/s. The net weight of the aerial seeding device was about 3 kg, and the UAV could carry about 5 kg rape seeds at a time.
## 3 Results
### Analysis of the Remaining Trajectory Precision Estimates for All Flights  
Processing the GNSS/INS data with and without the use of GAMS had a negligible impact on the mean precisions of the remaining single and dual-antenna GNSS/INS trajectory estimates as shown by the difference in means, column (\(\Delta\)) in Table 5. This table presents quantitative measures for all the single and dual-antenna trajectory precisions across the horizontal velocities tested within the experiment. The heading precision measures as presented in Table 4 are included for completeness.  
Figure 10: Single-antenna GNSS/INS heading precisions at the different horizontal velocities of the experiment best-fit to the theoretical GNSS velocity model.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Flight**} & \multirow{2}{*}{\begin{tabular}{c} **Hor.** \\ **Velocity** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Trajectory** \\ **Estimates** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Single-Antenna Precisions** \\ **Min.** \\ \end{tabular} } & \multicolumn{2}{c}{**Dual-Antenna Precisions**} & \multirow{2}{*}{\(\Delta\)} \\ \cline{3-3} \cline{5-10}  & & North & 0.019 m & 0.025 m & 0.020 m & 0.019 m & 0.023 m & 0.020 m & 0.000 m \\  & & East & 0.019 m & 0.024 m & 0.020 m & 0.018 m & 0.022 m & 0.019 m & 0.001 m \\ A & 2.5 m/s & Height & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.000 m \\  & & Roll & 0.03\({}^{\circ}\) & 0.07\({}^{\circ}\) & 0.038\({}^{\circ}\) & 0.03\({}^{\circ}\) & 0.06\({}^{\circ}\) & 0.037\({}^{\circ}\) & 0.001\({}^{\circ}\) \\  & & Pitch & 0.03\({}^{\circ}\) & 0.07\({}^{\circ}\) & 0.039\({}^{\circ}\) & 0.03\({}^{\circ}\) & 0.06\({}^{\circ}\) & 0.037\({}^{\circ}\) & 0.002\({}^{\circ}\) \\  & & Heading & 0.28\({}^{\circ}\) & 0.45\({}^{\circ}\) & 0.369\({}^{\circ}\) & 0.07\({}^{\circ}\) & 0.11\({}^{\circ}\) & 0.082\({}^{\circ}\) & 0.287\({}^{\circ}\) \\ \hline \multirow{6}{*}{B} & & North & 0.019 m & 0.024 m & 0.020 m & 0.019 m & 0.023 m & 0.020 m & 0.000 m \\  & & East & 0.019 m & 0.022 m & 0.020 m & 0.018 m & 0.022 m & 0.020 m & 0.000 m \\  & & Height & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.036 m & 0.000
## 3 Methods
### Classification Schemes  
We defined different pipelines by the combination of the six different word-embedding approaches described in Section 3.4, the three representation methods defined in Section 3.5, and the following five well-known classifiers: logistic regression (LR), multilayer perceptron (MLP), support vector machine (SVM), random forest (RF), and k nearest neighbor (kNN). The different pipelines were tested using macro average F1-score (F1-macro) with a 5-fold cross-validation scheme.  
We used the implementation from the Python library scikit-learn[31] for training and testing each pipeline. The parameters of the classifier were instantiated following a grid search scheme. In particular, for the MLP various combinations of neurons organized in 1 to 3 hidden layers were tested using the Adam solver and the values \(10^{-4},5\times 10^{-2}\) for the \(\alpha\). For the SVM, the polynomial, radial basis, and sigmoid kernels were tested with their default values together with an exponential growth sequence \(10^{-1},10^{0},\dots,10^{3}\) for the penalty. For the logistic regression, we varied the penalty term \(C(100,10,1,0.01)\). In \(k\)NN we assessed the values \(1,3,\dots,9\) for the number of nearest neighbors k. Finally, in RF we varied the number of estimators \((100,200,300)\), the minimum number of samples in a leaf node \((1,2,4)\), and the minimum number of samples required to split a node \((2,10,20)\).  
Our dataset is imbalanced, mainly associated with the class without SMM (WS). To limit the class imbalance impact, we undersampled the majority class (WS) over the training set. Instead of using undersampling in the rest of the dataset in a one-vs-rest scheme, we randomly removed samples from the majority class so that the size of the majority class had the same number of samples as the class with the second-highest number of samples. We chose this method because the classifiers do not always use the one-vs-rest strategy to deal with multiclass classification problems as is the case for MLP and kNN.  
For surveillance of SMM, both precision and recall are important. A high precision allows the system to assign the correct group to SMM; meanwhile, a high recall is intended to not miss a correct classification when the episode corresponds to a real condition associated with SMM. For that reason, we used the F1-macro as an evaluation measure, because it includes the precision and recall metrics and considers each class in the model.
