esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Measurement Results
### ML Model 1  
**Intrasubject classification**  
Figure 24a shows that the accuracy of the system for all the selected participants falls below 50%, with the lowest at 38.49%. Even though it is performing better than random chance, which is at 25% for four classes, this accuracy score shows that the system is not very reliable. Figure 24b also shows that for some pairs of all 3 participants, the precision and sensitivity can be as low as 0%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Events** & **Accuracy (\%)** & **Precision (\%)** & **Sensitivity (\%)** & **Specificity (\%)** \\ \hline GP (Grasp) vs. Non-GP & 93.8 & 96.1 & 91.8 & 92.0 \\ \hline RL (Release) vs. Non-RL & 94.2 & 93.1 & 95.5 & 92.9 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Grasp and Release results from the paper.  
Figure 24: (a) Accuracy of ML Model 1. (b) Precision and sensitivity of ML Model 1. RMSE is a benchmark that measures the overall energy of a signal over a short period.  
The accuracy of the intersubject classification for this model is 45.12%. Figure 25 shows the sensitivity of GP and RL performing at 0% and 1%, respectively, indicating that the model is not reliable at all for these two cases. The model performs quite well for the RV action, with both sensitivity and precision higher than 80%.  
One explanation for these low scores has been explained in earlier sections of the manuscript. The idea of grouping FW as one group and other actions as another group is to allow the CSP to generate filters that create FW vs. Others scalograms. These scalograms are then used to train the 'FW vs. Others' network, with the same method applied to the other three branches. The test set is created by combining FW, GP, RL, and RV from the scalogram pairs. Focusing on one branch, 'FW vs. Others', we can discuss the issue further since the same logic applies to the other branches. When the 'FW vs. Others' network predicts the test set labels, the assumption is that if GP, RL, or RV are tested, the network should predominantly classify them as 'Others' since they are not FW. However, because GP, RL, and RV are grouped as one pair when creating the input slices for this branch, the CSP filter turns this group of three into a single scalogram with characteristics that combine all three rather than their individual traits. In the test set, the FW, GP, RL, and RV scalograms retain the characteristics of individual actions, causing the 'FW vs. Others' network predicts the 'Others' class less frequently than the individual action classes. Since both intrasubject and intersubject classifications are not performing well, another model is explored, leading to the creation of ML Model 2. Given the theoretical explanation for the model's poor performance, ML Model 2 is designed to train the ML networks with scalograms that display individual characteristics rather than combinatorial ones.
# Deep Autoencoder Framework for Classifying Damage Mechanisms in Repaired CFRP
###### Abstract  
This study investigates the classification of damage modes in adhesively bonded carbon fiber-reinforced plastic (CFRP) composites, a critical factor in advancing lightweight automotive design. Adhesive bonding, replacing traditional riveting, improves structural integrity while reducing weight and CO2 emissions. Mechanical testing on CFRP composites was performed, and acoustic emission (AE) signals were collected to evaluate damage mechanisms. A deep autoencoder (DAE) framework was developed to automate damage characterization by reducing AE signal dimensionality through singular value decomposition (SVD) and classifying features using the k-means algorithm. This approach effectively identified three primary damage modes: matrix cracking, interfacial debonding, and fiber breakage. Traditional AE features, such as entropy and amplitude were also classified and validated using spectral analysis. The DAE-based strategy demonstrated superior capability in real-time damage mode differentiation. Fractographic analysis confirmed crack growth in the adhesive layer, leading to interfacial debonding, fiber-matrix separation, and eventual fiber rupture. These findings highlight the DAE framework's effectiveness in enhancing damage mode characterization, offering valuable insights for optimizing the structural performance of bonded CFRP composites in automotive applications.  
acoustic emission; deep autoencoder; k-means; CFRP composites; adhesive bonding; joggled lap shear joints; structure lightweight +
Footnote †: journal: Applied sciences
## 4 Experimental Analysis
### Limitations  
There are certain limitations to the proposed scheme in this work, which involves combining EEG data from multiple patients. For example, one disadvantage of combining EEG data from various patients is the potential for information leakage from the training data to the testing data. Since the data are temporal, there may be an unintentional correlation between the sets used for training and testing the model's performance. As a result, the model's true generalization capabilities may be overestimated. Another limitation is the uncertainty around how the model would perform on completely unseen data. While the model may perform well on the given dataset, its ability to appropriately generalize to new, previously unseen data from different patients cannot be guaranteed. When applied to completely new and different datasets, factors such as inter-subject variability, unique patient characteristics, and differences in data collection techniques can all have an impact on the model's performance.  
