esa-sceva/satcom-chunk-collection huggingface dataset:
# FF-MR: A DoH-Encrypted DNS Covert Channel Detection Method Based on Feature Fusion
## 5 Experimental Evaluation
### Parameter Sensitivity Experiments  
FF-MR extracts the first \(n\) bytes above the TCP layer as the session representation, aiming to extract the TCP transmission features, TLS handshake features, and the local patterns of encrypted DNS covert channel during transmission, as shown in Figure 13. In addition, the TLS messages after Server Hello are encrypted. Therefore, the principle for selecting \(n\) is that the byte sequence of length \(n\) should include at least the Client Hello and Server Hello, which are plaintext messages in the TLS handshake stage, with as many TCP messages as possible on top of that.  
We count the TCP and TLS layer bytes in the messages before Server Hello in a session. The distribution is shown in Figure 14, and the size of the bytes is mainly within 5000. According to the previous research on byte selection in the field of deep neural network traffic detection and identification, the number of bytes is usually taken to the power of two. Therefore, within 5000 bytes, \(n\) is selected as 512, 1024, 2048, and 4096 bytes. In addition, we also select 8192 bytes in order to minimize information loss. The results are shown in Figure 15.  
As shown in Figure 15a,b, the results with byte sizes less than 4096 are close because 512 bytes already contain the Client Hello message, which can achieve high F1-Score, precision, and recall. When \(n>4096\), the overall performance decreases significantly due to the excessive zero padding at a uniform length of 8192 bytes. Figure 15b focuses on the identification of the encrypted DNS covert channel traffic generated by iodine and dnscat2. The use of \(n=\)1024 gives the best results; therefore, 1024 is chosen as the value of \(n\).  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline  & **Metrics** & **1D-CNN** & **2D-CNN** & **1D-Resnet** & **2D-Resnet** & **MHA-Resnet** \\ \hline \multirow{3}{*}{benign\_DoH} & Precision & 0.9983 & 0.9981 & **0.9994** & 0.9989 & 0.999 \\  & Recall & 0.9929 & 0.9926 & 0.9987 & 0.9952 & **0.9995** \\  & F1-Score & 0.9956 & 0.9953 & 0.999 & 0.997 & **0.9992** \\ \hline \multirow{3}{*}{non\_DoH} & Precision & 0.9996 & 0.9996 & **0.9999** & 0.9997 & **0.9999** \\  & Recall & 0.9999 & 0.9999 & **1.0000** & 0.9999 & 0.9999 \\ \cline{1-1}  & F1-Score & 0.9997 & 0.9997 & **0.9999** & 0.9998 & **0.9999** \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 13: TLS handshake and encrypted messages transmission.
## 2 Materials and Methods
### Data Collection
#### 2.5.1 Questionnaire Survey  
The questionnaire surveys were administered to a total of 250 farmers from 10 villages. In other words, twenty-five farmers per village were interviewed. The structured questionnaires contained a range of items to assess farmers' knowledge, attitudes, and practices related to drought management in cattle farming. Each interview took no more than 20 min depending on the discussion with the farmer.
## 3 Results
### Classification Accuracy Evaluation  
#### 3.4.1 Data Set Split and Experiment Settings  
All experiments were conducted on NVIDIA workstations. The specific experimental setup and the deep learning framework used are shown in Table 1.  
In the two sets of experiments classified by CNNs, we divided the data set and investigated the changes in classification accuracy under different training set proportions. The settings of data set are represented in Table 2. And Table 3 shows the number of various geometric transformation in each class. In order to ensure the reliability of the data, each set of experiments was repeated three times.  
\begin{table}
\begin{tabular}{c c} \hline
**Hardware Platform** & **Parameter Name** \\ \hline CPU & E5-2068 v3 (2.50 GHz 2 cores) \\ Memory & 256 GB \\ GPU & NVIDIA GeForce RTX 2080 Ti \\ Memory & 11.0 GB \\ Software Platforms & Pycharm 2020.03 \\ OS & Windows 10 \\ DL Framework & Pytorch \\ CUDA & 11.1 \\ \hline \end{tabular}
\end{table}
Table 1: Workstation configurations.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Data Set Setting** & **Training Set Ratio** & **Test Set Ratio** & **No. Per Class for NWPU 45** \\ \hline  & & & Training & Test \\ (1) & 20\% & 80\% & 140 & 560 \\ (2) & 40\% & 60\% & 280 & 420 \\ (3) & 60\% & 40\% & 420 & 280 \\ \hline \end{tabular}
\end{table}
Table 2: The settings of the data set.  
