esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Feature Extraction in Multiple Domains
### Feature Extraction in the Time-Frequency Domain  
Wavelet analysis is employed to extract features of the raw signals in the time-frequency domain. The continuous wavelet transform (CWT) and discrete wavelet transform (DWT) are two commonly used methods that capture both frequency and location information (location in time) due to its temporal resolution, and have been shown to be effective in faults diagnosis, image processing, and many others. The following gives a brief introduction to the algorithm of CWT and DWT.  
The continuous wavelet transform of a vibration signal \(s(t)\) at a scale (a \(>\) 0) a \(\in\) R\({}^{+}\) and translational value b \(\in\) R is expressed by the following integral,  
\[c_{s}(a,b)=\frac{1}{\sqrt{|a|}}\int_{-\infty}^{\infty}s(t)\cdot\overline{\psi_{ a,b}(\frac{t-b}{a})}\ dt, \tag{5}\]  
where \(\psi_{a,b}(\bullet)\) is a continuous function in both the time domain and the frequency domain called the mother wavelet and the over line represents the operation of complex conjugate. Then, the wavelet family is generated by dilating and translating a fixed valued wavelet base \(\psi(b)\), expressed as  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}\psi(\frac{t-b}{a}), \tag{6}\]  
For the discrete wavelet transform (DWT), the Hilbert basis is constructed as the family of functions \(\{\psi_{a,b}\): \(a\), \(b\)\(\in\)Z\(\}\) by means of dyadic translations and dilations of \(\psi\):  
\[\psi_{j,k}(t)=2^{-j/2}\psi(2^{j}t-k), \tag{7}\]  
Here, \(a=2^{-j}\) is the binary dilation and \(b=\) k\(\cdot 2^{-j}\) donates the binary position.  
The continuous wavelet transform provides an overcomplete representation of a signal by letting the translation and scale parameter of the wavelets vary continuously, whereas in the discrete wavelet transform, the wavelets are discretely sampled. For the \(n^{th}\) level of decomposition, the WPT (one of the DWT methods) produces \(2^{n}\) different sets of coefficients, and each set of coefficients can be converted to a time series by signal reconstruction.
## 5 Discussion
### Results of the Application and the Influence of Current Regulation
#### 5.1.1 National Regulations in Application  
Fundamental aspects that must be taken into account are the resistance of the structural and constructive elements to fire, the maximum occupations or the number and dimensions of the accesses and evacuation routes Given that they are periodically updated, a periodic review of the building's Self-Protection Plan is necessary, as well as in the event of a change in current regulations or when new legal texts are published that modify or repeal the previous ones. Likewise, other specific standards for the design, installation, and maintenance of the different facilities that provide service to the residence, must be taken into account.
###### Abstract  
Electroencephalographic (EEG) signal processing and machine learning can support neurologists' work in discriminating Psychogenic Non-Epileptic Seizure (PNES) from epilepsy. PNES represents a neurological disease often misdiagnosed. Although the symptoms of PNES patients can be similar to those exhibited by epileptic patients,EEG signals during a psychogenic seizure do not show ictal patterns such as in epilepsy. Therefore, PNES diagnosis requires long-term EEG video. Applying signal processing and machine-learning methodologies could help clinicians find helpful information in the clinical diagnosis of PNES by analyzing EEG signals registered in resting conditions and in a short time. These methodologies should prevent long EEG recording sessions and avoid inducing seizures in the subjects. The aim of our study is to develop and validate several machine-learning models on a larger dataset, consisting of 225 EEGs (75 healthy, 75 PNES, and 75 subjects with epilepsy). A deep analysis of our results shows that changes in the evaluation strategy led to changes in accuracy from 45% to 83.98% for a standard Light Gradient Boosting Machine (LGBM) classifier. Our findings suggest that it is necessary to operate a very rigorous control in terms of experimental data collection (patient selection, signal acquisition) and terms of validation strategies to obtain and reproducible results.  
