esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Electrical Resistivity Tomography Surveys  
The purpose of electrical surveys is to determine the subsurface resistivity distribution by making measurements on the ground surface. From these measurements, the true resistivity of the subsurface can be estimated. The ground resistivity is related to various geological parameters, such as mineral and fluid content, porosity, and degree of rock water saturation.  
Resistivity measurements, or so-called resistivity sounding, are normally made by injecting a current into the ground through two current electrodes and measuring the resulting voltage difference at two potential electrodes. From the current \(I\) and voltage \(V\) values, an apparent resistivity \(\rho_{a}[\Omega m]\) is calculated:  
\[\rho_{a}=k\left(\frac{V}{I}\right), \tag{1}\]  
where \(k\) is the geometric factor, which depends on the arrangement of the electrodes. Using measurements of \(I\) and \(V\) for different \(k\), we can estimate the resistivity changes in vertical direction. A more accurate model of the subsurface is a two-dimensional (2D) model, where the resistivity changes in the vertical direction as well as in the horizontal direction, along the survey line. Details of this method can be also found in [55; 56; 57], and example applications of the method in studies of post-mining areas are stated in the Introduction section.  
Figure 5: Location of geophysical surveys in relation to topography and documented underground workings in test field three.  
In our case, ERT measurements were conducted in 15 profiles with a total length of 4413 m. The location of profile is shown in Figure 5. In the surveys, an asymmetric three-electrode scheme was used, with the spacing of the electrodes along the profile equal to 3 or 5 m. The scheme is presented in Figure 6. Data were recorded with the ARES II 850 W Automatic Resistivity System. The adopted methodology aimed for semi-detailed reconnaissance of the rock mass up to a depth of 70-80 m below surface level, in the central part of test field three, and detailed identification of test sites to a depth of 30-40 m below surface level. The collected data were processed by 2D inversion in profile lines. As a result, resistivity models of the ground were obtained. Electrical resistivity tomography is most often used in the 2D variant, which produces cross-sections showing the variability of resistivity along the measuring profile together with its depth. The results of ERT measurements are presented as profiles of apparent resistivity with geophysical interpretation.
# SAFP-YOLO: Enhanced Object Detection Speed Using Spatial Attention-Based Filter Pruning
## 4 Experimental Results
### Discussion
Compared to the conventional filter-pruning techniques [51, 52], the proposed SAFP could increase the execution speed of the baseline model with acceptable accuracy degradation. Figure 12 presents the attention maps obtained by applying Grad-Cam [61] to the models using the proposed method and the conventional filter-pruning techniques [51; 52]. Similar to Figure 11, it can be observed that in the case of the proposed method, attention is generally focused on the objects of interest to be detected. On the other hand, in the models applying the conventional filter-pruning techniques, some attention is directed towards objects; however, it is also noticeable that more attention is concentrated on areas such as the background that should not be detected. In other words, when applying the proposed method presented in this paper, it can be confirmed that more important filters for detection are retained compared to the conventional filter-pruning techniques, thereby aiding in the detection process. Figure 10: Accuracy–model size comparison of the filter-pruning techniques with YOLOv4 and YOLOv7 (**a,c**) shows the results on the pig dataset and (**b,d**) shows the results on the vehicle dataset. Compared to the conventional filter-pruning techniques [51; 52], the proposed SAFP could reduce the model size of the baseline model with acceptable accuracy degradation. Tables 4 and 5 present the results of applying the proposed method to TinyYOLOv4 and TinyYOLOv7, which are lightweight models of YOLOv4 and YOLOv7, respectively, on the pig and vehicle datasets. As shown in Tables 4 and 5, there was a decrease in accuracy; however, the relative increase in inference speed compared to the accuracy loss was substantial. Furthermore, the proposed method could significantly reduce the model size. This confirmed that the proposed method can be applicable to tiny models. Also, this confirms that the proposed method with YOLOv4 and YOLOv7 could provide better performance than TinyYOLOv4 and TinyYOLOv7 baseline models, regardless of the dataset type. With the pig dataset, the proposed method (applied to YOLOv4 with 78.6% accuracy at 20.9 FPS and YOLOv7 with 81.2% accuracy at 20.0 FPS) outperformed the tiny baseline (TinyYOLOv4 with 78.2%@19.2 FPS and TinyYOLOv7 with 81.9%@18.3 FPS). Additionally, it has been confirmed that the proposed method (applied to YOLOv4 with 72.6%@20.9 FPS and YOLOv7 with 75.7%@20.0 FPS) was superior for detecting vehicles when compared to the tiny baseline (TinyYOLOv4 with 68.5%@19.2 FPS and TinyYOLOv7 with 71.3%@18.3 FPS). Figure 11: Feature maps from pruned filters from the proposed method and the conventional filter-pruning techniques [51; 52]. Unimportant filters were pruned in the proposed method, while important filters were pruned in the conventional filter-pruning techniques.
