esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Development and Validation
### Model Validation  
To further verify the prediction accuracy and stability of the improved particle swarm algorithm for support vector machine regression, the genetic algorithm optimized support vector machine regression (GA-SVR), particle swarm algorithm optimized support vector machine regression (PSO-SVR), and traditional support vector machine regression (SVR) were compared and verified, as shown in Figure 7.  
According to Figure 6, SVR has the highest number of iterations and a large degree of adaptation, meaning that it is not a good choice. GA-SVR, although it shows some improvements over the traditional SVR algorithm in terms of the number of iterations and degree of adaptation, is still not ideal, and PSO-SVR is significantly better. IPSO-SVR ensures that the degree of adaptation is small enough while still satisfying the need for a faster convergence rate. The results show that the IPSO algorithm has a better ability to find the global optimal solution.  
As can be seen from Table 8, although PSO-SVR is superior in terms of fit, it is still not as stable as the improved particle swarm algorithm, and the prediction effect is not as good as the improved particle swarm algorithm; IPSO-SVR has the highest prediction accuracy and the best model stability, 94.00% and 8.71%, respectively, and the fit superiority R\({}^{2}\) is also higher in comparison; the prediction performance and fit effect of SVR are relatively poor, and the model is not stable, with the highest relative standard deviation (RSD) of 16.56. Generally speaking, the smaller the value of RSD, the lower the degree of dispersion of the data, and the higher the inter-data consistency and stability [41]. Therefore, by combining the calculation of different evaluation parameters, it is verified that IPSO has a good optimization effect on two important parameters of SVR and has a certain research value, indicating that the model has good stability. Comparing the prediction of aircraft seat comfort using artificial neural networks by [PERSON] [19] and others, the RMSE and R\({}^{2}\) obtained in the literature were 1.21 and 78%, respectively, and it was found that, in the model using IPSO to optimize the SVR, the data were better in terms of RMSE and prediction accuracy, which indicates the superior prediction accuracy of this method. The related research on the predictive analysis of airline seat comfort is still limited compared to other industries and needs to be further explored.  
Figure 7: Iteration process of each optimization algorithm.
# A Novel Dynamic Approach for Risk Analysis and Simulation Using Multi-Agents Model
## 6 Generic Dynamic ABMS (GDABM) for Risk Analysis
### Internal Failure Agent Communication (IFAC)
#### 6.4.2 FMs \(\rightarrow\) BMs  
The behavior of an agent \(i\) in a multi-agent environment is assumed to be initially in its nominal mode \(i_{Nom}\).  
Nominal mode \(i_{Nom}\) contains a set of activities \(v\). Each activity \(v\) has a set of FMs. The set \(i_{IFAC}\) covers all possible FMs that are generated within the agent during the execution of any activity \(v\). Moreover, \(i_{EFAC}\) covers the set of all possible failure modes that occur by other external agents.  
For each agent \(i\), a set of failure modes \(i_{FM}\) is defined as an in Equation (8):  
\[i_{FM}=i_{IFAC}\cup i_{EFAC} \tag{8}\]  
Equation (9) computes the behavioral mode \(i_{BM}\) of an agent \(i\) as a function \(\vartheta\) of the set of the active failure modes \(i_{FM}\) of that agent.  
\[i_{BM}=\vartheta(i_{FM}) \tag{9}\]  
If \(i_{FM}\) does not contain any FM elements, \(i_{FM}=\vartheta\), then the \(i_{BM}\) of the agent \(i\) is nominal \(i_{Nom}\). On the other hand, any addition of a failure mode element to the set of active failure modes leads to a disruption of the behavioral mode (connection **b**).  
Figure 8 represents the proposed model (GDABM). The figure shows the interaction between an agent i and its environment. The GDABM is composed of: (1) Risk Model Block (FM): It has the set of failure modes available in the GDABM in addition to their causes and consequences. Sources/ causes of failure modes are described in a dynamic fault tree and their consequences are illustrated in an event tree. For each failure mode, there is an associated behavioral mode to be triggered (2) Behavioural Mode (BM) module contains a set of degraded modes that are possible to occur in addition to the nominal mode. (3) Set of Variables: It holds the static characteristics of the agent. (4) Set of Attributes: It holds the dynamic characteristics of the agent. (5) Agent's environment: It holds the external agents.