epilepsy; PNES; quantitative EEG; data mining; classification
# Build-Launch-Consolidate Framework and Toolkit for Impact Analysis on Wireless Sensor Networks
## 4 Case Study: Analyzing Flooding Attack
### Flooding Attack  
A flooding attack is a form of DoS attack that aims to degrade or completely bring the service provided by the network to a halt. There are many forms and methods for carrying out a flooding attack that exploits weaknesses in the communication protocols stack. One such attack is a DODAG Information Solicitation (DIS) flooding attack. As per RPL specifications, the actual topology of a network is built dynamically as nodes in the network exchange information with each other. DIS messages are sent (usually as multicast) by new nodes to solicit information on available networks and candidate parents. Adjacent nodes already in the network respond by sending DIO messages to advertise their information [32]. After receiving DIO messages from neighbor nodes, the new node stops sending further solicitation messages and views all senders as prospective parents [33]. However, in a DIS flooding attack scenario, a malicious node would keep sending DIS messages excessively, overwhelming other nodes in the network and forcing them to generate additional DIO messages, which disrupts their ability to handle their roles in passing benign traffic, leading to congestion across existing links [34]. In our implementation of the attack, the malicious node is assumed to have infiltrated the network and acts as normally as other nodes until the attack is triggered. Flooding attacks are a classic example of DoS attacks. Their footprint on RPL networks has been studied widely and is shown to cause significant disruption to the networks in PDR, throughput, and power consumption [35; 36]. [PERSON] and [PERSON] [37] compared flooding attacks to other DoS attacks (namely blackhole, grayhole, and scheduling attacks) and demonstrated in a simulated experiment that they cause severe damage to the target network's lifetime and service availability.
# A Review of Fabrication Technologies for Carbon Electrode-Based Micro-Supercapacitors
## 5 Fabrication Technologies of Carbon Electrode-Based Micro-Supercapacitors
### Coating Methods
#### 5.2.3 Vacuum Filtration  
With vacuum filtration, a solid is separated from liquid, resulting in a stacked dense film, as electrodes with high energy density [164; 165; 166; 167; 168]. Several hybrid materials can be formed into uniform films using a range of homogeneous dispersions.  
Selective vacuum filtration was used in the fabrication of flexible graphene hydrogel micro-supercapacitors on paper chips [169], illustrated in Figure 10. Single-walled carbon nanotube thin films were also prepared by vacuum filtration, which can then act as electrodes in micro-supercapacitors [78]. A flexible, in-plane asymmetric micro-supercapacitor was fabricated with electrochemical-exfoliated functional graphene oxide and chemical-reduced, functional reduced graphene oxide films, made through mask-assisted vacuum filtration, and solid polyvinyl alcohol/sodium sulphate electrolyte [170]. In a very recent study, a new micro-supercapacitor was developed with MXene/carbon nanotubes composite, acting as the electrode, and fabricated using photolithography followed by vacuum filtration. In this process, the interdigitated structure micro-supercapacitor substrate was first produced on filter paper via photolithography and using a custom-made photomask, and then, silver paste, the MXene-carbon nanotubes composite, and polyvinyl alcohol/sulphuric acid were deposited layer-by-layer on the substrate, as collector, electrode material, and conductive gel electrolyte, respectively, by vacuum filtration [80].
