esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Pabs Model for Quantitatively Diagnosing Phosphorus Nutritional Status in Corn Plants
## 6 Conclusions  
This study achieved the remote sensing inversion of plant phosphorus concentration and aboveground biomass using field data and Sobol sensitivity analysis. Key findings demonstrate that the Pabs diagnostic model provides accurate quantitative estimation of phosphorus absorption in corn. Using PNI, a diagnostic model of Pabs was developed to estimate phosphorus demand at the regional level, revealing sufficient phosphorus levels across most areas of Changchun, with a phosphorus deficiency identified only in northeastern Yushu. Field validation indicated a 75% accuracy rate for the remote sensing diagnosis, underscoring the model's applicability for regional-scale phosphorus demand estimation in corn cultivation. Additionally, the phosphorus nutrition diagnosis model effectively estimates the phosphorus nutritional status of corn. The Sobol sensitivity analysis identified the vegetation index with the highest correlation, which was utilized to construct multispectral inversion models for these parameters. The R\({}^{2}\) values of 0.722 for phosphorus concentration and 0.721 for aboveground biomass confirmed the adequacy of remote sensing-based parameter estimation. These inversion results were applied within the phosphorus diagnostic model, yielding an R\({}^{2}\) of 0.706 and an RMSE of 0.082 for the PNI, demonstrating the model's effectiveness in accurately estimating corn phosphorus nutritional status.  
Conceptualization, X.Z.; methodology, X.Z. and Y.X.; software, X.Z. and Z.W.; validation, X.Z., Y.X. and Z.W.; writing--original draft preparation, X.Z.; writing--review and editing, S.C. and Z.W.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Changchun Satellite and Application Industry Major Science and Technology Project (grant No. 2024 WX06).  
Not applicable.  
The multispectral data used in this study for calculating vegetation indices are available from Google Earth Engine at [[https://eartheengine.google.com/](https://eartheengine.google.com/)]([https://eartheengine.google.com/](https://eartheengine.google.com/)), accessed on 1 July 2024. For maize classification in Changchun, a 10 m resolution crop type map was utilized, derived from Sentinel-2 imagery and processed using a random forest algorithm on the Google Earth Engine platform. Climate data, were retrieved from the China Meteorological Administration platform at [[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/)), accessed on 1 May 2024.  
The authors declare no conflicts of interest.
## 2 Materials and Methods
### Performance of AquaCrop Model  
The predicted rice yields during calibration were slightly higher compared to the observed yields of 5500 kg ha\({}^{-1}\) in the off-season and 5850 kg ha\({}^{-1}\) in the main season; the variation in yield in the corresponding season was 1.0% and 3.1%. However, the  
Figure 4: Projected changes in rainfall during the period of 2010–C2099 under emission scenarios RCP4.5, RCP6.0, and RCP8.5.  
Figure 5: Variation of the projected monthly reference evapotranspiration (\(ET_{o}\)) under emission scenarios RCP4.5, RCP6.0 and RCP8.5 over the period of 2010–2099.  
model performed fairly well in predicting the yields during the validation period (2009 to 2016). Indeed, reliable and high-quality experimental data can enhance the performance of calibration and validation of crop parameters in diverse environments. The model, however, effectively simulated rice yields in both crop seasons. As shown in Figure 6, the predicted and observed yields of rice are in good agreement during the validation period for both crop seasons. For the off-season, the root-mean-square error (RMSE), mean absolute error (MAE), prediction error (PE), and index of agreement (d) are 0.173, 0.157, \(-\)0.31 to 5.6 and 0.78, respectively. The corresponding indicators for the main season are 0.167, 0.127, \(-\)5.6 to 2.34 and 0.734. The performance indicators (RMSE, MAE, PE, and d) of the model indicating a yield difference of only 0.74% to 2.9% demonstrate that the AquaCrop model has good potential to accurately predict rice yields in Malaysia.  
The climate variables, by influencing the growth and development phases of rice, contributed increasing the simulated rice yield over time. Despite rising temperatures in the study area, there are still opportunities to increase rice yields because of favorable rainfall patterns. Model validation results indicate that AquaCrop model can simulate potential rice yields within reasonable accuracy. Only small fluctuations were observed in the simulated rice yields during model validation. The model could accurately predict the growth and yield of rice under different water management strategies. Therefore, AquaCrop model integrated with CSDSSS can promote development of adaptation strategies for the irrigation districts.
