esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Antioxidative Properties
The authors concluded that the higher antioxidant activity of mature vegetables might be due to the greater concentrations of polyphenols. The same conclusions were reached by [PERSON] et al. [56] and [PERSON] et al. [57] in the case of the comparison between buckwheat and amaranthus at different stages of vegetation. The antioxidant potential of _Nigella sativa_ L. plant parts (roots, seeds, and shoots) was assessed using the ORAC assay by [PERSON] et al. [58]. The authors obtained results which confirmed the important antioxidant activity of these extracts. The extract from the shoots was found to be the most active (with an ORAC value of 4.07 mmol of TE/mg), followed by the extract from the roots (with an ORAC value of 2.57 mmol of TE/mg). The extracts from the Tunisian components of the _Nigella sativa_ plants show broad activity. Through in vitro and ex vivo examinations, the methanol extracts from the roots, shoots, and seeds were found to have a high antioxidant potential, which implies that they can be used as a source of readily available natural antioxidants and as a possible dietary supplement to control organoleptic degradation caused by free radicals [58]. The differences which exist between the number of bioactive components and the variability in the antioxidant activity of the vegetable samples tested in this study and in studies by other authors may be due to a multitude of reasons. The variation between the vitamin C, carotenoid, and polyphenol content may be due to the natural variability of the material tested, growing and environmental conditions, the date of harvest, maturity status, as well as post-harvest storage conditions [59]. [PERSON] et al. [47] showed that popular commercially available microgreen types such as _Apiaceae_, _Lamiaceae_, _Brassicaceae_, and _Malvacae_ revealed very strong antioxidant activity ranging widely from 303.3 (jute) to 878.3 mmol/kg (cress). The _Brassicaceae_ species had the highest antioxidant potential, which was followed by the _Lamiaceae_ family. Additionally, [PERSON] et al. [50] proved that the plant extracts at the bud and flowering phases generally had the highest antioxidant activity in the polar systems (FRAP, DPPH, and TEAC tests). In these conditions, they showed the greatest antioxidant activity (when the amounts were calculated per fresh matter) at the ripe-pod phase. The amelina extracts at the flowering and ripe seed-pod phases exhibited the greatest antioxidant activity in the lipid emulsion system [50]. It is also worth noting that despite the existence of a number of methods for assessing antioxidant properties, there is a lack of standardization of the results obtained. Frequently, many variants of a given method are known, which differ in the measurement conditions to such an extent that a comparison of results is unavailable.
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
###### Abstract  
This paper proposes a modified nonsingular fast terminal sliding mode (NFTSM) guidance law to solve the problem of ground moving target tracking for fixed-wing unmanned aerial vehicle (UAV) in a planar environment. Firstly, the loitering algorithm is analysed, which can steer the UAV to follow and circle around a ground moving target with the desired distance by heading angle control. Secondly, the effects of different parameters on the convergence time of sliding manifold is presented which is helpful for the designing of sliding manifold. Singularity can be avoided by using a modified saturation function which is obtained through a small range around the null point. Moreover, the NFTSM sliding manifold is used in the loitering algorithm. By using the Lyapunov theory, the finite-time convergence of the proposed method has been proved in the the reaching phase and the sliding phase. In order to verify the approach's feasibility and benefits, numerical simulations are performed by using a moving target with three different motion states in comparison with the conventional sliding model control method. Simulation results indicate that, under the proposed NFTSM guidance law, the UAV can reach the desired distance in a short time.  
unmanned aerial vehicle (UAV); target tracking; finite-time convergency; sliding model control (SMC) +
Footnote †: journal: Applied  
_Article_  
Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 4 Data Analysis in Maritime IoT Applications
### Role of Data Analysis in Enhancing Maritime Operations
#### 4.2.4 Enable Predictive Maintenance  
One of the most significant benefits of data analysis in the maritime industry is the enablement of predictive maintenance. By analyzing real-time data from machinery and equipment, such as engine performance data, potential issues can be predicted before they result in major failures or downtime.  
For instance, by identifying patterns in engine vibration or temperature data, it may be possible to detect an impending engine failure. This allows for timely maintenance, leading to significant cost savings by avoiding unplanned downtime and expensive emergency repairs [82].
