esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Scenarios  
To investigate the performance of the proposed system thoroughly, two traffic scenarios were designed using a realistic model of the road infrastructure of two Slovak cities: Zilina and Bratislava. These two scenarios were selected as they represent a typical layout of urban road infrastructure, where the emergency lane can be implemented. The Zilina scenario consists of a road segment belonging to the city circle, which has a higher speed limit (70 km/h) and is a highway-like multilane road without intersections. The selected road segment is frequently used by ambulance vehicles as the main ambulance station in Zilina and the hospital are located close to the endpoint. The Bratislava scenario represents a typical dense urban network with many intersections and a low-speed limit (50 km/h). Hence, the scenarios were designed to study how different environments affect the system's performance. The geometries of the road networks were extracted from the openstreetmap.org project [37] and can be compared in Figure 5. In all simulations, a sufficiently long initial phase was used to populate the road infrastructure with vehicles, before the EV entered the scene. The EV entered the simulation and started to transmit messages at the time \(t_{start}\).  
The movement of vehicles was modeled in the Simulation of Urban Mobility (SUMO) simulator [38]. Parameters of vehicular flows were set according to the values available in the general traffic city plans of Zilina and Bratislava [39; 40]. The PEEV-WS was modeled in the Objective Modular Network Testbed in C++ (OMNeT++) discrete-event network simulator [41], using Vehicles in Network Simulation (VEINS) [42] and INET [43] simulation frameworks.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Field** & **Length** & **Description** \\ \hline Request code & 16b & Request code: Determines actions required from drivers. \\ \hline Distance & 16b & Distance in meters. \\ Timestamp & 64b & Timestamp (nanoseconds). \\ \hline \multirow{4}{*}{Payload} & \multirow{4}{*}{32b} & Data field containing additional information for drivers. The number of payload fields is given in the Payload length field. \\ \cline{1-1}  & & length field. \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 5: The road network with highlighted emergency routes (in blue): (**a**) Zilina—highway-like scenario; (**b**) Bratislava—dense urban scenario.
# Comparison of Machine Learning Methods to Up-Scale Gross Primary Production
## Materials and Methods
### Methods
#### 2.3.3 Validation of Up-Scaled GPP  
To assess the performance of the up-scaling GPP using machine learning methods, the up-scaled GPP were validated using GPP at flux tower sites, using weighted GPP in the footprint, and were compared with MODIS GPP products. The determination coefficient (R\({}^{2}\)) and root mean square error (RMSE) were used to quantify the accuracy of the results.  
Firstly, up-scaled GPP pixels were directly compared with corresponding GPP derived from ground eddy covariance systems. Moreover, time series (from May to September in 2014) of up-scaled GPP and GPP at flux tower sites were compared.  
Additionally, pixels in the footprint were selected as the validation pixels. Then the GPP at flux tower sites were weighted average in the footprint and were compared with corresponding up-scaled GPP pixels from five machine learning methods. In theory, the footprint of GPP from ground eddy covariance systems is infinite, as the ground-observed flux is the total integral of over an infinite region in the windward direction. In this paper, a 6 km \(\times\) 6 km area was used to compare the GPP at flux tower sites in the footprint and corresponding up-scaled GPP results. The weight of GPP at flux tower sites in the footprint was calculated by the following:  
\[R=\frac{f(x,y,z_{m})_{i}}{\sum_{i=1}^{N}f(x,y,z_{m})_{i}}\times 100\% \tag{4}\]  
where \(R\) is the weight of the pixel, \(f(x,y,z_{m})_{i}\) is the footprint of \(i\)-th pixel, \(N\) is the total number of the pixels.  
At last, the up-scaled GPP was compared to regional MODIS GPP products. Comparing pixel by pixel, Pearson coefficients and difference of up-scaled GPP with MODIS GPP were analyzed.
