esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Vessel Traffic Flow for Waterway Design-Port of Swinoujscie Case Study
## 3 Results
### Regression Analysis of Traffic Flow Parameters
#### 3.3.3 Model Including Length of the Leg \(L_{tr}\)  
The authors analysed the correlation between the standard deviation of the ship's position from the centre of the track and the length of the leg. Predictor \(L_{tr}\) had a \(p\)-value of 0.0065, which shows the statistical significance of the achieved results. The coefficient of determination \(R^{2}\) = 0.42 indicates that the model explained 42% of the variability in the response data around its mean. This led to the interesting conclusion that the standard deviation of ship position in traffic flow is higher when the length of the route increases, as shown in Figure 10. The research follows [PERSON]'s research, indicating that the distribution of the distance of vessel from the average route depends mainly on the length of the route [5]. The [PERSON] model is applicable for routes up to 600 Nm in length.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dependent** & **Ships** & **Coefficient** & **Coefficient** & \multirow{2}{*}{\(R^{2}\)} & \multirow{2}{*}{\(F\)} & \multirow{2}{*}{\(p\)} \\
**Variable** & **Type** & **Intercept** & & **B[m]** & & \\ \hline Mean & GC & 973.2 * & \(-\)12.7 * & 0.3799 * & 90.6729 * & 0.0000 * \\ \hline Mean & OPT & 1150.6 * & \(-\)23.3 * & 0.4562 * & 83.8872 * & 0.0000 * \\ \hline Mean & PAS & \(-\)6.2 & 23.9 & 0.0245 & 0.5532 & 0.4649 \\ \hline Std.Dev. & GC & 398.7 * & \(-\)0.7 & 0.0015 & 0.2211 & 0.6389 \\ \hline Std.Dev. & OPT & 304.2 * & 0.1 & 0.0000 & 0.0013 & 0.9715 \\ \hline Std.Dev. & PAS & 0.8 & 42.3 & 0.0000 & 0.0003 & 0.9857 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameter of simple regression B-model.  
Figure 9: Mean of ships distance from the centre to the width of the ship.
## 4 Experiments and Evaluations  
### Purpose  
We conducted experiments using the implementation system proposed in this paper. The purpose of the experiments was to show the optimal parameters for a malicious botnet to be disinfected by a white-hat worm, and to determine how each parameter of the white-hat worm affects the disinfection of the malicious botnet.
###### Abstract  
A tibial implant is necessary to provide mechanical support in open-wedge high tibial osteotomy (OWHTO) treatment of knee osteoarthritis. The pore structure and porosity of implants exert significant effect on tibia stress distribution and lower limb alignment stability. In this study, finite element (FE) analysis and in vitro biomechanical experiments were utilized to evaluate the impact of different tibial implants on postoperative tibial stress distribution. The biomechanical experimental results of experiments on tibial implants exhibit similar mechanical response patterns to the established finite element model, whose maximum displacement error is 1.18% under 1500 N compressive load. The hybrid porous implant developed in this study demonstrated significant stress reductions in both tibial bone (19.97% and 15.33% lower than mono-porous configurations at 73% porosity) and implant body (31.60% and 11.83% reductions, respectively), while exhibiting diminished micromotion tendencies. This consistent performance pattern was maintained across the entire porosity spectrum (53-83%) in implanted specimens. In summary, the finite element model established using authentic tibial CT data can effectively guide the structural design of tibial implants, and optimized pore structure design can provide enhanced mechanical support effects for tibial implants.  
open-wedge high tibial osteotomy; implant; honeycomb structure; finite element analysis; 3D printing Article
## 3 Frequency Deviation Suppression Based on VSG
### VSG Model and Control
Therefore, the optimum value selection of \(J\) and \(D\) could significantly ensure the system stability. The overall control block diagram of VSG is provided in Figure 8. The reference power is obtained from the power allocation Method#2. The \(P,Q\) calculation block computes the power output of the VSG via the Formula (13). \[P_{out} =\frac{1}{2}(\frac{1}{\tau s+1})(V_{d}I_{d}+V_{q}I_{q}) \tag{13}\] \[Q_{out} =\frac{1}{2}(\frac{1}{\tau s+1})(V_{q}I_{d}-V_{d}I_{q})\]  
where \(V_{d}\), \(V_{q}\), \(I_{d}\), \(I_{q}\) are \(dq-axis\) output voltage and current, respectively. A low-pass filter with cut-off frequency, \(f_{e}\)= 0.6366 Hz, is added to the measured power to diminish the noise from measurement device. From the VSG and \(Q-v\) droop control block, the virtual phase angle (\(\theta_{m}\)) and the voltage amplitude (\(E\)) are obtained, respectively. The multiplication of voltage amplitude (\(E\)) with the phase angle \(sin(\theta_{m})\) generates the sinusoidal \(PWM\) to control the switching time of inverter.
## 3 Experimental Results and Analysis
### Deep Learning Modeling Results
#### 3.4.2 Modeling Results of Deep Learning Models Based on Attention Mechanism
\begin{table}
\begin{tabular}{c c c c} \hline
**Model** & **R\({}^{2}\)** & **RMSE** & **MAE** \\ \hline CNN & 0.883029 & 1276.46 & 131.92 \\ +SA & 0.873534 & 1353.64 & 151.79 \\ +SE & 0.883679 & 1285.78 & 135.95 \\ +LA & 0.843451 & 1451.12 & 186.87 \\ LSTM & 0.893166 & 1219.79 & 118.88 \\ +SA & 0.903425 & 1208.32 & 113.48 \\ +SE & 0.938732 & 1102.43 & 105.94 \\ +LA & 0.825413 & 1608.54 & 211.49 \\ Transformer & 0.883673 & 1266.21 & 131.73 \\ +SA & 0.886291 & 1268.49 & 149.13 \\ +SE & 0.914283 & 1155.07 & 128.27 \\ +LA & 0.833451 & 1480.45 & 175.39 \\ \hline \end{tabular} The LSTM–SE with the best performance has 3 num_layers, 32 hidden_dim, 0.1 dropout_rate, and 16 batch_size.  
