esa-sceva/satcom-chunk-collection huggingface dataset:
# _CoVSCode_: A Novel Real-Time Collaborative Programming Environment for Lightweight IDE
## 5 Major Technical Issues and Solutions
### Utilizing Programming Interfaces (APIs) of the Underlying IDE  
Following the TA approach (Section 2.2), the client adaptor invokes programming interfaces (APIs) provided by the underlying IDE without knowing or changing its source code. To implement the proposed real-time collaboration functionalities and features, the system requires a collection of APIs, which are listed in Table 1. The left column of the table gives generic descriptions of the API requirements, while the right column presents the concrete _VS Code_ APIs that have been utilized in our prototype implementation.  
\begin{table}
\begin{tabular}{l l l} \hline  & **Generic API Requirement** & **Concrete _VS Code_ API (Descriptions Can Be Found at [38]) \\ \hline \multirow{4}{*}{(a)} & \multirow{4}{*}{Detecting editing operations on source code directories and files (i.e., source code tree)} & \multirow{4}{*}{vscode.workspace.createFileSystemWatcher (globPattern: GlobPattern, ignoreCreateEvents?: Boolean, ignoreChangeEvents?: Boolean, ignoreDeleteEvents?: Boolean): FileSystemWatcher} \\  & & & FileSystemWatcher.OnIdCreate: Event\textless{tri}\textless{} \\  & & & FileSystemWatcher.OnIdDelete: Event\textless{tri}\textless{} \\  & & & FileSystemWatcher.OnIdDelete: Event\textless{} \\ \hline \multirow{2}{*}{(b)} & \multirow{2}{*}{Detecting editing operations on source code file contents} & \multirow{2}{*}{vscode.workspace.onDidChangeTextDocument: Event\textless{}TextDocument: Event\textless{}TextDocument\textless{}} \\  & & & Event\textless{}TextDocumentChangeEvent\textless{} \\ \hline \multirow{2}{*}{(c)} & \multirow{2}{*}{Retrieving the positions of editing cursors} & \multirow{2}{*}{vscode.window.onDidChangeTextEditorSelection: Event\textless{}TextEditorSelectionChangeEvent\textless{}} \\ \hline \multirow{2}{*}{(g)} & \multirow{2}{*}{Retrieving the highlight ranges in source code} & \multirow{2}{*}{vscode.window.onDidChangeTextEditorSelection: Event\textless{}TextEditorSelectionChangeEvent\textless{}} \\ \hline \multirow{2}{*}{(h)} & \multirow{2}{*}{Setting the positions of editing cursors} & \multirow{2}{*}{vscode.textless{}TextEditor.setDecorations(decorationType: TextEditorDecorationType, rangesOrOptions: Range\textless{}[][DecorationOptions]): void} \\ \hline \multirow{2}{*}{(i)} & \multirow{2}{*}{Setting the highlight ranges in source code} & \multirow{2}{*}{vscode.textless{}TextEditor.setDecorations(decorationType: TextEditorDecorationType, rangesOrOptions: Range\textless{}[][DecorationOptions]): void} \\ \hline \multirow{4}{*}{(j)} & \multirow{4}{*}{Implementing other} & \multirow{4}{*}{vscode.window.createWebviewPanel(viewType: string, title: string, showOptions: ViewColumn[[preserveFocus: boolean, viewColumn: ViewColumn], options?: WebviewPanelOptions \& WebviewOptions \& WebviewOptions): WebviewPanelOptions \& WebviewPanel \\ \cline{1-1}  & \multirow{2}{*}{Implementing other collaboration-related user interface elements} & \multirow{2}{*}{vscode.window.lowinformationMessage(message: string, options: MessageOptions,... items: string[][
## 1 Introduction
However, deep-learning-based methods require large datasets and significant computing resources and, if the testing environment changes, a new training dataset is needed. In this paper, our goal is to achieve more accurate non-contact HR estimation under more challenging and practical conditions in a driving environment. With the rapid development of advanced driver assistance systems (ADASs) and automotive vehicles, ensuring driver and passenger safety is paramount. In particular, car accidents caused by medical emergencies afflicting the driver, such as a heart attacks, are a major concern. Using non-contact HR measurement, the status of a driver's heart can be monitored, and the driver can be notified when abnormal symptoms are detected. In addition, in automated vehicle systems, the vehicle can automatically be redirected to a nearby hospital. Although [PERSON] et al. previously attempted to employ the non-contact HR estimation approach in a driving environment, the extraction of pure HR signals was difficult, leading to poor performance [13]. Improving on previous research under driving conditions, a sliding window framework using a linear combination of RGB signals was proposed in Reference [14], achieving excellent performance and a rapid reaction time, making it suitable for driving conditions. In the same test framework as in Reference [14], our proposed method was tested in a public indoor environment, as well as under driving conditions, in order to verify the robustness of our multi-faceted approach. The structure of this paper is as follows: the overall proposed framework is described in detail in Section 2. In Section 3, two experiments are described: testing on a public indoor dataset to verify our method, and testing on a driving dataset containing rapid illumination changes, motion, and vibration. Finally, the experimental results are discussed in Section 4, and conclusions are drawn in Section 5.