## 3 Results and Discussion
### Environmental Radiological Risk  
The Lisava uranium mining sector is not an inhabited area; the closest village is about 5 km north. This area is included in a larger region where uranium mining has been the most prevalent activity for more than 40 years. The present study revealed that the soils from the Lisava sector had a relatively high concentration of \({}^{238}\)U. Therefore, the radiological risk was assessed by calculating the radium equivalent activity (Ra\({}_{\text{eq}}\)), absorbed gamma dose rate (DR), external hazard index (Hex) and representative level index (RLI) using the specific activities of \({}^{238}\)U, \({}^{232}\)Th and \({}^{40}\)K measured in the soil and comparing the calculated values to the median values provided by UNESCEAR. A descriptive statistic of the radiological parameters is presented in Table 1, and the individual values for each parameter are accessible in Appendix A.  
Figure 7: Spatial distribution of \({}^{40}\)K in soils from Lisava uranium mining sector.  
The radium equivalent (Ra\({}_{\rm eq}\)) is a commonly used risk index and is calculated using the specific activities of \({}^{23}\)U, \({}^{232}\)Th and \({}^{40}\)K. The radium equivalent (Ra\({}_{\rm eq}\)) ranged between 118.331 (Bq/kg) and 450.314 (Bq/kg) with a mean value of 262.228 (Bq/kg). Although the mean value was lower than the median value, in about 50% of the sampling points the calculated values exceeded 370 Bq/kg (Figure 8). The standard deviation (SD) of 88.91 Bq/kg indicated wide variations, suggesting a log distribution for Ra\({}_{\rm eq}\).  
The absorbed gamma dose rate (DR) had a minimum value of 57.55 (nGy/h) and a maximum value of 214. 552 (nGy/h). The mean of the absorbed gamma dose rate (DR) was 123.721 (nGy/h). Furthermore, at almost all the sampling points, the DR was assessed to be above the median value of 84 nGy/h provided by UNESCEAR  
In Figure 9, the external hazard index (Hex) values calculated for all the sampling locations are presented. The values of Hex varied from 0.32 to 1.39 and had a mean value of 0.708. Except for the values calculated for sampling points 11 and 23, in all the other sampled points, the values were below 1.  
The representative level index (RLI) of gamma radiation hazard associated with \({}^{238}\)U, \({}^{232}\)Th and \({}^{40}\)K from the soil varied from 0.877 to 3.148 with a mean value of 1.839. Almost all the calculated values were higher than the median value of one (Figure 9).
## 1 Introduction
Several geologists have made remote sensing data more effective in lithological and mineral mapping by employing advanced machine-learning algorithm techniques [26, 27, 28]. The spectral bands of the multispectral sensor are characteristic for absorptions of such minerals, and thus the bands of the sensor can be used to discriminate the ferric iron-rich weathered surfaces of harzburgites [29]. In the field of remote-sensing technology used to delineate ophiolite, previous research mostly focused on two or three spectral enhancement approaches or a single data source. Consequently, the current study combined remote-sensing data (Landsat8 and WorldView-2) of different resolutions, processed the integration of seven image enhancement techniques, and analyzed the utilization of the support vector machine algorithm (SVM) and anomaly extraction technique for mantle peridotites, delineating and extracting alterations in the Luobusa area for the first time.
## 2 Results and Discussion
### Brunauer-Emmelt-Teller (BET)  
N\({}_{2}\) adsorption-desorption isotherms of S0, S05, S07, and S10 with the distinct-type H\({}_{3}\) hysteresis loop are shown in Figure 3a, while the distribution of the microspore sizes of the same set is shown in Figure 3b. The BET results are listed in Table 1. The parameters under consideration included the BET surface area and total pore volume. For the S0 sample, the BET surface area and the total pore volume were recorded at 59.04 m\({}^{2}\)/gm and 0.26 cm\({}^{3}\)/g, respectively. The results of S0 are in agreement with other findings [26]. As the treatment of HNTs is conducted by adding SDS, PVA, and MA at 5%, 7%, and 10%, the BET surface area decreased to 40.13 m\({}^{2}\)/gm (about a 30% decrease) for S05, and then moderately increased to 54.68 m\({}^{2}\)/gm, almost approaching the level of S0. The result of 30% less surface area goes very well with the reduction of the total pore volume from 0.26 to 0.23 cm\({}^{3}\)/gm (density increases from 3.84 to 4.34 gm/cm\({}^{3}\)). As the treatment increased to 7% (sample S07), the BET surface area increased while the average pore size decreased, suggesting that S07 became less porous due to the crosslinking process. When the treatment was increased to 10%, BET was significantly increased to 511.12 m\({}^{2}\)/gm. This increase goes very well with the results of the total pore volume which decreased by almost the same percentage as the BET surface area. The results of the average pore size of S10 do not reflect these significant changes; however, the number of pores (not the size)  
Figure 2: FESEM microphotographs for: (**a**) S0; (**b**) S05; (**c**) S07; and (**d**) S10.  
