esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
The LCA methodology is based on the International Standard ISO 14040 (Environmental management--Life cycle assessment--Principles and framework) and ISO 14044 (Environmental management--Life cycle assessment--Requirements and guidelines). The two documents explain the goal and define the scope, the functional unit, the system boundary choice, the life cycle inventory analysis preparation, and the impact assessment analysis [33; 34]. The analysis is able to take into account different environmental impacts that are easy to overlook when the economical aspect prevails, such as energy consumption and atmospheric emissions, over the entire life cycle of a product or a service. Moreover, LCA can also consider a multitude of other environmental impacts on different compartments (i.e., air, water, soil, and human health). This methodology has proven to be useful to investigate and to highlight critical process phases and can be used as a decision tool for improving or optimizing production chain actions. In this study, an LCA analysis is applied firstly to two ideal valorization processes of corn cob, recovered from maize field. Then, the analogous LCA analysis has been conducted for the production processes of wood pellets and silicon carbide, in order to compare, respectively, the analogous products. The LCA investigates in detail the processes of the harvesting of corn cob, as a co-product in the field and not as a waste of maize grain production, and the subsequent use for the production of pellets and granulates, with a cradle to gate approach. The functional unit has been defined as 1 kg of final product of the four different materials (i.e., corn cob pellets, corn grits, wood pellets, and silicon carbide granulate). This functional unit has been chosen considering that all these products can be sold on the market, in both bulk and packs of different sizes according to the market segment. Primary data on maize grain cultivation have been collected from a case study farm. They consist of:  
* details of cultivation processes (i.e., sowing, tillage, irrigation);
* working time of processes;
* fuel consumption of tractors and machinery;
* pesticides and fertilizers type, compositions, and consumption data;
* distances performed by machinery/tractors.  
The secondary data from Ecoinvent 3.6 database [35] have been used as flows and process sources when primary data were not available. The analyses have been conducted through OpenLCA v1.10, an open source software for LCA analysis [36].
## 3 Performance Comparison between DL-LET and LET Joints  
### Validation by Finite Element Results  
As a baseline, the analysis of the LET joint was made to compare bending and tensile stiffness with the DL-LET joint. The material for the LET joint is assumed to be beryllium bronze, the overall dimensions are the same as the DL-LET joint, and others remain the same.  
Applying a bending moment of 50 N-mm and a tensile load of 30 N on two joints, the FEA results are shown in Figures 6 and 7, respectively. DL-LET and LET joints have a similar bending stiffness, while the tensile deflection of the benchmark LET joint is 7.1 times larger than that of the DL-LET joint. The bending angles and tensile deflections as loads functions are plotted in Figure 8. It can be found that the H18 aluminum foil significantly improves the tensile stiffness of the DL-LET joint without affecting its bending stiffness.  
Figure 5: (**a**) Angle versus bending moment curves and (**b**) Error versus bending moment curve (rotation angle).  
Figure 6: Bending deformations of DL-LET and LET joints obtained by FEA.
# Brain-Inspired Healthcare Smart System Based on Perception-Action Cycle
## 3 Electroencephalography
Among the issues involved in the treatment of digital signals, the linear and invariant digital filters stand out in time or Linear Time Invariant (LTI). Another important class of LTI filters are the frequency selective ones, which allow the passage of signals without distortion in one or a set of frequency bands and totally attenuate or eliminate the signals in the rest of the bands [12]. A filter can be seen as a continuous or discrete system to process the signals and it can modify the spectrum of the input signal according to certain specifications; these filters are divided into analog and digital. Some advantages of digital filters over the analog ones are that the formers respond to the frequency closest to the ideal one, they do not require tuning, their components are independent of the filter's operating frequency, can be multiplexed to process multiple signals and have a high degree of system integration. In addition, it is very important to consider the Nyquist-Shannon fundamental sampling theorem, which states that at least twice the frequency of operation at the input must be used so that sampling is reliable and information is not lost. That is why most EEG systems use a sampling rate of at least 128 Hz when anyone wants to study bands at less than 60 Hz, and in other cases up to 1 KHz when anyone wants to study bands higher than the previous ones with higher resolution [13]. Filtering techniques are widely used in the collection of raw data by EEG devices, since, without this  
Figure 4: Human brain activity represented in a raw electroencephalogram (EEG) signal. Figure 3: Main frequency bands present in human brain activity. stage, data analysis is compromised by the presence of environmental noise or electrical interference from other devices [8].
