esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments and Analysis
### Student Learning Analysis Lsing Classification
#### 5.5.2 Recursive Feature Elimination (RFE)  
The findings derived from the RFE approach indicate that the XGB and DT models exhibited efficacy in forecasting the target \"learning outcome\" category. Both models attained an accuracy rate of 78% and 77%, correspondingly. The ensemble model exhibited a satisfactory performance, achieving an accuracy rate of 75%. Nevertheless, the performance of the NN model was comparatively inferior to the other models, exhibiting a precision rate of merely 72%. The outcome of the ML models is depicted below in Figure 7.  
In summary, the findings indicate that the utilization of the RFE feature selection method in conjunction with XGB and DT models yield favorable outcomes in forecasting the \"learning outcome\" classification category. The aforementioned discoveries may aid educators in detecting students who are at a higher risk of academic failure and equip them with the essential assistance to excel in their academic endeavors.  
Figure 6: ML models’ outcome using RFI feature selection.  
In the face of closely aligned performance metrics--MAE and RMSE for regression tasks, and Accuracy, Precision, and Recall for classification tasks--determining the superior model demands a multifaceted approach. Statistical tests, such as paired t-tests, can offer insights into the statistical significance of the seemingly marginal differences in MAE or RMSE, thereby providing a scientifically grounded basis for model selection. Additionally, the quantification of relative errors and confidence intervals for these metrics can further delineate the models' performance nuances. Domain-specific criteria also come into play; in an educational context, the weightage given to different types of errors--be it false positives or false negatives--can tip the scale in favor of a particular model. Lastly, computational efficiency remains a pragmatic tiebreaker when performance metrics are too similar to definitively favor one model. Incorporating these diverse criteria provides a holistic, scientifically rigorous framework for model selection, elevating the analysis beyond the limitations of raw metrics alone.  
In our comprehensive assessment of predicting student performance, the Boruta feature selection technique combined with the Gradient Boosted Regression model emerged as the standout, achieving an impressive MAE of 12.93 and RMSE of 18.28. On the classification front, irrespective of the feature selection technique employed, the XGBoost model consistently outperformed its counterparts, registering an exemplary accuracy rate of 78%. These findings underscore the potency of a meticulous feature selection process coupled with robust machine learning models, illuminating a clear pathway for optimal predictive analytics in educational contexts.
## 5 Performance Evaluation and Discussion
### Experiment Results and Discussion
#### 5.2.2 System Performance
The reason for this is that the communication requirements for applications A, B, and C exceed the computation requirements. As a result, for these communication-intensive applications, LP policy is a better choice. D and E, on the other hand, are computationally intensive, so offloading policies are the best option. Furthermore, balancing the load among GBSs and employing UAVs as assistant nodes can improve the proposed model's performance compared with TOP policy.
## 3 Results
### Rotation Axis Estimation Module  
Table 6 shows a comparison of the number of detections of rotational axes before and after processing the prediction from the neural network. Before processing the prediction, 79.55% of the rotational axes were correctly detected, while after processing, only 61.02% remained. Detections that could be visually assigned to an existing front, before prediction processing, were taken into consideration. Correct detections after prediction processing were those axes that were successfully assigned to an existing front by the system. It is worth noting a large number of false detections of axes before prediction processing, which accounted for 35.32% of all results. Prediction processing is designed to remove the axes that cannot be assigned to a rotary front. In this case, false predictions are defined as detections that were assigned to a false front. They accounted for only 3.54% of all detections. The lower number of correct and false detections after processing the results is due to the rigorous way of assigning axes to the front, which depends on the quality of segmentation of the rotational front, the quality of handle detection, and the quality of segmentation of the rotational axis itself.  
Figure 7: Example segmentation results for various thresholds of the detection: 0.5 (**a**) and 0.95 (**b**).
