esa-sceva/satcom-chunk-collection huggingface dataset:
# Helicopter Blade-Vortex Interaction Airload and Noise Prediction Using Coupling CFD/VWM Method
## 2 Computational Method
### Acoustic Model  
Noise prediction in the current work depends on the well-known Ffowcs Williams-Hawkings (FW-H) Equation which is based on the Lighthill acoustic analogy method. Monopole and dipole sources physically denote the blade thickness and loading noise, and the quadrupole source involves the nonlinear effect from the flow-field compression. In this paper, it is assumed that the contribution to noise from the quadrupole term is negligible which simulates nonlinear noise at high speed, as strong blade-vortex interactions are known to occur at low-speed descent conditions. The integral formulation 1A developed by [PERSON] [22] is used in BVI noise prediction, i.e.,  
\[p^{\prime}(\overset{\rightharpoonup}{x},t_{a})=p^{\prime}_{t}(\overset{ \rightharpoonup}{x},t_{a})+p^{\prime}_{l}(\overset{\rightharpoonup}{x},t_{a}) \tag{12}\]  
where  
\[4\pi p^{\prime}_{t}(\overset{\rightharpoonup}{x},t_{a})=\int_{f=0}\left(\frac{ \rho_{0}\hat{v}_{n}}{r_{e}(1-M_{r})^{2}}\right)_{ret}ds+\int_{f=0}\left(\frac{ \rho_{0}v_{n}(r_{e}\dot{M}_{i}\dot{v}_{i}+c_{0}M_{r}-c_{0}M^{2}}{r_{e}^{2}(1-M_ {r})^{3}}\right)_{ret}ds \tag{13}\]\[4\pi p_{l}^{\prime}(\overset{\rightharpoonup}{x},t_{a}) =\frac{1}{c_{0}}\int_{f=0}\left(\frac{\frac{l_{r}l_{r}}{r_{e}(1-M_{ r})^{2}}}{r_{e}^{2}(1-M_{r})^{3}}\right)_{ret}ds+\int_{f=0}\left(\frac{l_{r}-l_{t}M_{t}}{r _{e}^{2}(1-M_{r})^{2}}\right)_{ret}ds \tag{14}\] \[+\frac{1}{c_{0}}\int_{f=0}\left(\frac{l_{r}(r_{e}M_{t}f_{t}+c_{0} M_{r}-c_{0}M^{2}}{r_{e}^{2}(1-M_{r})^{3}}\right)_{ret}ds\right.\]  
The subscript _ret_ indicates that the integrals are evaluated at the retarded time \(\tau_{e}=t_{a}-r_{e}/c_{o}\). The blade surface is divided into panels and the loads from each panel are used to obtain the total noise by integration. More descriptions about F1A formulation can be found in [22].  
Figure 2 shows the flowchart of the BVI airload and noise prediction. The calculation process of this method mainly includes three parts: (1) Calculate the spanwise sectional circulation around the blade using CFD and pass the vorticity to the WWM module; (2) Generate the source term, solve the rotor wake and the induced velocity using VWM, and feed the induced velocity back to the CFD domain; (3) Based on the desired precision, select the blade surface element, calculate the corresponding retarded time on the basis of observation time and location, and predict the BVI noise sound radiation using the FW-H equation.
## 3 Methodology
### Pattern Extraction Process  
In pattern extraction, we applied the NGT process to identify and extract patterns. Five KEs participate in the NGT process. The KEs have more than ten years of experience in biomedical text processing, analysis, and pattern extraction. In the first phase of NGT, we provided the same annotated hypertension guideline [33] to KEs for extracting patterns based on their heuristics. Heuristic-based decisions are premised on the cognitive ability, rule of thumb, intuitive judgment, an educated guess, and common sense of a person. The following five steps were performed in the NGT process for extracting the patterns. 1. Introduce all team members and nominate a leader to cordially handle meetings. The annotated CPG is provided to each member, the leader explained the purpose and process of the study and the voting process. 2. All panel members analyze the provided CPG independently and extract the patterns based on their heuristics that can identify recommendation statements in a CPG. 3. The leader collects all patterns extracted by each member and removes the duplicate patterns. A total of 21 unique patterns were identified by all KEs as shown in Table 2.
