esa-sceva/satcom-chunk-collection huggingface dataset:
# Effects of Long-Term Fertilization on Phosphorus Form and Availability in Black Soil
## 3 Results
### Analysis of Soil 31P NMR Spectra Under Long-Term Fertilization  
Using the 31P NMR technique, the Popper assay exhibited four classes of phosphorus compounds (Figure 4): orthophosphate (\(\delta\): 6.0 \(\times\) 10\({}^{-6}\)), pyrophosphate (\(\delta\): \(-\)4.4 \(\times\) 10\({}^{-6}\)-\(-\)4.8 \(\times\) 10\({}^{-6}\)), orthophosphate monoesters (\(\delta\): 7.0 \(\times\) 10\({}^{-6}\)-6.25 \(\times\) 10\({}^{-6}\) and 5.80 \(\times\) 10\({}^{-6}\)-\(2.50\)\(\times\) 10\({}^{-6}\)), and orthophosphate diesters (\(\delta\): 2.50 \(\times\) 10\({}^{-6}\)--1.50 \(\times\) 10\({}^{-6}\)).  
Based on the data in Table 3, orthophosphate can be inferred to be the dominant type of soil inorganic phosphorus, with contents ranging from 12.1 to 142.41 mg\(\cdot\)kg\({}^{-1}\). The soil orthophosphate content in all treatments increased with increases in chemical fertilizers and organic fertilizers in different amounts, which indicated that long-term fertilization could increase the orthophosphate content of the soil: the pyrophosphate content ranged  
Figure 3: Proportion of distribution of various forms of phosphorus in soil under long-term fertilization (%). Note: Different lowercase letters indicate significant differences among treatments at the \(p\) < 0.05 level.  
from 0.47 to 1.12 mg\(\cdot\)kg\({}^{-1}\); the soil orthophosphate content was 0.47 to 1.12 mg\(\cdot\)kg\({}^{-1}\) in the soil organic phosphorus fraction; the soil orthophosphate monoester content accounted for most of the soil organic phosphorus fraction, ranging from 19.77 to 25.63 mg\(\cdot\)kg\({}^{-1}\); the orthophosphate diesters content ranged from 1.37 to 4.34 mg\(\cdot\)kg\({}^{-1}\); and the changes in the soil organic phosphorus content between treatments were not obvious.
## 3 Research Framework
### Model Training and Evaluation  
This paper uses eight machine-learning algorithms, including LASSO regression [47], ElasticNet [48], k-nearest neighbors (KNN) [49], XGBoost [33], random forest [50], and AdaBoost [51] to predict roof stress. Below is a brief introduction to the algorithms. These models are classics in the field of machine learning and have achieved significant results across various domains, leading to their widespread use. Lasso regression excels in high-dimensional feature selection, while ElasticNet combines L1 and L2 regularization to address multicollinearity issues. KNN provides a simple and intuitive similarity measurement method, and XGBoost and random forest, as ensemble learning methods, offer robust predictive power and feature selection capabilities. AdaBoost enhances the overall model accuracy by boosting the performance of weak learners.  
Hyperparameters directly affect the model performance and learning capability, and different hyperparameter combinations can lead to significant differences in model accuracy, generalization ability, and training time. GridSearchCV is a powerful tool that systematically explores all possible parameter combinations, evaluates each combination's performance using cross-validation, and, ultimately, selects the best set of hyperparameters. Therefore, during model training, we used GridSearchCV in conjunction with five-fold cross-validation to find the optimal parameters for different models. The parameter grids for each model can be found in Appendix A.  
To comprehensively evaluate the model performance, we assessed three dimensions: interpretability, complexity, and predictive capability. Interpretability is measured using the coefficient of determination (R\({}^{2}\)), where an R\({}^{2}\) closer to 1 indicates a better model fit. Predictive capability metrics include root mean squared error (RMSE), mean absolute error (MAE), standardized root mean square error (SI), and mean bias error (MBE). The first three metrics should be minimized to indicate lower prediction errors, while MBE should have an absolute value closer to 0, with a positive MBE indicating overestimation and a negative MBE indicating underestimation. Complexity is assessed using the Akaike information criterion (AIC) and Bayesian information criterion (BIC), with smaller values indicating better models. Finally, the Kling-Gupta efficiency (KGE) considers the correlation, bias, and variability, with values closer to 1 indicating a better model performance. The calculation formulae for these evaluation metrics are provided in Appendix B.