# A Review of the Role of Critical Parameters in the Design and Operation of Biogas Production Plants
## 3 Process Parameters Involved in a Biogas Production Plant
### Feedstock
#### 3.1.3 Pretreatment
that consist of a small and specifiable number of monomers; usually less than five), while alkali pretreatment, involving NaOH and Ca(OH)\({}_{2}\), helps to break lignin together with the solubilization of hemicellulose [65]. Alkali pretreatment (also called saponification) is a commonly adopted technique to help accelerate the hydrolysis of the lipid-like substrate [87]. Compared to acid pretreatment, alkali pretreatment was demonstrated to be more effective in enhancing methane production [65; 88], although some issues concerning digestate land application and methanogen toxicity were identified [89]. Despite the certain advantages, the cost associated with the use of additives/chemicals may offset the benefit earned from the revenue of the biogas plants. Therefore, a careful trade-off between cost and biogas yield optimization needs to be practiced. The pros and cons of using a selective list of additives in an anaerobic digestion application are given in Table 3.  
Unlike thermochemical pretreatment, physical pretreatment does not require the addition of any external compounds but uses physical techniques such as physical operations (milling, grinding, lysing, centrifuge, high pressure, electroporation, ultrasound, and microwave irradiation). Mechanical pretreatment is a widely used physical method that improves bioconversion effectivity, particle densification and distribution, flow properties, porosity, bulk density, and the overall conversion of lignocellulosic biomass into biogas without producing any toxic side streams [91]. With mechanical milling, commonly achieved by the different mills such as attrition mills, ball mills, centrifugal mills, colloid mills, hammer mills, extruders, knife mills, pin mills, and vibratory mills [92], the crystallinity of cellulose, particle size, and the degree of polymerization is reduced and consequently the surface area and digestibility of feedstock are enhanced. Employing milling, such as screw press extrusion, results in an increase of up to 30% in the methane yield from dip litter (a combination of a solid fraction and straw) manure was accomplished [93]. The combination of milling (\(\leq\)2 mm) with fungal pretreatment was observed to achieve a profound methane yield increase of up to 160% [94]. Microwave irradiation is another physical pretreatment option, or a heating method, which directly applies electric and magnetic field components to the molecular structure of the substrate, stimulating physical, chemical, or biological reactions due to the heat and extensive collisions by the
## 4 Experiments and Analysis
### Experimental Results and Analysis
#### 4.4.3 Error Analysis  
In order to explore the factors affecting the improved Tri-Training algorithm model to identify medical entities, we analyzed the prediction performance of different models in Table 4. Among them, Model 1 is the supervised hybrid deep learning model BIBC proposed in this paper. The results of Model 1 are all lower than the other models, which is because the performance of supervised learning models is limited in the case of low-resource labeled data, while the semi-supervised learning approach can fully utilize the labeled data for learning. Meanwhile, compared with other supervised hybrid neural network models in Table 3, the results of the semi-supervised learning-based models are all higher than the results of other mainstream entity recognition models. In addition, the results of Model 2 with the original Tri-Training algorithm are poorer compared to Model 3 and Model 4 with the improved Tri-Training algorithm. This is because the original Tri-Training algorithm has the problem of error propagation and inadequate sample extraction. This demonstrates that the errors arising from sample extraction in semi-supervised learning algorithms and the prediction labeling errors caused by ensemble learning can lead to degraded model recognition performance. The improved model addresses these issues and improves the model entity recognition performance. Experiments validate the effectiveness of the improved Tri-Training algorithm. In this paper, the overall performance of the model is judged by the F1 value, and Model 4 achieves the highest F1 value. Compared with Model 3, which also incorporates the improved Tri-Training algorithm, Model 4 selects three different entity recognition models as base classifiers, further avoiding the problem of errors caused by low discrimination of the same classifiers. We also assign weights to the classifiers of different models to identify entities more accurately. Overall, all our improvements address the shortcomings of the original method and improve the performance of the Chinese medical entity recognition model.