# Fast Low-Precision Computer-Generated Holography on GPU
## 4 Experiments
### Visual Quality Experiments  
We generated holograms with a resolution of \(2048\times 2048\) pixels, a pixel pitch of \(p=4\) um and a wavelength of \(\lambda=532\) nm. The input data are a grayscale version of the standard \"Peppers\" test image, consisting of \(512\times 512\) pixels, decomposed into a point cloud (one point for every pixel). The virtual image plane is centered at the origin, displaced at a depth of 10 cm from the hologram plane.  
The image quality is evaluated by first backpropagating the hologram with the angular spectrum method (ASM) [23] to the image plane, taking the magnitude and scaling and  
Figure 6: Algorithm flowcharts. (**a**) Reference floating-point point-cloud CGH algorithm. (**b**) Proposed fixed-point algorithm pipeline, illustrating the batch subdivision and the different calculation phases.  
quantizing it to 8 bits, and then evaluating the peak signal-to-noise ratio (PSNR), defined for 8-bit images as follows:  
\[\text{PSNR}(I,\hat{I}):=10\log_{10}\left(\frac{255^{2}}{\|I-\hat{I}\|^{2}}\right) \tag{9}\]  
where \(\|\cdot\|\) is the Euclidean norm, \(I\) is the reference image and \(\hat{I}\) the approximation. The resulting images are shown on Figure 7. Comparing the quality of the reference floating-point algorithm to the triangular and piecewise fixed-point integer approximations, we obtain PSNR values of 34.58 dB and 51.19 dB, respectively.  
Please note that generally speaking, convolutional methods, such as discrete Fresnel diffraction or the ASM, should be used when creating holograms of planar images, which would be far more computationally efficient. However, these cannot directly calculate holograms of arbitrary 3D point clouds. Next, we investigate the impact of our proposed algorithm on 3D point cloud CGH.
###### Abstract  
In unmanned aerial vehicle photographs, object detection algorithms encounter challenges in enhancing both speed and accuracy for objects of different sizes, primarily due to complex backgrounds and small objects. This study introduces the PDWT-YOLO algorithm, based on the YOLOv7-tiny model, to improve the effectiveness of object detection across all sizes. The proposed method enhances the detection of small objects by incorporating a dedicated small-object detection layer, while reducing the conflict between classification and regression tasks through the replacement of the YOLOv7-tiny model's detection head (IDetetet) with a decoupled head. Moreover, network convergence is accelerated, and regression accuracy is improved by replacing the Complete Intersection over Union (CIoU) loss function with a Wise Intersection over Union (WIoU) focusing mechanism in the loss function. To assess the proposed model's effectiveness, it was trained and tested on the VisDrone-2019 dataset comprising images captured by various drones across diverse scenarios, weather conditions, and lighting conditions. The experiments show that [EMAIL_ADDRESS] and [EMAIL_ADDRESS] increased by 5% and 6.7%, respectively, with acceptable running speed compared with the original YOLOv7-tiny model. Furthermore, this method shows improvement over other datasets, confirming that PDWT-YOLO is effective for multiscale object detection.  
small-object detection; decoupled head; WIoU; YOLOv7-tiny model Article
## 2 Materials and Methods
### Ultrasonic Test  
Ultrasonic tests were also conducted on the same stones to estimate the dynamic elastic modulus (\(E_{d}\)). Ultrasonic inspection is a non-destructive method, in which high frequency sound waves are introduced into the material to be examined, in order to highlight any surface or internal defects. Longitudinal waves were produced by means of an electro-acoustic transducer (emitting probe in piezoelectric or magnetostrictive material) placed in contact with the stone surface under test; after passing through a path of known length, the train of waves was converted by a second transducer (receiving probe) into an electrical signal processed by electronic timing circuits that allow measurement of the transit time of the impulses. The ultrasonic test device consisted of: a Proceq Tico(r) pulse generator; two transducers of 54 kHz (one emitting and the other receiving); a pulse amplifier; and an electronic device for measuring the time interval (Figure 4). The speed of the wave (\(v\)) depends on the presence of any defects inside the material, and is related to the value of the dynamic elastic modulus (\(E_{d}\)) through the density (\(\rho\)) and the Poisson's ratio (\(\
u\)) of the material. Therefore, the value of the dynamic elastic modulus (\(E_{d}\)) of the stone can be obtained using the following relationship, in accordance with the technical standards ASTM D2845 1995 and UNI EN 12504-4: 2005 [27; 28]:  
\[E_{d}=v^{2}\ \rho\ \frac{(1+\
u)\ (1-2\
u)}{(1-\
u)} \tag{1}\]  
Figure 5: (**a**) Electric impact hammer; (**b**) Pimento model multichannel LMS signal analyzer.  
