esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Sources and Preprocessing
#### 2.2.2 Point of Interest (POI) Data  
POI data consists of geographic points closely related to human activities, such as schools, restaurants, hospitals, etc. These points indicate areas of human interest and can reflect the distribution and concentration of urban populations. We obtained POI data from Gaode Map (Amap), a leading Chinese map data provider with a comprehensive and up-to-date database. After registration, we accessed the data through Amap's open interface. The original dataset included 21 categories and 40,081 points. We then cleaned and filtered the data based on their relevance to residents' daily lives, resulting in eight categories: Catering Services, Shopping Services, Daily Life Services, Sports and Leisure Services, Medical and Health Care Services, Education and Culture Services, Government and Social Organizations, and Financial and Insurance Services, totaling 38,913 points. This selection ensures that the POI data used are directly relevant to urban population distribution and activities. The POI data were accessed at [[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)) (12 May 2024).
# Impacts of Drought and Heatwave on the Vegetation and Ecosystem in the Yangtze River Basin in 2022
## 4 Discussion  
### The Potentials and Limitations of Spatial Anomaly Analysis  
Through the study of the spatiotemporal anomaly distribution of various indices during this drought and heatwave period, we believe that the significant weakening of negative anomalies in NDVI and SIF in the western region from September to October is associated with the early recovery of normal temperature, VPD, and soil moisture levels in the western Yangtze River Basin region. After all, these two indicators are highly sensitive to climate and environmental changes [25]. The unique temperature, VPD, and soil moisture anomalies in this region may be related to local climate circulation and topographical conditions. Furthermore, there is strong consistency in the spatial distribution and evolution of NEE and GPP anomalies, which is determined by the relationship between GPP and NEE. GPP determines the total photosynthetic rate of ecosystems, i.e., the ability of plants in the ecosystem to fix carbon dioxide [40]. Therefore, ecosystems with low GPP due to summer droughs and heatwaves often exhibit poorer potential carbon uptake capacity, potentially resulting in NEE values close to zero or even positive, indicating less carbon absorption by the ecosystem. This can have significant implications for the carbon balance of ecosystems [41; 42].  
Formula (1) established based on the RST approach does not require any specific assumptions about the data and exhibits inherent robustness. Moreover, calculating anomalies pixel-by-pixel based on differences from observed values and mean, as well as standard deviation, allows for more intuitive comparisons of data changes across different times and locations, aiding in the discovery of potential patterns and trends. However, the RST method necessitates a significant amount of historical data to establish reference fields. In this study, the historical data span is insufficient, which may impact the statistical correlation of observed anomalies with historical data. Additionally, the lack of preprocessing for observational conditions (such as satellite viewing angles, solar angles, etc.) and cloud cover could potentially have adverse effects on the statistical correlation of anomaly calculation results [35].  
Furthermore, we condensed the data spanning a long period (over 100 days from July to October) into four-month averages for spatial anomaly analysis. This approach may smooth over some small yet significant features within this time span. This could potentially lead to the omission of important information, an increase in monthly average deviations, and a weakening of spatiotemporal sequence correlations. However, many scholars conducting similar studies have also used spatialized monthly averages and have identified reliable phenomena [10; 21]. Therefore, in our study, the negative impacts of this issue may be negligible.
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 4 Results  
To verify the proposed decoupling algorithm, we developed an LC-DM AO system suitable for a 2 m aperture telescope in the laboratory. First, the DM was controlled using the eigenmodes. The aberration in the infrared band was corrected, and the results obtained with the Zernike mode were compared. Then the LC was controlled to compensate the residual aberration in the visible band and to compare the imaging effects of the decoupling and nondecoupling algorithms.  
Figure 5: Curve of the wavefront correction RMS difference before and after decoupling.
## 3 Data Sets
### Optical Images  
All satellite images of Sentinel-3 data are available from The European Space Agency Copernicus Open Access Hub (ESA SciHub) [35], which is free to the public. We selected imaging products regarding AHDL in the mentioned time interval (2016 to 2019). Selected products were at processing level L1A. All the data relate to the relative orbit #249 (diurnal), crossing the AHDL for both instruments SLSTR and OLCI.  
