esa-sceva/satcom-chunk-collection huggingface dataset:
# Optically Transparent Honeycomb Mesh Antenna Integrated into OLED Light Source
## 3 Antenna Configurations
### Comparison between the Non-Transparent and Transparent Antennas  
Both designs were simulated and analyzed using an electromagnetic solver software, Ansys Electronics Desktop V.13.0.2. The electrical and radiation parameters of the antennas were compared. Figure 8 shows the comparison between the return losses for the compared antennas. For both candidates, the antenna impedance's were well matched to 50 \(\Omega\) at the resonance frequency of 5.15 GHz, because the return losses were less than \(-10\) dB. The bandwidth defined by (\(S_{11}\leq-10\) dB) was \(130\) MHz (5.19-5.32 GHz) for the non-transparent antenna and \(110\) MHz (5.09-5.2 GHz) for the transparent antenna. It should be  
Figure 6: Rectangular patch antenna geometry: (**a**) 3D view, (**b**) top view. \(W_{s}=25\) mm, \(L_{s}=25\) mm, \(L_{p}=11.76\) mm, \(W_{p}=15.8\) mm, \(t=1\) mm, \(L_{1}=5.4\) mm, \(L_{f}=5.8\) mm, and \(W_{f}=1.77\) mm.  
noted that the compared antennas had the same size; however, the disagreement observed between the resonance frequencies is justified by the resistance values for each structure (solid metal layer versus mesh metallic layer).  
The radiation patterns for the two antennas are also compared in Figure 9. These diagrams are obtained for the resonance frequencies of each antenna and for the two planes: the XZ-plane (Figure 9a) and YZ-plane (Figure 9b). These results are in acceptable agreement because the antennas' sizes are equivalent. The maximum gains were 6.23 dBi and 5.5 dBi for the non-transparent and transparent antennas, respectively. Both antennas achieved good gain in the operating band for WLAN applications. Therefore, increasing the resistance value of the sheet deteriorated the gain of the mesh antenna. This can be compensated by using meta-materials and applying array antenna techniques [40].
# Compact and Broadband Microstrip Band-Stop Filters with Single Rectangular Stubs
## 4 Results and Discussion
### Bandwidth Change of Rectangular Stubs  
Figure 8 shows the measurement results of \(\mid\!S_{21}\!\mid\) and \(\mid\!S_{11}\!\mid\) in the case of rectangular stubs. Four kinds of data indicated by line (a) show the case where the impedance ratio is \(Z_{1}/Z_{0}\) = 2, 1, 1/2, and 1/4 in the pattern (a) shown in Figure 5. The other two kinds of data indicated by lines (b) and (c) show the cases of \(Z_{1}/Z_{0}\) = 1/4 in pattern (b) and (c) shown in Figure 5. In the general EMC design, the threshold level of the transmission is often set to \(T\) = \(-\)30 dB, which can obtain sufficient shielding. However, since it is difficult to measure the bandwidth attenuated to \(-\)30 dB, we set the threshold as \(T\) = \(-\)10 dB. Table 1 shows the change of the bandwidth with respect to the threshold and the quarter-wavelength resonance point. The numerical values in parentheses represent the bandwidth expansion ratio and the increase/decrease ratio of the quarter-wavelength resonance point, with \(Z_{1}/Z_{0}\) = 1 as a reference. In the case of pattern (a), it shows that by setting \(Z_{1}/Z_{0}\) from 1 to 1/2, the stop bandwidth at each threshold increases by 1.8 times. From this result, it is also found that by increasing \(Z_{1}/Z_{0}\) from 1 to 1/4, the stop bandwidth increases 2.9 times. On the other hand, for patterns (b) and (c), the bandwidth increases 3.6 times. It was found that the taper deteriorates the stop band characteristics on the lower frequency side. It was also confirmed that the bandwidth of patterns (b) and (c) is wider than that of the rectangular stub of the pattern (a), even with the same characteristic impedance ratio. Furthermore, as the value of \(Z_{1}/Z_{0}\) decreases, the capacitance \(C\) of the circuit increases, and it is confirmed that \(Q\) decreases, as shown by the Equation (4). It should be noted that the reason why the quarter-wavelength resonance point shifts is considered to be that the propagation constant \(\beta_{1}\) changes as the stub line width \(W_{1}\) increases. From the above, it is found that the transmission zero frequency shifts to higher frequency, although the band is wider as \(Z_{1}/Z_{0}\) becomes smaller.  
