esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Modeling Program
### Prediction of ROP in Mixed Formation  
ROP is an important parameter for measuring the drilling efficiency, which is the depth of the drill bit into the rock per unit time. The level of ROP directly affects the progress and cost of the drilling operation. In practical core drilling, selecting suitable drilling parameters and optimizing the ROP can effectively improve the efficiency of drilling operations. To simulate a real complex-formation environment and to verify the prediction performance of the regression algorithm and the BP neural network in mixed formations, all the drilling experimental data were mixed and then randomly divided into training, validation, and testing sets at a ratio of 70%, 15%, and 15% respectively. After the model was trained, we evaluated the mechanical ROP prediction results, as shown in Figure 8.  
From Figure 8, it can be seen that the evaluation metrics of the BP neural network show better prediction performance compared with the other algorithms. Using R\({}^{2}\) as the evaluation metric, the prediction results of each algorithm are shown in Figure 9.  
Figure 8: Comparison of prediction results for the mixed-formation experimental sample data.  
Figure 7: Comparison of prediction results for the rate of penetration (ROP) using (**a**) MAE, (**b**) MSE, (**c**) RMSE, and (**d**) R\({}^{2}\).  
From Figure 9, it can be seen that after mixing the data, the difficulty of predicting the mechanical ROP increases. Compared with the previous prediction results, the MSE, RMSE, MAE, and other metrics have increased, the R\({}^{2}\) value has decreased, and the required computation time has significantly increased. The average prediction accuracy is around 80%. Among these, the ridge regression algorithm requires the least computation time, but due to its poor prediction accuracy, it was excluded. In contrast, the BP neural network algorithm's MSE, RMSE, MAE, and R\({}^{2}\) metrics are better than those of other algorithms, with a prediction accuracy of 90%. At the same time, the computation time needed is only about 20 s, which can meet actual needs. The predicted mechanical ROP values were compared with the actual mechanical ROP values, and the comparison results are shown in Figure 10.  
The blue line in the figure represents the predicted values, and the red line represents the true values. As can be seen from the figure, the error between the actual values and the predicted values is small. Through comparison and calculation, the root mean square error of the model's prediction results is 0.059, indicating that the prediction accuracy of the model can reach 94.1%. The R\({}^{2}\) value calculation result is 0.966, meaning the fit of the predicted mechanical ROP values to the true values can reach 96.6%. This indicates that the neural network performs well in predicting this formation and can effectively predict drilling speed optimization.  
Figure 10: Comparison between the real value and the predicted value of the ROP.  
Figure 9: Comparison of the predicted R\({}^{2}\) and the operation time.
# Impact of Peri-Implant Inflammation on Metabolic Syndrome Factors: A Systematic Review
## 2 Materials and Methods
### Focused Question  
Based on the PICO format (Population, Intervention, Comparison, and Outcome), this systematic review aims to address a focused question: What is the effect of peri-implant diseases on metabolic syndrome factors?  
* Population: Patients with osseointegrated dental implants.
* Intervention (exposure): Patients affected by peri-implantitis.
* Comparison: Patients with healthy peri-implant conditions.
* Outcome: Systemic metabolic factors in relation to MetS and its components.