## 3 Proposed Solution
### Incorporation of Mobile Nodes  
Mobile nodes introduce additional alterations in the system, making the detection process more complicating. However, the presence of mobile nodes should not affect the proper functioning of the WSN, _i.e.,_ they should be able to sense the environment, continue sending their own sensed values and be used as a routing hop in forwarding sensed values of other nodes as well. Due to their special nature, these nodes require special treatment in the proposed detection system.  
In order to minimize the disruption of the detection system, we propose the following principle: if the nodes encounter with a new node in their area, they will ask the base station about its reputation and continue the interaction assuming the existing reputation value. Concerning a mobile node that has changed the position, we distinguish two possible situations: the node remains in the same area, _i.e.,_ it has not changed its position significantly, which can be concluded by the base station if it still uses similar group of nodes to route its data, or if it starts using completely new routes, the old model has to be discarded and a new model has to be established and trained for the same node. This goes for both models: the one based on sensed values, _i.e.,_ if a node has changed its position significantly, it is very probable that sensed values will be different, thus a model has to be established by performing the training with new data, and also the one based on routing information.  
Regarding the nodes whose routes will be changed with the introduction of a new node that behaves properly, according to the presented model and the adopted distance function, it is not possible to introduce significant change so as to raise doubts, thus it is not necessary to perform the re-training. However, if more new nodes appear in the routes, it is advisable to perform the re-training (with both old and new data) in order to avoid false positives.
# Quantum 3D FFT in Tomography
## 3 Theory of Tomography
### Back Projection  
The back projection process is an attempt to reverse the Radon transform process. The problem here is that we have no specific information about the density variations, except its average for each projection, i.e., essentially measuring the attenuation along a line. One approach to receive the original object given the Radon transform of the object is to take all the values of the sinogram and try to spread them back into the image field, as shown in Figure 3 in 2D representation. The result we receive will be the original image but blurred. We can deal with this problem with various inversion methods of the Radon transform, as it is explained in the following.  
Some of the properties of the Radon transform are the following:  
_Linearity_  
If \(g(x,y)\ \leftrightarrow\ q(r,\varphi)\) then,  
\[a\cdot f(x,y)+\beta\cdot g(x,y)\ \leftrightarrow\ \alpha p(r,\phi)+\beta\cdot q(r,\varphi)\]  
_Displacement_  
\[f(x-x_{0},y-y_{0})\ \leftrightarrow\ p\ (r-x_{0}\cdot cos\varphi-y_{0}sin \varphi,\varphi)\]  
_Rotation_  
\[f(x\cdot cos\theta+y\cdot sin\theta,-x\cdot sin\theta+y\cdot cos\theta)\ \leftrightarrow\ p(r,\varphi-\theta)\]  
_Circular Symmetry_  
\[p(r,\varphi)=p(-r,\varphi\pm\pi)=p\Big{(}(-1)^{k}r,\phi\pm k\cdot\pi\Big{)}\]  
Figure 3: Back projection. The information of the sinogram \(p(r,\varphi)\) is spread back to the position of the initial image.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
###### Abstract  
Nacelle-based Doppler wind lidars have shown promising capabilities to assess power performance, detect yaw misalignment or perform feed-forward control. The power curve application requires uncertainty assessment. Traceable measurements and uncertainties of nacelle-based wind lidars can be obtained through a methodology applicable to any type of existing and upcoming nacelle lidar technology. The generic methodology consists in calibrating all the inputs of the wind field reconstruction algorithms of a lidar. These inputs are the line-of-sight velocity and the beam position, provided by the geometry of the scanning trajectory and the lidar inclination. The line-of-sight velocity is calibrated in atmospheric conditions by comparing it to a reference quantity based on classic instrumentation such as cup anemometers and wind vanes. The generic methodology was tested on two commercially developed lidars, one continuous wave and one pulsed systems, and provides consistent calibration results: linear regressions show a difference of \(\sim\)0.5% between the lidar-measured and reference line-of-sight velocities. A comprehensive uncertainty procedure propagates the reference uncertainty to the lidar measurements. At a coverage factor of two, the estimated line-of-sight velocity uncertainty ranges from 3.2% at 3 m\(\cdot\)s\({}^{-1}\) to 1.9% at 16 m\(\cdot\)s\({}^{-1}\). Most of the line-of-sight velocity uncertainty originates from the reference: the cup anemometer uncertainty accounts for \(\sim\)90% of the total uncertainty. The propagation of uncertainties to lidar-reconstructed wind characteristics can use analytical methods in simple cases, which we demonstrate through the example of a two-beam system. The newly developed calibration methodology allows robust evaluation of a nacelle lidar's performance and uncertainties to be established. Calibrated nacelle lidars may consequently be further used for various wind turbine applications in confidence.  