Figure 7: Compared results of \(\mid\!S_{21}\!\mid\) and \(\mid\!S_{11}\!\mid\) in the case of the rectangular stub when \(Z_{1}/Z_{0}\) = 1/2. Three kinds of data show the calculated values based on the TEM transmission line theory and the EM simulated value by using an Ansys HFSS simulator and measured values. Good agreement is obtained up to 5 GHz for the measured value and the simulated value.
# High-Accuracy Calibration Based on Linearity Adjustment for Eddy Current Displacement Sensor
## 4 Calibration Accuracy Verification  
In this paper, two methods are proposed to verify the adjusted calibration accuracy and precision: equidistance displacement error (EDE) and accumulated displacement error (ADE). The verification schematic diagram is shown in Figure 10.
## 4 Experiments and Results
### Comparative Analysis of Polyp Segmentation Performance Across Diverse Imaging Scenarios
The image (Row 5, Column 1) is from the ETIS-LaribPolypDB dataset, where the proportion of polyps to the entire image is very small and there are interference factors such as yellow residues. The U-Net, ResNet, PraNet, and CaraNet models generate incorrect segmentation results, identifying background pixels as polyps, while Polyp-PVT and HPANet are also affected by interference factors, resulting in segmentation errors but with clear segmentation boundaries. In summary, compared to other models, HPANet performs well in capturing fine details and segmenting boundaries, thereby improving segmentation accuracy.
# Fuel-Efficient Road Classification Methodology for Sustainable Open Pit Mining
## 2 Materials and Methods
### Data Acquisition  
#### 2.2.1 Elevation Data  
The elevation data was obtained from the point cloud pit model provided by the Haerwusu Open-pit Coal Mine. Point cloud model data was collected using the PH-25 EX hexacopter, developed by Chengdu Zongheng Dapeng UAV Technology Co., Ltd. (Chengdu, China), equipped with Light Detection and Ranging (LiDAR) technology. This enabled high-quality detection of multiple objects and accurate echo data acquisition. The acquired point cloud data was clear, retaining the distinctive characteristics of the object with high resolution. The specifications regarding the PH-25 EX and the LiDAR system are provided in Figure 2.  
Figure 1: Visual representation of step-by-step workflow.  
Figure 2: Specification of PH-25 EX and LiDAR system.  
The LiDAR scanner operates based on the principle of laser ranging to measure the distance to feature points on the surface of an object and calculate their three-dimensional coordinates. This data was then used to generate a three-dimensional surface that provides spatial information. The LIDAR system comprises three main components: the scanning system, the control system, and the computer module. The control system, managed via a computer bus, is responsible for controlling the scanning and ranging modules. Meanwhile, the computer module primarily consists of a microprocessor and memory, facilitating the exchange of information with the control system. The schematic representation of the principle and configuration of the LiDAR system are depicted in Figure 2(a) and Figure 2(b), respectively.
## 3 Results
### Built-Up Areas per Capita and Land Use Efficiency  
The LUE indicator (that is a dimensionless value) has been further characterized observing the built-up area per capita in the year 2015 (BpCt+n) in the LUE classes that were considered in the previous section. This operation presents an example of the spatial implications of the LUE indicator (Figure 4). The following arguments are based on the average built-up areas per capita that were calculated for each of the 10,000 urban centers and then averaged by the regional collocation of the center. Results demonstrate that in continents like Europe and North America in the class of LUE \(>\) 2, in which spatial expansion takes place at a rate that is at least double that of population growth, the built-up areas per capita exceed, respectively, 180 and 490 m\({}^{2}\) per inhabitant. Great variations are also observed in comparing the built-up area per capita in the same LUE class across regions. In the case of a development trajectory of population densification (0 < LUE < 1), the built-up areas per capita are almost 450 m\({}^{2}\) per person in Northern America and 175 m\({}^{2}\) per person in Europe. This drops by almost 50% in Latin America and the Caribbean (90 m\({}^{2}\) per person), to almost 70 m\({}^{2}\) per person in Asia, and to 52 m\({}^{2}\) per person in centers in Africa.  
