esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of a Land Area Measuring Instrument Based on an STM32 and a BeiDou Positioning Chip
## 3 Hardware System Design  
The system architecture comprises five integrated modules: microcontroller, power management, satellite navigation, OLED display, and key interface in Figure 4.  
The microcontroller (MCU) selects STM32 F103, which is a 32-bit MCU based on the ARM Cortex-M3 core (ARM, Cambridge, UK). It has high working frequency, rich memory, multiple peripheral interfaces and low power consumption characteristics.  
The power supply system uses a DC 5 V power supply, and the voltage is stably converted to 3.3 V through the Texas Instruments TPS76133 chip (TI, Dallas, TX, America) to meet the working requirements of each module. The filter capacitor is added to the conversion circuit to ensure the stability of the power supply, reduce interference, and improve the reliability of the system [19].  
The core of the BDS satellite module is an ATGM332D chip (Zhongke Microelectronics, Hangzhou, China), which adopts a 40 nm RF-SoC process, and the cold start sensitivity is \(-148\) dBm (2.3 dB higher than u-blox M8N). It has a unique BDSBAS satellite-based enhancement function through the GEO satellite broadcast differential correction number so that the single point positioning accuracy is from 3 m to 1.5 m. The built-in multipath suppression module of the chip adopts narrow correlator technology, which can reduce multipath error by more than 60% [20]. The chip supports both active and passive antenna modules. This design uses an active SMA interface antenna; it is compared with the general  
Figure 4: System hardware composition module. Arrows indicate primary signal/power flow: The Keystroke Module (user input) and Satellite Module (raw data) feed into the MCU, while the Power Module supplies it. The MCU (processed results) and Satellite Module (direct real-time data) output to the Display Module for visualization.  
u-blox NEO-M8N (GPS module) on the market. The comparison results are shown in Table 2.  
The liquid crystal display module adopts the 2.42-inch OLED liquid crystal display screen of Zhongjingyuan Electronics (Zhengzhou, China), which has the characteristics of high contrast, wide viewing angle and fast response. It can clearly display the measurement data, trajectory and other information, which is convenient for users to view in real time.  
The key module is powered by \(+5\) V DC, and the key processing thread reads each GPIO pin value circularly to realize the user's control of each functional thread, such as starting the measurement and ending the measurement. The operation is simple and the user experience is improved.  
The overall hardware design is compact and the function is comprehensive, which provides a solid foundation for the accurate measurement and stable operation of the area measuring instrument. Figure 5 is the hardware photo of the area measuring instrument.
# Analysis of Dust Detection Algorithms Based on FY-4A Satellite Data
## 4 Results and Validation  
These four algorithms/products were qualitatively compared with the color composite images during two typical dust events. The two case studies on 15 May 2019 and 16 March 2021 were chosen to demonstrate how the four dust detection methods performed in the two different types of events over China. The first dust event occurred mainly in northwest China, while another dust event affected a wider area, mainly originating in the Gobi Desert and affecting northern China. The BTD and NDDI algorithms were also applied to three months of FY4A/AGRI data. The dust detection results from four algorithms/products were quantitatively compared with the dust identification results from the PM10 concentration measurements in the spring of 2021.
# A Texture-Based Simulation Framework for Pose Estimation
## 3 Simulations and Design Criterion
### Verification Set Performance Comparison  
In this section, the performance of the CNN on the validation sets under different texture datasets is visually and quantitatively analyzed. The specific quantitative analysis results are shown in Table 1, and the visual analysis results are shown in Figure 3. In the table, (Err_X, Err_Y, Err_Z) represents the average angular error of the attitude angle corresponding to each coordinate axis; (Std_X, Std_Y, Std_Z) stands for standard deviation (Std); MAE stands for total mean absolute error; and RMSE stands for total root mean square error, which accounts for error magnitudes and is less prone to cancellation effects compared to the mean error metrics.  
Figure 2: Partial datasets generated by different textures. Figures (**a**–**f**) correspond to Texture_1_bw -Texture_3_color.  
