esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Time series remote sensing images can be used to monitor the dynamic changes of forest lands. Due to consistent cloud cover and fog, a single sensor typically provides limited data for dynamic monitoring. This problem is solved by combining observations from multiple sensors to form a time series (a satellite image time series). In this paper, the pixel-based multi-source remote sensing image fusion (MuTiFuse) method is applied to combine the Landsat time series and Huanjing-1 A/B (HJ-1 A/B) data in the Fuling district of Chongqing, China. The fusion results are further corrected and improved with spatial features. Dynamic monitoring and analysis of the study area are subsequently performed on the improved time series data using the combination of Mann-Kendall trend detection method and Theil Sen Slope analysis. The monitoring results show that a majority of the forest land (60.08%) has experienced strong growth during the 1999-2013 period. Accuracy assessment indicates that the dynamic monitoring using the fused image time series produces results with relatively high accuracies.  
time series; image fusion; dynamic monitoring; Landsat; HJ-1 A/B +
Footnote †: journal: Geo-Information
# Towards Digital-Twin Assisted Software-Defined Quantum Satellite Networks
## 2 Related Works
Specifically, the authors state that 68% of the architectures proposed in the literature follow a centralized approach, which involves a unique DT updated with data from the entire network. For instance, ref. [50] proposed a centralized solution based on graph neural networks aimed at optimizing a classical network. In particular, the proposed approach considers the coupling of a DT with an optimizer running on an SDN Controller. Furthermore, in [51] an SDN-DT architecture for the Industrial Internet of Things (IIoT) was proposed. In this case, the network was integrated with the industrial components that compose the system. The authors argued that the SDN controller collects all data describing the physical network, builds the network topology model, and supplies the essential intelligence to the DT. Moreover, the Third-Generation Partnership Project (3 GPP) produced a technical report on the management aspects of Network Digital Twins (NDTs) [52]. Recently, technologies such as DTs have also found applications in the quantum communications field. For instance, DT technologies applied to the IIoT were explored in [53]. Specifically, the paper proposed a channel encryption scheme based on quantum communications to guarantee communication security. In [54], the components of a possible Spacecraft Digital Twin (SDT) were discussed. In particular, the paper described the structure, including the data source, the system configuration, and the data service mode. Furthermore, ref. [25] analyzed the utility of the DT in both the design phase of a satellite network and for tuning and synchronization of the deployed physical systems. In [27] an Intelligent DT-based Software-Defined Vehicular Network (IDT-SDVN) scheme was proposed. Specifically, the IDT was constructed in the controller to perform a predictive verification before the application of a generated functional model as well as the maintenance diagnosis of operative networks in real scenarios. Few studies have been pursued so far on specific DT-based architectures for QSNs. Moreover, the application of a specific networking protocol and the theory of TNs on QNs composed of LEO satellites is a research area that should be further explored. Therefore, this manuscript, in addition to defining a possible architecture, proposes a networking approach based on the TN theory and a specific protocol for the E2E entanglement generation between a couple of GSs positioned at the antipodes by employing multiple QSRs.
# Topological Interference Management for K-User Downlink Massive MIMO Relay Network Channel
## 4 Precoded Spatial-Multiplexing MIMO Systems
\tag{14}\]  