4. The panel members discuss each pattern, and the concerned member explains the reason for selecting the corresponding pattern. 5. All five participants rank each pattern from one to five, where one is the lowest and five being the highest rank. The leader aggregate the ranks of each pattern. 6. A threshold value (total rank \(\geq\) 15 ) is selected with the consensus of all team members, which is the 60% of team members agreement on a pattern. 7. Select those patterns, which have a higher accumulative rank than the threshold value (15). Based on this criterion, 10 patterns are selected as final patterns shown in Table 3.  
In the first phase of the NGT, the KEs were unaware of the extracted salient terms while extracting these patterns so that they can extract the patterns based on their heuristics without any external bias. In the second phase of the NGT, we provided a list of salient terms to all KEs and asked them to reevaluate their extracted patterns. The aforementioned steps of NGT were performed again to reexamine the patterns with consideration of salient terms. The KEs modified the extracted patterns based on the salient terms and the final agreed-upon heuristics patterns list is given in Table 4. The patterns became more general compared to patterns without considering salient terms. Most of the selected patterns included some of the salient terms to boarder its scope. As an example the pattern \".*(recommend(ed)?) treatment.\"\" became \".*(recommend(ed)? | better) treatment.\"\" after reflecting salient term \"better\" in the pattern.
## 3 Result and Discussion
### FT-IR Analysis  
The chemical compatibility of the PEG/GNPs composites was characterized by FT-IR spectroscopy (Figure 2). As shown in Figure 2, in the spectrum of pure PEG, the absorption peak at 3435 cm\({}^{-1}\) belongs to the stretching vibration of O-H groups while the sharp peak at 2881 cm\({}^{-1}\) is attributed to the C-H stretching vibration. Moreover, the obtained bands at 1642 cm\({}^{-1}\) and 1110 cm\({}^{-1}\) represent the C=O stretching vibration and C-O asymmetric stretching vibration, respectively. In addition, the C-H bending vibration is shown at 841 cm\({}^{-1}\). Similar observations were reported in a previous study [34]. In terms of the spectrum of GNPs, it appears nearly a flat line because it is universally acknowledged that there are few functional groups on GNPs [18]. It can be clearly seen that the absorption peaks in the spectrum of PEG/GNPs composites are in accordance with the spectrum of PEG, which reveals that the previously mentioned peaks of PEG remain constant in the composites. Furthermore, no distinct new peaks are observed in the spectra of the mixtures, suggesting that there are only physical reactions between PEG and GNPs rather than chemical reactions.
## 2 Antenna Design and Performance
### Fractal Absorber Design and Performance
This propagation of the electromagnetic wave can be suppressed by inserting the proposed absorber. Metamaterial absorbers with different numbers of the fractal-shaped patch array layers are investigated in Figure 15c. As shown in Figure 15c, when the number of layers is less than three, suppression of the propagated wave is insufficient because the number of layers is not high enough to show the periodic characteristics [17]. Since it is difficult to increase the number of layers of the absorber to a very large number, an absorber with three layers is considered to be the optimized structure in this paper. Figure 14: Optimized unit cell of fractal absorber: (**a**) geometry of optimized unit cell, (**b**) simulated S-parameters and absorptivity, and (**c**) simulated absorptivity performances depending on incident angle.
# A Technical Survey on Delay Defects in Nanoscale Digital VLSI Circuits
## 11 Commercially Available EDA Tools
### Mentor Graphics  
Tessent product suite provides the test solutions for testing complex ICs [86]. It offers support for at-speed testing and performs delay testing through TDF ATPG, PDF ATPG, TA ATPG, multiple-detect ATPG, and CA ATPG. The transition delay fault ATPG supports LOC and LOS-based pattern generation. The PDF ATPG detects the faults by either robust, non-robust, or hazard-free tests. TA ATPG uses SDF [87] information to generate the patterns for detecting SDDs by propagating the fault effect through long paths. However, the run time is eight times more than the traditional TDF ATPG. Also, the test coverage of TA ATPG may sometimes be less than the conventional TDF ATPG as it tries to propagate the fault through the longest possible path. Besides, the TA ATPG does not support skewed load-based pattern generation.  
