esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Set-Up
### Motor Imagery EEG Data Collection  
For validation purposes, we utilize an electroencephalogram dataset from MI-based brain-computer interface experiments [71], comprising data from 52 healthy participants (mean age: \(24.8\pm 3.86\) years) (available online: [[http://gigadb.org/dataset/100295](http://gigadb.org/dataset/100295)]([http://gigadb.org/dataset/100295](http://gigadb.org/dataset/100295)), accessed on 1 December 2024). Of note, subjects labeled 29, 32, 34, 46, and 49 were excluded because their data lacked sufficient discriminative power, a practice also followed in [32].  
The experimental protocol requires participants to imagine left- and right-hand movements in response to standardized visual cues displayed on a screen. EEG signals are recorded using a 64-channel Biosemi ActiveTwo system at a sampling rate of 512 Hz. Additionally, simultaneous electromyographic recordings are conducted to ensure the absence of actual hand movements during MI trials. The experimental paradigm, illustrated in Figure 2, follows a structured MI task designed to elicit EEG data corresponding to imagined left- and right-hand movements. Participants are seated comfortably with armrests and positioned facing a monitor on which visual cues are displayed. Each trial begins with a black screen and a fixation cross for \(2\) s to allow for task preparation. This is followed by a visual instruction--either \"Left hand\" or \"Right hand\"--presented for \(3\) s, prompting participants to imagine sequential finger movements corresponding to the indicated hand. Afterward, a blank screen is displayed for a randomized interval of 4.1 to 4.8 s, serving as a rest period and reducing task predictability. Note that the protocol emphasizes kinesthetic experience of movement (i.e., the sensation of muscle activation), rather than visual imagery.  
All participants completed five to six runs per session, with each run consisting of 100-120 trials per MI class. Each trial is carefully structured to ensure consistency in data collection, as participants follow standardized visual cues to perform the MI task. Trials are evenly distributed between left- and right-hand imagery conditions, yielding a balanced dataset for analysis.  
Additionally, feedback is provided after each run to enhance participant engagement. This dataset enables robust validation through event-related desynchronization/synchronization analysis, classification accuracy metrics, and identification of noisy or artifact-contaminated trials. The comprehensive experimental design serves as a valuable resource for studying performance variability in MI-BCI systems and for developing subject-independent models.
## 6 Summary  
In this work, a method for estimating the significant wave height (SWH) from GNSS reflectometry using the SNR interference pattern was presented. The method uses the damping coefficient \(\delta\) of the oscillating SNR signal, which depends on the sea surface roughness and therefore correlates with the SWH. If the reflector height is known, \(\delta\) can be estimated using non-linear least squares adjustment. The coefficients from multiple satellites, gathered during different sea state conditions, can further be  
Figure 8: Analyzed ship segments in deep water and shallow water sorted by reference SWH. Reference SWH from the wave model (blue). Estimated SWH from the averaged damping coefficients \(\delta\) (red). (a) Analyzed segments with a water depth of more than \(25\,\mathrm{m}\). (b) Analyzed segments with a water depth of less than \(25\,\mathrm{m}\).  
used for empirical model calculation. The required data can be taken from other SWH sensors like wave buoys or numerical wave models.  
The methodology for static antennas, as well as for antennas installed aboard ships was presented. One month of data gathered from a static antenna and three months of data gathered aboard a ship were processed, and the specific processing steps were explained. SWH observations were taken from wave buoys, and if necessary, an additional correction was calculated from a numerical wave model. The estimated function was subsequently used for the calculation of SWH values valid for specific time spans. Due to the lack of useful independent in situ or remote sensing SWH observations, a validation was done with modeled data. Correlation coefficients of 0.92 for the static antenna and of 0.90/0.79 for the ships antenna in deep/shallow water were found. The mean differences were around \(-0.02\) m for the static antenna and \(-0.03\) m for ship measurements in deep water. In shallow water, where the handling of the hydrodynamic squat effect is quite complex, the mean difference was \(-0.11\) m.  