## 3 Middleware for WSN Virtualization
### Middleware for Sensor Virtualization
It Integrates state-of-the-art simulators and provides an accurate measurement of the power aware WSN design. The design can be traced, tested and varied using the power state model of the design. **WISeMid** middleware in [64] supports virtualization by providing services to the internet users it connects the sensors to the internet. It aggregates different sensor networks and concentrates on the energy consumption of the network. Middleware is based on SOA the nodes are programmed in nesC and the internet host is programmed in Java. The results show that stateless, Fire and Forget interaction model is the most energy efficient as it stops the process the instant the invocation is sent. **SenSer** in [65] is a generic middleware framework proposed to support remote access to the sensor data. SenSer virtualizes the functionalities by considering them as services. Programming language and WSN development platform are made independent of each other. SenSer middleware framework is divided into three layers: presentation layer where WSN admin services reside, second layer is composed of filter, stream and query manager. Third and final layer contacts directly with the sensors and comprises of a network and the repository manager. SenSer framework is based on the Web Service Technology providing solution to client end heterogeneity. At University of Virginia **SenQ** middleware [66] is a multi-layer embedded query system, based on declarative model. The middleware supports virtual sensors that can be dynamically discovered and shared. The algorithm reduces the network load and energy consumption, supporting lightweight network protocols. The middleware tackles the challenges of heterogeneity, dynamic deployment and in-network monitoring. **SENSIE**[67] is one of the mega projects, taking the real world information to facilitate business. The middleware design of SENSIE consists of three main modules resource manager, service manager and community manager. Furthermore, application layer contains the application management module. The main concept of the design is based on sensor virtualization. SOA based architecture of SENSIE connecting to the cloud allows multiple services to facilitate multi applications. SENSI uses RESTful design. The resource directory, entity directory and semantic query resolver help user to run multi applications ensuring QoS.
## 2 Classification of Electromagnetic Surfaces
### Space Wave and Surface Wave Features  
Two-dimensional surfaces can interact with EM fields of both space-wave modes and surface-wave modes, as shown in Figure 8a. When discussing space-wave modes, it is usually required to characterize reflection and transmission properties of given surfaces. In this case, the propagation constant along the normal direction to the surfaces, \(k_{z}\), is purely real, which means that EM fields can propagate along \(\hat{z}\) direction. Conversely, for surface-wave modes, EM fields would exponentially decay along \(\hat{z}\) direction and \(k_{z}\) is purely imaginary, since fields propagation is constrained along surfaces. Surface-wave modes are eigen propagation modes of given surfaces, where propagation constants of specific frequencies can be determined using eigen-mode solver of commercial simulation softwares like ANSYS High Frequency Structure Simulator (HFSS). Problems related to surface-wave modes can be modelled as special cases of space-wave modes, where incident angle \(\theta\) is equal to \(90^{\circ}+j\theta^{\prime\prime}\). The unknown value of \(\theta^{\prime\prime}\) is determined by both surfaces' characteristic parameters and frequencies. It is worthwhile to point out that there is also a complex mode with complex \(k_{z}\)[27].
