esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Cutting-Edge Sensor Technologies for Improved Flood Monitoring and Damage Assessment
## 3 Overview of Flood Sensor Development
### Flood Satellite Remote Sensing Sensors
Furthermore, LiDAR can compensate for the side-viewing nature of SAR for urban buildings and vegetation. For economic reasons, the DEMs of interferometric synthetic aperture radar (IISAR) and SAR are being used instead of the LiDAR system for large-scale monitoring in many areas [106]. Many organizations independently use various types of remote sensing data for flood monitoring, mapping, prediction, and assessment. However, combining data from different sensors can offset their individual limitations [107; 108; 109]. The integration of radar and multispectral imagery has demonstrateda marked enhancement in the delineation of flood extents, particularly in conditions characterized by cloud cover [110; 111]. Within recent years, machine learning algorithms have facilitated the robust fusion of disparate data sources, leading to significant advancements in the estimation of flood water bodies, assessment of flood risks, and the spatial mapping of flood events [112; 113]. It is worth noting that as technology in the field of remote sensing matures, there is a growing consensus to enhance satellite mission support for flood relief efforts [114]. The remote sensing satellite missions based on the sensor, principle, resolution, and launching country (Table 5) shows that there is a gradual transition of satellite missions from initial military reconnaissance to civil and scientific uses and from initial optical
## 2 Designed Photovoltaic System
### Operation of the System
#### 2.5.2 Maximum Power Point Tracking (MPPT)  
The MPPT unit employs three different algorithms to optimize the energy output of the PV array:  
ANN-Based MPPT: Based on input characteristics like temperature, load power, and solar irradiation, the ANN model forecasts the MPP. The operating point of the PV array is modified based on the anticipated MPP.  
RL-Based MPPT: The RL agent learns the optimal operating point through interaction with the environment. The Q-learning algorithm is used to update the Q-table based on the reward received for each action.  
Conventional MPPT: The P and O method is used as a baseline for comparison. The operating point is perturbed, and the resulting change in power is observed to determine the direction of the next perturbation, where VMPP and IMPP are the voltage and current at the MPP, respectively. The MPPT unit continuously adjusts the operating point of the PV array to maximize PPV.
## 7 Discussion and Future Work
### Positioning with Smart Devices  
In the results presented, we have seen that all smart devices showed reliability in providing a precise positioning regardless of the environment. While an increase in measurement noise could be seen in more challenging environments, such as S2, the devices maintained a suitable positioning accuracy. This is also the case for C/N0 values, which remained at a similar level throughout the surveys and do not seem to be a real determining factor in this case for positioning precision.  
Yet, a comparison of the raw measurement errors between the devices showed large differences from one device to another. With more advanced positioning techniques, this needs to be taken into account, as not all devices might be capable of performing similarly. The most evident discrepancies in measurements seen in our analyses were access to dual-frequency measurements, retrieval of carrier-phase measurements and pseudorange smoothing. Moreover, GP7 and PGW provided interesting comparison points as they had the same GNSS chipset model embedded, which showed that the platform has an impact on the measurements as well. The clearest example is the absence of L5 signals on the GPW.  
Another limiting factor in Android devices remains the missing characterizations of the hardware, such as the antenna used for reception of GNSS signal, as already pointed out in [11]. While access to antenna information has been added in the Android API, the information provided by the manufacturer is slim or non-existent. For advanced positioning processing, one has to rely on self-characterization of the hardware.  
A similar conclusion can be drawn for INS-related sensors, such as accelerometers and gyroscopes. They have not been reviewed in the analysis even though they are present in the dataset, as we preferred to focus on GNSS capacities, but will be part of the future analysis.
## 3 Frame Optimization Design
### Topology Optimization of Frame
#### 3.2.2 Topology Optimization Results  
After 33 iterations, the calculation was completed, and the iteration frequency curve is shown in Figure 10. With the increase in iteration frequency, the flexibility maintained a decreasing trend, with the lowest points of flexibility being 4760 and 5100 in the 10 th and 26 th iterations, respectively.  
Figure 9: Topological model of the frame.  
Pay attention to the iterative results of these two steps and obtain the material interpolation model based on the SIMP method, as depicted in Figures 11 and 12. This is achieved by assigning intermediate densities to represent the obtained topological result while penalizing them. In the model, the bright red area corresponds to a unit density of 1, the dark blue area represents a unit density of 0, and the other shades represent intermediate densities.  
Figure 11: The pattern after the iterative optimization solution is in step 10.  