Figure 7: Generation results of GANs and our method.
## 4 Stability Evaluation
### Result Analysis
#### 4.3.3 Volume Loss Rate  
The volume shrinkage rate is an important indicator for measuring the volume deformation of salt caverns. Figure 14 shows the curves of the volume shrinkage rates of deep and shallow salt caverns varying with time under different internal pressures. Seen from the figure, as the operation time increases, the volume shrinkage rate gradually increases, which indicates that the deformation of the salt cavern becomes larger and the volume loss becomes more severe. With the increase in the operating pressure, the volume shrinkage rate of the salt cavern gradually decreases, and the cavern tends to be more stable. In addition, under the same pressure, the volume shrinkage rate of the deep cavern is significantly larger than that of the shallow cavern, with a difference of about 3%, which also reflects the characteristic of large deformation in the deep cavern. The figure also shows that after 30 years of operation at an internal pressure of 10.5 MPa, the volume shrinkage rates of the shallow and deep caverns are 33.7% and 36.9%, respectively, both exceeding the allowable limit of 30%. This indicates that both salt caverns face a risk of deformation instability under this pressure. However, after 30 years of operation at an internal pressure  
Figure 12: Distribution diagrams of plastic zones in surrounding rock under different internal pressures. (**a**) 10.5 MPa; (**b**) 12 MPa; (**c**) 25.5 MPa; (**d**) 29 MPa.  
of 12 MPa, the volume shrinkage rates of the shallow and deep caverns are 27.9% and 31.0%, respectively, with an average of 29.5%. Compared with the internal pressure of 10.5 MPa, the volume shrinkage rate is significantly reduced, and the stability of the salt caverns is greatly improved.  
Figure 14: Time-dependent volumetric shrinkage rate curves of deep and shallow cavities under different internal pressures.  
Figure 13: Volume ratio–time curves of plastic zones in surrounding rock of salt caverns under different internal pressures.
# Co-Processing Parallel Computation for Distributed Optical Fiber Vibration Sensing
###### Abstract  
Rapid data processing is crucial for distributed optical fiber vibration sensing systems based on a phase-sensitive optical time domain reflectometer (\(\Phi\)-OTDR) due to the huge amount of continuously refreshed sensing data. The vibration sensing principle is analyzed to study the data flow of Rayleigh backscattered light among the different processing units. A field-programmable gate array (FPGA) is first chosen to synchronously implement pulse modulation, data acquisition and transmission in parallel. Due to the parallelism characteristics of numerous independent algorithm kernels, graphics processing units (GPU) can be used to execute the same computation instruction by the allocation of multiple threads. As a conventional data processing method for the sensing system, a differential accumulation algorithm using co-processing parallel computation is verified with a time of 1.6 \(\upmu\)s spent of the GPU, which is 21,250 times faster than a central processing unit (CPU) for a 2020 m length of optical fiber. Moreover, the cooperation processes of the CPU and GPU are realized for the spectrum analysis, which could shorten substantially the time of fast Fourier transform analysis processing. The combination of FPGA, CPU and GPU can largely enhance the capacity of data acquisition and processing, and improve the real-time performance of distributed optical fiber vibration sensing systems.  
distributed optical fiber sensor; \(\Phi\)-OTDR; GPU; parallel computation; real-time performance +
Footnote †: journal: Applied Sciences
## 3 Results
### Comparison of Triterpenoids between Syzygium formosum and Centella asiatica Leaves  
_Centella asiatica_ is a herbaceous medicinal plant recognized as an important drug by the Indian Herbal Pharmacopoeia, European Pharmacopoeia, Pharmacopoeia of the People's Republic of China, and German Homoepathic Pharmacopoeia [25]. It has been used to relieve skin rashes, heal wounds, and south itching. The functional molecule of _Centella asiatica_ has been reported the triterpenoids, particularly the asiaticosides. To compare the triterpenoid profiles and contents, _C. asiatica_ leaves from Indonesia and Korea were analyzed and compared to _S. formosum_ leaves.  