**Over the second time slot**: Transmitters 2 and 3 are kept active, whereas, transmitter 1 is made silent, as shown in Figure 5. The concept is identical as discussed for time slot 1; receiver 2, \(F^{[2,2]}[W_{2}]\) and receiver 3, \(F^{[3,3]}[W_{2}]\), can attain the superposition of the two desired symbols because receiver 1 can eavesdrop on the linear combination of the unintended interference signal from the active transmitters:  
\[\mathbb{F}^{[2,2]}[W_{2}] = \mathbf{h}_{[c_{2}]}^{[2,2]}[2]\mathbf{u}_{1}^{[2]}+\mathbf{h}_{[ c_{2}]}^{[2,2]}[2]\mathbf{u}_{2}^{[2]},\] \[\mathbb{F}^{[3,3]}[W_{2}] = \mathbf{h}_{[c_{3}]}^{[3,3]}[2]\mathbf{u}_{1}^{[2]}+\mathbf{h}_{[ c_{3}]}^{[3,3]}[2]\mathbf{u}_{2}^{[2]}. \tag{15}\]  
To deliver the same linear combination of undesired signals from receiver 1, the beamforming matrix is selected from transmitters 2 and 3 with a shared relay:  
\[ \begin{bmatrix}\beta\mathbf{h}_{[R_{3}]}^{(2,R)T}[n]\\ \beta\mathbf{h}_{[R_{3}]}^{(3,R)T}[n]\end{bmatrix} \mathbf{V}^{(1)}[n]= \begin{bmatrix} \mathbf{h}_{[c_{2}]}^{(2,2)T}[n]\\ \mathbf{h}_{[c_{3}]}^{(3,3)T}[n]\end{bmatrix} . \tag{16}\]  
**Over the third time slot**: In addition, for this time slot, we keep transmitters 3 and 1 active because transmitter 2 is kept in a silent mode, as shown in Figure 6. Receiver 3, \(F^{[3,3]}[W_{3}]\) and receiver 1, \(F^{[1,1]}[W_{3}]\), can attain the superposition of the two desired symbols because receiver 2 can eavesdrop on the linear combination of the unintended interference signal from the active transmitters:  
\[\mathbb{F}^{[3,3]}[W_{3}] = \mathbf{h}_{[c_{3}]}^{[3,3]}[3]\mathbf{u}_{1}^{[3]}+\mathbf{h}_{[ c_{3}]}^{[3,3]}[3]\mathbf{u}_{2}^{[3]},\] \[\mathbb{F}^{[1,1]}[W_{3}] = \mathbf{h}_{[c_{1}]}^{[1,1]}[3]\mathbf{u}_{1}^{[3]}+\mathbf{h}_{[ c_{1}]}^{[1,1]}[3]\mathbf{u}_{2}^{[3]}. \tag{17}\]  
To deliver the same linear combination of undesired signals from receiver 2, the beamforming matrix is selected from transmitters 3 and 1 with a shared relay:  
\[ \begin{bmatrix}\beta\mathbf{h}_{[R_{3}]}^{(3,R)T}[n]\\ \beta\mathbf{h}_{[R_{1}]}^{(1,R)T}[n]\end{bmatrix} \mathbf{V}^{(2)}[n]= \begin{bmatrix} \mathbf{h}_{[c_{3}]}^{(3,3)T}[n]\\ \mathbf{h}_{[c_{1}]}^{(1,1)T}[n]\end{bmatrix} . \tag{18}\]  
During all three of the time slots, the relay approximates the received transmitter symbols by multiplying the receiver beamforming matrix using the two received symbols [2]:  
\[\hat{S}^{[k]} = \begin{bmatrix}\hat{\mathbf{u}}_{[k]}^{[k]}\\ \hat{\mathbf{u}}_{2}^{[k]}\end{bmatrix} =\Big{(}\mathbf{H}_{[c_{k}]}^{[R,k]}(k) \Big{)}^{-1}\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!\!
# The POSEIDON Ocean Observing System: Technological Development and Challenges
## 4 System Efficiency  
Across the 20 years of operation, the percentage of valid data in the time series of the buoys network can be considered aa indicator of system efficiency. The loss or the rejection of the data is triggered for several reasons such as failures, equipment malfunctions, sensor drifts and quality control filtering. Sustainable funding is absolutely important, considering the requirements of expensive ship time, replacement parts, including spare instrumentation in case of failure or loss of equipment.  
An analysis performance of the three multiparametric buoys of the POSEDION system (Figure 12) is presented using the core parameters of temperature and salinity timeseries. The key target of the timeseries sampling is high-quality data with a minimum gap. The analysis uses the percentage of the quality-approved data, the rejected data during quality control (QC), as well as the percentage of non-operation period of the stations or sensors. The non-operation includes the time in which the buoy was retrieved from the field and stored on shore for several reasons (related to the challenges of the system operations). This gap period can be days, months or, on some occasions, years, and it is included in every depth non-operation percentage. However, the non-operation period of a sensor caused by a single failure in a specific depth is only included in that depth. The QC data set is the final product of delayed mode quality control. The QC ranges are customized for each site based on the climatology of the region and all the available data. The time range of data used is 28 May 2007 to 24 January 2022 for E1M3A, 9 November 2007 to 24 January 2022 for PYLOS, and for the ATHOS buoy--several periods between 2004 and 2022.
