esa-sceva/satcom-chunk-collection huggingface dataset:
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Smart Metering Application Projects
#### 4.1.3 Hook Norton  
Smart meters have been applied also during the realisation of the Hook Norton (UK) project targeting overall an examination of energy consumption in a rural area and monitoring the distribution substations. For the data transmission from the homes to a concentration hub within the substations, the NB-PLC technology has been used, whereas wireless solutions have been employed for further transmission of data to the control center. A feedback to the end users related to the total electricity consumption has been provided from the utility center via an Internet interface [76].
## 3 Results and Discussion
### Contents and Compositions of Volatile Compounds from Seeds and Cones of Greek Fir
\begin{table}
\begin{tabular}{r l r r r r} \hline \hline
**No.** & **Compound** & & **SEED** & **CONE** & **RI exp.** & **RI lit.** & **Identification Method** \\ \hline  & & [\%] & [\%] & & & **Method** \\ \hline
94 & (_E.E_)-Farneal & sto & tr. & n.d. & 1720 & 1722 & \({}^{1}\)H, RI, MS \\
95 & (_E.E_)-Farnesyl acetate & sto & tr. & n.d. & 1826 & 1822 & \({}^{1}\)H, RI, MS \\
96 & Manyl oxide & dto & n.d. & 0.1 & 1988 & 2007 & RI, MS \\
97 & Isopimara-7,15-diene & dt & n.d. & tr. & 1994 & 1999 & RI, MS \\
98 & Abieta-7,13-diene & dt & n.d. & 0.1 & 2080 & 2084 & RI, MS \\
99 & Abieta-8(14),13(15)-diene & dt & tr. & 0.1 & 2144 & 2152 & RI, MS \\
100 & Dehydroabetical & dto & n.d. & 0.1 & 2238 & 2260 & RI, MS \\
101 & Abietal & dto & n.d. & 0.3 & 2291 & 2261 & RI, MS \\ \hline \multicolumn{2}{l}{Sum of identified compounds} & 99.4 & 98.3 & & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) mt} & 95.4 & 88.4 & & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) mto} & 0.1 & 7.3 & & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) st} & 3.3 & 1.2 & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) sto} & 0.5 & 0.2 & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) dt} & tr. & 0.2 & & \\ \hline \multicolumn{2}{l}{\(\Sigma\) dto} & 0 & 0.5 & & \\ \hline \multicolumn{2}{l}{ Yield of essential oil (\%)} & 18.22 & 0.62 & & \\ \hline \hline \end{tabular} RI lit.—literature retention index on non-polar column; RI exp.—experimental retention index on non-polar column; n.d.—not detected; tr.—trace (amount lower than 0.05%); MS—mass spectrometry; \({}^{1}\)H—\({}^{1}\)H-NMR spectroscopy; m!—monoterpene hydrocarbons; mo—oxygenated derivatives of mt; st—sesuiterpene hydrocarbons; sto—oxygenated derivatives of st; dt—diterpene hydrocarbons; dto—oxygenated derivatives of dt.  
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{r r r} \hline \hline \multicolumn{1}{c}{**Enantioner/Enantioner Excess**} & \multicolumn{2}{c}{_Abies cephalonica_} \\ \cline{2-3}  & & **Seed** & **Cone** \\ \hline (S)-(\(-\))-Limonene (\%) & 97.6 & 93.5 \\ (R)-(+)-Limonene (\%) & 2.4 & 6.5 \\ ee & 95.2 & 87.0 \\ (1S,5S)-(\(-\))-\(\alpha\)-Pinene (\%) & 90.0 & 91.4 \\ (1R,5R)-(+)-\(\alpha\)-Pinene (\%) & 10.0 & 8.6 \\ ee & 94.2 & 82.8 \\ (S)-(\(-\))-Camphene (\%) & - & 81.8 \\ (R)+-Camphene (\%) & - & 18.2 \\ ee & - & 93.3 \\ (1S,5S)-(\(-\))-\(\beta\)-Pinene (\%) & 0.2 & 6.7 \\ ee & 100.0 & 86.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contribution of the levorotatory and dextrorotatory forms of enantiomers (%) with enantiomeric excess (ee) of the main Greek fir seed and cone monoterpenes. oils, mirroring previous findings on needle and bark essential oils from different Greek fir populations [1; 7; 12; 13; 14; 15].