# Complexity Reduction of MLSE and MAP Equalizers Using Modified Prolate Basis Expansion
## 2 Materials and Methods
### MLSE and MAP Equalizers
#### 2.2.1 MAP Equalization  
The MAP equalizer obtains an estimate of the transmitted symbol \(b_{l}\), by maximizing the a posteriori probability given the received signal sequence \(\mathbf{y}~{}=\left[y_{0},y_{1},\ldots,y_{L-1}\right]^{T}\)[20; 21; 22]. The latter is expressed as follows:  
\[\hat{b}_{l}=\underset{b\in X}{\mathrm{argmax}}Pr(b_{l}=b|\mathbf{y}), \tag{8}\]where \(b\) is chosen from the finite input alphabet \(\mathcal{X}\) with cardinality \(|\mathcal{X}|=M\). The MAP equalizer can be implemented using the Bahl, Cocke, Jelinek, and Raviv algorithm (BCJR) [20] as:  
\[Pr(b_{l}=b|\mathbf{y})=\sum_{\forall\mathbf{s}_{l}\rightarrow\mathbf{s}_{l+1}}\gamma(\mathbf{s} _{l}\rightarrow\mathbf{s}_{l+1})\alpha(\mathbf{s}_{l})\beta(\mathbf{s}_{l+1}), \tag{9}\]  
\(b_{l}=b\)  
where \(\mathbf{s}_{l}\in\mathcal{S}\) is the channel state in the instant \(l\), with \(\mathcal{S}~{}=~{}\{\mathbf{a}_{m}|m=0,\ldots,M^{P-1}-1\}\) being the set of all possible combinations of the previous \(P-1\) symbols \(\mathbf{a}_{m}~{}=~{}[b_{l-1},\ldots,b_{l-P+1}]\), \(\gamma(\mathbf{s}_{l}\rightarrow\mathbf{s}_{l+1})\) is the error probability in the transition from the state \(\mathbf{s}_{l}\) to \(\mathbf{s}_{l+1}\), and \(\alpha(\mathbf{s}_{l})\) and \(\beta(\mathbf{s}_{l})\) are the forward and backward coefficients in the state \(\mathbf{s}_{l}\), respectively, which are obtained through the following recursions:  
\[\alpha(\mathbf{s}_{l+1})=\sum_{m=0}^{M^{P-1}-1}\gamma(\mathbf{s}_{l}=\mathbf{a }_{m}\rightarrow\mathbf{s}_{l+1})\alpha(\mathbf{s}_{l}=\mathbf{a}_{m}), \tag{10}\] \[\beta(\mathbf{s}_{l})=\sum_{m=0}^{M^{P-1}-1}\gamma(\mathbf{s}_{l} \rightarrow\mathbf{s}_{l+1}=\mathbf{a}_{m})\beta(\mathbf{s}_{l+1}=\mathbf{a}_{m}), \tag{11}\]  
with \(\alpha(\mathbf{s}_{-1}=\mathbf{a}_{m})=1\) and \(\beta(\mathbf{s}_{L}=\mathbf{a}_{m})=1,\forall\mathbf{a}_{m}\in\mathcal{S}\).  
Note that \(\gamma(\mathbf{s}_{l}\rightarrow\mathbf{s}_{l+1})\) depends on the sequence \(\mathbf{b}_{l}=[b_{l},\mathbf{s}_{l}]\), composed of the \(l\)-th transmitted symbol and the previous \(P-1\); thus,  
\[\gamma(\mathbf{s}_{l}\rightarrow\mathbf{s}_{l+1})=Pr(\mathbf{s}_{l+1}|\mathbf{s}_{l})Pr(y_{l }|\mathbf{b}_{l}). \tag{12}\]  
Assuming an AWGN with a zero mean value and a variance of \(\sigma_{n}^{2}\), then  
\[Pr(y_{l}|\mathbf{b}_{l})=\frac{1}{\sqrt{2\pi\sigma_{n}^{2}}}e^{-\frac{\|y_{l}-( \hat{y}_{l}|\mathbf{b}_{l})\|^{2}}{2\sigma_{n}^{2}}}. \tag{13}\]  
To reduce the complexity of the BCJR, two algorithms were derived in the log-domain: the Max-Log-MAP [21] and the Log-MAP [22].
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Data Preprocessing  
Vis/NIR spectroscopy is generally affected by the overlapping spectral response of sample components, as well as interference from sources of error including instrument noise and drift, light scattering, and optical path changes during measurement [10]. Many researchers have shown that data preprocessing methods can effectively reduce the possible fluctuations in the Vis/NIR spectrum and improve the stability of the signal. Proper preprocessing can reduce the system and random errors of the instrument and the environment to gain better models [11; 12]. The preprocessing methods commonly used in spectral analysis generally include smoothing, derivative [13], standard normal variate (SNV), and multivariate scattering correction [14], etc.  
