esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion  
### Comparison Optimal of SPME Fiber Coating  
Five different SPME fibers (100 \(\upmu\)m PDMS, 85 \(\upmu\)m polyacrylate, 65 \(\upmu\)m CW/DVB, 85 \(\upmu\)m CAR/PDMS and 50/30 \(\upmu\)m DVB/CAR/PDMS) were used to adsorb the volatile constituents of \(P\). [PERSON]'s Pacific Sunset, respectively. The results determined that fibers with 50/30 \(\upmu\)m DVB/CAR/PDMS had optimal extraction ability (Figure 1).  
SPME fiber coatings are available in a wide assortment of thicknesses and polarities [11]. [PERSON] _et al._[12] used different fibers (100 \(\upmu\)m PDMS, 65 \(\upmu\)m PDMS/DVB and 50/30 \(\upmu\)m DVB/CAR/PDMS) to extract volatile compounds of yacon leaves. The best results for most of the target compounds were obtained using the DVB-CAR-PDMS fiber, which can be explained by the nature of the fiber, as well as its slightly larger capacity for the analytes. [PERSON] _et al._[11] reported that PDMS is a non-polar material such as DVB and CAR. [PERSON] _et al._[13] used SPME to analyze the volatile constituents of garlic flavor components and compared the extraction abilities of the five different adsorptive fibers. The DVB/CAR/PDMS fiber was found to be the most efficient among the five types of fibers investigated.  
Figure 1: Comparison of the contents of total volatile compounds detected in headspace of \(P\). Nobby’s Pacific Sunset by different SPME fibers (A: DVB/CAR/PDMS; B: CW/DVB; C: PDMS/DVB; D: polyacrylate; E: PDMS). Values are means \(\pm\) SD of triplicates. Values with different superscripts are significantly (\(p<0.05\)) different.
## 7 Research Challenges and Recommendations for Future Works
### Untrusted Gateways  
Gateways are transparent bridges between end nodes and network server. They create communication between end nodes and the server. In most of the implementation scenarios, a few number are deployed. The deployment of the gateways in an open environment makes them untrusted devices. Since the gateway is communicating directly with the network, if an intruder gains access to a gateway, the data passing through it can be easily recorded and even manipulated. The manipulation of these gateways can lead to higher power consumption of end devices which can breakdown the end devices. Also, the communication between the end devices and the rest of the network can be destroyed. To provide secure data transmission, an authentication mechanism for the gateways is necessary in order to prevent the network from attacks [97].
## Materials and Methods  
### Measurement System for Determining Path Loss Model  
The objective of this experiment was to determine the path loss introduced by the ground interfering the FFZ in a single radio frequency carrier propagated between the transmitter and receiver when changing height, by lowering both antennas from \(H_{1}\) = 40 cm to \(H_{2}\) = 20 cm. Furthermore, we will see the difference in the path loss when using three different frequency bands \(F_{1}\) = 868 MHz, \(F_{2}\) = 2.4 GHz and \(F_{3}\) = 5.8 GHz.  
In the transmitter end, a power of \(P_{t}\) = 6 dBm has been used. For 868 MHz, the frequency carrier was provided by the programmable synthesizer HM 8133-2 that generates frequencies less than 1 GHz. However, for 2.5 GHz and 5.8 GHz, the signal generator SRM40 has been used.  
The received power was measured by spectrum analyzer FSH6 and recorded in a laptop using the software FSH View. An averaging of 10 is used to reduce the noise in the data traces.  
Identical omnidirectional antennas were used in the transmitter and receiver ends. The antenna model for the 868 MHz pilot was EAD SCP868-5 (gain = 5 dBi, E-plane = 75\({}^{\circ}\), H-plane = 140\({}^{\circ}\)), and for the 2.4 GHz and 5.8 GHz pilots the antenna model was EM-6952 (gain = 4.41/4.71 dBi, E-plane = 70\({}^{\circ}\), H-plane = 125\({}^{\circ}\)). Figure 1 depicts a test performed in the lab with both antennas to verify the system operation. We should notice that the flat panel antenna used for 868 MHz shows circular polarization, meanwhile the log-periodic antenna used for 2.4/5.8 GHz is linearly polarized.  