# Influence of Solvent Selection in the Electrospraying Process of Polycaprolactone
## 5 The Applications of Electrosprayed PCL Particles
### Food Industry  
Biodegradable polymeric microspheres also show some applications in the field of food packaging [179]. On the one hand, some food additives (including flavoring agents, sweeteners, colors, nutrients, essential oils and anti-oxidants agents) are encapsulated into polymeric microspheres to achieving their controlling release. It helps to overcome both the inefficient utilization and the loss of food additives during the processing steps [180]. The polymeric shell, as an active barrier, plays a role in isolation and protection. On the other hand, some phase change materials are also encapsulated by the polymeric shell to obtain some temperature-controlled microspheres [181]. Using these temperature-controlled microspheres in the storage and transportation of food can prolong the activity and storage life of food. Also, adding some anti-oxidant agents into the polymeric shell and using this shell to encapsulate food improve the antioxidant capacity of food and extend the shelf life of food [182].
# Machine Learning-Supported Designing of Human-Machine Interfaces
## 2 Machine Learning-Supported HMI Optimisation  
Pattern mining-based filtering is more targeted than post-filtering methods in process mining tools, resulting in more focused process models. The proposed method can effectively support the process mining of any system in which events are stored.  
In this work, the applicability is demonstrated on an alarm management system. The concept discussed in the rest of the article will be alarm-related. From the log file in which the alarms, operator actions, and display actions are stored together, key events are identified through frequent pattern mining tools. If these kinds of events are stored in separate log files, they have to be merged. Patterns can be itemsets or sequences of events.  
These key events frequently occur with each other, and the set of these events will be the basis for log-file filtering. The filtered log file is the input of the process mining and the output is a typical process pattern. Different filtered log files can be created from the group of critical events, resulting in different process patterns. Once typical process patterns are available, with the help of existing system knowledge, situation-related action patterns are obtained, indicating amendments in the actual HMI layout. Each new pattern enriches the knowledge of the system. The concept of the proposed method can be seen in Figure 1.  
An essential condition for the development of HMI supported by ML is a properly designed log file [20]. For example, a process event in the log file can be an alarm, an adjustment of a process variable, a measured process value signal, or an alarm acknowledgement. HMI displays are drawn based on P and I diagrams, and one process event may belong to different subprocesses, so some controlled process variables are placed in more displays, as they can affect several parts of the plant. Based on the stored events, process models can be gained by using process mining algorithms, such as _Heuristic miner_[21].  
The design goal of a High-Performance HMI (HP-HMI) can be to minimise the number of displays and the amount of information on the displays, and to have supportive process-orientated display layouts. The applicability of the proposed method is highly dependent on the quality of the stored data in terms of proper labelling. If many subprocesses are related to more plant units, the creation of overview-like displays is recommended, containing only the relevant process values and controlled process variables.
## 5 LoW Mob Mobity Support Scheme
### Proposed Message Formats
#### 5.3.2 Message Format of a Data Packet Sent from a MN to the CNs  
The MN's data packet format is shown in Figure 7. It should be noted that the Fragmentation header is optional and is used when the packet size is large enough to be fit into the Maximum Transmission Unit (MTU) for IPv6 packets over IEEE 802.15.4. HC1 is the Dispatch header that indicates a compressed IPv6 header. An uncompressed IPv6 header is represented by IPv6 dispatch.  
When the SN receives the data packet, it first checks whether it has a routing entry for the destination or not. If the destination CN is located within the PAN, the SN forwards the packet to the node closer to the CN. If the SN is unable to locate the destination within the PAN, then it forwards the packet to the GW. In this case, it takes off the mesh header and inserts MN-GW dispatch and the MN-GW header. MN-GW dispatch indicates to the forwarding nodes that the source of the packet is a  
Figure 6: Location update (LU) message format.  