This research used the Savitzky-Golay (S-G) smoothing algorithm and standard normal variate (SNV) for preprocessing. S-G smoothing increases the signal-to-noise ratio without causing significant distortion to the spectrum [15]. It is a convolution process that uses the least squares method to fit a continuous subset of adjacent points with a low-order polynomial. SNV is widely used to eliminate random errors and fluctuations caused by equipment and external factors such as temperature, pressure, and pulse energy. It uses the standard deviation of the response to normalize each spectrum value to correct the spectrum. The formula is as follows:  
\[\text{SNV correction}:\ X_{snv}=\frac{X_{i}-\overline{x}}{\sigma} \tag{1}\]  
where \(X_{i}\) is the spectral value of the \(i\)-th wavelength point, \(\overline{x}\) is the average spectrum value of all wavelength points, and \(\sigma\) is the standard deviation of all wavelength points.
## 3 Results  
### Estimation of Forest Degradation Indicators  
#### 3.1.1 Canopy Cover  
From the period 1990-2014, a total of 74 canopy cover layers were extracted from the fractional maps that CLASlite(tm) produced. The forest canopy cover layers (derived from the fractional cover map) were 90% correct (kappa coefficient) with zero and 10% of quantity and allocation disagreement, respectively, based on 1000 pixels assessed independently using available high-resolution imagery from Google Earth(tm).
# Quadcopters in Smart Agriculture: Applications and Modelling
## 3 Agricultural Applications of Quadcopters
### Crop Mapping and Monitoring  
The use of quadcopters in agriculture mapping plays a significant role in helping farmers make more informed decisions and achieve more development in the farming sector.  
The authors in [60] equipped a DJI Matrice 100 quadcopter with a Light Detection and Ranging (LiDAR) sensor to observe crop fields and evaluate their production and environmental parameters.  
The commercial DJI Matrice 210 was used in [84] with image processing techniques to map and monitor crop fields in Northern Italy for better precision agriculture.  
A Pelican quadcopter captured multispectral images in [110] to extract agricultural features on the field and evaluate vegetation status.  
The DJI Inspire was used in [63] to take high-resolution images of crop fields and proved to be more effective than using conventional ground-observer methods.  
A combination of DJI Phantom 4 with an Unmanned Ground Vehicle (UGV) was proposed in [75] to provide 3D mapping of orchard fields. Additonally, the DJI Mavic Pro was employed with UGV in crop fields in Eschikon, Switzerland for precision agriculture [111].  
The authors in [29; 30] used a quadcopter in a WSN-based application to improve crop field management by nominating cluster heads among ground sensors to organize data collection, gathering information from the selected cluster heads, and localizing ground sensors.
## 3 Materials and Methods
### Experimental Setup and Procedures
#### 3.2.2 Ultrasonic Scanning Data Collection  
In this study, a fully noncontact ultrasonic test setup was used to generate and measure ultrasonic waves from the concrete specimen, as shown in Figure 4. An air-coupled transducer (NCG50-D50, Ultran) generated ultrasonic waves in the concrete sample with a center frequency of 32 kHz. The lift-off distance and incident angle for the transducer were set to 40 mm and 5\({}^{\circ}\), respectively. The corresponding ultrasonic responses were collected using the noncontact ultrasonic sensor array system (NUSAS) designed by the authors (see Figure 4) with a lift-off distance of 10 mm. The NUSAS consists of a linear array of 64 MEMS ultrasonic microphones (SPU0410 LR5H-QB, Knowles Acoustics) with a spacing of 5 mm and a 64-channel amplifying circuit to improve the signal-to-noise ratio (SNR). The amplifying circuit removes DC offsets, suppresses the low-frequency environmental noises from the obtained signals, and amplifies the filtered signals 2000 times (60 dB). Sixteen ultrasonic responses were collected at a time, and the sequential data collection throughout the 64 channels was controlled using a switch multiplexer (TB-2630B, National Instruments). The collected signals were digitized using a digital oscilloscope (PXIe-5105, National Instruments), and the digitized signal data were saved to a laptop computer. The sampling rate was set to 2 MS/s, and the signals were time-averaged over 50 repetitions.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \begin{tabular}{c} **Designed** \\ **Compressive** \\ **Strength (MPa)** \\ \end{tabular} & \begin{tabular}{c} **Maximum** \\ **Aggregate** \\ **Size (mm)** \\ \end{tabular} & \
\begin{tabular}{c} **Water-to-Cement** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Unit Weight (kg/m\({}^{3}\))** \\ \end{tabular} & \\ \hline
30 & 15 & 0.43 & 169 & 392 & 825 & 952 & 3.528 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mixture design proportions for the concrete specimen.  
