esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
In this study, based on the multiangle observation data of the reflectance of MODIS in the past 5 years, the surface reflection characteristics of the Hefei area were divided into eight categories according to the AFX index, and the corresponding BRDF archetype was constructed. The distribution characteristics of AFX and BRDF archetypes in the visible band in the Hefei area are analyzed. Albedo inversion based on FY-2G satellite data is realized based on eight BRDF archetypes as prior knowledge. A comparative analysis of the albedo inversion results based on FY-2G observation data shows that the inversion accuracy of the BRDF archetypes obtained by using the 5-year observation data from MODIS is better, and this method is feasible. With the help of the principal component analysis method, the relationship between AFX and the vegetation index of the FY-2G visible channel was discussed, and the orthogonal relationship between the two was revealed.  
The research in this paper is mainly based on satellite observation data and lacks the support of field measurement data. Therefore, the next work plan is to conduct a large number of BRDF measurement experiments in Hefei, China to further optimize the BRDF archetype database and verify the accuracy of the method.  
Conceptualization, Q.X.; methodology, L.H. and Y.C.; software, L.H. and M.S.; validation, L.H.; data curation, L.H. and J.S.; writing--original draft preparation, L.H.; writing--review and editing, L.H. and Y.C.; visualization, L.H. and W.L.; supervision, C.X.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (No. 42105082).  
Not applicable.  
All satellite remote sensing and field measured data used in this study are openly and freely available.  
We are grateful for the careful review and valuable comments provided by the anonymous reviewers.  
The authors declare no conflict of interest.
## 7 Processing Architecture
### CHP2 Data Link Layer  
The data link layer schedules operations, including transmission and reception, frame extraction, and timestamp packaging. This scheduler also controls the TRS amplifier board. The DLL also contains an Ethernet logger process, which exports system logging data at a rate of 10 Hz. This logging data includes range, position, and orientation estimates; channel SNR estimates; timing exchange information; and hardware debugging information.  
Figure 17: Optimized structure of the fine ToA estimation implementation. (**a**) Construct a micro-shifted version of the reference waveform. The consecutive waveforms are shifted by 1/200 th of a chip. (**b**) Memory-efficient subdivision of correlator bank.
# Analysis of Maize Sowing Periods and Cycle Phases Using Sentinel 1&2 Data Synergy
## 2 Materials and Methods
### Satellite Data
#### 2.2.2 Normalized SAR-Derived Index  
The potential of the combined use of VH and VV for agricultural monitoring has been explored in the scientific literature. The VH/VV ratio (Figure 2b) has been shown to be a very suitable vegetation index, being able to limit the double-bounce effect caused by the vegetation ground interaction [15; 16; 17; 37].  
The effect of soil moisture and bare soil roughness can be limited by normalizing VH/VV, which is able to detect the vegetation-induced backscattering setting the minimum signal for a specific type of crop, and then filtering the soil moisture-induced noise during the bare-soil period. However, even if Sentinel-1 enables the detection of high-resolution ground geometric changes, the aim of this study is to retrieve information of generalized behaviors of crop-specific souring, to compare the estimations with local available souring information and general crop calendars. For this reason, a large number of fields were considered in this case, in order to perform the EO-based analysis within a representative spatial sample of the area.  
For each acquisition, the average VH/VV ratio was calculated for every maize field through a GIS-based analysis. The VH/VV mean results were then converted into decibel units with:  
\[\left(\frac{VH}{VV}\right)_{dB}=10\cdot log_{10}\left(\frac{VH}{VV}\right)_{ lin} \tag{2}\]  
In order to compare the signals from different maize fields, a new index called IPR (Polarimetric Ratio Index) is proposed in this study; it is based on the normalized VH/VV ratio considering the maximum and minimum value from each field:  
\[IPR_{i}=\frac{\left(\frac{VH}{VV}\right)_{i}-\left(\frac{VH}{VV}\right)_{min,i} }{\left(\frac{VH}{VV}\right)_{max,i}-\left(\frac{VH}{VV}\right)_{min,i}} \tag{3}\]  
where the \(max,min\) pedexes identify the maximum and minimum values of \(VH/VV_{i}\) [-] during the growing season respectively, calculated for each \(i\)-th maize field. The IPR index allows a direct comparison of the backscattering variability during the first part of the season, avoiding misalignments due to different ground roughness in different maize fields. The minimum equal to zero corresponds to the context of bare soil. The maximum equal to 1 corresponds to the maximum of VH/VV ratio and then to the highest vegetation volume scattering. The IPR from pixels corresponding to each maize field (classified according to the procedure described in Section 2.3) were averaged for each acquisition day, to calculate the mean daily IPR of every maize field.  