The CA-based and TA-based CA test pattern generation detect internal cell defects requiring a bridge and open defect location extraction. For this purpose, the Tessent diagnostic tool uses LEF and DEF files to generate the Tessent Layout database (LDB). The Tessent fault extraction tool extracts the bridge defect's location from the LDB. Later, these defects are defined as fault models and written in UDFM format. The UDFM is used to describe the custom fault models [86]. The custom fault models represent the defects that standard fault models cannot model. The ATPG tool uses SDF and UDFM file information to propagate the fault effect through long paths.  
The tool also supports the on-chip clock control (OCC) circuitry insertion. The OCC supplies appropriate clock signals to the design under test based on the control signal. The test clocks (shift, launch, capture) generated from the OCC can be used by the Tessent ATPG tool for at-speed testing and FAST.
# An Aerial Mixed-Reality Environment for First-Person-View Drone Flying
## 4 Experimental Results  
### Experimental Environment  
To conduct user studies of an FPV drone flight in MR, we constructed an experimental drone racing environment in a drone airfield (8 m in length, 20 m in width, and 5 m in height), as shown in Figure 8. Three checkpoint flags and one destination were set in the drone flight path. While flying an FPV drone, virtual 3D obstacles were created at each checkpoint and mixed with the video feed from the drone camera. The flight time to the destination was set to approximately 1 min to complete the flight within a short time. To adjust the difficulty, more checkpoints can be added or the drone can be flown in a larger area. First, a user study was conducted to investigate the differences in immersion by stereoscopic sense and presence, in which an MR-based FPV flight in a simulated manner was compared with a VR-based FPV flight. The experiment data of the participants were collected after receiving written consent of each participant in accordance with the Declaration of Helsinki and our institutional regulations (Research ethics and integrity committee, 20200662, Sejong University). Furthermore, the participants received an explanation and video training from the researcher about each environment, and were notified before the experiment about the system components of each environment (viewing angle, monocular or binocular, resolution, etc.) A second user study was conducted to investigate the differences in FPV flights from the control of drones in real-word, VR, and MR environments by actual pilots.  
Figure 7: (**a–d**) First-person view (FPV) drone flight images in a time sequence within an aerial mixed-reality (MR) environment.
# A Hierarchical Security Event Correlation Model for Real-Time Threat Detection and Response
## 1 Introduction
### Research Background  
Generally, correlation techniques are categorized as (1) similarity-based, (2) sequential, and (3) case\(-\)based [3]. Similarity\(-\)based techniques correlate two events if they are similar by comparing their attributes or time. Typically, some measure of distance is used to decide the similarity. Common distance functions in use are the Euclidean, Mahalanobis, Minkowski, and Manhattan distances. To determine if a new alert should be included in a cluster, its distance to the cluster is compared to some predetermined threshold. If the distance is above the threshold, it is correlated and included in the cluster. These techniques are known for their simplicity and performance in aggregation and reduction of the data set [4]. However, sequences in the events cannot be captured. Additionally, similarity-based techniques cannot be used to detect complex and multistep attacks. On the other hand, sequence-based techniques correlate events that have a causal relationship with each other. One event is considered the precondition while the other is the consequence. The strength of these techniques is their scalability and the ability to detect even previously unknown attacks. Case-based methods require the existence of a database of earlier cases which can be correlated with a new alert to make a classification and prediction. Several of these approaches have been used by researchers to address different correlation problems. What is clear is that there is no single approach that is universally applicable to every problem [3].  
The problem being addressed by this paper can be characterized by the following requirements: (1) Correlation must be done on the events, rather than alerts. This approach is taken to deal with the issue of the high volume of alerts at the source, rather than after the fact. (2) The correlation must enable real-time attack detection so as to enable a fast response. This requirement means that approaches that require a long time or large data sets to build models and statistics do not apply, as those approaches will not satisfy the response times demanded by a real-time application.  