As shown in Table 1, the model's pose estimation accuracy improves progressively with an increasing texture complexity. Texture1 yield the highest errors (MAE = 1.178\({}^{\circ}\) for black and white and 0.974\({}^{\circ}\) for color; RMSE = 1.469\({}^{\circ}\) and 1.189\({}^{\circ}\)), while medium-complexity textures (Texture2) reduce the MAE to 0.842\({}^{\circ}\) and 0.791\({}^{\circ}\) (RMSE = 1.078\({}^{\circ}\) and 1.037\({}^{\circ}\)), respectively. Texture3, which is composed of complex textures, has the best performance, with the MAE = 0.492\({}^{\circ}\) in black and white type and 0.416\({}^{\circ}\) in color type and the RMSE reaching 0.661\({}^{\circ}\) and 0.543\({}^{\circ}\), which is 64.8% higher than texture 1. Notably, the RMSE values follow a similar decreasing trend as the MAE but emphasize a greater error magnitude reduction in the high-complexity textures. This trend shows that the complexity of the texture features may be highly correlated with the model's pose estimation ability.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Texture** & **Err\_X** & **Err\_Y** & **Err\_Z** & **Std\_X** & **Std\_Y** & **Std\_Z** & **MAE** & **RMSE** \\ \hline Texture\_1\_bw & 1.7485 & 0.663 & 1.121 & 0.706 & 0.529 & 0.873 & 1.178 & 1.469 \\ Texture\_1\_color & 0.769 & 0.654 & 1.499 & 0.493 & 0.454 & 1.534 & 0.974 &  
Color textures consistently outperform their black and white counterparts across all the complexity levels, with a lower MAE, RMSE, and Std, suggesting color information strengthens feature discriminability. However, in low-complexity scenarios (Texture_1_color), the \(z\)-axis errors increase significantly, implying potential noise interference from color in simple patterns. Figure 3 further reveals that a higher texture complexity concentrates error distributions in low-value regions, particularly for color textures, with improved consistency across all axes.
## 3 Results and Discussion
### Global Analysis of Results
#### 3.1.2 Roughness Analysis
Figure 9: Distinguished defects in IDR: (**a**); detail of material deformation by particle impact; (**b**) detail of fragments of detached material. Figure 11a-c shows SOM images of Tests 7, 21, and 12, respectively. In Test 7, it can be seen that there is no jet delay due to the high pressure and low feed rate. Therefore, the roughness results for this speed range are similar to those obtained for the rest of the zones. On the contrary, for Test 21, it can be seen that in the whole profile of the cut, striation marks appear; therefore, SCR is not generated. This is due to the use of low pressure and a high feed rate. Finally, for Test 12, it can be seen that there are both areas--SCR and RCR--due to the combination of parameters. This shows the influence of the different parameter levels and the need to limit their influence on the formation of different cutting regions. As for SEM analysis, Figure 12a,b shows the images of Test 7 and 21 analyzed above in order to observe the formation of the striation phenomenon. Specifically, in Test 21, the formation of jet delay marks is observed, accompanied by material drag at the jet exit. In addition, it is shown how, as the jet penetrates the material, the striation becomes deeper. This is due to the combined effects of loss of kinetic energy and jet opening at the exit of the cut. On the other hand, Figure 12c shows how, independently of the cutting parameters used and their influence, separate wear tracks or grooves plowed by individual abrasive grains are observed. The width of these tracks is related to the size of the abrasive particles, although they are not uniform [20]. This lack of uniformity is attributed to the size distribution of abrasive particles as they exit the nozzle and impact on the surface of the material. In addition, it may also be due to the fact that SiC particles are harder than Indian garnet, so they can fracture the abrasive by breaking down the wear grooves [21]. Figure 11: Difference between cutting profiles for (**a**) SOM image. Jet outlet. Test 7. WP = 500 = MPa; AMFR = 450 g/min; TFR = 100 mm/min; (**b**) SOM image. Jet outlet. Test 21. WP = 250 MPa; AMFR = 170 g/min; TFR = 500 mm/min; (**c**) SOM image. Jet outlet. Test 12. WP = 380 MPa; AMFR = 170 g/min; TFR = 500 mm/min.
## 3 Results and Analyses
### GPP Difference of the Two MODIS Products  
To assess the GPP inconsistency at 1 km and 500 m, a pixel-by-pixel comparison between the MODIS GPP at two resolutions was implemented with a time interval of 8 days during Julian days  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Pixel Resolution (m)**} & \multicolumn{2}{c}{**Min**} & \multicolumn{2}{c}{**Max**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**STD**} \\ \cline{2-9}  & **500** & **1000** & **500** & **1000** & **500** & **1000** & **500** & **1000** \\ \hline Altitude (m) & 2606 & 2647 & 4744 & 4671 & 3784 & 3787 & 501 & 489 \\ Slope (deg) & 0 & 0 & 38 & 28 & 18 & 12 & 8 & 6 \\ Aspect (deg) & 0 & 0 & 360 & 360 & 178 & 177 & 101 & 100 \\ LAI (m\({}^{2}\) m\({}^{-2}\)) & 0 & 0 & 7.0 & 5.7 & 1.88 & 1.58 & 1.93 & 1.61 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparisons of topographic factors and at 500 m and 1 km.  
