esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The increase in environmental and human-related changes (e.g., increase in the carbon cycle flux of plants) has increased the dynamism of ecosystems. Examining fluctuations in net primary production (NPP) is very important in adopting correct strategies for ecosystem management. The current study explores the spatiotemporal variations in NPP and its association with agricultural droughs in Iran's ecosystems over 20 years (2000-2020). [PERSON] and [PERSON]'s slope methods in each pixel were used to track changes in trends. Drought upsets the terrestrial carbon cycle balance. In this study, Vegetation Health Index (VHI) used to assess drought that extracted from different bands of images satellite. Then, the relationship between NPP rates and agricultural droughs was investigated through running Pearson correlation. The results demonstrated that Iran's annual share of carbon sequestration is 1.38 kg*C/m2/year. The highest carbon sequestration rate was recorded in Caspian Hyrccanian forests. In contrast, the lowest rate was observed in the Arabian Desert and East Sahero-Arabian serric shrulands in southwestern Iran. Moreover, the highest photosynthesis variations were recorded in Arabian Desert and East Sahero-Arabian serric shrulands and Tigris-Euphrates alluvial salt marsh, while the lowest changes were registered in Badghyz and Karabil. In total, 34.2% of the studied pixels showed a statistically significant rising or falling trend. Sen's slope estimator demonstrated that the sharpest negative trend in carbon sequestration belonged to Caspian Hyrccanian mixed forests (\(-\)12.24 g*C/m2/year), while the sharpest positive trend was observed in Azerbaijan shrub desert and steppe (12.29 g*C/m2/year). The results of the Pearson correlation revealed significant correlations between NPP and VHI in different ecosystems with coefficients ranging from \(-\)0.93 to 0.95. The largest area with a positive correlation (33.97%) belonged to the Zagros Mountains forest steppe. Identification of areas with the greatest carbon sequestration changes could result in prioritizing varied ecosystems management for carbon sequestering. It can be also utilized in environmental planning such as scaling up ecosystem values or estimating current and past ecological capacity.  
carbon cycle; Hyrccanian forest; MODES; Pearson correlation; trend analysis; vegetation health 2023  
1 2023 1 2023 1
# A CMOS-Based Terahertz Reconfigurable Reflectarray with Amplitude Control: Design and Validation
## 4 Experimental Verification
### Measurement Setup and Test Results  
The measurement setup for element performance testing is shown in Figure 10. The excitation feed horn illuminates the RRA or a metal plate at a 37\({}^{\circ}\) oblique incidence angle, while the receiving feed horn captures the terahertz waves at the same angle. The vertical distance \(H\) between the feed sources and the RRA plane is valued as 5.7 cm. By comparing the amplitude differences between the RRA and the metal plate, the element loss of the RRA can be determined.  
The test results are shown in Figure 11. At 290 GHz, the RRA exhibits an amplitude of \(-5\) dB with all switches in the ON state and \(-8\) dB with switches in the OFF state, revealing a 3 dB amplitude control capability.  
Figure 10: Measurement setups for the element performance.  
Figure 9: RRA and element chip tile structure.  
The RRA prototype successfully demonstrates amplitude control capability and low-loss performance within the target terahertz frequency range of 250-300 GHz. Experimental results not only validated the effectiveness of the terahertz switch but also confirmed the design concept. Although there were discrepancies in amplitude between the array test results and simulation results of the element, these differences were primarily due to interconnected jumpers and pads occupying too much space in the array, resulting in a fill factor of only 60.5%. This means that 39.5% of the array space was actually composed of the metal ground on the chip backside and the silicon substrate. These issues resulted in the energy from the feed horn being transmitted to the receiving horn through mirror reflection in the experiment. Consequently, the amplitude of terahertz waves in the experiments was higher than in simulations.  
To validate the influence of mirror reflection on test results, the numerical calculations can be employed to demonstrate their feasibility. Specifically, we model each center of an element as an equivalent Huygens source, where the amplitude is equal to the incident feed source amplitude minus the element losses. For positions without elements, the Huygens source amplitude is equal to the incident feed source amplitude. The calculation results are shown in Figure 11.  