In fact, the LBRA salts are often isolated as M\({}_{n}\)B\({}_{y}\)X\({}_{z}\) (M = alkali or alkaline earth metal, X = H, F, Cl, Br, I) hydrates that are dehydrated under vacuum at elevated temperatures (\(>\)200 \({}^{\circ}\)C) to obtain the anhydrous form [36]. Other alkali (Na, K, Cs) LBRA salts can be prepared from the acid form of the LBRA followed by cation exchange with an alkali hydroxide [7, 25]. The excellent water and thermal stability were the impetus for investigating a halogen-free lithium _closo_-borate salt in common liquid electrolyte systems. Additionally, the halogenated version of the _closo_-borates requires the use of fluorine or chlorine gas in their synthesis which has inherent safety concerns and may reduce their access in certain research settings. In this report we evaluate the electrochemical properties of a halogen-free _closo_-borate salt (Li\({}_{2}\)B\({}_{12}\)H\({}_{12}\)) in three different liquid solvents (propylene carbonate, ethylene carbonate:dimethyl carbonate, and 1-Butyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide). Symmetrical Li/Li cells were assembled and cycled to evaluate lithium stripping and plating of lithium at different current densities. Half cells were prepared with a lithium metal electrode and three different electroactive materials (titanium disulfide, lithium titanate, and perylenetetracarboxyliclic diimide) and cycled galvanostatically. The ionic conductivity and cycle performance of each liquid electrolyte is compared. FTIR is also utilized to investigate the interaction of the different solvent systems with the Li\({}^{+}\) and the B\({}_{12}\)H\({}_{12}\)\({}^{-2}\).
## 2 Materials and Methods
### ECAS Spray and Fog Disinfection Generation  
The electrochemically activated solution was sourced from Ecas4 Australia Pty Ltd. (Adelaide, Australia) and its physicochemical properties such as temperature, pH, and oxidation/reduction potential (ORP) were measured using a handheld meter (Model MC-80, TPS Pty Ltd., Brisbane, Australia). Free and total available chlorine was measured using a Free Chlorine Checker(r) HC-HI701 and a Total Chlorine Checker(r) HC-HI711 (Hanna Instruments, Melbourne, Australia), respectively. ECAS was stored at 4 \(\pm\) 1 \({}^{\circ}\)C and used within one week of preparation.  
The working concentration of ECAS (pH \(\approx\) 6.8-7.0, 150 mg/L of free available chlorine (FAC)) was freshly prepared each time prior to sanitisation experiment. We have previously optimised the aerosolised ECAS at 150 mg/L of FAC to significantly reduce the total microbial load in an animal farm environment, and for this reason we have chosen this concentration [28]. For the spray wash, ECAS was sprayed for 45 s using a handheld bottle sprayer. For the fog sanitisation, ECAS fog was generated using an ultrasonic humidifier that generates droplets sized between 1 and 3 \(\upmu\)m in diameter (Ultrasonic Humidifier HU-85, Contronics Engineering B.V., Sint-Oedenrode, The Netherlands) as previously optimised [28].
