esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning and Radiomics Approach in Lung Cancer for Predicting Histological Subtype
## 3 Methods
### Classification  
The main task of this work was to classify the histological subtype of lung nodules using radiomic features. In order to accomplish this task, we trained several classifiers considering a variable number of features. Specifically, features were added to the input pattern of each classifier according to their mean univariate AUC calculated in the features selection phase (as described in the previous section whose results are reported in Figure 4). The employed classification models include Logistic Regression (LogReg), Support Vector Machines (SVM) with a linear kernel, AdaBoost (AdaBo), Random Forest (RF), Multi-layer Perceptron (MLP), Gradient Boost (GB); these models were selected since they are widely used in radiomic studies for medical classification purposes [41; 42]. We also evaluated an ensemble of five models (En5) composed by the models mentioned above, except for GB. The ensemble model makes decisions following a voting strategy; the classification output by LogReg, SVM, and MLP was weighted 2; RF and AdaBo prediction was weighted as 1. Simpler models tend to be more robust and so they have received a larger weight. GB was not included since its adding to the ensemble did not lead to performance improvements. Moreover, it already suffered from low accuracies in many cases when more features were considered. The hyperparameters of all the models are reported in Table 3.  
To assess the performance of the classifiers and the reliability of the radiomic features considered, we performed four experiments:  
**Experiment 1:** Internal cross-validation on each dataset (D1, D2, D3) separately; this experiment aimed to investigate if the radiomic signatures found by our approach could be discriminative for this classification task;  
**Experiment 2:** To train models on D1 and validate them on D2 and D3; this experiment aimed to investigate if the classifiers trained on a single dataset could perform on different datasets;  
**Experiment 3:** To train models on D1 and D2 (as a single dataset) and validate them on D3; this experiment aimed to investigate if increasing the training sample size could have improved the classification performance on the external validation;  
**Experiment 4:** Internal cross-validation merging D1, D2 and D3; this experiment aimed to investigate if considering all the datasets together could have led to a better performance than considering datasets separately, even at the cost of lower generalisation capabilities of the models.
## 1 Introduction
Furthermore, although newer techniques such as the Cuckoo Search Algorithm (CSA) and Pigeon-Inspired Optimization (PIO) have successfully been used in various aerospace applications [26], to our knowledge, these techniques have not been used in LPREs. This research has three aims. First, to estimate the mass and dimensions of a pump-fed cycle LPRE using mathematical model and not via statistical or empirical estimates. Second, to compare seven techniques, including a novel hybrid GA-PSO technique (GA-Swarm), and four OFs on GG and SC cycles and determine the most suitable technique and function for each cycle. Thirdly, a comparison is made between the two cycles for a given mission. The structure of this paper is as follows: a description of the modelling and optimization process used in this research is provided in Section 2; a validation of the mass model and results of the optimization algorithms and OFs are discussed in Section 3; conclusions and future work are provided in Section 4.
# Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends
## 4 Cyber-Attack Surfaces and Vulnerabilities in the Civil Aviation Industry
### Aerospace and Avionic Systems
#### 4.1.1 Aircraft Communications Addressing and Reporting System (ACARS)  
Aeronautical Radio Incorporated (ARINC) introduced the ACARS data link protocol to reduce crew workload and improve data integrity. ACARS is an ARINC 618-based air-to-ground protocol that transfers data between on-board avionics systems and ground-based ACARS networks [64]. The ACARS system consists of a Control Display Unit (CDU) and ACARS Management Unit (MU); the MU sends and receives digital messages from the ground using existing very high frequency (VHF) radios; on the ground, the system--a network of radio transceivers--receives and transmits data link messages, as well as routing them to various aircraft on the network.  
[PERSON] et al. stated in [65; 66] that the current use of ACARS by stakeholders extends beyond its original application to serve as flight trackers and the crew automated timekeeping system. The works in [65; 66] demonstrate how current ACARS usage systematically breaches location privacy; the authors of [65] showed how sensitive information transmitted over an ACARS wireless channel can lead to a privacy breach for users, supporting a known fact that ACARS messages are susceptible to eavesdropping attacks. The article in [65] was concluded by proposing a privacy framework, and in [66] the use of encryption and policy measures was recommended to arrest known eavesdropping attacks on the communication channel.