Both test results and simulation results show an amplitude control performance of approximately 3 dB. The test results' amplitude is lower than the simulation results because the loss of the silicon medium in the mirror reflection area was not accounted for. Thus, there is good agreement between the test results and the calculated results, which implies that improving the element fill factor can make the array test results closer to the element simulation results. We will optimize the size of the bonding pad in future designs to minimize the unoccupied space between arrays and enhance the overall performance of the RRA prototype.
# High-Efficiency Clustering Routing Protocol in AUV-Assisted Underwater Sensor Networks
## 3 Design of the HECRA  
Conventional LEACH methods have several drawbacks in terms of energy efficiency and data transmission reliability, which are more significant, especially in underwater environments. The random CH selection method of LEACH may lead to the overloading of some CHs, which accelerates energy consumption. In addition, hydroacoustic signals are more susceptible to interference and attenuation, threatening the reliability of data transmission.  
Given the analysis above, this paper proposes HECRA, which aims to optimize the performance of underwater sensor networks. HECRA employs more energy-efficient cluster head selection strategies and data transmission strategies to balance the node loads and prolong the network lifetime. HECRA is divided into four phases: cluster head selection, cluster formation, data transmission, and network optimization, which are described in detail below.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 3 Applications of Artificial Intelligence in Viticulture
### Disease Prediction Models
Notably, the integration of these models into decision support systems could significantly reduce fungicide applications by over 50%, contributing to more sustainable viticultural practices [35]. A recent study by [PERSON] et al. presents an innovative approach using satellite imagery, real-time weather data, and deep learning (DL) and a custom convolutional neural network (CNN) architecture tailored for plant disease classification using vineyard imagery. This model was tested against other state-of-the-art models, such as ResNet50 and MobileNetV2, and showed comparable performance, with MobileNetV2 achieving a remarkable accuracy of 99.375%. In addition, the front-end uses Mapbox for satellite data visualization, providing an intuitive user interface for vineyard managers. This system not only facilitates accurate disease classification and treatment, but also incorporates geo-tagging to map disease hotspots, improving targeted intervention strategies [43]. Moreover, integrating these models with mobile applications and user-friendly interfaces allows vineyard managers to access real-time data and recommendations, facilitating rapid decision-making. These tools empower even small-scale vineyard operations to leverage advanced AI technologies for disease management, enhancing overall productivity and sustainability [42]. Recent advancements have also shown that combining AI with IoT sensors and machine learning models improves the precision of forecasting models by capturing microclimatic variations critical for disease prediction [40]. Although disease prediction models for grapes have shown high accuracy in controlled studies, their practical application can vary significantly depending on regional and environmental conditions. For instance, a novel deep learning model for grape disease detection that integrates multimodal data such as soil type, climate, and grape variety, have been successful in specific locations, but their effectiveness may be limited when applied to different regions without further adaptation [44]. Factors such as local climate variability, different disease prevalence, and varying agricultural practices can affect the precision of these models. Therefore, while they are promising for improving vineyard management, it is crucial to adjust them using localized data to ensure reliable performance across diverse regions.
## 4 Proposed SFO Estimation Scheme
### MSE Analysis
With those implications in mind, we obtain the MSE from Equation (30)\(\sim\) Equation (32)  
\[E\left\{\left|\hat{\Delta}_{s}-\Delta_{s}\right|^{2}\right\}=\frac{1}{N_{s}^{ 2}}\sum_{m=1}^{N_{s}}\frac{N^{2}}{4\pi^{2}F_{m}^{2}D^{2}N_{u}^{2}N_{m}}\frac{ \sigma_{Z}^{2}}{\sigma_{H}^{4}E_{X}^{2}}\left(1+\frac{\sigma_{Z}^{2}}{2\sigma _{H}^{4}E_{X}^{2}}\right) \tag{35}\]  
which can be further expressed as  
\[E\left\{\left|\hat{\Delta}_{s}-\Delta_{s}\right|^{2}\right\}=\frac{1}{N_{s}^{ 2}}\sum_{m=1}^{N_{s}}\frac{N^{2}}{2\pi^{2}F_{m}^{2}D^{2}N_{u}^{2}N_{m}}\left( \frac{1}{\gamma}+\frac{1}{2\gamma^{2}}\right)\left[1+\left(\frac{1}{\gamma}+ \frac{1}{2\gamma^{2}}\right)\right] \tag{36}\]  
where \(\gamma=\sigma_{H}^{2}E_{X}/\sigma_{Z}^{2}\) is the average SNR. It is obvious from Equation (36) that the choice of some design parameters \(D\), \(N_{s}\), \(F_{m}\), and \(N_{m}\) has a significant impact on estimation performance. Once the time separation \(D\) is determined to be used for synchronization, the parameters \(N_{s}\), \(F_{m}\), and \(N_{m}\) are optimized by means of the subset selection considering tone mask requirements.