# Solar Irradiance Forecasting Using Dynamic Ensemble Selection
## 5 Experiments
### Discussion  
Due to the number of computational results presented in Table 3, it is possible to discuss many aspects regarding the performance of the models. One can observe that the best RMSE value does not necessarily correspond to the best values for the other error metrics for a given method [73]. Previous works on time series forecasting have indicated such a behavior, which can occur when using distinct error metrics [56; 74].  
In two scenarios, a single model achieved the smallest error. Precisely, the smallest MAPE for Salvador, and the smallest MAPE, MAE, ARV, and IA for Florianopolis were achieved by SVR. In all other cases, the proposed model stood out. In terms of RMSE, Table 4 reveals the proposed approach overcame the single propositions, and \(\text{Het}_{\text{mean}}\) and \(\text{Het}_{\text{median}}\).  
After the application of the Friedman-Nemenyi [26] statistical test using 95% confidence, it is observed that the proposed method (\(\text{HetDS}(m\),\(k)\)) achieved the best overall results in almost all comparisons, outperforming the single methods considering the \(\ell\)4 our datasets, as highlighted in Figure 8.  
Figure 5: Boxplot graphic regarding Florianópolis.  
Figure 6: Boxplot graphic regarding Salvador.  
Figure 7: Boxplot graphic regarding São Paulo.  
The analysis of the boxplot graphics in Figures 4-7 leads to some interesting remarks. As expected, the ARMA and SVR do not present dispersion since their training processes are performed using closed-form solutions. In general, the proposed approach achieved the best solutions and relatively small dispersion. Moreover, it did not find the best general solution for Salvador.  
It is clear that the linear ARIMA model did not overcome any machine learning approach. One possibility of enhancing its prediction capability is to use bio-inspired metaheuristics [75]. Regarding the other single models, we observe high variability in terms of the performance or dispersion. It indicates how difficult it is to solve solar irradiation forecasting problems.  
Finally, Figures 9-12 present the predictions provided by the best models in comparison with the original series in the test set. It is important to observe the performance on the peaks of the solar irradiance, where most of the deviation occurs.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 2 Related Works
The Route Chaining System (RCS) [22] presented the concept of using SDN networks to allow ASes to select routes that do not follow the standard BGP algorithm and explore the possibility of Internet diversity paths. The RCS explores path diversity in inter-domain routing systems with SDN, focusing on controlling the network traffic in transit ASes from the AS source to their destinations. The RCS also requires an inter-domain communication layer between the ASes to enable this solution. The main drawback of the RCS is the propagation of new types of prefix messages in the inter-domain environment for representing the fine-grain rules. This new inter-domain routing system dramatically increases the signaling, making the solution inviable once it consumes a lot of bandwidth. Here, the idea of this architecture is to mitigate the utilization of the bandwidth to control messages between domains using compression techniques and exchanging the network state by sending it in binary format, and not in plain text. Furthermore, regarding routing logic, [PERSON] et al. [23] proposed SIREN, a solution to offload the routing functions of a customer AS to an external trusted contractor. Thus, this contractor would be responsible for optimizing and managing the network traffic of its customers. The main idea of SIREN is that, with multiple customers, the contractor would eventually get the global inter-domain network state, be able to identify conflicts in inter-domain policies and take actions to optimize customer traffic. The idea of SIREN goes towards the opposite direction of this paper, where it is claimed to give more control to customer ASes that own and advertise its prefixes. Using the architecture of this work, a customer AS can manage the network traffic on the Internet that otherwise, using only the BGP, cannot be achieved. In other words, SIREN pushes more control for ISPs to manage the inter-domain routing of its customer; here, customers have more control of the inter-domain routing once it is the customer's responsibility to apply the routing logic.
## 5 Discussion  
This article uses the data envelopment analysis method as a tool to evaluate the spatial efficiency of rail transit stations. A specific variable set is selected, and the station spatial  
efficiency is object-oriented. A new input-output system is established. Compared with other rail transit station evaluation models [39; 40; 41; 42], the new DEA model is optimized in terms of object-oriented and variable selection to more accurately measure a station's efficiency. An important innovation of this study is the construction of a new station input system based on four aspects: human, material, land, and financial resources. A new station output system was constructed based on four aspects: land efficiency output, transportation efficiency output, social efficiency output, and spatial efficiency output. It provides a scientific basis for optimizing the layout of rail transit stations and improving resource allocation efficiency.  