Figure 1: Aswan High Dam Lake, selected area shows Nubia Lake and Allaqi Khor, area clipped from Sentenel-3 image shows most of Egypt and the Red Sea in the footprint (image taken in October 2018).  
The moderate spatial resolution is useful for our study area to achieve full coverage of the AHDL in the same swath and relatively manageable memory occupation. The moderate spatial resolution is compensated for by a high radiometric performance for both instruments in terms of noise and quantization error [36], which is fundamental for experimenting on the retrieval of depth from optical measurements. In particular, we used the reflectance of green, red, and NIR bands for both SLSTR and OLCI instruments to estimate the lake's depth. (Table 1) shows the main specifications of the two instruments, and (Table 2) lists the spectral bands used in this paper [4].  
Figure 3: In situ water level of AHDL between 2016–2019 obtained from (NRI) [32].  
Figure 2: (**a,b**) show the use of new survey technology and the multi-beam echo sounder to determine the depth [25]; (**c**) shows paths of a bathymetric field trip in the study area, and (**d**) shows the locations of the bathymetry points used in building the bathymetry model.
## 5 Performance Testing
### Deployment Position Test (Ceiling vs. Wall)  
The deployment position test was conducted in Room ZB214, and the wall and ceiling placement of iBeacon were deployed and compared, in order to study whether simple placement choices could provide substantial increases in positioning accuracy at little to no extra cost and labour. For the wall deployment, the iBeacons were stuck on the wall with tape at 2 m height, while the ceiling deployment involved installing the iBeacons to the ceiling with the location shown in Figure 6. The test compared the positioning accuracy of deployment heights of 2 m up the wall versus on the ceiling, which was 2.38 m high. Eight and ten iBeacons were tested for each deployment scheme with the locations shown in Figure 6. The discrepancy between the two placement methods was the effect of geometry, as the iBeacons were located at the edge of classroom for wall placement because of the physical limitation, whereas the iBeacons could be evenly distributed in the classroom for the ceiling placement.  
The results of deploying iBeacons to the ceiling or wall with 8 or 10 iBeacons, respectively, are presented in Figure 7. As shown in Figure 7, the positioning accuracies from the ceiling and wall deployment schemes were compared with the cumulative distribution function (CDF). From Figure 7, we observed a lower error rate when 8 iBeacons were deployed on the ceiling compared with being deployed on the wall, and the superior accuracy of the ceiling deployment scheme with 10 iBeacons echoed the results obtained with 8 iBeacons. The root-mean-square error (RMSE) for the ceiling deployment was also lower than that of the wall placement method for the deployments of both 8 and 10 iBeacons.  
Figure 6: Deployment plans. **(a)** 8 iBeacons; **(b)** 10 iBeacons.  
Figure 7: Cumulative distribution function (CDF) comparison of the two deployment methods using 8 and 10 iBeacons, respectively.  
The positioning accuracy improved with ceiling placement due to the reduction of obstructions between the iBeacons and the mobile device. By mounting iBeacons on the ceiling, there were fewer obstructions like people, furniture, hanging light fixtures and computers, etc. This mounting method allows a more consistent line of sight and enables flexibility in placement geometry, as iBeacons can be placed anywhere on the ceiling to ensure balanced coverage, while wall-based schemes are confined to the edges of the space. The results confirmed a drop in the positioning accuracy when using wall-based schemes, in particular, poorer accuracy in larger areas, as poor geometry results in the lack of signal in the centre as well as higher susceptibility to obstructions. If ceilings are too high, or ceiling deployment is impracticable because of the environment, wall-mounted iBeacons should be placed at a height higher than the average person, to ensure the line of sight is not blocked as suggested in the environmental conditions test.
