esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Graph Neural Network for Garbage Classification Based on Multimodal Feature Fusion
## 6 Conclusions and Outlook  
### Conclusions  
The garbage classification method proposed in this study effectively integrates deep semantic information with color histogram features through a design that fuses multimodal features and employs a dynamic graph neural network. Moreover, the graph structure--constructed via an adaptive neighbor selection strategy--significantly enhances the discriminative performance across various categories. Extensive experimental results demonstrate that the proposed method not only achieves higher accuracy and robustness when handling imbalanced and challenging samples but also maintains a low misclassification rate even in complex scenarios. In comparison to state-of-the-art methods such as DenseNet121, InceptionV3, MobileNetV2, and conventional ResNet50, our approach displays clear advantages in highly heterogeneous categories, such as hazardous garbage and other difficult-to-classify groups, thereby substantiating its strong potential for practical applications.
## 2 Background
### Pedometers and Accelerometers in Physical Activity Measuring  
The simplest and most popular devices allowing activity measurements are pedometers, which record the number of steps. Thanks to the ability to display the result on a regular basis, they are considered as a motivating tool to perform more physical activity in everyday life. However, measurements by pedometers in scientific research have many limitations. Devices provide information on the frequency of movement, but they do not determine the intensity of physical exercise. Pedometer step counts are also more inaccurate at slow speeds (\(<\)60 m/min); therefore, they may be inappropriate for older adults, and the result may not be reliable. Pedometer readings can also vary according to where the pedometer is mounted. In addition, its weakness is also the possibility of falsifying and increasing results by intentionally shaking the device or by shocks caused by driving a car, which do not prove that the subject was more active [14].  
Currently, the most accurate motion sensors used to assess physical activity are accelerometers. The devices detect the acceleration of body movement, giving the opportunity to measure reliably the intensity and duration of physical activity, as well as the number of steps taken, and sedentary analysis [15]. Those parameters of the motion are read by the piezoelectric sensor, which converts the analog signal into the digital one in the range (0.1-3.6 Hz). Thanks to this, very accurate monitoring of physical activity is possible. An example of a commonly-used accelerometer is ActiGraph.
## 2 Non Destructive Sensing
### Disease Warning Systems  
In a susceptible crop, the increase of a foliar parasite during a growing season often is determined primarily by weather factors, particularly duration of wetness and air temperature [79]. Consequently, the effects of these factors on foliar parasites have been investigated extensively [80]. In many experimental studies [81, 82], wetness duration and temperature are manipulated under controlled conditions to evaluate their effects. Variation in some measure of the parasite's response, such as rate of sporulation or germination, infection efficiency, latent period, lesion density, disease incidence, or disease severity, is assessed [83].  
Forecasting systems are useful for diseases that are important but sporadic. Mango anthracose forecast in the seasonal tropics would be most useful in dry seasons, when sporadic rain is possible, or during transitional periods between dry and wet seasons. Once the rainy season is established, calendar-based fungicide applications are the best strategy for chemical control, since conditions are usually favorable for disease development. The most advisable strategy would depend on the time of flowering of a given orchard in a given region of production [84].  
Electronic leaf wetness sensors (LSW) determine surface water deposition by a change in sensor resistance or capacitance, ranging in size from crude wires to fine grid networks etched on a printed circuit board [85, 86]. Direct LSW measurement was obtained by placing an electronic grid directly on the leaf [87]. These sensors have not gained widespread adoption as daily maintenance is required to ensure that they have good contact with the leaf surface [88]. The LSW size and shape sensor should be similar to that of the leaf or organ. In onions, a cylindrical shaped sensor has been found to be useful [89], although it was proved with a form similar to the fruit shape [88]. The wetness sensor represented leaf wetness reasonably well, although most sensors over-predicted canopy wetness. The drawback is that it gets the measurement of only a single location in the production area, which may not be a complete representation of the entire crop zone.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 3 Results and Analysis  
### Characteristics of Climate Factor Variations  
Analysis of temperature data in the Turpan-Hami Basin from 2001 to 2020 (Figure 3a) revealed relative stability in the mean annual temperature over the observation period. However, the study area exhibited pronounced seasonal disparities in temperature variations. Data from Figure 3a indicate that autumn was the only season displaying a significant warming trend, with a warming rate of 0.086\(\cdot\)a\({}^{-1}\), suggesting that autumn serves as the core driving period of regional climate warming. Among the other seasons, summer showed a weak and statistically insignificant warming trend, while spring and winter exhibited slight cooling trends, neither of which reached statistical significance (\(p=0.30\), \(p=0.92\)), likely attributed to interannual climatic fluctuations.  
