esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### GPP Estimation Model Based on LUE and Environmental Factors  
Considering the effects of the environmental factors on the GPP accumulation, [PERSON] (2013) built two GPP estimation models based on LUE [44]. Like other LUE models, for example, VPM, CASA, MODIS-GPP, and EC-LUE, the models were based on the one minimum or coupled effect of the environmental factors on crop growth, and were designed as follows:  
\[\text{GPP1}=\text{PAR}\times\text{LUE}_{\text{max}}\times\text{ScaledWDRVI} \times\text{ScaledLST}\times\text{ScaledVSDI}\times\text{GNDVI} \tag{3}\]  
\[\text{GPP2}=\text{PAR}\times\text{LUE}_{\text{max}}\times\text{ScaledWDRVI} \times\text{min}(\text{ScaledLST},\text{ScaledVSDI},\text{GNDVI}) \tag{4}\]  
\[\text{ScaledWDRVI}=\text{WDRVI}/1.54 \tag{5}\]  
\[\text{ScaledLST}=\text{min}\bigg{(}\frac{\text{LST}}{23},\ (-0.059\times\text{LST})+2.35\bigg{)} \tag{6}\]  
\[\text{ScaledVSDI}=\ (\text{VSDI}-0.5)/0.5 \tag{7}\]  
where LUE\({}_{\text{max}}\) is the maximum light use efficiency of the crop, 1.02-3.71 gC/MJ for wheat [45]; ScaledWDRVI is the influence of the sunshine; WDRVI is the wide dynamic range vegetation index [46]; ScaledLST is the influence of temperature; LST is the land temperature product from MODIS (MOD11A2); ScaledVSDI is the influence of soil moisture; VSDI is the visible and shortwave infrared drought index [44]; and GNDVI is the influence of N, which is defined as the green normalized difference vegetation index [47].  
Solely considering the effect of the worst effect of one factor or the coupled effect of factors was not practical for field crop growth, because light, temperature, soil moisture, and N affected the crop growth simultaneously. The crop growth was the joint result of external environmental factors, and heat, soil moisture, and N played complementary roles in crop growth. A sufficient hydrothermal environment could compensate the yield-reducing effect that was caused by the N deficit. Similarly, an increase in the N rate could improve the yield-reducing effect that was caused by the water deficit.  
The GPP accumulation was mainly controlled by photosynthesis, which was determined by the PAR, FPAR, and LUE. The LUE was determined by the LUE\({}_{\text{max}}\) and adjustment coefficients of the N deficit, water droughts, and temperature. Thus, based on the LUE theory, the study modified the aforementioned model and designed it as follows:  
\[\text{ACPM}=\text{PAR}\times\text{LUE}_{\text{max}}\times\text{FPAR}\times( \text{ScaledLST}+\text{ScaledVSDI}+\text{MRVI}) \tag{8}\]  
where ACPM is the Agriculture Crop Photosynthesis Model and FPAR is the product from MODIS (MCD15A2H).  
Using the three above-mentioned models, we estimated the GPP for the winter wheat in Hengshui City in 2017. The DAM and yield were then estimated based on the GPP accumulation across the whole growth period of the winter wheat.
# VANET Clustering Based Routing Protocol Suitable for Deserts
## 4 Clustering and Cluster Management
### Cluster Head Election Principles
#### 4.1.4 Overall Election Priority  
The overall priority is a function of \(P_{e}\), \(P_{l}\) and \(P_{v}\) as:  
\[P=\frac{1}{w_{1}}P_{e}+\frac{1}{w_{2}}P_{l}+\frac{1}{w_{3}}P_{v} \tag{16}\]  
\(w_{1},w_{2}\) and \(w_{3}\) are weighting values used to determine the weight of priority factors (equipment, location and velocity), where \(w_{1}+w_{2}+w_{3}=1\).  