# Research on Environmental Vibration Induced by High-Speed Maglev Transportation
## 1 Introduction
[10] studied the propagation laws of the environmental vibration induced by urban rail transit moving on the ground and proposed general propagation rules by comparing statistical data on environmental vibration in field tests. [PERSON] et al. [11] established a simplified three-dimensional finite element model to simulate ground vibrations caused by metro trains; the numerical analysis results showed that vertical ground vibrations are generally larger than lateral vibrations in the area close to the central line of the railway, and they successfully simulated the vibration-amplifying zone caused by wave reflection and interference 20 m away from the railway's central line. [PERSON] et al. [12] compared different existing mitigation systems and provided a benchmark reference for the railway industry when searching for the best solution for a project. Different from wheel-track transit, maglev transportation is fairly new, and the number of maglev lines is rather small; therefore, research on the vibration effects of maglev transportation is not quite sufficient. The research is based more on numerical simulations rather than in situ tests. [PERSON] et al. [13] arranged vibration measurement points along and perpendicular to the maglev line and found that the ground vibration induced by a maglev train is far less than that caused by a wheel-track train, and the vibration will not obviously increase with the increase in train speed, as is the case for wheel-track transits. [PERSON] et al. [14] measured the vibration magnitude 26-80 m away from a maglev line and utilized a logarithmic function to fit the vibration propagation law. [PERSON] et al. [15] proposed an interaction model of a maglev vehicle-bridge system to calculate dynamic magnetic forces between a maglev train and a guideway bridge and then used the force to calculate the ground vibration response. The numerical results showed that the ground vibration attenuation law is similar to that induced by wheel-rail vehicles, but there is no amplifying zone at about 25 m away from the track centerline. When the vehicle speed increased from 125 km/h to 430 km/h, the vibration level rose about 10 dB at the same point. The research mentioned above generally showed the propagation and attenuation laws of the ground vibration induced by maglev transportations, but the lack of in situ tests may cause overfitting problems. Thus, the current research involved the following: (1) A series of in situ tests were conducted around the Shanghai high-speed maglev line, where practical vibration data from the running of maglev trains were gathered. (2) The vibration propagation law in the vertical direction was discovered, and an empirical formula related to height was proposed. (3) A ground vibration distribution graph was drawn using the measured data, and vibration propagation patterns different to those of wheel-track transportation were revealed.
## 4 Discussion  
This study aimed to investigate the critical issues that arise when measuring AOX concentrations in aqueous waste and to assess the potential impact of AOX-containing substrates on various aerobic biomass types.  
UNI EN ISO 9562:2004 specifies the standard method for the determination of the AOX concentration in water and wastewater samples. Standard-compliant preparation of the sample is a crucial aspect for a reliable AOX analysis. In this study, four different laboratories --Lab-1, Lab-2, Lab-3 and Lab-4--were asked to carry out measurements of the AOX content of two samples: sample S1 was expected to have a low concentration of AOXs, while sample S2 was expected to have a high concentration of AOXs. The results showed that the standard procedure was followed by all the laboratories. They employed 100 mL of diluted samples. However, each laboratory used a different dilution factor (DF) and obtained very different values for the AOX concentration. In order to verify whether a cause/effect relationship between the DF of the sample and the AOX concentration actually exists, Lab-1 was commissioned to carry out the AOX analysis using four different DFs. The outcomes of this investigation showed that the AOX value was not influenced by the different dilution ratios. Moreover, the results of the two series of tests showed a concordant trend in the normalized data, which could argue in favor of a systematic problem. Therefore, further investigations will be necessary.  
In this study, both batch tests and continuous tests were carried out in order to evaluate the actual biological treatability of AOX-containing substrates. Five different types of aerobic biomasses from plants with different capacities were tested. The results obtained can be summarized as follows:  
* By changing the pH value from about 9-10 to about 8, the kinetics of the oxygen consumption increased. Thus, the optimum pH range to ensure the best biomass respiration kinetics is close to neutrality;
* The industrial biomass B1 showed higher biological kinetics compared to the civil biomass B2 when put in contact with aqueous waste containing AOXs. The explanation for this lies in the fact that the industrial biomass was already accustomed to \"unconventional\" pollutants;
* Lower biological kinetics was observed for small civil plant biomasses, proving that biomasses coming from plants with a medium-low capacity need to be acclimatized;
* When the substrate was more diluted, i.e., it had a lower concentration of AOXs, the kinetics of the oxygen consumption was higher;
* The type of AOX did not affect short-term oxygen consumption for both biomass B1 and biomass B2;
* There was no inhibition of the tested aerobic biomasses, either in the short term nor in the medium term. However, low oxygen consumption was noticed.