## 3 Methods  
### Determination of Parameters for Channel Lateral Migration  
Channel width adjustment was considered to be closely associated with channel lateral migration [31; 34]. In this study, the parameter of bankfull width (\(W_{i}\)) was used to investigate the channel width adjustment in the Xiaobeiganliu reach. In general, the lip-top level of an active floodplain at a section is termed the bankfull level, and the main-channel width between the two floodplain margins on left and right banks is defined as the bankfull width [16; 35]. The thalweg migration distance (\(D_{i}\)) was used to represent the magnitude of lateral migration in the current study, which was determined to be the lateral distance between two thalweg points in the previous and current years at one section [5]. For example, Figure 4 shows the post-flood cross-sectional profiles measured in 1986 and 1987 at the section of CS61. According to the method, the bankfull widths were determined to be 1435 m in 1986 and 1664 m in 1987, and the thalweg migration distance in the two years was determined to be 163 m.  
To investigate the channel lateral migration in the whole reach, a reach-averaged method proposed by [PERSON] et al. [35] was used in the current study to calculate the reach-scale parameters. The reach-scale bankfull width and thalweg migration distance can be expressed as follows:  
\[\overline{W}=\exp\left(\frac{1}{2L}\sum_{i=1}^{N-1}\left(\ln W_{i+1}+\ln W_{i }\right)\times\Delta x\right) \tag{1}\]  
\[\Delta\overline{D}=\exp\left(\frac{1}{2L}\sum_{i=1}^{N-1}\left(\ln\left| \Delta D_{i+1}\right|+\ln\left|\Delta D_{i}\right|\right)\times\Delta x\right) \tag{2}\]  
where \(L\) represents the channel length, and \(N\) represents the number of cross sections. \(\overline{W}\) and \(\Delta\overline{D}\) represent the reach-scale bankfull width and thalweg migration distance, respectively. \(W_{i+1}\) and \(W_{i}\) represent the bankfull widths at the (\(i+1\))th and \(i\)th sections, respectively. \(\Delta D_{i+1}\) and \(\Delta D_{i}\) are the thalweg migration distances at the (\(i+1\))th and \(i\)th sections, respectively. \(\Delta x\) is the longitudinal distance between adjacent sections. A dimensionless parameter of the lateral migration index (\(M\)) was defined as \(\overline{W}/\overline{D}\), which can be used to represent the lateral stability of the whole reach [5].
# PFDA-FMEA, an Integrated Method Improving FMEA Assessment in Product Design
## 3 The PFDA-FMEA Approach  
The proposed PFDA-FMEA method (Pythagorean Fuzzy Dimensional Analysis--Failure Mode and Effect Analysis), intends to minimize the uncertainty in the FMEA assessment and improve the way to make decisions based on the risks identified in the PD process. The method uses FMEA as a basis to collect and organize the main PFMs for the process to beanalyzed by the SME group, following which the PFDA method is applied. The suggested method is generalized in the five steps given below and illustrated in Figure 1.  
**Step 1.** Creation of SMEs group. Depending on the process to assess, a group of \(n\) experts on the matter to be assessed are required to identify, to perform the assessment.  
**Step 2.** Assign weights to SME. Once the SME group is selected, a weight is assigned to each of the experts; the weight to each SME can be assigned depending on the grade of expertise on the matter under analysis.  
**Step 3.** SME group identifies PFMs. The group of experts agrees and establishes the main internal and external characteristics that affect direct or indirect process under analysis.  
**Step 4.** Assess FMEA using Linguistic Values. The list of PFMs is evaluated by each SME independently and based on their experience on the matter, using Table 1 to assign the corresponding Linguistic Values. Table 1 collects the main areas of impact on the process, defined by the SMEs group. These areas are quality (Q), time (T), budget (B), and market (M); each of these areas is divided into levels of impact, low (L), regular (R), and high (H) impact; based on this, the SMEs group assigns the membership and non-membership functions according to the SMEs' experience in each area of the process.  
**Step 5.** Computing and obtain PFDA-FMEA. The results obtained in the previous step are used to apply PFDA per Equation (3); then using Equation (2) the values are de-fuzzified getting the PFDA-FMEA results for S, O, and D.  
**Step 6.** Obtain the PFDA-FMEA index. The mathematical product of PFDA-FMEA S, O, and D, gives us the PFDA-FMEA index.  
**Step 7.** Determine the PFM ranking. The results are ranked to identify the PFM risks and to support the decision to be taken.  
Figure 1: PFDA-FMEA Methodology step by step diagram.
# A Novel Inverse Simulation Method of Helicopter Maneuvering Flight
## 5 Discussion and Conclusions  
A degeneration method of inverse simulation is proposed in this article, and was successfully applied to a helicopter system flying pull-up and slalom maneuvers. The inverse simulation results are compared with the flight test data and the reference data, which indicate that this method has fidelity and is effective. It is different from the conventional inverse simulation method, and can improve the numerical stability during inverse simulation, because the core principle is to solve the output vector in each simulation time step, by degenerating the system of first-order differential equations into nonlinear equations.  