Before deploying the proposed model for clinical decision support, real-time experimental trials should be carried out to assess the model's performance under real-world conditions. For instance, the model can be tested with a continuous stream of EEG signals  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Method** & **Accuracy** & **Sensitivity** & **Specificity** & **False Positive \%** \\ \hline [PERSON] et al. [22] & ANN+DWT using 5 statistical features & 89.01\% & 88.39\% & 89.62\% & 10.47\% \\ [PERSON] et al. [58] & SSTFT+GNMF-based LogE+FKNN & 88.92\% & 88.43\% & 89.22\% & 10.5\% \\ [PERSON] et al. [59] & LSTM & 87.46\% & 87.42\% & 86.11\% & 12.53\% \\ [PERSON] et al. [60] & 2D CNN STFT+LSTM & 89.21\% & 89.30\% & 88.69\% & 12.91\% \\ [PERSON] et al. [14] & Gaussian mixture models & 86.93\% & 86.26\% & 87.58\% & 12.75\% \\ [PERSON] et al. [61] & fractional Fourier transform & 89.67\% & 89.5\% & 89.75\% & 11.49\% \\ [PERSON] et al. [62] & CNN+DWT & 83.27\% & 74.08\% & 92.46\% & 7.92\% \\ [PERSON] et al. [63] & MVAR Sample entropy STFT & 90.10\% & 88.60\% & 91.23\% & 8.05\% \\ [PERSON] et al. [25] & SVM+DWT & 90.68\% & 83.37\% & 88.03\% & 11.76\% \\ \hline This work & RNN+DWT & 93.27\% & 90.10\% & 96.53\% & 3.4\% \\  & RNN+FFT & 84.60\% & 79.17\% & 89.60\% & 10.40\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of the proposed RNN-based model on the CHB-MIT dataset with other methods in the literature.  
Figure 11: Confusion matrix: RNN+DWT performance-based CHB-MIT dataset with 70% train ratio and 0.05 LR.  
to evaluate its performance in real time. This can provide useful insights into how the model works with previously unseen data, allowing for a more accurate assessment of its performance metrics. Furthermore, feedback from clinicians and patients, as well as a collection of data, can help in identifying areas for improvement and fine-tuning the model's performance. As new data become available, regular retraining and calibration can help maintain accuracy metrics.
## 3 The Sensor Integration
### Strapdown Algorithm  
Within the SDA, the MEMS IMU readings are used to propagate the attitude, the velocity and the position of the system.  
Attitude: The gyroscopes of the IMU measure the attitude change \(\omega_{ib}^{b}\) of the b-frame with respect to an inertial frame represented in the b-frame. Since the Earth rotation rate \(\omega_{ie}^{e}\) is small compared to the measurement noise of the MEMS gyroscopes, the approximation:  
\[\omega_{eb}^{b}\approx\omega_{ib}^{b} \tag{23}\]is valid. Assuming that the direction of the angular rate vector \(\omega_{eb}^{b}\) remains fixed in space during the time interval \(\Delta t=t_{k}-t_{k-1}\), the rotation angle \(\Delta\sigma_{k}\) can be determined with the trapezoidal rule:  
\[\Delta\sigma_{k}\approx\left(\frac{\omega_{eb,k-1}^{b}+\omega_{eb,k}^{b}}{2} \right)\Delta t \tag{24}\]  
With the quaternion \(\Delta\mathbf{q}_{k}\):  
\[\Delta\mathbf{q}_{k}=\begin{bmatrix}\cos(\left|\Delta\sigma_{k}\right|/2)\\ a_{s}\Delta\sigma_{x,k}\\ a_{s}\Delta\sigma_{y,k}\\ a_{s}\Delta\sigma_{z,k}\end{bmatrix},\;\;a_{s}=\frac{\sin(\left|\Delta\sigma_{ k}\right|/2)}{\left|\Delta\sigma_{k}\right|} \tag{25}\]  
the current attitude \(\mathbf{q}_{k}\) results from:  
\[\mathbf{q}_{k}=\mathbf{q}_{k-1}\Delta\mathbf{q}_{k} \tag{26}\]  
where \(\mathbf{q}_{k}\) represents the quaternion relating the b-frame to e-frame axes at the epoch \(k\).  