While nine triterpenoid compounds were found in the ethanolic extracts of _S. formosum_ leaves, only asiatic acid and macdessic acid were found in _C. asiatica_ samples from both Indonesia and Korea.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Triterpenoids**} & \multicolumn{2}{c}{_Syzygium formosum_} & \multicolumn{2}{c}{_Centella asiatica_} \\ \cline{2-5}  & **Phu Tho** & **Ha Noi** & **Indonesia** & **Korea** \\ \hline Madecassic acid & 0.24 \(\pm\) 0.02 c & 0.41 \(\pm\) 0.01 b & 2.02 \(\pm\) 0.03 a & 0.53 \(\pm\) 0.11 b \\ Asiatic acid & 2.78 \(\pm\) 0.23 c & 8.59 \(\pm\) 0.48 a & 3.83 \(\pm\) 0.41 b & 0.81 \(\pm\) 0.10 d \\ Hederagenin & 0.02 \(\pm\) 0.00 b & 0.05 \(\pm\) 0.00 a & ND & ND \\ Maslinic acid & 0.89 \(\pm\) 0.08 b & 1.26 \(\pm\) 0.08 a & ND & ND \\ Corosolic acid & 3.19 \(\pm\) 0.18 b & 3.91 \(\pm\) 0.45 a & ND & ND \\ Betulin & 0.11 \(\pm\) 0.00 b & 0.15 \(\pm\) 0.02 a & ND & ND \\ Betulinic acid & 6.14 \(\pm\) 0.49 a & 3.58 \(\pm\) 0.23 b & ND & ND \\ Oleanolic acid & 0.19 \(\pm\) 0.04 b & 0.27 \(\pm\) 0.02 a & ND & ND \\ Ursolic acid & 0.62\(\pm\) 0.11 b & 0.78 \(\pm\) 0.15 a & ND & ND \\ \hline Total & 14.18 \(\pm\) 1.15 b & 19.00 \(\pm\) 1.51 a & 5.85 \(\pm\) 0.44 c & 1.34 \(\pm\) 0.21 d \\ \hline \hline \end{tabular} ND: No detection. a, b, c, d means followed by the different letters in a row are significantly different at \(p\) < 0.05. Values are expressed as a mean \(\pm\) standard deviation.  
\end{table}
Table 4: Triterpenoid contents (mg/g dry leaf) in _Syzygium formosum_ and _Centella asiatica_ leaves.  
The results showed that the triterpenoid content in Korea _C. asiatica_ was significantly lower than in other countries. The concentration of masdecassic acid was 0.53 \(\pm\) 0.11 mg/g dry leaf that was similar or slightly higher than that observed in Vietnam samples. However, the asiatic acid concentration in the Korean sample was less than 1.0 mg/mL, showing only 9-30% of other samples from Vietnam and Indonesia. The madecassic acid content in Indonesia samples were 5- to 10-fold higher than that of other countries. Asiatic acid concentration was also higher than the samples from Korea and Phu Tho region, albeit no other triterpenoids were found including their hexose or pentose glycoconjugates (Table 4).
## 5 Experiment and Results
### Evaluation of Level 1  
In this level, we proposed an algorithm to improve the efficiency of traffic lights at intersections. To evaluate our proposed algorithm for a smart traffic light (STL), we compare our work with ITMS [67]. ITMS also aims to improve the efficiency of a traffic light at an intersection by using differential time-slots for congested streets. Since STL also uses a similar approach, in our experiments we compared both algorithms (i.e., ITMS and STL) with a traditional traffic light (i.e., a fixed time-slot for each street). The main difference between STL and ITMS is that STL periodically (after every 10 s) checks the congestion rate on each street of the intersection, whereas ITMS checks only once and then opens the street with the maximum number of vehicles to pass all the vehicles. However, in both algorithms there is a maximum waiting limit for other signals that should not be exceeded.  
We used MATLAB for simulations to evaluate the algorithms (STL, ITMS and traditional) for the average waiting time and number of vehicles serviced for each street. We used same parameters during simulation as were used in the ITMS experiments. These were as follows:  
* The total evaluation time was 1 h (3600 s).
* The number of streets at the intersection was 4.
