esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Summary and Future Works  
In this paper, we proposed a BMI prototype for controlling wheelchairs by using decoded EEG signals recorded while the user performs tasks to drive the wheels. The novelty of the proposed system lies in including a shadow detection module based on an adaptive direction tracking filter to extract target features along the direction of boundaries. Note that the present study is intended as preliminary work for a future full BMI system implementation. Here, we propose a theoretical framework for controlling the navigation of a wheelchair. At this stage, BMI experiments and validation have been conducted through a laboratory set-up. A novel shadow detection strategy is developed based on an adaptive direction tracking filter for use in the BMI wheelchair system. Note that the integration of the proposed detection algorithm with the BMI, including real-time wheelchair control testing, is proposed as future work. Future development can also focus on motor imaginary experiments, EEG recordings, classification of the control signal using ML-techniques, acquisition of real scenes from a camera installed on the wheelchair and a detailed shared control strategy that adapts to the situation and alarms the user of possible obstacles. In addition, motivated by the promising shadow detection results achieved, in this study, our follow-up work will consider a wider set of features (such as those reported in [12, 68]). Moreover, optimization techniques [69] for tuning the training parameters and deep learning approaches [70, 71, 72] will be explored in an attempt to enrich the shadow detection accuracy.  
Shadow detection methodology, simulations and data curation Z.J.; machine learning models design C.I.; brain machine interface design M.M. and C.I.; writing original draft and review Z.J. and C.I.; editing Z.J., L.G., A.H., M.M. and C.I.; conceptualization and supervision, L.G., A.H. and C.I. All authors read and approved the final manuscript.  
This research was funded by Key R & D projects of Sichuan Science and Technology Department under Grant 2016 FZ0120. [PERSON] would like to acknowledge the support of the UK Engineering and Physical Sciences Research Council (EPSRC)--Grants Ref. EP/M026981/1, EP/T021063/1, EP/T024917/1.  
The authors are grateful to [PERSON] at University of Electronic Science and Technology of China and [PERSON] at Chongqing University for their valuable comments and suggestions to improve the quality of the paper.  
The authors declare no conflict of interest.  
Figure 14: Comparison of shadow detection results achieved by MLP, AE, 1D-CNN, SVM, Huang’s and Lalonde’s classification models on a realistic scene. Note that the best MLP and AE classifiers are reported (i.e., MLP\({}_{1}\), AE\({}_{3}\)).
## 3 Backscattering Scenarios
### Experimental  
A corner reflector, a cylinder, and a detonator frequently used in the manufacturing of Improvised Explosive Devices (IEDs) in Colombia were the metallic objects used for the two experimental setups. The frequency response was obtained using a VNA up to 3 GHz for free space and up to 6 GHz for the buried scenarios, two TSA600 Vivaldi antennas, and three distances from the source to the object. Furthermore, clutter, antenna coupling, and the background for the experimental signals were removed using the GPR signal acquisition methodology presented in [15].  
Figures 4 and 5 show the experimental configuration for the metal cylinder in free space, Figure 6 shows an example of the reconstructed signals in the frequency domain, and Figure 7 shows the calculated CNRs in the complex plane. The difference in the reconstructed signals and poles location between Cauchy and VCM becomes more evident in the experimental signals, and it was again calculated using FSV. The difference in the reconstructions for the frequency domain input signals using Cauchy and VCM methods was evaluated with FSV. The curves appear to be similar, but only the numerical evaluation will provide a real comparison, as in Section 4. Again, the SVD-based system order calculation and linearized problem solution can be treated as in [16].  
Figure 3: CNRs calculated with the Cauchy and VCM methods for a simulated metal sphere.  
Figure 4: Free space experimental setup with no objects.  
Figure 5: Free space experimental setup with a metal cylinder.  
Figure 6: Frequency domain backscattered signal obtained from a metal cylinder in free space at 60 m.  
Now, in the experimental setup for buried objects, the same antennas were used, this time targeting the objects buried 15 cm. Figures 8 and 9 present the configuration. Both experiments included IED detonators used in Colombia; the one used here is shown in Figure 10 with an approximate dimension of 54 mm.  