Interannual and seasonal precipitation in the Turpan-Hami Basin from 2001 to 2020 exhibited no statistically significant increasing or decreasing trends, mainly dominated by interannual fluctuations (Figure 3b). As shown in Figure 3b, although there were fluctuations in the interannual precipitation, there was no obvious long-term trend, with an extremely low mean annual precipitation of 53 mm. Summer experienced relatively higher precipitation (mean: 133 mm), showing a weak and non-significant increasing trend. In contrast, spring, autumn, and winter displayed minor decreasing trends, none of which reached statistical significance (\(p>0.05\)). These results are consistent with the typical  
Figure 2: Flowchart of this work.  
features of extremely arid regions, characterized by minimal precipitation and pronounced interannual variability, which is potentially influenced by oscillations in the westerly wind belt or localized topographic effects.  
The PET in the Turpan-Hami Basin exhibited a pronounced seasonal variation from 2001 to 2020 (Figure 3c). Autumn PET demonstrated a significant upward trend, with an average annual increase of 4.34 mm-a\({}^{-1}\), indicating that autumn was the critical period during which regional evapotranspiration water consumption intensified. This trend may be potentially attributable to elevated temperatures or enhanced wind speeds. While PET showed a marginal upward tendency over the years, it did not pass the significance test, suggesting that its fluctuations were primarily governed by interannual variability. In other seasons, no significant trends were observed in spring, summer, and winter PET, aligning with the seasonal disparities in radiation energy distribution characteristic of extremely arid regions. It is noteworthy that the mean annual PET in the study area surpassed 900 mm, remarkably exceeding the annual precipitation of 53 mm, thereby exacerbating the water supply-demand contradiction to an extreme degree.  
Figure 3: Annual and seasonal decadal change trends of MAT (**a**), MAP (**b**), and PET (**c**) in the Turpan-Hami Basin from 2001 to 2020.
# Development of a Novel Deployable Solar Panel and Mechanism for 6U CubeSat of STEP Cube Lab-II
## 5 Experimental Validation
### Solar Panel Deployment Test
However, the release time variation from 0 to \(+60\)\({}^{\circ}\)C was nominal. Relative to the space radiation environment, the radiation hardness of electronic devices is defined according to two key aspects: cumulative effects and single event effects [33]. The most important parameter to consider when selecting electrical components  
Figure 12: Release times of the proposed mechanism in repetitive release function test. for space application is the tolerance of the cumulative effects of ionizing radiation. The TID test of the electrical interface PCB was conducted under the low-level energy spectrum of 1.17 to 1.33 MeV by Cobalt-60 (\({}^{60}\)Co) radiation source for radiation hardness assurance of the P-HRM in a harsh space environment. The dose rate of \({}^{60}\)Co was 1.67 krad per hour. The TID test duration was set to 1 h because the solar panel deployment is generally initiated within 1 h of the orbital injection of CubeSat, which gives confidence that the mechanism will not have in-orbit issues to release the panel due to radiation exposure. The voltage on the pogo pin was monitored during the test period to observe the electrical malfunction of the circuit due to the cumulative radiation exposure. The TID test result did not report any drastic variation in voltage in the circuit, while the electrical interface PCB was exposed to a 1 h cumulative radiation dose. However, the 1-year TID estimated by the space environment information system (SPENVIS) software for STEP Cube Lab-II in accordance with the system description is 10 krad. The mechanism will be activated within 1 h of the orbital injection of STEP Cube Lab-II for solar panel deployment, although further TID testing of P-HRM is scheduled to be executed under the projected 1-year total ionizing dose of 10 krad in order to assure radiation hardness over the mission life. Furthermore, the SEE test of the electrical interface PCB of P-HRM was performed to ensure that the electrical components were robust to the in-orbit expected radiation environment. The test specimen was subjected to a high-level energy spectrum of 100 MeV in a 5 krad radiation environment. The electrical current at the pogo pin was measured during the test. The result exhibited steadiness of the current, which revealed that no SEE event occurred in the circuit. The results of these radiation tests did not report a malfunction or electrical failure in the electrical system of the P-HRM.