Figure 10: Function image of flexibility change.  
Figure 12: The pattern after the iterative optimization solution is in step 26.  
These two resolved patterns represent the material distribution of the frame. The area with a unit density of 1 serves as the main component of the frame, responsible for load transmission, while the areas with intermediate densities represent other components of the frame.  
Through comprehensive analysis of Figures 10-12, it is observed that although the flexibility in the iterative operation of Step 10 is slightly smaller than that in Step 26, the difference is not significant. Considering the material distribution effect, Step 26 yields the best result with a more pronounced material distribution. Therefore, the difference in flexibility is disregarded, and the iterative result obtained in Step 26 is selected as the final result of topology optimization.  
Finally, some adjustments are made to the pipe fittings based on the results of the topology optimization. As shown in Figure 13, a solid line indicates the addition of a pipe fitting in the front compartment. In the frame structure considered in this paper, the main body comprises thin-walled circular steel tubes. Designing solely based on the results of topology optimization would lead to practical manufacturing issues and non-compliance with competition rules. Thus, it becomes necessary to incorporate manufacturability treatment, which involves combining the optimization result with actual processing technology and modifying it based on engineering experience. This ensures that the optimized frame can be manufactured smoothly and meets the requirements set by the competition rules. Figure 14 illustrates the addition of a pipe fitting to the front partition support structure, while Figure 15 depicts the removal of a pipe fitting from the rear compartment (represented by a dashed line).  
Figure 14: Front diaphragm support structure after topology optimization.  
Figure 13: Front compartment model after topology optimization.
## 5 Discussion
### Results per Topology  
In star topology, almost all nodes communicate through the central node. In this case, the central node controls the infection--other nodes can only contact no more than one infectious source. However, when the central sensor node has been infected and failed, The spread of malware will be accelerated, and MCC will plummet to 0. Therefore, controlling the infection and failure of the central sensor is an effective way to improve the robustness.  
The lattice-2d topology presents a 2D lattice network; the average degree of nodes is about 4. It can maintain a stable connection between nodes in case of a node failure and will control the spread of malware to a certain extent in the earlier stage--each node is only connected to two to four nearby nodes. However, it will spread and maintain a certain number of infected nodes eventually. Moreover, if the number of failed nodes exceeds a particular proportion, the MCC of lattice-2d topology will deteriorate more than random topology.  
In general, random topology has the best MCC performance. In this network type, each node has a connection probability (between 0 and 1) of being connected to each other node, which means that all nodes are able to communicate with distant ones, and the connection between nodes is homogeneous. In this case, the network can always maintain a certain degree of connectivity, even if a considerable number of nodes fail.  
The topology that performs the best is the random topology. We try a simulation that only considers the failure caused by subsystem failure and external physical damage; then, the performance of random topology is also excellent. However, with the integration of malware propagation factors, the global spread of malware will drastically reduce the reliability of the nodes, thereby reducing the functionality of the network. Furthermore, a completely uniform connectivity network does not exist in reality; ignoring the distance between nodes to establish an average connection is unacceptable in cost. The star topology can be considered to have the same MCC and number of normal nodes--unless the central node fails, it will lead to the collapse of network performance. Finally, the lattice-2d topology has a performance in the middle of the other two.
# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 2 Background
Thus, recurrent neural networks (RNNs) are proposed to handle the temporal evolution of traffic status [21]. An RNN comprises hidden layers having closed-loops, thereby capturing nonlinear time-series problems. However, the chain-like structure of the RNN is unable to train time series data with long time lags due to a gradient vanishing problem during the back-propagating process. The gradient vanishing problem, an inherent problem of the RNN, is addressed by a long short-term neural network (LSTM). The LSTM is designed to model long-term dependencies and determine the optimal time lag for time series problems by giving the memory unit the ability to decide to remember or forget certain information. Thus, the LSTM has been exploited in traffic forecasting [7; 8; 22; 23]. Even though the LSTM has a substantial ability to memorize long-term dependencies for traffic forecasting, it has a complex structure, and its training takes a long time. Therefore, the gated recurrent unit (GRU) is applied to resolve traffic problems. The GRU has fewer parameters than the LSTM, which accelerates training and equivalent capability [22; 24; 25; 26]. In addition, in recent research, hybrid integrated deep learning models for traffic forecasting have been proposed. For example, the graph neural network or the spatio-temporal residual network have been integrated into the LSTM in order to improve its prediction accuracy, and computational cost [27; 28; 29; 30]. This study aims to predict traffic speed on highway sections using the GRU method. The prediction performance is compared with alternative methods, i.e., the ARIMA and LSTM models. Furthermore, this study investigates the computational cost of the GRU method compared with the LSTM.