Figure 1: Path loss measurement system: (**a**) 868 MHz antenna setup; (**b**) 2.4/5.8 GHz antenna setup.
## 3 Methodology  
### C Fluxes Data  
The C flux data included three variables (GPP, Re, and NEE), but only NEE was measured directly using EC instrumentation. The GPP and Re were estimated by NEE decomposition techniques using the methodology below.  
The NEE time series were decomposed into GPP and Re according to the following four equations:  
\[NEE=\frac{a_{1}Q_{PPFD}}{a_{2}+Q_{PPFD}}+R_{D} \tag{1}\]where NEE at no light (Q\({}_{\rm PPFD}\) = 0) is estimated as R\({}_{\rm D}\) (the mean of daytime respiration of the time window), ais the maximum photosynthetic uptake, and a\({}_{2}\) is the light level (Q\({}_{\rm PPFD}\)) corresponding to half the maximum photosynthesis rate.  
To estimate daytime ecosystem respiration, nighttime NEE data were calculated according to the Arrhenius type function [40] below:  
\[NEE_{night}=R_{e}T_{ref}e^{(E_{a}/R)(1/T_{ref}-1/T_{k})} \tag{2}\]  
where R is the gas constant(8.314 J\(\cdot\)K\({}^{-1}\cdot\)mol\({}^{-1}\)), T\({}_{\rm ref}\) is the reference temperature constant of 283.15 K, T\({}_{\rm k}\) is soil temperature (K), and E\({}_{\rm a}\) is the activation energy in J\(\cdot\)mol\({}^{-1}\)[38].  
GPP was calculated as the difference between NEE and the total ecosystem respiration (R\({}_{\rm e}\)).  
\[GPP=NEE-R_{e} \tag{3}\]  
Daily ecosystem respiration Re is calculated as the sum of daytime respiration (R\({}_{\rm e,day}\)) and nighttime respiration (R\({}_{\rm e,night}\)):  
\[R_{e}=R_{e,night}+R_{e,day} \tag{4}\]  
Nighttime net exchange flux is assumed to be equivalent to nighttime ecosystem respiration. Temperature response models obtained from nighttime net exchange flux (R\({}_{\rm e,night}\)) were applied at daytime to estimate daytime ecosystem respiration (R\({}_{\rm e,day}\)) [38].
## 2 Related Work
### Trajectory-Aware Routing in FANETs Networks  
In FANETs, UAV mission planners can often generate highly accurate future trajectory sequences. Nevertheless, most existing studies rely on historical data for trajectory prediction rather than leveraging planner-generated trajectories directly [30].  
In [17], the authors proposed EORB-TP, which predicts near-future movement based on time-series modeling of historical positions to estimate link duration and optimize forwarder set selection in opportunistic routing. Similarly,Vinti [PERSON] et al. in [31] applies an optimized 3D interpolation model for energy-efficient trajectory prediction. In [30], a prediction module is incorporated into a reactive greedy protocol to estimate link stability and improve path durability. While these approaches improve stability, they generally suffer from limited prediction accuracy and high computational complexity, which undermines real-time applicability.  
Only a few works have directly utilized trajectory data from task planners for routing. In [21], the authors propose a mission-driven protocol where UAVs fly in a predefined formation and determine packet deliverability based on their current phase. Though effective in reducing packet loss in idealized scenarios, this method lacks flexibility in next-hop selection and adaptability to dynamic environments. [PERSON] et al. in [32] introduce a multi-agent deep reinforcement learning framework to jointly optimize trajectory, spectrum allocation, and routing. Although performance improves holistically, the solution's high computational overhead and reliance on deep models hinder its deployment in resource-constrained UAV nodes.  
In summary, trajectory-aware routing in FANETs is still underexplored. Most existing work depends on prediction rather than directly using planner-generated trajectories. Furthermore, such knowledge has not been fully integrated into position-based protocols like GPSR. To fill this gap, this paper proposes a novel integration of future trajectory information and Q-learning into GPSR, constructing time-aware two-hop neighbor tables to support adaptive and predictive routing decisions.