# A Software Defined Radio Based Anti-UAV Mobile System with Jamming and Spoofing Capabilities
## 3 System Functionalities
### System Security  
Technology is a double-edged sword, and we must always see both sides. Along with the benefits of each step forward comes the possibility that it may be used for malicious purposes. Some examples of what is possible if there is a misuse of the anti-UAV system is the possibility that a malicious user may be able to divert drones in order to keep them in his possession. Furthermore, it could also be used with the aim of interfering and diverting the route of a self-driving car with potentially dangerous consequences.  
For these reasons, it is important to include security measures in a portable anti-UAV system such as the one described in this paper. In the proposed implementation, only previously authorized users can use the system to transmit jamming or spoofing signals. The fingerprint scanner--TTL (GT-521F32), chosen as a biometric sensor due to its small and compact size, was used for system implementation. The fingerprint scanner primarily converts biometric information, i.e., the surface or subsurface of the skin of a finger, into a digital signal, typically a digital image. In practice, this conversion process can never be perfect. The persistent and largely invariant part of the imperfections induced by the fingerprint scanner in this process is called the scanner pattern [26]. By switching on the anti-UAV mobile system, the user is required to authenticate himself with a biometric sensor in order to boot up the system [27].  
Once the fingerprint details are stored, whenever the fingerprint is scanned the identity can be authenticated. To build a complete and more accurate fingerprint image, the scanner often asks to take multiple prints from the same finger.  
Figure 5: UAV localization method.  
As a means for preventing and enhancing system security through mobile network communication, the system sends a periodic (configurable time) message with the location information of the system to a supervisory entity. In addition, whenever someone initializes the system or initiates the transmission, a message with the respective username is also sent. For the communication with a supervisory entity, later identified, the Arduino SIM900 shield was used, as it allows sending messages using general packet radio service (GPRS) technology. By sending the messages to the supervisor entity, it is possible to create a timeline of system usage. This type of monitoring is necessary to increase the security of system operation, by recording all its activity.
## 3 The Bike-Share Case
### Evaluation--Integration and Policy Analysis
#### 3.6.1 Starting Inventory Policy  
A natural candidate for a high-leverage control variable is the station's starting inventory for the day. Since overnight balancing actions are standard practice, setting an optimal starting inventory can significantly reduce the need for extraordinary measures, such as offering incentives or deploying the provider's fleet for additional balancing.  
The performance comparison between LSTM and the statistical method showed that LSTM significantly reduces residual errors by detecting patterns from past demand and, crucially, other demand correlates. Therefore, when setting the starting inventory, LSTM's superior pattern recognition must be considered, in addition to the uncertainty introduced by randomness and unaccounted-for demand factors.  
This dual requirement leads to a straightforward approach for setting the starting inventory. Forecast errors are assumed to follow a normal distribution with zero mean (indicating no systematic bias) and a standard deviation equal to the Root Mean Squared Error (RMSE). Based on a confidence interval, random errors are generated and scaled by the RMSE to reflect forecast uncertainty. Adding these errors to the forecast creates multiple demand scenarios for rentals and returns. These scenarios are then applied in a simplified simulation model (single station, without feedback loops for user incentives) to determine the most robust inventory value. Along with the confidence interval and the number of scenarios, the relative weighting of bike and dock outages is an essential parameter in evaluating performance.  
Figure 14 below shows demand scenarios for the station \"E 16 St and 5 Ave\", generated for 24 August, with a confidence interval of 80% and 300 scenarios. Since the provider must determine the target inventory for overnight balancing before the day begins, the  
forecasts are generated on a rolling basis. As much as possible, actual historical demands are used (adjusted to account for the time needed to execute physical balancing actions). However, when forecasting into the future--where actual demand data is unavailable--the previously generated forecasts are used instead. This approach enables the creation of a continuous forecast time series covering the entire day.
# Lightweight Infrared and Visible Image Fusion Based on Nested Connections and Res2 Net
## 4 Training Strategy  
During the training phase, our image fusion network needs to possess superior performance based on two key factors: one is the feature extraction capability of the encoder network and the feature reconstruction capability of the decoder network, and the other is the capability of R2 FN to extract dual-mode multi-scale features. Therefore, a two-stage training method is adopted in this study. Firstly, the encoder network and the decoder network are trained as a whole, with the objective of reconstructing the network input. Then, R2 FN is trained using multimodal images, with the parameters of the encoder and decoder obtained from the first stage being fixed during this phase.
# A Non-Invasive Fetal QRS Complex Detection Method Based on a Multi-Feature Fusion Neural Network
## 2 Signal Collection and Preprocessing
### Signal Multi-Feature Extraction and Combination
#### 2.5.1 ECG Signal Feature Values  
Calculating specific data feature values of ECG signals, such as extremal values, area, and extremal points, represents the signal's energy and time domain distribution characteristics.