## 4 Proposed Epileptic Seizures Detection Model
### Classification Techniques
#### 4.4.1 Support Vector Machine (SVM)  
SVM is a powerful supervised learning algorithm known for its effectiveness in high-dimensional spaces and its robustness against overfitting, especially in cases where the number of dimensions exceeds the number of samples. SVM works by finding the optimal hyperplane that separates the data into different classes with the maximum margin. This property is particularly useful in EEG data classification, where the distinction between seizure and non-seizure events can be subtle. SVM's ability to handle complex, non-linear  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Description** \\ \hline Wavelet Type & Daubechies 4 (db4) & Selected for its suitability in analyzing EEG signals with transient behaviour. \\ \hline Number of Decomposition Levels & 3 & Allows decomposition into relevant EEG frequency bands (delta to beta). \\ \hline Signal Length & 178 data points per 1-s segment & Based on UCI dataset sampling rate (173.61 Hz). \\ \hline Sampling Frequency & 173.61 Hz & Defines the effective frequency resolution for DWT. \\ \hline Features Used & Detail coefficients (D1, D2, D3) & Captures high-frequency signal changes linked to seizure events. \\ \hline Implementation Library & PyWavelets (PyWT) & Python (Pandas 2.2.3) library used for wavelet transformation and feature extraction. \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters and settings used for DWT algorithm.  
boundaries through kernel functions further enhances its suitability for our task. Therefore, SVM stands out for its margin-maximizing decision boundaries, and kernel-based flexibility makes it highly suitable for sparse and imbalanced datasets. Additionally, SVM-based methods have demonstrated reliable performance across a variety of EEG domains [36]. For instance, in decoding motor intentions for predicting movement directions from EEG signals, SVM is employed to tackle noisy data while maintaining robustness. Similar to this, in brain-machine interfaces [37] SVM has been successfully applied to classify moment intention. The choice of SVM is driven by its high accuracy and ability to generalize from training data to unseen data.
# A New Sentiment-Enhanced Word Embedding Method for Sentiment Analysis
## 4 Experiment
### Analysis of Convergence Time for Downstream Tasks  
Under TextCNN, TextRNN, TextRCNN, TextBiRCNN, ABCDM, and CNN-LSTM using Word2 Vec and GloVe with or without S-EWE\({}_{\text{c}}\) and S-EWE\({}_{\text{f}}\) on SemEval-2013 and SST-2 datasets, their convergence times were analyzed on one NVIDIA 2080 Ti (12 GB), as shown in Table 7. For all baseline models using Word2 Vec enhanced by S-EWEs on the SemEval-2013 dataset, the convergence times of TextCNNs-EWE\({}_{\text{c}}\), TextRCNN\({}_{\text{S-EWE}_{\text{c}}}\), and TextBiRCNN\({}_{\text{S-EWE}_{\text{c}}}\) were on average increased by 22.56% over their original models; however, the convergence times of TextRNNS-EWE\({}_{\text{c}}\), ABCDM\({}_{\text{S-EWE}_{\text{c}}}\), and CNN-LSTM\({}_{\text{S-EWE}_{\text{c}}}\) on average decreased by 31.16%. Meanwhile, for all baseline models using Word2 Vec enhanced by S-EWEs on the SST-2 dataset, the convergence time of CNN-LSTM\({}_{\text{S-EWE}_{\text{c}}}\) was the most significant increase, by 440.32% over CNN-LSTM; the convergence time of TextRNNS-EWE\({}_{\text{c}}\) decreased the convergence time of TextRNN by 0.35%; and the convergence times of TextCNNs-EWE\({}_{\text{c}}\), TextRCNN\({}_{\text{S-EWE}_{\text{c}}}\), and ABCDM\({}_{\text{S-EWE}_{\text{f}}}\) were on average increased by 21.98% of their original models.  