## 5 Results and Discussion
### Verifying our Results with Real Optimization Run  
In this subsection, the results of our fitness landscape analysis technique are verified by optimization test runs. The tested operators can also be used in the Genetic Algorithm (GA), so this algorithm was selected for testing. The Genetic Algorithm maintains a population of solutions. Starting from an initial population, it continuously improves elements of the population with crossover and mutation techniques.  
Table 7 presents the parameters and results of our test run. The test run was set up with OX in Test 1, PMX in Test 2, CX in Test 3, and 2-opt in Test 4. In Test 5, all three crossing operators were given equal emphasis.  
Since the objective is to minimize the fitness value, it can be read from Table 7 that Test 4 was the best, in addition, Tests 1 and 2 also provided high-quality results. The most efficient operator was 2-opt, but the PMX and OX operators also proved to be effective. Using the CX operator and setting equal sharing of each crossover has not been shown to be effective.
## 2 Previous Work
### Neural Network Quantization  
The reduction in memory requirements while maintaining the NN accuracy was attempted to be achieved by quantization of the network. The first very efficient approaches to aggressive NN quantization were given in 2015. when [PERSON] et al. [36] presented BinaryConnect, an NN that uses binary weight values while maintaining model accuracy. [PERSON] et al. [37] presented XNOR-Net, a binary CNN, by evaluating it on the ImageNet data set. Two different approaches were tested: in the first one, only the weights were binarized (Binary-Weight-Networks), while in the other (XNOR-Networks), the input to the network is also binarized. In both approaches, a memory saving of \(\sim\)32\(\times\) was achieved, and the binary weights allowed the performance of convolutions without using the multiplication operation, which in the first case results in a speedup of \(\sim\)2\(\times\), and in the second, of \(\sim\)58\(\times\), compared to the model where the network and data were used in complete precision. The first approaches to network quantization were performed so that the network is trained and then quantized, called post-training quantization, a technique that mainly suffers from a significant loss of accuracy. In the quantization-aware training approach, quantization is performed during training, which generally gives a smaller accuracy drop compared to the previous technique. The QKeras [38] and Larq [39] libraries are designed as a quantization extension of the Keras API. In the paper, QKeras enables the approximation of NN weights, biases, and activation functions with low bit values that significantly reduce the network size.  
Therefore, the development of such tools and the emergence of ML-compatible FPGA devices have opened up new possibilities for implementing classification networks on L1T. The paper investigates the possibility of additional resource reduction using data selection and quantization. Applied methods enable the usage of the proposed quantized models in classification within the set time limit of 12 \(\upmu\)s.
# Effective Feature Selection Methods to Detect IoT DDoS Attack in 5G Core Network
###### Abstract  
The 5G networks aim to realize a massive Internet of Things (IoT) environment with low latency. IoT devices with weak security can cause Tbps-level Distributed Denial of Service (DDoS) attacks on 5G mobile networks. Therefore, interest in automatic network intrusion detection using machine learning (ML) technology in 5G networks is increasing. ML-based DDoS attack detection in a 5G environment should provide ultra-low latency. To this end, utilizing a feature-selection process that reduces computational complexity and improves performance by identifying features important for learning in large datasets is possible. Existing ML-based DDoS detection technology mostly focuses on DDoS detection learning models on the wired Internet. In addition, studies on feature engineering related to 5G traffic are relatively insufficient. Therefore, this study performed feature selection experiments to reduce the time complexity of detecting and analyzing large-capacity DDoS attacks in real time based on ML in a 5G core network environment. The results of the experiment showed that the performance was maintained and improved when the feature selection process was used. In particular, as the size of the dataset increased, the difference in time complexity increased rapidly. The experiments show that the real-time detection of large-scale DDoS attacks in 5G core networks is possible using the feature selection process. This demonstrates the importance of the feature selection process for removing noisy features before training and detection. As this study conducted a feature study to detect network traffic passing through the 5G core with low latency using ML, it is expected to contribute to improving the performance of the 5G network DDoS attack automation detection technology using AI technology.  
