esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Multi-Model Performance Comparison  
The linear fitting effects of the measured \(N_{u}\) values in the test set and the corresponding predicted \(N_{u}\) values obtained from the six different methods are shown in Figure 7. It is evident from the first four subgraphs that PSVM has the best prediction performance, followed by DT, and the worst is GP. Though MLR is a common linear model, its performance is still better than the nonlinear model of GP, indicating that the linear model is not necessarily worse than the nonlinear model. Additionally, as a combined model, the prediction performance of PSVM is better than that of a single model. This viewpoint can be proven by the performance evaluation results of different models in Tables 2 and 3. Generally speaking, the prediction accuracy of SVM optimized by PSO is about 5% higher than that of SVM optimized by the grid search technique (GST) [57]. In Section 4.2, PSO converges after less than 100 iterations, which shows that the operation efficiency of PSO is also better than that of GST. Additionally, PSO can adjust SVM parameters adaptively, so that the combined PSVM model has better generalization ability.  
The last two subgraphs in Figure 7 are the calculation results of the two design methods, GJB 4142-2000 and AIJ 1997 expressions. Compared with the above four MLAs, the design expressions have physical meaning and are easy to calculate, resulting in lots of practical applications. As for prediction performance, the prediction accuracy of the two expressions is not as good as that of PSVM  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Evaluation Indexes** & **DT** & **GP** & **MLR** & **PSVM** & **AIJ 1997 Expression** & **GJB 4142-2000 Expression** \\ \hline \(R^{2}\) & 0.825 & 0.432 & 0.522 & 0.932 & 0.635 & 0.770 \\ _MAPE_ & 0.204 & 0.512 & 0.320 & 0.143 & 0.316 & 0.210 \\ _MAE_ (x10\({}^{3}\) kN) & 0.369 & 0.743 & 0.592 & 0.239 & 0.606 & 0.423 \\ _RMSE_ (x10\({}^{3}\) kN) & 0.503 & 0.906 & 0.831 & 0.314 & 0.727 & 0.576 \\ _CEI_ & 0.239 & 1.000 & 0.718 & 0.000 & 0.622 & 0.329 \\ \hline \end{tabular}
\end{table}
Table 2: Prediction performance evaluation of DT, GP, MLR, PSVM, AIJ 1997, and GJB 4142-2000 expressions in the training set.  
Figure 6: Radar chart of performance evaluation of PSVM in the (**a**) training set and (**b**) test set.  
and DT, but better than that of MLR and GP. Combined with Tables 2 and 3, it can be found that the calculation results of GJB are more accurate than those of AIJ, but there is still a gap with PSVM.
# Towards Optimizing Multi-Level Selective Maintenance via Machine Learning Predictive Models
## 4 Proposed Methodology
### Predictive Model for Multi-Level Selective Maintenance Using ANN and SVR
#### 4.2.3 Hyperparameter and Model Evaluation  
The model training process involves selecting the optimal hyperparameters that the learning algorithm will use to learn the optimal parameters and properly map the input features (independent variables) to the labels (dependent variables). Hyperparameters are used to control the process of learning and determine the model parameter values that a learning algorithm ends up learning. There are several training techniques introduced into machine learning to find these optimal hyperparameters, and the most widely used method is random search. Indeed, this technique uses a random combination of parameters at each iteration to find the best solution for the built model. In addition, it has been shown theoretically and empirically that a randomized search is computationally efficient, especially when it comes to ANN models [42]. In fact, hyperparameter values should be settled before the training of the model begins. Then, a backpropagation algorithm (BP) is applied to the model in order to find the optimal values of the weights \(w_{i,j}\).  
In this study, the hyperparameters considered for the ANN model are the number of hidden layers, the number of nodes for each layer and the learning rate of the optimization algorithm used to fit the model. So, defining the network configuration is the first step in developing the model.  
Therefore, the second step is to compile and fit the network to build our model. To train the model, the compilation applied an optimization algorithm and a loss function to evaluate the network. As we studied a regression problem, an adaptive optimizer called Adam updates the network weights iteratively, and a mean squared error loss function is implemented to calculate the average differences between the actual and predicted labels [43].  