In this case, from scientific literature sources [29; 30] a \(\
u\) value equal to 0.20 was considered for the sandstone. Three speed measurements were made for each sample.
## 2 Materials and Methods
### Forest Structure Updating  
For determining the CHM while not using the most recently acquired LiDAR data (from 2013), it was necessary to recalculate the tree height according to spruce age--see Figure 2 and Formula (1). For determining the forest structure parameters, LiDAR data and the derived DSM (CHM) were used. Since the default DSM data density was 1 m \(\times\) 1 m, it was necessary to verify how the actual spruce heights differ from the heights determined by the DSM. Verification was done using a photogrammetric evaluation of the aerial  
Figure 3: Dependence of forest structure determination.  
photographs and geodetic method (tachymetry) for accuracy verification--see also [21]. Geodetic measurements of the positions of 116 trees and their heights were carried out by a total station Leica TC 1500. Geodetic survey of the tree heights was taken as the most accurate measurement method. Those tree height estimations were determined photogrammetrically in different time periods with the aid of color aerial photographs. The Military Geographical and Hydrometeorological Office (VGHMUf) Dobruska took them at regular photographing periods in the Czech Republic (2003, 2006, 2009, 2012, and 2014). A detailed description of the photogrammetric evaluation was given in [21]. Tree height LiDAR data do not match those more accurate from aerial photographs due to the fact that the density of the reflected laser beams (1 m \(\times\) 1 m) is not sufficient enough to catch the peaks of trees--see the red dots in Figure 4. The LiDAR average tree height is 6 m less than the average height determined by the photogrammetric method [22]. Photogrammetric evaluation and tachymetric verification revealed that, because of the lower density of the LiDAR data (1 m \(\times\) 1 m), the treetops were not captured, and DSM needed to be corrected (increase in height)--see the red lines in Figure 4.  
Figure 4 shows a series of average spruce heights from 2003, 2006, 2009, 2012, and 2014 determined for the photogrammetry (blue points) and the average height of the same forest determined from the DSM in 2013 from the LiDAR data (red point). The height difference for 2013 was about 6 m; that is, we needed to adjust the values of the DSM heights (CHM) for this constant. Due to the corrected elevations of the DSM, it was possible to define a new forest structure (see the methodology above) and calculate the tree distances and stem diameters. For the forest structure simulation, the normal Gauss distribution of distances and \(DBH\) were used. The mean values of height, distance, and \(DBH\) were used at 18 m, 4 m, and 0.25 m.
# Energy Efficiency Analysis of Fixed-Speed Pump Drives with Various Types of Motors
## 2 Characteristics of the Pump Unit and the Motors  
The structure of the drive of a pump unit with one electric motor, powered directly from the mains, is shown in Figure 2[8]. The electric motor is fed directly from the mains and coupled to a centrifugal pump without intermediate mechanical gears.  
The input electrical power from the mains \(P_{1}\) is converted by the electric motor into the mechanical power (output power) \(P_{mech}\). Power \(P_{mech}\) is less than \(P_{1}\) by the value of the electric motor loss [8]:  
\[P_{mech}=P_{1}-\Sigma P_{loss,M}, \tag{1}\]  
where \(\Sigma P_{loss,M}\) is the sum of the motor losses.  
The mechanical power \(P_{mech}\) of the electric motor is transmitted to the pump, and therefore, in the absence of intermediate mechanical gears, is equal to the input mechanical power of the pump. In the pump, the mechanical power \(P_{mech}\) is converted to the hydraulic power \(P_{hydr}\). The difference between \(P_{mech}\) and \(P_{hydr}\) is the total loss \(\Sigma P_{loss,pump}\) in the pump [8]:  
\[P_{hydr}=P_{mech}-\Sigma P_{loss,pump}. \tag{2}\]  
The hydraulic power is determined using the flow \(Q\) and the pump head \(H_{pump}\). The pump head depends on the flow in accordance with the Q-\(H\) coefficient of the pump at a given pump rotational speed \(n\).  
The required electrical power \(P_{1}\) depends on the flow \(Q\)[8]:  
\[P_{1}=\rho\cdot g\cdot Q\cdot H_{pump}+\Sigma P_{loss,pump}+\Sigma P_{loss, M}. \tag{3}\]  
Figure 2: Schematic representation of a single pump unit for fixed-speed operation [8].  