# Spatial Deformation Calculation and Parameter Analysis of Pile-Anchor Retaining Structure
## 4 Engineering Background and Numerical Simulation Model
### Comparative Analysis of Results
#### 4.3.1 Comparison of Crown Beam Deformation  
It is observed from Figure 8 that the deformation of the crown beam is symmetrically distributed, with smaller deformations occurring at the corners of the foundation pit and larger deformations occurring at the middle position of the crown beam. This is because the numerical simulation model imposes a constraint on the deformation in the Y direction at the corner of the foundation pit. With the constraint on the deformation in the Y direction of the two points at the corner, the crown beam is equivalent to a simply supported beam. Under the combined influence of the soil pressure exerted on the retaining wall and the deformation of the pile, the crown beam demonstrates a deformation pattern akin to that of a simply supported beam subjected to a uniform load. This deformation behavior is a result of the forces and displacements acting on the crown beam due to the surrounding soil pressure and the response of the supporting pile system.  
Figure 9 shows the crown beam deformation curve obtained by the theoretical calculation method, numerical simulation method, and on-site data. It can be concluded that:  
(1) Theoretical calculations of the crown beam's deformation align with the observed deformation curve derived from on-site monitoring data. The horizontal deformation curve of the crown beam exhibits a distinctive \"V\"-shaped distribution. Conversely, the numerical simulation method produces a horizontal deformation curve that shows a \"U\"-shaped distribution. These findings highlight the correspondence between the theoretical predictions and the actual behavior of the crown beam, as well as the insights gained from the numerical simulation method. This is because the pile and the crown beam are integral structures in the numerical simulation, resulting in a greater stiffness of the retaining structure. The deformation gap in the middle area of the foundation pit is small, while the deformation gap in the surrounding area is large. During the theoretical calculation and on-site construction, there is no tie contact between the crown beam and the pile, which differs from the situation in the numerical simulation method.  
(2) Comparing the results obtained from the theoretical calculation, numerical simulation, and on-site data, it is evident that the maximum horizontal deformations of the crown beam are 13.23 mm, 12.44 mm, and 12.60 mm, respectively. These values demonstrate that the differences between the three methods are relatively small. This finding supports the practicality of both the theoretical calculation and the numerical simulation model. It is important to note that the theoretical calculation results and the numerical simulation results are presented as smooth curves, as they do not consider the variation in soil layers or construction steps.  
Figure 8: Cloud chart of crown beam horizontal deformation.
## 4 Results and Discussion
Figure 11 shows the S curve, which is a direct approximation of ITU-R P.1410-2. However, the prediction is more accurate. Generally, the LoS probability increases when increasing the elevation angle. LoS probability reached a maximum at 30\({}^{\circ}\) elevations in the urban area. However, the LoS probability reaches a maximum at 60\({}^{\circ}\) and 70\({}^{\circ}\) in the case of suburban and dense urban areas, respectively. Figure 12 depicts the LoS probability based on a different elevation angle from the smart drone for different environments. The LoS probability changes according to the environment either an urban, suburban, or dense urban area. However, in urban areas the elevation angle should be small to keep the LoS probability small and the elevation angle should not be greater than 70\({}^{\circ}\); otherwise, the connection will be lost. It is observed that the LoS probability shows similarities with the LoS presented in [16], but here the prediction was more accurate because of the estimation of the LoS probability at any location and elevation angle. Figure 11: LoS probability of drone in different environments vs. elevation angle. Figure 12: LoS probability with different elevation angle and environment vs. elevation angle.
