esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Privacy-Preserving Deep Learning (PPDL)
#### 3.1.7 Works Based on Instance-Encoding Technique  
A more recent approach to preserving privacy in deep learning is to encode the training instances before feeding them to a deep learning model. [PERSON] et al. propose InstaHide [64], which is an instance encoding scheme for private learning. They assert that this scheme is efficient for distributed learning in federated learning, despite a reduction in accuracy. InstaHide [64] comprises two steps: the first involves mixing the images by taking the private image's linear combination with randomly selected images from the private set (Inside dataset) or a public dataset (Cross dataset). The second step uses a random pattern of sign flips on the mixed image. [PERSON] et al. demonstrated in their work [65] that InstaHide fails to maintain privacy, as it does not comply with recognized privacy notions and is susceptible to privacy attacks. Moreover, they developed an attack strategy that successfully recovered source images with high similarity, thereby undermining the privacy protection offered by InstaHide.  
Generally, InstaHide [64] is the work most closely related to ours, as it introduces the concept of encoding training instances before inputting them into a machine learning model, and it also proposes preserving privacy within a collaborative framework. However, as the authors themselves acknowledge, using the Mixup technique to blend instances with other instances does not guarantee privacy [64]. Additionally, random sign-flipping does not offer inherent privacy protection [65]. In contrast, our work efficiently preserves both the privacy and utility of data within collaborative deep learning frameworks.  
In summary, the reviewed literature highlights significant challenges including the trade-off between privacy and utility, threat model assumptions, encryption, and communication and computation overheads, as well as the inability to handle complex deep learning models. These challenges underscore the need for a framework that safeguards privacy while addressing these issues. Our work steps in to address this gap by protecting the privacy of the training data, the model, and the prediction. Furthermore, compared to cryptographic methods like Multi-Party Computation (MPC) and Homomorphic Encryption (HE), our use of Restricted Boltzmann Machines to generate encodings introduces a significantly lower computational overhead.
# Input Shaping Control of a Flexible Structure for Rest-to-Rest and Non-Rest-to-Rest Maneuvers
## Appendix B Dynamic System Constants  
In Equation (2), the mass matrix has the form  
\[\mathbf{H}=\left[\begin{array}{cccc}M&0&0&0\\ 0&M&0&0\\ 0&0&H_{33}&H_{34}\\ 0&0&H_{34}&H_{44}\end{array}\right] \tag{1}\]where  
\[H_{33} = \frac{1}{4M}\Big{[}m_{1}m_{2}(l_{1}^{2}l_{2}^{2}+2l_{1}l_{2}\cos\theta _{2})\Big{]}+(i_{1}+i_{2}) \tag{11}\] \[H_{34} = \frac{1}{4M}\Big{[}m_{1}m_{2}(l_{2}^{2}+l_{1}l_{2}\cos\theta_{2}) \Big{]}+i_{2}\] (12) \[H_{44} = \frac{1}{4M}\Big{[}m_{1}m_{2}l_{2}^{2}\Big{]}+i_{2} \tag{13}\]  
The centrifugal terms matrix \(\mathbf{C}\) has the form  
\[\mathbf{C}=\left[\begin{array}{cccc}0&0&0&0\\ 0&0&0&0\\ 0&0&C_{33}&C_{33}-C_{43}\\ 0&0&C_{43}&0\end{array}\right] \tag{14}\]  