## 2 Materials and Methods
### Coupling Coefficient and Impedance  
As was already demonstrated in Reference [32], a sinusoidal modulation in the profile of a transmission line produces a stop band filter response. Using the coupled-mode theory, as proposed in Reference [33], a full analytical synthesis can be achieved. To do so, the desired frequency response is used in order to determine the variation of the coupling coefficient \(K(z)\) along the propagation direction (\(z\)) that provides that response. For complex responses, the coupling coefficient \(K(z)\) can be obtained using the zeros and poles of a ideal frequency response expressed as a rational function of polynomials, as proposed in Reference [29]. However, for a simple response as in a stop band filter, the coupling coefficient can be directly expressed as a sinusoidal variation of the form [31]:  
\[K(z)=A_{k}\sin\left(\frac{2\pi}{\Lambda}z+\theta\right),\;\;z\in[0,\;n_{p}\cdot\Lambda] \tag{1}\]  
where \(A_{k}\) represents the oscillation amplitude, \(\Lambda\) is its period, \(z\) is the propagation direction, and \(\theta\) is the phase of the sinusoidal. \(\theta\) should be set to 0 in order to facilitate the subsequent manufacturing process, avoiding sharp changes in the profile at the beginning of the filter.  
The value of \(\Lambda\) controls the central frequency of the stop band. If we want to locate the center of the stop band at a certain frequency \(f_{0}\), the period \(\Lambda\) should be:  
\[\Lambda=\frac{\pi}{\beta_{0}} \tag{2}\]  
where \(\beta_{0}\) is the phase constant of the propagating mode at the central frequency \(f_{0}\).  
The amplitude \(A_{k}\) and the length (or number of periods \(n_{p}\)) of the sinusoidal variation of \(K(z)\) control the depth of the rejection (insertion losses \(IL\) at \(f_{0}\)) and the fractional bandwidth of the stop band (\(FBW\)), as is described in Section 2.4.  
Figure 2 shows the variation of the coupling coefficient \(K(z)\) for \(\Lambda=13.52\) mm, \(A_{k}=50\), \(n_{p}=15\) and \(\theta=0\). The value of \(\Lambda\) has been calculated using \(\beta_{0}=232.3\) rad/m, which is the phase constant of an ESICL with \(Z_{0}=50\,\Omega\) (\(A=6\) mm, \(B=2.618\) mm, \(t=0.866\) mm and \(w=1.8148\) mm) at \(f_{0}=11\) GHz.  
For a device propagating a TEM mode, the relationship between the coupling coefficient and the impedance along the propagation direction is [33]:  
\[K=-\frac{1}{2}\frac{1}{Z_{0}}\frac{dZ_{0}}{dz} \tag{3}\]  
This equation can be used to express the impedance \(Z_{0}\) as a function of the coupling coefficient as:  
\[Z_{0}(z)=Z_{0}(0)e^{-2\int_{0}^{z}K(r)dr} \tag{4}\]  
Substituting the coupling coefficient of Figure 2 into Equation (4), gives the impedance variation of Figure 3. The next step is to obtain an ESICL with a profile that provides this impedance variation.  
Figure 3: Variation of the impedance (\(Z_{0}\)) along the propagation direction.  
Figure 2: Variation of the coupling coefficient (\(K(z)\)) along the propagation direction (\(\Lambda=13.52\) mm, \(A_{k}=50\), \(n_{p}=15\) and \(\theta=0\)).
## 2 Results and Discussion
### Basic Characteristics of the Samples  
The acidity, peroxide value (PV), K\({}_{232}\) and K\({}_{270}\) of investigated VOOs were assessed by EU method (Table 1).  