This study expands the application scenarios of the data envelopment analysis method and supplements relevant research on spatial evaluation models of rail transit station areas. The research findings have broad practical application value, with primary beneficiaries including urban planners, transportation management departments, real estate developers, and academic researchers. Moreover, the framework can be extended to other cities or regions, offering decision-making support for rail transit optimization, efficient land resource utilization, and sustainable urban development in the context of rapid urbanization. Additionally, it provides academic researchers with a new analytical perspective and theoretical reference.  
Due to data limitations, particularly regarding the temporal sequence of station construction and surrounding environmental development, differences in land parcel characteristics, and the impact of dynamic changes, the efficiency evaluation results for certain stations may exhibit biases. Therefore, future research should further explore the impact of station construction at different historical stages on spatial efficiency and incorporate dynamic data as well as finer-grained field investigations to enhance the accuracy and applicability of station area efficiency evaluations.
# Study on Contamination with Some Mycotoxins in Maize and Maize-Derived Foods
## 3 Results
### Fumonistins  
Fumonistins (Figures 7 and 8) are also called _Fusarium_ toxins. The main producing species for fumonisins are _Fusarium verticillioides, F. proliferation, F. sacchari, F. fujikuroi_ and \begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Country**} & **The** & **No.** & **Zearalenone** & **Zearalenone** & **Zearalenone** & **Reference** \\  & **Analyzed** & **Samples** & **Incidence\%** & **Mean** & **Range** & **Reference** \\  & **Product** & & & & **\#g/kg** & **\#g/kg** & \\ \hline Romania & Corn & 12 & 8.00 & 3.44 & nd–18.565 & \\  & Corn flakes, & & & & & \\  & breakfast & 10 & 0.00 & nd & nd & \\  & cereals, canned & & & & & \\  & corn, puffs & & & & & \\  & Maize & 90 & 0.00 & 0.00 & 0.00 & [45] \\  & Maize & 29 & 41.00 & 267.00 & nd–565.00 & [33] \\  & Maize & 106 & 64.80 & 100.50 & 71.20–1085.60 & [34] \\ Belgium & Maize & 106 & 40.70 & 158.50 & 0.00–1085.60 & [34] \\  & Maize & 106 & 42.40 & 175.50 & 0.00–2791.60 & [34] \\ Germany & Maize & 120 & 96.00 & 819.00 & nd–3910.00 & [35] \\ Brazil & Maize & 40 & 95.00 & 22.20 & 1.80–99.00 & [46] \\ Egypt & Maize & 30 & 33.34 & 2.39 & 1.13–3.70 & [47] \\  & Maize & 50 & 94.00 & 109.10 & 0.20–3613.00 & [35] \\ China & Maize & 44 & 13.60 & 50.80 & 40.70–1056.80 & [48] \\ \hline \end{tabular}
\end{table} 
Table 4: Studies on the incidence of zearalenone in maize and maize-derived foods. Figure 6: (**a**) Zearalenone in maize-derived food samples; (**b**) Zearalenone percentage in maize-derived food samples. F. subglutintans.[49]. The twenty-eight fumonisin analogs identified so far are split into four groups: fumonisins A, B, C and P. Group Fumonisins B includes FB1, FB2 and FB3. Of these, FB1 is the most frequent and has the highest concentrations [50]. Fumonisins have been identified in several food categories including cereals [51], foods derived from cereals [52; 53], asparagus, garlic [54], grapes, beer, milk and raisins [55]. The study on the presence of fumonis was performed on 14 samples of maize and maize-derived foods (canned corn and corn flakes) produced in Romania, as presented in Table 5.  
From all the maize samples, samples 4, 5 and 6 are not significantly different (\(p<0.05\%\)). All the other samples are significantly different from each other. No significant (at 95% confidence level) difference was found between the canned corn samples. The result obtained for the two cornflakes samples showed a significant difference (\(p<0.05\%\)) between them. Figure 8: Fumonisin B2 chemical structure. Figure 7: Fumonisin B1 chemical structure.
## 4 Field Experiments
### Effect of Foam on Water Spewing  
To investigate the influence of foam parameters on the water spewing, \(P_{o}\) and \(P_{f}\) have been compared under the different foam types, FERs, and FIRs. Accordingly, the foaming agent with good stability was replaced starting from the 220 th ring, replaced from the 240-250 th rings to increase the FER, and replaced from the 250-280 th rings to increase the FIR.  