Therefore, in order to evaluate the predictive performances and the accuracy of the model based on ANN or SVR, certain evaluation indices have been used. These indices are explained below and are among the most commonly used performance measures with \(y_{m,i}\) as the \(i^{th}\) actual value and \(y_{p,i}\) as the \(i^{th}\) predicted value.  
Absolute error (\(e_{i}\)): the absolute error is given by the absolute value of the difference between the measured (actual) value and the predictive value  
\[e_{i}=\big{|}y_{m,i}-y_{p,i}\big{|} \tag{17}\]  
Mean Absolute Error (MAE): measures the average of absolute errors of predictions and observations  
\[MAE=\frac{\sum_{i=1}^{n}e_{i}}{n} \tag{18}\]  
Mean Squared Error (MSE): measures the average squared difference between the observed and predicted values  
\[MSE=\sqrt{\frac{\sum_{i=1}^{n}e_{i}^{2}}{n}} \tag{19}\]Mean Absolute Percentage Error (MAPE): a statistical measure to define the accuracy of a machine learning algorithm on a particular dataset. It can be considered as a loss function to define the error termed by the model evaluation  
\[MAPE=\frac{1}{n}\ \sum_{i=1}^{n}e_{i} \tag{20}\]
###### Abstract  
In order to reduce the environmental impact of aircraft operation in the terminal area, this paper researched the route optimization method. Firstly, this paper constructed the air pollutant emission and noise assessment model, and the flight performance model. Secondly, aiming at reducing air emissions and noise level, the multi-objective terminal area route optimization model is established based on the principles of flight safety and flight procedure construction. Then this paper puts forward the path optimization method of emission and noise reduction of terminal area route network, through the research on the priority setting method of terminal area approach and departure route planning. The route segmentation method and NSGA-II algorithm are employed to solve the problem. Finally, a numerical case study is carried out for the Shanghai terminal area, and yields the following results: (1) Compared with the original route network, the optimized route network in the terminal area can significantly reduce emission and noise by reducing pollutant emission by 51.4% and noise influence by 21.5%; (2) The method can also reduce fuel consumption by 60.5% and the total route length by 21.1%.  
Terminal area; Arrival routes; Path optimization; Air pollutant emissions; Aviation noise 1 Air Traffic Department, College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China 1
## 3 Results and Discussion
### dsDNA and GNPs  
The key point of this study was to investigate the efficiency of adsorption (on the surface of GNPs) of DNA duplexes (Figure 2), including those carrying \"anchor\" ssDNA motifs that are intended to enhance the affinity for GNPs. DNA duplexes (or dsDNA) were assembled from ssDNA (strand concentrations from 0.5 to 100 nM) in a 4 mM sodium citrate solution. The concentration of GNPs was constant (0.5 nM) in all experiments.  
Initially, we determined thermal stability of the formed duplexes under the conditions utilized for the assembly of the noncovalent nanoassociates. According to the experimental data, the melting temperature (T\({}_{\text{m}}\)) of all dsDNA (at 4 \(\upmu\)M total concentration of ssDNA) is significantly higher than 25 \({}^{\circ}\)C (Table S1). This finding indicates that at a high concentration of interacting complementary DNA strands (100 nM), in solution, all duplexes being analyzed should be predominantly in the double-stranded state. At 37 \({}^{\circ}\)C, under such conditions, the DNA duplex is half-dissociated, whereas at 45 \({}^{\circ}\)C, the DNA should be predominantly represented by separate strands. According to the calculated data (Table S2), a decrease in the total concentration of ssDNA (to 0.5 nM) at 25 \({}^{\circ}\)C should not significantly destabilize dsDNA. Note, however, that the proportion of ssDNA released from the complex goes up as the concentration of interacting DNA strands decreases [48].