## 4 Methodology
### Route Maintenance  
Some other routes are found with the help of route maintenance if the energy rates of the selected next-hop nodes drop to a particular threshold value [39]. As a result, data degradation and re-transmission are prevented. When an energy-inefficient node is found during the routing stage, it stops transmitting data and sends an error message to the origin node. The next subsequent hop is chosen to continue the data routing after the originating node uses the heuristic function to identify the node with the highest weight. The application of an energy threshold for the maintenance of the route significantly decreases the time of network and route intrusions [40]. Additionally, the quantity of packets received that the next-hops of the BS's neighbors broadcast to them determines the congestion ratio. The proposed method is used to determine the traffic rate (\(Tf_{r}\)) among the neighboring hops (\(i\)) or the BS given by Equation (7).  
\[Tf_{r}=\frac{b_{l}-P_{l}}{B_{m}} \tag{7}\]  
where \(b_{l},P_{l}\), and \(B_{m}\) are the bandwidth of the link, the transmitted packets on the link, and the maximal bandwidth from \(i\) to the BS, respectively.
## 2 Principles of WIFFT and DE Algorithms
### Principle of DE Algorithm  
Differential Evolution is a simple and efficient algorithm for global optimization over continuous spaces [25; 26]. Let \(f(\textbf{X})\) be the target function, the crossover rate is \(C\), the scaling factor is \(F\) and the evolution generation is \(t\). The steps of DE are as follows [27].  
(1) Initialization step. Set the generation number t = 0 and randomly initialize a population of \(M\) individuals \(\textbf{{P}}_{t}=[\textbf{{X}}_{1}(t),\ldots,\textbf{{X}}_{M}(t)]^{\text{T}}\) with \(\textbf{{X}}_{i}(t)=[x_{i,1}(t),x_{i,2}(t),\ldots,x_{i,n}(t)]\).  
(2) Mutation step. Generate a donor vector \(\mathbf{Z}_{i}(t)\) corresponding to the \(i\)-th target vector \(\mathbf{X}_{i}(t)\) via  
\[\mathbf{Z}_{i}(t)=\mathbf{X}_{r1}+F\cdot(\mathbf{X}_{r2}-\mathbf{X}_{r3}) \tag{3}\]  
where \(r_{1},r_{2},r_{3}\) are mutually exclusive integers randomly chosen from the range \([1,2,\ldots,M]\).  
(3) Crossover step. Generate a trial vector \(\mathbf{U}_{i}(t)=[u_{i,1}(t),\ldots,u_{i,n}(t)]\) for the \(i\)-th target vector \(\mathbf{X}_{i}(t)\) according to  
\[u_{i,j}=\left\{\begin{array}{ll}z_{i,j}&\text{if }rand(0,1)<\text{Corj}=j_{ \text{rand}}\\ x_{i,j}&\text{otherwise}.\end{array}\right. \tag{4}\]  
where \(j_{\text{rand}}\in[1,2,\ldots,\text{n}]\) is a randomly chosen index.  
(4) Selection step. Evaluate the trial vector \(\mathbf{U}_{i}(t)\) through  
\[\mathbf{K}_{i}(t)=\left\{\begin{array}{ll}\mathbf{U}_{i}(t)&\text{if}f(\mathbf{U}_{i}(t ))\leq f(\mathbf{X}_{i}(t))\\ \mathbf{X}_{i}(t)&\text{otherwise}\end{array}\right. \tag{5}\]  
If \(f(\mathbf{K}_{i}(t))\leq\varepsilon\) or \(t=t_{\text{max}}\), then output \(\mathbf{K}_{i}(t)\) as the optimal solution. Otherwise, \(\mathbf{X}_{i}(t+1)=\mathbf{K}_{i}(t)\) and return to step (2).