Again, as an example of the results to be shown in the next section, the backscattered frequency domain signal for the metal detonator and its associated CNRs for both the Cauchy and VCM methods are displayed in Figures 11 and 12.  
The CNR of the highest frequency (imaginary part) for VCM in the complex plane represents a more accurate length of the detonator.  
Figure 10: Electric IED detonators commonly used in Colombia. The approximate dimension is 54 mm, with a diameter of 4 mm.  
Figure 9: Buried objects experimental setup with a metal IED detonator.
# Long-Period Grating with Asymmetrical Modulation for Curvature Sensing
## 4 Conclusions  
In this article, a curvature sensor based on LPFG with asymmetric index modulation in a single-mode fiber is proposed and experimentally demonstrated. Femtosecond laser micromachining is used to inscribe the grating structure into the single-mode fiber. The grating structure is not introduced in the central fiber core, but is located off-axis with a distance of a few micrometers. Experimental results indicate that the offset distance affects the grating spectra. By utilizing such an asymmetric structure, two-dimensional vector curvature sensing can be realized. The curvature sensitivity increases with the amount of offset, and for an LPFG with offset distance of 6 \(\upmu\)m, the curvature sensitivity is around 29 nm/m\({}^{-1}\) in the 0\({}^{\circ}\) and 180\({}^{\circ}\) direction and about 20 nm/m\({}^{-1}\) in the 90\({}^{\circ}\) and 270\({}^{\circ}\) direction. Larger offset also leads to larger sensitivity difference in different curvature orientations. The difference in curvature sensitivity in different bending planes makes the sensor capable of distinguishing the curvature orientations. The temperature response of the sensor is experimentally investigated, and the temperature sensitivity of the sensor is measured to be 0.087 nm/\({}^{\circ}\)C. The temperature cross-sensitivity is calculated to be 0.003 m\({}^{-1}\)/\({}^{\circ}\)C, which indicates that the influence of temperature on the measurement accuracy of curvature is negligible. The sensor structure is compact and the sensitivity is high, making it an ideal candidate to be applied in the fields of structure health monitoring or intelligent robotics.  
Conceptualization, S.L.; methodology, X.Q.; validation, R.G.; formal analysis, Y.J.; investigation, C.Y.; writing--original draft preparation, L.S.; writing--review and editing, S.L.; visualization, L.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (Grant No. 51675350 and Grant No. 51575361). Not applicable. Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The authors declare no conflicts of interest.
## 2 Materials and Methods
### GPR Data Acquisition and Processing
\[A(i)=\frac{\sum_{t=t_{1}}^{t_{2}}|x(t)|}{N}\] (2) where \(i\in M,t_{1},t_{2}\) are the start and end time of the time-window, and \(N\) is the number of sampling points in the time-window. 3. Calculation of the weight \(w(i)\) of the time-varying automatic gain, which is the weight of the center of the time-window. \[w(i)=\frac{1}{A(i)}\] (3)
4. The weight of the center point of the adjacent time-window is linearly interpolated to obtain the gain weight of other points. The flowchart for the calculation program is shown in Figure 6.  