# A Deep Learning-Based Mapping Model for Three-Dimensional Propeller RANS and LES Flow Fields
## 4 Experiments  
### Dataset  
The dataset used in this work was obtained through 3-dimensional RANS and LES flow field simulations. The computational grid employed in the simulations was non-structured and utilized hexahedral meshing generated using Hexpress software (2022.1). RANS simulation had 1.2 \(\times\) 10\({}^{6}\) grid elements and LES simulation had 4.8 \(\times\) 10\({}^{6}\) million grid elements, with the same space boundary in both simulations. This means that 1.2 \(\times\) 10\({}^{6}\) grids for RANS simulation were processed to give 646,432 voxels and were mapped to 4.8 \(\times\) 10\({}^{6}\) million grid elements. Both of them were 3D simulations. The turbulence model used in the RANS simulation was the shear-stress transport model (SST) model. The subgrid model used in LES was WALE. In the simulations, the flow was characterized by a Reynolds number of 1.4 \(\times\) 10\({}^{7}\), a CFL (Courant-Friedrichs-Lewy) number of 0.3, and a y+ value of 24. The forward velocity of the propeller was described by a coefficient of 0.925. When compared with experimental data using this model, the calculated errors for ship resistance, propulsion force, and torque were 0.6%, \(-\)4.5%, and 1.6%, respectively.  
The dataset comprises 1000 samples, each containing RANS and LES simulation data for the central region of the propeller. The flow field information in both cases is provided in CSV format, including spatial coordinates (\(x\), \(y\), \(z\)) and velocities (\(u\), \(v\), \(w\)) representing the angular velocity components. Each sample contains approximately 6,000,000 points with three velocity components. These points are distributed within the range of (\(-9\leq x\leq 2.2\), \(-1.5\leq y\leq 1.5\), \(-1.3\leq z\leq 0\)). The reason why RANS data for the propeller flow field contain velocity data in the axial, tangential, and radial directions is related to the nature of turbulent flow and the approach used in RANS. Turbulent flow is three-dimensional, unsteady, and multiscale, which makes precise simulation computationally expensive. In RANS, the transient momentum is averaged by using the time-averaged Navier-Stokes equations, with the Reynolds stress term involved in the averaging equation. Reynolds stress describes the rotational motion of vortices in turbulent flow and includes velocity components in the axial, tangential, and radial directions. Therefore, RANS data for the propeller flow field include velocity data in these three directions to reflect the nature of turbulent flow.
# Digital Content Management Using Non-Fungible Tokens and the Interplanetary File System
## 4 Digital Content Management (DCM) System
### Distributed Hash Table (DHT)  
DHTs comprise six APIs, namely findPeer, findProvs, get, provide, put, and query, that manage the routing process and DHTs, as shown in Table 1. A P2P network can be realized without a centralized server by allowing nodes participating in the network to manage their own hash tables.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**API Name** & **Function** \\ \hline \hline dht.findPeer & Find multi-addresses using the PeerID \\ \hline dht.findProvs & Find peers to provide a specific value (CID) \\ \hline dht.get & Query the routing system for a given key \\ \hline dht.provide & Alarm the network with the given values \\ \hline dht.put & Write a key or value pair to the routing system \\ \hline dht.query & Find the closest PeerID to the given PeerID or CID \\ \hline \hline \end{tabular}
\end{table}
Table 1: APIs of DHTs.  
Rather than using a centralized system, DHTs use hash tables to locate files by mapping the name of each node to a value (Figure 5). The DHT operation determines network efficiency and how nodes enter or leave the network and register new content. Therefore, it reduces the network load and allows searching the network content quickly and accurately, depending on the configuration.  
Explaining the DHT algorithm defined in Algorithm 3, initially, 'contents1 (DC.p1): S1' denotes a portion of digital contents allocated to server S1, while 'contents2 (DC.p2): S2' and 'contents3 (DC.p3): S3' represent different segments of digital contents assigned to S2 and S3, respectively. 'n' represents the total number of servers, and the distributed hash table (DHT) contains pairs of keys and values. Here, 'key' signifies the content name, and 'value' represents the IP address. 'peers(DATABASE)' refers to querying peers regarding the database. 'query' performs a query on the database, and if the database matches a specific key, it returns that database. In cases of inserting values into the database, 'insert(key, value) by peers into database' indicates peers inserting key-value pairs into the database. Within these operations, the message count is O(log(N)), and state changes are O(log(N)). Here, 'O(log(N))' represents the algorithm's time complexity, indicating that the execution time of operations is logarithmically proportional to the database size (N).  
However, it is important to define an optimal nearness metric, maintain a minimum hop count, and define a minimum hash table for optimal DHT routing and fast processing. Hence, efficient generates O(log(N) messages for each lookup, and scalable generates O(log(N) states.  
```
define contents1 (DC.p1): S1 define contents2 (DC.p2): S2 define contents3 (DC.p3): S3 \(n\) = the total number of servers DHT <- pair(key, value); key <- content name value <- IP address query <- peers(DATABASE) if database == key returns (database) insert (key, value) by peers into database messages == O(log(N)) state == O(log(N))
```  
**Algorithm 3**_//DHT API Algorithm_  
Figure 5: Hash function in a DHT.