## Materials and Methods
### Cytotoxicity Assay  
In this study, the MTT assay was used to evaluate the cytotoxic effect of mushroom-synthesized ZnO NPs, and that of methanolic _P. ostreatus_ mushroom extract on two cell lines, namely, human embryonic kidney (Hek293) and hepatocellular carcinoma (HepG2). A modified method from [36] was adopted, whereby the HepG2 and Hek293 cells were grown in 25 cm\({}^{2}\) flasks using complete culture medium (CCM; DMEM with 10% fetal bovine serum, 1% L-glutamine, and 1% penicillin/streptomycin/fungizone) and incubatedat 37 \({}^{\circ}\)C and 5% CO\({}_{2}\). Confluent flasks were trypsinized to resuspend the cells, and the cells were counted using the trypan blue method. The cells were transferred into a sterile 96-well microtiter plate (15,000 cells/well, 200 \(\upmu\)L CCM per well) and allowed to attach overnight. The medium was removed, and adherent cells were treated in triplicate with the prepared extracts (0-2000 \(\upmu\)g/mL, 200 \(\upmu\)L/well); a 4000 \(\upmu\)g/mL stock solution of the mushroom ZnO NPs and _P. ostreatus_ mushroom extract was used to prepare the treatment dilutions. A vehicle control (1% DMSO in CCM) was included. After 24 h, the treatment medium was discarded and replaced with 20 \(\upmu\)L MTT salt solution (5 mg/mL in 0.1 M PBS) and 100 \(\upmu\)L CCM at 37 \({}^{\circ}\)C. After a 4 h incubation, the MTT salt was removed, and the formazan crystals were solubilized by adding 100\(\upmu\)L of DMSO to each well and incubated for an hour at 37 \({}^{\circ}\)C. The absorbance of the samples was read at 570 nm/690 nm using a Bio-Tek \(\upmu\)Quant spectrophotometer (USA). The cell viability was calculated using the following formula:  
Cell viability = ((absorbance of sample)/(absorbance of control) \(\times\) 100)  
Furthermore, the cell viability vs. log treatment concentration curve was analyzed using nonlinear regression analysis (GraphPad Prism V5.0, GraphPad PRISM(r), La Jolla, CA, USA). The 25% inhibitory concentration (IC\({}_{25}\)) was extrapolated from the curve; at this concentration, cells retained 75% cell viability. The cell viability at the respective MIC was also extrapolated from the curve.
## 5 Algorithm Descriptions
### Feature Extraction
#### 5.1.3 WT (Wavelet Transform)  
Firstly, we will decompose the signal with five-layer wavelet [29]. In this procedure, we could get the detailed and approximation coefficient. Then, we get the spectrum features of each coefficient-reconstructed signal by applying FFT. Correlation between mother wavelet and signal should be taken into consideration in wavelet decomposition. PIR signal shows strong symmetric feature, while at the same time high vanishing moment is required in noise reduction. Taking all these factors into account, we choose bior wavelet. It is symmetric which has a high vanishing moment and is regular.
## 4 Discussion  
### SOC and MOC in Grassland Soil Samples  
Both the SOC and MOC contents of grassland soils decreased with increasing temperature (Figure 3a) [48]. This result was consistent with the findings that alpine grasslands are more likely to accumulate SOC [49]. Higher temperatures lead to higher soil temperatures, which activated microbial activity and accelerated the rate of SOC decomposition [50; 51]. The SOC and MOC contents of grasslands other than the MP transect MS increased with increasing precipitation gradients in different grassland transects [52]. The study showed that precipitation has a positive effect on SOC accumulation. The impact of the kind of grassland on the variance in organic carbon storage was further examined in [PERSON]'s study [7]. The temperature precipitation gradient between the MP transect MS and the TS was less different compared to other transects. It has also been shown that the correlation between the loss of SOC and the increase in the MAT and MAP was stronger. However, the increase in SOC input was not sufficient to offset the loss due to more favorable decomposition conditions [53]. Therefore, the LP had the highest percentage of MOC and high SOC stability. However, its uptake was constrained by SOC. The MOC content was not the highest among the three sample strips. The lower the temperature is, the more sensitive the MOC content of the grassland transects was to changes in precipitation. The MOC content of MS soils in the MP was lower compared to other meadow grasslands. This was partly because changes in MOC may be related to the level of carbon input [9; 54; 33]. For example, precipitation can also increase SOC sequestration by promoting plant growth under certain conditions [8]. On the other hand, it may be related to a combination of conditions, such as the associated decomposition environment [54]. In arid and semi-arid grasslands, high soil water content can accelerate the rate of soil respiration, leading to SOC loss. The amount of water in the soil is a major factor in determining how the carbon cycle responds [16]. A recent study showed that under wet conditions, warming stimulates soil carbon uptake; under very dry conditions, warming inhibits carbon absorption [55]. The MOC/SOC content ranged from 65.33 to 66.67%, which is consistent with the results of Cai [23]. MOC/SOC increased with decreasing precipitation for the MP and LP and vice versa for the TP. The decomposition of soil organic carbon in high temperature and humid areas is usually not limited by soil water availability [53]. Therefore, water deficiency will limit carbon uptake and soil respiration, thus reducing MOC content [16].  