Although the normalized combination of VV and VH has already been used for crop phenology monitoring [16], to the best of our knowledge, this is the first time that a noise-filtered index based on the normalized VH/VV was directly used to retrieve crop-specific emerging periods.
## 3 Experiments
### Visualization  
In this section, we set the parameters of Equation (16) to \(f_{min}=0.001\), \(f_{max}=80\) and \(n=255\). Semi-quantitative visualization is a non-mean quantization method that assigns color range based on distance. This method is more suitable for human attentional habits for distance and assigns a unique color to each depth range.  
As KITTI does not make the official visual color map available, we were unable to show the visual colors in Figure 4 for comparison purposes. Therefore, we used a self-supervised depth completion from a previous study [45] and the algorithm in this paper to compare the visual differences between the mean-quantization visualization commonly used for depth completion and the semi-quantitative visualization proposed in this paper, as shown in Figure 4c.  
The second row of the figure shows the depth maps obtained by the two algorithms using the mean-quantization visualization. This method only generates information on the relative distances in a single image. However, as the maximum and minimum depth values do not coincide from image to image, the color of each pixel obtained according to Equation (9) will also vary. The third row of the figure shows the depth maps obtained by the two algorithms using the semi-quantitative visualization. As can be seen from Figure 4, the two algorithms do not differ much in their depth predictions, but they produce a significant visual difference compared to the mean quantization visualization. The observation of the whole image shows that the mean quantization visualization assigns the color map equally to the entire depth range, which may result in a wasted color map, i.e., producing the visual effect of an all-blue color in the image. In contrast, the depth map  
Figure 4: Visual comparison between ours (**left**) and [45] (**right**) algorithms using different visualization methods. (**a**) RGB images; (**b**) mean-quantization visualization; (**c**) semi-quantitative visualization.  
visualized by the semi-quantitative visualization proposed in this paper is smoother in terms of color distribution, resulting in a better color range to the nearer parts, which is in line with human visual observation habits.  
The car marked in Figure 4 is about 22 metres away. If mean-quantization visualization is used, it can be obtained from Equation (9) that 0-22 m can be assigned approximately 71 bits of color in an image of 256 bits. If semi-quantitative visualization is used, we can obtain a color map according to Equations (10) and (16). By referring to the color map, we can determine that 0-22 m months is assigned to 181 bits of the color system. That is about 1.5 times more colors.  
These results show that the semi-quantitative visualization has advantages in the visualization of depth completion, and is able to produce depth maps that are more in line with human visual observation habits than the traditional mean quantization visualization. This provides a useful reference for further research and application of depth completion algorithms.