Velocity: The velocity propagation is based on the integration of the measured accelerations \(a_{ib}^{b}\):  
\[\dot{\mathbf{v}}_{eb}^{e}=\mathbf{C}_{b}^{e}\mathbf{a}_{ib}^{b}-2\omega_{ie} ^{n}\times\mathbf{v}_{eb}^{e}+\mathbf{g}_{l}^{e} \tag{27}\]  
The assumption of a constant direction of the acceleration vector during the time interval \(\Delta t\) is also valid, with the result that Equation (27) can be simplified:  
\[\dot{\mathbf{v}}_{eb}^{e}\approx\mathbf{C}_{b}^{e}\mathbf{a}_{ib}^{b}+ \mathbf{g}_{l}^{e} \tag{28}\]  
Then, the velocity at time \(k\) can be determined applying Equation (29).  
\[\mathbf{v}_{eb,k}^{e}=\mathbf{v}_{eb,k-1}^{e}+\mathbf{C}_{b}^{e}\left[ \mathbf{a}_{ib}^{b}+\frac{1}{2}\Delta\sigma_{k}\times\mathbf{a}_{ib}^{b} \right]\Delta t+\mathbf{g}_{l}^{e}\Delta t \tag{29}\]  
The vector \(\mathbf{g}_{l}^{e}\) is the local gravitation vector represented in the e-frame.  
Position: Finally, the current position may be derived integrating the velocity. For this integration, again, the trapezoidal rule is used, since the integration time is short here:  
\[\mathbf{x}_{p,k}^{e}=\mathbf{x}_{p,k-1}^{e}+\left(\frac{\mathbf{v}_{eb,k-1}^ {e}+\mathbf{v}_{eb,k}^{e}}{2}\right)\Delta t \tag{30}\]
# DAB Signal Preprocessing for Passive Coherent Location
## 1 Introduction
The main contribution of this work is a novel, simple yet effective algorithm of received signal preprocessing in the temporal domain (at the DAB frame level) that improves the readability of bistatic velocity/range images obtained from DAB-illuminated PCL in cases of extended integration time (i.e., much longer than 96 ms).
# Load-Balancing Routing for LEO Satellite Network with Distributed Hops-Based Back-Pressure Strategy
## 6 Simulation Performance and Analysis
### Result Discussion
#### 6.2.3 Bursty Traffic  
In LEO satellite networks, the bandwidth occupied by non-real-time services is difficult to predict. Bursty traffic often occurs. Bursty traffic can lead to the degradation of network quality, which can cause congestion and increase forwarding delay. In severe cases, packet loss may occur, resulting in degraded or even unavailable service quality. The performance under bursty traffic is demonstrated in Figure 11.  
Different from CBR traffic and Poisson traffic, the transmission period of bursty traffic consists of a bursty phase and a normal phase. The average duration of a bursty phase is 1 second with a random rate change of between 3 Mbps and 11 Mbps. According to Figure 11, performances of DHBP are similar to results under CBR traffic and Poisson traffic. The robustness of DHBP against traffic bursts is verified. Thus, we can conclude that the proposed method outperforms the state-of-the-art routing protocols, with several improvements in transmission cost and throughput.  
The average throughput and data delivery ratio under Poisson traffic and bursty traffic are demonstrated in Figure 12. We can see that the proposed scheme outperforms DBPR, SALB, and OSPF in terms of throughput and data delivery ratio. Compared to Poisson  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Scheme** & **Average Delay (\%)** & **Average Forwarding (\%)** & **Throughput (\%)** & **Data Delivery Ratio (\%)** \\ \hline OSPF [43] & \(-32.67\) & \(10.77\) & \(61.65\) & \(40.00\) \\ DBPR [4] & \(12.42\) & \(13.13\) & \(10.47\) & \(11.59\) \\ SALB [8] & \(25.14\) & \(21.47\) & \(23.74\) & \(14.93\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative analysis of network performance under heavy traffic load.  
Figure 10: Simulation results under Poisson traffic.  
traffic, DHBP has more significant advantages under bursty traffic. Since DHBP takes the cache queue length and remaining hops into account, it can divert the bursty traffic to relatively idle nodes within a short period of time, which effectively improves the QoS of the LSN. OSPF and SALB, etc. only consider global congestion without taking the current satellite and neighboring satellites into account. Therefore, it is difficult to make reasonable traffic diversions. The performance deterioration of such routing protocols is more severe when there is a burst of traffic.