For one cluster, the overall priority of the vehicles can be obtained by:  
\[P_{I}=\left[\begin{array}{cccc}P_{1}&P_{2}&P_{3}&...&P_{N_{c}}\end{array}\right] \tag{17}\]  
These values can be normalized by scaling them between zero and one as:  
\[P_{norm}=\frac{P(i)-min(P_{I})}{max(P_{I})-min(P_{I})}\quad\forall i\in\phi_{n} \tag{18}\]  
Usually, \(w_{1}\) takes the biggest value because the equipment is quite important for providing reliable service in the network, particularly in intra-cluster communication. The location \((w_{2})\) should take the second weight, because the vehicle with the best location has a good possibility to serve the others with the minimum number of hops; consequently, this will reduce the end-to-end delay.
## 2 Virtual Wafer-Based Method
### One-Wafer Cyclic Schedule  
By the backward strategy, after a wafer is loaded into a PM at Step \(i\) by the robot, the wafer stays in the PM for being processed. When the robot comes to the PM again, it unloads the wafer and moves to a PM at Step \(i+1\) for loading the wafer. Then, the robot moves to a PM at Step \(i-1\) for unloading a wafer. At this time, it may need to wait there since the wafer may be not processed. After the wafer is processed, the robot unloads the wafer and moves to the empty PM at Step \(i\). When it arrives at the PM, it loads a wafer into the PM again. This implies that a wafer is completed at the PM at Step \(i\). Thus, the time taken for completing a wafer at the PM is \(\tau_{i}+2\alpha+2\beta+3\mu+\omega_{i-1}\). Since there are \(n_{i}\) parallel PMs serving for Step \(i\), \(i\in\)N\({}^{+}\)n, the time taken for completing a wafer at Step \(i\) is  
\[\theta_{i}=(\tau_{i}+2\alpha+2\beta+3\mu+\omega_{i-1})/n_{i},i\in\)N\({}^{+}\)n (1)  
With WRTCs, \(\rho_{i}\leq\tau_{i}\leq\rho_{i}+\delta_{i}\), \(i\in\)N\({}^{+}\)n, should hold. By the backward strategy, the robot task sequence \(\sigma\) is repeatedly performed, implying that the robot cycle time is the time taken for performing \(\sigma\). Let \(\psi\) denote the robot cycle time. Then, we have  
\[\psi=(n+1)(\alpha+\beta)+2(n+1)\mu+\sum_{d=0}^{n}\omega_{d}=\psi_{1}+\psi_{2} \tag{2}\]  
In Equation (2), \(\psi_{1}=(n+1)(\alpha+\beta)+2(n+1)\mu\) is known in advance and \(\psi_{2}=\sum_{d=0}^{n}\omega_{d}\) is determined by a schedule. This work focuses on the scheduling problem of process-bound SACTs with WRTCs and chamber cleaning requirements. For a process-bound SACT, the robot is not busy all the time, and has time to wait at some PMs. Thus, the key to schedule an SACT is to regulate the robot waiting time in a cycle such that a one-wafer cyclic schedule is obtained.  
**Definition 1**.: _An SACT is operated under a one-wafer cyclic schedule if the robot waiting times \(\omega_{i}\), \(i\in\)N\({}_{n}\), in each cycle keep unchanged._  
In Definition 1, if robot waiting time \(\omega_{i}\), \(i\in\)N\({}_{n}\), and each cycle is unchanged, the robot cycle time \(\psi\) must be fixed. Further, by Equations (1) and (2), if an SACT is operated by a one-wafer cyclic schedule, we have  
\[\theta_{i}=\psi,i\in\)N\({}^{+}\)n (3)  
[PERSON] et al. [15] present an efficient method to obtain a feasible and optimal one-wafer cyclic schedule by properly regulating the robot waiting time. Readers can refer to [15] for more details. Based on a one-wafer cyclic schedule, this work introduces a virtual wafer-based method to deal with the chamber cleaning requirements next.