One advantage of the method is that it can invert the helicopter model, and simulate almost any maneuver in the longitudinal, lateral, and directional channels, and even any maneuver in the three-dimensional space, by introducing a transitional vector that can express the output and state vectors at the same time. Thus, our method has good versatility. In addition, because the present results are independent of simulation time steps, and the results of the time histories of the cockpit controls have no high-frequency oscillations (HFOs), our method also demonstrates good robustness. However, there are still two derivatives of Euler angles that cannot be directly expressed in explicit forms; thus, these angles have to be expressed numerically, using a differentiation-based approach.  
We applied our novel inverse simulation method to the helicopter system in the maneuver flight, and successfully obtained the required control inputs. These inverse simulation ideas can also be further applied by those working on inverse dynamics problems, in areas such as fixed-wing aircraft, unmanned aerial vehicles, rockets, and even robotics, and in other industrial fields, such as the direct trajectory control of ground vehicles and surface vessels, and even trajectory optimization problems in spacecraft. Next, we will further develop and improve this method, and attempt to conduct in-depth research on the direct trajectory control of any aircraft, any ground vehicle, and even any surface vessel, using this method.  
Conceptualization, G.L. and Y.C.; Methodology, G.L. and Y.C.; Software, G.L.; Validation, G.L. and Y.C.; Formal analysis, Y.C.; Writing--original draft, G.L.; Writing--review & editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: UH\(-60\)A (**a**) collective, (**b**) longitudinal cyclic, (**c**) lateral cyclic, (**d**) pedal, and (**e**) stabilator angle, during the slalom maneuver.
# A Copula-Based Attack Prediction Model for Vehicle-to-Grid Networks
## 1 Introduction  
Global warming is a phenomenon manifested by an increase in average temperatures due to significant greenhouse gas emissions. It constitutes a situation of a global nature and is worrying such that all the nations of the world are mobilized to find the ideal solutions to preserve the environment and leave a livable planet for future generations. Gasoline vehicles are one of the main sources of greenhouse gas emissions. It becomes urgent to move towards electric vehicles and renewable energy sources. Faced with these challenges, the Smart Grid, or the intelligent electricity grid, has emerged. The Smart Grid was born from the merger between the conventional electricity network and information systems. It promotes renewable energies and manages the balance between production and consumption. However, as with any system, the Smart Grid--and, more precisely, the V2G--has vulnerabilities that can be exploited by malicious parties. The V2G is a part of the Smart Grid; it is responsible for bidirectional exchanges between the electric vehicle and its charging station.  
As with other networks, V2G networks need solutions in order to secure communications between the vehicle and the charging station. Thus, one obstacle to the large-scale deployment of electric vehicles would be overcome. To resolve the security issue in V2G, it is possible to draw inspiration from Intrusion Detection Systems (IDS). These IDS have proven their worth in the networks that preceded the V2G networks.  
The objective of our work is to propose a model capable of predicting DoS and MitM attacks. This model is based on copulas.  
The rest of this paper is organized as follows. In Section 2, general information about V2G networks is exposed. In Section 3, the state of the art on security requirements in V2G networks and methods to detect attacks in multiple networks are presented. In Section 4, the model is described. In Section 5, the process of obtaining the attack database is presented, and finally, the obtained results are discussed in Section 6.
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
###### Abstract  
Globally, more and more attention has been paid to the integrity of Girth Welds (GW) of oil and gas pipelines due to their failures with high consequences. A primary concern is that defects originate during field construction but over time may be subject to external loads due to earth movement. GW defects in newly built pipelines are also assumed to exist but would be much smaller in size, and more difficult to detect, which motivated the investigation into minimum defect detection capabilities of the inspection technologies. This study presents the evaluation results of UltraScan(tm) Circumferential Crack-Like Detection (USCCD) technology for oil pipeline GW inspection, based upon the pull test and in field data from Inline Inspection (ILI) of pipeline by PetroChina Pipeline Company (PPC) using GE PII (General Electric Company, Pipeline Integrity Inspection) 32\" UltraScan(tm) CCD Tool. The performance of USCCD is given according to the ILI data, pull test results and dig NDE (Non-Destructive Examination). It can be concluded that crack-like defects with clear edges can be detected during ultrasonic propagation; however, the irregular shape of weld makes the inspection more difficult. It is still a challenge to identify the type of defects, and depth sizing can only be classified not quantified, which would require more excavations. However, this technology is feasible for the alternative technology of GW defect inspection.  