# Energy and Exergy Analyses of an Existing Solar-Assisted Combined Cycle Power Plant
## 2 Plant Description
### Combined Cycle  
The combined cycle field consists of one MS6001 FA heavy-duty gas turbine with a generator of a rated electric power output of 70 MW at 20 \({}^{\circ}\)C ambient dry bulb temperature. It has one HRSG that receives about 206 kg/s flue gas at about 600 \({}^{\circ}\)C from the gas turbine at full load operation. The flue gas leaves the HRSG at about 100 \({}^{\circ}\)C. Under the rated conditions of the gas turbine and HRSG, full load operation and solar heat input of 50 MW and 20 \({}^{\circ}\)C ambient dry bulb temperature, the steam turbine generator output is about 65 MW.  
The HRSG of the ISCC power plant in Kureimat comprises three high-pressure economizers (HP ECO), a high-pressure evaporator (HP EV), a high-pressure steam drum (HP Drum at a pressure about 80 bar), and five high-pressure superheaters (HP SH) for a feed of the high-pressure section of the steam turbine, as shown in Figure 1. Besides this, it includes a low-pressure evaporator (LP EV), low-pressure steam drum (LP Drum at a pressure about 11 bar), and low-pressure superheater (LP SH)  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Solar Field Operation Parameter** & **Unit** & **Value** \\ \hline Solar Field Total Aperture Area & m\({}^{2}\) & 130,800 \\ Number of Collectors & N\({}^{\circ}\) & 160 \\ Number of Collector Loops & N\({}^{\circ}\) & 40 \\ Design Irradiation & W/m\({}^{2}\) & 700 \\ Maximum Solar Field Thermal & MW & 61 \\ Power Output & \multirow{2}{*}{MW} & \multirow{2}{*}{61} \\ Output Temperature of the HTF & \({}^{\circ}\)C & 393 \\ Input Temperature of the HTF & \({}^{\circ}\)C & 293 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The solar field design parameters [15,16,22].  
for a feed of the steam turbine low-pressure section. The solar-generated steam is injected into the high-pressure drum.
# A Mamdani Type Fuzzy Inference System to Calculate Employee Susceptibility to Phishing Attacks
## 7 Testing, Evaluation and Results
The rules were altered to cater for the most logical outcome where only logical rules were included enabling a rule base that was efficient and well structured. The entirety of the rule base contained the AND operator. It was found that when the OR operator was used to define rule input variables, there were a number of illogical errors as well as abnormal decision of events observed. For example, if the employee experience is'security analyst OR phishing attack level is weak then employee susceptibility is High'. This result is deemed inaccurate, however, a very small number of OR rules were included to satisfy the outcome and not negate the earlier defined rules. This is further proven through Figure 14 where the fifth column (SOM) shows values that are of no use, e.g., a high susceptibility rating is only 60. The AND operator was shown to have the optimal result when configuring the rulebase (Centroid). A series of tests were developed to push the bounds of the system to ensure full robustness and that it meets the requirements and expectations. A spreadsheet was created as a secondary input process. The spreadsheet acted as a alternative method of inputting values into the system without having to reconfigure the system. This external file contained values for each defined variable, this was linked to the system using C code. The spreadsheet allowed for multiple to test cases (employees) to be ran simultaneously. The input values in the external spreadsheet was executed through the system evaluated through the predefined rules. This resulted in a value being written to the spreadsheet for analysis (Figure 15). The analysis took the form of cross checking all the defuzzification values returned and calculating the most accurate susceptibility rating. Each row of the sheet mimicked a employee test case that was fed into the system. For example, row 1 was predicted to be a high susceptible employee and the variables inputted catered for this. There are various defuzzification methods (Centroid, LOM, MOM, SOM), however, after evaluation of the system outputs, it was found that the MOM method produced the most effective output. Figure 15 shows the MOM values result in accurate and efficient  
Figure 14: Defuzzification Output Values. output values that are congruent with the system aims. The centroid method produces results closely matching the MOM method (Figure 14). The MOM and centroid values were analysed to have the correct and most logical results for the susceptibility rating. However, comparing the two methods, MOM was found to be a more suitable. The LOM, SOM and Bisector methods were unsuitable and were not utilised for the system where inaccurate values returned within the defuzzification method as illustrated in columns 4, 5 and 6 in Figure 14.