## 3 Experiments and Analysis
### Improvement Experiments
#### 3.5.2 A Comparison of Different Attention Mechanisms  
In order to verify the superiority of introducing the CBAM module into the grape disease identification model, this paper replaces the CBAM with SE [31] and CA [32] (Coordinate Attention) modules, respectively, based on GFCD-YOLOXS, for comparative experiments. The results are shown in Table 2, where the introduction of the SE attention mechanism resulted in the highest AP values for the identification of grape anthracose, grape felt, and grape leaf roll; the introduction of the CA attention mechanism resulted in the highest AP values for the identification of grape sour rot, grape deficiency, grape downy mildew, and healthy grapes; and the introduction of the CBAM attention mechanism resulted in the highest AP values for identifying healthy grape leaves, grape phylloxera, and grape powdery mildew. In a comprehensive comparison, the introduction of the CBAM module had the highest mAP value of 99.10% for all disease identification, which was 0.25% higher than the introduction of the SE module and 0.08% higher than the introduction of the CA module. This result indicates that introducing the CBAM module allows the model to more effectively focus on the key features of grape diseases and reduce the influence of noise, thus improving the accuracy of grape disease identification.  
Figure 14: mAP curve of ablation experiment.  
Figure 13: Loss diagram of ablation experiment.
# Microchannel-Embedded D-Shaped Photonic Crystal Fiber-Based Highly Sensitive Plasmonic Biosensor
###### Abstract  
An improved design of a D-shaped photonic crystal fiber (PCF)-based refractive index (RI) sensor is proposed that uses the surface plasmon resonance phenomenon. The sensor consists of a large semicircular open channel that is inserted at the upside of the D-shaped PCF. A thin plasmonic sensing layer is deposited on the interior surface of the channel to excite the surface plasmon wave that eliminates the requirement of additional effort to fabricate a well-polished sensing layer of the D-shaped sensor. The sensor's optical properties are numerically explored by the finite element method. The sensor is optimized to detect the RI of biological and biochemical analytes in the range of 1.33 to 1.44, shows spectral sensitivity as high as 63,000 nm/RIU with a spectral resolution of \(1.59\times 10^{-06}\) RIU, and maximum amplitude sensitivity of 1439 RIU\({}^{-1}\) with a resolution of \(6.94\times 10^{-06}\) RIU. It is also found that the sensor's linearity parameter is very high with a large figure of merit of about 839. Additionally, the sensor's fabrication tolerance is studied by varying its structural parameters. Therefore, high sensing parameters with a wide detection range make this microchannel-based D-shaped PCF sensor an appropriate device for the application of biological and biochemical analyte detection.  
D-shaped PCF; microchannel; optical fibers for sensing; surface plasmon resonance +
Footnote †: journal: Applied Physics Letters
## 4 Performance Evaluation
### Affective Feedback Using Projected Avatars  
With this experiment, we explored an alternative method to represent affective expressions visually by the robotic device. We evaluated the affective expressiveness of projected images in comparison with a more conventional visual feedback method, such as colored light patterns. The expressions for a physically constrained robot developed by [39] represented four emotions: happy, sad, angry, and relaxed. Their proposed methodology suggested that it is possible to represent all the emotions that a user can perceive by implementing only these four emotional expressions, as they are found in one of each quadrant of valence-arousal. With this design, any emotion on the same quadrant is considered to be similar to the representative one (e.g., happy and delight), and it is easier to distinguish it from others from different quadrants (e.g., angry and calm). This also implies that increasing the variety of emotions to be expressed does not always benefits the quality of interaction, on the contrary, it could confuse the user especially in the case of physically constrained robotic devices. To avoid this issue, we adopted a simplified set of the robot's expressions such as one positive and one negative expression. For this reason, we are designing visual representations of a positive affective expression (happy-like) and negative affective expression (sad-like) using both avatars and colored lights.  
The avatars were animated in a sequence that displayed a character that shared features with the physical robot. The avatars featured facial expressions as well as images such as blooming flowers for happy, dry flowers for sad, or changes in the color of leaves (Figure 12).  
