esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
We then use the resulting formulas to fit with a single parameter the measured phytoplankton absorption spectra as a function of concentration [9; 10]. The backscattering cellular interfaces are assumed to be composed of cellulose and the reflectance spectra are computed for several types of algae and underwater vegetation. The spectra show that, as is well known, vegetation is actually translucent which means that when it grows over a mineral substrate the reflectance spectra changes significantly as a non-linear function of the thickness. We use a normalized version of the Albert and Mobley model for finite depth [1] to evaluate this effect. The reflectance from the mineral substrate replaces the bottom reflectivity in the model and the water column absorption and backscatter properties are replaced by those of the vegetation. If the vegetation cover is complete over one pixel, the complete vegetation model depends on three parameters: the concentration of chlorophyll-a, the chloroplast absorption saturation parameters and the thickness. The aim of the present work is to help limit the number of fit parameters in order to better constrain the water depth value. This is particularly significant in conditions were there is little or no a priori knowledge of the bottom type. For convenience, Table 1 lists the symbols we use and their definitions and units. \begin{table}
\begin{tabular}{c c} \hline
**Symbol or Abbreviation** & **Definition, Units** \\ \hline \(a(\lambda)\) & Absorption coefficient, m\({}^{-1}\) \\ \(a_{cl}(\lambda)\) & Cellulose absorption coefficient, m\({}^{-1}\) \\ \(a_{w}(\lambda)\) & Pure water absorption coefficient, m\({}^{-1}\) \\ \(a^{*}(\lambda)\) & Extended Bricaud specific absorption coefficient, m\({}^{2}\)/mg \\ \hline \end{tabular}
\end{table} 
Table 1: List of abbreviations, symbols, definitions and units.
# A _uv_-Cellular Network: Design, Implementation and Experiments
## 4 Simulation Study
### Simulation Environment  
In order to make the simulation results more realistic, we have conducted Point-to-Point communication tests in a real field environment and used the results of this test as a parameter for the simulation system. In this test, we used the OFDM-based Underwater Acoustic Modem (UAM) with the communication frequency of 21-27 kHz. The physical layer technologies are described in the previous Section 3.3.1. The maximum transmitting power consumption of this UAM is 70 W. The communication range of the UAM can achieve up to 5 km in the smooth underwater channel conditions.  
As shown in Figure 5, we deployed six sets of buoys equipped with acoustic communicators at a distance of 1 km apart in the designated sea area. Among them, A1-A5 are the receiving nodes. A0 is the transmitting node and sends a 400B packet every 60s. The whole experiment lasted for more than 48 h and A0 sent more than 3000 packets. The experimental results, which are shown in Table 1, verify that increasing the communication distance leads to a decrease in the communication success rate and the communication \(SNR\). We use the results of this experiment to simulate the communication results.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Communication Distance** & **Communication Success Rate** & **Average SNR** \\ \hline
0–1 km & 98\% & 12 \\
1–2 km & 90\% & 10 \\
2–3 km & 80\% & 7 \\
3–4 km & 65\% & 5 \\
4–5 km & 45\% & 3.5 \\ \textgreater{}5 km & 10\% & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: P2P communication test results in underwater environment.  
Figure 5: Point-to-Point communication tests in a real field environment.  
In the simulation, there is only one destination node in the test area which act as the BS node. The coverage area of the base station is a circular area with a radius of 5 km. We set up three different network topologies to simulate the number of 1 to 3 hops of forwarding required between the source and destination nodes under ideal conditions. In each topology, there is only one source node, one destination node and several relay nodes. The value of the nodes' minimum speed is 0.5 m/s and maximum speed is 2.5 m/s. Due to the mobility of the nodes, the distance between nodes varies periodically between 3 km and 5 km. The network topologies are shown in Figure 6. To increase the credibility of the results, each set of tests lasted for 24 h without intervention. Due to the packet size limitation, each node maintains at most two hops of forwarding information.