lidar; calibration; uncertainties; nacelle-mounted; wind turbine; power performance +
Footnote †: journal:  
0000-0001-6558-7800][PERSON] *
# Synergistic Effect of Nonthermal Plasma and ZnO Nanoparticles on Organic Dye Degradation
###### Abstract  
The synergetic effect of nonthermal plasma and ZnO nanoparticles (NPs) on the degradation efficiency of methylene blue was investigated. First, the ZnO nanoparticles were synthesized via the hydrothermal route; the spherical nanoparticles had diameters of 30-50 nm, as observed with a scanning electron microscope (SEM), and had hexagonal ZnO lattice structures, which was confirmed by both X-ray diffraction (XRD) and Raman spectroscopy. The X-ray-photoemission spectroscopy confirmed the ZnO composition and the presence of oxygen vacancies; meanwhile, the optical band gap energy was 3.17 eV. The optical emission of plasma radiation confirmed the presence of various active plasma species. Second, it was found that the maximum degradation efficiency of MB after 60 min was 85% in plasma alone and increased to 95% when combined with 0.2 gl.\({}^{-1}\) ZnO; but this decreased to 75% when ZnO loading increased to 0.4 gl.\({}^{-1}\). These results clearly show that combining plasma with the right amount of ZnO is a promising advanced oxidation technique as it provides an additional source of hydroxyl radicals and, at the same time, a source of photons that can excite the ZnO catalyst. The degradation mechanism for plasma alone and the plasma in combination with ZnO was presented.  
n onthermal plasma; ZnO nanoparticles; methylene blue degradation; XPS; XRD; OES; Raman 2023
## 2 The Method of Ray Tracing
### Effect of a Flow Field on Light Transmission  
The optical refractive index of a gas is affected by several state parameters of the gas [15]. This article assumes that the refractive index of a gas is determined by the gas density under the condition of room temperature. For a discrete spatial density distribution, the refractive index can be calculated by corresponding to any point using the Lorentz-Lorenz formula [15; 18]:  
\[n=1+K_{GD}\rho, \tag{1}\]  
where \(n\) and \(\rho\) denote the index of refraction and the density respectively, and \(K_{GD}\) is the Gladstone-Dale constant, which is weakly dependent on the optical wavelength. The wavelength dependence is given by the following equation [2]:  
\[K_{GD}=2.23\times 10^{-4}(1+\frac{7.52\times 10^{-3}}{\lambda^{2}})\, \tag{2}\]  
where \(\lambda\) denotes the wavelength and the unit of \(K_{GD}\) is m\({}^{3}\)/kg.  