## 2 Description of the Multi-Sensor Monitoring System
### Signal Processing Architecture and Software  
Our sensor software is a three-layered set of modules written in various languages to facilitate data collection and analysis. In the case of radar motes, channels I and Q channel data are recorded in order to obtain the target range and velocity information, see Figure 4. Our goal is to make a smooth transition from raw mote data collection to a Matlab-based signal processing tool. The layers we used are the sensor mote layer, base station layer, and the analysis layer. Each layer abstracts a portion of the implementation for modularity. Communication between layers is done through network interfaces. The layers are described as follows:  
* The first layer is the _mote layer_, which performs the actual sensing. The software at this layer is written in the nesC language [10] and executed in TinyOS environment [9]. TinyOS is an operating system environment designed for small embedded systems in which resources are highly constrained. nesC is a component-based language and uses syntax similar to C. Motes are programmed via a USB connection to a PC. We developed a sensing software program for two types of motes. The sentry sensor-mote monitors its visual light sensor and detects when shadows of objects pass by it. When motion is detected it broadcasts an 'Alert' message via its radio transmitter. 'Alert' messages are sent to other sensor-motes in the area that then began to sense their environment. Sensor-motes run a separate sensing program designed to sample sensors at high speeds for a short amount of time. Their clocks are synchronized with the sentry mote. Here our main constraint is the size of memory available. The TelosB mote hardware used by the sensor-motes has only 10 KB of RAM. Each capture run is saved onto the flash memory and, when all runs are finished, this data is transmitted to the second layer running on a base station composed of a PC and a mote attached to the PC.
* The _base station layer_ includes several applications written for the base station. First, data is immediately received by the base station application running on the mote attached to the PC. Second, a Serial Forwarder application on the PC receives data from the base station application via USB and sends this to our service component written in Java. The service layer tests the validity of the data by checking for lost packets and requests retransmission for any incomplete data sets. This layer also has some control functionality. Users can change sampling periods and the number of runs per experiment from the Java console.
* Data from the mote are available to the _analysis layer_ written in Matlab. Matlab can instantiate Java objects that we use to contact the service layer. Thus in the Matlab environment a user only needs to create a Java object and read raw mote data from that object. At this layer the user can perform any required signal processing techniques.
## 3 Results and Discussion
### Nanomechanical Properties  
Figure 3 shows the Load-Displacement curves of nanoindentation tests for samples under 10 mN normal load. Table 1 shows the mechanical properties obtained by the L-D plots. In general, a higher ratio between hardness and elastic modulus (H/E) serves as a better wear-resistance [24]. In this test, the elastic modulus (E) and hardness (H) of the implanted samples decreased to 78% and 61.7% of the substrate, respectively. In addition, the H/E was approximately 78% of the substrate. Studies have shown that the irradiation effect during the implantation may increase the surface temperature of the samples, causing the substrate to soften [25]. The associated bombardment and cascade collisions will form a large number of defects on the surface, such as vacancy, dislocation and dislocation group, strengthening the substrate to some extent.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **H(GPa)** & **E(GPa)** & **H/E** \\ \hline substrate & 4.677 & 146.645 & 0.032 \\ Nb implanted & 2.888 & 114.743 & 0.025 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Calculated values of elastic modulus and hardness of samples.  
Figure 3: The nanoindentation Load–Displacement curves of the substrate and the Nb-implanted sample.  
Figure 2: XRD patterns of substrate and the Nb-implanted sample.
# Atmospheric and Fog Effects on Ultra-Wide Band Radar Operating at Extremely High Frequencies
## 2 Propagation of a Linear FM Signal in a Dielectric Medium
Dispersive behavior of the atmospheric medium will cause deviations in the frequency calculated in Equation (10), and thus produce errors in the measured distance. We now demonstrate the effects of atmospheric fog on the resulting measured distance at millimeter wavelengths.
## 5 Experimental Results
### Results
#### 5.4.4 LMVSegRNN Is Robust towards Mesh Tessellation  
Naturally occurring small changes in mesh topology and geometry may be observed in the analyzed data, as they are influenced by the scanning properties, which may vary depending on the clinic, scanner, etc. We believe that methods should learn the structural nature of the real-world object represented by the mesh rather than the discrete approximation encoded in raw mesh data. To examine this, we evaluate the robustness to various geometry tessellations to see how individual methods handle changes that do not affect object semantics. The test sets are modified with the subsequent methods:  
* Random vertex displacements (RVD). Vertices are displaced by a value from \(\langle 0.0\,\mathrm{mm}\), \(0.2\,\mathrm{mm}\rangle\), sampled uniformly. The maximal displacement value is set so the perturbation does not affect shape semantics and is determined according to the typical error of modern intra-oral scanners [(27)].