Figure 3: Spatial distributions of altitude (**a**); slope (**b**); aspect (**c**) and LAI (**d**) at 500 m and 1 km.  
169-209. Figure 4a presents the relationships between MODIS products at 500 m and 1 km during the study period. From Julian day 169 to Julian day 209, the 1 km MODIS GPP explained between 24% and 47% of MODIS GPP at 500 m, with RMSEs varying between 1.34 and 2.14 gC m\({}^{-2}\)d\({}^{-1}\). In general, the MODIS GPP at 1 km had a significant inconsistency with MODIS GPP at 500 m (R\({}^{2}\) = 0.36, RMSE = 1.62) during the whole study period, showing that the simplification of complex terrain within each modeling pixel might cause the model results to be considerably biased.  
Figure 4: Density scatterplot between MODIS GPP and downscaled GPP at 500 m during Julian days 169–209. (**a**) represents the comparison between MODIS GPP products; and (**b–e**) represent the comparison between downscaled GPP and MODIS GPP at 500 m. The solid lines are the regression lines, while the dashed lines are the 1:1 lines. The green and red represent the low-density and high-density areas, respectively.  
To investigate the effects of topography on GPP, a mismatched MODIS GPP between a 1 km resolution and a 500 m resolution was analyzed. Figure 5 illustrates the relationships between topographic factors (i.e., altitude, slope) and the inconsistency in the MODIS GPP at 500 m and 1 km during Julian days 169-209. Altitude and slope were divided by 500 m and 10\({}^{\circ}\) intervals, respectively, and the relative error (RE) was adopted to evaluate the inconsistency in the MODIS GPP at 500 m and 1 km. It was found that the inconsistency in MODIS GPP at two resolutions increased with the increase of slope and altitude. In addition, there was not an obvious relationship between the aspect and the inconsistency in MODIS GPP at two resolutions.
## 3 Results and Discussion
### Dependence of the TPPE Plots on Environments  
Figure 5 shows the dependence of the TPPE plots for copper samples subjected to ultrasonic cleaning and 10-minute abrasion on the alcohols and water, including the data points for air [26]. In each sample, the TPPE plots exhibit a similar trend for the environments, except that for 10-minute abrasion in C\({}_{3}\)H\({}_{7}\)OH. The PE total count (\(N_{\rm T}\)) slowly increases, reaches a maximum, and then decreases. In the case of 10-minute abrasion (sample A), the \(N_{\rm T}\) value at 250 \({}^{\circ}\)C considerably decreased in the order H\({}_{2}\)O \(>\) CH\({}_{3}\)OH \(>\) C\({}_{2}\)H\({}_{5}\)OH \(>\) (CH\({}_{3}\))\({}_{2}\)CHOH \(>\) C\({}_{3}\)H\({}_{7}\)OH. On the other hand, in the case of ultrasonic cleaning (sample C), the \(N_{\rm T}\) value decreased in the order C\({}_{3}\)H\({}_{7}\)OH \(>\) (CH\({}_{3}\))\({}_{2}\)CHOH \(>\) C\({}_{2}\)H\({}_{5}\)OH \(>\) CH\({}_{3}\)OH \(>\) H\({}_{2}\)O. The orders of \(N_{\rm T}\) values for the two samples were completely opposite. This finding triggered the present study.  
Figure 4: [Sample C] Plots of PE total count (\(N_{\rm T}\)) during the 1 st and 2 nd temperature increase and subsequent temperature decrease processes for copper samples ultrasonically cleaned in various liquids for 5 min, which are named (a) (Up1 scan, blue line) and (b) (Down1 scan, red line), and (c) (Up2 scan, grey line) and (d) (Down2 scan, yellow line), respectively, versus measurement temperature: (**1**) H\({}_{2}\)O, (**2**) CH\({}_{3}\)OH, (**3**) C\({}_{2}\)H\({}_{5}\)OH, (**4**) (CH\({}_{3}\))\({}_{2}\)CHOH, and (**5**) C\({}_{3}\)H\({}_{7}\)OH. It is noted that the liquids are arranged in the increasing order of the \(N_{\rm Tmax}\).