Figure 6: Flow chart of calculation program based on time-varying automatic gain. Wavelet transform has been widely applied in signal processing, image processing, radar, seismic exploration, CT imaging and other fields in the past 30 years [19]. The wavelet transform is a time-frequency analysis method in which the time-frequency window has a fixed size but its shape can be changed. The main characteristics of the wavelet transform are a high-frequency resolution and low-time resolution in the low-frequency range, and a high-time resolution and low-frequency resolution in the high-frequency range. So it's highly adaptive, and also known as a mathematical microscope. Continuous wavelet transform (CWT) is defined:  
\[\big{(}CWT_{\psi}f\big{)}(a,b)=\frac{1}{\sqrt{|a|}}\int_{-\infty}^{+\infty}f(t )\overline{\psi\Big{(}\frac{t-b}{a}\Big{)}}dt \tag{4}\]  
where  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{|a|}}\psi\Big{(}\frac{t-b}{a}\Big{)} \tag{5}\]  
\(a\) = coefficient of time scale dilation; \(b\) = coefficient of time translation; \(f(t)\) = original radar signal. The above series of functions is called the wavelet function, or wavelet for short, which is obtained by different time scale dilation and different time translation. Thus, \(\psi(t)\) is the wavelet prototype, which is called the mother wavelet or basic wavelet. In the radar signal, the useful signal is usually manifested as a low-frequency signal or some relatively stable signal, and the noise signal is usually manifested as a high-frequency signal. Therefore, after wavelet decomposition, the noise part is usually included in the high frequency, and the amplitude of the high-frequency coefficient of the noise is rapidly attenuated with the increase in scale and decomposition levels. Additionally, the performance of noise on different scales is also unrelated. Thus, the high-frequency wavelet coefficients are processed in the form of threshold values and then the processed signals are reconstructed to achieve the purpose of denoising. [PERSON]'s research showed that Daubechies wavelet was more suitable for radar signal processing, and through the study of various wavelet bases of Daubechies, it was found that the \"db4\" wavelet had the best effect in denoising and maintaining the original signal characteristics [20]. In this experiment, the decomposed layer \(N\) is equal to 3.
# Advanced Modulation Formats for 400 Gbps Optical Networks and AI-Based Format Recognition
## 3 AI-Based Modulation Format Recognition
### Results and Discussion  
#### 3.2.1 Modulation Format Identification Results  
The test results of the loss values for the training and test sets are shown in Figure 9, where the loss function is used to evaluate the convergence of the model. It can be observed that the loss values for both the training and test sets significantly decrease with the increase in the number of training epochs, indicating a very stable convergence of the model. After 50 epochs, the loss values for the training and test sets are 0.0713 and 0.101, respectively. The low loss values suggest that the model has good robustness.  
Figure 9: Loss values for training and test sets.  
The MFI for the training and test sets is depicted in Figure 10. Each column of the matrix signifies the actual output types of modulation formats, while each row represents the target types of modulation formats. By examining the data between the rows and columns, one can discern the number of instances correctly and incorrectly identified. There are 1267 training images and 317 test images for each modulation format. As shown in Figure 10a, only the DQPSK format achieves a 100% MFI classification accuracy, while the MFI classification accuracy for the Apol-CRZ-FSK and CRZ-FSK formats can reach up to 99%. Figure 10b indicates that only the DQPSK format can attain a 100% MFI classification accuracy. In contrast, misclassification occurs for the modulation formats Apol-CRZ-FSK and CRZ-FSK. For example, for the 317 images using Apol-CRZ-FSK, four instances were misclassified as CRZ-FSK. The classification accuracies of Apol-CRZ-FSK and CRZ-FSK were 98.73% and 99.68%, respectively, and their MFI errors were because the format of Apol-CRZ-FSK is an orthogonal polarization based on CRZ-FSK, and both formats share similar characteristics. However, the probability of recognition error is extremely small, and excellent performance has been achieved in the recognition of three optical signal formats.
# Systematic Analysis of the Hydrogen Value Chain from Production to Utilization
## 3 Hydrogen Storage Technologies  
Despite having a higher energy density per unit mass than other energy carriers, hydrogen is the lightest molecule in the universe, presenting a volumetric density of 0.08376 kg/m\({}^{3}\) (at 20 \({}^{\circ}\)C and 1 atm) and being in the gaseous state under ambient conditions. These characteristics make the hydrogen storage process much more complex than other energy carriers, such as natural gas, oil, and coal. To make this process economically feasible, hydrogen's volumetric density needs to be increased so that the volumetric energy density of the stored hydrogen is at least comparable to other energy carriers [65]. Hydrogen storage technologies are classified as physical-based or material-based technologies [66]. In Figure 13, an overview of the main hydrogen storage technologies can be found [66].  