The International Olive Oil Council determined acidity below 0.8% for extra VOOs (IOC/T.15/NC n\({}^{\circ}\) 3/Rev.7, 2012 [12]). From Table 1 it can be seen that _Oblica_ variety exhibited the lowest acidity (0.12%), while the highest value was found in _Lastovka_ oil (0.17%). Statistically significant differences were obtained in free acidity and peroxide value among the oils from the studied cultivars. Although significant differences were determined by [PERSON]'s test, the absolute values of measured acidity of all oils are very similar. Peroxide value should amount less than 20 mEq O\({}_{2}\)/kg for extra VOO. Determined PVs ranged from 2.96 to 4.00 mEq O\({}_{2}\)/kg. The lowest PV showed _Oblica_ oil (2.96 mEq O\({}_{2}\)/kg) and the highest _Lastovka_ oil (5.32 mEq O\({}_{2}\)/kg). Determined PVs are significantly different in all tested oils, but in absolute values they are very close. K\({}_{232}\) value demonstrates conjugated dienes and their oxidation products (absorbtion at \(\lambda\) = 232 nm) and K\({}_{270}\) value indicates conjugated trienes and secondary oxidation products (carbonyl compounds; absorbtion at \(\lambda\) = 270 nm). K\({}_{232}\) and K\({}_{270}\) values ranged from 1.70 to 2.11 and from 0.12 to 0.19 (Table 1). _Oblica_ oil showed the lowest K\({}_{232}\) and K\({}_{270}\)  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**No.** & **Cultivar** & **Acidity x (\%)** & **Peroxide Value y (mEq O\({}_{2}\)/kg)** & **K\({}_{232}\) x** & **K\({}_{270}\) x** \\ \hline
1. & _Oblica_ & \(0.12\pm 0.00\)a & \(2.96\pm 0.05\)a & \(1.70\pm 0.10\)a & \(0.12\pm 0.02\)a \\
2. & _Lastovka_ & \(0.17\pm 0.00\)b & \(5.32\pm 0.04\)b & \(2.11\pm 0.40\)b & \(0.19\pm 0.01\)b \\
3. & _Levantinka_ & \(0.13\pm 0.00\)c & \(4.00\pm 0.06\)c & \(1.93\pm 0.80\)c & \(0.15\pm 0.02\)c \\ \hline \end{tabular} All values are expressed as mean of triplicate determinations \(\pm\) SD; x threshold value for extra VOO is \(\leq 0.8\); y threshold value for extra VOO is \(\leq 20\); z threshold value for extra VOO is \(\leq 2.5\); x threshold value for extra VOO is \(\leq 0.22\); the mean values within each column labeled with different letters are significantly different (Tukey’s test, \(p<0.05\)).  
\end{table}
Table 1: Basic characteristics of _Oblica_, _Lastovka_ and _Levantinka_ virgin olive oils (VOOs).  
values while _Lastovka_ oil showed the highest values. The investigated parameters (Table 1) were within the limits of EC Reg. 1989/2003 (2003) [13] indicating the category of extra VOOs.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 2 Geometric Errors of Spindle  
According to ISO 230-7:2015 [15] and ASME B89.3.4-2010 [16], the rotating shaft has six error motion terms because of imperfect design, manufacturing, and assembly of machine parts, including three translational errors and three rotational errors, as shown in Figure 1. The translational errors are two radial error motions (\(\delta_{\mathrm{X}}\) and \(\delta_{\mathrm{Y}}\)) and an axial error motion (\(\delta_{\mathrm{Z}}\)). The rotational errors are two tilt motions (\(\varepsilon_{\mathrm{X}}\) and \(\varepsilon_{\mathrm{Y}}\)) and an angular positioning error motion (\(\varepsilon_{\mathrm{Z}}\)). Axis average line represents the mean location of the axis of rotation, and it is determined by connecting the calculated least-squares centers of two data sets of radial error motion taken at axially separated locations.