# A Wireless EEG System for Neurofeedback Training
## 3 System Architecture of the EEG Device
### Transmission Module
#### 3.2.6 Electrode Sensors and Cap  
The wireless system with dry sensors was more applicable in outdoor environments (Figure 3 and Table 2). The time to prepare such a system was less than 5 min without using conductive gel and cleaning the skin. Each dry EEG sensor was configured as a matrix with a stellated geometry of 16 long-length golden and spring-loaded pins that better contacted an irregular scalp [29]. Other foam-based sensors made of a polymer and covered in electrically conductive fabric were used as ground and reference electrodes. Both types of sensors achieved low impedance of the skin-electrode interface (less than 5 k\(\Omega\)). The dry sensors were built permanently into soft elastic 64-channel configured EEG caps [30] with three sizes (56, 58, and 60 cm), chosen in accordance with the child's head. The adjustability of the EEG caps is a limitation of most devices, requiring different headsets to collect data for people with scalp size discrepancies that increase the cost of the system.  
Figure 2: Comparisons of EEG of two EEG systems for one subject during a visual task with low spatial frequency sinusoidal grating, recorded from the same brain locations with a 12 Ag/AgCl channel Nikon Kohden EEG system (**A**) and with the proposed Wi–Fi EEG system with 12 dry sensors (**B**). Comparisons of time–frequency spectrogram of data collected with Nikon Kohden EEG system (**C**) and with the proposed system (**D**). Event–related responses and the spectrograms were aligned according to the onset of the stimuli.
# Exploring the Bioethanol Production Potential of _Miscanthus_ cultivars
## 3 Results and Discussion
### Analysis of Miscanthus Structure Using Scanning Electron Microscopy  
Scanning electron microscopy was used to observe the cell wall structure before and after the diluted acid pre-treatment and also after the isolation of holocellose, \(\alpha\)-cellulose and Klason lignin, respectively. There were no distinguishing features observed between cultivars of _Miscanthus_.  
There were, however, pronounced differences observed following different methods. The raw samples (untreated) _Miscanthus_ had been milled, causing some mechanical damage to the material. Each milled sample consisted of fragments approximately 1-2 mm long (Figure 3A,B). The tissues of untreated samples were largely intact; the epidermal layers were visible and covered in phytoliths (microscopic deposits of silica found in plants) (Figure 3A,B). Following acid pre-treatment, considerably more degradation was observed. While some pieces of _Miscanthus_ stem looked unchanged, most of the material had disintegrated into smaller pieces. There were fewer surface layers visible, and the internal structures were much more easily observed (Figure 3C,D).  
Figure 2: _Cont._  
After acid degradation using the Klason method, the \(\alpha\)-cellulose and hemicellulose were removed and the lignin-carbohydrate complex disintegrated. The lignin content exhibited a highly deformed structure. Cellulose provides the structural rigidity and overall morphology of the plant; lignin merely reinforces it. With the cellulose removed, the lignin structure appeared to have curled, and the apparent cellular structure could not be distinguished (Figure 3E).  
The structure of the holocellulose appears similar to the acid pre-treated biomass when observed using SEM; the structure was highly disintegrated, and the internal cellular structure of the plant was easily distinguished. There is, however, one distinct difference. Holocellulose determination involves the chlorination and extraction of lignin. There were no visible lignin structures in any of the samples; the vascular bundles were not apparent, and there was no annular strengthening visible in the holocellulose samples (Figure 3F).  
To obtain \(\alpha\)-cellulose from holocellulose, the hemicelluloses are removed with an alkaline solution, and all that remains is insoluble cellulose. Hemicellulose is found throughout the secondary cell wall; therefore, when lignin and hemicellulose were removed, there was nothing left to hold the cellulose together and the fibres separated (Figure 3G).  
Figure 2: Pre-treatment of _Miscanthus_ and the fermentation profile of _Miscanthus_ hydrolysate. (**A**) Xylose and glucose concentrations of _Miscanthus_ cultivars following an acid pre-treatment; (**B**) cellulose hydrolysis yield of _Miscanthus_ cultivars following saccharification (% of theoretical yield). (**C**) Fermentation profile of _S. cerevisiae_ NCYC2592 on a hydrolysate derived from _Miscanthus_ cultivar B (BE04). Data representative of triplicate values.  