could support the results by assuming that the surface and the bulk of S10 have a huge increase in the number of the pores, which makes S10 very porous compared to the S0 sample, as shown in Figure 3b.
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 4 Design of the Space-Based Time System for Navigation Constellations
### Time Scale Model  
The clock source is a clock group composed of multiple hydrogen atomic clocks and cesium atomic clocks. The atomic clocks on each satellite serve as independent time references. For a clock group consisting of \(N\) atomic clocks, the time observation equation of any atomic clock \(i\) can be written in the form of the following formula, thus obtaining \(N\) time references:  
\[T_{i}=x_{0}+y_{0}t+\frac{1}{2}Dt^{2}+\varepsilon_{x}(t), \tag{18}\]  
where \(T_{i}\) is the time deviation of the atomic clock, \(x_{0}\) is the initial time deviation, \(y_{0}\) is the initial frequency deviation, \(D\) is the linear drift rate of the atomic clock, and \(\varepsilon_{x}(t)\) corresponds to the random variable of the time deviation, i.e., the noise of the atomic clock. Here, we use a quadratic polynomial model for clock error prediction, taking into account the physical characteristics of the satellite clock error, frequency accuracy, frequency drift rate, and frequency stability, which are widely used clock error extrapolation methods in broadcast ephemeris. The clock error characteristic data of the satellite are constants fitted based on the on-orbit measurement results. These constants can be used to predict the satellite clock within a certain period of time.  
The purpose of the time scale algorithm design is to optimize the stability of the time scale, so the atomic time algorithm is used. The weighted average of all atomic clocks can finally lead to higher reliability, stability, and accuracy of the final comprehensive atomic time scale than any atomic clock.
## 3 The OpenFlow Protocol
### OpenFlow Specifications
#### 3.2.6 Summary of OpenFlow Specifications and Controllers  
Table 2 provides the supported protocols and available match fields of the discussed OpenFlow versions. Table 3 compiles the types of statistics collected by a switch, which can be queried by a controller.  
A list of open source controllers is given in Table 4. The NOX controller [29] was initially developed at Stanford University and can be downloaded from [30]. It is written in C++ and licensed under the GNU General Public License (GPL). The NOX controller was used in many research papers. The POX [30] controller is a rewrite of the NOX controller in Python and can be used on various platforms. Initially, POX was also published under the GPL, but has been available under the Apache Public License (APL) since November, 2013. The Beacon [31] controller was also developed at Stanford University, but is written in Java. The controller is available under a BSD license. The Floodlight controller [32] is a fork of the Beacon controller and is sponsored by Big Switch Networks. It is licensed under the APL. The  
\begin{table}
\begin{tabular}{c c c c c} \hline  & **OF 1.0** & **OF 1.1** & **OF 1.2** & **OF 1.3 \& OF 1.4** \\ \hline Ingress Port & X & X & X & X \\ Metadata & & X & X & X \\ Ethernet: src, dst, type & X & X & X & X \\ IPv4: src, dst, proto, ToS & X & X & X & X \\ TCP/UDP: src port, dst port & X & X & X & X \\ MPLS: label, traffic class & & X & X & X \\ OpenFlow Extensible Match (OXM) & & & X & X \\ IPv6: src, dst, flow label, ICMPv6 & & & X & X \\ IPv6 Extension Headers & & & & X \\ \hline \end{tabular}
\end{table}
Table 2: OpenFlow (OF) match fields.  
Maestro controller [33] was developed at Rice University and written in Java. The authors emphasize the use of multi-threading to improve the performance of the controller in larger networks. It is licensed under the GNU Lesser General Public License (LGPL). The NodeFLow [34] controller is written in Java and is based on the NodeJS library. It is available under the MIT license. The Trema [35] controller is written in C and Ruby. It is possible to write plugins in C and in Ruby for that controller. It is licensed under the GPL and developed by NEC. Finally, the OpenDaylight controller [36] is written in Java and hosted by the Linux Foundation. The OpenDaylight controller has no restriction on the operating system and is not bound to Linux. The controller is published under the Eclipse Public License (EPL).