# LoRa Architecture for V2X Communication: An Experimental Evaluation with Vehicles on the Move
## 7 Conclusions  
Communication technologies like LoRa play an important role in developing a reliable and secure communication architecture for V2X. It can offer both reliability and modularity for the prevailing vehicles with no additional circuitry. Moreover, it also addresses challenges like security and power consumption. This paper presents a reliable and robust architecture for V2X communication with LoRa which involves the design, implementation, and real-world testing with the prototypes. It addresses some of the fundamental challenges such as reliability, data handover, time criticality, modularity, and energy efficiency with vehicles on the move under different practical scenarios which is lacking in the existing LoRa V2X literature. This research work also helps to fill up the void in the literature regarding robust architecture and evaluation of V2X under real traffic scenarios. The results show the suitability of the LoRa architecture for V2X communications where mobility of the vehicles presents some unique challenges. This work inspires other researchers to work further and improve LoRa-based V2X as it has the potentiality to bring revolution in ITS applications.  
Conceptualization, K.F.H. and K.Y.; methodology, K.F.H., and K.Y.; software, K.F.H.; validation, K.F.H., and K.Y.; formal analysis, K.F.H., and K.Y.; supervision, A.A. and K.Y.; writing--original draft preparation, K.F.H.; writing--review and editing, K.F.H., A.A., V.P.Y., and K.Y. All authors have read and agreed to the published version of the manuscript. This research is funded in part by the National Science Foundation, grant number 1542368.  
We would like to thank the Research Experience for Teachers (RET) team: [PERSON], [PERSON], and [PERSON] for helping with collecting data. The authors declare no conflict of interest.
## 5 Results & Discussion
### Simulation Results of the ZigBee WSN
#### 5.2.1 Throughput  
Throughput is the average number of bits or packets that are successfully transmitted from the source to the destination. The results of the steady state are 0.041, 0.034 and 0.028 Mbit/s for the star, mesh and tree topologies, respectively (Figure 16). The maximum throughput can be achieved in the star topology. The mesh topology has the second highest throughput, followed by the tree topology. The reason for this finding is that the star topology interacts with the personal area network (PAN) coordinators and ZigBee devices, which are more efficient than the end devices of the system.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## 3 Results
### Backscatter Signal VH versus VV  
As an essential component of the seasonal threshold approach, the backscatter signal from the Sentinel-1 data built the basis of the computation. In this study, the co-polarization vertical-vertical (VV) and the cross-polarization vertical-horizontal (VH) were used and tested separately. For each of the 39 possible validation test sites (Table 1), two SAR timeseries (VV and VH) containing 100 to over 200 data points from April 2014 to August 2020 were built.  
The two timeseries from the same sites always had the same data size, whereas there was a significant difference between the values of the VV and VH timeseries. As shown in the left panel of Figure 3, the values of the VV timeseries from the chosen example sites were generally higher than the values of the VH timeseries. This phenomenon can be observed in all test sites, which means that, on a whole, the backscatter signal of the VH polarization was weaker than the signal of VV in the study area.  
The overall trends of the two sets of timeseries were roughly similar, and seasonal fluctuations were fairly obvious. Compared with the more even distribution of VV data, there were more extreme values in VH data.  
Considering the commonly used lower limit of \(-30\), the quality of VH data was slightly worse since there were significantly more sites with substandard VH data, which were increasingly under \(-30\). This difference can be observed at many sites. As shown in Figure 3, there were several points in each of the stations shown that were slightly lower than \(-30\), while the whole timeseries was located in a value range clearly closer to \(-30\) compared to their corresponding VV timeseries. This situation was particularly strong at the sites shown in the right panel of Figure 3. These stations were located in special or complex environments (P4 on a sandy plain with seashells; P31 close to Clearwater Lake; P38 on an island at the center of the Robert-Bourassa reservoir), for which the captured backscatter signal itself had a certain possibility of being negatively affected by the surrounding environment.  
