esa-sceva/satcom-chunk-collection huggingface dataset:
# Production of Sorbitol via Catalytic Transfer Hydrogenation of Glucose
## 3 Results and Discussion
### Influence of the Reaction Temperature and Catalyst Loading
This transformation was very efficient in the lowest tested temperature conditions (130 \({}^{\circ}\)C), and sorbitol yields around 60% were, thus, produced after reacting for 6 h. Total carbon balance was, under these conditions, very high, and about 80% of the starting substrate could be quantified as different products after reacting for 6 h. This result is strong evidence of the catalytic transfer hydrogenation mainly taking place from the sacrificing ethanol to sorbitol, not from the sugar disproportionation, and a confirmation of the high potential of CTH for the transformation of glucose into sorbitol using sacrificing alcohols. Figure 2: Cumulative product yields achieved in blank reactions performed at different temperatures. Reaction conditions: substrate concentration = 90 mM; reaction time = 6 h.  
Figure 3: Cumulative product yields (bars, **left** axes) and substrate conversion (dots, **right** axes) achieved in the catalytic transfer hydrogenation of glucose in ethanol in the presence of Raney nickel catalyst with different substrate-to-catalyst weight ratios (1:8—**left** column, graphs **a–d**); (1:1—**right** column, graphs **e–h**). Reaction conditions: substrate concentration = 90 mM; reaction time = 6 h; reaction temperature = 130 \({}^{\circ}\)C (**a**,**e**), 150 \({}^{\circ}\)C (**b**,**f**), 170 \({}^{\circ}\)C (**c**,**g**), and 190 \({}^{\circ}\)C (**d**,**h**). Increasing the reaction temperature led to, as stated above, acceleration of the substrate conversion, as well as diminishing sorbitol yields at long reaction times. The reasons for this phenomenon can be found in the enhancement of side reactions, such as sugar isomerization, as well as some other transformations related to the consumption of sorbitol, such as its hydrogenolysis. These side reactions usually take place in high-temperature conditions, such that these are highly influenced by a thermal effect. The transformations occur through the cleavage of sorbitol into different smaller polyols. The analysis of the liquid phase of the reaction media using GC allowed detecting some of the heavier products coming from these transformations, such as ethylene glycol, 1,2-propanediol, and glycerol.
## 2 Study Area and Data
### Se Soil Content  
An iterative method was applied to remove outliers in the original Se soil data that exceeded \(X+3S\) or fell below \(X-3S\) (where \(X\) and \(S\) are the mean and standard deviation of the original Se soil data, respectively). This process ensured that all data values fell within the range of \(X+3S\) and \(X-3S\), forming a background dataset [29]. Geochemical parameters such as mean, median, standard deviation, and coefficient of variation (CV) were calculated for Se soil content before and after outlier removal to describe the central tendency and dispersion of Se soil content [30]. The average Se soil content in the study area was compared with the national background values to evaluate the enrichment and distribution characteristics of Se soil in the region.  
The Se soil content in the study area ranged from 0.105 to 5.653 mg/kg, with an average of 0.347 mg/kg, which was higher than the national background value. This indicates an enrichment of Se soil. Since the maximum Se content far exceeded the average, the coefficient of variation was used to describe the degree of dispersion in Se soil content and to analyze the distribution of high Se values. The CV for Se in the raw data was 0.72, which decreased to 0.24 after outlier removal, resulting in a CV ratio of 3. This substantial change in CV indicates a high degree of dispersion in Se soil content, with a notable number of high-value areas and regions exhibiting elevated Se soil levels [31].  
A Se soil content distribution map was created using kriging interpolation based on the processed data (Figure 1a). Accordingly, as shown in Figure 1b, Se soil content was categorized into five levels [2,13]: Se-deficient (<0.125 mg/kg), Se-marginal (0.125-0.175 mg/kg), Se-sufficient (0.175-0.4 mg/kg), Se-rich (0.4-3.0 mg/kg), and Se-excessive (>3.0 mg/kg). The distribution map shows that Se soil content follows a clear zonal pattern, aligning closely with the distribution of geological strata and parent materials. The Permian is identified as the primary Se-rich strata, contributing to a large area of Se-rich soils in the study region.