It can be concluded that GNSS reflectometry based on the analysis of SNR interference pattern is an easy-to-apply technique, especially if existing stations are used. Applying the proposed method, stations located near the coast or on artificial structures could be used to gather observations of the SWH. The ship experiment showed how such data can be handled, as well. Nearly all ships are equipped with GNSS for navigation purposes, but better antennas/receivers are necessary to obtain reliable results. Additionally, the ship's hydrostatics and hydrodynamics must be known. If all these requirements are fulfilled, measurements from ships will be a great opportunity to gather more SWH data in the open ocean or in areas where no other techniques are available.
## 2 Related Works
### Ensemble-Based Imbalanced Data Classification Methods
#### Hybrid Combined Ensembles  
EasyEnsemble [50] was proposed by [PERSON] and [PERSON] in the context of imbalanced data sampling. The main motivation of this method was to keep the high efficiency of under-sampling but reduce the risk of ignoring potentially useful information contained in majority class examples. It adopts a very simple strategy. First, it randomly generates multiple subsamples \(S_{maj1},S_{maj2},...,S_{majn}\) from the majority class sample. The size of each subsample is the same as that of the minority class sample \(S_{min}\), i.e., \(|S_{maji}|=|S_{mini}|\), \(1\leq i\leq n\). Then, the union of each possible pair (\(S_{maji}\), \(S_{min}\)) is used to train an adaboost ensemble. The final ensemble is formed by combining all the base learners in all the adaboost ensembles. It can get better results than adaboost, bagging, random forest, SMOTEBoost and BRF for binary imbalance problems [23]. It seems that using an ensemble as base classifier is more effective (though less efficient) for imbalance classification than using a single classifier.  
BalanceCascade [50] tries to use _guided_ rather than random deletion of majority class examples. In contrast to EasyEnsemble, it works in a supervised manner. In the \(i\)th round, a subsample \(S_{maji}\) is randomly generated from the current majority class data set \(S_{maj}\) with sample size \(|S_{maji}|=|S_{mini}|\). Then, an ensemble \(H_{i}\) is trained from the union of \(S_{maj}\) and \(S_{min}\) by adaboost. After that, the majority class data examples that are correctly classified by \(H_{i}\) are removed from \(S_{maj}\). Since BalanceCascade removes correctly classified majority class examples in each iteration, it should be more efficient on highly imbalanced data sets. The method outperforms adaboost and random forest combined with both random undersampling and oversampling schemes on binary-class imbalanced data sets. However, despite the underlying guided sampling procedure, the reported results are not better than those achieved by EasyEnsemble. Furthermore, some borderline instances of majority class face the risk of being removed.
# A Speed Tracking Method for Autonomous Driving via ADRC with Extended State Observer
###### Abstract  
This paper proposes the extended state observer (ESO)-based active disturbance rejection control (ADRC) for the speed tracking of an autonomous vehicle. Uncertainties, both in the vehicle plant and in the sensors, such as nonlinear uncertainties due to the powertrain dynamics, variations in rolling resistance and air resistance, are all estimated in real-time by an extended state observer (ESO). Furthermore, a simple vehicle longitudinal dynamics model, including a mean value engine model (MVEM), is implemented to obtain the parameters in ADRC and design a feedforward controller to enhance the controller's performance. The proposed controller is validated through CarSim(r)/Simulink(r) simulations and road tests. The simulation validates the adaptiveness of the proposed controller against the well-tuned proportional integral derivative (PID) controller, and the speed tracking error of the proposed controller is within 1.26% in simulation. Simulation results also show that fuel consumption can be improved by 3.6% by changing the accelerator pedal depth and positive rate. Finally, the road tests are completed under four kinds of road conditions, and the maximum tracking error is smaller than 0.5 km/h.  