In terms of average change of built-up areas per capita between 1990 and 2015, the values are also diverse. In the class 0 < LUE < 1, the built-up areas per capita shrink between 10% (in Europe) and 26% (in Africa). In the class of LUE > 2, built-up areas per capita more than doubled in Latin America and the Caribbean, Asia, and Africa, increased by half in Northern America, by 15% in Europe, and by 6% in Oceania.  
As a comprehensive overview of the SDG 11.3.1 quantified with GHSL data over the 25 years between 1990 and 2015, the results show that urban centers follow a development trajectory in which the rate of population growth prevailed over that of spatial expansion, with a LUE that is equivalent to 0.72. In absolute terms, the urban centers expanded globally over a land surface that is equivalent to almost 67,800 km\({}^{2}\) (approximately the surface of Ireland) to settle about 1.1 billion new people (almost the population of India in 2015).  
Figure 3: Comprehensive visualization of the Land Use Efficiency (LUE) value in circa 10,000 urban centers that were computed in the period 1990–2015.
## Results
### Comparison of Phenological Transition Dates (PTDs) Derived from Different VIs  
The comparison of PTDs extracted from different VIs is shown in Figure 5. Generally, the PTDs derived from the different VIs show smaller difference between each other at Dry-down periods (POS2, EOS\({}_{\text{trs}}\), RD) compared to the Green-up periods (UD, SO\({}_{\text{trs}}\), POS1). Specifically, PTDs derived from GCC have the smallest differences with PTDs derived from CamNIRv, while they are significantly  
Figure 4: Scatter plots between monthly mean air temperature (Ta), monthly sum of precipitation (Prec), and monthly mean gross primary productivity (GPP) during day time of (**a**,**b**) winter, and (**c**,**d**) spring in hydrological year 2014 to 2016 in four Mediterranean tree–grass ecosystem in Spain. The data in hydrological years of 2014, 2015, and 2016 are colored with green, red, and blue color, respectively. The regression line and the formula are shown if the linear regression is statistically significant (\(p<0.05\)). The gray area represents 95% confidence interval. \(R^{2}\): determination coefficient of linear regression.  
advanced from the ones from CamNDVI at the Dry-down period (Figure 5). During Green-up periods, the PTDs derived from GCC are more advanced than the ones from CamNDVI and CamRVI, while they are also delayed compared to PTDs extracted from CamNIRv. By contrast, the PTDs derived from CamNDVI are all more delayed than the PTDs from CamNIRv, in particular, with more than 10 days difference at Green-up period. However, they have small differences compared to the PTDs extracted from CamRVI (<5 days), but they are significantly advanced at Green-up and delayed at Dry-down periods, respectively. Similar to GCC, the PTDs derived from CamNIRv are also advanced than the ones derived from CamRVI with large difference at Green-up period (>15 days).  
Figure 5: Barplots of the differences between phenological transition dates (PTDs) extracted from different PhenoCam-based vegetation indexes: green chromatic coordinate (GCC), normalized difference vegetation index (CamNDVI), near-infrared reflectance of vegetation index (CamNIRv), ratio vegetation index (CamRVI). The error bars represent the standard error of the PTDs derived from different experimental sites, and hydrological years. The statistically significant differences were tested using Wilcoxon signed-rank tests (when PTDs were not normally distributed) and paired Student’s \(t\) tests (when PTDs were normally distributed). \(p\)-values are as follows: ***\(p\)\(\leq\) 0.001, **\(0.001\)\(<\)\(p\)\(\leq\) 0.01, * \(0.01\)\(<\)\(p\)\(\leq\) 0.05, ns for \(p\)\(>\) 0.05. Please see the definition of each PTD in the Table 2.