where  
\[C_{33} = -\frac{1}{4M}\big{(}m_{1}m_{2}l_{1}l_{2}\dot{\theta}_{2}\sin \theta_{2}\big{)} \tag{15}\] \[C_{43} = \frac{1}{4M}\big{(}m_{1}m_{2}l_{1}l_{2}\dot{\theta}_{1}\sin \theta_{2}\big{)} \tag{16}\]  
The force matrix includes the input torque \(u(t)\) applied at the spacecraft bus, link \(\mathcal{L}_{1}\), and the force of the torsional spring at point \(A_{12}\)  
\[\mathbf{G}=\left[\begin{array}{c}0\\ 0\\ u\\ -k\theta_{2}\end{array}\right] \tag{17}\]  
Note that \(m_{1}\), \(m_{2}\), \(l_{1}\), \(l_{2}\), \(i_{1}\), and \(i_{2}\) designate the mass of the spacecraft bus, mass of the solar panel, side length of the bus, length of the solar panel, and moments of inertia of the two bodies, respectively. \(M\) denotes the total mass of the system, \(M=m_{1}+m_{2}\). Moreover, if the thickness of the solar panel is represented by the term \(l_{3}\), then \(i_{1}\) and \(i_{2}\) take the following form:  
\[i_{1}=\frac{1}{6}m_{1}l_{1}^{2}\qquad i_{2}=\frac{1}{12}m_{2}\Big{(}l_{2}^{2}+ l_{3}^{2}\Big  
Note that \(c_{1}\), \(c_{3}\), \(b_{2}\) and \(b_{4}\) can be written as follows:  
\[c_{1} =\frac{12k(4l_{2}^{2}m_{1}+l_{2}^{2}m_{2}+l_{3}^{2}m_{1}+l_{3}^{2}m_ {2}+3l_{1}l_{2}m_{1})}{l_{1}^{2}m_{1}(8l_{2}^{2}m_{1}+5l_{2}^{2}m_{2}+2l_{3}^{2}m _{1}+5l_{3}^{2}m_{2})} \tag{11}\] \[b_{2} =\frac{12(4l_{2}^{2}m_{1}+l_{2}^{2}m_{2}+l_{3}^{2}m_{1}+l_{3}^{2} m_{2})}{l_{1}^{2}m_{1}(8l_{2}^{2}m_{1}+5l_{2}^{2}m_{2}+2l_{3}^{2}m_{1}+5l_{3}^{2}m _{2})}\] (12) \[c_{3}^{2} =\frac{12k(2l_{1}^{2}m_{1}^{2}+l_{2}^{2}m_{2}^{2}+l_{3}^{2}m_{2}^ {2}+5l_{1}^{2}m_{1}m_{2}+4l_{2}^{2}m_{1}m_{2}+l_{3}^{2}m_{1}m_{2}+6l_{1}l_{2}m_ {1}m_{2})}{(l_{1}^{2}m_{1}m_{2}(8l_{2}^{2}m_{1}+5l_{2}^{2}m_{2}+2l_{3}^{2}m_{1} +5l_{3}^{2}m_{2})}\] (13) \[b_{4} =\frac{-12(l_{2}^{2}m_{2}^{2}+l_{3}^{2}m_{2}^{2}+4l_{2}^{2}m_{1}m _{2}+l_{3}^{2}m_{1}m_{2}+3l_{1}l_{2}m_{1}m_{2})}{l_{1}^{2}m_{1}m_{2}(8l_{2}^{2 }m_{1}+5l_{2}^{2}m_{2}+2l_{3}^{2}m_{1}+5l_{3}^{2}m_{2})} \tag{14}\]
## Mesoscale Typhoon Field Simulation and Analysis
### Selection of Physical Scheme and Parameter Settings  
The #12 Typhoon \"Nuri\" in 2008 was chosen as the simulation object. Based on the information provided by the Typhoon Website of China Meteorological Administration, it landed on coastal regions of Saigon, Hong Kong on 16:55 on 22 August and the maximum wind near the center was 12-level (33 m/s). On the same day, it landed again at Nanlang Town, Zhongshan City, Guangdong Province at 22:10, and the maximum wind near the center was eight-level (25 m/s). For effective numerical simulation of typhoon path as well the air pressure field and wind speed field, the simulation covers the whole process from the first landing at Saigon, Hong Kong to the second landing at Zhongshan City, Guangdong Province. The initial atmospheric conditions and time-dependent boundary conditions, which were applied in the WRF model, were all based on the FNL (Final Global Data Assimilation System) [27; 28; 29; 30]. It covered 27 atmospheric layers. The sampling interval was set at 6 h, and the spatial resolution was 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\). The WRF simulation region had 37 vertical layers, and the air pressure at the top layer was set 5000 Pa.  