# Latest Advances in the Global Navigation Satellite System--Reflectometry (GNSS-R) Field
## 6 New Science Investigations
### Ionospheric Plasma Depletions  
The first evidence of ionospheric plasma depletions using CYGNSS GNSS-R data was found by authors in [142]. Their theory suggests that electromagnetic waves experience signal delay, polarization change, direction of arrival, and fluctuations in signal intensity and phase due to temporal and spatial variations in the total electron content (TEC) and the ionosphere dynamics. The authors proposed the use of the CYGNSS constellation to explore ionospheric activity and travelling equatorial plasma depletions (EPBs). Using GNSS-R data, the authors were able to detect ionospheric bubbles in ocean regions where ground stations are unavailable. The results showed that the bubbles' dimensions and duration could be measured, and the increased intensity scintillation (S4) occurring in the bubbles could be estimated. The study found that the bubbles detected had S4 values of around 0.3-0.4, lasting from a period of time ranging from a few seconds to a few minutes.
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 5 Influence of Structure Parameters on Torque Density
### Pole-Pitch  
From Equation (25), if the pole-pitch is selected as the unique variable, the torque density of motor will be improved when the pole-pitch is decreased. However, the leakage flux between adjacent permanent magnets will also be increased with the decrease of pole-pitch, which brings a negative influence on the torque density. Thus, there is a reasonable range of pole-pitch.  
The pole-pitch \(\tau_{\text{m}}\) is analyzed as the unique variable by 3D FEM, and the results are shown in Figure 11. From Figure 11: (1) the average torque is improved significantly with the decrease of pole-pitch when pole-pitch is larger than 7.3 mm; (2) the average torque is nearly kept constant when pole-pitch is in the range of 5.8-7.3 mm; (3) the average torque is reduced with the decrease of pole-pitch when pole-pitch is smaller than 5.8 mm; and (4) the torque ripple is reduced with the decrease of pole-pitch. The pole-pitch should be chosen in the range of 5.8-7.3 mm from the FEM results.  
Figure 11: The influence of pole-pitch \(\tau_{\text{m}}\) on average torque and torque ripple.
## 4 Simulation Results and Analysis
### Influence of Iteration Times on Total System Cost  
Figure 6 shows the comparison of total system costs under different iterations. In this section, the maximum number of iterations \(Max\_gen=100\), the number of tasks \(N=50\), and the weight factor \(\alpha=0.5\) are selected. Since Local and MEC algorithms only offload tasks locally or on the server, the number of iterations has no influence on the simulation results. From the simulation results, it can be seen that the total system cost of the PSO, BES, and IBES algorithms level off at 65, 52, and 38 iterations, respectively. Compared with the PSO algorithm and BES algorithm, the total system cost of IBES decreases by 33.07% and 22.73%, respectively. Therefore, under the influence of the number of iterations, the total system cost of the proposed scheme is lower than that of the PSO algorithm and BES algorithm.
# Improving Prostate Image Segmentation Based on Equilibrium Optimizer and Cross-Entropy
## 4 Minimum Cross-Entropy by EO for MRIs
### MCE-EO Implementation  
The equilibrium optimization (EO) algorithm is implemented for the solution of thresholding problems with both standard and prostate MRI images. The entire process is summarized as follows. First, the image \(\mathbf{I}(\mathbf{x},\mathbf{y})\) is loaded into memory, and its grayscale histogram \(h^{Gr}(\mathbf{I})\) is computed. Then, the configuration parameters of the MCE-EO are selected. Afterwards, the iterative process of the EO is started with a randomly generated population and the solutions are iteratively enhanced through the operators of EO and the evaluation of the fitness function MCE. The MCE-EO approach stops iterating until a stop criterion is met; in this case, a fixed number of generations. Finally, the optimal set of thresholds \(\mathbf{th_{best}}\) is used to generate the segmented image. A graphical representation of the MCE-EO approach is depicted in Figure 1. The detailed implementation of MCE-EO is described in the pseudo-code of Figure 2.  
Figure 1: Overview of the proposed MCE-EO methodology for prostate MRI segmentation. This figure illustrates the stages of the multilevel cross-entropy equilibrium optimizer (MCE-EO) approach, from MRI image acquisition and preprocessing to threshold selection and segmentation. The methodology optimizes threshold values using cross-entropy, enhancing segmentation accuracy.  