Based on the problem defined and the approaches reviewed, this paper develops an event correlation model for real-time intrusion detection based on a hierarchy of two approaches: (A) a similarity correlation method is used to reduce the size of the data set and form clusters, and (B) graph correlation is applied on the clusters to discover cluster interconnections which can reveal unusual network traffic patterns that may indicate a possible attack. The target architecture is a distributed IDS where data collection components stream events to the correlation unit in real time. The proposed correlation model is described in detail in the rest of this paper.
## 2 Designed 1D CNN-BiLSTM-AdaBoost Model
### Detailed Model Description
#### 2.2.3 BiLSTM Architecture  
BiLSTM is a variant of LSTM. Unlike conventional LSTM, which can only utilize past time-series data for predictions, BiLSTM incorporates both forward and backward LSTM layers, enabling more comprehensive information processing. The fundamental principle of LSTM is illustrated in Figure 3.  
In a BiLSTM model, two independent LSTMs operate in parallel: a forward LSTM and a backward LSTM, which is presented in Figure 4.  
Forward LSTM: Processes the input sequence \(\mathrm{x_{1}},\mathrm{x_{2}},\ldots,\mathrm{x_{T}}\) sequentially from time step \(\mathrm{t=1}\) to \(\mathrm{t=T}\).  
\[\overrightarrow{h}_{t}=\mathrm{LSTM}_{\mathrm{forward}}\ \left(x_{t}, \overrightarrow{h}_{t-1}\right) \tag{2}\]  
Backward LSTM: Processes the input sequence \(\mathrm{x_{T}},\mathrm{x_{T-1}},\ldots,\mathrm{x_{1}}\) in reverse order sequentially from time step \(\mathrm{t=T}\) to \(\mathrm{t=1}\).  
\[\overleftarrow{h}_{t}=\ \mathrm{LSTM}_{\mathrm{backward}}\ \left( \overleftarrow{x}_{t},\overleftarrow{h}_{t+1}\right) \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Fusion Type** & **Advantages** & **Disadvantages** \\ \hline Data-level fusion & Provides rich information with high accuracy & Requires handling different data formats and precision \\ \hline Feature-level fusion & Combines features from different sensors, enhancing robustness and efficiency & Effective feature extraction and selection can be complex \\ \hline Decision-level fusion & Allows independent decision-making by each sensor, offering flexibility & More complex; decision-making at the sensor level is challenging \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of data fusion methods.  
Figure 3: Basic principle of LSTM.  
Bidirectional Hidden State: At each time step, the hidden state of the BiLSTM is formed by concatenating the hidden states of the forward and backward LSTMs:  
\[h_{t}=\left[\overset{\rightarrow}{h}_{t};\overset{\leftarrow}{h}_{t}\right] \tag{4}\]  
where \(\overset{\rightarrow}{h}\) represents the hidden state of the forward LSTM at time step t; \(\overset{\leftarrow}{h}\) is the hidden state derived from the backward LSTM; and \(h_{t}\) is the final hidden state at time step t, obtained by concatenating the forward and backward hidden states.  
The final output of the BiLSTM is typically computed by mapping the bidirectional hidden states at each time step h\({}_{t}\) to the desired output dimension through a linear layer:  
\[y_{t}=W_{y}.h_{t}+b_{y} \tag{5}\]  
where \(W_{y}\) is the weight matrix and \(b_{y}\) is the bias term.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 1 Introduction
### Objectives  
We classify multi-path routing to be of two types--FMP and MMP--based on path control and verify that the effectiveness of multi-path with real networks deployed globally, especially MMP routing, shows more resilience than FMP used currently. MMP routing enables changing to alternate paths to mitigate the network problem in other hops, which cannot be controlled by FMP routing. In this study, we evaluated the network performance in terms of latency and loss rate [12].  