Figure 3: Cross-sectional dimensions of the tested concrete specimen. The unit is in mm.
## 4 Aperture Design: Parametric Analyses and Performance Evaluation
### Hexagonal Ridged Radiating Elements
Instead, the hexagon with six ridges shows better performance in terms of XPD than the other apertures, with values better than 18 dB when it scans up to \(50^{\circ}\). The performances of the proposed designs are listed in Table 3. The scan angle of \(40^{\circ}\) and \(50^{\circ}\) are also reported to provide direct comparison to the literature data. All the data refer to circular polarization. Figure 19: Comparison of ARC, scan loss and XPD for all analysed apertures for circular polarization. (**a**) S\({}_{11}\) at scan angle limit. The green box indicates the operative frequency band; (**b**) S\({}_{21}\) at broadside. The green box indicates the operative frequency band; (**c**) S\({}_{21}\) at scan angle limit. The green box indicates the operative frequency band; (**d**) scan loss at 20.2 GHz; (**e**) XPD at 20.2 GHz. Reference [5] reports an EQA with a matching section providing acceptable reflection and coupling up to \(\pm 50^{\circ}\) within a bandwidth of 4.5% (simulated data). The worst case XPD level is only 8 dB. For the radiated elements presented in this work, no matching section is included. In this way, the reported data show the intrinsic performance of such radiating apertures (open-ended waveguides), i.e., the performance that can be achieved with minimum complexity. The reported data can be used as a reference for the future design of more complex elements including matching discontinuities. The performance for scan angles of \(60^{\circ}\) is not satisfactory for all the elements. The coupling is above \(-10\) dB even at \(50^{\circ}\) scan angles. From these considerations, we can conclude that such elements can be suitable for single polarized DRAs (which, to the best of the author's knowledge are the configurations currently being adopted for LEO satellites) operating up to \(50^{\circ}\) scan angles, when the unexcited port is closed on a matched load. This will reduce the element radiation efficiency. However, it will provide a smooth frequency response (without spurious resonances/spikes).
## 4 HFAJAYA for the Multi-Constraint OPF Problems
### OPF Solutions, Including Stochastic Solar and Wind Power
#### 4.2.5 Case 8  
This case focuses on the minimization of the generation cost incorporating stochastic wind and solar power with the carbon tax.  
Many nations have put significant pressure on the whole energy industry to decrease carbon emissions in recent years because of global warming [22]. The carbon tax (\(C_{tax}\)) is levied on every unit quantity of released greenhouse gases to stimulate investment in greener types of power such as wind and solar. The cost of emission (in dollars per hour) is expressed as:  
\[C_{E}=C_{tax}E \tag{49}\]  
\[J_{8}=J_{7}+C_{tax}E \tag{50}\]  
This case study sough to reduce total generating costs by imposing a carbon tax on emissions from traditional thermal power producers. As calculated by (50), the total cost must be kept to a minimum. The \(C_{tax}\), or carbon tax rate, is estimated to be USD20 per tonne [22]. Because wind and solar are spotless sources of energy, the carbon tax component is intended to boost their adoption. Table 11 shows the optimal generating schedule, reactive generator power, entire generation cost (counting \(C_{tax}\)), and other computed factors. When a carbon price was imposed in Case 8, the penetration of both solar and wind energy was higher than in Case 7 where no carbon tax was considered. The volume of emissions and the pace at which a carbon price is applied determine the amount of growth in the ideal renewable producing schedule.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Optimal Values** \\ \hline Total Cost (USD/h) & 782.2795 \\ \hline Emission (t/h) & 1.76202 \\ \hline Power losses (MW) & 5.7724 \\ \hline _V.D._ (p.u.) & 0.45423 \\ \hline \hline \end{tabular}