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 2 Topology Based Scalability
### Distributed Controller Designs
#### 2.2.3 Hybrid Topology  
This design differs from the flat and hierarchical topologies in a way that the involvement of control plane as well as data plane devices on the decision processing and controlling the network and that is why this design is called hybrid, see Figure 6.  
Aiming at improving scalability, this design assigns certain control functionalities to data plane leading to reduction in the workload of the controllers. For instance, some rules can be installed proactively or reactively in the switches. Furthermore, allowing the data plane to handle the flows where possible leads to enhancing the performance in terms of latency and bandwidth utilization.  
The DIFANE architecture [32] aims to concentrate traffic in the data plane through assigning the packet management to the \"Authority Switches\". It contains one controller that maintains an algorithm that divides the switches rules to mitigate rule fragmentation among authority switches. This enables the network to react efficiently to policy changes, topology changes and host mobility.  
DevoFlow [19] (Devolved Flow) aims to obtain global view and full control of the network by enabling frequent communication between the data and control planes. In order to reduce the overhead resulted from such communication, DevoFlow assigns some functionalities of the control plane to the data plane. Thus, the controller only has to control significant and long-lived flows. Additionally,  
Figure 6: Hybrid Design.  
wild-cards are used to combine different rules leading to reduction in the communications required between the controller and the switch. However, DevoFlow has some drawbacks regarding security and traffic engineering.  
The Fibbing architecture [20] tackles the traditional distributed link-state protocols and aims to apply a central control over these protocols. The centralized controller is responsible for path computation which is based on the needs of operators. However, the traditional protocols that are implemented on the distributed controller of the network is responsible for the computation of Forwarding Information Base (FIB) entries and installing them on data plane devices. Therefore, Fibbing improves scalability and gets both the advantages of distributed traditional protocols and centralized control.  
The hybrid design aims to reduce the overload on the controller. Overloading controllers leads to complications in the processing and causes data latency. By involving data plane devices in the network control, the overload of the controllers can be reduced. However, several existing approaches still suffer from inconsistency between data plane devices.
# A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms
## 3 Materials and Methods
### Support Vector Classifier  
The Support Vector Classifier (SVC) is used for both regression and classification. Here, we have used regression to find suitable crop detection. We identify the production rate as a parameter for it and expect the crops to be suitable for output. The independent variables are the production rate and area name, whereas the dependent variable crop is taken into consideration. The SVC shall evaluate the non-linear decision boundaries and classify them using its kernel functions. Here, we can apply four kernel functions, Gaussian, Gaussian Kernel Radial Basis Function, Polynomial Kernel, and Sigmoid Kernel, to identify the most suitable function for improving the performance of the SVC. The classification of crops based on variations in crop production in different areas is to be obtained here.  
In the SVC, the hyperplane is used to identify the closest points to the margin. Here, the margin shall be the maximum for classification.  
Based on the input features, the hyperplane can be a line or a 2D plane. The standard equations for classification using the SVC are presented in Equations (6)-(8).  
Equation (6) shows the decision function; w is the direction of the plane, b is the threshold value, and x is the feature vector.  
\[w*x-b\ \text{shall\ be\ within\ range}\ \{-1,\ 0,\ 1\} \tag{6}\]  
In Equations (7) and (8), \(w*x-b\) shows the distance of the hyperplane along with vector \(w\).  
For positive class (1), it will be  
\[w*x-b\geq+1 \tag{7}\]  
For negative class (\(-1\)), it will be  
\[w*x-b\leq-1 \tag{8}\]  
In a multi-class SVM with three classes, we create hyperplanes to separate each pair of classes. To classify a new sample, we use a one-vs-rest approach, where a hyperplane is constructed for each class against all other classes combined.  
\[f_{(x)} =0\] \[f_{(x)} =w\cdot x+b\] \[f_{(x)} >0\] \[f_{(x)} <0\]  
In Equation (9), a hyperplane for separating the data points of three classes the decision function \(w\cdot x_{i}+b\) calculates the distance between \(x_{i}\) and the hyperplane.  
\[y_{i}\ (w\cdot x_{i}+b) \tag{9}\]
## 5 Performance Evaluation
### Evaluation Schemes  
The performance of MFLBS can be reflected by the throughput and average delay in two different network models.  