## 4 Performance Analysis of QoS Metrics
### Analysis of Throughput Considering Data Encryption and Deployment Scenario
However, these QoS metrics, especially the network lifetime, and energy efficiency, can be improved significantly by adopting the data aggregation approach at the end nodes based on correlation among the sensor data as demonstrated by [56; 57]. This can be a great solution to improve the performance metrics as the data recorded by the closer nodes are correlated; thus, the total payload can be cut down significantly by compressing the data upon aggregation. Moreover, many researchers have also suggested the time division multiple access (TDMA)-based approach with Zigbee instead of traditional CSMA/CA, which has shown great potential in achieving better performances in terms of throughput and energy consumption [58; 59; 60]. However, this approach may encounter problems due to inefficient clock synchronization, which can be pro-actively addressed by a priority-based direction aware media-access control protocol by [PERSON] et al. [61]. The authors aimed to propose an energy-efficient, optimized, and improved Zigbee protocol based on the performance analysis of this study.
# Fast Scalable Architecture of a Near-ML Detector for a MIMO-QSM Receiver
## 4 Proposed Hardware Architecture for the Digital QSM Detector
### fd and ctrl Modules  
The fd module made the decision of accepting the current detected indexes as valid or not. It achieved this by comparing the detection results with predefined parameters specific to QSM detectors (\(V_{th1}\), \(V_{th2}\), \(d_{min}\), in Algorithm 3). In case the results were accepted, the detection process ended; otherwise, det2 started over with different data. This was implemented with counters and a state machine, so it emulated the behavior of the _for_ cycle in Algorithm 3.  
The ctrl module grouped three independent modules that controlled the three main parts of the detector (det1, det2_P1, and det2_P2).  
The control modules for det1 and det2_P1 were composed mainly of counters that checked the number of elapsed clock cycles, representing their respective _for_ cycles in the algorithms.  
The control module for det2_P2 differed from the other two control modules. This one consisted of a state machine and counters that performed the _for_ cycles in Algorithm 2, specifically Lines 11 and 13. As is seen in Line 13, the _lim_ variable was known until execution time and changed depending on the data, the reason why a state machine was required for proper control.
## 4 Discussion  
This study employs 3D-FDTD simulations to investigate LIV variations under lightning strikes at different locations on Gaussian-shaped mountains. Unlike previous numerical studies, the proposed Gaussian-shaped terrain better captures curved boundaries, which significantly influence lightning EM wave propagation. Previous studies [31] have confirmed that lightning striking a mountain can lead to an increase in LIVs on nearby distribution lines. In this study, the impact of mountains exhibits distinct patterns at different strike locations. The curved surface of the Gaussian-shaped mountain exhibits wave-focusing behavior, enhancing LIVs through stronger reflections when EM waves propagate along the surface while increasing shielding effects when waves propagate across the mountain. This effect is also influenced by the size of the mountain, as shown in the results, mountain height dominates LIVs more than width, with higher mountains amplifying both reflection and shielding effect. These results demonstrate that realistic mountain curvature must be considered in lightning protection design, as standard rectangular or cone-shaped models may underestimate LIV variations. Future work could extend this approach to complex terrain such as multi-peak terrains. From the results shown in Figures 11 and 12, simulations reveal the necessity of distinguishing soil conductivity between mountain and ground due to their altitudinal positions and humidity. Future studies should pay more attention to the nonlinear correlation between soil conductivity and LIVs.
## 2 Abnormal Data Feature Identification  
Firstly, time domain analysis is used to process the data. The data is visualized to transform time series data into images for better classification based on their characteristics of them. The horizontal coordinate of each image is the time, and the vertical coordinate is the acceleration. Each image occupies the duration of one hour of the original data, and the data characteristics of one hour of acceleration are reflected in this graph roughly. The characteristics of different categories of monitoring data are analyzed by the trend of acceleration amplitude in images over time and space. Each category of data will be divided into six categories: normal, drift, gain, noise, missing, and outlier. Figure 1 shows an example of each category of image.  
