esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Performance Evaluation
### Simulation Results
#### 5.2.5 Changes in Duty Cycle vs. Energy Consumption  
The basic performance of the protocols in terms of the energy consumption per node under different node densities with varying active radio periods is shown in Figure 11. The simulation was executed with a single-packet transmission from source to sink. As shown in the figures, the energy consumption increased for all protocols when the duty cycle per node increased. However, our protocol consumed less energy compared with existing protocols. As the duty cycle increased, the difference in performance increased proportionally.  
The simulation setup in Figure 11 slightly differs from the simulation setup in Figure 6. In Figure 6, the packet rate was 0.1, meaning a source node initiates a packet transmission after a 10 s interval. However, in Figure 11, the setup shows the basic performance. Hence, only a single packet was processed during the entire simulation. Thus, only one packet was transmitted from the source node. Consequently, even for the same duty cycle and with the same number of nodes, the energy consumption shown in Figure 6 differs from that in Figure 11.  
Moreover, the existing implemented protocols and our proposed protocols use the BoX-MAC protocol on the MAC layer. According to the MAC protocol mechanism, after the completion of any type of operation (such as data packet transmission and ACK sending), the nodes return to their previous duty-cycle schedule because, before performing the operations, the nodes cancel all their duty-cycle scheduled timers. Because the scheduled duty-cycle timer has a constant active time duration, the energy consumption graph shows a comparatively smoother linear growth with increasing active time duration (Figure 11). Owing to the MAC mechanism in our Castalia implementation, the duty cycle graph, which should be slightly curved, shows a smoother linear increase.
## 4 Vibroacoustic Analysis for Cabin Interior Noise  
### Frequency Limit for Calculation of Vibroacoustic Model: Modal Overlap Factor  
SEA assumes that the energy balance between each element is achieved by the resonance mode. The mode density is a factor indicating how many resonance modes participate in energy balance in the corresponding frequency section. The SEA equation is derived under the assumption that the value of mode density is significant. Therefore, SEA is suitable for analysis in the high-frequency range. The mode density is a value obtained by dividing the number of resonant modes by the corresponding frequency section and is defined as follows:  
\[n(f)=\frac{dN}{d\omega}. \tag{46}\]  
Estimation of mode density can be obtained experimentally, theoretically, and analytically. In this study, the modal frequencies of the front windshield, side windows, and cabin cavity were calculated using FEM, and the modal density was computed using Equation (19). The mode density of the front windshield, side windows, and cabin cavity is shown in Figure 12.  
Figure 11: (a) Three-dimensional PSD diagram (left window), (b) two-dimensional diagram at \(k_{x_{2}}=0\), and (c) PSD levels of total, compressible, and incompressible pressure for left window of HSM (reference pressure 20 uPa).  
Figure 12: Mode density of side windows, front windshield, and cavity.  
On the basis of the predicted modal density, the modal overlap factor (MOF) was computed. This parameter gives approximate frequency criteria according to which FEM or SEA is chosen as a vibroacoustic model, and it is defined as follows [27]:  
\[MOF=n(f)\cdot\Delta_{n}=n(f)\cdot\frac{\pi}{2}f\eta, \tag{47}\]  
where \(n(f)\) is the mode density, \(\Delta_{n}\) is the bandwidth of the modal oscillators, \(f\) is the center frequency, and \(\eta\) is the damping loss factor of the subsystem in the corresponding frequency band. When \(MOF<1\), a deterministic method such as FEM can be used, whereas, when \(MOF>1\), statistical methods such as SEA are more qualified [27].  
Figure 13 shows the MOF calculated using the modal density of the front windshield, side windows, and cavity.  
It can be seen that the frequency criterion of \(MOF=1\) was around 400 Hz. Therefore, in this study, to predict vehicle interior noise, the FEM was used in the frequency range of less than 400 Hz, and the SEA was used elsewhere.