## 5 Results and Discussion
Figure 8: Effects of the first-order slip parameter \(\sigma\) and second-order slip parameter \(\delta\) on the dual (a) velocity profile \(f^{\prime}(\eta)\) and (b) temperature profile \(\theta(\eta)\). Figure 10: Effects of the stretching/shrinking parameter \(\varepsilon\) on (a) the velocity profile \(f^{\prime}(\eta)\) and (b) the temperature profile \(\theta(\eta)\). Figure 9: Effects of the Soret number \(Sr\) and Dufour number \(Du\) on (a) the temperature profile \(\theta(\eta)\) and (b) the concentration profile \(\phi(\eta)\). As the thermal boundary layer thickness grew larger, the heat transfer coefficient was expected to decrease (Figure 5) [59]. The stability analysis was performed to verify if either the first or second solution was stable and hence physically realizable by substituting the set of ordinary differential Equations (22)-(24) together with the respected boundary condition (25) into the codes. The purpose of the analysis was to determine the smallest eigenvalue \(\gamma\) in order to identify the solutions in which a positive eigenvalue corresponded to a stable solution whereas a negative eigenvalue corresponded to an unstable solution. The first solution presented a positive value and was a stable solution characterized by a slightly disturbance in the boundary layer separation that did not interrupt the flow system (Table 1). However, the second solution presented a negative value which led to an initial growth of disturbance that interrupted the boundary layer separation and, hence, was an unstable solution. Thus, the first solution was a stable solution and, hence, it can be obtained physically because of its physical properties, whereas the second solution was an unstable solution.
# Phased Array Antenna Analysis Workflow Applied to Gateways for LEO Satellite Communications
## 4 Patch Antenna Design
### Design of Reception Patch Antenna  
The gateway reception frequency bands for LEO constellations are slightly different: SpaceX and OneWeb are from 17.8 GHz to 19.3 GHz and are centred at 18.55 GHz, Telesat is from 17.8 GHz to 20.2 GHz and is centred at 19 GHz while Amazon is from 17.7 GHz to 20.2 GHz and is centred at 18.95 GHz. Since the frequencies are not very different, the same substrate will be used for all of them. In this case, the chosen substrate was Taconic TLY-5 with \(\varepsilon_{r}=\)2.2 and \(h=1.58\) mm. To glue the two layers, the same pre-impregnated material as for the reception antenna is used (TPG-32). Figure 9 shows the ADS substrate model of the six-layer PCB used for EM simulations.  
Figure 10 shows a physical representation of the reception antenna. In this case, the width and length of the antenna are 6.2 mm and 3.8 mm, respectively, and the feeding point for a 50 \(\Omega\) input impedance is located \(X_{f}=\) 3.1 mm and \(Y_{f}=\) 2.6 mm. As the frequencies of the four considered systems are not very different, there were no major differences between the results.  
In Figure 11a is shown the reflection coefficient, which is below \(-\)10 dB over all the frequency bands selected. The antenna's radiation pattern is illustrated in Figure 11b,c, obtaining a maximum gain of 6.2 dB.  
Figure 8: (a) \(S_{11}\) for the transmission patch antenna (b) 3-D transmission antenna radiation pattern.  
Figure 9: ADS model of the selected substrate for the reception antenna composed of two TLY-5 substrates glued back-to-back with TPG-32 pre-impregnated material.