## 3 Results and Discussion
### Modified Combustion Efficiency (MCE) in Oxy-Combustion of Australian Coal  
Combustion efficiency (CE) is defined as the ratio of carbon released as CO2 divided by the carbon released in the form of all carbon-containing products of combustion (such as CO2, CO, non-methane hydrocarbons, and PM2.5) [45; 46]. It can be used to determine the completeness of the carbon oxidation that occurs during combustion. Since CE is difficult to measure, the modified combustion efficiency (MCE), which is defined as the ratio of carbon emitted as CO2 divided by carbon emitted as CO2 plus CO [47], is typically used as an estimate of CE [48; 49]. The modified combustion efficiency (MCE; n) was calculated using the following equation [31; 50; 51; 52]:  
\[\eta=\frac{[\text{CO}_{2}]}{[\text{CO}_{2}]+[\text{CO}]}\times 100\%, \tag{2}\]where [CO\({}_{2}\)] is volume concentration of CO\({}_{2}\) on a dry basis, and [CO] is volume concentration of CO on a dry basis. Pure flaming combustion usually exhibits an MCE approaching 99% [49]. The MCEs have also been reported to be greater than 99.9%, such as when burning gaseous fuels ([PERSON] et al. [50]), solid fuels ([PERSON] et al. [51]; [PERSON] et al. [52]), and liquid fuels ([PERSON] et al. [31]). In this investigation, CO and CO\({}_{2}\) were measured. However, non-methane hydrocarbons and particulate matter content were not measured. Therefore, Equation (2) was used to calculate the modified combustion efficiency (MCE), as reported in our previous study [31] and other literature [50; 51; 52]. Based on Equation (2), modified combustion efficiencies above 99.9% were obtained in the present study, which agrees well with [PERSON] and [PERSON]'s study [36]. This indicated that excellent combustion efficiency was achieved.  
In view of the above discussion, we realized that unburned carbon (UC) in fly ash (FA) is also an indicator of combustion efficiency [53]. The high proportion of UC in FA reflects a large amount of energy loss corresponding to low combustion efficiency. In addition, UC is an obstacle to the beneficial use of FA, especially in the cement and concrete industries. In this study, ash analyses were not carried out. However, before starting this work, a loss on ignition (LOI) analysis of air-fuel combustion was performed in various positions, including the ash pot, convective section, and bag house under the following operating conditions: a coal feed rate of 21.54 kg/h and an air supply rate of 210 Nm\({}^{3}\)/h. It is worth noting that the LOI level of each position was lower than 6% (by mass). In other words, fly ash can meet the CNS 3036 [54] and ASTM C618 [55] requirements. [PERSON] and [PERSON] [36] reported that oxy-coal combustion is expected to achieve higher combustion efficiency (close to 100%) than air-coal combustion (greater than 99%). The modified combustion efficiency (MCE) of oxy-coal combustion was higher than 99.9%, which supports the argument of [PERSON] and [PERSON] [36].
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 5 Experiment and Result Analysis
### Key Parameters Selection  
We attempted to experimentally determine the impact of various key parameters on the localization accuracy of the actual DNCL system, namely the window size for each DOA estimation, interrogation interval of the reader antenna, the number of reference tags, and the distance from the reader antenna to tags.
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 6 Bioremediation Applications Using Adapted Microbes
### Field Trials
#### 6.2.2 Bioaugmented Phytoremediation Trials  
[PERSON] and [PERSON] [186] showed that the inoculation of _Acacia saligna_ and _Eucalyptus tamaldulensis_ with AMF and PGPRs to remove Cd, Pb, As, and Zn from an industrial polluted soil significantly contributed to enhanced plant growth and metal uptake. According to them, the benefits of the triple association plant-fungi-bacteria could limit metal spreads. On the contrary, in an 18-month field trial, [PERSON] et al. [187] revealed that AMF inoculant from _Glomus intraradices_ did not increase metal extraction in _Salix viiminalis_ and _Populus generosa_ from a non-saline soil contaminated with Cd, Zn, Cu, and Pb. Finally, a field study of [PERSON] et al. [158] compared the impact of several metal-chelating organic acids and a _Pseudomonas fluorescens_ bacteria on the metal phytoextraction capacity of the halophyte _Suaeda vera_. The results showed that _P. fluorescens_ significantly increased the accumulation of Cu and Zn in the roots and stems in comparison with the control plants.  
In view of these examples, such cost-effective and eco-friendly remediation techniques should be more and more concretely studied to assess field methods, taking into account all the parameters that influence the success of metal extraction or stabilization [28]. Halophytes-associated microbial strains that have already shown promising PGP effects should be further investigated in field trials to properly assess their applicability, a necessary step to be concretely used by professional operators.