Using the refractive index calculated via Equation (1), the optical path length (OPL) of a beam passing through the beam expanding system can be calculated accurately using the following formula:  
\[OPL(x,y)=\int_{0}^{L}n(x,y,z)dz. \tag{3}\]  
Due to the instability of the flow field, there will be a certain density gradient in the flow field in the beam expanding system, which will result in differences in the refractive index along the beam propagation path. These differences will lead to deflection, jitter, and phase changes of the beam, and result in a difference relative to the ideal optical path length, called here the optical path difference (OPD):  
\[OPD=\mathrm{OPL(x,z)-<OPL(x,z)>}. \tag{4}\]
## 2 Materials and Methods
### Data Analysis Method  
In this research, Mutual information theory (MIT) [19] was applied to evaluate the correlation between sample feature and target output (TSS value). Partial least squares regression (PLSR) [20] and back propagation neural network (BPNN) [21] were applied to build the detection model and test the detection ability. Due to the complex network structure of BPNN, principal component analysis (PCA) [22] was needed to simplify and reduce the structure and dimensions of the input data before conducting BPNN. The introduction of MIT, PLSR, PCA, and BPNN were as follow.  
MIT is usually used to evaluate the correlative degree between variables, and the correlation can be either linear or nonlinear. By conducting MIT, the amount of common information between variables can be represented by a mutual information (MI) value. The PLSR technique is based on multiple linear regression, which is a statistical method that bears some relation to principal components regression. Instead of finding hyperplanes of maximum variance between the response and independent variables, it finds a linear regression model by projecting the predicted variables and the observable variables to a new space. PCA is a multivariate technique that analyses a data table in which the observations are described by several inter-correlated quantitative dependent variables. The goal of PCA is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components to simplify the data structure and improve the analysis efficiency. BPNN is one of the most commonly used neural networks and includes input, hidden, and output layers. In the process of training a BPNN for analysis, the weights and threshold values of each layer are constantly revised. The BPNN adjusts the weights and threshold values repeatedly based on the differences between the expected outputs and actual outputs. Thus, a BPNN is a neural network that spreads information in the forward direction and returns the difference in the reverse direction. This training lasts until the difference between the expected outputs and actual outputs is reduced to a preset range or until the scheduled training times are achieved.  
All the data analysis was performed by the software Matlab R2017a (MathWorks Inc., Natick, MA, USA). The PCA and BPNN were conducted by invoking the build-in toolbox, and the MIT and PLSR were conducted by self-programming due to no related build-in toolbox.
# Around View Monitoring-Based Vacant Parking Space Detection and Analysis
## 4 Experimental Results
### Performance Evaluation
#### 4.4.2 Space Occupancy Classification Rates  
Table 6 lists the space occupancy classification rates for both indoor and outdoor settings. We provide an in-depth discussion of the classification rates obtained from different features. In indoor parking settings, we observed that the region growing method produced higher error rates than the edge detection method in detecting features. This was caused by dim lighting in indoor parking structures. Inadequate lighting decreased the contrast between the road and cars and led to errors in region growing. In outdoor parking settings, particularly for roadside parking, the edge detection method produced higher error rates than the region growing method. This is because a vacant parking space may contain objects with a high number of edges and a high amount of noise such as manhole covers or storm drain covers. This noise is easily mistaken for a car in the parking space. These results showed that road features and obstacle features are each advantageous in different settings. Thus, both types of features were included to offset the inadequacies of using only one type of features. The final results confirmed that the fusion of both feature methods produced the lowest error rates in both indoor and outdoor settings.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scene** & \multicolumn{2}{c}{**Indoor**} & \multicolumn{3}{c}{**Outdoor**} \\ \hline
**Feature type** & Edge-based [10] & Growing -based & Fusion & Edge-based [10] & Growing -based & Fusion \\
**Computing time (s)** & 0.14 & 0.13 & 0.27 & 0.13 & 0.1 & 0.24 \\
**\# of occupied slots** & 175 & 173 & 179 & 394 & 333 & 333 \\
**(positive)** & 88 & 90 & 84 & 349 & 410 & 410 \\
**\# of vacant slots** & 17 & 21 & 12 & 6 & 11 & 7 \\
**(negative)** & 4 & 6 & 3 & 77 & 20 & 18 \\
**\# of false positive** & 9.7 & 12.1 & 6.7 & 1.5 & 3.3\% & 2.1 \\ FFR (\%) & 4.5 & 6.7 & 3.6 & 22 & 4.9 & 4.4 \\
**Error Rate (\%)** & 7.1 & 9.4 & 5.2 & 11.8 & 4.1 & 3.3 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Indoor and outdoor space occupancy classification rates. FNR—false negative rate; FPR—false positive rate.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scene** & **Indoor** & **Outdoor** \\ \hline
**Computing time (s)** & 0.17 & 0.16 \\ \hline
**Parking type** & Perpendicular parking & Perpendicular parking & Parallel Parking & Total \\ \hline
**\# of existing slots** & 311 & 545 & 233 & 778 \\
**\# of detected slots** & 274 & 521 & 224 & 745 \\
**\# of correctly slots** & 263 & 519 & 224 & 743 \\
**Recall (\%)** & 84.6 & 95.2 & 96.1 & 95.5 \\
**Precision (\%)** & 96 & 99.6 & 100 & 99.7 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Indoor and outdoor parking space detection rates.