# Conception of a System-on-Chip (SoC) Platform to Enable EMG-Guided Robotic Neurorehabilitation
## 3 System Requirements and Architecture Implementation
Quad SPI block, configures the AFE, while the second stream, connected to the output of ADS Ctrl SPI, manages the captured data received from the AFE. The multiplexer selection is controlled via an AXI4-Lite slave interface. * AXI Quad SPI: This is a standard Vivado IP block responsible for managing and establishing SPI communication between the AFE and the processor. It reduces the input clock frequency to achieve a 4 MHz SPI output clock necessary for AFE communication. The block provides SPI outputs for data transmission (MOSI), clock (SCK), and chip select (CS), along with an input for data reception (MISO). * Processing Algorithm: This IP has been generated from Vitis HLS, and is responsible for implementing the low-level processing algorithms required for the data streams received from the ADS1298. These include signal processing to obtain the on-set detection [22], and buffering for each channel (e.g., ch0 buffer, ch1 buffer, etc.). It is connected to the AXI4-Lite bus for configuration and uses the AXI Stream interface to interact with the DMA, enabling efficient data transfer to the DDR memory for further processing. Different on-set detection algorithms have been implemented with Vitis HLS, such as moving average, moving RMS, low-pass filter with FIR (Finite Impulse Response), and TKEO (Teager-Kaiser Energy Operator). Due to its simplicity and efficiency, the moving average implementation with a fixed threshold has been used for this work. This consists of rectifying the scaled data and calculating the moving RMS for 64 samples, as shown in Equation (1), where \(x_{rms}\) is the result of the moving RMS; \(N\) is the number of samples, in this case 64; and \(|x_{i}|\) is the absolute value of each scaled sample. \[x_{rms}=\frac{1}{N}\sum_{i=0}^{N-1}|x_{i}| \tag{1}\]  
Then, a threshold is applied to the moving RMS signal to determine whether muscle activation has occurred. This threshold is configurable and is used to obtain a digital signal, as shown in Equation (3), where \(x_{th}\) is the result of the thresholding; \(x_{rms}\) is the moving RMS signal; and \(th\) is the threshold. \[x_{th}=\left\{ \begin{array}{ll}\text{if }x_{rms}>th&1\\ \text{if }x_{rms}\leq th&0\end{array} \right. \tag{2}\]
# Nuclear Analysis of High-Power LIEBE Molten Target at CERN for the Production of Radioisotopes
## 2 MCNP Code  
The MCNP code was used to simulate the proton beam's interaction with the target and generated neutron and gamma transport. In neutron and gamma transport simulations, typical output is neutron and gamma fluxes. To estimate the cell flux, the track length, collision, absorption and point flux substitution methods are used [6].  
The particle fluence over a cell volume in MCNP is defined by the formula:  
\[\Phi(V)=\frac{1}{V}\int_{V}dV\int_{E}dE\int_{4\pi}d\Omega\Phi(r,E,\Omega), \tag{1}\]  
where \(V\) is the volume of the cell, \(E\) is the energy of the particle at the volume and \(\Phi(r,E,\Omega)\) is the energy and angular distribution of the fluence as a function of position, which can be expressed as the sum of the contributions of each scattering [7].  
It is also necessary to find an estimate of the standard deviation of the mean of the results \(\mu\) and the relative error of the particle histories. The standard deviation of the mean in MCNP is calculated as follows:  
\[\sigma_{\mu}=\frac{\sigma}{\sqrt{N}}, \tag{2}\]  
where \(\sigma\)--standard deviation of history results, \(N\)--number of particles per source particles.  
To reduce the standard deviation \(\sigma_{\mu}\), it is required to decrease the variance of the histories or increase the number of histories [8]. Since the model is very detailed, to obtain statistically acceptable results, a longer computing time is needed to increase the number of particle histories. However, for high-fidelity mesh calculations, the relative standard deviation is less suitable for obtaining a convincing estimate of the relative error. The relative error is usually calculated as follows:  
\[R=\frac{\sigma_{\mu}}{\mu}. \tag{3}\]  
MCNP can provide information on the reliability of results using ten statistical tests, and if any of the ten tests fails, MCNP will produce additional outputs about the failed tests [7]. This paper considers the neutron and gamma-induced dose rates around the LIEBE target. The dose rate estimation can be obtained after the neutron and gamma flux calculations using the Formula [9]:  
\[H=\sum\Phi(V)h, \tag{4}\]  
where \(h\) is the fluence-to-dose conversion coefficient.