## 4 Numerical and Experimental Investigation of MR Semi-Active Control
### Comparison between MR Isolation and LRB Isolation System  
For the same model, the isolation experiment with lead rubber bearing has been done in the relevant literature [38], using four diameter of 100 mm of lead rubber bearing (LRB). The overall yield force is 8.652 kN. The test results are shown in Table 3 under different working conditions.  
Compared with the displacement of the isolation layer, the smart base isolation with MR dampers, regardless of the maximum displacement of the isolation layer or the amplification factor of the upper acceleration, is significantly less than the passive isolation with LRBs. In this way, the smart isolation system has the capability to resist larger earthquakes. Thus, it can be seen that the MR smart isolation device has better performance compared with the passive LRBs.
## 4 Discussion
As Equation (1) reveals, the DGD is demodulated from the sinusoidal power variation \(P_{1}\) that oscillates at a frequency \(f_{\rm s}\) on the order of \(10^{10}\) Hz. Consequently, the sensitivity of DGD measurement is determined by \(f_{\rm s}\). Similarly, the white-light-interferometry-based technique interrogates the GBD of a PMF by measuring the phase delay of two optical signals. The phase delay is demodulated from the sinusoidal power variation that oscillates at an optical frequency \(\
u\), and the measurement sensitivity of the white-light-interferometry-based technique is determined by \(\
u\). For a light wave operating at C band, the optical frequency is on the order of \(10^{14}\) Hz, which is \(10^{4}\) higher than \(f_{\rm s}\). Therefore, the measurement sensitivity of the white-light-interferometry-based technique is \(10^{4}\) higher than that of the proposed approach.
# Advanced Security Framework for 6G Networks: Integrating Deep Learning and Physical Layer Security
## 2 System Model
### Use Case  
The use case is a simulated representation of signal transmission and reception between two entities, [PERSON] and [PERSON], within the context of a 6G air interface. This simulation, conducted using MATLAB, focuses on a 2 \(\times\) 2 multiple input multiple output (MIMO) system, enhancing communication performance through the use of multiple antennas for both transmitting and receiving signals. In this simulation, [PERSON] and [PERSON] each generate a set of random information bits to transmit. For [PERSON], 1000 random bits are generated and modulated using binary phase shift keying (BPSK). The modulated bits are then transmitted through a Rayleigh fading channel, represented by a complex matrix \(H_{AB}\). In order to simulate realistic channel conditions, Gaussian noise is added to the transmitted signal. [PERSON] receives the signal, applies a decoding process to estimate the transmitted bits, and then generates his own set of random information bits to transmit back to [PERSON] through a similar channel \(H_{BA}\). The key parameters of the simulation include the number of information bits (1000), SNR set at 15 dB, and the use of a 2 \(\times\) 2 MIMO system. The channel model used is Rayleigh fading, and the modulation scheme is BPSK. The dataset columns capture critical aspects of the communication process, including the information bits transmitted by [PERSON] and [PERSON], the real and imaginary parts of the transmitted signals from both [PERSON] and [PERSON] and the real and imaginary parts of the signals received by both parties. This information is essential for analysing the performance and reliability of the 6G air interface communication system, providing insights into the behaviour of MIMO channels under various noise conditions and signal strengths.
## 2 Materials and Methods
### Data Collection  
Data were collected using the WIMU PRO(tm) device (RealtrackSystems S.L., Almeria, Spain), a wearable inertial measurement unit (IMU) widely used in sports science for monitoring physical activity and external load. The WIMU PRO(tm) integrates multiple sensors, including Global Positioning System (GPS) and Local Positioning System (LPS) technologies, allowing precise tracking of player movements during training and matches. The GPS and the LPS both function at a frequency of 18 Hz. The system also includes four 3D accelerometers (\(\pm\)16 g, \(\pm\)32 g, and \(\pm\)400 g) with sampling rates adjustable from 10 to 1000 Hz, three 3D gyroscopes (\(\pm\)500\({}^{\circ}\)/s, \(\pm\)2000\({}^{\circ}\)/s, and \(\pm\)4000\({}^{\circ}\)/s full-scale output range) operating at 1000 Hz, a 3D magnetometer, and a barometer. Validation studies have demonstrated the accuracy and reliability of this device for measuring various physical parameters [35; 36; 37; 38; 39]. The sensor was positioned between the players' scapulae using a specially designed tight vest to ensure stability and consistent data collection, as described in Figure 2.  