The normal data images have acceleration amplitudes that fluctuate in an orderly manner above and below the image centerline with significant fluctuations. The drift data image has a tendency for the data to deviate from the centerline and shows a tendency to tilt or fluctuate above and below the centerline. The local gain data images show a tendency for a sudden increase in acceleration amplitude in some time periods, and their amplitude is much higher than the data in other time periods. The noise data images whose acceleration amplitude tends to be consistent throughout the time period without significant fluctuations. The missing data image has some data missing; the acceleration amplitude at the missing point is zero. Additionally, a straight line is shown in the image, or the whole image is blank with all data missing. The outlier data image has a sudden increase in acceleration amplitude at a certain moment, and one or more isolated data points are presented in the image as well as the overall features are significantly reduced.  
Figure 1: Schematic diagram of data characteristics of each category. (**a**) normal. (**b**) drift. (**c**) local gain. (**d**) noise. (**e**) missing. (**f**) outlier.
## 1 Introduction
The objective of this study is to evaluate the impact of various operational parameters of a modified light-duty single-cylinder methane/diesel engine, including engine rotational speed (rpm), methane mass, diesel mass, the SOI, and the duration of diesel injection on indicated specific fuel consumption (ISFC), PFP, and emissions. The characteristics of the base engine are presented in Table 2, and the diesel and the RCCI engine models of this engine, in addition to the piston bowl shape, are shown in Figure 1. The sensitivity to the operational parameters is investigated to determine the most appropriate one in different circumstances. The non-dominated sorting algorithm NSGA II is used for the optimization study and to minimize ISFC simultaneously with decreasing tailpipe emissions. The research analyzing the variables that affect the combustion in RCCI engine, including the amount, timing, and duration for both premixed fuel, as well as direct injection fuel in addition to the effect of the engine rotation, is inadequate. The primary goal of this study is the extensive optimization of performance and tailpipe emissions, in comparison to the baseline conventional diesel engine, across a wide range of engine loads. Studying the effect of all the variables mentioned above and exploring the range of RCCI loads and on which excess air ratios methane/diesel light-duty engine work is a new gap that has been examined to recognize the main variable parameters that control RCCI mode to cope with the essential difficulties facing RCCI combustion, which is the second consequence of \begin{table}
\begin{tabular}{c c c} \hline \hline
**Properties** & **n-Heptane** & **Methane** \\ \hline Chemical formula & C\({}_{7}\)H\({}_{16}\) & CH\({}_{4}\) \\ Density at 15 \({}^{\circ}\)C (g/m\({}^{3}\)) & 0.688 & 0.664 \\ Lower heating value (MJ/kg) & 44.24 & 49.94 \\ Auto-ignition temperature (K) & 558 & 810 \\ Molecular weight & 100.21 & 16.042 \\ Cetane number & 56 & - \\ Octane number & - & 130 \\ Boiling point (K) & 372 & 111.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Methane and n-Heptane physio-chemical properties [19]. this study. Finally, a sensitivity analysis is carried out to assess how the decision variables independently affect the objective functions, assisting in discovering the key variables affecting RCCI combustion. Moreover, this work is carried out to prepare and check the validity of our calculation methodology for the future H\({}_{2}\)-diesel combustion study.
## 4 Defense Strategies Against Deliberate Attacks on IECPS
### Post-Event Recovery and Summarizing
#### 4.3.3 Defense Evaluation and Enhancement  
Enhancing the defense capability of IECPS against deliberate attacks is crucial to protect the system from threats and maintain its safe and stable operation. Scientifically and rationally evaluating the effectiveness of defensive strategies against deliberate attack events is the foundation for enhancing defense capabilities. Following each defense against a deliberate attack, it is essential to analyze the weaknesses in the current defense system and the effectiveness of the defense strategies, providing guidance for optimizing the defense system. Specifically, defense evaluation typically combines both static and dynamic methods, dividing the process into static analysis and dynamic assessment. Static analysis mainly focuses on reviewing aspects, such as system architecture, access control policies, communication protocols, and device configurations, analyzing the strengths and weaknesses of the defense system from a configuration and planning perspective. Literature [187] analyzes the framework of the entire energy system based on the System of Systems approach. It establishes multidimensional evaluation metrics to capture future changes in the system architecture, providing theoretical support for the updating of defense systems. In [188], based on trust evaluation of security management nodes, a data-driven method was used to establish a security analysis model for communication protocols, providing an effective solution for analyzing the security of communication protocols. In [189], various configuration schemes within the integrated energy system were evaluated in detail, and the economic aspects of the configurations were also analyzed.  