Figure 3: _Cont._
# Multi-Class Sleep Stage Analysis and Adaptive Pattern Recognition
## 2 Methods  
### PSG Data Acquisition  
The data studied include the own database of 184 polysomnography overnight observations [38; 39; 40] of patients recorded, diagnosed and classified by the authors in the sleep neurological laboratory. Table 1 presents a summary of the data for (i) 63 healthy individuals (36 males, 27 females); (ii) 77 individuals with sleep apnea (M = 48, F = 29); (iii) 16 individuals with restless leg syndrome (M = 8, F = 8); and (iv) 28 individuals with the restless leg syndrome combined with sleep apnea (M = 19, F = 9). Each of these records is about 8 hours long, segmented and classified into 5 sleep stages:  
* Wake stage (Wake): normal body functions,
* No Rapid Eye Movement 1 (NonREM1): the initial sleep stage with eyes closed,
* No Rapid Eye Movement 2 (NonREM2): the light sleep stage with slower heart rate and body temperature going down,
* No Rapid Eye Movement 3 (NonREM3): the deep sleep stage during which the body repairs and regrows tissues,
* Rapid Eye Movement (REM): the specific sleep period with faster brain activities, faster breathing and heart rate (period of dreams).  
These stages were specified by a neurologist specializing in sleep analysis.  
The matrix of recorded sleep PSG data of each individual include an information text file and 8 EDF files with subsequent one hour long multichannel records of 22 variables acquired during the night. While EEG, EOG, ECG, sound and movement data were observed with a sampling frequency of 200 Hz, breathing records (Flow) were acquired with a sampling frequency of 10 Hz.  
A selected EEG data segment 2 h long of a chosen individual together with its manual classification into 5 classes is presented in Figure 3a. The time localization of these classes specified by an experienced neurologist is shown in Figure 3c.
## 3 Sensor Technologies in Chemical and Elemental Detection
### Hyperspectral Imaging  
Hyperspectral cameras are high-tech image devices that are able to capture a wide range of the electromagnetic spectrum. Contrary to conventional RGB cameras that capture only three bands (red, blue, and green) and instantly combine them, hyperspectral camera captures a dense sampling of the spectrum range, usually hundreds of narrow bands, which provides rich spectral information about the target in each pixel. These systems can cover wavelengths from ultraviolet to thermal infrared and provide a unique insight into plant biochemical traits while recording a dense spectral signature for each pixel [9]. Apart from plant biochemical traits detection hyperspectral cameras have wide application in remote sensing where they can be used to asses water quality materials [9], in biochemical for non-invasive medical diagnostics [10], quality control in industry [11], cultural heritage preservation [12], military and security [13], and many more. But despite the potential, hyperspectral cameras face challenges such as high cost, and imaging generates large datasets, requiring advanced algorithms for efficient storage and analysis [12]. Future advancements are being focused on, making cheaper cameras with wider spectral range and miniaturization of the systems.  
Data dimensionality reduction algorithms are usually needed for working with hyperspectral images due to the large volumes of data generated with every picture, which can become computationally heavy and hard to store. Techniques like PCA, PLSR, or CARS, among many, have proven to be crucial in reducing the complexity of generated data, reducing noise and computational load, which enables faster and more efficient processes while maintaining or increasing accuracy.
## 4 Routing in 3D Sensor Network without Internal Holes
### Theoretical Insights
#### 4.1.2 Volumetric Harmonic Function  
Our goal is to construct virtual coordinates for a 3D sensor network to support successful greedy routing. The virtual coordinates must be one-to-one correspondent to the sensor nodes. To this end, we resort to volumetric harmonic mapping (VHM) under spherical boundary condition. A computational method for volumetric harmonic mapping under a spherical boundary condition can be found in [35].  
In general, a function, \(f\), is harmonic if it satisfies the Laplace's equation \(\triangle f=0\). If the Dirichlet boundary condition is imposed on this partial differential equation, a harmonic function is the solution of [PERSON]'s problem.  