# Path Planning Strategy for Implementing a Machine Control System in Grader Operations
## 3 Path Generation Algorithm for Graders
### Work Path Generation Algorithm
#### 3.3.4 Set Work Pattern and Start Point  
After setting the safety line, the working patterns and directions of the equipment were determined. The working pattern of the equipment refers to the method of changing work lines, which can be one of the following: (a) the 3-point turning method, (b) turning after the backward method, or (c) turning while reversing, as shown in Figure 6. Once the working pattern is chosen, the method for changing the work lines is determined accordingly.  
Next, the starting position for the work was set, as shown in Figure 10. Once the starting position is defined, the work direction is determined based on the direction of progression of the road. If the starting position is assumed to be A, the equipment works in the direction from A to D, and the work lines are generated sequentially from A to B. If the starting position is B or C, then the direction vectors df1 and df2 are exchanged, allowing the existing direction vector calculation to be used without modification. Similarly, if the starting position is D or C, the order of df1 and df2 is reversed and the existing process continues without requiring any additional modifications.
# Co-Channel Interference Suppression for LTE Passive Radar Based on Spatial Feature Cognition
###### Abstract
The clean reference signal can be found by directing a narrow beam in the direction of the specific CCBS, which is unfortunately not known in advance usually. The directions of the CCBSs can be obtained by estimating the directions of the corresponding arriving signals. Because the LTE base stations are densely distributed, the traditional MUSIC and CAPON spatial spectrum estimation algorithms cannot get an accurate estimation of the directions of the arriving signals because the number of signals far exceeds that of the array elements. In this paper we propose a cascaded cancellation method for suppressing the DMI and co-channel interference in LTE passive radar. We first propose a spatial spectrum recognition method based on the cyclostationary characteristics of the LTE signal and the compressed sensing technique, through which we can estimate the spatial distribution of the interference signals. The reference signal of each strong interference base station is then obtained through beamforming, and a clean version of the reference signal is reconstructed with the signal bits demodulated from the received signal. Third, the co-channel interference from each base station is cancelled. Finally, a termination criterion is checked to determine whether the interference in the remaining signal has been adequately suppressed. If not, the cancellation procedure above is repeated. The termination of the cancellation procedure is determined by comparing the interference to noise ratio (INR) in the remaining signal with a predefined threshold. The INR is estimated through a cycle autocorrelation method and is referred to in our previous work [10]. If the estimated INR is smaller than the threshold, then the cascaded cancellation algorithm terminates. The advantages of the proposed method are two-fold. First, the proposed method can obtain a good estimation of the spatial distribution of the strong interference signals even with a small sized antenna array. Additionally, the proposed method uses a termination criterion to check whether the interference is suppressed adequately. If not, the cancellation procedure repeats. In contrast, the traditional cancellation methods only perform once and there is no indicator about how the interference is suppressed. The proposed method is analyzed through a range of simulations. The results demonstrate the effectiveness of the proposed method.
# Dynamic Correction of the Influence of Long Measuring Path Irregularity in Antenna Tests
## 5 Measurement Results
### 1P Calibration  
If it is necessary to measure the reflection coefficient at the input of a device under test (DUT), a 3-term error model of a one-port (1P) VNA is considered. This model is derived from a 12-parameter model of a two-port (2P) VNA (EURAMET CG No. 12 <<Guidelines on the Evaluation of Vector Network Analyses (VNA)>> Version 3.0) through simplification. To calibrate the VNA, it is necessary to measure the reflection from three different known loads--open circuit (OC), short circuit (SC) and matched load (ML). As a result of solving a system of linear equations, the following VNA error factors are determined: directivity \(\dot{E}_{D}\), source match \(\dot{E}_{S}\), and reflection tracking \(\dot{E}_{R}\). Then, the corresponding correction for the reflection coefficient of the measured load is written as:  
\[\dot{\Gamma}^{a}=\frac{\dot{\Gamma}^{m}-\dot{E}_{D}}{\dot{\Gamma}^{m}\dot{E}_{ S}-\dot{E}_{D}\dot{E}_{S}+\dot{E}_{R}} \tag{21}\]  
where \(\dot{\Gamma}\) is the reflection coefficient of the DUT; subscripts \"\(a\)\" and \"\(m\)\" refer to actual and measured value, respectively; \(\dot{E}\)--error terms of 1P VNA model.  