To design the expressions using only colored lights, we followed the methodology proposed by [36], and used the following function:  
\[f(t)=\begin{cases}\begin{array}{cc}-\frac{1}{2}\cos\left(\frac{2\pi t}{xT} \right)+\frac{1}{2}&0<t\leq\frac{xT}{2},\\ 1&\frac{xT}{2}<t\leq\frac{T}{2},\\ \frac{1}{2}\cos\left(\frac{2\pi t}{xT}(t-\frac{T}{2})\right)+\frac{1}{2}&\frac {T}{2}<t\leq\frac{T}{2}\left(1+x\right),\\ -1&\frac{T}{2},(1+x)<t\leq T.\end{array}\end{cases}\end{array} \tag{2}\]  
Figure 12: Representation of the robot’s affective expressions: (**a**) happy with Avatar; (**b**) sad with Avatar; (**c**) happy with LED; (**d**) sad with LED.  
Positive expressions like happiness are related to yellow light with a high frequency and square waveform. Negative expressions like sadness are related to blue light with a low frequency and sinusoidal waveform. In this experiment, we set T = 900 ms and x = 0.25 for the happy state, and T = 3350 ms and x = 0.75 for the sad state. These selected values were similar to the ones proposed by [36], and they have already been proven to be effective at conveying these affective expressions.
## 1 Introduction
Using principal component and cluster analysis of air quality data and meteorological monitoring data from major cities in the UK in 1996, [PERSON] concluded that wind speed and maximum gust, pressure, and pollutant concentrations correlate well and that there is a significant negative correlation between SO\({}_{2}\) concentrations and wind speed, maximum gust, and minimum temperature [16]. [PERSON] et al. based their typology on the circulatory background of the pollutant concentrations in Portugal concluded that pollution is [PERSON] et al. concluded, based on the typology of the circulation background of pollutant concentrations in Portugal, that pollution is more likely to occur in dry air intrusion systems controlled by anticyclones and northerly winds [17]. Domestic research on valley wind circulation can be traced back to the last century. In 1959, [PERSON] led a research team to select dozens of observation points in the Badong, Fengjie, Wanxian, and Fuling areas of the Three Gorges region of the Yangtze River to carry out meteorological observations to study the phenomenon of valley winds [18]. Master's thesis at Chongqing University to study the valley wind phenomenon in 2009, [PERSON] et al. used the RAMS model to simulate the local circulation characteristics in the winter valley area of Lanzhou [3]. In 2014 [PERSON] et al. used the WRF model coupled with the lake model to simulate the local circulation in the Cangshan-Erhhai area of Dali [6]. It reproduced the local circulation formed by the interaction between the valley winds and the lake winds in this area [19]. In 2017, [PERSON] et al. analyzed the observed Doppler acoustic radar wind field data and sounding data and confirmed the existence of the valley wind phenomenon near the surface of Huangsi in Xingtai City [20]. In 2019, [PERSON] et al. used the WRF to simulate the local valley wind circulation under the complex topography of Chongqing with high accuracy [12]. They explored the characteristics of the formation of valley wind circulation under the effect of sheer thermal difference [21]. When conducting research on pollutant dispersion in complex terrains, the traditional methods mainly include on-site monitoring and experimental simulation. The on-site monitoring method has obvious limitations. Due to the limited number of monitoring stations, it is difficult to achieve full coverage of complex terrain areas, and monitoring blind spots will inevitably emerge. Moreover, this method incurs high costs. The experimental simulation method also has its drawbacks. Since the experimental models are scaled-down models, they may cause scale effects, resulting in certain deviations between the experimental results and the actual situation. The powerful spatial analysis and modeling capabilities of GIS enable the rapid generation of three-dimensional models of complex terrains. These models can comprehensively depict the geographical features of the study area, providing precise geometric shapes and boundary conditions for CFD simulations.