Autonomous driving; mean value engine model; longitudinal speed tracking; active disturbance rejection control (ADRC)
# AoI Analysis of Satellite-UAV Synergy Real-Time Remote Sensing System
## 3 Derivation for MGF of AoI\({}_{0}\)
### Calculate the MGF of AoI\({}_{0}\) under LCFS-PW
The transitions \(l\in\{1,  
Similar to Section 3.2, with some algebraic operations, the MGF of AoI\({}_{0}\) under _LCFS-PW_ is given by Equation (21). \[MGF_{PW}(s)=\frac{2\eta_{1}\mu\left( \begin{array}{c}4\lambda_{2}^{2}\mu^{2}\eta _{3}^{2}-s^{3}\eta_{2}\eta_{3}\eta_{4}+\lambda_{1}^{4}\eta_{5}+2\lambda_{1}^{3} \eta_{6}+2\lambda_{1}\lambda_{2}\mu\eta_{7}+\lambda_{1}^{2}\eta_{8}\\ -\bar{s}\lambda_{1}\eta_{13}+2s^{2}(\lambda_{1}^{3}\eta_{3}+2\mu\eta_{14}\eta_ {3}^{2}+2\lambda_{1}^{2}\eta_{9}+\lambda_{1}\eta_{10})\\ -\bar{s}(\lambda_{1}^{4}\eta_{3}+2\lambda_{2}\mu\eta_{15}\eta_{3}^{2}+4 \lambda_{1}^{3}\eta_{11}+\lambda_{1}^{2}\eta_{12})\\ -\bar{\beta}_{1}\beta_{2}\beta_{3}\gamma_{3}\big{(}4\lambda_{2}^{2}\mu^{2}\eta _{3}^{2}+\lambda_{1}^{4}\eta_{5}+2\lambda_{1}^{3}\eta_{6}+2\lambda_{1}\lambda_ {2}\mu\eta_{7}+\lambda_{2}^{2}\eta_{8}\big{)}\end{array} \right)}, \tag{21}\]  
where \(\eta_{1}=\lambda_{1}\lambda_{2}(\lambda_{1}+\lambda_{2})\), \(\eta_{2}=\lambda_{1}+2\mu\), \(\eta_{3}=\lambda_{2}+2\mu\), \(\eta_{4}=\lambda_{1}+\lambda_{2}+2\mu\eta_{5}=\lambda_{2}^{2}+2\lambda_{2}\mu+4 \mu^{2}\eta_{6}=\lambda_{2}^{3}+4\lambda_{2}^{2}\mu+8\lambda_{2}\mu^{2}+8\mu^ {3}\), \(\eta_{7}=\lambda_{2}^{3}+8\lambda_{2}^{2}\mu+16\lambda_{2}\mu^{2}+8\mu^{3}\), \(\eta_{8}=\lambda_{2}^{4}+8\lambda_{2}^{3}\mu+20\lambda_{2}^{2}\mu^{2}+32\lambda _{2}\mu^{3}+16\mu^{4}\), \(\eta_{9}=\lambda_{2}^{2}+4\lambda_{2}\mu+5\mu^{2}\), \(\eta_{10}=\lambda_{2}^{3}+8\lambda_{2}^{2}\mu+18\lambda_{2}\mu^{2}+16\mu^{3}\), \(\eta_{11}=\lambda_{2}^{2}+3\lambda_{2}\mu+4\mu^{2}\), \(\eta_{12}=4\lambda_{2}^{3}+22\lambda_{2}\mu+44\lambda_{2}\mu^{2}+40\mu^{3}\), \(\eta_{13}=\lambda_{2}^{4}+12\lambda_{2}^{3}\mu+44\lambda_{2}\mu^{2}+72\lambda_ {2}\mu^{3}+32\mu^{4}\), \(\eta_{14}=\lambda_{2}+\mu\), \(\eta_{15}=\lambda_{2}+4\mu\). According to (15), the mean of AoI\({}_{0}\) under the _LCFS-PW_ queuing discipline is derived as \(\text{AAoI}_{PW}=\{\mathbf{E}[MGF_{PW}(s)]|_{s=0}\}^{(1)}\). Based on the above derivation, Figure 6 briefly compares the average AoI performance under the three queuing disciplines, for \(\lambda_{1}=10,\lambda_{2}\in\{1,2,\ldots,10\},\mu=10\). From the figure, it is evident that as parameter \(\lambda_{2}\) increases towards \(\lambda_{1}\), \(\text{AAoI}_{NP}\), \(\text{AAoI}_{PS}\), and \(\text{AAoI}_{PW}\) decrease. The AAoI initially decreases significantly as the update rate \(\lambda_{2}\) increases from zero, and the AoI reduction diminishes as the \(\lambda_{2}\) continues to increase. Specifically, as \(\lambda_{2}\) increases, \(\text{AAoI}_{PW}\) becomes lower compared to \(\text{AAoI}_{NP}\) and \(\text{AAoI}_{PS}\), signifying better AoI performance from the _LCFS-PW_. In the next section, the \(\text{AAoI}_{PW}\)-related performance is simulated and analyzed in the satellite and UAV synergy remote sensing scenario.