\end{table}
Table 10: _Cont._  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variables** & **Optimal Values** \\ \hline \(P_{G1}\) (MW) & 122.91239 \\ \hline \(P_{G2}\) (MW) & 31.4629 \\ \hline \(P_{ws1}\) (MW) & 45.1894 \\ \hline \(P_{G3}\) (MW) & 10 \\ \hline \(P_{ws2}\) (MW) & 38.0721 \\ \hline \(P_{ss}\) (MW) & 41.0406 \\ \hline \(V_{G1}\) (p.u.) & 1.0701 \\ \hline \(V_{G2}\) (p.u.) & 1.0566 \\ \hline \(V_{G5}\) (p.u.) & 1.0354 \\ \hline \(V_{G8}\) (p.u.) & 1.0403 \\ \hline \(V_{G11}\) (p.u.) & 1.0999 \\ \hline \(V_{G13}\) (p.u.) & 1.0573 \\ \hline \(Q_{G1}\) (MVAR) & \(-\)2.82122 \\ \hline \(Q_{G2}\) (MVAR) & 12.1528 \\ \hline \(Q_{ws1}\) (MVAR) & 22.9933 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The optimal values by HFAJAYA for Case 8.  
Constraints on load bus voltage are critical in the OPF problem since load bus operating voltages are frequently near their limitations. In our investigation, the load bus voltage had to be kept between 0.95 and 1.05 p.u.
## 2 Methodology
### Robust Data Reconciliation Framework  
In a thermal system, all known parameters (including the sensors' measurement values and assumed empirical parameters) can be arranged into the vector \(\mathbf{x}\), and the unknown parameters can be arranged into the vector \(\mathbf{u}\). In a standard heat balance computation, several equations should be constructed to calculate the values of \(\mathbf{u}\) from \(\mathbf{x}\), where the components of \(\mathbf{x}\) are mostly the design parameters of the equipment deployed in the system. In the data reconciliation method, however, most of the elements in \(\mathbf{x}\) are the measurements from various types of sensors. Suppose all equations can be expressed as a residual vector \(\mathbf{f}\), that is:  
\[\mathbf{f}=\mathbf{f}(\mathbf{x},\mathbf{u}) \tag{1}\]  
When all parameters are precise, there should be \(\mathbf{f}=\mathbf{0}\). The length of \(\mathbf{x}\) is denoted as \(n\), the length of \(\mathbf{u}\) as \(r\), and the length of \(\mathbf{f}\) as \(m\). As a necessary condition for data reconciliation, one should ensure the following:  
\[m>n-r \tag{2}\]This condition ensures that there are more equations (or correspondingly, more known parameters) than needed in order to solve the system so that redundancy exists for data correction. Otherwise, when both sides in Equation (2) are equal to each other, it implies that one has to suppose all known parameters are precise to solve the unknown parameters, just like in the heat balance calculation.  
Under the robust data reconciliation framework, the following optimization problem was constructed to acquire the best correction results for known and unknown parameters:  
\[\begin{split}&\min_{\mathbf{v}}\sum\limits_{i=1}^{n}\rho(\xi_{i}), \quad\xi_{i}=\frac{v_{i}}{\sigma_{i}}\\ & s.t.\ \mathbf{f}(\mathbf{x}-\mathbf{v},\mathbf{u})=\mathbf{0} \end{split} \tag{3}\]  
In Equation (3), the vector \(\mathbf{v}\) stands for the correction vector of known variables \(\mathbf{x}\) (which should have the same length), which should be subtracted from the original measurements as an offset to acquire the corrected results \(\mathbf{x}-\mathbf{v}\). The target function of the optimization problem is the summation of the normalized correction \(\xi_{i}=v_{i}/\sigma_{i}\) transformed by the estimator \(\rho\), where \(\sigma_{i}\) is the standard deviation of the \(i\)th parameter and can be transformed from its measurement uncertainty \(a_{i}\) when it obeys a normal distribution [18]:  
\[\sigma_{i}=\frac{a_{i}}{1.96} \tag{4}\]