In order to compare the energy consumption of the electric motors of the pump unit when adjusting flow using a throttle valve, a centrifugal pump NM4 40/25B (manufactured by Calpeda S.p.A., Montorso Vicentino, Vicenza, Italy) with the rated power \(P_{RATE}=2.2\) kW and with rated rotational speed \(n=1450\) rpm [17] was considered. Pump data is shown in Table 1. \(Q_{BEP}\) denotes the flow at the best efficient point (BEP), and \(H_{BEP}\) denotes the pump head at BEP.  
The calculation was performed for eight different four-pole electric motors with a power of 2.2 kW, namely three LSPMSMs of the IE4 class (Bharat Bijlee SynchroVERT [18], WEG [19], SEW-Eurodrive [20]), two IMs of the IE4 class (Siemens [21] and WEG [22]), and three IMs of the IE3 class (Siemens [21], WEG [23], and ABB [24]). Efficiency data for the electric motors are shown in Table 2.
## 3 Testing the RLE and BSL Single Cylinder Engines under Idle Conditions
### BSL Testing under Reduced Compression Ratio
This is evident by the fact that the sharp pressure rise \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Number** & **PCP (bar abs)** & **PMP (bar abs)** & **Difference (bar)** \\ \hline
1. BSL high compression (old) but combustion continues after TDC. & 59.5 & 38 & 21.5 \\ \hline
2. BSL low compression, med advance & 44.5 & 28 & 16.5 \\ \hline
3. BSL low compression, retarded timing. & 39.0 & 28 & 11.0 \\ \hline
4. RLE old (combustion completed well before TDC) & 49.8 & 33 & 16.8 \\ \hline
5. RLE new (some combustion taking place past TDC) & 45.6 & 33 & 12.6 \\ \hline
6. RLE new, slightly cooler coolant, further retarded timing. & 42.5 * & 33 & 9.5 \\ \hline \hline \end{tabular}  
* In other tests with different injectors, we have seen PCP as low as 40 bar when the temperature approaches 71 C (160F).  
\end{table} 
Table 1: Summary of PCP minus PMP for 750 rpm and 71 \({}^{\circ}\)C (160F) coolant/oil idle conditions. RLE cases in italics font. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Number** & **Difference Adjusted to RLE** & **Total Adiabatic Heat** & **CO2 (\%)** & **\% Idle Fuel Economy Improvement** \\  & **Clearance Volume (bar)** & **Release (jcycle)** & **Based on PCP-PMP** \\ \hline
1. & 20.5 & 487 ** & N/A & \\ \hline
2. & 18.6 & N/A & 2.52\% & \\ \hline
3. & 12.4 & N/A & 2.54\% & \\ \hline
4. & 16.8 & 328 & 2.02\% & 18\% compared to 1, \\ \hline
5. & 12.6 & 319 & 1.31\% & 32\% compared to 2, 38\% compared to 1. \\ \hline
6. & 9.5 & 325 & N/A &of the premixed combustion takes place at a compression pressure of at least 3 bar below the maximum motoring pressure and of course, prior to any local maxima of the pressure. When the temperature actually reached 71 \({}^{\circ}\)C, the peak pressure was about 44.5 bar abs. The combustion behavior of this case is shown in the Appendix A (Figure A4). This case is called Case 2. Now, let us explore the PCP response when the timing is retarded by 4 degrees. The combustion characteristics are again shown in the Appendix A (Figure A5). The fact that these conditions are of a retarded injection timing can be concluded by the fact that the premixed combustion started at a pressure about identical to PMP, indicating that combustion started around the TDC and most of the heat release took place after TDC. For some reason, the premixed combustion is not as well defined in this case, possibly due to the reduced compression ratio. The combustion seems to be slower than other cases. The PCP for that case was about 39.0 bar absolute. This case, which we call Case 3, is also shown in the Appendix A. Obviously, we cannot use the difference of PCP-PMP to compare this case with, for example, the RLE cases of Figure 11 or Figure 12, since in these cases, most of the heat release was completed by TDC. However, an RLE case with similarly retarded timing is presented in Section 3.5 in order to show that the trend of lower PCP-PMP in the RLE persists.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 1 Introduction
### Limitations  
As this work extends the concept of velocity obstacle that increases its applicability, it inherits some limitations of VO method. First, it requires the passive agents to be of circular shape. It is a logical assumption for pedestrians or mobile robots, as it simplifies the problem. For a non-circular passive agent, collision avoidance is achieved by selecting a circle enclosing geometry of a passive agent. Second, this work proposes a local navigation approach. Thus it requires a global motion planner to converge to goal in the presence of the large static obstacles. The paper presents one possible implementation of the global navigational plan in Section 5, which results in model predictive partial motion planning framework.  