5G; sensor network; machine learning; feature selection; IoT DDoS; DDoS detection
## 2 Materials and Methods
### Calculating Fishing Pressure  
Fishing pressure by beam trawling was calculated by using Vessel Monitoring System (VMS) data (yearly kilometers of each vessel authorized by the National Agency for Fisheries and Aquaculture--NAFA), buffered with the average gear width and aggregated to calculate the swept area by dividing it to the total area of the extended zone of ROSCI0066, adapting the existing Swept Area Ratio (SAR) methodology [(36; 31)], according to the formula:  
Figure 1: Study area—Danube Delta Marine Zone (ROSCI0066 extended area, 20–40 m). Sampling stations for macrozoobenthos with broad-scale benthic habitat according to the EMODnet Seabed Habitats.  
SAR = Towed gear distance covered (km) \(\times\) Cear width (km)/Total area investigated (km\({}^{2}\)) Vessel monitoring system (VMS) data were accessed and analyzed to reconstruct the trawling lines of Romanian vessels > 12 m equipped with beam trawls for the period 2015-2022. The development of VMS as a surveillance and law enforcement tool transformed the study of fishing activity and footprints [37], offering total or nearly complete coverage of targeted fleets as well as high-resolution information on the positions of specific fishing vessels [38]. In the case of the Danube Delta--Marine Zone, management authorities can use VMS data to verify if a vessel is in an area where fishing is allowed. VMS data were used to point out the hotspot locations and dynamics of beam trawl fishing activity during the analyzed 8-year period.  
In this particular case, only VMS data for vessels fishing with the beam trawl (TBB) targeting the gastropod _R. venosa_ were used. As there is no actual fixed width of beam trawl used at the Romanian coast, local ecological knowledge (LEK) [39; 40] from fishermen and authorities (NAFA) was used to estimate the average width of the gear used in our calculations (4.2 m). Moreover, in order to make a clear separation between the total number of kilometers recorded by the VMS and the actual trawling operations (contact of the gear with the seabed), local ecological knowledge was also used with the result that 50% of the total distance covered by one vessel was actually used for beam trawling.
## 5 Experimental Setup and Results Evaluation
### Status Monitoring  
To provide medical doctors with a user-friendly GUI, the LabVIEW webserver is used to share the designed front panel. As can be seen in Figure 12, medical doctors can easily track the status of their cardiac patients by analyzing the indicated processed data (information) in the front panel of the GUI. The proposed healthcare monitoring system can be particularly helpful for cardiac patients who are required to consult with their doctor for follow-up to prevent developing more risk factors from emerging. Additionally, it would work well to reduce the expenses of visiting a healthcare facility or remaining in a hospital for an extended period of time. For instance, if a patient has angina pectoris, she/he should receive immediate treatment to prevent the emergence of risk factors that may lead to myocardial infarction [40]. Consequently, the developed monitoring system can be used to reduce care costs while maintaining cardiac patients' health and providing them with more confidence and securing their health status. Therefore, this would highly assist in improving the overall performance of the healthcare system by delivering intelligent and dependable decision-making techniques.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Testing Dataset (30\%)**} & \multicolumn{2}{c}{**All Dataset (100\%)**} \\
**K** & **Range** & **Accuracy** & **Range** & **Accuracy** \\ \hline K1 & \(1\to 22\) & \(100\%\) & \(1\to 75\) & 97.3\% \\ \hline K2 & \(23\to 45\) & 95.45\% & \(76\to 150\) & 100\% \\ \hline K3 & \(46\to 68\) & \(100\%\) & \(151\to 225\) & 97.3\% \\ \hline K4 & \(69\to 91\) & 95.45\% & \(226\to 300\) & 100\% \\ \hline K5 & \(92\to 114\) & \(100\%\) & \(301\to 375\) & 100\% \\ \hline K6 & \(115\to 137\) & \(100\%\) & \(376\to 450\) & 98.67\% \\ \hline K7 & \(138\to 160\) & 95.45\% & \(451\to 525\) & 97.3\% \\ \hline K8 & \(161\to 183\) & \(100\%\) & \(526\to 600\) & 100\% \\ \hline K9 & \(184\to 206\) & 95.45\% & \(601\to 675\) & 98.67\% \\ \hline K10 & \(207\to 225\) & \(100\%\) & \(676\to 750\) & 98.67\% \\ \hline \multicolumn{2}{c}{AVE} & 98.18\% & \multicolumn{1}{c}{AVE} & 98.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Accuracy evaluation of the proposed ML encoding model using (10%) K-fold cross validation.  