# Internet of Things (IoT) and the Energy Sector
## 4 IoT in the Energy Sector
### Smart Cities  
Nowadays, the staggering rate of urbanization as well as overpopulation has brought many global concerns, such as air and water pollution [118], energy access, and environmental concerns. In this line, one of the main challenges is to provide the cities with clean, affordable and reliable energy sources. The recent developments in digital technologies have provided a driving force to apply smart, IoT based solutions for the existing problems in a smart city context [119]. Smart factories, smart homes, power plants, and farms in a city can be connected and the data about their energy
## 3 Results and Discussion
### Adsorption Mechanism Analysis  
The mechanism of O-MWCNTs' adsorption of Pb(II)/Cd(II) was determined using XPS [46; 47; 48]. Figure 7 shows the XPS spectra of the powdered samples of the fresh and spent O-MWCNT\({}_{\rm{S}}\). Figure 7a displays the fine spectra of the C 1s orbital for the spent O-MWCNT\({}_{\rm{S}}\). The peak of C-C, C-O-C, and O-C=O has a combining energy of 284.8, 286.0, and 289.3 eV, respectively, which clearly indicates that an oxygen-containing functional group is in the C-O-C and O-C=O states expected for the native oxide of MWCNTs. The figures presented in Figure 7b,c illustrate the distinctive peaks of C 1s of the spent O-MWCNT\({}_{\rm{S}}\). The combining energies of C-O-C and O-C=O are observed to be lower than those illustrated in Figure 7a. The observed decrease in binding energies implies that the oxygen-containing functional groups (C-O-C and O-C=O) reacted with Pb(II)/Cd(II). Figure 7d,e shows O-Pb and O-Cd spectra collected from the surface of the power sample. The combined energy of O-Pb and O-Cd is 534.5 eV and 535.1 eV, respectively. Figure 7f provides further evidence that the adsorption process is facilitated with the C-O and C=O groups present. Figure 7g shows that the Pb 4f binding energy is 138.9 eV and 143.8 eV. Similarly, the binding energy of Cd 3d is observed at 406.1 and 412.7 eV in Figure 7h. These findings suggest the successful adsorption of Pb(II)/Cd(II) onto O-MWCNT\({}_{\rm{S}}\). In contrast to the conventional Pb 4f profile, the binding energy shifts to slightly higher sites. In addition, the binding energies of these peaks are in agreement with those reported by [PERSON] et al. and [PERSON] et al. [49].  
Based on the research conducted on the basis of XPS, the adsorption mechanism of O-MWCNTs\({}_{\text{S}}\) may be inferred [50].  
MWCNTs + H2O2 \(\rightarrow\) MWCNTS-COO\({}^{-}\) + MWCNTS-O\({}^{-}\)  
2(-COO\({}^{-}\)) + Pb\({}^{2+}\) \(\leftrightarrow\) (-COO)2 Pb  
(-COO\({}^{-}\)) + (-OO\({}^{-}\)) + Pb\({}^{2+}\) \(\leftrightarrow\) (-COO)Pb(-O)  
2(-COO\({}^{-}\)) + Cd\({}^{2+}\) \(\leftrightarrow\) (-COO)2 Cd  
(-COO\({}^{-}\)) + (-O\({}^{-}\)) Cd\({}^{2+}\) \(\leftrightarrow\) (-COO)Cd(-O)
## 3 MALDI Non-Imaging MS Analysis of GSLs
### MALDI Non-Imaging MS Analysis of GSLs in the Healthy State
#### 3.4.2. GSLs from Human Brain Tissues
Utilizing an AXIMA-QIT (quadrupole ion trap)-TOF MS (Shimatzu, Japan), employing a nitrogen laser emitting at 337 nm and 2, 5 DHB as a matrix, and operating in the negative ion mode, mass spectra of normal human brain tissue were obtained (specifically gray matter from the hippocampus), with \(m/z\) 1544 (d18:1) and \(m/z\) 1572 (d20:1) for GM1, \(m/z\) 1544 (d18:1), \(m/z\) 1572 (d20:1), \(m/z\) 1857 (d18:1), and \(m/z\) 1885 (d20:1) for GD1a, \(m/z\) 1544 (d18:1), \(m/z\) 1572 (d20:1), \(m/z\) 1857 (d18:1), and \(m/z\) 1885 (d20:1) for GD1b, and \(m/z\) 1544 (d18:1), \(m/z\) 1572 (d20:1), \(m/z\) 1835 (d18:1), \(m/z\) 1885 (d20:1), and \(m/z\) 2198 (d20:1) for GT1b. While simple gangliosides (e.g., GM3) prevail in peripheral tissues [165], more than 90% of the adult human brain ganglioside content is shown to be represented by the few complex gangliosides (GM1, GD1a, GD1b, and GT1b), followed by very low amounts of GQ1, GM3, and GM2 [166,167]. The age-related decrease in ganglioside content with similar modifications in phospholipid and cholesterol levels [168; 169] could be associated with tissue atrophy, cell death, or the change in brain cytoarchitecture (e.g., loss of synapses, increased number of neuroglia, etc.). A specific alteration of the brain ganglioside profile occurs with aging in healthy humans, with a shift from complex to simple gangliosides: a relative decrease in GD1a and GM1 and a relative increase in GD1b, GM3, and GD3. This shift in the ganglioside pattern might reveal metabolic and/or cellular modifications [169; 170]. While the abundance of GM, GD, and GT species identified in normal adult brain tissues in the above-presented studies underlines their critical roles in maintaining brain health [154], the lesser-explored GQ and GP species are a peculiar finding and need further investigation in order to provide insights on the complex functions of gangliosides in the human brain. Nonetheless, the scarcity of normal human brain tissue samples hinders extensive exploration in this domain. In the presented protocols, each method for sample preparation was proposed for keeping GSLs as intact as possible, with sialic acid loss being an issue to be addressed by improved sample preparation procedures.