* The inter-arrival time for vehicles on the first street ranged between 1 and 20 s per vehicle. This implies that the traffic congestion on the road decreases with the increase in this value.
* The inter-arrival time for vehicles on the second, third, and fourth streets was fixed at 30 s per vehicle. This implies that these streets were not congested.
* The time to get ready for an orange light was 2 s.
* The maximum waiting time for any signal was 200 s.
* The maximum opening time was 60 s (Green light).
* The minimum opening time on the green light was 5 s (in case all cars had passed).
* The street without vehicles did not open at all.
## Appendix A  
Before proposition 1 is put forward, we discuss the prerequisite of VB-TPHD filter that the measurement noise covariance is stochastic with the independent dynamic models and the survival probability along with detection probability are also independent of the covariance, \(p_{s,k}\left(\overline{X}\right)=\ p_{s,k}\big{(}x^{i-1}\big{)}\), \(p_{D,k}\big{(}\overline{X}\big{)}=\ p_{D,k}\big{(}x^{i}\big{)}\). Therefore, according to [29], there is the equation as follows:  
\[f\Big{(}x^{i},R^{i}|x^{i-1},R^{i-1}\Big{)}=\ f\Big{(}x^{i}|x^{i-1}\Big{)}g \Big{(}R^{i}|R^{i-1}\Big{)} \tag{1}\]  
Connected with Equation (12), the surviving extended trajectory PHD can be written as  
\[\begin{split} D_{\xi_{k}}\Big{(}t,x^{1:i},R^{1:i}\Big{)}& =p_{s,k}\big{(}x^{i-1}\big{)}f\Big{(}x^{i},R^{i}|x^{i-1},R^{i-1} \Big{)}D_{\pi_{k-1}}\Big{(}t,x^{1:i-1},R^{1:i-1}\Big{)}\delta_{N_{k}}[t]\\ &\quad=p_{s,k}\big{(}\overline{X}\big{)}f\Big{(}x^{i}|x^{i-1} \Big{)}g\Big{(}R^{i}|R^{i-1}\Big{)}D_{\pi_{k-1}}\Big{(}t,x^{1:i-1},R^{1:i-1} \Big{)}\delta_{N_{k}}[t],\\ &\quad=p_{s,k}\big{(}x^{i-1}\big{)}\cdot f\Big{(}x^{i}|x^{i-1} \Big{)}g\Big{(}R^{i}|R^{i-1}\Big{)}D_{\pi_{k-1}}\Big{(}t,x^{1:i-1},R^{1:i-1} \Big{)}\delta_{N_{k}}[t],\end{split} \tag{2}\]  
where \(f(\cdot)\) and \(g(\cdot)\) denote state transition density as well as noise covariance transition density. Obviously, the newborn extended trajectory PHD \(D_{\mathcal{T}_{k}}\Big{(}t,x^{1:i},R^{1:i}\Big{)}\) follows by Equation (11):  
\[D_{\mathcal{T}_{k}}\big{(}\overline{X}\big{)} =D_{\mathcal{T}_{k}}\Big{(}t,x^{1:i},R^{1:i}\Big{)}=D_{\mathcal{T }}\Big{(}t,x^{1:i},R^{1:i}\Big{)}\delta_{k}[t], \tag{3}\] \[D_{\omega_{1}}(\overline{X}) =D_{\gamma}\Big{(}t,x^{1:i},R^{1:i}\Big{)}\delta_{k}[t]+p_{s,k}(x^ {i-1})\cdot f(x^{i}|x^{i-1})g(R^{i}|R^{i-1})D_{\pi_{k-1}}\Big{(}t,x^{1:i-1},R^ {1:i-1}\Big{)}\delta_{N_{k}}[t] \tag{4}\]Therefore, the whole extended trajectory PHD consists of the surviving extended trajectory PHD and the newborn extended trajectory PHD.
## 2 Materials and Methods
### Proposed CO\({}_{2}\) Sensor Node  
#### 2.3.1 Design Overview  
The proposed wireless sensor node comprises four units; an air-quality sensor, a processor, a wireless transceiver, and a battery charger, as shown in Figure 2. The CO\({}_{2}\) air-quality sensor is used to sense indoor CO\({}_{2}\) concentration. The main component is a microcontroller to be used for data acquisition and as a main processing unit. The acquired CO\({}_{2}\) data will be recorded and sent to the cloud over an IoT system using a Wi-Fi unit. A rechargeable power storage unit is applied as the main sensor node power source. It stores power from external energy sources (wall adapter power supply, ambient energy) and provides a regulated voltage source to the sensor node through a voltage regulator unit.  