These results indicate that MMP can mitigate a DDoS attack of a transit-link and provide high availability on a global network using scalability, control, and isolation on next-generation networks (SCION) [13; 14]. SCIONLab [15; 16] is a global research network used to test SCION. We can join the SCION network using our own computation resources and set up and run our own ASes. An AS will actively participate in routing in a SCIONLab network and enable realistic experimentation using the unique properties of the SCION architecture. We conducted an evaluation of multi-path selection on the MMP and compared it with FMP. For a global evaluation, we used a SCIONlab network with nodes from six countries, that is, the United States, Switzerland, Germany, the Republic of Korea, Singapore, and Japan, and considered 18 SCION ASes. For a multi-path test, we assume that there is an attacker attempting to consume the entire bandwidth for communication. To compare MMP with FMP, a situation is assumed in which heavy traffic occurs among the ASes affecting both links configured for SD-WAN, but which cannot be controlled by it.
# A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images
## 4 Discussion
Therefore, future research may need to explore some semi-supervised or unsupervised DL methods to alleviate the need for annotated data [44; 45]. In addition, we will also explore how to incorporate features such as vegetation parameters and phenological changes into our MAFANet model to further improve the richness of the model and its ability to UGS segmentation [46].
## 3 Development History of Navigation Signal Authentication Technology
### Authentication Performance Evaluation  
The authentication performance evaluation results represent the service performance of the BDS authentication service. It is necessary to build a complete authentication performance indicator system to comprehensively represent the security, robustness, authentication, and other performances of the authentication service. The authentication performance indicators are shown in Figure 14.  
Figure 12: Satellite offset transmission.  
Figure 13: SBAS message format [52].  
1. Security.  
Security describes the ability to resist spoofing attacks, including key length, key security level, key management and authentication protocols [71]. The NMA is embodied in the unpredictable message bit/symbol, and the SCA is embodied in the unpredictable spreading code, that is, the entropy of the authentication signal.
2. Robustness.  
Robustness describes the maximum bit error rate or signal distortion that can be tolerated under channel transmission [21]. The NMA is reflected in the maximum message bit error rate, which will lead to the failure of the entire frame of message authentication; the SCA is embodied in the maximum signal distortion, which will cause signal correlation peak attenuation, resulting in missed alarms and false alarms in authentication.
3. Authentication.  
Authentication describes the ability of the receiver to perform message/spreading code authentication, including the time between authentication, authentication latency, time to first authentication, and authentication time to detect [18], etc.
4. Other indicators.  
Other indicators include communication overhead, data loss tolerance, the scalability of key management, and receiver requirements. Communication overhead refers to the communication bandwidth required for authentication messages/spreading codes; data loss tolerance refers to the ability to restore authentication services or to minimize service impact in the event of data loss; the scalability of key management refers to being faced with the scalability of key distribution, storage, and update under a large number of users; receiver requirements refer to the cost of additional authentication services for receivers, such as SCA, which will increase receiver storage resources.
## 2 Materials and Methods
### Methodology
#### 2.2.1 Physics-Informed Data-Driven Models
The long short-term memory (LSTM) model is an advanced technological neural network that has been selected as an effective model for time-series simulation and/or prediction [44]. LSTM is a type of recurrent neural network (RNN) that addresses the drawbacks of the RNN vanishing gradient by adding additional interactions [45]. The LSTM model has been applied in various areas, including rainfall-runoff simulation, handwriting identification, speed identification, and road traffic forecasting, because of its nonlinear predictive capability, faster convergence, efficient learning trends, and ability to capture the long-term correlation of the sequential series [46]. The LSTM model comprises a forget gate, an input gate, an output gate, and a cell state, as shown in Figure 3. In general, LSTM utilizes the previous state information to perform further operations. The forget gate determines the value that should be maintained or discarded. Values from the previous hidden state (h\({}_{\text{t-1}}\)) and current input (x\({}_{\text{t}}\)) move through the activation function and are computed from 0 to 1. If the computed value is close to zero, the gate forgets it, and if the value is close to 1, the gate retains it. An input gate is used to modify the state of the cell. First, we used a sigmoid function to combine the prior concealed state and the current input. The phrase \"long-term memory\" is frequently used to refer to the cell state. A forget gate was used to modify the cell state, whereas an input modulation gate was used to regulate the cell state. Finally, the output gate determines the next hidden state. The hidden state includes the values of previous inputs and is also used for prediction. The hidden state was represented by the output. The new cell state, as well as the new hidden state, is then transferred to the next time step in the process [47]. Figure 3: Architecture of the LSTM considered in this study.