## Return loss  
Figure 8a shows return losses \(RL^{p}\) measured for the two ports (\(p=\rm H\), V) of the antenna. Measurements are performed for the frequency range of 1,000 MHz to 2,000 MHz using a spectrum analyzer and a directional coupler with directivity of about 20 dB-30 dB. The \(RL^{p}\) [dB] shown are achieved after fine-tuning the length of the \(\lambda\)/4-structures receiving the radiances at H- and V-polarization, respectively (see Section 2.3). The measurements show that the specified value of \(RL^{p}\leq\) -20 dB are well met for the radiometer center frequency of 1,413.5 MHz.
## 1 Introduction
[PERSON] et al. [22] use a subset of the same database as a stimulus for their experiment. The scores of nine subjective material qualities (glossiness, hardness, roughness, etc.) are 90% accurate in predicting the perceived categories of materials. In the aforementioned research, glossiness is often selected as the primary target of the investigation, and some scholars have conducted more comprehensive studies specifically focused on glossiness. Initially, researchers investigated the elements that influence the perception of glossiness from the local picture characteristics of near-field stimuli. [PERSON] et al. [23] discovered that high light coverage, edge sharpness, and contrast have a direct effect on perceived glossiness intensity. [PERSON] et al. [24] demonstrated the effect of visual attributes on glossiness by manipulating the perceived glossiness by altering these aspects. According to [PERSON] et al. 's research on VR visual perception [25], the vergence-accommodation conflict may be a significant element influencing VR perception. [PERSON] et al. [26] have emphasized the significance of this component since this physiological conflict might have a significant impact on the perception of a variety of reflection qualities. In this work, psychophysical experiments are utilized to establish the mapping relationship between glossiness physical quantities and virtual material empirical parameters, producing experimental scenarios in VR. This not only allows designers to alter material properties faster but also provides adequate validity for visual perception research in VR. The article is organized based on the paper's three primary components: Section 2 introduces a psychophysical experiment that uses the 2 AFC design to measure the association of materials with different glossiness levels in both reality and VR; Section 3 introduces a material matching adjustment experiment that measures how much glossiness is psychologically equivalent under different PBR parameter levels. Section 4 develops the Gloss-Bridge mapping model from physical glossiness to PBR parameters based on the results of the two experiments and introduces the Gloss-Bridge mapping model. Section 5 summarizes the content of the research.
# An Application of Instantaneous Spectral Entropy for the Condition Monitoring of Wind Turbines
###### Abstract
Inspection [15] is nowadays widely accepted as the standard for condition-based maintenance, in particular for gearbox condition monitoring. Several signal processing techniques and approaches have been proposed throughout the last 30 years for this specific application; a recent and well-documented review can be found in [16]. In fact, the Structural Health Monitoring (SHM) of the wind turbines' several components has attracted much interest from the researchers' community in the last decade (see, e.g., [17; 18; 19; 20]). The procedure discussed here is based on the instantaneous definition of the Shannon Spectral Entropy (SSE, [21]). That is to say, the Instantaneous Spectral Entropy (ISE) is applied as a time-dependent, damage-sensitive feature for damage event detection. Indeed, Spectral Entropy has been suggested for the assessment of rolling-element bearing performance [22]. In its standard definition, it has been applied to stationary signals for the Structural Health Monitoring of masonry buildings [23; 24] and steel pipelines [25; 26]. In previous studies, ISE was validated for an aluminium frame structure undergoing structural changes under controlled laboratory conditions [27]. However, to the best of the Authors' knowledge, it has never been investigated for condition monitoring purposes, let alone on experimental data originating from wind turbines under operating conditions. The remaining of this article is organised as follows. In Section 2, the theory of SSE and ISE is recalled. Section 3 resumes some basic definitions for the Continuous Wavelet Transform (CWT) and the Generalised Morse Wavelet (GMW), which have been utilised here to define the time-frequency (TF) representations needed for the ISE analysis. Section 4 describes the experimental case study. Section 5 reports the results and Section 6 briefly discusses them. The Conclusions end this discussion.