Figure 5: Structural equation model of the influence of environmental and soil factors on MOC. The numbers next to the path arrows indicate the standardized effect estimates and the direction of their relationship (Red for positive values, blue for negative values). MAP: mean annual precipitation; MAT: mean annual temperature; I/Sme: mixed-layer illite/smectite; MOC: minera-associated organic carbon; Sa: silica-alumina ratio.
# Simulative Investigation of the Radar Cross Section of Wind Turbines
## 4 Discussion
### Further Considerations  
Exchange of operational parameters (via internet or cellular network) between radar installations and wind farms in order to reduce negative mutual influence (e.g., by communicating \"forbidden positions\" for the azimuth angle to the wind turbines or by communicating current azimuth angles/pitch angles/rotational speed to the radar installation for inclusion in their signal processing) could be considered. If severe weather can reasonably be expected (by forecast), weather radar installations should be given preference. This approach would probably require the least amount of investments into existing wind parks but would negatively influence the wind farm's yield (i.e., generated electricity).  
An issue of radar-absorbing materials is their quite narrow-band absorptive behavior. Applying coating for several frequencies could, therefore, very well be cost prohibitive in practice.  
Additionally, using radar-absorbing material or coatings reduces the RCS of wind turbines, and rotor blades do not alleviate the problem of causing \"blind zones\" behind them (relative to the radar source). This issue will be of greater importance in the future, as tower heights and rotor diameters grow, with total heights reaching more than 200 m. Therefore, a wind-turbine clearance zone around radar systems for civic protection should be upheld. Where new on-shore wind parks are constructed, an analysis of the expected radar shadowing should be performed. If \"blind zones\" accumulate, additional radar installations should be considered.
## 3 Results
### Postural and Environmental Determinants of Work Activity  
The workers' perceptions about the types of demands related to their work activity were mainly physical, and differences between the two divisions' shift types were present in both data collection moments. The WaSaD workers reported a higher percentage of both mental and physical demands in their work activity (Table 2). \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline
**2017** & **SoWD (n = 82)** & \multicolumn{4}{c}{**WaSaD (n = 212)**} & \multicolumn{4}{c}{**Shift Type Effect**} \\ \hline  & \multicolumn{2}{c}{**Fixed Night (n = 33)**} & \multicolumn{2}{c}{**Fixed Early (n = 49)**} & \multicolumn{2}{c}{**Fixed Daytime (n = 126)**} & \multicolumn{2}{c}{**Rotating (n = 86)**} & \multicolumn{2}{c}{**Total (n = 294)**} & \multicolumn{2}{c}{\(\chi^{2}\)} & \multicolumn{1}{p}{\(p\)} \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Mental & 3 (9.1\%) & 5 (10.2\%) & 5 (4.0\%) & 5 (5.8\%) & 18 (6.1\%) & \multirow{4}{*}{33.408} & \multirow{4}{*}{\(<\)0.001 *} \\ \cline{2-2} \cline{5-8}  & Physical & 23 (69.7\%) & 39 (79.6\%) & 72 (57.1\%) & 33 (38.4\%) & 167 (56.8\%) & & \\ \cline{2-2} \cline{5-8}  & Both & 7 (21.2\%) & 5 (10.2\%) & 49 (38.9\%) & 48 (55.8\%) & 109 (37.1\%) & & \\ \hline
**2019** & \multicolumn{2}{c}{**SoWD (n = 94)**} & \multicolumn{2}{c}{**WaSaD (n = 196)**} & \multicolumn{2}{c}{**Shift type effect**} \\ \hline  & \multicolumn{2}{c}{**Fixed night (n = 36)**} & \multicolumn{2}{c}{**Fixed early (n = 58)**} & \multicolumn{2}{c}{**Fixed Daytime (n = 177)**} & \multicolumn{2}{c}{**Rotating (n = 79)**} & \multicolumn{2}{c}{**Total (n = 290)**} & \multicolumn{1}{p{42.7 pt}}{\(\chi^{2}\)} & \multicolumn{1}{p}{\(p\)} \\ \hline \multirow{4}{*}{
\begin{tabular}{} \end{tabular} } & Mental & 3 (8.3\%) & 6 (10.3\%) & 4 (3.4\%) & 4 (5.1\%) & 17 (5.9\%) & \multirow{4}{*}{27.923} & \multirow{4}{*}{\(<\)0.001 *} \\ \cline{2-2} \cline{5-8}  & Physical & 23 (63.9\%) & 42 (72.4\%) & 66 (56.4\%) & 29 (36.7\%) & 160 (55.2\%) & & \\ \cline{2-2} \cline{5-8}  & Both & 10 (27.8\%) & 10 (17.2\%) & 47 (40.2\%) & 46 (58.2\%) & 113 (39.0\%) & & \\ \hline \end{tabular}
\end{table} 
Table 2: Perception of work demands (Frequency (%)) in 2017 and 2019 by shift type and results of Chi-square test for homogeneity comparing shift types.