# Comparing Sentinel-2A and Landsat 7 and 8 Using Surface Reflectance over Australia
## 6 Conclusions  
This comparison of Sentinel-2A and Landsat 7 and 8 shows that the new instrument has very good compatibility with the existing Landsat instruments. However, it does also show that there are some small but systematic differences, which could be important in some contexts. These differences are in the range 1% to 9%, depending on the band in question.  
Simple adjustment equations were derived for each of the corresponding bands, which enable adjustment of Sentinel-2A surface reflectance to match either Landsat-7 or Landsat-8, as required. These adjustments bring the average systematic difference to less than 1% in all bands.  
It was also shown that the differences in reflectance carry through to produce corresponding differences in estimates of biophysical quantities such as green vegetation cover, with systematic differences of up to 8%. However, adjusting the reflectances as discussed was able to reduce this to around 1%, on average. This increases the confidence with which one might transfer models and thresholds from Landsat, for use with Sentinel-2 imagery.  
The comparison of the adjustments fitted here with those fitted for the Harmonized Landsat-8 Sentinel-2 product suggests that there may be substantial regional variation implicit in such adjustments, and that they should be tested locally when used in a specific region.  
The Joint Remote Sensing Research Program is supported financially by the governments of the Australian states of Queensland and New South Wales.  
The author declares no conflict of interest.
## 4 HFM-Based AUV Localization
### Localization Process  
The localization process consists of three steps, chirp rate distribution, packet transmission and localization. One of ANs is selected as representative AN (R-AN). It is assumed that R-AN knows the total number of ANs in the networks. Based on the information, the R-AN divides the bandwidth into sub-channels, and then distributes them to each AN. In addition, the R-AN determines the first transmission time and the transmission period for the ranging. Based on those timing parameters, all the ANs periodically transmit their packet at the same time. At that time, each AN uses the assigned chirp rate to generate the transmission signal. The packet includes the transmission time and their position. Meanwhile, the localization should be periodically performed to relive the cumulative position error. If the number of receiving packets is more than the minimum required, the AUV estimates its current position based on the received information as illustrated in the previous section. Decoupling the localization performance from the MAC delay allows to AUV localization algorithms to use all kinds of ranging technique. In addition, the ranging initiated by the ANs' transmission considerably enhance the spectral efficiency.
# Real-Time Mitigation of the Mobility Effect for IEEE 802.15.4g SUN MR-OFDM
## 3 Related Works  
### Overview of IEEE 802.15.4g Mr-Ofdm  
In the global industry, the IEEE 802 is considered to be one of the most demanding standardization organizations over the last decade. In 2012, the IEEE 802.15.4g task group standardized the PHY amendment for the IEEE 802.15.4 specifications by supporting the SUN technique to create a new direction for the LPWA sensor network. The IEEE 802.15.4g PHYs SUN specification defined two configuration modes: low data rate and high data rate. When considering the applications, the IEEE 802.15.4g PHYs included multi-rate MR-FSK, multi-rate MR-OFDM, and MR-O-QPSK. The configuration summaries of IEEE 802.15.4g SUN OFDM PHY are shown in Table 1. This mode can support a range of data rates from 50 kbps to 800 kbps with the 10.416 kHz constant of the subcarrier spacing with four configuration options. Every option mode is differentiated by the number of active tones, the channel bandwidth, the sensitivity level, and the data rates. The total signal bandwidth for each mode ranges from 1.2 MHz to 200 kHz depending on the number of active tones. All devices are required to support the particular hardware options, which are supported by the BPSK, the QPSK modulation, and the 16-QAM option. The PHY frame structure was modified with forward error coding information and a header up to a 1500-octet frame length from the start frame delimiter. The preamble includes the short training field (STF) for synchronization and the long training field (LTF) for the signal-to-noise ratio (SNR) estimation. In each STF symbol, there are five 16-length symbols. One LTF defines two symbols, S1 and S2, with a double CP for N samples.  