The same concepts can be well formulated on volumes in a discrete setting. To this end, we first introduce the definition of edge weight.  
**Definition 10**.: _For edge \(e_{ij}\), which connects vertices \(v_{i}\) and \(v_{j}\), its edge weight, \(k_{ij}\), is a real value determined as follows. Suppose edge \(e_{ij}\) is shared by \(t\) adjacent tetrahedra. Then, it lies against \(t\) dihedral angles \(\{\theta_{m}|1\leq m\leq t\}\). The weight of \(e_{ij}\) is defined as:_  
\[k_{ij}=\frac{1}{t}\sum_{m=1}^{t}l_{m}\cot\theta_{m} \tag{2}\]  
_where \(l_{m}\) is the length of edge to which \(e_{ij}\) is against in the UTC mesh._  
Based on edge weight, we next define the piecewise Laplacian under discrete setting.  
**Definition 11**.: _The piecewise Laplacian is the linear operator \(\triangle_{PL}f=0\) on the space of piecewise linear functions. Let us define a map \(f:\mathbb{T}\rightarrow\mathbb{R}^{3}\), where \(f=(f_{0},f_{1},f_{2})\). \(f_{0},f_{1}\) and \(f_{2}\) are corresponding to three dimensions, and each of them is a real valued function defined over the vertices of the UTC mesh. The piecewise Laplacian of \(f\) is:_  
\[\triangle_{PL}f=(\triangle_{PL}f_{0},\triangle_{PL}f_{1},\triangle_{PL}f_{2}) \tag{3}\]  
_where \(\triangle_{PL}f_{m}=\sum_{e_{ij}\in\mathbb{E}}k_{ij}(f_{m}(v_{j})-f_{m}(v_{i}))\) for \(m=0,1,2\)._  
Our goal is to find \(f\), such that \(\triangle_{PL}f=0\), _i.e._, the volumetric harmonic function. It is equivalent to minimizing the following volumetric harmonic energy.  
**Definition 12**.: _The volumetric harmonic energy of \(f\) is:_  
\[E(f)=\sum_{m=0}^{2}E(f_{m}) \tag{4}\]  
_where \(E(f_{m})=\sum_{e_{ij}\in\mathbb{E}}k_{ij}||f_{m}(v_{j})-f_{m}(v_{i})||^{2}\)._  
If \(f\) minimizes the volumetric harmonic energy, then it satisfies the condition \(\triangle_{PL}f=0\), _i.e._, \(f\) is harmonic.
## 4 Proposed Method for Detecting Force Applied to a Column Specim in an Earthquake  
### Voltage Signals Generated by Post-Embedded Piezoceramic Sensors under Dynamic Loading  
Since the lateral force excitation from the dynamic actuator induced the piezoelectric effect of the post-embedded piezoceramic sensors in each specimen, the sensors produced the voltage signals under the dynamic testing. For Sensor 1 and Sensor 2 in each specimen, charge amplifiers were used to amplify and filter their output signals. Additionally, the charge amplifier was set to a gain of 10 dB, and the frequency range of its band-pass filter was set to between 0.1 Hz and 1 kHz. The data acquisition system with a sampling frequency of 100 Hz was set to record data. The signals that were received by the sensors could not be used directly in the following analysis because the loading machine and the electrical equipment generated noise. Therefore, the frequency ranges of the band-pass filter for the output signal in the analysis was set to the frequency of the input signal 0.5-4 Hz.  
Equation (3) was used to calculate the coefficient of correlation between the signals that were obtained using a post-embedded piezoceramic sensor and the force that was applied by the dynamic actuator \(\rho_{F}\). As well as the force under dynamic testing, the coefficient of correlation between the voltage signals and the member deformation were evaluated with reference to the same equation (Equation (3)).  
\[\rho_{F}=\frac{\sum(s-\overline{s})\big{(}f-\overline{f}\big{)}}{\sqrt{\sum(s- \overline{s})^{2}\sum\big{(}f-\overline{f}\big{)}^{2}}} \tag{3}\]  
where \(s\) and \(\overline{s}\) are the voltage signal from a piezoceramic sensor and its average value, respectively; and \(f\) and \(\overline{f}\) are the force applied by the dynamic actuator and its average value, respectively.  