We compare the MFLBS algorithm with the following two algorithms, which simply represent the scheduling methods of the two sub-nets in our proposed model, respectively, evaluating our algorithm from the perspectives of global routing switching and specific bandwidth allocation:  
* One-hop DLBS. This is an algorithm that dynamically adjusts the network load. Through real-time monitoring of the network status, the unbalanced flow on the link is dynamically scheduled to maximize the network throughput.
* First come first server, FCFS/first in first out, FIFO. This is a classic single-objective task algorithm, which gives priority to meeting the task requirements of the data flow that arrives at the network first. In this article, we adopt the following two transmission models [37] to simulate the transmission patterns in the network:  
* Uniform pattern. Each host is equally likely to send and receive data in each time slot. The flow is evenly distributed in the network.
* Semi-central pattern. Each experiment selects a fixed half of the number of hosts to send data packets and randomly sends them to each host on the network, that is, all hosts have the same probability of receiving data packets.
* We will evaluate our algorithm from the following three main evaluation indicators:
* Average throughput. This is an important indicator to measure network throughput. Here, we divide the total task volume by the time when the scheduling ends to obtain the average throughput of the network. It is worth noting that the unit of the throughput characterization in the figure below is a bit.
* Average delay. Delay is a classic indicator to measure the scheduling algorithm. The calculation method of average delay in this article is the average time interval from a source node to a destination node of data packets. The small flow delay is the average of the sum of all ping delays.
* Global real-time load. The utilization of network bandwidth can be observed from the load. Here, we divide the throughput of the entire network per unit time by the sum of the bandwidth of each link in the topology.
## 2 Methodology
### Support Vector Machine (SVM)  
The works of [PERSON] [41] paved the way for what would become known as support vector machines (SVMs), which were initially titled \"Support-vector networks\" [42]. SVM is extensively utilized for classification and regression, enhancing prediction accuracy in areas such as hydrology through statistical learning theory. SVM, as a supervised learning method, identifies a singular optimal solution for a given dataset, in contrast to other algorithms that may yield multiple potential solutions. This characteristic enhances its effectiveness in mitigating overfitting. This is achieved through the use of a kernel function that defines decision boundaries in nonlinear applications [43]. In the realm of kernel-based machine learning methods, SVM serves as a classifier aimed at categorizing data or predicting a fitness function through the minimization of classification errors or the fitness function itself. In linear data classification, the algorithm identifies a decision boundary that optimizes the margin between classes [44]. Figure 5 shows a sample of the SVM of the structure.  
The versatility and robust performance of SVM in regression and classification tasks position it as a prominent method in machine learning, supported by numerous documented successful applications. Numerous modifications and enhancements to the fundamental SVM framework have demonstrated encouraging outcomes [45; 46; 47]. In the context of regression problems, SVM is known as support vector regression (SVR) [48; 49]. The primary aim is to minimize statistical learning errors, which enhances the model's predictive capabilities and robustness [50]. For further information regarding the theoretical foundations of SVR, refer to [PERSON] [51], [PERSON] [41], and [PERSON] et al. [52]. The mathematical formulation of SVM is presented in Equation (2), defining the relationship between input and output variables:  
\[f(x)=(w,\phi(x))+b \tag{2}\]  
where \(f(x)\) denotes a high-dimensional feature space, \(w\) represents the weight of the output variable, and \(b\) refers to the bias term.  
Figure 5: The schematic of SVM architecture.
## 4 Conclusions  
At present, there is no suitable target detection model for detecting smoke from straw burning, and the image background of straw burning is also affected by distance, season, and crop type. In order to explore a recognition algorithm of straw-burning smoke that is suitable for mainstream areas in China, this study designed a two-year collection experiment to enrich the training dataset. Due to the large sizes of the existing target detection models, they are not suitable for deployment on embedded devices with a limited computing performance. Therefore, the Yolov5s target detection network was improved, and deep separable convolution and ECA mechanisms were introduced into the new network model (Yolo-Light) to considerably decrease the number of parameters and calculations. The obtained model volume of the Yolo-Light network was only 4.76 M. Deploying this network on the Jetson Xavier NX could propel the inference speed up to 28.65 FPS.  