When moving from the classic 1P calibration, which assumes static measurements, i.e., fixed cables, to the case of dynamic process, no additional measuring equipment is required. It is assumed that each error term now depends not only on frequency, but also on the spatial coordinates of the measuring stand components--measuring antenna and antenna under test. In other words, the dynamic calibration allows one to determine terms of the VNA error model and perform an unique correction of the measured data depending on spatial coordinates of antennas. Since this technique implies data post-processing, the measurements associated with it can be carried out both before and after the main measurements of the DUT. The process for measuring the reflection of calibration standards should be the same as for DUT measuring, that is, process is repeated for the available OC, SC and ML standards, connected consistently to the measuring cable instead of the DUT.  
For example, the considered calibration can be applied in the far field antenna measurements jointly with the transmission coefficient normalization when measuring a reference antenna. The described process takes advantage of the unmodulated load reflection method and assumes ensuring the repeatability of the measurement conditions.
## 3 Results and Discussion
Therefore, in the specified range of \(a_{bl}/b_{bl}\), the maximum intensity of mixing of the mixture was achieved. The mixture moved from laminar to turbulent regime. The rate of mass transfer of the foam concrete mixture in the axial direction increased. Moreover, an increase in the parameter \(a_{bl}/b_{bl}\) led to an increase in power consumption. In the \(a_{bl}/b_{bl}\) range of 0.35-0.45, some stabilization of \(k_{N6}\) was observed. Therefore, the optimal value of the \(a_{bl}/b_{bl}\) parameter, which ensured the maximum homogeneity of the foam concrete mixture at optimal values of \(k_{N6}\), was in the range of \(a_{bl}/b_{bl}\) ratios from 0.35-0.45. Effect of the Inclination of Activator Blades on the Homogeneity of Foam Concrete Mixture and the Power Consumption of Mixer Drive (Stage 7)  
Earlier, this effect was studied in [25; 27]. To assess the influence of the inclination of activator blades relative to the plane perpendicular to the rotation axis of activator on the homogeneity of foam concrete mixture and the power spent on mixing the foam concrete mixture, a number of experiments were carried out. In the experiments, a cylindrical mixer with a conical part \(V=250\) L was used, and the rotation speed of the activator was 500 rpm. Blade activators with three flat blades were used. On each of the activators, the blades were installed radially and inclined at an angle \(\alpha_{l}=20^{\circ}\), \(30^{\circ}\), \(45^{\circ}\), \(50^{\circ}\), and \(60^{\circ}\) relative to the plane perpendicular to the axis of rotation of the mixer (for straight blades, \(\alpha_{l}=0^{\circ}\)). The study of the influence of the inclination angle of blades \(\alpha_{l}\) on the uniformity coefficient \(K_{chm}\) showed that the homogeneity of foam concrete mixture decreased with an increase in the inclination angle of blades (see Figure 17). This was due to a decrease in the rotation speed of the mixture in accordance with the intensity of mixing. With an increase in the inclination angle of blades \(\alpha_{l}\), the power \(k_{N7}\) spent on mixing decreased (Figure 18). Figure 5:4 Influence of the parameter \(a_{bl}/b_{bl}\) on the power factor \(k_{N6}\) (experimental data). From Figure 18, it follows that, in the case when the blades were inclined under an angle \(\alpha_{l}=45^{\circ}\) to the rotation plane of mixer, the power consumption coefficient was half in the turbulent flow region compared to the value for similar mixer blades with an angle of inclination \(\alpha_{l}=0^{\circ}\). At the same time, the effect on the homogeneity of the foam concrete mixture was minimal. This was confirmed by the results presented in Figure 5:5  
The obtained experimental data on the influence of the inclination angle of activator blades on the quality of foam concrete mixture and the efficiency of mixing show that the optimal inclination angle of blades was in the range \(\alpha_{l}=30\)-\(45^{\circ}\).