###### Abstract  
Terrestrial vegetation, a critical component of the Earth's land surface, directly impacts the planet's material and energy balance. This study investigated the dynamics of terrestrial vegetation in China from 2000 to 2019 using three remote sensing products (NDVI, EVI, and SIF) and explored the driving mechanisms behind these changes. We considered three meteorological factors, nine land use types, and two socio-economic factors while employing mathematical models to analyze the data. Additionally, we used the CA-Markov model to predict the spatial distribution of vegetation remote sensing products for 2020-2025. Our findings indicate the following: (1) Throughout the study period, the vegetation indices, NDVI, EVI, and SIF, all exhibited increasing trends. The SIF showed a more direct response to vegetation cover changes and was less influenced by other driving factors. The SIF outperforms the NDVI and EVI in detecting vegetation trend changes, particularly regarding sensitivity. (2) Vegetation cover changes are driven by multiple meteorological factors, such as temperature, precipitation, and relative humidity. These factors exhibit a strong spatial correlation with the distribution of vegetation remote sensing products. Among these factors, the SIF shows a higher sensitivity to temperature compared to the NDVI and EVI, while the NDVI and EVI display greater sensitivity to precipitation and relative humidity. (3) Within the study area, land use types reveal a gradient from northwest to southeast, which is consistent with the spatial distribution of the vegetation remote sensing products. For green vegetation types, the three remote sensing products exhibit varying sensitivity levels, with the SIF demonstrating the highest sensitivity to green vegetation types. (4) Overall, the future vegetation outlook in China is promising, especially in the southeastern regions where significant vegetation improvement trends are evident. However, the vegetation conditions in some northwestern areas remain less favorable, necessitating the reinforcement of ecological construction and improvement measures. Additionally, a significant positive correlation exists between population size, GDP, and vegetation remote sensing products. This study highlights the variability in the dynamics and driving mechanisms of terrestrial vegetation remote sensing products in China and employs the CA-Markov model for predicting future vegetation patterns. Our research contributes to the theoretical and technical understanding of remote sensing for terrestrial vegetation in the Chinese context.  
**Keywords:** NDVII (Normalized Difference Vegetation Index); EVI (Enhanced Vegetation Index); SIF (Solar-Induced Chlorophyll Fluorescence); spatiotemporal variation; driving factors; CA-Markov model
## 3 Archaeological Feature Extraction from SAR: Methodological Approaches
### Multi-Temporal Analysis
Moreover, the ratio operator is useful for the further mitigation of multiplicative speckle noises in SAR images. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Acquisition (Day** & \multirow{2}{*}{**No.**} & **Acquisition (Day** & \multirow{2}{*}{**No.**} & **Acquisition (Day** & \multirow{2}{*}{**No.**} & **Acquisition (Day** & \multirow{2}{*}{**Month Year)**} \\  & & **Month Year)** & & & **Month Year)** & & **Month Year)** \\ \hline
1 & 27 February 2013 & 11 & 20 April 2013 & 21 & 23 June 2013 & 31 & 26 August 2013 \\
2 & 3 March 2013 & 12 & 24 April 2013 & 22 & 27 June 2013 & 32 & 30 August 2013 \\
3 & 7 March 2013 & 13 & 2 May 2013 & 23 & 5 July 2013 & 33 & 7 September 2013 \\
4 & 15 March 2013 & 14 & 6 May 2013 & 24 & 9 July 2013 & 34 & 11 September 2013 \\
5 & 19 March 2013 & 15 & 10 May 2013 & 25 & 21 July 2013 & 35 & 15 September 2013 \\
6 & 23 March 2013 & 16 & 26 May 2013 & 26 & 25 July 2013 & 36 & 23 September 2013 \\
7 & 31 March 2013 & 17 & 3 June 2013 & 27 & 29 July 2013 & 37 & 27 September 2013 \\
8 & 4 April 2013 & 18 & 7 June 2013 & 28 & 6 August 2013 & 38 & 9 October 2013 \\
9 & 8 April 2013 & 19 & 11 June 2013 & 29 & 10 August 2013 & 39 & 13 October 2013 \\
10 & 16 April 2013 & 20 & 19 June 2013 & 30 & 14 August 2013 & 40 & 17 October 2013 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Revisit-cycle acquisition dates for the COSMO-SkyMed data to ensure similar illumination geometry. The data were acquired along the ascending orbit, with Horizontal Transmit-Horizontal Receive (HH) polarization and an angle of incidence of 19.95\({}^{\circ}\).