## 4 Ternary and Multivariate Mn-Based Multi-Metal Oxide Catalysts
### MnO\({}_{x}\)-CeO\({}_{2}\)-X Catalysts
When the V to Mn molar ratio is set at 0.18:1, the optimal VO\({}_{\text{x}}\)-MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst achieves 50% NO conversion at 80 \({}^{\circ}\)C and maintains a high conversion rate exceeding 90% across a broad temperature range of 122 to 400 \({}^{\circ}\)C. The incorporation of vanadium oxide introduces intermediate and robust acid sites while moderately reducing the redox sites. The synergistic interaction between vanadium oxide and manganese oxide enhances the surface acidity (including both Lewis and Brensted acids) and provides a moderate redox performance. Overall, the balance between surface acidity and redox performance facilitates the adsorption and activation of NH\({}_{3}\) and NO molecules, thereby making the VO\({}_{\text{x}}\)-MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst highly active for NH\({}_{3}\)-SCR. Sn doping can significantly enhance the Lewis acid sites on the surface of the MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst. Chang et al. (2019) synthesized a SnO\({}_{2}\)-MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst using the co-precipitation method for the NH\({}_{3}\)-SCR reaction. With a molar ratio of Sn/(Sn + Mn + Ce) at 0.1, NO is completely converted within the temperature range of 80-230 \({}^{\circ}\)C. During the SCR reaction with SO\({}_{2}\) at 200-500 \({}^{\circ}\)C, surface sulfonation notably increases the Lewis acid sites, resulting in exceptionally high activity, N\({}_{2}\) selectivity, and SO\({}_{2}\) resistance. It has been observed that the presence of a small amount of water can alleviate the SO\({}_{2}\) poisoning of the MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst to a certain extent at lower temperatures. Ji et al. (2019) found that adding H\({}_{2}\)O greatly enhanced the SO\({}_{2}\) resistance of the MnO\({}_{\text{x}}\)-CeO\({}_{2}\) catalyst at an ultra-low temperature of 100 \({}^{\circ}\)C. SO\({}_{2}\) alone can reduce the catalyst's activity from 100% to 20%, but the presence of water alleviates the deactivation, retaining 60% of the activity. By combining experimental characterization with DFT calculations, we find that H\({}_{2}\)O promotes the formation of hydroxyl groups at defect sites, which in turn facilitates the formation of sulfites and hinders the sulfate-interfering active Mn sites with electron-withdrawing solid ability. Compared to sulfate, sulfite causes less interference with the adsorption bed, ensuring the occurrence of both E-R and L-H mechanisms.
# Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle
## 5 Evaluation of Model Performance  
The performance of the ANN models was statistically measured with respect to the MSE, RMSE, and CR.  
\[\text{MSE}=\frac{1}{\text{N}}\sum_{i=1}^{\text{n}}{(\hat{y}_{i}-y_{i})}^{2}, \ \ \text{RMSE}=\sqrt{\frac{1}{\text{N}_{\text{p}}}\sum_{i=1}^{\text{N}_{\text{p}}} {(\hat{y}_{i}-y_{i})}^{2}}, \tag{8}\]  
\[\text{CR}=\frac{\text{n}\sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}\hat{y}_{i}}- \sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}\sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}}}} {\text{n}\sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}^{2}}-\left(\sum_{i=1}^{\text{ N}_{\text{p}}}{y_{i}^{2}}\right)\times\text{n}\sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}^{2}}- \left(\sum_{i=1}^{\text{N}_{\text{p}}}{y_{i}^{2}}\right)}, \tag{9}\]  
where y, represents the observed values, \(\hat{y}_{i}\) denotes the predicted values, and N is the training data size. The MSE, RMSE, and CR for the estimation model were determined to be 0.00043, 0.02084, and 0.73, respectively. In the ANN model, CR = 0.73, which indicated a good correlation between the target and predicted outputs. Furthermore, the MSE and RMSE were low. Therefore, the model can be considered to be appropriate for predicting the NO\({}_{\text{x}}\) concentrations from Euro 6 diesel vehicles while driving on realistic roads.  
Figure 4 describes the comparison of estimated and actual values, where red and blue lines correspond to the predicted and actual outputs, respectively. The graph depicts the NO\({}_{\text{x}}\) concentrations on the \(y\)-axis and the number of input datasets on the \(x\)-axis. As shown, the developed ANN model is close to the actual values. The NO\({}_{\text{x}}\) concentrations were relatively high during engine starting, but NO\({}_{\text{x}}\) concentrations tended to decrease and stabilize due to emission reduction technology.