The inertial devices were synchronized and calibrated before use. The calibration procedure was carried out manually in accordance with the manufacturer's guidelines. Users were instructed to activate the device outdoors, ensuring minimal signal interference from nearby structures. Consistent with the recommendations of [PERSON] and [PERSON] [40], all devices were activated 30 min before data collection to facilitate satellite signal acquisition and synchronize the GPS clock with the satellite's atomic clock [41]. Eighteen different parameters were extracted (Table 1).  
The external load variables, such as total distance, high-speed running distances (>19.8 km/h and >25.2 km/h), and mechanical work, were derived from the GPS and IMU data. Mechanical work was defined as the cumulative sum of acceleration/deceleration events greater than 3.5 G. To mitigate drift in horizontal displacement, calibration protocols were regularly performed, and post-processing corrections were applied using the manufacturer's software.  
Figure 1: Description of the box-to-box run test.  
Figure 2: Sensor placement: WIMU PRO(tm) device positioned between the scapulae using a specific tight-fitting vest. Adapted from [PERSON] et al. (2024) [39].  
The ACWR can be calculated using various window durations, with the 7:28-day ratio being the most common in soccer. This is because weekly fixtures justify a 7-day acute window [15; 42]. Consequently, acute and chronic loads for the external load variables were determined using 7-day and 28-day rolling averages, respectively. The ACWR was categorized into three groups: underload (<0.8), sweet spot (0.8-1.5), and danger zone (>1.5) [7]. Running parameters were then assigned to these groups based on their ACWR values.
## 4 Case Study  
The data of the coil detector in the intersection of Beijing is selected as the experimental data source during the week (13-17 August 2018). There is a coil in front of the stop line in each lane of the intersection. There are eight coils in total. Each detector generates about 555 data a day. Figure 6 is a working day inspection data. In coil 3 and coil 7, faults occur between data points 150 to 200, 300 to 350. After adding high frequency random white noise to all data, the data are used as the data to be checked with fault and noise.  
Figure 6: Traffic flow data of eight detectors.  
In the parameter setting of simulation, the DB3 wavelet is used as the basis function of wavelet decomposition and wavelet packet decomposition in traditional MSPCA and the improved MAPCA model proposed in this paper. The confidence level is 99% when calculating the parameters of statistics and SPE statistics. The cumulative contribution rate of principal component is not less than 85%.