# Dynamic Routing in Flying Ad-Hoc Networks Using Topology-Based Routing Protocols
## 3 Simulation Setup and Performance Metrics  
### Simulation Setup  
To demonstrate the performance of the topology-based routing protocols presented above, we chose one reactive routing protocol (i.e., OLSR), two proactive routing protocols (i.e., DSR, and AODV) and one hybrid routing protocol (i.e., ZRP). These routing protocols were selected on the basis of having the best performances mentioned in the literature study for FANETs, and to test the basic features of each class of topology-based routing. We used OPNET Modeler 14.1 [37] for the modeling and simulation of our work. We modified the node model of the MANET node for FANETs in the node editor, as shown in Figure 3. We set the data rate to be 11 Mbps and 54 Mbps for all the UAVs, according to the IEEE 802.11 g standard. Their working was implemented in the function block of the process model editor. We simulated various scenarios in which the quantity of UAVs was selected to be 30 and 100 nodes. The speed of the UAVs was set to be 25 m/s and 60 m/s, respectively, with the random waypoint mobility model. The UAVs were placed in the area of 1000 m \(\times\) 1000 m and 2000 m \(\times\) 2000 m. The altitude of the UAVs was kept constant to 40 m for all the varying scenarios. All metrics are displayed in averages. The rest of the parameters are
## 2 Data and Methods
### Methods
#### 2.2.2 Lanczos Filter  
The Lanczos filter consists of a weight function, which is a combination of the sinc function and the Lanczos window function (a simple sinusoidal shape function). The following is the weight function of the Lanczos filter [42]:  
\[\omega(k)=\frac{\sin(2\pi f_{c}k)}{\pi k}\cdot\frac{\sin(\pi k/N)}{\pi k/N} \tag{1}\]  
where \(f_{c}\) represents the cutoff frequency, \(k\) is the weight index, and \(N\) is the size of the filter's half-window width, which represents the number of weights on one side of the filter window, that is, the distance from the center point to one side of the window (excluding the center point itself). The total number of weights is \(2N+1\), including the center point and \(N\) weights on both sides. The first term in Equation (1), \(\frac{\sin(2\pi f_{c}k)}{\pi k}\), is used to extract signals from specific frequencies.  
As mentioned in the Section 1, the Lanczos filter is one of the most popular methods used in climate studies. Thus, in this study, the Lanczos filter and its filtering output are considered as the ground truth for training and evaluating the 1D-CNN filter. Specifically, a 121-point Lanczos bandpass filter is applied to the temperature records to extract the QBWO and ISO signals (10-60-day) based on the wavelet spectral analysis results (Figure 1). The filtered outputs are then used for model training and validation in the next step.
## 4 Discussion
Therefore, some nutrition depression in insects may be advantageous to reduce intraspecific competition pressure, although this needs to be confirmed in the future. At the population level, the cereal aphid growth rate is mainly affected by the field microclimate [53]. Several meteorological factors could exert direct influences on aphids in the monoculture system. [PERSON] et al. (2020) found that rainfall caused a significant reduction in the survival rate of the aphid, _Melanaphis sacchari_, on sugarcane. A substantial reduction in the aphid population was observed because of stimulating rainfall [54]. A previous study reported a 30-80% reduction in the population density of the lower-instar nymph of _S. avenue_ due to rain [55]. In southern Xinjiang, a 15-year pattern revealed that the gate season typically lasts from April through July [56]. This period perfectly coincides with the occurrence period of _S.avenae_ on wheat. It may partly explain the relatively higher aphid population under the walnut-wheat intercropping pattern within the ecological function of windbreak working and the lower population in the wheat monoculture fields. The excessive application of pesticides has aggravated pesticide residues, intensified environmental pollution, and enhanced the pesticide resistance of aphids [57, 58]. Therefore, the Ministry of Agriculture and Rural Affairs of PRC formulated a plan named the course of action for zero growth of agricultural chemicals consumption by 2020. The goal of reducing pesticide consumption and improving pesticide utilization rate has been defined. Compared with a traditional artificial knapsack sprayer, an unmanned aerial vehicle (UAV), as the newly emerging plant protection machinery in China, has a higher operational efficiency, a faster spraying speed, and a better feature to control time-sensitive target organisms [59, 60, 61, 62]. Considering our study results, regular dosage treatments could give better efficiency in both major wheat varieties XD20 and XD60 than their corresponding 30% dosage reduction treatments for all three surveys. Sulfoxaflor and thiamethoxam, two out of six experimental pesticides, were the only ones to exhibit reasonably effective control effects higher than 60% on the aphids fed by XD20. On the other side, the control effect of all reduction dosage treatments was shown to be more than 70% on the _S. avenue_ that infested XD60. Among them, the control effect of imidalcoprid, sulfoxaflor, thiamethoxam, and thiamethoxam-cyhalothrin even exceeded 86%. Some studies demonstrated that different features among varieties, such as height, canopy density, leaf area index, and leaf inclination angle, could affect the utilization efficiency of specific pesticides by changing the pesticide's deposition [63, 64]. The inherent variations in the physical and physiological structure of XD20 and XD60 may account for the variations in control effect performances observed in the current study.