## 3 Multi-Sensors Spatio-Temporal Multi-Correlation Fusion Analysis
### Temporal Correlation Analysis  
The above formula is used to combine the signal with the time deviation to obtain the synchronization signals \(x(t)\), \(y(t+\tau_{1})\) and \(z(t+\tau_{2})\) of the three sensors. \(x(t)\), \(y(t+\tau_{1})\) and \(z(t+\tau_{2})\) signals are deterministic signals with limited energy, so the correlation coefficient between \(x(t)\) and \(y(t+\tau_{1})\), \(x(t)\) and \(z(t+\tau_{2})\) is defined as:  
\[d_{xy}=\frac{\sum\limits_{n=0}^{\infty}x(t)y(t+\tau_{1})}{\left[\sum\limits_{ n=0}^{\infty}x^{2}(t)\sum\limits_{n=0}^{\infty}y^{2}(t+\tau_{1})\right]}=\frac{R_{xy}} {\sqrt{E_{x}E_{y}}} \tag{11}\]  
\[d_{xz}=\frac{\sum\limits_{n=0}^{\infty}x(t)z(t+\tau_{2})}{\left[\sum\limits_{ n=0}^{\infty}x^{2}(t)\sum\limits_{n=0}^{\infty}z^{2}(t+\tau_{2})\right]}=\frac{R_{xz}} {\sqrt{E_{x}E_{z}}} \tag{12}\]  
where the denominator \(\sqrt{E_{x}E_{y}}\) was the square root of the normalized energy product of the signals \(x(t)\) and \(y(t+\tau_{1})\), and the denominator \(\sqrt{E_{x}E_{y}}\) was the square root of the normalized energy product of the signals \(x(t)\) and \(z(t+\tau_{2})\). It can be seen from the formula that the power of the denominator energy is a constant, so the size of the correlation coefficient is determined by the numerator. \(R_{xy}\) is correlation function of the signal \(x(t)\) and \(y\) (\(t+\tau_{1})\). It can be obtained \(\left|d_{xy}\right|\leq 1\) according to [PERSON]'s inequality, when the two signals are completely equal, the numerator is equal to 1, \(d_{xy}=1\); when the two signals are not completely equal, \(d_{xy}<1\); when the two signals are completely unrelated \(d_{xy}=0\). In other words, \(d_{xy}\) can be used to describe the degree of similarity between two signals.  
The correlation function of vibration signals describes the correlation of two sample functions at different moments, which reflects the close relationship between two ran dom vibration signal waveforms when they move on the time coordinate [36]. The cross correlation function of discrete random vibration signals is expressed as:  
\[R_{xy}(t)=\frac{1}{N-m}\sum_{i=1}^{N-m}x(t)y(t+m)(m=1,2,\cdots,k) \tag{13}\]  
where \(N\) is the number of sampling points, and the size of the cross-correlation function directly reflected the correlation between the two signals and it was a measure of waveform similarity.
## 3 Results and Discussions  
### Flow Behavior  
The high temperature flow behavior of asphalt binders is an important property to establish in-plant and in-field temperatures [42; 43]. The viscosity of binder depicts its competence for pumping through an asphalt plant, its ability to properly coat the aggregate particles in asphalt mixture and the workability required to place the asphalt mixture in field and appropriate compaction. Therefore, RV test was carried out from 135 \({}^{\circ}\)C to 175 \({}^{\circ}\)C with an increment of 10 \({}^{\circ}\)C.  
Figure 4 demonstrates the changes in viscosity of asphalt binders in an unaged condition and after RTFO aging on temperature increments. It can be clearly seen that inclusion of oil into asphalt decreased its viscosity and construction temperatures, which is in line with previous literature.  
In-field mixing and compaction temperatures were selected based on the viscosity of binder. As recommended by Asphalt Institute [44], temperature at 170 \(\pm\) 20 cP is to be taken as mixing temperature while 280 \(\pm\) 30 cP depicts the compaction temperature of bitumen. It is evident from Figure 4a and Table 7, there was an overall 5-8 \({}^{\circ}\)C decrement of compaction and mixing temperature for the unaged binder condition. FR had the highest effect on reducing the mixing and compaction temperatures due the fact that fresh oil had not been aged before and had very low viscosity compared to waste oil.  