* Planar flipping optimization (PFO). We utilize edge flip operation to locally remesh the input shape, increasing local triangle quality. Again, a similar procedure is applied during training data augmentation.  
Figure 5: Visualization of two segmentation results generated by the competing methods, our proposed method, and ground truth. From left to right, top row first: input mesh, PointNet without normal vectors, PointNet with normal vectors, SparseMeshCNN with attention blocks, our LMVSegRNN, zoomed-in input, PointNet++ without normal vectors, PointNet++ with normals, Graph Cut, ground truth. Zoom in to better see the details.  
Figure 6: Detailed visualization of the results of the best-performing setup of LMVSegRNN on four randomly selected complex test cases. For each sample, we visualize the results from the occlusal view, and the lingual and buccal surfaces in orthographic projections. Zoom in for details.  
* Explicit remeshing (ER). We apply local remeshing operators edge flip, edge collapse, relax, and refine, to obtain isotropically remeshed geometry.  
The obtained results are summarized in Table 3. From the results, the following conclusions emerge. The highest drop in performance is observed when RVD transformation is applied, as it introduces the most considerable changes in geometry. All methods are collectively sensitive to vertex displacements, with an average performance drop of approximately 3% on _W-IoU_ metric. When visually examined, we find that the measured increase in error is reflected in more jagged region boundaries. The remaining remeshing adjustments aim to increase the mesh quality by introducing changes in the topology. Though, in general, no significant performance drop is observed, the smallest performance drop is measured in the results of our projection-based method.
# Evaluation of HY-2 Series Satellites Mapping Capability on Mesoscale Eddies
###### Abstract  
With the launches of HY-2B, HY-2C and HY-2D, the National Satellite Ocean Application Service (NSOAS) successfully established the first batch of HY-2 constellation on 19 May 2021, dedicated to detecting the marine dynamic environment. While the validation and calibration of HY-2 series satellites have been well studied, using both in situ and cross-track measurements, further evaluation is required of HY-2 series satellites mapping capability on mesoscale eddies. In this study, we conducted two groups of pseudo-observations obtained by ocean models output at positions and times of the satellite along ground tracks. The two altimetry satellite combinations are the Jason-3+Sentinel-3A/B configuration (reference group) and the Jason-3+Sentinel-3A/B+HY-2B/C/D configuration (evaluation group). Three regions at different latitudes were selected as the study areas. Then, we gridded the satellite along ground tracks into synthetic SSH products for each satellite configuration group. Finally, the attributes of mesoscale eddies extracted from gridded products were analyzed to evaluate the mapping capability of HY-2 series satellites. Our results suggest that the gridded products of evaluation groups are more similar to model data compared to reference groups, with a maximum difference reduced by 23%. In terms of eddy identification, the evaluation groups using HY-2 series satellites can alleviate eddy incorrect estimation and add more details to the spatial distribution of eddy radius and amplitude. Additionally, the evaluation groups displayed better performance in mesoscale eddy detection, with a maximum enhancement of 14% and 16% in eddy recognition rate and accuracy.  
HY-2 series satellites; mesoscale eddy; sea surface height; optimal interpolation 2022
# A Symmetric Matrix-Aided MIMO to Improve Reliability for Maritime Visible Light Communications
## 5 Conclusions  
In this paper, we propose a symmetric matrix-assisted MIMO-VLC system for maritime communication to improve reliability performance. By exploiting the repeatability of the elements of the symmetric matrix, the Euclidean distance can be enlarged to combat the adverse conditions of channels and thus the reliability performances can be improved. Then we derive the theoretical SER expressions of the proposed scheme. Moreover, simulations are conducted to verify the effectiveness of the proposed scheme and to validate that the proposed scheme is superior to the benchmark schemes over atmospheric channels, underwater channels, and air-water interface channels. Then we simulate the proposed scheme over atmospheric channels, underwater channels, and air-water interface channels with different channel parameters. These simulation results show that the proposed symmetric matrix scheme has advantages in improving communication reliability under the three channels. Moreover, when changing the environmental parameters, we can observe that the shorter the propagation distance, the cleaner the water and the lower the wind speed, the transmission reliability can be improved. For atmosphere channel, when the SER is \(1\times 10^{-5}\), the symmetric scheme is about 4 dB better than the SMP scheme when \(\sigma_{I}^{2}=0.18\) and about 3 dB better than the RC scheme when \(\sigma_{I}^{2}=1.68\). Therefore, the symmetric matrix-aided MIMO transmission scheme provides a promising solution for reliable transmission. Further research directions on improving the reliability performance include applying the geometric mean decomposition (GMD) or combining the SVD with the GMD for enhancing the reliability performance.  