# TEC and ROTI Measurements from a New GPS Receiver at BOWEN University, Nigeria
## 1 Introduction
The Center for Space Science and Engineering Research (Space@VT) at Virginia Polytechnic Institute and State University, Blacksburg, Virginia, in the United States and Bowen University, Nigeria, collaborated on the deployment of a CASES GPS receiver. The CASES GPS receiver differs from standard GNSS receivers in two important ways: it was specifically made to measure TEC and scintillation parameters, and it has a unique feature that enables it to function reliably in the presence of strong ionospheric scintillation. Since there are no GPS receivers around that record scintillation measurements, measurements of the TEC and ionospheric irregularities from this newly installed CASES GPS receiver will be investigated. A comparison of the CASES GPS receiver will be made with a typical GNSS receiver located in a nearby country, the Benin Republic (BJCO). Section II of this introduction includes a description of the CASES's hardware platform and the performance specifications. Section 2 is about the data and methodology we employed in this investigation. We present all our important results and discussions in Section 3. Section 4 is about our conclusions.
## 3 Experimental Results
### Result of the Collision Grid Map  
In order to generate the Collision Grid Map, the map is set 150 * 100 in size and 0.01 m in resolution. Based on the sensor coordinate, x, y with the width of 1.5 m, the length of 1.0 m and depth data are used up to 2 m. When data is measured in the collision situation, Figure 5 shows the result of the Collision Grid Map. The example image was captured by a depth camera sensor shown in Figure 6a. Figure 6b shows raw 3D point cloud data. Figure 6c,d show the result of Collision Grid Map that consists of the first channel and second channel. In the second channel about depth data, the result shows that the nose of the cow becomes green due to the nearest distance based on a sensor coordinate. Similarly, Figure 7 shows results of the Collision Grid Map about a non-collision situation. Figure 7a,b show the image and 3D point cloud data each. Figure 7c,d show the result of the Collision Grid Map.  
Table 1 shows a comparison of data size by types. The data format unifies pickle to compare data sizes. Raw point cloud data size averages 4508.9 KB per data and the Collision Grid Map data size is 5.7 KB per data. The Collision Grid Map data size shows a significant reduction relative to raw point cloud data due to a transformation more than 100,000 raw data to 150 \(\times\) 100 size of pixel data. Vox grid, which is set 200 * 200 * 200 size, 0.01 m resolution and hit map type for VoxNet [25] data size shows 8.0 KB. The size of voxnet data is relatively larger than the Collision Grid Map data size since map size is different.  
Figure 6: Result of the Collision Grid Map about the collision situation. (**a**) The example image that was captured by a depth camera sensor. (**b**) Raw 3d point cloud data. (**c**) The result of CGM in first channel. (**d**) The result of CGM in second channel.  
Figure 7: Result of the Collision Grid Map about the non-collision situation. (**a**) The example image that was captured by a depth camera sensor. (**b**) Raw 3d point cloud data. (**c**) The result of CGM in first channel. (**d**) The result of CGM in second channel.
# Breast Density Transformations Using CycleGANs for Revealing Undetected Findings in Mammograms
## 2 The Proposed System Overview
### The Deep Learning Module
#### 2.2.2 Generative Adversarial Networks (GANs)  
Recently, the idea of adversarial training has gained popularity, and deep learning research has advanced significantly. Since their initial presentation, generative adversarial networks (GANs) have attracted attention worldwide, and every year, even more studies are published and presented in different research areas, especially in medical image analysis. GANs have been used for data augmentation in several recent works [35, 36, 37, 38, 39], including medical image analysis.  
Generally, training on a set with a large number of samples, performs well and gives high accuracy rates. However, biomedical datasets usually contain only a relatively small number of samples due to the limited number of patients that can be involved in different studies. To solve this problem, data augmentation can be used to increase the size of the input data by generating new data from the original input data. Given the rapid progress of generative models in synthesizing realistic images and the known effectiveness of simple data augmentation techniques (e.g., horizontal flipping, rotation, shifting, brightness adjustments), we have integrated two GAN models in our CAD system to synthetically augment the extracted patches from the training database. In this way, we can balance the class ratio of normal, benign, and malignant samples in the training set.  