How this difference between VV and VH timeseries further affects the estimation result (Section 3.4) and whether some of these stations should be eliminated from the processing (Section 3.2) are tested and presented below.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & \multicolumn{8}{c}{**Gamma3-Scale3**} \\ \hline  & & \multicolumn{8}{c}{**Without \(<-30\)**} & \multicolumn{8}{c}{**With \(<-30\)**} \\ \hline
**Station-** & **Amount** & \multicolumn{2}{c}{**VV**} & \multicolumn{2}{c}{**VH**} & \multicolumn{2}{c}{**Diff.**} & \multicolumn{2}{c}{**VH**} & \multicolumn{2}{c}{**Diff.**} \\ \cline{3-10}
**Depth** & **\(<-30\)** & **Threshold** & **Accuracy** & **Threshold** & **Accuracy** & **VV-VH** & **Threshold** & **Accuracy** & **VHwo-VHw** \\ \hline
5–5 & 11 & 0.66 & 0.71 & 0.41 & 0.57 & 0.14 & 0.74 & 0.58 & \(-0.01\) \\
6–0 & 5 & 0.62 & 0.76 & 0.64 & 0.84 & \(-0.08\) & 0.72 & 0.84 & 0.00 \\
6–5 & 5 & 0.62 &
## 3 Results  
### Overview of Recorded Trials  
In total, 220 trials were recorded, with 11 trials for each speed being recorded for each subject's leg. Of the 220 total trials, 20 trials, where the subject exhibited a lowering recovery strategy, were excluded from analysis along with 16 trials where the trip occurred in the wrong gait phase and 20 trials where irregular events such as multiple trips occurred. Furthermore, 15 trials were disregarded owing to the obstruction of a marker. Consequently, 20 trials were disregarded as they were outliers. Therefore, 129 trials where the subject exhibited an elevating recovery strategy were analyzed. An example of the recovery motion observed in this experiment is shown in Figure 4.  
Figure 4: Stick picture of recovery steps. The horizontal position was adjusted considering the velocity of treadmill belt.  
Figure 3: Definition of parameters: (**a**) Joint angles. (**b**) Kinematic parameters.  
Tripping in the analyzed trials occurred between 65% and 80% of the gait cycle. The average stride time of subjects walking normally, which was recorded prior to the tripping trials, ranged from 1.11 to 1.42 s at 3.5 km/h and from 1.06 to 1.28 s at 4.0 km/h. The average step length ranged from 0.47 to 0.59 m at 3.5 km/h and from 0.53 to 0.63 m at 4.0 km/h.
# PPG and Bioimpedance-Based Wearable Applications in Heart Rate Monitoring--A Comprehensive Review
## 5 Heart Rate Monitoring
### Algorithms  
#### Troika  
The TROIKA algorithm combines several steps to clean and enhance heart rate (HR) signals obtained from a wrist-worn PPG device. It utilizes sparse signal reconstruction (SSR) as one of the main processing components, specifically combined with the FOCUSS method, to capture heartbeat signals in a sparse spectrum. The process begins with band-pass filtering to isolate the relevant signal, followed by signal decomposition using singular spectrum analysis (SSA) for denoising, and temporal difference analysis to enhance the signal clarity. Finally, spectrum peak tracking (select and verify) ensures accurate heart rate estimation. This approach, as illustrated in Figure 17, effectively combines previous estimations to refine the current heart rate estimation, making it suitable for use during physical activity [103].  
Figure 16: Partition of the PPG signal into windows for further calculation [9].  