The sensor node is developed considering the requirements for indoor air-quality monitoring applications. It is designed in a compact size to fit and to be easy to install in limited space. The commercial off-the-shelf (COTS) components available in the market and low-power consumption components are chosen to meet low-cost development and reduce the sensor node's energy consumption, respectively. The low-power mode features are applied using a low-power algorithm to minimize total energy consumption during CO\({}_{2}\) data acquisition and processing. In addition to that, to reduce overall sensor node power consumption, the cloud storage integrated with the IoT system is utilized to store the acquired CO\({}_{2}\) data. During sensor-node active mode (data sensing, processing, transmitting, and logging), the cloud storage system consumes less energy than data storage implemented with a commercial data logger (hardware). With a low-power strategy, the developed sensor node has the potential to integrate with a micro energy harvester as a power source in the further development toward an autonomous CO\({}_{2}\) sensor node. Furthermore, integrating sensor nodes with IoT systems provides indoor CO\({}_{2}\) concentration levels that can be recorded and monitored in real time, are continuous and reliable, and are remote anytime and anywhere. Thus, the indoor CO\({}_{2}\) concentration data will be useful as a strategy tool to minimize the risk of indoor SARS-CoV-2 airborne transmission.
## 3 Analysis of Optical OFDM for Underwater Communications
### Aco-Ofdm  
ACO-OFDM is proposed for optical wireless communications, which can totally avoid the zero clipping distortion [22]. By using only the odd frequency subcarriers modulating data and leaving the even subcarriers blank, a bipolar signal is generated, as shown in Figure 6. Although zero clipping affects the ACO-OFDM signal, the interference caused by the zero clipping only happens on the even subcarriers. Therefore, no extra DC bias is needed. However, due to the zero clipping, the power of the data modulated using ACO-OFDM is reduced by half [22].  
Similar to the DCO-OFDM, we can derive the pdf of the ACO-OFDM signal, which is represented as:  
Figure 6: Block diagram of the ACO-OFDM transmitter [35].  
\[f_{\rm A}(x)= \frac{1}{\sqrt{\pi\sigma_{x}^{2}}}\exp\left(\frac{-x^{2}}{\sigma_{x} ^{2}}\right)\Pi(x,P_{\rm m}) \tag{17}\] \[+\frac{1}{2}\delta(x)+\frac{1}{2}\mathrm{erfc}\left(\frac{P_{\rm m }}{\sqrt{\sigma_{x}^{2}}}\right)\delta(x-P_{\rm m}).\]  
Figure 7 shows the simulation and analytical results of the ACO-OFDM signal's pdf. From the results, a larger \(\beta\) can introduce more peak power clipped distortions, as well as a higher SNR. Thus, there is a compromise of \(\beta\). Based on the [PERSON] theorem, the clipping coefficient of ACO-OFDM can be obtained by:  
\[\alpha_{\rm A}=\frac{1}{2}-\frac{1}{2}\mathrm{erfc}\left(\frac{P_{\rm m}}{ \sqrt{2\sigma_{x}^{2}}}\right). \tag{18}\]  
Due to the peak transmitted power constraint, the variance of clipping noise of ACO-OFDM can be calculated by:  
\[\sigma_{\rm A}^{2} =\int_{P_{\rm m}}^{\infty}(x-P_{\rm m})^{2}\cdot f_{\rm A}(x)dx \tag{19}\] \[=\frac{1}{2}\left(P_{\rm m}^{2}+\sigma_{x}^{2}\right)\mathrm{erfc }\left(\frac{P_{\rm m}^{2}}{2\sigma_{x}^{2}}\right)-\frac{1}{2}\sigma_{x}P_{ \rm m}\exp\left(\frac{-P_{\rm m}^{2}}{2\sigma_{x}^{2}}\right).\]  
The received SNR using ACO-OFDM can be calculated by:  
\[\gamma_{\rm A}(d,\beta)=\frac{(\bar{h}h_{o}(d)\beta\alpha_{\rm A})^{2}\sigma_ {x}^{2}}{2N((\bar{h}h^{2}_{o}(d)+\sigma_{h}^{2})\sigma_{\rm A}^{2}+\sigma_{h} ^{2})}. \tag{20}\]  
Then, the average SNR can be calculated by:  
\[\bar{\gamma}_{\rm A}(d,\beta)=\int_{0}^{\infty}\gamma_{\rm A}(d,\beta)f_{\bar {h}}(\bar{h})d\bar{h} \tag{21}\]
## 2 Presentation of Components, Data Acquisition Methodology and the System  
The structure of the analysis and EEG signal classification system presented in this paper consists of many components. A raw EEG signal (raw data) was registered with Emotiv EPOC Flex Gel. The acquisition of measurements was performed with dedicated EmotivPRO software. Figure 1 provides more information on how the classification system works, it helps to understand its features and structure. Figure 2 shows the electrodes configuration in the set. It also shows the research environment. Here can be seen a participant in the study, a program (EmotivPRO) running on the computer, and the vehicle constructed by authors based on LEGO EV3 cube.  