On the other hand, the designed GUI prioritizes simplicity and clarity to ensure usability, particularly for monitoring cardiac patients. It is tailored to display critical patient information effectively while supporting multiple-patient monitoring. Additionally, all displayed information is automatically stored in an Excel sheet on the server computer, providing a robust solution for maintaining patient records and enabling further analysis.
# Towards Improved Inertial Navigation by Reducing Errors Using Deep Learning Methodology
## 2 Methods  
### IMU Data Sampling  
The typical format of an IMU output is a digital data series in the time domain as shown in Figure 1. To make the IMU output fit the input format of a deep learning network, such as a CNN, the IMU data is divided into small step sizes by time division. Within each time step, the IMU output data is approximated as having a constant acceleration for the accelerometer and angular velocity value for the gyroscope. This approximation would hold as long as the time divisions are small enough. Figure 1 shows that the smaller the time divisions, the less change in the acceleration and angular velocity would occur; therefore, the more accurate representation of the acceleration and the angular velocity would be obtained. The IMU data is sampled by using Mathworks MATLAB.
# Investigation of Aerosol Climatology and Long-Range Transport of Aerosols over Pokhara, Nepal
## 3 Results and Discussion
### Investigation of Aerosol Sources and Types  
Figure 8 shows the seasonal clusters of five days air mass back trajectories arriving at the observation site Pokhara at an altitude of \(500\) m and active fire spots (red dots) for \(2017\). The percentage contribution of each cluster is also shown in the figure for each season. The air masses reaching Pokhara valley follow two distinctive pathways from the Indo Gangetic Plain (IGP) region, during winter and pre-monsoon (Western Nepal, West India, and Pakistan), when the influence of strong western disturbances occur. During monsoon, majority of air masses arrived from the eastern IGP region and Bay of Bengal. Dense fire spots are observed during winter and pre-monsoon period over the region, which can enhance the emission of aerosols from biomass burning, that could be transported to Pokhara and influence the air quality and enhance AOD during that period [53]. In addition, during the post-monsoon, widespread crop residue burning occurs in the north-west part of India [54]. Comparatively few active fire spots are detected during the monsoon season, which might be due to cloud cover and heavy rainfall in South Asia, caused by moist air from the Arabian Sea and Bay of Bengal.  
Figure 7: Monthly mean spectral SSA (**top two figures**), averaged within the months of the study period of years 2010–2018, from January to May, along with AAOD (**bottom two figures**) in the logarithmic scale. The dotted lines are the linear fit of spectral AAOD in the logarithmic scale.  
It is evident from previous analyses of aerosol chemical composition over the Himalayan football in Nepal that the emission from crop-residue burning over the IGP has a significant impact on the air quality over the regions [24,28,46,55,56,57,58,59]. The cluster analysis of trajectories in this study also indicates the high likelihood that the aerosol population's physical and optical properties over the Pokhara valley could be influenced significantly by the regional transport of air masses from polluted regions of South Asia. This could be further confirmed by looking at MODIS visible imagery, showing continuous aerosol haze layers extending up from the IGP into the Himalayan valleys (Figure 9a,b). The MODIS satellite image taken on 27 October 2017 reasonably shows emissions from biomass burning. The biomass burning smoke funnels through the densely populated and industrialized areas on IGP, and after mixing with anthropogenic pollution background, it comfortably transports to Nepal's Pokhara valley, and is also suggested by the HYSPLIT back trajectory analysis (Figure 9b).  
Figure 8: Clusters of five days air mass back trajectories computed with HYSPLIT model reaching Pokhara and at altitude 500 m and all active fire spots; each season is symbolized with red dots at different seasons (winter, pre-monsoon, monsoon and post-monsoon) of 2017. The percentage contributions of each cluster are shown in different colors.