## 4 Applications in Food Analysis
### Aroma
Compounds responsible for the sweet and fruity aroma in jackfruit were identified as ethyl isovalerate, 3-methylbutyl acetate, 1-butanol, propyl isovalerate, isobutyl isovalerate, 2-methylbutanol, and butyl isovalerate [231]. Fruit origin and authenticity (species idenfication) can also be successfully investigated by means of SPME analysis of volatile compounds [229]. For example, the typical sensory expectations of apricot fruits are sweetness and juiciness, which are strongly related to the ripening stage apricots at harvest [302]. Aroma compounds of apricots from different regions of the world were extensively studied [303]. Major identified aroma compounds were ethyl acetate, hexyl acetate, limonene, 6-methyl-5-hepten-2-one, menthone, E-hexen-2-al, linalool, beta-ionone and cyclo-decalactone [228]. Recently the European Union established the research program Horizon 2020 to develop a validated analytical method for determination of volatiles resulting from virgin olive oils by means of SPME-GC/MS. [PERSON] _et al_. [244] published a validated method including calibration curves for 29 volatile compounds. The analytical precision of 67% of these compounds had a relative standard deviation lower than 10% and accuracies were determined for 97% of the analyzed volatile compounds. The limits of detection ranged from 0.1 to 2.54 mg/kg for determined volatile compounds [244].
# Automated Monitoring of Bluefin Tuna Growth in Cages Using a Cohort-Based Approach
## 2 Materials and Methods
### [PERSON]'s Method for Modal Analysis  
Taking into account that the fish stock comprised individuals from various age groups, a modal analysis was essential to identify different cohorts before analysing the evolution of length and width. The modal analysis outlined in this section follows the recommendations of [22], specifically applying [PERSON]'s method [23] through FiSAT II (FAO-ICLARM Fish Stock Assessment Tools) Version 1.2.2 software [24]. This method involves the separation of normal distributions, each representing a cohort of fish, from the overall distribution.  
In spite of the usefulness of objective criteria in allowing researchers to conduct effective and standardised analyses, FiSAT II software allows the operator to decide on the selection of points defining each modal group, and [PERSON]'s method is sensitive to such selections. Therefore, different results may be obtained depending on operator decisions. In this case, as a first step to obtaining reliable results, available information on BFT growth rates, both in the wild and under farming conditions [25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36], were analysed to obtain reference values regarding the expected amplitudes of annual cohorts and the separation index between them throughout the life cycle. Moreover, to ensure the coherence among different analyses, in addition to the basic criteria to identify and characterise modal groups stated in [24], the following criteria were strictly applied:  
Figure 3: Sequence of processes performed automatically in our proposal.  
* SI (separation index) values between successive cohorts should be similar in different analyses, with a minimum value of 2 considered as a general reference, occasionally accepting slightly lower values in older cohorts.
* SD (standard deviation) values of identified modal groups should fall within the range of 3 to 7 cm, considering background information about the variability within annual cohorts.
* The proportions of specimens belonging to given cohorts should be consistent among different analyses.  
Prior to applying the method, length-frequency data underwent preprocessing with different configurations regarding class intervals and smoothing. The bin width or class interval, which represents the distance between classes or subintervals of the data, was set to 2 cm, 3 cm, or 5 cm, whereas data smoothing was applied using running averages over 3 and 5 classes. Based on preliminary analyses considering the available knowledge on Atlantic BFT annual growth, the optimal configuration for characterising annual cohorts was determined to be a bin width of 3 cm and applying a running average over 3 classes to smooth the data, since larger bins tend to mix several annual cohorts within the same modal group. Hence, the modal analysis presented in the Results section used these preprocessing options.