After RTFO aging, in Figure 4b, even though the compaction and mixing temperatures increased due to stiffening of binder, oil addition reduced the compaction temperature of BB by 6-7 \({}^{\circ}\)C while mixing temperature decreased by up to 5 \({}^{\circ}\)C. No significant difference was observed in construction temperatures between FO and FR, while UFO depicted 1 \({}^{\circ}\)C increment from other  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Binder Property and Condition**} & \multicolumn{5}{c}{**Temperature (\({}^{\circ}\)C)**} \\ \cline{2-6}  & & **BB** & **UFO** & **FR** & **FO** \\ \hline \multirow{2}{*}{Compaction} & Unaged & 146.5 & 141.5 & 138 & 140 \\  & RTFO & 156.5 & 150.5 & 149 & 149 \\ \multirow{2}{*}{Mixing} & Unaged & 155.5 & 151 & 147 & 150 \\  & RTFO & 166 & 162 & 160 & 160 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Compaction and mixing temperature changes with engine oil addition.  
Figure 4: Viscosity variations in different binders: (**a**) Unaged condition and (**b**) RTFO aged.  
This 1 \({}^{\circ}\)C difference might have been caused by comparatively high viscosity of UFO due to the metal traces present in the asphalt matrix.
## 3 Inherent Optical Properties (IOPs)
### Ocean IOPs--Bio-Optical Models
#### Algae Particle IOPs  
The absorption coefficient for _pigmented_ particles (algae particles or phytoplankton) can be written [61]:  
\[\alpha_{\text{pig}}(\lambda)=A_{\phi}(\lambda)\times[\text{CHL}]^{E_{\phi}( \lambda)} \tag{96}\]  
where \(A_{\phi}(\lambda)\) and \(E_{\phi}(\lambda)\) are given by [61], and where CHL is the chlorophyll concentration, which represents the concentration of pigmented particles (algae particles or phytoplankton).  
The attenuation coefficient for pigmented particles at 660 nm is given by [62]:  
\[\gamma_{\text{pig}}(660)=\gamma_{0}\times[\text{CHL}]^{\eta};\qquad\gamma_{0} =0.407;\qquad\eta=0.795\]  
and its spectral variation is taken to be [63]:  
\[\gamma_{\text{pig}}(\lambda)=\gamma_{\text{pig}}(660)\times(\lambda/660)^{\
u} \tag{97}\]  
where  
\[\
u=\begin{cases}0.5\times[\log_{10}\text{CHL}-0.3]&0.02<\text{CHL}<2.0\\ 0&\text{CHL}>2.0.\end{cases}\]  
The spectral variation of the scattering coefficient for pigmented particles follows from the difference:  
\[\beta_{\text{pig}}(\lambda)=\gamma_{\text{pig}}(\lambda)-\alpha_{\text{pig}} (\lambda). \tag{98}\]The scattering phase function for pigmented particles may be described by the Fournier-Forand phase function (see below) with a backscattering ratio equal to 0.006 [63; 64].
## 4 Formulation of an Operation and Maintenance Strategy for Equipment with a High Failure Rate
### Verification of Equipment Value Improvement
#### 4.2.2 Cost Coefficient  
Assuming that the everyday operation service life of each set of equipment is 3 years, according to the \"Highway Engineering Estimation Index\" (JTG/T3821-2018)  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Transfer** & \(\mathbf{\lambda_{01}}\) & \(\mathbf{\lambda_{12}}\) & \(\mathbf{\lambda_{2F}}\) & \(\mathbf{\mu_{H00}}\) & \(\mathbf{\mu_{H10}}\) & \(\mathbf{\mu_{H20}}\) & \(\mathbf{\mu_{F0}}\) \\ \cline{2-9}
**Probability** & **0.0275** & **0.073** & **0.001** & **0.9725** & **0.927** & **0.999** & **1** \\ \hline State & \(S_{0}\) & \(S_{1}\) & \(S_{2}\) & \(S_{\text{F}}\) & \(I_{0}\) & \(I_{1}\) & \(I_{2}\) \\ \hline \(S_{0}\) & 0.8077 & 0.0275 & 0 & 0 & 0.1648 & 0 & 0 \\ \(S_{1}\) & 0 & 0.7622 & 0.073 & 0 & 0 & 0.1648 & 0 \\ \(S_{2}\) & 0 & 0 & 0.8342 & 0.001 & 0 & 0 & 0.1648 \\ \(S_{\text{F}}\) & 1 & 0 & 0 & 0 & 0 & 0 & 0 \\ \(I_{0}\) & 0.9725 & 0.0275 & 0 & 0 & 0 & 0 & 0 \\ \(I_{1}\) & 0.927 & 0 & 0.073 & 0 & 0 & 0 & 0 \\ \(I_{2}\) & 0.999 & 0 & 0 & 0.001 & 0 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Matrix table of optimal maintenance strategy.  