## 3 Blur Kernel Estimation
### Blure Kernel Estimation Algorithm
#### 3.2.2 Blur Kernel \(h\) Estimation  
When solving variable \(h\). It has been pointed out in the literature [28] that directly using the intermediate latent image estimated above for calculation will reduce the accuracy of the results. Using gradient image for calculation can more effectively ensure the accuracy of estimation. So, we can get \(h\) by solving the following:  
\[h^{\prime}=\underset{h}{\text{argmin}}\parallel h\otimes\
abla X-\
abla G \parallel_{2}^{2}+\gamma\parallel h\parallel_{2}^{2} \tag{14}\]  
Using fast Fourier transform solution (14):  
\[h=F^{-1}\Bigg{(}\frac{\overline{F(\
abla X)}F(\
abla G)}{\overline{F(\
abla X) }F(\
abla X)+\gamma}\Bigg{)} \tag{15}\]  
Since the blur kernel element is non-zero, and the sum is \(1\), for the negative elements produced by each iteration of \(h\), we set them to zero and normalize \(h\) at last. As suggested by [28], we decrease \(\lambda\) gradually to make more information available for kernel estimation.  
To sum up, the flow of the blur kernel estimation algorithm is shown in Figure 2.  
Figure 2: The flow of the blur kernel estimation algorithm.
# Context-Aware Wireless Sensor Networks for Smart Building Energy Management System
## 3 Research Methodology
### Existing Energy Policy and Control Approach  
The existing energy policy of heaters control, before this project, was based on the identification of the context from a high-level perspective rather than identifying the context of each room. Therefore, the energy policy consisted of controlling the building as a whole based on some general knowledge, such as the season of the year and outdoor temperature. As a result, we adopted WSN and ICT components in order to identify the context of each room all the time. Such an approach helped us  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Month and Year** & **Energy Consumption (MAD)** \\ \hline September 2017 & 477,693.00 \\ \hline October 2017 & 620,662.00 \\ \hline November 2017 & 925,342.00 \\ \hline December 2017 & 1,248,436.00 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Electricity consumption of the university during the fall semester of 2017.  
collate data pertaining to temperature, humidity, motion, etc. Hence, the energy policy was further enhanced as more data became available for the EMS.  
To achieve the required results, we also investigated existing EMS technologies in order to understand the workflow between its different components. We also investigated the different control approaches which are adopted in the Smart Energy Efficient Building (SEEB). These control approaches were leveraging WSN for data acquisition, context recognition, and appliance control. After surveying the state of the art modeling approaches for the university campus, we adopted the Context-Based Reasoning Model (CBR) [15] through which we could successfully model the different buildings and rooms of the university campus. Then we drafted a \"sample\" energy policy dedicated to heaters control. Afterward, we developed custom sensors and actuators based on temperature modules, motion modules, Arduino programming boards, and XBee radio frequency modules. These sensors sample building rooms and forward data to the server via an intermediate gateway device that connects the WSN to the control server. Moreover, this WSN also forwards the control decision back from the control server to the actuators.
## 4 Results of the Bridge Dynamic Responses  
### Ambient Vibration of the 336-m Span  
S1 was set on the basement of the pier #8, at the center of the bridge width, and the IBIS-S sensor was located 28.6 m below the bridge. The radar antennas were pointed to the middle of the 336-m span. The field campaign was carried out during the bridge maintenance (00:30 to 04:30, 10 th July 2018) and without train activity. This field campaign aimed to study the ambient vibration behavior of the 336-m span. The radar instrument was working in the dynamic mode with a maximum range  
Figure 4: Google Earth image of the NDHRB southern part. The IBIS-S installations (S1, S2, S3) are marked with yellow triangles and the approximated radar LOS is highlighted with yellow dotted lines; the four pictures show the corresponding views (view S1, view S2a, view S2b and view S3); the positions of the hydrostatic leveling are also indicated with green arrows.  
of 250 m. The total duration of the acquisition was approximately ten minutes, at a sampling rate of 148.91 Hz and 2.0-m of resolution in range.  