According to Tables 5 and 6, the voltage signals that were obtained using a post-embedded piezoceramic sensor were more closely correlated with the applied force than with the applied deformation under dynamic testing. Based on these results, the following section examines the relationship between the output signals of a sensor and the applied force under dynamic testing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Damage Levels**} & \multicolumn{3}{c}{**Description of Damage**} \\ \cline{2-4}  & **Residual Crack Widths** & & \\ \cline{2-4}  & **Japan** & & **State of Concrete** & **State of Rebars** \\ \hline I & \(<\) 0.2 mm & Narrow cracks on concrete surfaces & - \\ (Slight) & & & \\ II & 0.2–1.0 mm & Visible cracks on concrete surface & - \\ (Light) & & & \\ III & 1.0–2.0 mm & Local crush of covered concrete & - \\ (Moderate) & & & \\ IV & \(>\) 2.0 mm & Cover concrete spalled off & Crushing of concrete \\ (Severe) & & & with exposed rebars \\ V & & & \\ (Total damage or collapse) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Definition of damage levels in JBDPA [25; 26].  
Figure 10: Maximum residual crack widths for each specimen. (**a**) FF-15S; (**b**) FSF-15S; (**c**) SF-30S.
## 3 SD-GPSR Routing Method
### Design of the Control Layer  
In the SD-GPSR routing architecture, the SDN controller can collect information from all nodes in the network for centralized control. Therefore, we design to use the SDN controller as a location server to provide node location information. In order to provide more accurate location information, the location server needs to complete the discovery and update of location information. Additionally, interruptions may occur during dynamic network operation, requiring a timely response mechanism.
# Environment-Aware Knowledge Distillation for Improved Resource-Constrained Edge Speech Recognition
## 5 Experimental Results and Discussion
### Accuracy of SNR and RT60 Estimators  
First, we need to validate if the proposed noise and reverberation level estimators are accurate. To this end, clean speech samples taken from the dev-clean set of the LibriSpeech dataset were corrupted with additive noise randomly sampled from the DCASE dataset. The SNR is uniformly sampled from 0 to 30 dB. A total of 2800 noisy test files are used for this experiment. Figure 4 shows the scatterplot between estimated and true SNR values. An overall correlation of 82.5% is achieved. It is important to remember, however, that as mentioned previously, here we are employing two compressed models, one optimized on low SNR levels ranging from 0 to 10 dB and another for high SNR levels (10-30 dB). Using the WADA algorithm to detect signals within these two classes results in an overall accuracy of 94.2%, suggesting that the model can be accurate enough for deployment.  
Figure 5, in turn, shows the scatterplot between the inverse of the SRMR and the true RT60. The signals used in this ablation study are speech samples taken again from the dev-clean set of the LibriSpeech dataset convolved with R  
Figure 4: Scatterplot of estimated and true SNRs for noisy test signals using the WADA algorithm.  
the BIRD dataset. As can be seen, an overall correlation of 82.2% is achieved. Again, as the proposed solution utilizes models optimized on low reverberation (RT60 smaller than 500 ms) and high reverberation (RT60 grater than 500 ms) levels, only a binary classifier is needed. Unlike the previous experiment that maps the signal directly to the estimated quantity, this algorithm maps the signal to the inverse of the SRMR. Thus, we use an SVM to map between this quantity and the desired RT60. We perform a 90/10% train/test split on the data and use an SVM to perform the classification. Experiments using a vanilla SVM with a radial basis function (RBF) kernel classifier resulted in an 88.9% classification accuracy, indicating again that the model can be accurate enough for deployment.