The straw-burning smoke target detection method proposed in this paper significantly improves the detection speed while considering the recognition rate, and it can be effectively applied to embedded devices with limited computing performance. It also provides a possible solution for miniaturized, low-cost, and rapid straw-burning smoke target detection equipment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Correct Pictures** & **Recognition Rate** \\ \hline Yolov5s & 902 & 90.2\% \\ Yolov5s+ECA & 907 & 90.7\% \\ Yolov5s+Dconv & 876 & 87.6\% \\ Yolo-Light & 926 & 92.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Results of model-performance testing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Params** & **Model Size** & **mAP5** & **Recognition Rate** & **FPS on Jetson** \\ \hline Yolov5s & 7.01 M & 14.4 MB & 0.709 & 90.2\% & 22.57 \\ Yolov8s & 11.1 M & 22.5 MB & 0.708 & 89.3\% & 15.97 \\ Yolo-Light & 4.76 M & 9.9 MB & 0.750 & 92.6\% & 28.65 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison of Yolo-Light, Yolov5s, and Yolov8s.  
**Author Contributions:** Conceptualization, R.H.; methodology, R.H.; software, C.W.; validation, H.W. ([PERSON]); formal analysis, H.W. ([PERSON]); investigation, R.H.; resources, Y.F.; data curation, X.W.; writing--original draft preparation, R.H.; writing--review and editing, H.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by Major Science and Technology Projects in Anhui Province (NO. 202203a07020004 and NO. 202003a07020005); the National Natural Science Foundation of China (NO. U2133212), and the Natural Science Foundation of Anhui Province (NO. 2208085 MF162).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Modeling and Parametric Simulation of Microplastic Transport in Groundwater Environments
## 3 Results and Discussion
However, the amount of attached MPs increased more rapidly in this case, and the final amount of attached MPs was larger for an increased water flow velocity. It is not possible to remove soil into which MPs have leaked while a leakage accident is occurring. However, it may be a good precaution to initially construct a base soil with a large porosity for industries affected by MPs. Figure 7 shows the simulation results for the transport of MPs with a decreased first-order attachment coefficient, _ka_. The simulation results were obtained by setting \(d_{50}=2\)\(d_{50,0}\). The concentration of attached MPs, S, decreased as the attachment performance in Equation (6) decreased. A combination of decreased S and a constant value of C indicates that a relatively larger amount of floating MPs existed in this case, and therefore adverse effects in terms of controlling the leakage of MPs were present. Figure 5: Simulation results for case S2 (\(v=2\,v_{0}\)): (**a**) distribution of C at various times, (**b**) variation in C at various locations, (**c**) distribution of S at various times, and (**d**) variation in S at various locations. Figure 6: Simulation results for case S3 (\(\theta=2\,\theta_{0}\)): (**a**) distribution of C at various times, (**b**) variation in C at various locations, (**c**) distribution of S at various times, and (**d**) variation in S at various locations. Figure 7: Simulation results for case S4 (\(k_{a}=0.5\,k_{a0}\)): (**a**) distribution of C at various times, (**b**) variation in C at various locations, (**c**) distribution of S at various times, and (**d**) variation in S at various locations. Figure 8 shows the simulation results for the transport of MPs with an increased soil bulk density. Substituting Equation (6) into Equation (4) results in Equation (7), in which the soil bulk density term is eliminated, and its influence does not manifest. \[\theta\frac{\partial C}{\partial t}=\theta D\frac{\partial^{2}C}{\partial x^{2} }-v\theta\frac{\partial C}{\partial x}-k_{a}\theta C \tag{7}\]  
The simulation results may be summarized as follows. When the diffusivity is varied (S1), the change in the concentration of attached MPs, S, is small, while the change in the concentration of MPs floating in the water, C, is more noticeable. Furthermore, changes in the soil bulk density (S5) do not affect the transport of MPs. The other examined cases (S2, S3 and S4) showed significant differences in terms of the amount of attachment. From the above findings, it can be derived that a combination of low diffusivity, fast water flow velocity, and high soil porosity may reduce the amount of MPs being leaked into groundwater environments.
## 3 Proposed Method
### Optimal Training Method Based on Adam  
Considering that gradient-directed search is more suitable for constellation optimization in terms of complexity and real-time performance, we select an appropriate gradient descent algorithm. For constellation optimization design, the real and imaginary parts of constellation points can be set as network weights to optimize the constellation through loss minimization. When the loss function represents system performance, this essentially identifies the constellation that maximizes communication-sensing system performance.  