## 6 Conclusions  
This paper considered the sparse synthesis optimization of CCAAs. We aimed to turn off a specific number of antennas while reducing the sidelobe of CCAAs. First, we formulated an optimization problem and presented the solution space. Then, we proposed an EWOA method for solving the optimization problem. EWOA introduced hybrid solution initialization, crossover, and update methods. Finally, we conducted simulations to show the effectiveness of the proposed EWOA and the proposed improvement factors.  
Data curation, B.T. and J.X.; Supervision, L.C.; Writing--original draft, S.Y. and Y.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China [grant number 51675506], and the Foundation for Excellent Young Scholars of Jilin Province, China [grant number 20190103015 JH].  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline
**(DB)** & **AVE.** & **MAX.** & **MIN.** \\ \hline DA & \(-\)8.73719 & \(-\)8.39645 & \(-\)9.46009 \\ GWO & \(-\)9.05977 & \(-\)8.16812 & \(-\)9.57519 \\ SSA & \(-\)8.99624 & \(-\)8.71156 & \(-\)9.36257 \\ WOA & \(-\)9.90705 & \(-\)8.32748 & \(-\)9.47902 \\ SCA & \(-\)8.91788 & \(-\)8.3524 & \(-\)9.36407 \\ EWOA & \(-\)9.41423 & \(-\)9.15111 & \(-\)9.6363 \\ \hline \end{tabular}
\end{table}
Table 6: Statistical results of the objective values obtained by different approach in the case with 50 populations and 120 elements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**(DB)** & **AVE.** & **MAX.** & **MIN.** \\ \hline DA & \(-\)8.27269 & \(-\)7.7332 & \(-\)9.08744 \\ GWO & \(-\)8.68996 & \(-\)7.24128 & \(-\)9.26944 \\ SSA & \(-\)8.59402 & \(-\)7.72822 & \(-\)9.31861 \\ WOA & \(-\)8.67264 & \(-\)7.46379 & \(-\)9.38475 \\ SCA & \(-\)8.56307 & \(-\)7.73576 & \(-\)9.14937 \\ EWOA & \(-\)9.1513 & \(-\)8.67405 & \(-\)9.45595 \\ \hline \end{tabular}
\end{table}
Table 7: Statistical results of the objective values obtained by different approach in the case with 10 populations and 120 elements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**(DB)** & **AVE.** & **MAX.** & **MIN.** \\ \hline DA & \(-\)22.492 & \(-\)21.281 & \(-\)23.5388 \\ GWO & \(-\)22.7027 & \(-\)21.8035 & \(-\)23.5196 \\ SSA & \(-\)22.7403 & \(-\)22.0197 & \(-\)23.8178 \\ WOA & \(-\)22.7205 & \(-\)21.647 & \(-\)23.4629 \\ SCA & \(-\)20.1068 & \(-\)18.9223 & \(-\)22.2253 \\ EWOA & \(-\)23.0437 & \(-\)22.5879 & \(-\)23.6858 \\ \hline \end{tabular}
\end{table}
Table 5: Statistical results of the objective values obtained by different approach in the case with 10 populations and 440 elements.