## 6 Evaluation Metrics of the Network Performance
### Delay  
The time for a signal to travel across the network from the UAV to SAR is called delay. Delay is a critical metric for assessing a communication network's performance. The concept intends to reduce the travel time between the UAV and SAR by allowing direct contact between the two. The processing, queuing, and transmission delays of a network link are all included in E2E delays. The E2E delay \(D_{En42 End}\) depends on processing delay \(D_{p}\)_processing_, transmission delay \(D_{t}\)_transmission_, and propagation delay \(D_{p}\)_propagation_, as well as the number of nodes \(N\).  
\[D_{E2E}=N\times(D_{p}\text{\emph{processing}}+D_{t}\text{\emph{transmission}}+D_{ p}\text{\emph{propagation}}) \tag{8}\]
## Appendix B  
In order to derive an expression for \(\mathbf{p}_{t}(x)=E[(\mathbf{c}_{t}(x)-\widehat{\mathbf{c}}_{t}(x))(\mathbf{c}_{t}-\widehat{ \mathbf{c}}_{t})]\), we first observe that the linear minimum variance estimate \(\widehat{\mathbf{c}}_{t}(x)=\sum_{i=1}^{t}\mathbf{g}_{i}^{T}(x)\widetilde{\mathbf{Y}}_{i}\) is the orthogonal projection of \(\mathbf{c}_{t}\) onto \(\widetilde{\mathbf{Y}}\), by the orthogonality principle. Therefore,  
\[E[\widehat{\mathbf{c}}_{t}(x)(\mathbf{c}_{t}-\widehat{\mathbf{c}}_{t})]=0. \tag{14}\]  
To simplify \(\widehat{\mathbf{c}}_{t}=\sum_{i=1}^{t}\mathbf{G}_{i}\widetilde{\mathbf{Y}}_{i}\), we can write it in terms of another equivalent BLUE  
\[\widehat{\mathbf{c}}_{t}=\sum_{i=1}^{t}\mathbf{A}_{i}\mathbf{Y}_{i}=\sum_{i=1}^{t}\mathbf{A} _{i}(\mathbf{W}_{i}\mathbf{c}_{i}+\mathbf{W}_{i}\mathbf{F}^{T}\mathbf{m}_{i}+\mathbf{v}_{t}) \tag{15}\]  
We also have that \(\mathbf{A}_{i}\mathbf{W}_{i}\mathbf{F}^{T}=\mathbf{0},i=1,2,\ldots,t\). Therefore, we obtain:  
\(\begin{split}&\mathbf{p}_{t}(x)=E[\mathbf{c}_{t}(x)(\mathbf{c}_{t}-\sum_{i=1} ^{t}\mathbf{A}_{i}(\mathbf{W}_{i}\mathbf{c}_{i}+\mathbf{W}_{i}\mathbf{F}^{T}\mathbf{m}_{i}+\mathbf{v}_{t}) )]\\ &\qquad\qquad\qquad=E[(Z_{0}(x)+\sum_{i=1}^{t}\mathbf{\varepsilon}_{ i}(x))(\mathbf{c}_{t}-\sum_{i=1}^{t}\mathbf{A}_{i}\mathbf{W}_{i}\mathbf{c}_{i}]\\ =E[(Z_{0}(x)+\sum_{i=1}^{t}\mathbf{\varepsilon}_{i}(x))(\mathbf{Z}_{0}+ \sum_{i=1}^{t}\mathbf{\varepsilon}_{i})]-\sum_{i=1}^{t}\mathbf{A}_{i}\mathbf{W}_{i}\mathbf{E} [(Z_{0}(x)+\sum_{i=1}^{t}\mathbf{\varepsilon}_{i}(x))(\mathbf{Z}_{0}+\sum_{i=1}^{t} \mathbf{\varepsilon}_{i})]\\ =(a+t)\mathbf{k}(x)-\sum_{i=1}^{t}\mathbf{A}_{i}\mathbf{W}_{i}(a+i)\mathbf{k}(x) \end{split}\) (16)  
Next, we replace \(\mathbf{k}(x)\) by \(\mathbf{K}\mathbf{K}^{-1}k(x)\)and use \(\mathbf{P}_{t}=[\mathbf{p}_{t}(x_{1}),\ldots,\mathbf{p}_{t}(x_{n})],\mathbf{K}=[\mathbf{k}(x_{1}),\ldots,\mathbf{k}(x_{n})]\) to obtain:  