# A Hybrid Optimization Algorithm for the Synthesis of Sparse Array Pattern Diagrams
## 2 Quantum Hybrid Particle Swarm Optimization Algorithm Combined with Quantum Behavior
### Classical Particle Swarm Optimization Algorithm
In the definition of \(c_{1}\), the inertia weight \(W\) is introduced. By coupling \(W\) and \(c_{1}\), its initial value is larger (strong exploration ability), and it decreases later (strong exploitation ability) [21]. The dynamic coupling strategy between the individual learning factor \(c_{1}\) and the inertia weight \(W\) is the key innovation used to enhance the algorithm's global search ability, especially in complex high-dimensional problems such as sparse array optimization, reducing the dependence on the historical optimal position and enhancing the local search ability. For \(c_{2}\), the balance between the particle's global exploration and local exploitation ability is adjusted in real time through a cosine function [20].
## Materials and Methods
### Online Gaze-Based Selection  
In the conditions with vocalization (T, N, F), the digitized audio signal was sent in packages \(\sim\)14 ms long, which were combined into a circular buffer 181 ms long. The data were filtered with a Chebyshev Type 2 filter with stopband edge frequencies of 1 Hz and 5000 Hz, passband from 300 to 3400 Hz, 10 dB of stopband attenuation and 3 dB of passband ripple. The selection occurred when two criteria were met: (1) The prospective object for selection was pursued by gaze (details and parameters are described below), (2) the voice confirmation was detected. To detect the voice confirmation, the median of filtered data amplitudes computed over the circular buffer was computed. If the median exceeded a certain threshold, this meant that the participant confirmed the selection. The threshold value was set based on pilot tests in a way that participants could trigger selection by comfortable pronouncing the confirming words, without the need to raise their voice.  
In the condition with manual (mouse) action confirmation (M), the data from the mouse left button were continuously monitored. Before starting the task in condition M, the participants were asked to place the mouse cursor to the dark grey zone of the playing field (see Figure 1; it was not visible in that area; this action was required, due to technical limitations of the software, which could not receive the mouse clicks when the cursor was outside the screen).  
In the first ten experiments (participants from #1 to #10), we used the parameters of the ball selection algorithm from the study [22], where gaze was used for selection without voice confirmation. Specifically, when a confirming action occurred, regardless of the condition, the median of the distances from the gaze position to a ball center was calculated for each ball in a window of \(\sim\)867 ms length. We noticed, however, that some participants had issues with the delay of the selection algorithm triggering. The most probable cause of this problem could be a long algorithm window size and the \"features\" of the circular audio buffer. After the tenth experiment (i.e., for participants from #11 to #23), the window size was reduced from 867 ms to 400 ms, which reduced the number of such delays.  
If the median of the distances computed for a ball did not exceed 60 pixels (in other words, the distance did not exceed 60 px for at least half of samples in the time window) and was the smallest  
Figure 1: A screenshot of a typical experimental layout. Here, ball #3 was selected, which was indicated with the green color.  
among all balls, this ball was selected. To prevent unintended multiple sequential selections, the next selection was allowed only 800 ms or later after the previous one.
###### Abstract  
This study describes a new algorithm developed to detect local cells of minimum or maximum heights in grid Digital Elevation Models (DEMs). DEMs have a low variance in digital levels due to the spatial continuity of the data. Traditional algorithms, such as SIFT, are based on statistical variance, which present issues to determine these highlighted cells. However, one of the main purposes of this identification is the use of these points (cells) to assess the positional accuracy of these products by comparing those extracted from the DEM with those obtained from a more accurate source. In this sense, we developed an algorithm based on a moveable window composed of variable sizes, which is displaced along the image to characterize each set of cells. The determination of highlighted cells is based on the absolute differences of digital levels in the same DEM and compared to those obtained from other DEMs. The application has been carried out using a great number of data, considering four zones, two spatial resolutions, and different definitions of height surfaces. The results have demonstrated the feasibility of the algorithm for the identification of these cells. Thus, this approach expects an improvement in traditional procedures. The algorithm can be used to contrast DEMs obtained from different sources or DEMs from the same source that have been affected by generalization procedures.  
image matching; low-variance feature detection; DEM matching; DEM quality control Article