# Constant-Beamwidth LCMV Beamformer with Rectangular Arrays
## 2 Signal and Array Model
Beamformer MetricsBeampattern \(\mathcal{B}\), as a function of beamformer \(\mathbf{h}\) and direction \(\theta\) (through steering vector \(\mathbf{d}_{\theta}\)) is given by  
\[\mathcal{B}(\mathbf{h},\mathbf{d}_{\theta})=\mathbf{h}^{\mathsf{H}}\mathbf{d} _{\theta}. \tag{5}\]  
Given the desired signal steering vector \(\mathbf{d}\), the white noise gain (WNG), desired signal distortion index (DSDI), and directivity factor (DF) are, respectively,  
\[\mathcal{W}(\mathbf{h},\mathbf{d})=\frac{\left|\mathbf{h}^{ \mathsf{H}}\mathbf{d}\right|^{2}}{\mathbf{h}^{\mathsf{H}}\mathbf{h}}, \tag{6a}\] \[v_{\mathsf{d}}(\mathbf{h},\mathbf{d})=\left|\mathbf{h}^{ \mathsf{H}}\mathbf{d}-1\right|^{2},\] (6b) \[\mathcal{D}(\mathbf{h},\mathbf{d})=\frac{\left|\mathbf{h}^{ \mathsf{H}}\mathbf{d}\right|^{2}}{\mathbf{h}^{\mathsf{H}}\mathbf{h}}, \tag{6c}\]  
where \(\mathbf{\Gamma}(\omega)\) is the spherical isotropic noise field coherence matrix [27]. Using the DSDI, the distortionless constraint can also be written as \(v_{\mathsf{d}}(\mathbf{h},\mathbf{d})=0\).
## 3 Materials and Methods
### Sorbent Phases  
The sorbent phases used for coating the BA\(\mu\)E devices were from several ACs and polymers. The novel four ACs were prepared from biomaterials waste, characterized, and provided by the Industrial Biotechnology Laboratory from Tiradentes University (Aracaju, Sergipe, Brazil). AC1 was obtained from coconut fiber (pH\({}_{\text{PZC}}\) 6.3, 1130 m\({}^{2}\) g\({}^{-1}\) (BET) surface area, <20 A pore size); AC2 was obtained from coffee residue (pH\({}_{\text{PZC}}\) 7.3, 1308 m\({}^{2}\) g\({}^{-1}\) (BET) surface area, <20 A pore size); AC3 was obtained from sugarcane chaff (pH\({}_{\text{PZC}}\) 5.9, 1185 m\({}^{2}\) g\({}^{-1}\) (BET) surface area, <20 A pore size); and AC4 was obtained from sugarcane bagasse (pH\({}_{\text{PZC}}\) 6.9, 791 m\({}^{2}\) g\({}^{-1}\) (BET) surface area, <20 A pore size). The polymeric sorbents used were Strata-X (SX) (polymer containing N-vinylpyrrolidone; 33 \(\upmu\)m particle size, 85 A pore size, 800 m\({}^{2}\) g\({}^{-1}\) surface area), Strata-CN (SCN) (reversed phase polymer containing ciano; particle size 55 \(\upmu\)m, 70 A pore size, surface area 500 m\({}^{2}\) g\({}^{-1}\)) and Strata SDB-L (SDVB) (reversed phase styrene-divinylbenzene polymer; particle size 100 \(\upmu\)m, 260 A pore size, 500 m\({}^{2}\) g\({}^{-1}\) surface area) from Phenomenx (Torrance, CA, USA); Onasis HLB (HLB) (reversed phase N-vinylpyrrolidone-divinylbenzene co-polymer; 30 \(\upmu\)m particle size, 80 A pore size, 830 m\({}^{2}\) g\({}^{-1}\) surface area and pH stability at 0-14) from Waters (Milford, MA, USA); ENVI-18 (C18) (reversed phase octadecyl silica polymer; 45 \(\upmu\)m particle size, 60 A pore size, 475 m\({}^{2}\) g\({}^{-1}\) surface area) from Supelco (Darmstadt, Germany); LiChroult EN (DVBM) (reversed phase ethylvinylbenzene-divinylbenzene co-polymer;particle size 40-120 \(\upmu\)m, 60 \(\AA\) pore size, surface area 1200 m\({}^{2}\) g\({}^{-1}\)) from Merck Millipore (Darmstadt, Germany).