Software, Y.Z.; investigation, Y.F.; writing--review and editing, L.Z.; project administration, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 11: Comparisons with different wind speeds for the air–water interface channel.
# A New SEPIC-Based DC-DC Converter with Coupled Inductors Suitable for High Step-Up Applications
## 3 Semiconductor Stresses and AC Analysis of the Proposed SEPIC-Based Converter
### Steady State Theoretical Waveforms  
The theoretical waveforms associated to the converter are presented in Figures 5 and 6.  
Figure 5: Main waveforms associated to the passive components of the proposed converter.  
The magnetizing inductor voltage \(v_{LM}\) is piecewise constant, exhibiting a rectangular shape, while the magnetizing inductor current \(i_{LM}\) is piecewise linear, increasing until \(D\)-\(T_{s}\), because \(v_{LM}\) is positive in the first topological state and decreasing in the second topological state, when the voltage is negative. Similarly, the same theoretical waveforms are associated for inductor voltage \(v_{L3}\) and inductor current \(i_{L3}\). Internal capacitor current \(i_{C1}\) and output capacitor current \(i_{C2}\) are piecewise constant, resulting in a piecewise linear internal capacitor voltage, \(v_{C1}\), and output capacitor voltage, \(v_{C2}\). The semiconductor waveforms, depicted in Figure 6, result from taking into account that, when \"\(ON\)\", the semiconductor currents are linear combinations of inductor currents and the same is true for semiconductor voltages. These linear combinations values are also specified in Figure 6.  
Figure 6: Main waveforms associated to the semiconductor devices.
## 4 Control System Design
### Preliminary Knowledge
#### 4.1.3 Input Saturation  
In practice, the control force and moment are subject to saturation nonlinearities due to the physical limitations of the actuator. It can be described as follows:  
\[\tau_{i}=\left\{\begin{array}{ll}\tau_{i\max},&\quad if\ \tau_{0i}>\tau_{i\max} \\ \tau_{0i},&\quad if\ \tau_{i\min}\leq\tau_{0i}\leq\tau_{i\max}\\ \tau_{i\min},&\quad if\ \tau_{0i}<\tau_{i\min}\end{array}\right. \tag{33}\]  
where \(i=u,r\), \(\tau_{i\max}\) and \(\tau_{i\min}\) are the maximum and minimum output that the actuator can provide, respectively, \(\tau_{0i}\) is the control command without considering input saturation and \(\tau_{i}\) is the final control command with the same meaning as (9).  
To prevent the saturation problem of implementing agencies, an auxiliary dynamic system is constructed as follows [23]:  
\[\dot{e}_{i}=\left\{\begin{array}{ll}-K_{ei}e_{i}-\frac{|i_{e}\cdot\Delta\tau _{i}|+0.5\Delta\tau_{i}^{2}}{|e_{i}^{2}|}\cdot e_{i}+\Delta\tau_{i},&\quad|e_{ i}|\geq\xi_{i}\\ 0,&\quad|e_{i}|<\xi_{i}\end{array}\right. \tag{34}\]  
where \(K_{ei}\) is a positive design parameter, \(e_{i}\) is an introduced variable, \(\xi_{i}\) is a small positive parameter and \(i_{e}\) is a variable, which will be introduced in the next subsection. \(\Delta\tau_{i}=\tau_{i}-\tau_{0i}\).