The following steps briefly describe the main components used by this algorithm:  
1. Signal decomposition: This part aims to denoise and sparsify the spectra of PPG signals. SSA helps to remove motion artifacts (MA) frequency components and to sparsify the spectrum coefficients. The time series is decomposed into oscillatory components and noise as shown below: \[\mathbf{y}=\sum_{i=1}^{Q}y_{i},\] (18)
2. Sparse signal reconstruction (SSR): SSR aims at obtaining the sparsest high-resolution spectrum of the PPG signal, which is robust against noise, using FOCUSS algorithms. This process is essential for distinguishing the heart rate component from other dominant frequencies caused by MAs. The basic model of SSR can be written as follows: \[\mathbf{y}=\ \Phi\mathbf{x}+\mathbf{v}\] (19) Here, \(\Phi\) is a known basis matrix, the vector \(\mathbf{y}\) is the observed signal, \(\mathbf{x}\) is an unknown solution vector and \(\mathbf{v}\) is an unknown noise vector. The goal is to find the sparsest vector \(\mathbf{x}\) by minimizing the square of the \(l_{2}\) norm of \((\mathbf{y}-\Phi\mathbf{x})\).
3. Spectral peak tracking: The final step involves selecting and verifying the correct spectral peak corresponding to the heart rate. This involves an initialization phase where users are required to minimize motion, followed by the selection of spectral peaks within certain ranges, and a verification process that employs decision mechanisms to ensure the correct peak is chosen despite potential motion artifact interference. \[N_{\text{cur}}=\begin{cases}N_{\text{prev}}+\tau,&\text{if }N-\hat{N}_{\text{prev}}\geq\theta,\\ N_{\text{prev}}-\tau,&\text{if }N-\hat{N}_{\text{prev}}<\theta,\\ \hat{N},&\text{otherwise}.\end{cases}\] (20) where \(\hat{N}\) is the location of spectral peak selected by the user, \(N_{cur}\) is the modified value based on the previous observations and \(\theta=6\) and \(\tau=2\)[103].
# Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters
## 3 Methods
### System Model  
The SMP system model is below. Equation (1) is the governing equation. It produces the final trajectory for the system's variable of interest, \(y\), using a forcing function, \(f\), from the SHC formulation.  
\[\tau\ddot{y}=\alpha_{y}(\beta_{y}(g-y)-\dot{y})+f \tag{1}\]  
The forcing function (2) is summed over the number of kernel functions, \(K\), used in the system. \(K\) is chosen based on the complexity of the system (e.g., actuatable degrees of freedom), and the desired smoothness of the produced trajectory. SMPs are \(O(K^{2}t)\) at their most complex, where \(t\) is the number of timesteps calculated [1]. In this work, the kernels are color-matched to show which state space waveform corresponds to each region of the task space trajectory.  
\[f(x)=\sum_{i=1}^{K}x_{i}w_{i} \tag{2}\]  
The canonical state Equation (3) is based on competitive Lotka-Volterra (LV) equations [4].  
\[\tau dx_{i}=x_{i}\Bigg{(}\alpha_{i}-\sum_{j=1}^{K}\rho_{ij}x_{j}\Bigg{)}dt+ \sum_{j=1}^{N}C_{ij}z_{j} \tag{3}\]  
External perturbation is a critical factor in the use of LV kernels; external perturbation (modeled as Gaussian noise in this work), \(z_{j}\), ensures that the system variable, \(x\), passes close to the SHC saddle point, but not so close that the system remains in static equilibrium [7]. The effect of noise on SHCs has been explored in other work, and for practical uses, we can establish a reasonable noise magnitude compared to the rest of the system [13; 14; 52; 53; 54; 55].  
All of the variables across the SMP system model are defined in the following table (Table 1).  
\begin{table}
\begin{tabular}{c l} \hline _Variable_ & _Definition_ \\ \hline \(y\) & relevant system variable \\ \(\tau\) & time-scaling term \\ \(\alpha_{y}\) & system damping \\ \(\beta_{y}\) & system stiffness \\ \(g\) & system’s “goal” position \\ \(f\) & controller force (applied to system) \\ \(K\) & total number of kernel functions \\ \(w_{i}\) & kernel function weight \\ \(x_{i}\) & canonical state of the system (for a single kernel) \\ \(\alpha_{i}\rho_{i}\) & system behavior parameters \\ \(N\) & number of sensors \\ \(C_{ij}\) & coupling matrix \\ \(z_{j}\) & noise \\ \hline \end{tabular}
\end{table}
Table 1: SMP system variable definitions.