## 2 Research Method
### Statistical Indicators  
In the present research, evaluation of the two databases was performed with the help of statistical indices of correlation coefficient (R\({}^{2}\)), efficiency coefficient (EF), agreement coefficient (IA), slope, normalized root mean square error (NRMSE), and BIAS in the MATLAB environment (MathWorks, MA, USA). The coefficient R\({}^{2}\) is calculated from Equation (1) [19]:  
\[R^{2}=\frac{\left[\sum_{i=1}^{n}\left(P_{i}-\overline{P}\right)\left(O_{i}- \overline{O}\right)\right]^{2}}{\sum_{i=1}^{n}\left(P_{i}-\overline{P}\right) ^{2}\left(O_{i}-\overline{O}\right)^{2}} \tag{1}\]  
where P\({}_{i}\), O\({}_{i}\), \(\overline{P}\), \(\overline{O}\), are, respectively, TRMM precipitation rate, the observational precipitation rate, the average TRMM precipitation, the average observational precipitation.  
Figure 1: The study area and geographical locations of synoptic stations.  
EF is a statistical indicator, having the most application in the comparison between TRMM precipitation data and observed precipitation. When EF = 1, the efficiency of TRMM satellite in the estimation of the real precipitation sounds appropriate. This indicator is computed by Equation (2) [20]:  
\[EF=1-\frac{\sum\limits_{i=1}^{n}{{{({O_{i}}-{P_{i}})}^{2}}}}{\sum\limits_{i=1}^{ n}{{{({O_{i}}-\overline{O})}^{2}}}}. \tag{2}\]  
The agreement coefficient (IA) designates the rate of conformity between the values of the two time series, and is obtained by Equation (3):  
\[IA=1-\frac{\sum\
olimits_{i=1}^{n}{{{({P_{i}}-{O_{i}})}^{2}}}}{\sum\
olimits_{ i=1}^{n}{{{({\left|{{P_{i}}-\overline{P}}\right|}+\left|{{O_{i}}-\overline{O}} \right|})}^{2}}}. \tag{3}\]  
In linear regression, the more the slope between TRMM and observational precipitation is close to 1, the more direct the relationship between the two variables would be. The slope is obtained from Equation (4):  
\[Slope=R\frac{\sigma_{P}}{\sigma_{O}} \tag{4}\]  
where R, \(\sigma_{P}\), and \(\sigma_{O}\) are, respectively, correlation coefficient between TRMM and observational precipitation, standard deviation of TRMM precipitation data, and standard deviation of observational precipitation data.  
The NRMSE represents the mean square error dimensionless, and it is calculated by Equation (5) [21]:  
\[NRMSE=\frac{1}{\overline{O}}\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{i=n}{{{({P_{ i}}-{O_{i}})}^{2}}}}} \tag{5}\]  
where n is the number of data. The BIAS coefficient, which determines the discrepancy between observational and TRMM data, is computed through Equation (6) [15]:  
\[BIAS=\frac{1}{n}{\sum\limits_{i=1}^{n}{{({P_{i}}-{O_{i}})}}}. \tag{6}\]  
It is necessary to be able to identify bias in results, since BIAS is the tendency of a statistic to overestimate or underestimate the parameter, such as TRMM precipitation. The BIAS index indicates the average error of the model in the overestimation or underestimation of observed values. A value of zero for this index indicates that the model was able to predict the observed values well. Positive and negative values also indicate overestimation and underestimation, respectively [20].