For all baseline models using GloVe enhanced by S-EWEs on the SemEval-2013 dataset, the convergence times of TextCNN\({}_{\text{S-EWE}_{\text{c}}}\), TextRCNN\({}_{\text{S-EWE}_{\text{c}}}\), TextBiRCNN\({}_{\text{S-EWE}_{\text{c}}}\), and ABCDMs\({}_{\text{S-EWE}_{\text{c}}}\) were on average increased by 50.17% compared to their original models. The convergence time of CNN-LSTM\({}_{\text{S-EWE}_{\text{c}}}\) was decreased by 26.24% compared to CNN-LSTM. Meanwhile, using GloVe enhanced by S-EWEs on the SST-2 dataset, the convergence times of TextBiRCNN\({}_{\text{S-EWE}_{\text{c}}}\) and ABCDM\({}_{\text{S-EWE}_{\text{c}}}\) were on average increased by 19.67% compared to their original models; the convergence times of TextCNN\({}_{\text{S-EWE}_{\text{c}}}\), TextRCNN\({}_{\text{S-EWE}_{\text{c}}}\), and CNN-LSTM\({}_{\text{S-EWE}_{\text{c}}}\) were on average decreased by 25.34% compared to their original models.  
In all baseline models with the S-EWE method, the convergence time of ABCDM\({}_{\text{S-EWE}_{\text{f}}}\) was significantly decreased by 51.21% compared to ABCDM on the SemEval-2013 dataset, and the convergence time of CNN-LSTM\({}_{\text{S-EWE}_{\text{c}}}\) vastly decreased by 41.34% compared to CNN-LSTM on the SST-2 dataset.  
Based on the performances of various sentiment classification models and models' convergence times, we conclude that the proposed S-EWE method can enhance the ability of sentiment classification on SemEval-2013 and SST-2 datasets.
## 2 Literature Review
### Time Domain Analysis Methods  
Time domain analysis is often affected seriously by noise in the signal. Pre-conditioning of the signals, especially high frequency signals, is an important task that can dictate correctness of the analysis outcome. [PERSON] et al. [7] studied the hovering problem of an unmanned aerial vehicle. The time domain response data was processed in a linear time- invariant model of the vehicle. The analysis outcomes were used to improve stability of the flight control system. Likewise, [PERSON] et al. [8] used an adaptive fuzzy clustering concept to group the vibration data into a data matrix with nine common time domain parameters. Exception processing could then be initiated according to the limits of the varying signal.  
One of the important time domain methods is Statistical Process Control (SPC) [9]. SPC aims to monitor outcomes of a manufacturing process. If the process continues to produce good outcomes, no action is taken. However, if the process starts to produce rejects, a decision on whether to intervene will be taken. [PERSON] et al. [10] used an SPC control chart to monitor the process deterioration caused by vibration in the manufacturing. An SPC control chart can also be applied to monitor the ecological system Shore [11]. This research demonstrated that SPC is a time-based out-of-limit detection system. Efficacy of the system depends on the limit value that needs to be set in such a way that it can raise the alarm when the system has the trend to produce more bad signals, but at the same time does not raise the alarm too quickly while the system produces one or two bad signals occasionally (by chance). It is therefore a matter of risk of the system going bad too quickly.
## Material and Methods
### Assessment of Nitrogen Removal in Domestic Wastewater  
To test the potential application of the strain F19 in low-C/N domestic wastewater, an integrated bioreactor (500 L) for sewage treatment was designed and installed outdoors. The strain F19 was cultured in 20 L of NM for 16 h as seed culture. Then, 200 L of domestic wastewater from the living quarters of Zhejiang A & F University was added in the culture system, and next, 20 L of F19 seed culture was added. The culture chamber was aerated with an air flow rate of 2.0 vvm for 24 h. Subsequently, domestic wastewater incessantly flowed into the chamber at a constant flow rate of 0.28 L/min and up to 400 L of total volume. According to the design, the treated water in the chamber at more than 400 L will automatically overflow out with the same rate, which effectively ensures that all inflow water was treated by strain F19 for 24 h. Air flow rate in the aerobic zone was regulated consistently with the dissolved oxygen (DO) levels at \(4.5\pm 0.2\) mg/L, and in the anaerobic zone with the DO levels at \(0.5\pm 0.1\) mg/L. The experiment was carried out for one month, from May 4 to June 4, 2018. After the 20 th day, glucose was added with a nitrogen and carbon ratio of 1:2. Nitrogen removal efficiency of the bacterium was measured (ammonium, nitrite, nitrate, total nitrogen (TN), and chemical oxygen demand (COD)) at regular intervals of 2 days for a period of 30 days. During the experimental period, neither water change was made nor any external carbon source added.