\[\mathbf{p}_{t}(x)=((a+t)\mathbf{K}-\sum_{i=1}^{t}\mathbf{A}_{i}\mathbf{W}_{i}(a+i)\mathbf{K})\mathbf{K }^{-1}\mathbf{k}(x)=\mathbf{P}_{t}\mathbf{K}^{-1}\mathbf{k}(x) \tag{17}\]
## 3 Results
### Concentration Effect of Syr and Van for Regeneration of Trp from the Trp\({}^{\bullet+}\)
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{[**Syr**]} & \multicolumn{3}{c}{[**Van**]} \\ \hline  & **50 \(\upmu\)M** & **100 \(\upmu\)M** & **150 \(\upmu\)M** & **50 \(\upmu\)M** & **100 \(\upmu\)M** & **150 \(\upmu\)M** \\ \hline \(k_{\mathrm{obs}}/\mathrm{s}^{-1}\) & \(5.0\times 10^{3}\) & \(7.0\times 10^{3}\) & \(1.0\times 10^{4}\) & \(3.2\times 10^{3}\) & \(4.2\times 10^{3}\) & \(5.0\times 10^{3}\) \\ \hline \(k_{\mathrm{q}}/\mathrm{M}^{-1}\cdot\mathrm{s}^{-1}\) & \(4.7\times 10^{7}\) & \(4.3\times 10^{7}\) & \(4.9\times 10^{7}\) & \(1.1\times 10^{7}\) & \(1.5\times 10^{7}\) & \(1.6\times 10^{7}\) \\ \hline \(k_{\mathrm{q}}\) (average) & & \(4.6\times 10^{7}\) & & & \(1.4\times 10^{7}\) & \\ \hline \end{tabular}
\end{table} 
Table 1: Observed pseudo-first-order rate constants (\(k_{\mathrm{obs}}/\mathrm{s}^{-1}\)) for reduction of Trp\({}^{\bullet+}\) by \(\mathrm{Syr}\) and Van as measured at 560 nm. Figure 3: Concentration dependence of plant phenols in the regeneration of Trp from Trp\({}^{\bullet+}\). Kinetic traces of Trp\({}^{\bullet+}\) were recorded at 560 nm in the presence of 0, 50, 100, and 150 \(\upmu\)M (**A**) syringe acid (Syr) and (**B**) vanillic acid (Van) at pH 7.2. Thicker solid lines are derived from curve fitting. Effect of concentration of \(\mathrm{Syr}\) (**C**) and Van (**D**) on pseudo-first-order rate constant for regeneration of Trp from Trp\({}^{\bullet+}\) as calculated by radical decay at 560 nm. Furthermore, a significant pH dependency on Pyr and Van was observed as shown in Figure 4C. Under alkaline conditions, Pyr exhibits a stronger pH dependence than that of Van. This trend follows the deprotonation curve associated with the pKa value, allowing the determination of the proportion of each deprotonated group at different pH environments, as summarized in Table 2. As pH increases, the proportion of \(\varphi\)-O\({}^{2-}\) increases, enhancing the reducing ability of plant phenols and accelerating the reduction rate. Specifically, as the pH shifts from acidic to alkaline, the proportion of \(\varphi\)-O\({}^{-}\) in the environment exceeds 70%. Consequently, the bimolecular reaction rate of Pyr with Trp\({}^{\bullet+}\) increases from 4.8 \(\times\) 10\({}^{6}\) M\({}^{-1}\cdot\)s\({}^{-1}\) at pH 2 to 1.7 \(\times\) 10\({}^{8}\) M\({}^{-1}\cdot\)s\({}^{-1}\) at pH 10. Similarly, for Van, the reaction rate rises from 1.4 \(\times\) 10\({}^{6}\) M\({}^{-1}\cdot\)s\({}^{-1}\) at pH 2 to 4.6 \(\times\) 10\({}^{7}\) M\({}^{-1}\cdot\)s\({}^{-1}\) at pH 10.