Figure 7: A typical 6 lowpan message format of a data packet.  
MN, and the packet needs to be forwarded to the GW. The packet's header includes source MN's address and destination CN's address. Thus using the dispatch value eliminates the need of having a tunnel between the SN and the GW. The packet format of the MN's data packet that the SN forwards to the GW is shown in Figure 8.  
When the GW receives the packet, it takes off the MN-GW header, assembles the fragments, and decompresses the IPv6 header, if needed before forwarding the packet to CN.
# Underestimates of Grassland Gross Primary Production in MODIS Standard Products
## 2 Materials and Methods  
### GPP\({}_{\text{MOD}}\) Algorithm  
The MOD17A2H GPP product (Collection 6) is a standard global product with a 500-m spatial resolution and eight-day temporal resolution [22]. It is based on the light use efficiency approach, which calculates GPP by using the amount of photosynthetically active radiation (_PAR_) absorbed by vegetation over a growing season [37]. The algorithm was developed below.  
\[GPP_{MOD}=\varepsilon\times FPAR\times PAR \tag{1}\]  
where \(\varepsilon\) is the actual light use efficiency, _PAR_ is the photosynthetically active radiation (MJ m\({}^{-2}\)) per unit time, and _FPAR_ is the fraction of _PAR_ absorbed by vegetation canopy.  
\[\varepsilon=\varepsilon_{max}\times TMIN_{scalar}\times VPD_{scalar} \tag{2}\]  
where \(\varepsilon_{max}\) (g C m\({}^{-2}\) d\({}^{-1}\) MJ\({}^{-1}\)) is the maximum light use efficiency, which is given in a Biome Parameter Look-up Table (BPLUT) for each land cover type in the PSN model. The _TMIN\({}_{scalar}\)_ and _VPD\({}_{scalar}\)_ are environmental stress factors of temperature (daily minimum temperature, Tmin, \({}^{\circ}\)C) and water (maximum daily vapor pressure deficit, VPD, Pa) and are parameterized according to Equations (3) and (4).  
\[TMIN_{scalar}=\begin{cases}1&TMIN>TMIN_{max}\\ (TMIN-TMIN_{min})/(TMIN_{max}-TMIN_{min})&TMIN_{min}\leq TMIN\leq TMIN_{max}\\ 0&TMIN<TMIN_{min}\end{cases} \tag{3}\]  
\[VPD_{scalar}=\begin{cases}0&VPD>VPD_{max}\\ (VPD_{max}-VPD)/(VPD_{max}-VPD_{min})&VPD_{min}\leq VPD\leq VPD_{max}\\ 1&VPD<VPD_{min}\end{cases} \tag{4}\]  
where _TMIN_ and _VPD_ are the daily minimum temperature (\({}^{\circ}\)C) and the average vapor pressure deficit (Pa), _TMIN\({}_{max}\)_ and _VPD\({}_{max}\)_ are the daily minimum temperature and the average vapor pressure deficit at which \(\varepsilon=\varepsilon_{max}\), and _TMIN\({}_{min}\)_ and _VPD\({}_{min}\)_ are the daily minimum temperature and average vapor pressure deficit at which \(\varepsilon=0\)[28; 38]. These parameters were determined, according to the BPLUT. The NASA Global Modeling and Assimilation Office (GMAO) provided input data for the GPP\({}_{\text{MOD}}\) algorithm including global meteorological simulations of incoming _PAR_, daily maximum/minimum temperature, and daily maximum/minimum _VPD_. We downloaded MODIS Collection 6 data for 2000 to 2014 and we extracted GPP\({}_{\text{MOD}}\) from pixels with the 500-m resolution in which the flux towers were located. GPP\({}_{\text{MOD}}\) for each site was transformed from monthly totals to monthly means and were converted from kg C m\({}^{-2}\) to g C m\({}^{-2}\).