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 1 Introduction
### Mathematical Modeling of the Measuring System
\begin{array}{l}t(z+z_{s},\theta)=\left(\zeta_{x}-\zeta_{x}\right)\cos( \varphi)+\left(\zeta_{y}-\zeta_{y}\right)\sin(\varphi)=\left[r(z+z_{s}, \theta+\varphi)-r_{0}\right]\\ +\left[e_{cx}+z\tau_{cy}-e_{sx}+z_{s}\left(\tau_{cy}-\tau_{sy}\right)\right] \cos(\varphi)+\left[e_{cy}+z\tau_{cx}-e_{sy}+z_{s}\left(\tau_{cx}-\tau_{sx} \right)\right]\sin(\varphi).\end{array}  \tag{4}\]where, \([r(z+z_{s},\theta+\varphi)-r_{0}]\) is the cylindrical profile. Equation (4) indicates that output of the probe is a mixture of cylindrical profile and all kinds of error motions in measuring system, in which \([e_{cx}+z\tau_{cy}-e_{sx}]\), \([z_{s}(\tau_{cy}-\tau_{sy})]\), \([e_{cy}+z\tau_{cx}-e_{sy}]\), and \([z_{s}(\tau_{cx}-\tau_{sx})]\) are the mutual independent error motions. Therefore, the minimum number of the probes in order to fully eliminate the error motions is five [10]. From Figure 2 and Equation (4), the three-point roundness EST [3; 4; 5; 9] arranges three probes in one cross-section of the cylinder, and the error motions \([e_{cx}+z\tau_{cy}-e_{sx}]\) and \([e_{cy}+z\tau_{cx}-e_{sy}]\) are sensed by the probe, and can be canceled. If all error motions are expected to be eliminated, a few probes must be arranged in the axial direction to sense the error motions \([z_{s}(\tau_{cx}-\tau_{sx})]\) and \([z_{s}(\tau_{cx}-\tau_{sx})]\). The literature on the subject of in situ measurement of cylindricity is limited. [PERSON] [11] proposed the four-point EST for in situ measurement of cylindricity, which combines the concepts of two-point straightness and three-point roundness ESTs. The signals from the three probes were used to calculate radius deviations and out-of-roundness of each cross-sectional profile. Whereas the signals from the two probes placed at the same generatrix in the cylinder were employed to determine out-of-straightness of the generatrix. However, this method sidesteps the problem of accurately determining curved median line profile of the cylinder, although it was successfully verified on a dedicated measuring device by the experiments. Several valuable five-point ESTs for in situ measurement of cylindrical profile have been reported [8; 10; 12; 13]. They are clearly different in the arrangement of probes, error separation model, reconstruction method, adaptability to service environment, and accuracy of measuring and reconstructing. Related technical notes are presented in the following for comparing and evaluating pros and cons. Suggestions for best meets the requirements of modern manufacturing and the most prospective one for industrial applications will then be made.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 7 Challenges and Future Research Directions  
To better grasp how the occupation pattern behaves in spectrum bands, a channel white-space mapping may be helpful. As the authors of [90] demonstrated, distinct areas on the map (Figure 13) may have a varied spectrum behavior. Therefore, a data exploration study like this is essential to improve the PU arrival prediction and, consequently, enhance the spectrum handover. In addition, a white-space mapped area may be crucial in order to achieve an UAV flight path guided by spectrum occupancy.  
Although some studies [153; 154; 155; 156] have successfully employed Machine Learning algorithms to predict the PUs arrivals, most of them use simulated data. Even though simulated data have importance in proof-testing new approaches, real data are indispensable when integrating UAVs to CR. Likewise, these studies generally concentrate on prediction of one or few channels, although a large range of channels may be preferable on UAV context.  
\begin{table}
\begin{tabular}{|c|c|c|c|c|c|} \hline
**Publication** & **Q1** & **Q2** & **Q3** & **Q4** & **Q5** \\ \hline [PERSON] et al. [135] & & & & & \\ \hline [PERSON] et al. [136] & & & & & \\ \hline [PERSON] et al. [137] & & & & & \\ \hline [PERSON]. [PERSON] et al. [138] & & & & & \\ \hline [PERSON] et al. [139] & & & & & \\ \hline [PERSON] et al. [140] & & & & & \\ \hline [PERSON] et al. [141] & & & & & \\ \hline [PERSON] et al. [142] & & & & & \\ \hline [PERSON] et al. [143] & & & & & \\ \hline [PERSON] et al. [144] & & & & & \\ \hline [PERSON] et al. [145] & & & & & \\ \hline [PERSON] et al. [146] & & & & & \\ \hline [PERSON] et al. [147] & & & & & \\ \hline [PERSON] et al. [14] & & & & & \\ \hline [PERSON] et al. [148] & & & & & \\ \hline [PERSON] et al. [149] & & & & & \\ \hline [PERSON] et al. [150] & & & & & \\ \hline [PERSON] et al. [151] & & & & & \\ \hline [PERSON] et al. [152] & & & & & \\ \hline \end{tabular}
\end{table}
Table 2: _Cont._As noted before, the proportion of SDR devices presents as a major challenge. Although devices such as Hope's RF RFM22B may initially comply with this requirement, a device with a wider frequency range may be desirable for the use in UAV communication. In consonance with hardware improvements, to the extent of our knowledge, there has been no study concerning the software adjustments required when combining SDR and UAVs.