Two main parameters are considered to characterize each storage technology, namely the system gravimetric capacity, which represents the ratio of the mass of the stored hydrogen to the mass of the system (hydrogen + container), as a percentage, and the volumetric capacity, which represents the mass of hydrogen that can be stored in one unit of volume of the container, expressed in kg/m3 (or g/L) [67]. Furthermore, the storage technologies can be distinguished between short-scale and large-scale storage, long-term and short-term storage, and mobile and static storage [68].
# Seismic Resilience in Critical Infrastructures: A Power Station Preparedness Case Study
## 2 Background
### Secondary Phenomena (Aftershocks and Beyond)
#### 2.2.3 Amplitudes of Seismic Waves  
Recent research has illuminated that specific site conditions can significantly amplify seismic wave intensity and increase damage during earthquakes [36, 37, 38, 39]. The primary factor influencing a site's response is the structure of the shallow subsurface rock layers, especially when soft rock layers with low seismic velocities overlay hard rock layers with high seismic velocities. This geological configuration can lead to resonance in the soft upper layer, enhancing the seismic waves' intensity and delaying their decay. Deep and narrow basins in the subsurface and specific topographies such as mountainous or cliff areas contribute to the intensity of seismic vibrations. However, this work does not cover their risk assessment and the development of mitigation due to a lack of information on these factors.  
This knowledge facilitates the early identification of areas at risk for seismic vibration amplification through subsurface geology and local topography analyses. Consequently, qualitative and quantitative risk maps can be produced based on available data on the geotechnical and seismic properties of the rock layers and predictive models. Nevertheless, conducting a precise quantitative evaluation of risk levels for this and other factors requires a detailed study at a specific site.  
Risk levels related to this amplification factor are delineated in the Israeli Standard 413 [23,24], drawing from a map identifying areas prone to exceptional site amplification (see Figure 1 at the middle top).
# Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance
## 3 Methodology and Results
### Final Grades and Other Metrics
#### 3.4.4 Cosine Similarity  
In the context of students transitioning between states in Moodle, Cosine Similarity is employed to unveil the similarity between two student profiles, based on their transitionswithin the platform. In our study, we intend to compute the cosine of the angle between two vectors representing each student's profile and the mean student profile in a 7-dimensional space. Each dimension of the vector corresponds to a specific state within Moodle (such as Folder, Forum, Quiz, Assignment, etc.). The cosine similarity gauges the cosine of the angle between these vectors, which ranges from \(-1\) to \(1\).
# Deep Error-Correcting Output Codes
## 3 Deep Error-Correcting Output Codes (DeepECOCs)
### The ECOC Framework  
The general ECOC framework consists of two steps: coding and decoding. In the coding step, the ECOC coding matrix \(\mathbf{M}\in\{-1,0,1\}^{C\times L}\) is first defined or learned from the training data, where each row of \(\mathbf{M}\) is the codeword of a class, each column corresponds to a binary classifier (dichotomizer), \(L\) is the length of the codewords, \(C\) is the number of classes, symbol '\(1\)' indicates a positive class, '\(-1\)' indicates a negative class, and '\(0\)' indicates the class that can be neglected during training the binary classifiers. Subsequently, the binary classifiers (dichotomizers) are trained according to the partition of the classes in each column of **M**. Figure 1 shows some coding matrices encoded with different coding strategies. The white grids are coded by 1, the dark grids are coded by \(-1\), and the gray grids are coded by 0. In the decoding step, the test data are predicted based on an adopted decoding strategy.  
Applying a decoding strategy on the outputs of the binary classifier, the ECOC framework can be used for multi-class learning; meanwhile, applying the sigmoid function on the values of the discriminant functions of the binary classifiers, ECOC can be used for representation learning [6]. This is also the foundation of our DeepECOCs model. Specifically, to speed up the training of the binary classifiers in the ECOC modules of DeepECOC, we employ the online SVMs as binary classifiers [45].
## 3 Results
### Fault Diagnosis
#### 3.2.6 Daubechies' Wavelets  
A more advanced technique for denoising purposes is that based on the wavelets, a family of functions with a fixed shape, but which can be shifted and dilated in the time starting from the definition of a root function, i.e., the mother wavelet. Specifically, Daubechies' orthogonal wavelets of the fifth level have been chosen, determining the extent and threshold values of the blocks with the James-Stein method.  