The first GAN (GAN-NBM) was used to produce synthetic patches from normal/benign/malignant classes to ensure the robustness of the CNN-based classifier and variability of the samples. The second one (GAN-ACR) was implemented to produce synthetically augmented patches belonging to the four different tissue ACR categories. Figures 4 and 5 depict patches generated by the GAN model that belong to the specified classes. In Figure 4, the GAN-NBM produces ROI patches from the normal, benign, and malignant classes. In Figure 5, ROI patches representing the ACR breast density class are generated. The role of both GANs is to augment the training dataset in an unsupervised manner. For both cases, the INbreast annotation, which refers to the annotation of masses and microcalcifications in mammograms, was used, imposing at least an 85% ratio of overlap with the breast tissue mask that was estimated from the previous image segmentation step, especially for the patches of classes ACR-A, -B, -C, and -D.  
Figure 4: Examples of artificially generated images of the normal, malignant and benign classes using the GAN-NMB network. (Note that checkerboard effects and artifacts are present.)
## Materials and Methods
### Adsorption Isotherns and Data Modeling
#### 2.5.1 Non-Linear Regression  
Langmuir adsorption isotherm:  
\[q_{\rm e}=\frac{q_{\rm m}K_{\rm L}c_{\rm e}}{1+K_{\rm L}c_{e}} \tag{3}\]  
Dubinin-Radushkevich adsorption isotherm:  
\[q_{\rm e}=q_{\rm s}exp\Bigl{(}-\beta c^{2}\Bigr{)} \tag{4}\]  
\[\varepsilon=RT\ln\big{(}\frac{c_{s}}{c_{e}}\bigr{)} \tag{5}\]  
\[E=\frac{1}{\sqrt{2\beta}} \tag{6}\]  
Dubinin-Radushkevich becomes:  
\[q_{e}=q_{\rm s}exp\biggl{(}-\beta R^{2}T^{2}\Bigl{(}ln\frac{c_{s}}{c_{e}} \Bigr{)}^{2}\biggr{)} \tag{7}\]  
Hill adsorption isotherm:  
\[q_{e}=\frac{q_{m}c_{e}^{n_{H}}}{K_{\rm D}+c_{e}^{n_{H}}} \tag{8}\]where \(K_{\rm L}\) is the Langmuir equilibrium constant of adsorption (1/mg) or apparent affinity constant, \(q_{\rm m}\) is the theoretical maximum adsorption capacity of \(\beta\)-glucan (mg/mg), \(q_{\rm s}\) is the theoretical isotherm saturation capacity (mg/mg), \(\beta\) is a constant related to the adsorption capacity (mol\({}^{2}\)/\({}^{2}\)), \(\varepsilon\) is Polanyi potential (J/mol), \(R\) is the gas constant (8.314 J/mol K), \(T\) is the temperature (K), \(E\) is the adsorption mean free energy (J/mol), \(c_{\rm s}\) is the theoretical saturation concentration or solubility (mg), \(n_{\rm H}\) is the Hill cooperativity coefficient of the binding interaction, and \(K_{\rm D}\) is the Hill constant (mg)\({}^{\rm nH}\).  