## 2 Mechanisms Specific to QoS and Multicast in Ad Hoc Networks
### Mechanisms for Resource Estimation  
Both admission control and resource reservation depend on resource estimation. The resource estimation for bandwidth depends on the type of MAC layer. In addition, some of the protocols also estimate other resources such as delay, buffer level, and power level. The resource estimation mechanisms are therefore divided into methods for estimation of bandwidth in contention-free MAC, methods for estimation of bandwidth in contention MAC, and methods for estimation of resources other than bandwidth.
## 4 Fundamentals of n-Pulse Scheme
### Modulation  
For transmitting data using an LED, we can employ a modulation technique known as count pulse, where symbols are represented by different numbers of pulses within a symbol period. Each symbol is composed of a specific number of pulses with the same width (\(\tau_{\text{on}}\) [s]) and amplitude (\(A\) [Lux]). Within a set of symbols, the pulse width is determined by the maximum number of pulses (\(n_{\text{max}}\)) and the duty cycle (\(D\)) parameter. The duty cycle is defined as the fraction of the sub-period during which the signal is active relative to the total symbol duration. The duration of the pulse, \(\tau_{\text{on}}\), for a given duty cycle (\(D\)), the maximum number of pulses (\(n_{\text{max}}\)), and symbol time (\(T_{\text{s}}\)) is given by  
\[\tau_{\text{on}}=D\frac{T_{\text{s}}}{n_{\text{max}}}, \tag{1}\]  
Meanwhile, the duration of the no activation of the pulse (\(\tau_{\text{off}}\)) is given by  
\[\tau_{\text{off}}=(1-D)\frac{T_{\text{s}}}{n_{\text{max}}}, \tag{2}\]  
and the guard time (\(\tau_{\text{g}}\)) for the i-symbol (\(\text{s}_{\text{i}}\)) is given by,  
\[\tau_{\text{g,i}}=T_{\text{s}}-(\tau_{\text{on}}\cdot i)-(\tau_{\text{off}} \cdot i), \tag{3}\]  
Finally, the expression for any symbol, given the prior parameters, is  
\[S_{i}=\left\{\begin{array}{ll}A&(j-1)\cdot\frac{T_{\text{s}}}{n_{\text{max} }}<t\leq(D+j-1)\cdot\frac{T_{\text{s}}}{n_{\text{max}}},\quad\forall j\in\{1, \ldots,i\}\\ 0,&\text{otherwise}.\end{array}\right. \tag{4}\]  
Figure 4 illustrates a modulation example presenting a specific set of symbols. It can be seen that the \(\tau_{\text{g}}\) is different for every symbol, being the largest when the pulse number is minimum (one pulse, \(s_{1}\)) and the shortest when the number of pulses is maximum (\(s_{\text{max}}\) pulses, \(s_{4}\)). With this modulation, we can encode up to \(\lfloor\log_{2}(n_{\text{max}})\rfloor\) bits. For simplicity, we exclude the symbol with no present pulse.  
Figure 4: A set of symbols illustrating n-pulse modulations is displayed. The parameters used include a maximum number of pulses (\(n_{\text{max}}\)) equal to 4 and a duty cycle (\(D\)) equal to 0.5. Each symbol contains a specific number of pulses within the symbol time. With this configuration, we can encode 2 bits per symbol.