The MR-OFDM was formatted as shown in Figure 2. Based on IEEE 802.15.4g, the MR-OFDM includes four modes; each mode is determined by the number of active tones. This standard supports the following baseline DFT sizes: 16, 32, 64, and 128. Table 1 shows more details about the four modes of the MR-OFDM. The MR-OFDM symbol rate was 8-1/3 ksymbol/s; each symbol corresponds to 120 us. This symbol period includes the CP (24 us) and a base symbol part (96 us). The synchronization header (hereinafter SHR) includes the STF and the LTF. It is used as a preamble for detecting the start of the frame. After SHR, the PHY header carries the frame configuration, code rate, frame length, scrambler, and other information fields. The packet service data unit is the payload of the packet that carries the useful data. According to the standards, the STF field is divided into four OFDM symbols; in the fourth symbol, the last half was negated in the time domain, which is important for defining the synchronization algorithm. In addition, a quarter-duration of CP is added to each symbol. The LTF field is described by two OFDM symbols. A half-symbol CP is inserted into two consecutive copies of the LTF symbol; this prefix is used to estimate IFO.  
Figure 1: Scenario of a practical system.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 5 Experiments
### Segmentation Quality  
The information in Table 4 indicates that EO outperformed its counterparts in terms of the fitness function for prostate MRIs. However, the excellent performance of the algorithm over the search-space does not necessarily guarantee that it will reflect on image quality. Thus, an evaluation not associated to the fitness function is required. Here, the objective quality of the segmented image is evaluated using the three non-referenced metrics described in Section 5.3: _PSNR_, _SSIM_, and _FSIM_. In all metrics, a higher value points to higher-quality segmentation. Figure 6 shows a graphical comparison of the _PSNR_ value over the different methodologies represented as seven groups along the horizontal axis. Inside each group, the average _PSNR_ value is given for all images, considering thirty runs for a given number of thresholds (color-coded). To facilitate the interpretation of results, the best value is marked with an *. In terms of _PSNR_, EO outperformed the other approaches at most of threshold levels. Only GA managed to have a slightly better score on three thresholds.  
Figure 6: _PSNR_ comparison across methodologies. This figure shows the _PSNR_ values of seven segmentation methods. Each group represents average PSNR values for different thresholds, with the best marked by an asterisk (*).  
In Figure 7 we can observe the same type of graphical representation for the comparison of the _SSIM_ value over the experiments over the horizontal axis. Each group takes the average _SSIM_ value for all images for a given number of thresholds. In this case, the GA algorithm was able to outperform over five thresholds. In the rest of the experiments, EO consistently performed better than the other approaches. Following the same representation, the _FSIM_ is analyzed in Figure 8, where GA has a marginal victory with five thresholds, while EO scores better in the other cases. In summary, we can observe that _PSNR_, _SSIM_, and _FSIM_ objectively indicate that EO is better than the other approaches for the segmentation of prostate MRIs.
# Numerical Analysis of Flow in Building Arrangement: Computational Domain Discretization
## 1 Introduction
### Numerical Accuracy  
Numerical accuracy and its estimation is an important issue not only in CFD but also in broader areas of computational physics. Numerical errors resulting from an algorithm and mesh are difficult to be distinguished from physical modelling errors. The common practice of publishing comparisons based on coarse mesh solutions without systematic error testing is observed, although individual researchers strive to control numerical accuracy.  
One issue connected with the numerical accuracy estimation is the iterative convergence: in most cases, it can be assumed that it is achieved with at least three orders of magnitude decrease in the normalized residuals for each equation solved.  
The computational domain discretization resulting in mesh resolution is also a crucial factor influencing the solution of an analysed case. One such method for discretization error estimation is the Richardson Extrapolation (RE) [30]. The local RE values of predicted variables may not exhibit a monotonic dependence on mesh resolution. Moreover, in transient calculations, RE will also be a function of time and space. Despite its shortcomings and generalizations, it is currently the most reliable method available for the prediction of numerical uncertainty. The Grid Convergence Index (GCI) is based on the RE. It is recommended by the Fluids Engineering Division of the American Society of Mechanical Engineers to estimate the discretization error and was applied within this paper.