The parameters from the two-parameter Langmuir isotherm determined by the regression are \(q_{\rm m}\) and \(K_{\rm L}\). Dubinin Radushkevich is a three-parameter isotherm and the parameters that could be determined are \(q_{\rm s}\), \(\beta\), and \(c_{\rm s}\). The \(c_{\rm s}\) represents the theoretical saturation capacity, so it should be large than the largest observed \(c_{\rm e}\). A convenient constraint value for these data is that it not be smaller than 0.1 mg. With this constraint the non-linear least squares solutions occurred at this value. An equivalent result is obtained by fixing the \(c_{\rm s}\) value at the level of 0.1 mg. A least squares value of \(c_{\rm s}\) near 0.1 mg occurred automatically for the quercetin-3-rhamoside data, so no constraint was found to be needed in that case. Along with the \(c_{\rm s}\) value, we determined \(q_{\rm s}\) and \(\beta\). From \(\beta\), the energy \(E\) was calculated. Altogether, the reported parameters of the Dubinin-Radushkevich isotherm are \(c_{\rm s}\), \(q_{\rm s}\) and \(E\). The parameters for the three-parameter Hills model are \(q_{\rm m}\), \(n_{\rm H}\) and \(K_{\rm D}\). An approximate least squares values of the Hills constant \(K_{\rm D}\) was found at the level 0.0001 for quercetin-3-galactosidase and quercetin-3-glucoside (smaller \(K_{\rm D}\) values that show potential insignificant improvement were numerically unstable, so we chose the indicated level). The stable least squares value of \(K_{\rm D}\) was 0.033 for quercetin-3-rhamoside. Along with \(K_{\rm D}\), we determined and reported the two additional Hill parameters \(q_{\rm m}\) and \(n_{\rm H}\).
## 5 Determination of Translation Magnitude from GNSS Measurements
In addition, the user's previous position vector \(\vec{\mathrm{R}}_{r}(t-1)\) has been optimally estimated by the filter of the integrated system, and the present position vector \(\vec{\rm R}_{r}(t)\) in Equation (16) can be initially determined by inertial navigation given a short time interval. The \(\vec{\rm R}_{r}(t)\) at this stage is not for the ultimate position solution at the present time \(t\) but for the upcoming \(\delta\)R\({}_{r}\) estimation. Note that after the dot product operation, the desired unknown becomes the scalar \(\delta\)R\({}_{r}\) instead of \(\delta\vec{\rm R}_{r}\). Combining the Equations (15) and (16) with rearranged known/unknown quantities, the revised differential equation is expressed as:  
\[\delta P^{r_{i}}=-\cos(\theta_{r}^{i})\cdot\delta\rm R_{r}+c\cdot\delta t_{r}+ \delta\epsilon^{i}\hskip 56.905512 pt\hskip 56.905512 pti=1,...,k \tag{17}\]  
where:  
\(\delta P^{r_{i}}\) is the pseudorange differential term compensated by the known quantities of the dot product operation;  
\(\theta_{r}^{i}\) represents the included angle between the vectors of \(\delta\vec{\rm R}_{r}\) and \(\vec{\rm u}\). With two or more available pseudorange observations, the unknown terms of \(\delta\)R\({}_{r}\) and \(\delta t_{r}\) can be derived by implementing the least-square fit. It is worth noting that the LOS measurements play an important role in accurate \(\delta\)R\({}_{r}\) estimation as a contaminated measurement does not purely satisfy the above-mentioned satellite/user relationship and will give rise to a biased estimate of \(\delta\)R\({}_{r}\). In an effort to conduct multipath detection and mitigation in challenging GNSS environments, previous researchers have applied several approaches [32, 33] by leveraging pseudorange, carrier phase or signal-to-noise ratio (SNR) measurements based on the test statistics. However, difficulties arise to road applications as a result of undesirable detection performance and required hardware upgrades [34]. Another easier option uses a weighting scheme by evaluating the satellite elevation angle and carrier-to-noise ratio (C/No) to restrain the multipath effect. With the synchronized GNSS and camera observations, the camera scale ambiguity can be finally resolved by the estimation of the baseline magnitude of position change \(\delta\)R\({}_{r}\) between two GNSS epochs.