## 4 Experiments
### Discussion and Relevance
#### 4.5.3 Imbalanced Class Distribution  
The Data3 dataset constructed in this study exhibits class imbalance, with certain classes having significantly fewer samples than others. Such imbalance biases model training toward majority classes, thereby compromising classification performance for minority classes. Traditional accuracy-centric evaluation metrics often prove ineffective in such scenarios. For instance, when a minority class constitutes merely 1% of the data, blindly predicting majority classes yields 99% accuracy yet renders the model virtually useless.  
To address this challenge, researchers have proposed multi-tiered solutions. At the data level, oversampling and undersampling techniques mitigate bias by adjusting sample distributions. At the algorithmic level, cost-sensitive learning optimizes loss functions by assigning higher misclassification penalties to minority-class samples. Additionally, ensemble methods and generative adversarial networks (GANs) have demonstrated efficacy in enhancing model representation for minority classes. Recently, improved loss functionssuch as Focal Loss have further advanced model optimization in imbalanced scenarios by suppressing gradient contributions from easily classified samples.  
The present study primarily focused on comparing classification algorithms and face recognition techniques for dragonfly species identification; consequently, the aforementioned imbalance-handling methods were not implemented.
# Analysis of GNSS Displacements in Europe and Their Comparison with Hydrological Loading Models
## 2 Materials and Methods  
### CNES-CLS Multi-GNSS Orbits and Clock Products  
The GNSS satellite orbit and clock files used in this study were generated as part of the GRGS participation in the third International GNSS Service (IGS) reprocessing campaign effort (REPRO3) to contribute to the realisation of ITRF2020. These products (referenced here as MG3) are the result of a homogeneous reprocessing of multi-GNSS data (GPS, GLONASS, and Galileo) between 2000 and 2020 using the zero difference with integer ambiguity fixing method described in [34; 45]. We used up-to-date models listed in Table 1, following the IGS recommendations ([[http://acc.igs.org/repro3/repro3.html](http://acc.igs.org/repro3/repro3.html)]([http://acc.igs.org/repro3/repro3.html](http://acc.igs.org/repro3/repro3.html)) (accessed on 7 November 2021)). The MG3 reference frame solutions were first evaluated and then preliminarily combined by the IGS [46]. The station network includes a set of about 300 selected IGS stations that are distributed over the globe (Figure 1). The number of available satellites for each constellation and the daily number of available stations varied over the observing period as shown in Figure 2. The CNES-CLS MG3 products are available in the CDDIS (Crustal Dynamic Data Information System) archive at [[https://cddis.nasa.gov/archive/gnss/products/wwwwww/repro3/GRG6](https://cddis.nasa.gov/archive/gnss/products/wwwwww/repro3/GRG6) RE3 FIN]([https://cddis.nasa.gov/archive/gnss/products/wwwwww/repro3/GRG6](https://cddis.nasa.gov/archive/gnss/products/wwwwww/repro3/GRG6) RE3 FIN)*.gz (accessed on 7 November 2021).
# Adaptive Harmony Search for Tuned Liquid Damper Optimization under Seismic Excitation
## 4 Discussion  
In this study, the contribution of adaptive algorithm parameters, in the HS algorithm, to the optimization process, in single and multi-story buildings, in different periods, exposed to earthquake loads, was investigated. Classical HS and adaptively obtained AHS algorithm performances were compared in three different construction periods for a single-story structure and a ten-story structure. As a single case, AHS was compared with the other two variants of HS. AHS was found to be more effective than IHS and LHS in the reduction of displacement.  
Concerning the displacement and total acceleration values obtained as a result of the optimization, the reduction percentages, compared to the structure without a TLD, are shown in Table 10. The mean and standard deviation values of the 10 optimizations are provided in Table 11 for the HS and AHS algorithms.  
The standard deviations of the maximum displacement values of the critical seismic analysis, obtained as a result of 10 optimizations, were small in both algorithms, and these values were almost zero for the AHS algorithm. While optimum results were obtained from the HS algorithm for the ten-story structure and 0.5s, 1s, and 1.5s in the single-story structure, in 17, 925, 942, and 871 iterations, respectively, in the AHS algorithm, optimum results were obtained in 640, 969, 995, and 403 iterations, respectively. In this case, it cannot be said that one is more advantageous than the other in terms of computation time, concerning the HS and AHS algorithms; however, it shall be noted that AHS has lower structural responses. From this, the importance of adapting algorithm parameters for optimum selection is understood.