# LP-Order Time-Frequency Spectra and Its Application in Edge Detection
## 4 Examples
### Actual 3D Seismic Data Example  
In order to better use the actual 3D seismic data for geologic body detection by LTFS, a four-step procedure was followed. First, HT was applied to the input actual 3D seismic data in the time (or depth) direction to generate its analytical representation. Second, MSTFT was applied to the analytical representation of the input actual 3D seismic data in the time direction to generate the time-frequency spectra. Third, the 3D instantaneous amplitudes obtained by HT, GHT, and LTFS were calculated. Finally, time slices of the instantaneous amplitudes obtained by HT, GHT, and LTFS were extracted for comparison.  
Figure 5a,b shows the seismic data of inline 160 and crossline 96 extracted from the input actual 3D seismic data, respectively. The time-sample interval is one millisecond, thus the Nyquist frequency is 500 Hz.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Methods**} & **GHT with \(p=0.2\) (or LTFS with** & **LTFS with \(p=0.2\),** \\  & \(p=0.2\),\(f_{1}=0\) Hz, and \(f_{2}=500\) Hz)** & \(f_{1}=10\) Hz, and \(f_{2}=100\) Hz \\ \hline Time costs (s) & T1 = 4.21 & T3 = 0.77 \\ \hline Ratios of time costs & T1/T3 = 5.47 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Time costs of GHT and LTFS results in Figure 3e,f.  
Figure 4: Zoomed-in views of yellow rectangles in Figure 3. (**a–f**) show the zoomed-in views of yellow rectangles in Figure 3a–f, respectively.  
Figure 6a shows a time slice of 1.7 s extracted from the input actual 3D seismic data. Figure 6b shows the same time slice of instantaneous amplitudes obtained by HT. Figure 6c shows the same time slice of instantaneous amplitudes obtained by GHT with \(p=6\) (an even number). Figure 6d shows the same time slice of the instantaneous amplitudes obtained by LTFS with \(p=6\),\(f_{1}=0\) Hz, and\(f_{2}=90\) Hz. For comparison, the same time slices of curvature and third-generation coherence (C3) attributes used for characterizing the channels are shown as Figure 6e,\(f\), respectively.  
Figure 5: Two profiles seismic data extracted from the input actual 3D seismic data. (**a**) Inline 160; (**b**) Crossline 96.  
Figure 6: Time slices at 1.7s: (**a**) amplitudes; (**b**) instantaneous amplitudes; (**c**) instantaneous amplitudes obtained by GHT with \(p=6\); (**d**) instantaneous amplitudes obtained by LTFS with \(p=6\), \(f_{1}=0\) Hz, and \(f_{2}=90\) Hz; (**e**) most positive curvatures; (**f**) third-generation coherences (C3).  
Figure 7a-f shows the time slices in Figure 6a-f, respectively, and with two interpreted channels (shown as the two yellow curves). Comparison of Figure 7b with Figure 7a,c-f indicates that HT shows the main features of the two channels, but lacks the details obtained by GHT, LTFS, most positive curvatures, and third-generation coherences. A comparison of Figure 7d with Figure 7a-c,e,f indicates that Figure 7d is consistent with the two interpreted channels, and exhibits higher resolution and more detail than Figure 7a-c,e,f.
# An Elliptical-Shaped Dual-Band UWB Notch Antenna for Wireless Applications
## 3 UWB Notch Antenna Design Approach  
Figure 3 shows the design of an elliptical UWB notch antenna. After completing the design of the elliptical-shaped UWB antenna, the notches have to be introduced. There are many techniques to obtain notches. Two parasitic resonators, an inverted-U-shaped resonator and an iron-shaped resonator, are used to achieve the notch. The inverted-U-shaped resonator helps obtain a notch at 5.3 GHz, and the iron-shaped resonator helps in obtaining a notch at 8.1 GHz. Figure 4 shows the dimension of the suggested antenna. Table 1 lists the antenna's dimensions.  
Figure 1: Progression stage of UWB antenna.  
Figure 2: S\({}_{11}\) characteristics of UWB antenna.  
Figure 4: Dimension of elliptical UWB notch antenna: (**a**) patch; (**b**) ground plane.  
Figure 3: Elliptical UWB notch antenna: (**a**) UWB to UWB notch; (**b**) S\({}_{11}\).  