and \"Highway Engineering Budget Quota\" (JTG/T3832-2018), the manual maintenance cost incurred during maintenance is calculated at 106.28/person-day; the material fees and machinery and shift costs incurred during maintenance are shown in Table 5, in which minor and sporadic materials and small construction machinery are not all listed and are included in \"fees for other materials\" and \"fees for the use of small machines and tools\", respectively. During the inspection and repair of the cameras in the toll plaza, it is necessary to arrange trucks within 4 t and hydraulic lifts within 300 kg to operate. According to the Highway Construction Machinery shift fee quota (JTG/T3833-2018), the cost for each shift of trucks within 4 t is CNY 29.28 per shift for hydraulic lifts within 79.56300 kg. Based on this, it can be calculated that the total cost of each working day for horizontal and vertical transportation is CNY 8.3 when overhauling the video surveillance equipment in the toll square.  
According to the \"Budget preparation method for Highway Construction projects\" (JTG3830-2018), the working hours per comprehensive working day are calculated according to 8 h. Based on this, the minor maintenance cost (\(c_{1}\)), the general maintenance cost (\(c_{2}\)), and the deep maintenance cost (\(c_{3}\)) are 13.28, 15.71, and 21.24, respectively. The cost of staffing and tool materials generated by the inspection strategy is the average value of the three maintenance modes, \(c_{I}=16.74\).
# OPERA the Radar Project
## 2 Materials and Methods  
### European Radar Network  
In January 2019, OPERA had 30 members. Its metadata database listed 200 operational radars from these members, and 164 of these (from 25 countries) were exchanged regularly. A total of 71 radars were from the 20 th century; furthermore, 163 are C-band, 29 are S-band, and 8 are X-band (see Figure 1).  
To understand both the variability and common factors in this grouping and to share experiences between members, OPERA has executed several surveys and studies. Results from a survey about maintenance showed large differences in national maintenance policies, and revealed that the primary causes of missing data were not related to the radar itself, but were related to issues with the electricity supplies or telecommunications [2]. A survey about application priorities showed that the most important uses of radar data were for aviation and severe weather warnings. The importance of other applications, such as hydrological applications, NWP assimilation and verification, television, and web had large variability across Europe. These differences of priorities of national meteorological services were the main reasons for the wide variability in measurement schedules observed over Europe. The other main reason was that radars of different generations were used at the same time. Many new radars are more agile than the old ones, allowing more flexibility in scanning. On the other hand, many meteorological services want to collect more samples than before to get the full benefit of their Doppler and dual-polarization capabilities.  
The subject of another survey was the use of polarimetric radar variables in Europe. Currently, the largest benefit of dual-polarization (dual-pol) radars is in the quality improvement of other data (reflectivity and Doppler), especially in the removal of unwanted echoes, such as radio interference and wind turbines. Only a limited number of OPERA members are testing dual-pol attenuation correction, and even less have started using this operationally. Many of the new dual-polarization-based quality control algorithms are implemented in the radar signal processor, and are part of the proprietary software. So, they must be applied at the radar site during the time of measurement.  
Solar monitoring is a method used to monitor antenna direction. The direction from where microwaves are received from the Sun are compared to the nominal pointing of the antenna and the power from a solar observatory [3; 4]. The solar hit detection software in ODC is also able to calculate solar ZDR values [5]. At present (April 2019), polarimetric data (ZDR or TV) is provided by two members (16 radars, in total). In the OPERA data-hub, the solar signals are analyzed in the polar volume data, which helps the members to notice changes in the radar direction. This is a good example of a benefit provided to participating weather services.
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 4 Proposed System for the EEG-based Preference Detection
### Data Pre-Processing  
We used the preprocessed EEG dataset from the DEAP database, where the sampling rate of the original recorded data of 512 Hz was down-sampled to a sampling rate of 128 Hz, with a bandpass frequency filter that ranged from 4.0 to 45.0 Hz, and the EOG artifacts were eliminated from the signals using a blind source separation method, namely independent component analysis ICA. The data were averaged and segmented to 60-s trials. Then, we applied a channel selection step with a dimensionality reduction technique. The aim of this step was to reduce the number of features and/or channels used by selecting a subset that excludes very high-dimensional and noisy data. Ideally, the features that are meaningful or useful in the classification stage are identified and selected, while others, including outliers and artifacts, are omitted. Moreover, it reduces the computational cost of the subsequent steps. Therefore, we keep only the channels in which we are interested (Fz, AF4, AF3, F4, and F3).