## 2 Materials and Methods
### Data Processing Method
#### 2.2.1 PPP Functional Model  
In PPP algorithm processing, real-time orbit and clock products provided by CNES are used to produce high-precision solutions based on dual-frequency combined observations. The ionospheric delay in the observations is eliminated by using dual-frequency ionosphere-free (IF) combination observations, while the tropospheric delay error is estimated by introducing parameters. The conventional PPP IF combination observation model is shown in Equations (1) and (2) [28], and the ZTD of the station can be extracted according to Equations (1) and (2):  
\[P_{IF}^{s}=\rho_{r}^{s}+c\cdot(dt_{r}+dt^{s})+M_{wet}\cdot ZWD+\varepsilon_{P, IF} \tag{1}\]  
\[L_{IF}^{s}=\rho_{r}^{s}+c\cdot(dt_{r}+dt^{s})+M_{wet}\cdot ZWD+B_{IF}+ \varepsilon_{L,IF} \tag{2}\]where \(B_{IF}=\frac{f_{1}^{2}}{f_{1}^{2}-f_{2}^{2}}\lambda_{1}N_{1}-\frac{f_{2}^{2}}{f_{1} ^{2}-f_{2}^{2}}\lambda_{2}N_{2}\); indexes \(r\) and \(s\) refer to the receiver and the satellite; \(P_{IF,r}^{s}\) and \(L_{IF,r}^{s}\) are the IF combination of pseudorange and carrier phase observations, respectively; \(c\) denotes light speed; \(f_{i}\) is the frequency; \(\rho_{i}^{s}\) is the geometrical range between the satellite and the receiver; \(dt_{r}\) and \(dt^{s}\) are receiver clock error and satellite clock error, respectively; \(M_{wet}\) is the zenith wet delay (ZWD) mapping function; \(ZWD\) is the zenith wet delay at the station and the zenith hydrostatic delay (ZHD) corrected by the model; \(\lambda_{1}\) and \(\lambda_{2}\) are the wavelengths corresponding to \(L_{1}\) and \(L_{2}\), respectively; \(N_{1}\) and \(N_{2}\) are the integer ambiguities corresponding to \(L_{1}\) and \(L_{2}\), respectively; \(\varepsilon_{P,IF}\), and \(\varepsilon_{L,IF}\) are the multipath effects and other unmodeled errors of the pseudorange and carrier phase, respectively.  
The observation processing strategy, error correction model, and parameter estimation method used in the PPP solution are shown in Table 2.
# Method for Select Best AIS Data in Prediction Vessel Movements and Route Estimation
## 4 Data Analysis and Preliminary Experiments
### Data Adjustment
**Skew** identifies the degree of distortion of the distribution and is a characteristic most used as a discriminant. **Kurtosis** is a statistical measure to discriminate by focusing on the tails of the distribution. It identifies outliers and routes that have deviations. **Standard Error of Mean (SE)** rates the uncertainty in estimating the mean of the target route data compared to the other historical routes. The selection operation is not strict because the routes can have a statistical similarity from the point of origin to the set point. The consequence is selecting routes with different weights for training and influencing the precision of the method. Of the previous variables, MAD is one of the most stable variables depending on its nature, regardless of the number of observations, as shown in Figure 6.  
Therefore, it is the variable that determines the selection of significant routes. The range is quite sensitive to the characteristics of the dataset because it is altered significantly by the existence of outliers. As mentioned above, Table 1 shows the statistical values of each route as features. The data have a marked difference in most of the statistical values, specifically those related to latitude. The method applies the cluster technique to those statistical values to help us choose the best prediction routes. The cluster is on densities. The dense region of objects contains a target path and similar paths. The low-density section is those routes with a statistical difference. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l l l} \hline
**Routes** & **Lat** & **Lat** & **Lars** & **Lat** & **Lat** & **Lars** & **Long** & **Lat** & **Lat** & **Lars** & **Long** & **Long** & **Long** & **Long** & **Long** & **Long** & **Long** & **Long** \\  & **Mean** & **SD** & **Median** & **Vramed** & **Med** & **Range** & **Saw** & **Kurtosis** & **SE** & **Mean** & **SD** & **Median** & **Minimum** & **MAD** & **Range** & **Saw** & **Kurtosis** & **SE** \\ \hline \hline
**Round** 1 & 46,782 & 0.3696 & 46,7206 & 46,7206 & 46,723 & 1.327 & 0.1064 & -1.2307 & 0.0129 & -58,1877 & 0.9065 & -84,8766 & -85,0109 & 1.0152 & 2.3322 & -4,5702 & -1.0045 & 0.0134 \\
**Round** 2 & 46,798 & 0.3813 & 46,7065 & 46,7456
## 4 Results and Discussion
### Research Limitations
#### 4.3.2 Model Selection and Tuning  
* Generalization capability of models: Although gradient boosting regression (GBR) demonstrated excellent performance in this study, its generalization capability across different cities or time points requires further validation. Subsequent research could apply this model in other urban contexts or regions to assess its adaptability and generalizability.