# Machine Learning-Driven Calibration of Traffic Models Based on a Real-Time Video Analysis
## 3 Methodology for Analyzing Video Stream Data
### Tracking Algorithm and Indirect Calculation of Vehicle Characteristics  
Four straight-line equations were formulated to define the boundaries of the visibility area along the route to improve the accuracy of tracking vehicle trajectories. The centers of objects within this area were identified in each frame, and the distances between these centers in consecutive frames were calculated using the Euclidean metric. When the distance traveled by a vehicle between frames exceeded a set threshold, its movement was recorded, and its trajectory was determined.  
The distance traveled in pixels between two frames for each object was used to calculate the speed of vehicles as an example of indirect characteristics. This distance allowed for determining the speed at the current time step:  
\[v_{\text{km/h}}=\frac{d(C_{1},C_{2})}{3600\cdot EPS}, \tag{3}\]  
where \(d(C_{1},C_{2})\) is the calculation of the Euclidean metric, and the distance in kilometers is then calculated from the resulting value through interpolation. \(C_{i}\) is the selected object center coordinates on the frame's pixel grid. \(EPS\) is the number of frames per second.  
One-dimensional Kalman filtering was applied to all indirect characteristic calculations to provide a recursive estimation of the current speed value:  
\[\dot{x}_{k}=\dot{x}_{k-1}+KG\big{(}z_{k}-\dot{x}_{k-1}\big{)}, \tag{4}\]  
where \(z_{k}\) is the calculated speed value, \(\dot{x}_{k-1}\) is the speed estimate at the previous step, and \(KG\) is Kalman Gain, which is calculated:  
\[KG=\frac{E_{EST_{i}}}{E_{EST_{i}}+E_{EMA}}, \tag{5}\]  
where \(E_{EST_{i}}\) is the uncertainty of the current value estimate, and \(E_{EMA}\) is the tracking algorithm's error in calculating the speed.
# A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD
## 5 Comparison with Advanced Methods  
In order to highlight the novelty and superiority of the proposed method, the identification results of frequency and amplitude were compared with several advanced methods, as shown in Table 3.  
As Table 3 shows, the method proposed in this paper, CS-SP-EEMD, accurately identifies frequencies in noisy environments. The ABPF method uses an adaptive bandpass filter (ABPF) to identify the frequency accurately, but it does not consider the interference  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Frequency and Error** & **Amplitude and Error** & **Robustness to Noise** \\ \hline CS-SP-EEMD & 50, 75, 200, 400 (0, 0, 0, 0) & 0.85, 0.2, 0.45, 400 & 15 dB-30 dB \\  & & (0.539\%, 7.0\%, 5.28\%, 4.33\%) & 15 dB-30 dB \\ \hline ABPF & 1.5, 25, 300, 400 (0, 0, 0, 0) & 5, 5, 5, 5, 5 & — \\  & & (1.312\%, 1.988\%, 2.228\%, 2.228\%) & \\ \hline IA-VMD & 0.68, 1.95, 26.58, 21.94, 93.42 & 1.46, 2, 0.49, 0.22, 0.32, 0.2 & White noise with an amplitude of 0.2 \\  & (0, 1.55\%, 0.90\%, 0.46\%, 0.45\%) & (6.16\%, 0, 2.04\%, 9.09\%, 0) & amplitude of 0.2 \\ \hline PSO-VMD & 28, 50, 72, 150 & 45, 25, 18.5, 8.5 & 5 dB-20 dB \\  & (0.0614\%, 0.0066\%, 0.0125\%, 0.036\%) & (2.8337\%, 0.2691\%, 4.2087\%, 10.302\%) & 5 dB-20 dB \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with the existing methods.  