The signal processed via Wavelets and SES techniques appears denoised and cleaned up from masking components (Figures 30 and 31); however, the fault spectral lines, albeit visible, are not as emerging as would be desirable.
# Integrating Machine Learning Algorithms: A Hybrid Model for Lung Cancer Outcome Improvement
## 3 Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model Name** & **Previous Models ([PERSON] et al. [41])** & **Proposed Models** \\ \hline Logistic regression & 87.50 & 93.08 \\ KNN & 92.86 & 93.30 \\ Support vector machines & 85.71 & 95.19 \\ Decision tree & 89.29 & 94.76 \\ Random Forest & 85.71 & 95.40 \\ Gaussian naïve Bayes & 91.07 & 88.46 \\ Gradient Boosting & 89.29 & 95.61 \\ Multilayer perceptron & 89.29 & 94.13 \\ Multinomial naïve Bayes & - & 74.84 \\ Hybrid model & - & 96.42 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of previous and proposed models for lung cancer detection. Gaussian naive Bayes reached 88.46%, showing an improvement over the previous 91.07%. Meanwhile, multinomial naive Bayes, with significant accuracy, jumped to 74.84%. The hybrid model was developed by first performing cross-validation on the Gaussian naive Bayes (GNB) model, followed by integrating the AdaBoost algorithm, a boosting technique, to enhance performance. In this study, the proposed hybrid model achieved an accuracy of 96.42%, as shown in Table 5.  
These findings confirm a valid comparison of the proposed and previous machine learning and deep learning methods. The hyperparameters, as detailed in Table 3, were utilized to achieve optimal performance in lung cancer prediction. This outcome, in turn, proves helpful in the ML research community and for professionals in terms of better classification predictive performance with a focus on optimizing strategies. We conclude that the Gradient Boosting and Gaussian naive Bayes models have performed better in this problem, given smaller datasets and binary characteristics. Such models would be more suited to situations where the attributes/features of the dataset are highly independent of each other. In contrast, the performance was restricted in the other models based on correlation and training/testing splits.
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 3 Proposed TL-Based UWA OFDM Receiver  
The proposed TL-based UWA OFDM receiver utilizes a three-step transfer learning procedure to address the domain mismatch between the source (watermark channels) and target (Qingdao Lake) tasks. By pre-training a DNN model on the source task and fine-tuning it on the target task, the proposed approach significantly reduces the need for extensive training while maintaining high prediction accuracy.
# A Preemptive-Resume Priority MAC Protocol for Efficient BSM Transmission in UAV-Assisted VANETs
## 5 Numerical Results  
In this paper, we idealize the definition of a message to contain only one packet, which can be adjusted in size as \(\beta\) bits. Multiple packets contained within a message can be treated as one large packet. In this paper, the packet size \(\beta\) is set to 10,240 bits.  
To evaluate the effectiveness of the above expression, we conducted simulation experiments on MATLAB. In the simulation, the set parameters are shown in the following table, where the G2A link delay is based on Table 3 and referenced from the literature [19].  
Furthermore, this paper defines the number of priority levels \(\tau\) as 3. The control frame rate is \(\text{CR}=\
icefrac{{\text{R}_{\text{W}}}}{{\text{I}0}}\). The DIFS for each priority level is defined as \([\text{HDIFS};\text{MDIFS};\text{LDIFS}]=[10;80;200]\text{s}\). The vehicle speed range is defined as \([\text{v}_{\text{min}}^{1}\text{-v}_{\text{max}}^{1}]=[33;42](\text{m}/\text{s} );\left[\text{v}_{\text{min}}^{2}\text{-v}_{\text{max}}^{2}\right]=[25;33]( \text{m}/\text{s});\left[\text{v}_{\text{min}}^{3}\text{-v}_{\text{max}}^{3} \right]=[17;25](\text{m}/\text{s})\).