The simulation center chose a district in north Shenzhen. Typhoon \"Nuri\" ran through this region at 20:00 on 22 August. With comprehensive considerations to data demands and computational conditions, the three-dimensional (3D) nesting program was used to calculate typhoon field in the WRF model. The horizontal mesh resolution was 13.5 km, 4.5 km, and 1.5 km, respectively. The longitude and latitude of center of the simulation region was (114.1\({}^{\circ}\) E, 22.5\({}^{\circ}\) N). The coarse grid space D01 in the external surface was 13.5 km, and the number of grids was 211 \(\times\) 211. The grid space D02 in the second layer was 4.5 km, and the number of grids was 217 \(\times\) 217. The grid space D03 in the inner layer was 1.5 km, and the number of grids was 241 \(\times\) 241. The map projection applied the Lambert program. A total of 37 vertical layers were set, forming upper-sparse and lower-dense stratification patterns. There were 19 layers below 1000 m. The simulation domain is shown in Figure 2. The altitudes of different vertical layers are shown in Table 1.  
Figure 1: Meshing of the Weather Research Forecasting (WRF) model. (**a**) Horizontal grids (space between coarse and thin grids is 3:1); (**b**) Vertical grids.  
Figure 2: Simulation domain of the Typhoon \"Nuri\".  
The hub height of the wind turbine is generally lower than 150 m, which is below the thickness of the boundary layer. The hub height is significantly influenced by airflow mass, humidity, and heat transmission in the boundary layer [31; 32; 33; 34; 35]. Therefore, the settings of the physical parameterization scheme can affect the simulation accuracy of boundary layer of the typhoon field directly. In this paper, the physical parameters of WRF model are listed in Table 2.
## 3 Theoretical Comparisons Between Estimators
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \(\omega\) & \multicolumn{4}{c}{2} & \multicolumn{4}{c}{4} & \multicolumn{4}{c}{6} \\ \hline \hline \multicolumn{1}{c}{} & \(\gamma=0.95\) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \(\gamma=0.95\) & \multicolumn{1}{c}{} & \(\gamma=0.95\) & \multicolumn{1}{c}{} \\ \hline \(\hat{\alpha}^{PIG-MLE}\) & 7.203 & 3.286 & 2.193 & 2.130 & 9.763 & 4.081 & 2.380 & 2.172 & 10.407 & 4.932 & 2.645 & 2.220 \\ \hline \(\hat{\alpha}^{PIG-RRE}\) & 4.251 & 1.819 & 1.072 & 1.038 & 5.757 & 2.454 & 1.104 & 1.041 & 6.187 & 3.045 & 1.184 & 1.140 \\ \hline \(\hat{\alpha}^{PIG-LE}\) & 4.133 & 1.805 & 1.072 & 1.032 & 5.603 & 2.381 & 1.127 & 1.041 & 6.006 & 2.935 & 1.205 & 1.162 \\ \hline \(\hat{\alpha}^{PIG-ISE}\) & 3.596 & 1.642 & 1.096 & 1.065 & 4.873 & 2.038 & 1.110 & 1.061 & 5.195 & 2.462 & 1.210 & 1.122 \\ \hline \(\hat{\alpha}^{PIG-TPE}\) & 3.436 & 1.614 & 1.103 & 1.071 & 4.650 & 1.967 & 1.115 & 1.098 & 5.960 & 2.354 & 1.125 & 1.117 \\ \hline \(\hat{\alpha}^{PIG-KLE}\) & 2.911 & 1.367 & 1.042 & 1.036 & 4.118 & 1.622 & 1.016 & 1.075 & 5.330 & 1.919 & 1.113 & 1.100 \\ \hline \(\hat{\alpha}^{PIG-MLE}\) & **2.732** & **1.305** & **1.034** & **1.032** & **3.876** & **1.519** & **1.100** & **1.047** & **4.071** & **1.779** & **1.105** & **1.076** \\ \hline \multicolumn{1}{c}{} & \(\gamma=0.99\) & \multicolumn{1}{c}{} & \multicolumn{1}{c}{} & \(\gamma=0.99\) & \multicolumn{1}{c}{} & \(\gamma=0.99\) & \multicolumn{1}{c}{} & \(\gamma=0.99\) & \multicolumn{1}{c}{} \\ \hline \(\hat{\alpha}^{PIG-MLE}\) & 45.884 & 20.772 & 7.035 & 5.806 & 53.706 & 25.398 & 9.405 & 7.780 & 56.620 & 33.407 & 11.446 & 9.476 \\ \hline \(\hat{\alpha}^{PIG-RRE}\) & 25.701 & 12.607 & 