girth weld; defect; UltraScan Circumferential Crack-Like Detection; pull-through test; excavation
# Adaptive Beamformer Combined with Phase Coherence Weighting Applied to Ultrafast Ultrasound
## 2 Materials and Methods
### Experimental Methods and Evaluation Metrics  
#### 2.4.1 Experimental Setup  
In the present study, an ultrasound imaging phantom (model 040 GSE, CIRS, Norfolk, VA, USA) was used for evaluation of image quality. A linear array ultrasonic probe at a nominal center frequency of 7.5 MHz was used. The element pitch of the linear array and the number of the transducer elements were 0.2 mm and 192, respectively. Ultrasonic echo signals received by individual transducer elements were acquired by a custom-made ultrasound scanner (RSYS0002, Microsonic, Tokyo, Japan) at a sampling frequency of 31.25 MHz. The beamforming procedure was performed off-line using the numerical analysis software MATLAB (The MathWorks Inc., Natick, MA, USA). The transmit-receive procedure is described in [9]. In the present study, plane waves were emitted using 96 transducer elements, and then, 24 receiving beams were created in response to one emission. By repeating such a transmit-receive procedure four times, \(24\times 4=96\) receiving beams were created at intervals of 0.2 mm. The frame rate achieved under such transmit-receive response condition was 1302 Hz at the pulse repetition frequency of 5208 Hz.
# A Comprehensive Review on Music Transcription
## 3 Transcription Techniques: Insights and Advances
### Multi-Instrument Transcription  
Multi-instrument transcription involves transcribing an audio recording containing multiple musical instruments or sound sources. The objective is to separate and extract the individual musical lines of each instrument, capturing their respective pitches, timings, and contributions to the overall musical texture. Recently, deep learning-based multi-instrument transcription [73; 74; 75; 76; 77] has been widely used and is crucial for analyzing and understanding the interactions between different instruments in a polyphonic musical piece. A recently published paper [73] jointly considers the instrument recognition module, the transcription module, and the source separation module, which is capable of transcribing, recognizing, and separating multiple musical instruments from the audio signal. Similarly, the work in [74] adapts the concept of computer vision methods like multi-object detection and instance segmentation for multi-instrument note tracking. The multi-instrument multi-pitch estimation (MI-MPE) task, which is a clustering-based method for music transcription, was proposed by [75]. It estimates piano rolls for diverse musical instrument categories. The authors in this article claim that their method became the very first MI-MPE to be published. Similarly, the paper in [76] implements the concept of multi-object semantic segmentation to solve the problem of pitch activity detection and instrument recognition. Moreover, the work presented in [77] focuses on transcribing snare drums, bass drums, and hi-hats using a convolutional neural network and convolutional recurrent neural network after synthesizing a large number of training data. There are some other methods that carry out multi-instrument transcription after source separation. In this type of multi-instrument transcription task, the algorithm first separates each independent instrument using a source separation algorithm and estimates the pitch and note for each source separately [78; 79]. Finally, the transcribed notes and musical elements from each instrument are combined to create a comprehensive representation of the polyphonic music, capturing the interplay between different instrumental lines.
## 4 Building the Reputation System
### Detection Phase
#### 4.3.2 Independent Detection Scheme: Analysis and Discussion  
As nodes' behavior does not change during the simulation, every node that forwards a message in which it is not the source is good and will never change its status from the moment it is classified. Therefore, this detection scheme always leads to a 0% false-negative rate because not a single malicious node will be classified as good. The false-positive rate and detection rate have various results depending on the routing scheme used and the number of malicious nodes in the simulation. The results simulated in the ONE, for the settings presented in Table 2, are depicted in Figure 3 when using the Prophet routing protocol with 20% of black-hole nodes. The 95% confidence intervals are also plotted, showing great trust in the achieved results, particularly by the end of the simulation.  
The simulation results show that the false-positive ratio decreases to zero. Initially, nodes only have messages created by themselves. Thus, when nodes try to forward their messages, they will be considered malicious and added to the black-node list. Having 20% malicious nodes in the network results in about 20% being correctly classified as malicious and about 80% incorrectly classified as malicious. This is why the false-positive rate starts  
Figure 1: Independent Detection Scheme example.  
Figure 2: Independent Detection Scheme flowchart.  
at about 80%. As good nodes forward messages, their classification will change to good, and the false-positive rate decreases, tending to zero after about 24 h.  
The simulation results also show that the detection ratio increases as time goes by. In the beginning, black-node lists and good node lists are empty, so the detection ratio accounts for zero black holes detected by every node. As nodes generate messages, they will be classified as malicious unless they contribute to the forwarding process, so the detection ratio gradually increases, reaching about 97% after 24 h.  
If a modification is done such that a node is only classified as malicious after being the source of a message \(n\) times without forwarding messages, the detection ratio is much slower, and the false-positive ratio does not improve.  
In the independent detection scheme, every node classifies other nodes independently. Thus, the correct classification is not very fast. If nodes cooperate in the classification process, the classification can be faster.