Figure 5 shows the range profile of the bridge deck. Due to the corner reflector effect of the steel beams in the lateral direction, a fairly high SNR (\(>\)55 dB) was obtained for all the bins. The radar bin corresponding to the middle of the span, i.e., the most sensitive position, was selected as representative to obtain the vibrations. A portion of the time history of this bin is depicted in Figure 6a. This was compared to the synchronized result achieved from the hydrostatic leveling installed at the same position and measuring with a sampling rate of 1 Hz (Figure 6b). To perform the comparison, all the displacements were projected along the vertical direction, which was considered the main displacement component. To the noted, the hydrostatic leveling has a relative lower precision (\(\pm\)0.1 mm) compared with the radar IBIS-S. On contrary, the IBIS-S has a displacement accuracy of 0.02 mm, while it can be affected by the change of environment. We can see from Figure 6 that: the magnitudes of the displacements are both within \(\pm\)0.3 mm and non-periodic displacement behavior was observed from both sensors. The inconsistency in figure can be explained as the uncertainty of the measurements. This agrees with the rigid characteristic of a railway bridge, which has a relatively rigid response to ambient noise.  
Figure 5: Range profile of the bridge deck. Bold gray line indicates the radar bin (number 76) selected for estimating the deck displacements.  
Figure 6: Displacements in mm due to ambient vibration in the middle of the 336 m-span achieved from (**a**) IBIS-S and (**b**) the hydrostatic leveling.
## 3 Preliminaries and Problem Statement
### Greedy Algorithm  
In this subsection, we propose a heuristic algorithm in which, at each step, the available maximal cover sector of an unselected sensor that covers the uncovered targets with greatest importance is identified. In our algorithm, we assign each target a weight indicating its importance. We first consider the maximally covered number (MCN) [31] for each target \(t_{k}\), where \(\text{MCN}(t_{k})=|\left\{D_{ij}\middle|\middle|t_{k}\in T(D_{ij})\right.\), \(1\leq i\leq n\), \(1\leq j\leq p_{i}\right\}|\) indicates the number of maximal sectors that cover \(t_{k}\). The weight of target \(t_{k}\) is \(1/\text{MCN}(t_{k})\). Therefore, targets with a smaller MCN value have greater weight (i.e., coverage of targets covered by fewer sectors should be considered more critical). We determine the weight of each maximal cover sector by summing the weights of all targets covered by the maximal cover sector. We then begin scheduling by selecting the sector with the highest weight among all unselected sectors. When a sector is selected, the corresponding sensor is scheduled as active with the orientation of the corresponding selected sector, and all remaining cover sets are eliminated from the unselected sectors for that sensor. The weights of all targets covered by the selected sector are set to 0, and the weights of the remaining unselected sectors that cover those targets are updated. This scheduling process continues until the set of unselected sectors is empty. The pseudocode of our proposed greedy algorithm is shown in Algorithm 1.  
```
1: Find the set of cover sectors \(D=\{D_{ij},\,1\leq i\leq N,\,1\leq j\leq p_{i}\}\), where \(D_{ij}\) is the \(j\)th maximal cover sector of sensor \(i\). Find the weight of each target \(t_{k}\), \(1\leq k\leq M\), and the weight of each sector in \(D\).
2:\(Z=\{\}\)
3:while (\(D\) is not empty) do
4: Choose the cover sector in \(D\) with maximum weight, denoted \(D_{nq}\)
5:\(Z=Z\cup D_{nq}\)
6: Delete \(D_{nj}\) from \(D\) where \(1\leq j\leq p_{n}\)
7: Set the weight all targets covered by \(D_{nq}\) to 0
8: Update the weight of all sectors in \(D\) that contain the targets covered by \(D_{nq}\)
9:endwhile
10:return\(Z\)
```  
**Algorithm 1**Greedy Algorithm (GREEDY)  
The time complexity of the greedy target coverage algorithm, denoted as GREEDY, is as follows. Determining the MCN values of all targets requires \(mn\) steps. The running time for identifying all cover sectors is thus bounded by \(O(mn)\). In the worst case, \(D\) has \(O(mn)\) cover sectors. Therefore, we can identify the cover sector in \(D\) with maximum weight in \(O(mn\text{log}(mn))\). Because at most \(n\) loops are required to select the cover sectors and each update takes at most \(O(m\text{log}(mn))\), the time complexity of GREEDY is \(O(mn\text{log}(mn))\) in the worst case.