4.332 & 3.484 & 29.055 & 15.021 & 6.001 & 4.843 & 30.797 & 19.288 & 7.225 & 5.906 \\ \hline \(\hat{\alpha}^{PIG-LE}\) & 24.798 & 11.718 & 3.866 & 3.093 & 28.652 & 14.309 & 5.382 & 4.212 & 30.233 & 18.652 & 6.495 & 5.215 \\ \hline \(\hat{\alpha}^{PIG-TPE}\) & 22.897 & 10.367 & 3.512 & 2.899 & 26.800 & 12.675 & 4.695 & 3.884 & 28.254 & 16.671 & 5.713 & 4.730 \\ \hline \(\hat{\alpha}^{PIG-ISE}\) & 22.063 & 9.816 & 3.396 & 2.834 & 25.978 & 11.995 & 4.461 & 3.766 & 27.361 & 15.848 & 5.433 & 4.560 \\ \hline \(\hat{\alpha}^{PIG-KLE}\) & 20.575 & 8.621 & 2.925 & 2.509 & 24.672 & 10.689 & 3.771 & 3.310 & 25.896 & 14.385 & 4.662 & 3.987 \\ \hline \(\hat{\alpha}^{PIG-MLE}\) & **19.771** & **8.045** & **2.738** & **2.381** & **23.869** & **10.005** & **3.485** & **3.113** & **25.036** & **13.575** & **4.317** & **3.740** \\ \hline \hline \end{tabular}
\end{table}
###### Abstract  
Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, one can either have a small sample of in situ measurements or use satellite data to observe large areas of land surface phenology (LSP). In the latter, a tradeoff exists among platforms with some allowing better temporal resolution to pick up discrete events and others possessing the spatial resolution appropriate for observing heterogeneous landscapes, such as urban areas. To overcome these limitations, we applied the Spatial and Temporal Adaptive Reflectance Model (STARFM) to fuse Landsat surface reflectance and MODIS nadir BRDF-adjusted reflectance (NBAR) data with three separate selection conditions for input data across two versions of the software. From the fused images, we derived a time-series of high temporal and high spatial resolution synthetic Normalized Difference Vegetation Index (NDVI) imagery to identify the dates of the start of the growing season (SOS), end of the season (EOS), and the length of the season (LOS). The results were compared between the urban and exurban developed areas within the vicinity of Ogden, UT and across all three data scenarios. The results generally show an earlier urban SOS, later urban EOS, and longer urban LOS, with variation across the results suggesting that phenological parameters are sensitive to input changes. Although there was strong evidence that STARFM has the potential to produce images capable of capturing the UHI effect on phenology, we recommend that future work refine the proposed methods and compare the results against ground events.  
urban heat island (UHI); phenology; STARFM; remote sensing; TIMESAT; growing season Article
## 2 Experiments  
The scroll compressor used in this study is made of a silicon-manganese aluminium alloy, i.e., Al 4007A, as it is quite difficult to evaluate fatigue characteristics in the scroll compressor due to its complicated geometry and loading conditions. Therefore, a newly designed specimen, by considering the most dangerous region under operating conditions, is proposed and used in this study. To design the customized C-specimen, the stress distribution and deformation profile during the operation are considered, as shown in Figure 2. Prior to performing the experiment with the C-specimen, 4007A's material fatigue curve (S-N curve) was evaluated. A rotary bending test machine from Shimadzu Corporation, Kyoto, Japan was utilized. The advantage of the machine is that it increases speed to 50 Hz, which is five times faster than a tensile test machine. Furthermore, the rotary bending machine's fatigue R-ratio corresponds to \(-1\), which is the ideal number for the fatigue curve.  