Stochastic Gradient Descent (SGD) is a gradient-based optimization algorithm that minimizes loss functions by updating parameters using gradients computed from randomly sampled data batches. Define the constellation mapping function as \(f_{\mathbf{c}}\in\mathcal{F}\), where \(\mathcal{F}:\mathbf{b}_{\mathbf{B}}(n)\to\mathbf{c}(n)\) is the set of all possible constellation mapping relationships. For constellation diagram optimization, the real and imaginary parts of constellation points \(\Re(c(i)),\Im(c(i))\) are directly parameterized as network weights, where \(\mathbf{W}=\{\Re(c(1)),\Im(c(1)),\ldots,\Re(c(N_{c})),\Im(c(N_{c}))\}\), and \(M\) is the modulation order.  
Compared to SGD, which only considers the first moment estimate and is prone to becoming trapped in local optima, Adam combines momentum gradients with adaptive learning rates, taking into account both first- and second-moment estimates. This allows for adaptive adjustment of the learning rate, enabling faster escape from local optima, resulting in more accurate and efficient convergence. The process is depicted as Algorithm 1. The parameter settings for Adam include the learning rate, exponential decay rate (EDR) for the first-moment estimates, EDR for the second-moment estimates, and a small constant for numerical stability.  
```
0: Random bitstream \(\mathbf{b}_{\mathbf{B}}\), The set of network weights of neural network \(\mathbf{W}_{0}\).
1: Initial \(\mathbf{W}_{0}\), Adam learning rate = 0.001, 1 st EDR = 0.9, 2 st EDR = 0.999.
2:for epoch \(=1\) to \(N_{\text{max}}\)do
3: Forward pass: \(\hat{b}=\text{Decoder}(H\cdot\mathbf{f_{\mathbf{c}}}(W)+n)\)
4: Compute \(\mathcal{I}_{\text{ce}},\text{CRB}(\mathbf{\theta})\)
5: Evaluate joint loss:
6:\(Loss=(1-\alpha)\mathcal{I}_{\text{CS}}(\mathcal{I}_{\text{ce}};\mathbf{f_{\mathbf{c} }})+\alpha\mathcal{I}_{\text{SC}}(\text{CRB}(\mathbf{\theta});\mathbf{f_{\mathbf{c}}})\)
7: Backpropagate gradients: \(\
abla_{\mathbf{W}}Loss\leftarrow\text{Backprop}(Loss)\)
8: Adam update \(\mathbf{W}_{epoch+1}\leftarrow\mathbf{W}_{epoch}\).
9:endfor
```  
**Algorithm 1** End-to-End Constellation Optimization Algorithm Based on Adam
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
###### Abstract  
The Integrated Multi-satellite Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band 60\({}^{\circ}\)N-S at unprecedented 0.1\({}^{\circ}\)\(\times\) 0.1\({}^{\circ}\) and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at 2\({}^{\circ}\)\(\times\) 2\({}^{\circ}\) resolution; this study focusses on summertime months when the diurnal cycle shows stronger features. Harmonics are fitted to the diurnal cycle using a non-linear least squares method weighted by random errors. Results suggest that mean-to-peak amplitudes for the diurnal cycles of occurrence and accumulation are greater over land (generally larger than 25% of the diurnal mean), where the diurnal harmonic dominates and peaks at \(\sim\)16-24 LST, than over ocean (generally smaller than 25%), where the diurnal and semi-diurnal harmonics contribute comparably. Over ocean, the diurnal harmonic peaks at \(\sim\)0-10 LST (\(\sim\)8-15 LST) over open waters (coastal waters). For intensity, amplitudes of the diurnal and semi-diurnal harmonics are generally comparable everywhere (\(\sim\)15-35%) with the diurnal harmonic peaking at \(\sim\)20-4 LST (\(\sim\)3-12 LST) over land (ocean), and the semi-diurnal harmonic maximises at \(\sim\)5-8 LST and 17-20 LST. The diurnal cycle of accumulation is dictated by occurrence as opposed to intensity.  
diurnal cycle; satellite precipitation; IMERG; global; summertime 2019; Accepted: 26 July 2019; Published: 30 July 2019