Figure 7: The identification results for the measurement signal.  
of noise, and the amplitude recognition of each mode has different degrees of error when the amplitude is constant [15]. The IA-VMD method uses the adaptive variational mode decomposition method to identify wideband oscillating signals with five different modes in a white noise environment with an amplitude of 0.2, but there are errors in the frequency and amplitude [16]. Although the PSO-VMD method [17], which uses particle swarm optimization (PSO) to improve VMD, satisfies the robustness to noise criterion, there are also errors in the identification of frequency and amplitude. In addition, the above three methods do not involve the sampling and processing of high-frequency signals.
## 9 Stokes Space Polarization and Field Recovery Principle
### Polarization Recovery Techniques in the Direct Detection Stokes Vector Receiver
#### 9.1.2 Training-Assisted Polarization Characterization
This brings about a limited reach, owing to the fact that it will be highly challenging to transmit the signal over hundreds of kilometers. This challenge can be addressed by means of channel linearization. This can be achieved by maintaining the \(Y\) polarization as a constant carrier while the signal is used to modulate the \(X\) polarization only. Based on this approach, the subsequent Jones vector is \(\mathbf{J}=[X,Y]^{T}\), and the Stokes vector can be defined as [24]:  
\[\mathbf{S}=\Big{[}|S|^{2}-|C|^{2},\Re(S\cdot C^{*}),\Im(S\cdot C^{*})\Big{]}^{T}. \tag{51}\]  
In general, the SV-DD algorithm concept is based on the combination of the second and the third components of the Stokes vector to achieve the final output \(S\cdot C^{*}\), which contains the full phase diversity of the signal. Therefore, the signal can be fully recovered from the output without being marred by the chromatic dispersion-induced fading. Furthermore, the SV-DD algorithm helps associate the nonlinearity term with the first Stokes vector component without impairing the recovered signal derived from the second and the third Stokes vector components. Therefore, it is remarkable that the SV-DD signal is complex modulated, and there is no need for a frequency gap between the carrier and the signal. In this regard, the optical fiber channel can be considered as a linear complex optical channel that is comparable to that of COH detection [24]. As a result, compared with single-polarization-modulated COH detection, the SV-DD can realize 100% nominal SE. In relation to the offset-OFDM and VSSB, the SV-DD offers electrical spectrum efficiency of 4 \(\times\) and 2 \(\times\), respectively [8; 13; 24]. It is noteworthy that the implementation of the training-assisted equalization scheme imposes distinct conditions on the transmitter. For instance, there should be a symmetric power on the \(X\)- and \(Y\)- polarizations in the Jones space. It should be capable of controlling the interpolarization differential phase through its adjustment to \(0^{\circ}\) or \(90^{\circ}\). Moreover, regarding the Stokes space field recovery, the PDM-SSB and SCOH-SSM can support and transmit the three required orthogonal bases [13].
## 3 Energy Efficient Joint Power Control and User Association Optimization Algorithm
### Joint Optimization Algorithm
#### 3.2.2 The Master Problem: User Association Optimization Problem  
Problem P4 is a mixed-integer nonlinear programming (MINLP) problem, which can be converted into a convex optimization problem with the help of the Lagrange's dual decomposition method. The Lagrange function of Problem P4 can be expressed as Equation (21).  
\[\mathcal{L}\left(\mathbf{x},\mathbf{k},\lambda,\mu\right)=\sum\limits_{j} \sum\limits_{k}x_{jk}\ln\left(a_{jk}\right)-\sum\limits_{j}k_{j}\ln\left(k_{j }\right)+\sum\limits_{j}\lambda_{j}\left(k_{j}-\sum\limits_{k}x_{jk}\right)+ \mu\left(\sum\limits_{j}k_{j}-K\right) \tag{21}\]  
objective functions, respectively. It can be proven that there will be \(g\left(\mathbf{x}^{*},\mathbf{k}^{*}\right)-g\left(\mathbf{x},\mathbf{k}\right) \leq\Omega\), where \(\Omega=\sum\limits_{j}k_{j}\ln\left(k_{j}\middle/e^{k_{j}+\mu-1}\right)\). The proof can be seen in Appendix B.