As previously mentioned, the structure of C-specimen was revised based on the operating conditions, as shown in Figure 2, to apply severe loads comparable with the pressure. By removing a part of the wrap, the experimental device can accurately apply a vertical load at Point A, which is the weak point during scroll compressor operation as per operation and simulation data. The experimental device for the fatigue test of C-specimen was MTS 810 (MTS Systems Corp., Minneapolis, MN, USA). An additional guide structure was designed and used to prevent unexpected loading types (e.g., tangential loading between zig and wrap) as shown in Figure 3. The operation frequency was 10 Hz,  
Figure 1: Plastic shakedown in a direct cyclic analysis [20].  
and the R-ratio of the loading condition was 0.05. Additionally, five amplitude loads were applied as follows: 1900, 1662.5, 1444, 1140, and 948 N.
# Simulation Analysis of a Three-Degree-of-Freedom Low-Frequency Resonant Mixer
## 3 Dynamic Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
### Modal Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer
#### 3.1.2 Results of Modal Analysis of the Three-Degree-of-Freedom Low-Frequency Resonant Mixer  
Modal analysis yields the natural frequencies and mode shapes of the three-degree-of-freedom low-frequency resonant mixer. By comparing the natural frequencies obtained from computer simulation with those from theoretical calculations, the correctness of the simulation results and the structural design of the equipment can be verified.  
The three-degree-of-freedom low-frequency resonant mixer, with its core dynamics model, exhibits resonance phenomena near its natural frequencies. During vibration, the low-order mode plays a dominant role, while the contribution of high-order modes is negligible and they decay rapidly [22]. Therefore, this study selects the first three modes and presents the mode shapes in Figure 3.  
From Figure 3, the following can be observed:As the excitation frequency increases, the equipment reaches the third-order resonance at 58.95 Hz. At this frequency, the amplitude of the payload assembly is relatively large, reaching approximately 3.90 mm, while the amplitude of the excitation assembly is only about 1.00 mm. Calculations indicate that at the third-order natural frequency, the amplitude of the payload assembly is approximately 390.00% of that of the excitation assembly, which effectively achieves amplitude amplification. The larger amplitude of the payload assembly is beneficial for the efficient mixing of materials at the payload assembly location; conversely, the smaller amplitude of the excitation assembly is conducive to the stable operation of the drive assembly connected to the excitation assembly.  
Figure 3: _Cont._  
To verify the correctness of the computer simulation results, a comparison was made between the calculations performed in Matlab and the simulation results obtained in ANSYS Workbench. The results are presented in Table 2.  
The following can be inferred from Table 2:  
The maximum data deviation is only 5.42%, indicating the correctness of the computer simulation results. Based on the analysis of the results, the reason for the deviation is that although both the Workbench simulation and the Matlab calculation methods took into account key parameters such as the mass of each component of the equipment and the stiffness of the springs, the Matlab calculation, compared to the Workbench simulation, is more simplified and did not consider specific structural factors. The structure of each component in Workbench simulation and the simulation of materials will slightly change the equivalent spring stiffness of the system, resulting in deviation from the theory. Therefore, the obtained result is an approximate solution with a minor deviation.
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
###### Abstract  
In the Fifth Generation of telecommunications networks (5G), it is possible to use massive Multiple Input Multiple Output (MIMO) systems, which require efficient receivers capable of reaching good performance values. MIMO systems can also be extended to massive MIMO (mMIMO) systems, while maintaining their, sometimes exceptional, performance. However, we must be aware that this implies an increase in the receiver complexity. Therefore, the use of mMIMO in 5G and future generations of mobile receivers will only be feasible if they use very efficient algorithms, so as to maintain their excellent performance, while coping with increasing and critical user demands. Having this in mind, this paper presents and compares three types of receivers used in MIMO systems, for further use with mMIMO systems, which use Single-Carrier with Frequency-Domain Equalization (SC-FDE), Iterative Block Decision Feedback Equalization (IB-DFE) and Maximum Ratio Combining (MRC) techniques. This paper presents and compares the theoretical and simulated performance values for these receivers in terms of their Bit Error Rate (BER) and correlation factor. While one of the receivers studied in this paper achieves a BER performance nearly matching the Matched Filter Bound (MFB), the other receivers (IB-DFE and MRC) are more than 1 dB away from MFB. The results obtained in this paper can help the development of ongoing research involving hybrid analog/digital receivers for 5G and future generations of mobile communications.  