Figure 11 shows the average \(P_{o}\) and \(P_{f}\) values during the construction of the 190-285 th rings. Under the same foam and bentonite parameters, a ZD30 foaming agent (\(T_{\rm{hl}}\) = 27.4 min) was used starting from the 220 th ring. As shown in Figure 11 (area C), the \(P_{o}\) values of the 220-240 th rings were lower (the maximum was less than 0.5 bar), than those of the 200-220 th rings. Observing the construction process of the 220-240 th rings, the water spewing frequencies were significantly reduced after replacing the ZD30 foam. The results of this experiment indicated that the foam with a higher stability was more effective in preventing water spewing.  
The FER was increased from 10 to 20 at the 240 th ring (as the FER increased, the volume of liquid injection was reduced, and the FIR remained the same). Figure 11 (area D) shows that the \(P_{o}\) values of the 241-249 th rings are between 0.3-0.5 bar, and \(P_{f}\) is greater than 1.5 bar (the maximum value reached 2.3 bar). Moreover, discontinuous water spewing occurred from the 241 th ring. However, compared with the data of the 220-240 th rings in Figure 11 (area B), the \(P_{f}\) in area D was larger, but the \(P_{o}\) was smaller. The results showed that a high FER foam increased the \(P_{o},P_{f}\) and the pressure drop per unit length of the screw conveyor.  
Starting from the 251 th ring, the FER was restored to 10. The flow of the foam pump was increased from the original 31 L/min to 50 L/min, at which time the FIR was approximately 48%. The data of the 250-265 th rings in Figure 11 (area E) show that the \(P_{o}\) average does not change significantly after increasing the FIR when(compared to area C). Upon increasing the FIR value further to 80% (adding a foam pump of 35 L/min), the \(P_{o}\) and \(P_{f}\) values remained unchanged (as shown in the F area in Figure 11). The data of the three areas C, E, and F in Figure 11 shows that increasing the FIR has little effect on \(P_{o}\). When the FIR \(>\) 33%, increasing the FIR was not an effective way to prevent the water spewing.  
Figure 11: The average of \(P_{o}\) and \(P_{f}\) from the 190 th to 285 th rings.
# An Advanced AFWMF Model for Identifying High Random-Valued Impulse Noise for Image Processing
## 3 The Proposed Adaptive Fuzzy Weighted Mean Filter Model
### Algorithm of the Proposed AFWMF Model  
The algorithm of proposed AFWMF model is summarized in detail below; this is an improved and enhanced version of the ROR algorithm.  
* The enhanced coarse stage includes the following six steps, as follows: 1. Choose parameters for \(L_{c}=1\); initialize the flag matrix to all zero. 2. For every pixel in the image, find its ROR, the relative divergence d among the filtering window's median, and the active pixel. 3. Use the coarse stage of ROR described in Section 2.2 to detect the noise in the active pixel. Good and noisy pixels are represented by zeros and ones, respectively. 4. Following the use of Equations (7)-(14) to build the input membership function and according to three-rules-based filter stage described in Section 3.2, we obtain the pixel weights in a filtering window. 5. By obtaining the value of restoring pixels from Equation (17), let \(L_{c}=L_{c}+1\). 6. Use Equation (5) for judging and stopping the enhanced coarse stage; otherwise, go to Step 2.
* The enhanced fine stage includes the following six steps: 1. Choose parameters for \(L_{f}=1\); initialize the flag matrix to all zeros. 2. For every pixel in the image, find its ROR, the relative divergence d among the filtering window's median, and the active pixel. 3. Use the fine stage of ROR described in Section 2.2 to detect the noise in the active pixel. Good and noisy pixels are represented by zeros and ones, respectively. 4. Use Equations (7)-(14) to build the input membership function. According to the three rules-based filter stage described in Section 3.2, obtain the pixel weights in the filtering window. 5. By obtaining the value of restoring pixel from Equation (17), let \(L_{f}=L_{f}+1\). 6. Use Equation (5) for judging and stopping the enhanced fine stage; otherwise, go to Step 2.