The length of truncation in the ground plane for achieving UWB is calculated using Equation (1).  
\[\begin{array}{ll}L_{trunc}&=h(perimeter)+j+f+l3+i(perimeter)\\ &=1.5+1.9+2+2.47+1.25\\ &=9.12\;\text{mm}\\ \end{array} \tag{1}\]  
The relative permittivity of the FR-4 substrate is \(\varepsilon_{\text{r}}=4.4\), so effective permittivity can be calculated as Equation (2),  
\[\begin{array}{ll}\varepsilon_{eff}&=\frac{\varepsilon_{r}+1}{2}+\frac{ \varepsilon_{r}-1}{2}\Big{[}1+12\frac{H}{w}\Big{]}^{-0.5}\\ &=2.7+\frac{1.7}{\sqrt{1+12(\frac{1.6\times 10^{-3}}{3\times 10^{-3}})}}=3.325 \\ \end{array} \tag{2}\]  
As we can observe that the first notch frequency, \(f_{mct1}\) is around 5.2 GHz, and is due to the effect of the upper resonator. Therefore, mathematically, the length of the upper resonator can be calculated using Equation (3).  
\[\begin{array}{ll}L_{upper\;resonator}=\frac{\lambda_{0}}{2\sqrt{\varepsilon_ {eff}}}=\frac{c}{2\;f_{mct1}\;\sqrt{\varepsilon_{ref}}}\\ &=\frac{3\times 10^{-8}}{2\times 52\times 10^{6}\sqrt{3.325}}=15.55\;\text{mm} \\ \end{array} \tag{3}\]  
Similarly, the second notch frequency, \(f_{mct2}\) is around 8.2 GHz and is due to the effect of the lower resonator. Therefore, mathematically, the length of the lower resonator can be calculated using Equation (4).  
\[\begin{array}{ll}L_{lower\;resonator}&=\frac{\lambda_{0}}{2\sqrt{\varepsilon _{ref}}}=\frac{c}{2\;f_{mct12}\;\sqrt{\varepsilon_{ref}}}\\ &=\frac{3\times 10^{-8}}{2\times 8.2\times 10^{6}\sqrt{3.325}}=10\;\text{mm} \\ \end{array} \tag{4}\]
## 3 DNA as Functional Layers for OTFTs
[32] integrated water-soluble DNA into OTFTs, in which an 8-nm thick DNA film was deposited as a buffer layer between electrode and pentacene film through spray-coating. Based on the characterization of surface morphology and microstructure of pentacene film, the authors suggested that the increase of carrier mobility from 0.035 to 0.153 cm\({}^{2}\)/Vs can be ascribed to the decreasing of charge trapping at conducting channels as well as the effective inhibition of the bulk-like phase transition of pentacene. Figure 4: **(a)** Schematic configuration of the OFET memory device and chemical structure of DNA–CTMA and pentacene; **(b)** Arrhenius behavior of the metal/insulator/metal(MIM) device; **(c)** Transfer characteristics of DNA–CTMA based OTFT memory device at different temperature. (Reproduced with permission from [19], Elsevier, 2016). Moreover, DNA was also used as the interlayer for the solution processed \(n\)-type or ambipolar OTFTs, as shown in the reports by [PERSON] et al. [33]. The schematic of OTFT structure and electric performance are shown in Figure 5a,b, respectively. The mobility and threshold voltage for \(n\)-type PC\({}_{70}\)BM and ambipolar BTDPP2 based device are summarized in Table 1. It demonstrated the DNA interlayer effectively enhanced charge carrier injection in OFETs. The improvement of the performance is attributed to the dipole layer originated from DNA and the enhanced injection from Au source electrode to the semiconducting layers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**PC\({}_{70}\)BM (\(p\)-Type)**} & \multicolumn{3}{c}{**BTDPP2 (Ambipolar Type)**} \\ \hline  & \(\mu_{\textbf{lin}}\) & \(\mu_{\textbf{sat}}\) & \(\textbf{V}_{\textbf{th}}\) & \(\mu_{\textbf{lin}}\) & \(\mu_{\textbf{sat}}\) & \(\textbf{V}_{\textbf{th}}\) \\  & [**cm\({}^{2}\)/Vs**] & [**cm\({}^{2}\)/Vs**] & [**V**] & [**cm\({}^{2}\)/Vs**] & [**cm\({}^{2}\)/Vs**] & [**V**] \\ \hline With DNA & \(1.7\times 10^{-2}\) & \(1.6\times 10^{-2}\) & 10 & \(2.3\times 10^{-3}\) & \(1.1\times 10^{-2}\) & 6.5 \\ Without DNA & \(7.5\times 10^{-3}\) & \(9\times 10^{-3}\) & 16 & \(4.8\times 10^{-4}\) & \(4.4\times 10^{-3}\) & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Device parameters comparison of OFETs fabricated with or without DNA interlayer [33]. Figure 5: (**a**) Schematic configuration of the OFET device based on DNA as interlayer. Output and transfer characteristics of \(n\)-type (**b**,**d**) and \(p\)-type (**c**,**e**) modes with DNA and without DNA as the interlayer. (Reproduced with permission from [33], Wiley-VCH, 2012).