# New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
## 5 Dynamic Algorithms Based on BetaRED
### Simulations
As can be seen in Figure 8, when fluctuations and congestion level grow, all performance metrics of the DBetaRED algorithm outperform all other AQM algorithms. Moreover, in Figure 9 a greater stability around the target value of the average queue length is also observed. Figure 7: Performance comparison of the Dynamic Beta RED algorithm according to a number \(N\) of active flows that vary dynamically over time and different values of the weight parameter \(w\). Other tunable parameters: \(T_{\text{target}}=40\,\text{ms}\) (\(q_{\text{target}}=250\,\text{packets}\)) and \(\theta=0.1\). Scenario 2 on the dumbbell topology (described in Section 3.1) is used. We observe that the trend is for the performance of the DBetaRED algorithm to increase as the value of \(w\) increases. Figure 8: Performance comparison between related AQM algorithms when considering different levels of congestion by varying dynamically the number \(N\) of active flows. Other tuning parameters: \(T_{\text{target}}=40\,\text{ms}\) for all AQM algorithms; \(\theta=0.1\) and \(w=0.1\) for ABetaRED and DBetaRED. Scenario 2 on the dumbbell topology (described in Section 3.1) is used. The performance of the DBetaRED algorithm outperforms the other algorithms as the congestion level increases. Figure 9: _Cont_.
## 2 Materials and Methods
### Ecosystem Assessment Methods
#### 2.2.1 Forest Status Indicators  
Based on the global GLC_FCS30 product with a 30 m spatial resolution, the classification of forests, which includes evergreen broadleaved forest (EBF), deciduous broadleaved forest (DBF), evergreen needle-leaved forest (ENF), deciduous needle-leaved forest (DNF), and mixed-leaf forest types (MF), was initially separated from other land cover types. Subsequently, FCI was calculated as the percentage of forests within a spatial resolution of 500 m. Moreover, three indexes (i.e., the annual average LAI, the annual maximum FVC, and the annual cumulative GPP) were used to analyze the forest growth status. The details of these four indicators are listed in Table 1. For areas without any vegetation cover, all four indicators in Table 1 are set to 0. A higher value of the FCI indicates a larger proportion of forest coverage. A higher value of the annual average LAI and the annual maximum FVC indicate better vegetation growth status. A higher value of the annual cumulative GPP indicates a stronger capacity for carbon sequestration by vegetation.
# Satellite-Observed Global Terrestrial Vegetation Production in Response to Water Availability
## 2 Materials and Methods  
### Data  
#### 2.1.1 Remote Sensing Data  
The actual evapotranspiration, potential evapotranspiration and soil moisture (including surface soil and root-zone soil moisture) data were derived from the Global Land Evaporation Amsterdam Model (GLEAM), which is designed to be determined by remote sensing observations only [26]. The GLEAM_V3.3a dataset has a 0.25\({}^{\circ}\) spatial resolution at a monthly time step that spans the 39-year period from 1980-2018 (www.gleam.eu (accessed on 25 March 2021)). Fraction of Photosynthetically Active Radiation (FPAR)3g data were generated from AVHRR GIMMS NDVI3g using an Artificial Neural Network (ANN) derived model, with a spatial resolution of \(1/12^{\circ}\) and a 15 days time interval [27]. We used the MODIS MCD12C1 product, which contains yearly, worldwide distributions of 17 land cover types from 2001-2017 at a spatial resolution of 0.05\({}^{\circ}\) ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)) (accessed on 10 December 2020)) [28]. In this study, barren land, water bodies, permanent snow, and ice were removed.