Finally, the presented approach is probabilistic in nature. It is possible that in some cases the solution may not be found even if one exists. However, simulation results show that the presented approach has much higher success rate compared to existing VO based approaches.
# Toward a Detailed Evaluation of Wireless Industrial Data Distribution Approaches +
## 3 Methods for Industrial Data Distribution
### Decentralized Methods for Industrial Data Distribution  
The composition of the two decentralized methods is shown in Table 2, labeled as D1 and D2. Method D1 is operating as follows: publish-subscribe on the application level, RPL in storing mode on the network level, CSMA/CA on the MAC level and IEEE 802.15.4 on the physical level. Method D2 on the other hand is operating as follows: publish-subscribe on the application level, source routing on the network level, WirelessHART on the MAC level and IEEE 802.15.4 on the physical level.  
The common aspect of the decentralized methods is that they cache the generated data not at the CNC, but at selected nodes, distributed in the network. Method D1 caches the data at the locations of the lowest common ancestors (LCAs) for each producer-consumer pair, while method D2 caches the data at the locations of the proxies. The proxies are selected before the industrial data distribution process begins, in the following manner: the CNC is initially set as the first proxy of the network. Then, the number of proxies is gradually increased, until it reaches a number such that the average access latency does not violate the maximum data access latency threshold. In every iteration, the exact selection of the next proxy in the network is performed using a myopic greedy addition. Each candidate node is examined, and the one whose addition to the current solution reduces the average access latency the most is added to the incumbent solution. To this end, the latency between a candidate proxy and a consumer is estimated as the length of the shortest path that is connecting them multiplied by the expected latency on each hop. The NET layers of the two methods are different. Method D1 uses RPL in storing mode in order to route the relevant data to the LCAs, while method D2 uses source routing to the proxies.
## 3 Results and Discussion
### Frontier Molecular Orbitals
Its ionization potential value (6.280 eV) is higher than its electron affinity, which suggests the higher electron donor than lower electron acceptor capabilities (2.365 eV) of DIT-SAL. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Molecule**} & \multirow{2}{*}{**Orbital**} & \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **(eV)** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Energy** \\ **(\(E_{g}\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Electron** \\ **Affinity** \\ **(\(EA\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Ionization** \\ **(\(EA\))** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Chemical** \\ **(\(IP\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Chemical** \\ **Hardness** \\ **(\(\
u\))** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Chemical** \\ **(\(\
u\))** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Electrophile** \\ **(\(\
u\))** \\ \end{tabular} } \\ \hline \multirow{2}{*}{SAL-DIT} & \(E_{HOMO}\) & \(-6.280\) & \multirow{2}{*}{3.915} & \multirow{2}{*}{2.365} & \multirow{2}{*}{6.280} & \multirow{2}{*}{1.958} & \multirow{2}{*}{0.511} & \(-4.323\) & \multirow{2}{*}{4.772} \\ \cline{2-2} \cline{4-10}  & \(E_{LUMO}\) & \(-2.365\) & & & & & & \\ \hline \multirow{2}{*}{Dithranol} & \(E_{HOMO}\) & \(-6.368\) & \multirow{2}{*}{4.062} & \multirow{2}{*}{2.306} & \multirow{2}{*}{6.368} & \multirow{2}{*}{2.031} & \multirow{2}{*}{0.492} & \(-4.337\) & \multirow{2}{*}{4.631} \\ \cline{2-2} \cline{4-10}  & \(E_{LUMO}\) & \(-2.306\) & & & & & & \\ \hline \multirow{2}{*}{Salicylic acid} & \(E_{HOMO}\) & \(-6.576\) & \multirow{2}{*}{5.153} & \multirow{2}{*}{1.423} & \multirow{2}{*}{6.576} & \multirow{2}{*}{2.568} & \multirow{2}{*}{0.389} & \(-4.000\) & \multirow{2}{*}{3.115} \\ \cline{2-2} \cline{4-10}  & \(E_{LUMO}\) & \(-1.423\) & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: FMO energies and global chemical reactivity descriptors. Figure 10: Shapes and energies of frontier molecular orbitals of DIT-SAL. Figure 11: Shapes and energies of frontier molecular orbitals of dithranol. Figure 12: Shapes and energies of frontier molecular orbitals of salicylic acid.