## 3 Results
### Rotational Angle  
The scans for each abutment demonstrated a statistically significant deviation in the angle of rotation (\(p\)-value \(<\) 0.001).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pin** & **Parameter** & **Mean** & **STD** & **\(p\)-Value** \\ \hline
**MIS** & **x (mm)** & \(-\)0.007 & 0.083 & 0.656 \\ \hline  & **y (mm)** & \(-\)0.082 & 0.072 & \textless{}0.001 \\ \hline  & **z (mm)** & \(-\)0.002 & 0.016 & 0.475 \\ \hline  & **D (mm)** & 0.125 & 0.058 & \textless{}0.001 \\ \hline  & **rotational angle (deg)** & 0.506 & 0.233 & \textless{}0.001 \\ \hline  & **longitudinal angle (deg)** & 0.324 & 0.236 & \textless{}0.001 \\ \hline
**ZZ** & **x (mm)** & \(-\)0.013 & 0.015 & \textless{}0.001 \\ \hline  & **y (mm)** & 0.03 & 0.024 & \textless{}0.001 \\ \hline  & **z (mm)** & 0.026 & 0.005 & \textless{}0.001 \\ \hline  & **D (mm)** & 0.046 & 0.022 & \textless{}0.001 \\ \hline  & **rotational angle (deg)** & 0.468 & 0.245 & \textless{}0.001 \\ \hline  & **longitudinal angle (deg)** & 0.304 & 0.269 & \textless{}0.001 \\ \hline
**AB** & **x (mm)** & \(-\)0.033 & 0.02 & \textless{}0.001 \\ \hline  & **y (mm)** & \(-\)0.025 & 0.016 & \textless{}0.001 \\ \hline  & **z (mm)** & \(-\)0.007 & 0.089 & \textless{}0.001 \\ \hline  & **D (mm)** & 0.069 & 0.075 & \textless{}0.001 \\ \hline  & **rotational angle (deg)** & 1.045 & 0.121 & \textless{}0.001 \\ \hline  & **longitudinal angle (deg)** & 0.194 & 0.165 & \textless{}0.001 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean and STD for MIS, ZZ and AB scan abutments (\(\alpha\) =0.001).
## 5 Conclusions  
According to the environmental risk characteristics of a sudden oil spill accident in the Taihu Basin, the Taipu River, a typical transboundary river, is the main research object in this paper. Based on the MIKE21 water environment numerical simulation software package developed by the Danish Institute of Water Environment, a two-dimensional oil spill model of the Taipu River was established. Through model calibration and verification, it is proved that the model has met the requirement of early warning accuracy. The arrival time and thickness of oil film in different rivers after an oil spill accident are simulated from the angles of oil film length, drift speed, and influence range of upstream and downstream. The main contents include the following:  
1. For the two-dimensional hydrodynamic model of the Taipu River, the measured data of three sections in March were selected to verify the water level of the model. The relative error of the simulation results was less than 0.25, which was within the acceptable range. It shows that the model is feasible to simulate the two-dimensional hydrodynamic dynamics of the typical reaches of the Yangtze River, and the model can be used to simulate the oil spill accident in the Taipu River area.
2. Through the simulation of the oil spill accident at Zhaojiabang in the upper reaches of the Taipu River, the results show that the evaporation rate and water content of the oil spill are close to the results of other scholars. The decreasing rate of oil film thickness gradually decreases with time, and the maximum thickness of oil film decreases from 1.94 mm to 0.48 mm.
3. The established oil spill model can better simulate the migration and transformation processes such as oil film expansion, oil particle transport with the flow, wind conduction drift, weathering, and emulsification after oil spill. The establishment of this model can reduce the blindness of the emergency treatment of oil spill accidents, and is of great significance to the establishment of risk assessment and decision management systems for oil spill accidents in the Taipu River.  
Conceptualization, F.H. and Q.L.; data curation, G.W.; formal analysis, Q.L.; investigation, F.H. and J.M.; methodology, F.H.; supervision, W.L.; visualization, J.M.; writing--original draft, Q.L.; writing--review & editing, F.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The Project of Ecological and Environmental Protection Integration Research Institute in Yangtze River Delta (No. ZX2022 QT046), Major Science and Technology Program for Water Pollution Control and Treatment (No. 2017 ZX07301006), and The Special Fund of Chinese Central Government for Basic Scientific Research Operations in Commonwealth Research Institute (No. GYZX220405).  
Not applicable.  
The data used during the study appear in the submitted article.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.