* Automation of hyperparameter tuning: This study improved model performance via manual hyperparameter tuning; however, this process demands substantial human effort and computational resources. Future work might benefit from integrating automated tools (such as GridSearchCV or RandomizedSearchCV) for more systematic hyperparameter optimization, thereby enhancing efficiency and effectiveness.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 2 Materials and Methods
### ANN and ANFIS Techniques
#### 2.5.2 AnFIS  
[PERSON] proposed fuzzy logic in 1965 to find answers to various uncertainty problems [41]. Fuzzy logic is closely related to human thinking through differences in membership functions. The framework includes a fuzzy set, fuzzy if-then rules, and fuzzy reasoning. Combining fuzzy systems and ANN models provides a powerful tool based on learning from the ANN model and increasing performance through the fuzzy inference model. An adaptive neuro-fuzzy inference system (ANFIS) is a hybrid model integrating an adaptive ANN and a fuzzy inference system using [PERSON]'s Fuzzy model to define the output. The ANFIS has a mathematical structure that can detect complex non-linear systems. The ANFIS structure consists of five layers: the fuzzy layer, product layer, normalized layer, de-fuzzy layer, and total output layer, as shown in Figure 4. The ANFIS has been applied through various applications. The selection of membership functions varies depending on the characteristics of the datasets.  
ECG signals are complex and have large dataset sizes. Therefore, an optimal membership function is essential to help screen AF patients. In this research, triangular (trimf), trapezoidal (trapmf), generalized bell-shaped (bellmf), gaussian (gaussmf), and gaussian combination (gauss2 mf) are used in the ANFIS model to compare their performances with symmetry membership functions. These membership functions performed well in many applications and were recommended in many works [42; 43; 44].
## 3 Data and Methods
### Data Availability  
The data availability for the different measurement systems is shown in Figure 4. The FMI permanent weather station close to the lighthouse is part of the official Finnish weather observation network and is operational year-round. Data from this station was therefore available without major quality issues for the entire observation period. The automatic weather station, installed on the sea ice, was operated between 11 and 27 February. Due to a damaged backup battery, which was causing a drop in voltage, some data was lost. In particular, the slow-response data seemed to be affected by this issue. The EC system, running on the same data logger, stopped recording on 13 February due to a broken data cable from the sonic anemometer, which was replaced on 15 February. Furthermore, some of the EC data was of poor (flag 2, see Section 3) or intermediate quality (flag 1). Good and intermediate quality data are marked in green and orange in Figure 4 and were both used for further analysis, whereas poor quality data were removed. The optical lens of the LIDAR was subject to significant icing from the inside, especially at the beginning of the campaign. After defrosting the lens several times, this was not an issue any longer, but probably, due to very low aerosol concentration, the carrier-to-noise ratio (CNR) was rather poor for most observed levels for almost the entire campaign. The SODAR system was subject to flooding due to snow melt and water pushing up through the ice, causing some loss of data in the middle of our campaign. Green and orange colors in Figure 4 refer to the availability of instantaneous observations used to compute a 10-min average. For the good quality data, the lower threshold was 66.7% and for limited quality data, it was 33.3%.  
The operation of the different UAVs requires significant manpower, typically involving one safety pilot and one ground control station operator. These systems were therefore mainly operated during intensive observational periods, when the atmospheric conditions were most interesting, i.e., strong static stability and weak winds in the SL. Smaller technical problems and human endurance during rough environmental conditions prevented higher numbers of flights. A fair amount of flights were also carried out during conditions when the stability was relatively weak. In total, 139 scientific flight missions were carried out during the campaign and were distributed as follows: 53 Bebop2 Met; 39 SUMO; 30 AMOR; 14 MASC-2; and 3 miniTalon flights. Around one third of the flights (39%) were carried out during conditions with strong atmospheric stability (\(Ri_{B}>0.2\)). For 12% of the cases, the 2-m wind speed was, in addition, below \(0.5\,\text{m}\,\text{s}^{-1}\). The irregular flight times, with a focus on stable conditions and rather moderate and low wind speeds, as well as the maximum flight altitudes of the different UAV systems, may have caused a significant sampling bias. It is therefore not recommended for general conclusions to be drawn based on the UAV data alone. These data should primarily be used for the analysis of case studies.