## 3 Results  
### Carbon Emission Calculation of the Case Study  
#### 3.1.1 Building Materials Production  
The data sources of the construction materials used for refurbishment are the \"Construction tender quotations for overground landscape and underground space refurbishment of Zero-Carbon Pavilion in Shanghai Yangpu Riverside\". The list of building materials is based on the budgets of each sub-project. The summary of carbon emissions in the production phase of refurbished buildings is shown in Table 2. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Building Materials**} & \multirow{2}{*}{\
\begin{tabular}{c} **Carbon Emission** \\ **Intensity** \\ \end{tabular} } & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{**Quantity**} & \multirow{2}{*}{**Unit**} & \multirow{2}{*}{
\begin{tabular}{c} **Carbon** \\ **Emissions** \\ **(kgCO\({}_{2}\)e)** \\ \end{tabular} } \\ \hline \multirow{2}{*}{Steels} & Steel structure components & 2050 & kgCO\({}_{2}\)e/t & 19.31 & t & 39,575.65 \\  & Rebar & 2340 & kgCO\({}_{2}\)e/t & 28.96 & t & 67,770.63 \\ \hline \multirow{3}{*}{Concrete} & Ready-mixed concrete & \multirow{3}{*}{335} & kgCO\({}_{2}\)e/m\({}^{3}\) & 227.79 & m\({}^{3}\) & 76,309.63 \\  & (non-pumping type) C35 & \multirow{3}{*}{248} & kgCO\({}_{2}\)e/m\({}^{3}\) & 24.64 & m\({}^{3}\) & 6111.33 \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Concrete blocks} & Concrete brick & 336 & kgCO\({}_{2}\)e/m\({}^{3}\) & 6.73 & m\({}^{3}\) & 2260.83 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Materials list of the refurbished building. Since the type and quantity of building materials of the assumed new building are unknown, the statistic result based on the architectural geometry model constructed in the DesignBuilder software is exported for calculation. The geometry model is shown in Figure 5.  
The carbon emission intensity in the production phase of solar photovoltaic systems refers to existing case studies [33; 34; 35; 36; 37]. The average value is 8971.79 kgCO\({}_{\rm 2e}\)/kW, which is suitable for both refurbishment construction and new construction, and the power generation of the photovoltaic system is derived from software statistics. The selection of carbon emission factors for building materials refers to the Building Carbon Emission Calculation Standard.
# Data Augmentation Method for Plant Leaf Disease Recognition
## 2 Related Studies  
Recently, various data augmentation studies on the early diagnosis of plants using Generative Adversarial Networks (GAN) are being conducted. Most of them aim to correct problems with existing models to verify that more realistic images can be generated or to solve overfitting problems by replacing insufficient data.  
[PERSON] et al. [24] worked on recovering tomato disease data via DCGAN (Deep Convolutional GAN) [25]. Their contribution demonstrates that samples generated via DCGAN can improve the efficiency of classification models. However, it was confirmed that the generated samples are not clear and are not likely images in reality. Also, there is a problem of not being able to control the class of the image to be created.  
[PERSON] et al. [26] proposed an RHAC_GAN model that improves ACGAN (Auxiliary Classifier GAN) to solve the problem of tomato disease data augmentation. RHAC_GAN aims to solve the problem of lack of diversity among generated images by using hidden variables obtained from the discriminator along with inputs from the generator. Regarding the lack of diversity in the generated images, the authors explain that traditional ACGAN generators have difficulty learning different information within a class. In addition, a network was constructed by combining the Attention module and the Residual Block so that the generated images reflected obvious disease features. Plant leaf images generated by RHAC_GAN are sharper and more realistic compared to DCGAN and ACGAN.  
[PERSON] et al. [27] proposed a LeafGAN model that solved CycleGAN's problem of changing unwanted content such as background when transforming plant leaf images. Their contribution is to propose an LFLSeg module that can segment leaf regions relatively simply and use it to control the transformation of background regions. The author proposed a background loss function to control the transformation of the background area by the generator and used only the input from which the background was removed as the input of the discriminator so that the discriminator for the background area did not react. The experiment was conducted based on actual data collected in the natural environment, and it was reported that classification performance was partially improved when learning was performed with a dataset augmented by the proposed model.  
[PERSON] et al. [28] proposed a Style-Consistent Image Translation (SCIT) model that aims at converting to the target domain while preserving information about the parts related to styles independent of labels in the translation process of plant images. To this end, we proposed a style loss calculated through a pre-trained vgg19 network. The authors verified through experiments that the amount of change to the target domain can be adjusted according to the change in the coefficient value of style loss.
## 4 Results and Discussion
### Detailed Analysis of Model Performance
#### 4.7.2 Two Sensors with 50% Overlapping Data  
With overlapping data, TCN's performance improves to a test accuracy of **0.9765**. LSTM and GRU also see performance gains, achieving test accuracies of **0.9762** and **0.9750**, respectively. The overlapping data provide additional temporal context, enhancing the models' ability to capture dependencies. Traditional models show modest improvements but remain behind deep learning models.