## 2 Collection and Statistics of Case Histories
### Statistics of Case Histories
#### 2.2.2 Structure Forms and Dimensions of the Deep Excavation and Tunnel  
According to the statistics [7], the form of deep excavation in the case histories is mainly the basement of high-rise buildings, which usually has a large excavation area, deep excavation depth, and strong unloading effect, and it has a significant effect on the adjacent existing tunnels. Figure 2 shows a histogram of excavation depth \(H_{\rm e}\). It is observed that the distribution range of \(H_{\rm e}\) is 3-27 m, in which 9-12 m and 15-18 m account for the highest proportion, and the number of deep excavations with \(H_{\rm e}\leq 18\) m accounts for about 81.0% of the total case histories.  
The form of retaining structure of the deep excavation mainly includes a diaphragm wall, bored pile, etc. Diaphragm walls are widely used in engineering owing to their high rigidity and good integrity. The form of horizontal struts in the pit mainly includes concrete struts and steel pipe struts, which have been widely used in excavation engineering, particularly in large-scale excavation engineering. The structure form of the adjacent tunnel is a shield tunnel with a circular cross-section, and the diameter of the tunnel is 6 m or 6.2 m.  
The above statistical results provide data support for the setting of structure forms and dimensions of the deep excavation and adjacent tunnel in the following numerical simulations.  
Figure 1: Dimensions, relative positions and structural deformations of the deep excavation and tunnel: (**a**) Plane diagram; (**b**) Cross section diagram; (**c**) Deformation diagram.
## 1 Introduction
[28] simulated the collapse process of the Matsurube Bridge which collapsed in the 2008 Japan Iwate-Miyagi inland earthquake due to strong ground excitations and the sliding of the rock mass beneath the bridge by FE model. Besides, some researchers carried out a seismic analysis for new complex bridges. The collapse mode is predicted by a numerical model or investigated by the shaking table test, mainly for the arch bridge [29; 30], cable-stayed bridge [31; 32; 33], skew bridge [34], and other special-shaped bridges [35]. However, the collapse control strategy is rarely studied from the point of view of control methods and there are few pieces of research on the common continuous girder bridge, so it is necessary to study the collapse control method of the conventional continuous girder bridge. Due to the large number and relatively low cost of continuous girder bridges, many continuous girder bridges do not even carry out seismic isolation design. If the collapse design of many continuous girder bridges is to be carried out, the cost of its control device should not be too high. The simplest idea is to select a suitable collapse control method among the commonly used passive control methods. Rubber bearings, viscous fluid dampers, and restraint devices are taken into account. In this paper, a two-span continuous girder bridge model with a 1:3 scale, which has been studied by the shaking table test, is the study object. The collapse control effects of three different control methods which are high damping rubber (HDR) bearing, fluid viscous damper (FVD), and lock-up clutch (LUC), are analyzed in order to provide the basis for the anti-collapse design of continuous girder bridges.
# Partial Discharge Pattern-Recognition Method Based on Embedded Artificial Intelligence
## 2 Materials and Methods  
PD data from various insulation defect types and different severity levels are acquired through discharge simulation experiments using HV equipment (Makers: Wuhan Sanxin Power Equipment Manufacturing Co; Source City: Wuhan, China). The collected data is then transformed into n-q-\(\varphi\) maps using the PRPD map analysis method for feature extraction. Subsequently, these maps are converted into grayscale representations.
## 4 Channel State Prediction via ML Algorithms  
Theoretically, the channel's state characteristics may change over distances of a half wavelength. So, dynamic channel variations could lead to rapid changes in the channel's state information, including AoA and AoD. However, as we have seen in Section 3, these changes happen at much wider scales. Identifying the scale, where the channel state prediction methods may still provide accurate approximations of AoA and AoD, are of critical importance for both beam-tracking algorithms and speeding up ray-tracing simulations.  
Recently, deep neural networks (DNNs) have received considerable attention due to their decisive advantages: (i) DNNs can model complex non-linear relationships using multiple layers of an artificial neural network, and (ii) DNNs can achieve low complexity of mathematical operations. Thus, a DNN can be a suitable candidate for modeling the temporal behavior of mmWave channels.  
In this section, we propose a channel state prediction method that models rapidly-varying mmWave channels using DNNs. We employ a convolutional long short-term memory (ConvLSTM) architecture to predict the temporal evolution of \(V_{\mathrm{X}}(s)\), based on \(N\) previous channel estimates \(V_{\mathrm{X}}(s-N),\ldots,V_{\mathrm{X}}(s-1)\).