## 2 Simulation Details
### Initial Structure
The composite precursor is presented in Figure 1b at the initial state and after exposure at 300 K, simulated with the help of different potential functions. The size of the simulation cell at the initial state was \(10.1\times 10.0\times 8.4\) nm. The exposure process is discussed further below. Then, hydrostatic compression was used to reduce large pores between the individual elements of the composite precursor. The structures were compressed to approximately the same size: \(4.2\times 4.2\times 4.2\) nm. Then, hydrostatic compression was used to obtain the composite with the maximum possible densities. Hydrostatic compression was applied with a strain rate of \(\dot{\epsilon}=0.01\) ps\({}^{-1}\). Details of the structure fabrication are presented in [2; 27]. For simplicity, the results for a structure \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & **BOP** & **EAM-Morse** & **Morse** & **BOP [75]** & **Experiment [20]** \\ \hline \(T_{m}\), K & 830 & 797 & 730 & 723 & – \\ \(h\), Å & 3.2 & 6.4 & 6.4 & – & 3.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Melting temperature of Cu nanoparticle \(T_{m}\) and equilibrium distance between Cu atom and graphene layer \(h\) obtained with different methods. Figure 1: (**a**) Graphene flake with Cu nanoparticle inside and (**b**) composite precursor at the initial state and after exposure at 300 K. Cu atoms are shown in orange and C atoms are shown in black color. obtained with the BOP potential are referred to as CG-Cu\({}_{BOP}\), with the hybrid EAM-Morse potential as CG-Cu\({}_{EM}\), and with the Morse potential as CG-Cu\({}_{M}\). The structures were studied at a constant temperature to trace the dynamics of the interaction of Cu nanoparticles with graphene using different potentials. The simulation was conducted using a large-scale atomic/molecular massively parallel simulator (LAMMPS). Equations of motion for the atoms were integrated numerically using the fourth-order Verlet method with a time step of 0.1 fs. A Nose-Hoover thermostat was used to control the system temperature. The periodic boundary conditions were applied in all directions. To study the mechanical properties of the composites, uniaxial tension was applied along the \(x\)-axis, with a strain rate of 0.005 ps\({}^{-1}\).
## 4 Energy Efficient Protocols, and Scheduling in WBSNs
### Energy Efficient Protocols in MAC Layer
WBSN devices are classified into full function device (FFD) and reduce function device (RFD). A Body Node (BN) can either be FFD or RFD and can respond accordingly to instructions received from the Body Network Controller (BNC). BNs are set in a sleep mode as a default state and the wakeup (managed by BNC) when they need to transmit or receive data (transition to idle state). A superframe with a beacon period and contention-free period (CFP) with 15 guarantee time slots (GTS) is advised. The communication process is divided in two phases: in the first phase BN receives the wake up radio signal from the BNC and verifies itself as the indicated receiver and sends an acknowledgement message; in the second phase, the main radio transceiver is triggered on and sends a beacon to guarantee the time slot for transmission. The behavior of BNC and BN are different depending on the priority of traffic. At normal traffic, BNC uses a table with wakeup schedule for every node where the wakeup interval is calculated from inter-arrival packets and the wakeup process is according to traffic intensity. When an emergency scenario (random traffic) occurs, BN wakes up and sends a wakeup signal to the BNC, then BNC acknowledges it and sends the beacon to the BN to allocate it into an available channel, finally BN sends its data to BNC. The evaluation of this MAC protocol demonstrates an increase in the network lifetime thanks to an overhead reduction. Better performance in energy saving and delay is found compared to WiseMAC, ZigBee, B-MAC, and X-MAC protocols. An energy efficient and QoS (EQ-MAC) protocol is presented in [47]. This protocol introduces a hybrid approach of scheduled TDMA and contention-based Carrier Sense Multiple Access (CSMA) schemes. It shows as advantage the use of service differentiation, handling highest priority packets to be processed immediately over those of the lowest priority. In terms of energy consumption and efficiency, EQ-MAC adapts better with high data rates, resulting in less energy compared with the standards S-MAC and Q-MAC protocols. Also, the energy consumption is not affected by the change in priorities. In terms of average packet delay, EQ-MAC achieves better performance under high priority data traffic, and in terms of delivery ratio, EQ-MAC adapts avoiding packet collisions by using scheduling of nodes. In conclusion, prioritizing traffic is a key aspect to improve performance with minimum delay and with energy efficiency.