The electrodes in Emotiv EPOC Flex Gel were named in compliance with the following channels: Cz, Fz, Fp1, F7, F3, FC1, C3, FC5, FT9, T7, TP9, CP5, CP1, P3, P7, O1, Pz, Oz, O2, P8, P4, CP2, CP6, TP10, FC6, C4, FC2, F4, F8 and Fp2. Table 1 shows the names of the particular channels, which are presented on visualization from Figure 2.  
Two persons took part in the investigation, who were subjected to a few hundred tests. The signals were registered at rest and giving order to move forward, backward, to the right and to the left. Being concentrated is a significant element determining the test results. A person subjected to the test should be in a state of deep concentration, otherwise the course of the tests will surely be disturbed and thus their final result will be incorrect. After collecting a large number of measurements, their further processing and classification using various methods was possible, which is described in detail in Section 2.1.
## 4 Discussion
### Interpretation of SIF Retrievals  
Thus far, this discussion has focused on the influence of various instrument attributes and retrieval conditions on the potential quality of SIF retrievals. However, it is important to consider that once SIF retrievals are acquired with sufficient accuracy, additional information will aid in their meaningful interpretation. A SIF signal in a remote sensing observation arises because of a complex integration of many factors in the sampled area. For example, the coverage and composition of vegetation are major determining factors in the total SIF signal per pixel. The high resolution (\(\sim\)3 cm) RGB camera integrated into G-LiHT could certainly provide estimates of vegetation cover (Figure 23), and has already shown promise in identifying some vegetation species in work currently in progress. In addition, hyperspectral data from G-LiHT's imaging spectroscopy could provide spectral un-mixing, if appropriate end-members for local species can be derived from coincident ground measurements or pre-existing studies.  
Vegetation condition, in terms of its fluorescent light yield at the time of overpass, and the relative action of photosystems I and II, mediates the SIF signal resulting from a particular coverage and composition. The respective action of the photosystems can be expected to co-vary with phenology and diurnal cycle, as well as many potential vegetation health conditions and underlying stressors that are of ecological interest. However, to study particular influences on SIF variation, or to infer vegetation condition from SIF retrievals, repeated measurements may be required to establish and disentangle background diurnal and phenological cycles. Static platforms, overhead trans, or unmanned autonomous vehicles (UAVs) can be used to facilitate repeated observations of a target, and aid in the interpretation of airborne and satellite observations of SIF.  
Vegetation structure will also influence the observed SIF in a retrieval. For example, structurally convoluted forests will show the effects of complex patterns of reabsorption and reemission. The aspects of SIF signal that can be attributed to structure can also be expected to be highly dependent on view-angle. The coincident lidar observations from G-LiHT, supported by the high-quality inertial navigation system and Global Navigation Satellite System may provide some insight into the vegetation structure and view-angle for FIREFLY SIF retrievals (Figure 18). However, it is likely that the most useful insight into the dependence of airborne SIF retrievals on vegetation structure, and its sensitivity to view angle, will arise from ongoing work to simulate SIF with ray-tracing in complex vegetation.