Dynamic evaluation mainly assesses the system's response to deliberate attacks, including load loss evaluation, the degree of protection for information integrity, among others. By analyzing the resistance capability of specific events, the vulnerabilities and issues of the existing defense system are intuitively revealed. After completing the defense evaluation, corresponding improvement measures are formulated for the existing defense system's issues, including but not limited to improving the accuracy of attack detection and localization, enhancing the targeting of redundant equipment configurations, and improving the coordination of cyber-physical restoration. Additionally, considering the uncertainty of deliberate attacks, periodic attack-defense exercises can be conducted to broaden the scope of dynamic evaluation objects. In conclusion, through continuous evaluation and improvement of the defense system against deliberate attacks, a dynamic defense system for IECPS is formed, ensuring that it possesses sufficient resistance capabilities to maintain the security and stability of energy supply in the face of real deliberate attacks.
## 4 Experiment and Discussion  
The availability of ARAIM is the core evaluation index. In this section, the availability performance evaluation is divided into two subsections: the classical worldwide availability coverage and the particular fixed-point availability verification. The former one tests the ARAIM worldwide coverage of ARAIM 99.5% availability with the nominal constellation configurations. It is also the most frequently used for ARAIM evaluation. The latter one will fix the receiver at one particular point and collect the actual GNSS signals to assess the ARAIM availability. It aims to evaluate the ARAIM performance under the current constellations. Different ARAIM modes will be evaluated under the LPV-200 criteria.  
The numerical values are given in Table 1. At the same time, as the determination of parameter values in ISM is still a major challenge before enabling ARAIM service, the choice of ISM and ARAIM integrity parameters are taken from the ARAIM TSG Milestone 3 Report and given in Table 1, too.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Classification** & **Parameter** & **Value** \\ \hline \multirow{5}{*}{LPV-200} & VAL & 35 m \\  & HAL & 40 m \\  & EMT & 15 m \\  & \(\sigma_{\text{A}\text{C},\text{req}}\) & 1.87 m \\ \hline \multirow{5}{*}{ISM} & \(P_{\text{comst},j}\) & \(10^{-5}\) \\  & \(P_{\text{sat},j}\) & \(10^{-4}\) \\  & \(\sigma_{\text{URA},i}\) & 1 m \\  & \(\sigma_{\text{URE},i}\) & \(2/3\sigma_{\text{URA},i}\) \\  & \(b_{\text{nom},i}\) & 0.75 m \\ \hline \multirow{3}{*}{Integrity parameters for ARAIM} & \(P_{\text{THIRES}}\) & \(9\times 10^{-8}\) \\  & \(P_{\text{THMI}}\)\({}_{\text{VERT}}\) & \(9\times 10^{-8}\) \\ \cline{1-1}  & \(P_{\text{FA}}\) & \(4\times 10^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters for ARAIM availability evaluation.  
For the comparative test, the test objects are divided into three ARAIM modes and further refined to five groups. As shown in Table 2, three ARAIM modes are: ARAIM with two fixed constellations, ARAIM with four constellations, ARAIM with constellation dynamic selection. Then in the first model, 'GPS + Galileo' and 'GPS + BDS' are chosen as two groups in this test. The third model is also refined to two groups based on the analysis of sorting rules in the above section: the two constellations with minimum VDOPs and the dual-constellation combination with minimum VDOP.  
Location points spread on a grid of 5-by-5 degree on earth and the period is one sidereal day. The time step is 600 s. Therefore, 2592 \(\times\) 144 = 373,248 points are available for analysis. In turn, the main outputs of ARAIM include availability, VPL, HPL, EMT, and vertical positioning accuracy.