Figure 2: Pseudocode for the implementation of the MCE-EO algorithm. This figure presents the pseudocode outlining the key steps of the MCE-EO algorithm. It includes initialization of parameters, the optimization process using the equilibrium optimizer (EO), and the evaluation of threshold values based on the minimum cross-entropy criterion. The pseudocode highlights the iterative process that leads to the selection of optimal thresholds for accurate prostate MRI segmentation.
# Solid-State Transformation of an Additive Manufactured Inconel 625 Alloy at 700 \({}^{\circ}\)[PERSON]
## 1 Introduction
A separate strategy is to completely remove the microsegregation using a high-temperature homogenization heat treatment. For example, a heat treatment at 1150 \({}^{\circ}\)C for one hour completely homogenizes the alloy. However, this heat treatment promotes grain growth and can be both challenging and costly to implement for industrial-scale large parts due to the time required for the temperature to equilibrate as well as the high annealing temperature required. These complicating factors contribute to an industrial need to investigate the feasibility of using lower temperature stress-relief heat treatments. To understand the microstructural responses of AM IN625, in this study, we investigate the solid-state transformation kinetics of an AM IN625 alloy at 700 \({}^{\circ}\)C primarily using synchrotron-based in situ scattering and diffraction methods. Specifically, we use X-ray diffraction to monitor the phase transformation kinetics and small-angle X-ray scattering to evaluate the morphological changes in the precipitates. In contrast to most studies of the effect of heat treatment on nickel-based superalloys, where experimental evidence is mainly gathered from microscopy and from in-house X-ray diffraction data, synchrotron measurements probe a fixed and significantly larger sample volume through in situ experiments that allow the annealing kinetics to be unambiguously determined. Such results are also more statistically representative. The kinetics results from the same sample volume are elucidated with thermodynamic predictions by CALPHAD (Computer Coupling of Phase Diagrams and Thermochemistry) methods.
# Augmented Lagrangian-Based Reinforcement Learning for Network Slicing in IIoT
###### Abstract
(2) _Primal- dual method_: it uses the Lagrangian relaxation technique to transform the original problem into a dual problem, for instance [17; 18]. (3) _Direct policy optimization_: it replaces the objective or constraint in the original problem by a more tractable function, such as [19; 20; 21]. (4) _Safeguard_ uses an extra step mechanism to guarantee the constraint in each training step [22]. In addition to the constraint problem, the discrete-continuous mixed action space is involved in our work. Inspired by the concepts of the augmented Lagrangian similar to the _primal-dual method_, we propose an augmented Lagrangian-based soft actor-critic (AL-SAC) algorithm to solve the network slicing problem with constraints and the hybrid action space. The main contributions of this paper are as follows. * A two-stage action selection is designed by considering a hierarchical policy network to solve the hybrid action space problem in RL, which can significantly reduce the action space;
* A penalty-based piece-wise reward function and a constraint-handling part involving neural networks for Lagrangian multipliers and cost functions are introduced to solve the constraint problem;
* Simulation results show that our proposed algorithm satisfies the constraints, and AL-SAC has a higher reward value than the DDPG algorithm with a penalty item.