## 5 Experiments
### Experiment Result Analysis
Furthermore, the improved Faster R-CNN algorithm exhibits enhanced detection performance across four GUI error types. Notably, the detection performance for image absence and null values achieved high precision scores of 0.964 and 0.930, respectively. The detection of text overlap and component occlusion also demonstrated improvements, reaching precision scores of 0.784 and 0.812, respectively. However, some areas warrant further improvement. The generation of a realistic dataset, crucial for training the algorithm, is based on automatically generating screenshots \begin{table}
\begin{tabular}{l l l l l l} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{3}{c}{**Average Accuracy AP/\%**} & \multirow{2}{*}{**mAP/\%**} \\ \cline{2-2} \cline{4-6}  & **Text Overlap** & & **Image Missing** & **Component Occlusion** & **Empty Values** \\ \hline Faster R-CNN & 0.69 & 0.936 & 0.686 & 0.899 & 0.803 \\ Faster R-CNN(R) & 0.703 & 0.94 & 0.714 & 0.907 & 0.816 \\ Faster R-CNN(RF) & 0.741 & 0.959 & 0.753 & 0.917 & 0.843 \\ Faster R-CNN(RFA) & 0.766 & **0.966** & 0.771 & 0.922 & 0.856 \\ Faster R-CNN(REFA) & **0.784** & 0.964 & **0.812** & **0.93** & **0.873** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of accuracy between original and improved algorithms. Figure 11: Comparison of different algorithms. with real GUI display issues from the Rico dataset. The quality of this generated dataset significantly influences the experimental results. Therefore, there is a need for optimization in aspects related to the algorithm for training data generation. In the experiment, screenshots are employed to detect GUI display issues. Due to the minimal differences observed in screenshots from various platforms, the methodology can be transferred and extended to detect GUI display problems on other platforms. For instance, we identified similar display issues on the Windows platform, affirming the universality of these problems. Further, more in-depth experiments will be conducted in the future. Regarding the computation complexity, the enhanced Faster R-CNN algorithm is expected to have a higher computational complexity compared to the traditional Faster R-CNN due to the use of ResNet-50, EMA, FPN, and ROI-Align. While these enhancements contribute to improved performance, they come at the cost of increased computational requirements.
# Toward High Bit Rate LoRa Transmission via Joint Frequency-Amplitude Modulation
## 3 QR\(-\)LoRa Design
### Joint Frequency-Amplitude Demodulation
#### 3.3.2 Amplitude Dimension Demodulation  
\(A(t)\) is sampled at \(f_{s}=BW\), and its discrete sequence can be represented by  
\[A(n){=}\Bigg{\{}\begin{array}{l}\frac{n-n_{0}}{2^{SF}}+1,0\leq n\leq n_{0}-1 \\ \frac{n-n_{0}}{2^{SF}},n_{0}\leq n\leq 2^{SF}-1\end{array} \tag{13}\]  
Then, a differential operation is performed on the sequence \(A(n)\) to achieve the amplitude-encoded value,  
\[|\mathrm{diff}[A(n)]|{=}\Bigg{\{}\begin{array}{l}\frac{2^{SF}-1}{2^{SF}} \approx 1,n=n_{0}\\ \frac{1}{2^{SF}}\approx 0,n=else\end{array} \tag{14}\]  
Figure 6 depicts the difference operation result, where the expected peak with an amplitude of 1 appears at the amplitude jump point, the 256 th sampling point.