## 2 Theory and Method  
### Forward Simulation Theory  
In the inversion process, forward simulation is a crucial factor affecting computational efficiency. In particular, 3D forward simulation of a complex model is generally extremely time consuming and requires high computational power. Therefore, through comprehensive consideration of two aspects of 3D forward simulation, namely, its adaptability in modeling complexity and computational efficiency, 3D forward simulation is performed in this study using the staggered mimetic finite volume approach [47; 48].  
We begin with [PERSON]'s equations of time-varying electromagnetic fields. In forward simulation theory, [PERSON]'s equations can be written as follows:  
\[\
abla\times E+\mu\frac{\partial H}{\partial t}=0 \tag{1}\]  
\[\
abla\times H-\sigma E-\varepsilon\frac{\partial E}{\partial t}=S(r,t) \tag{2}\]  
where \(E\) and \(H\) are the intensities of magnetitic and electric fields, respectively, \(S(r,t)\) is the emitter term, \(\sigma\) is the electrical conductivity, \(\varepsilon\) is the permittivity, and \(\mu\) is the magnetic conductivity (\(\mu=\mu_{0}\)).  
An electromagnetic field consists of primary and secondary fields:  
\[E=E_{p}+E_{s} \tag{3}\]  
\[H=H_{p}+H_{s} \tag{4}\]  
where \(E_{p}\) and \(H_{p}\) are the intensities of primary electrical and magnetic fields, respectively, and \(E_{s}\) and \(H_{s}\) are the secondary electrical and magnetic fields, respectively. Neglecting the displacement current allows for the transformation of Equations (1)-(5).  
\[\
abla\times\
abla\times E_{s}+\mu\sigma\frac{\partial E_{s}}{\partial t}=- \mu\big{(}\sigma-\sigma_{p}\big{)}\frac{\partial E_{p}}{\partial t} \tag{5}\]  
where \(\sigma_{p}\) is the electrical conductivity of the half space.  
Transforming the volume integral to a surface integral based on [PERSON]'s law gives the following discrete state-space equation:  
\[\iiint_{V}\mu\sigma\frac{\partial E_{s}}{\partial t}dV+\iint_{\partial V}n \times(\
abla\times E_{s})dS=-\iiint_{V}\mu\big{(}\sigma-\sigma_{p}\big{)} \frac{\partial E_{p}}{\partial t}dV \tag{6}\]  
where \(n\) is the unit normal vector of \(\partial V\).  
Finally, [PERSON]'s method is employed to temporally discretize the equation. Specifically, \(H\) and \(E\) are discretized on the edges and surface, respectively.  
The following equations give the initial and boundary conditions for temporal discretization:  
\[E_{p}(r,0)=0 \tag{7}\]  
\[E_{s}(r,0)=0 \tag{8}\]\[H_{p}(r,0)=H_{0} \tag{9}\]  
where \(H_{0}\) is the initial static value of the magnetic field.