# Prediction of the Absorption Characteristics of Non-Uniform Acoustic Absorbers with Grazing Flow
## 3 Impedance Boundary Navier-Stokes Equations (IBNSE) Method
### Semi-Empirical Impedance Models of the Perforated Plate under Grazing Flow
#### 3.1.4 Goodrich Model  
A representative semi-empirical model for predicting the absorber impedance is the Goodrich impedance model proposed by the Goodrich Aeronautical Structure Group [43]. The highest Mach number used in the original simulation was 0.2. In the Goodrich model, the frequency normalized linear acoustic impedance of the perforated plate is calculated as follows:  
\[Z_{G}=Z_{of}+S_{r}V+R_{cm}+\mathrm{i}(S_{m}V) \tag{17}\]  
where the impedance of the perforated plate \(Z_{of}\) can be expressed as  
\[Z_{of}=\mathrm{i}\omega\frac{t+\varepsilon d}{c_{0}\sigma}F(k_{s}r) \tag{18}\]  
where the end correction \(\varepsilon d\) is  
\[\varepsilon d=(0.2d+0.85\mathrm{i}d)(1-0.7\sqrt{\sigma}) \tag{19}\]  
The cross-section averaged hole velocity profile \(F(k_{s}r)\) is  
\[F(k_{s}r)=1-\{2I_{1}(k_{s}r)/[k_{s}r]_{0}(k_{s}r)]\},\ k_{s}^{2}=-\mathrm{i} \omega\rho_{0}/\mu \tag{20}\]The nonlinear acoustic resistance slope \(S_{r}\), normalized grazing flow acoustic resistance \(R_{cm}\) and nonlinear mass acoustic reactance slope \(S_{m}\) are expressed as  
\[S_{r}=\frac{1.336541}{\rho c}\left(\frac{\rho_{0}}{2C_{d}^{2}}\frac{1-\sigma^{2 }}{\sigma^{2}}\right),\ R_{cm}=\frac{M}{\sigma\Big{(}2+1.256\frac{\delta^{ \omega}_{d}}{d}\Big{)}},\ S_{m}=-0.0000207\frac{k}{\sigma^{2}} \tag{21}\]  
For micro-perforates, the flow coefficient \(C_{d}\) is  
\[C_{d}=0.584854\sqrt{\sigma^{0.1}/\mathrm{e}^{-1.151d/t}} \tag{22}\]
## 1 Introduction
Internet mapping services, which are also called web-based mapping services, such as Google Maps ([[https://www.maps.google.com](https://www.maps.google.com)]([https://www.maps.google.com](https://www.maps.google.com))), Amap ([[https://www.amap.com](https://www.amap.com)]([https://www.amap.com](https://www.amap.com))), Baidu Map ([[https://www.baidu.map.com](https://www.baidu.map.com)]([https://www.baidu.map.com](https://www.baidu.map.com))), are used to acquire the travel costs and complete point of interest (POI) objects directly based on a route planning application programming interface (API) and POI API [17; 20]. (1) Route planning APIs consider different modes of transport, such as driving, walking, cycling, and public transport [13; 21]. It is feasible to measure the travel time costs through route planning API [22; 23]. (2) POI data, a burgeoning data source for regional research, is applied for the automated identification and characterization of parcels (AICP) [24], extracting hierarchical landmarks [25], and the hierarchical semantic cognition of urban functional zones [26; 27]. POI data can estimate employment locations, and implement interzonal commuting patterns [20]. (3) LBS data contain information on people's hobbies and places, and have provided new informat ion in terms of understanding individual activity patterns [28]. Representative LBS data include Twitter data and Weibo data, which can be used for dynamic land-use mapping [28] and assessing human travel activity patterns [29]. Tencent LBS data ([[https://heat.qq.com](https://heat.qq.com)]([https://heat.qq.com](https://heat.qq.com))) are quintessential data that record more than 50 billion location data of dominant Chinese from China's largest social media operator [30]. Tencent LBS data provide rich user information, including population location data and population category descriptions, which afford more microcosmic, dynamic, and real population data for assessing SARS-RA. Thus, we can use POI data and LBS data as the data of the quality/quantity of opportunities. Based on the above description, Internet map and LBS data that provide micro-scale knowledge about residential areas will be pertinent to SARS-RA. In the next section, we will introduce and analyze detail data information and the measurement framework.
## 3 Results
### Remotely Sensed Variables and \(R_{\rm s}\)
#### 3.3.2 Mean Canopy Height  
We found no significant relationship between mean canopy height calculated from LiDAR data and average growing season \(\mathrm{R_{s}}\). We also found no significant relationship between average nongrowing season \(\mathrm{R_{s}}\) and mean canopy height. Mean canopy height correlated with the September sample date (\(p=0.0254\), \(\mathrm{R^{2}}=0.30\)).