MIMO; mMIMO; SC-FDE; IB-DFE; MRC +
Footnote †: journal: Electronics Letters
## 3 Results and Discussion
### Crack Evolution Process
#### 3.1.2 Two-Dimensional Analysis of Cracks  
Figure 6 shows 2D crack distributions along the loading direction (\(Z\)-axis) on the XY plane (perpendicular to the loading direction) at different loading stages (cracks are marked with red dotted lines and numbers). Original pores and microcracks were distributed inside cement mortar (Figure 6a), and there were original microcracks at the edge of the unhydrated cement (position 1). New cracks appeared around the original pores and cracks under 2000 N load (positions 2 and 3 in Figure 6b), indicating that the boundary of unhydrated cement was the weakest area; thus, pores and initial defects around it caused localized fractures easily. With the increase in the load, the original cracks continued to expand and gradually formed joint cracks, and other new cracks appeared. When the load reached its maximum value of 5000 N, internal cracks presented a crazing distribution. These cracks increased in scale and had a complex shape and mode (Figure 6c-f). Comparing 3D and 2D crack images under the same loading condition, it is worth mentioning that no significant damage occurred on the mortar surface; however, the inside fracture was very obvious.
## 4 Simulation Results and Analysis
### Hybrid Adaptive Compensation Simulation of Sliding Mode NDI
(**c**) Tracking curves of the pitch derivative \(\dot{\vartheta}\). (**d**) Tracking curves of the attack angle derivative \(\dot{\alpha}\). (**e**) Tracking curves of the track inclination \(\mu\). (**f**) Tracking curves of the attack angle \(\alpha\). (**g**) Tracking curves of the pitch angle \(\theta\). Table 4 shows the performance comparison result of the proposed method in the HFV model without disturbance, uncertain parameters, and modeling error and the HFV model in this paper. To ensure the fairness of the comparison, the proposed finished algorithm is used in both models. Table 4 shows that the proposed method can also adapt to normal non-severe flight conditions. The only drawback is the response time, as the complex algorithm slows down the HFV response, resulting in a minimum response time of about 1 s. Other performance indicators are better than the harsh environment considered in this paper. Table 5 shows the comparative analysis result of the proposed method and the existing state-of-art method in reference [5]. To ensure fairness, the controlled object is set to the longitudinal HFV model. Simulation result shows that the maximum response time of the existing method is beyond the simulation time interval [0, 20s] allowed by the experiment, so it cannot meet the requirements of HFV flight simulation in a real environment. The time of the proposed method is reduced by 18% of the existing method, and the steady-state errors are compressed by more than 10,000 times, which greatly improves the performance of HFV under the co-existence of multiple uncertainties in sea-skimming flight, and has obvious advantages.
###### Abstract  
In arid and semi-arid regions, it is essential to monitor the spatiotemporal variability and dynamics of vegetation. Among other provinces of Pakistan, Punjab has produced a significant number of crops. Recently, Punjab, Pakistan, has been described as a global hotspot for extremes of climate change. In this study, the soil adjusted vegetation index (SAVI), normalized vegetation difference index (NDVI), and enhanced vegetation index (EVI) were comprehensively evaluated to monitor vegetation change in Punjab, Pakistan. The time-series MODIS (Moderate Resolution Imaging Spectroradiometer) data of different periods were used. The mean annual variability of the above vegetation indices (Vis) from 2000 to 2019 was evaluated and analyzed. For each type of vegetation, two phenological metrics (i.e., for the start of the season and end of the season) were calculated and compared. The spatio-temporal image analysis of the mean annual vegetation indices revealed similar patterns and varying vegetation conditions. In the forests and vegetation areas with sparse vegetation, the EVI showed high uncertainty. The phenological metrics of all vegetation indices were consistent for most types of vegetation. However, the NDVI result had the greatest variance between the start and end of season. The lowest annual VI variability was mainly observed in the southern part of the study area (less than 10% of the study area) based on the statistical analysis of spatial variability. The mean annual spatial variability of NDVI was <20%, SAVI was 30%, and EVI ranged between 10-20%. More than 40% of the variability was observed in the NDVI and SAVI vegetation indices.  
vegetation dynamics; vegetation indices; arid and semi-arid; time-series; crop phenology 4.0.1