\end{table} 
Table 5: Prediction results of the heat transfer coefficient of the straight flow channel based on deep learning with attention mechanism.
## 4 Analysis of Optimization Calculation and Results
### Further Discussions
#### 4.3.2 Comparative Analysis of Optimization Performance  
In the two optimization modes, the comparison of optimization performance based on optimal decisions is shown in Figure 8. The diagram shows that the primary energy efficiency of the bi-level optimization model is 2.6% lower than that of the single-level optimization model, and it is slightly lower than that of the single-level multi-objective  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Life Cycle** & \begin{tabular}{c} **Life Cycle** \\ **Carbon** \\ **(CNV)** \\ **(kg)** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Emissions** \\ **(Efficiency)** \\ \end{tabular} & \begin{tabular}{c} **Total Operating** \\ **Cost for Lower** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **GB** \\ **(m\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **PV** \\ **(m\({}^{2}\))** \\ \end{tabular} & \begin{tabular}{c} **SC** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **GSHP\({}_{\star}\)** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **GSHP\({}_{\star}\)** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **ASHP\({}_{\star}\)** \\ **(MW)** \\ \end{tabular} & \begin{tabular}{c} **HE** \\ **(MW)** \\ \end{tabular} & \
\begin{tabular}{c} **ES** \\ **(MW)** \\ \end{tabular} &
\begin{tabular}{c} **HS** \\ **(MW)** \\ \end{tabular} \\ \hline
2,349,200 & 1,440,000 & 8.74 & 2,396,600 & 126.74 & 339.72 & 32.24 & 199.57 & 144.37 & 33.09 & 3.62 & 120.13 & 136.59 & 36.90 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Optimal compromise solution of bi-level multi-objective optimization of the MCS.  
Figure 7: Comparison of objective cost and carbon emission vales under optimal solution strategies.  
optimization model. However, its renewable energy penetration rate is 1.2 times greater than that of the single-level planning optimization. High renewable energy penetration has been achieved to compensate for a slightly lower primary energy efficiency of fossil fuels in the life cycle.  
According to the relatively complex bi-level optimization model, the iteration time of the simulation calculation of the model is long, and the average iteration time is 1.7 times that of the single-level multi-objective optimization model. In terms of objective value, the overall optimization results of the bi-level optimization model are indeed better than those of the single-level programming optimization model. However, considering the running time, since the intelligent optimization algorithm needs widespread debugging and system simulation to make the optimal decision, the bi-level optimization calculation is time-consuming, which gives the method a large time limit. Further independent experiments may be carried out in order to obtain more reliable results in terms of the actual engineering optimization. Therefore, in practical engineering applications, the optimization time can be calculated according to the complexity of the project, as well as which optimization scheme is comprehensively considered for system optimization.
# Application of Machine Learning Models in Optimizing Wastewater Treatment Processes: A Review
## 5 Results
### Comparative Analysis of ML Methods
#### 5.1.1 Applications of Supervised ML Techniques in the Optimization of Treatment Processes
The random forest and hybrid models used in wastewater treatment give operators a clear idea of the reasons behind certain decisions. LSTM models are the best at forecasting the results of wastewater treatment plants because they are the best at understanding how things change over time. Figure 10: Comparative analysis of supervised learning methods used in wastewater treatment. Curiously, other methods, such as random forest or hybrid models, may also be able to perform very well, showing that there is no single best way to solve these forecasting problems. This gives plant operators a suite of reliable tools from which to choose, based on their individual needs. The computational efficiency of SVM is the highest, with ANN in second place. Simpler models, like SVM, run faster and use less energy, which is essential for the limited-resource context of treatment plants. While it is probably the most accurate model, LSTM requires a significant amount of computing power, which creates bottlenecks that limit its real-world usage. For real-time control, the best performer is ANN, followed closely by SVM. Both make decision-making possible within a timeframe of milliseconds, which is absolutely essential for maintaining effective treatment control. Random forest seems to have a moderate speed, which places it in a nice dimension between speed and accuracy. Hybrid models, if they exist in a certain configuration, do not have sufficient speed for real-time control. LSTM is too slow for real-time applications. Limited data is not an issue for SVM and random forest because these two machine learning methods work well with small datasets, which is excellent because we know that collecting good-quality wastewater data is both expensive and difficult. LSTM and ANN are expensive in terms of the size of the dataset they require to achieve acceptable performance. Thus, LSTM and ANN are much less practical for new wastewater treatment facilities if we want to apply machine learning. As shown in Figure 11, no single model excels in every respect. For resource-constrained environments, SVM provides the best balance of efficiency, speed, and data demands while still achieving acceptable accuracy. Random forest gives the best balance between accuracy and interpretability for regulatory compliance. LSTM is the highest performer in terms of accuracy when the available computational resources and data volume are extensive. ANN is the best in terms of speed, and the best for accuracy under time constraints. Hybrid models offer compromise solutions for moderately demanding applications. Figure 11: Performance metrics and characteristics of supervised learning methods. Colors indicate performance levels: red (undesirable), orange (moderate), green (desirable).
# A Framework for Bluetooth-Based Real-Time Audio Data Acquisition in Mobile Robotics
## 5 Novel Implementation Methodology
### Android Application Development  
The Android application was developed in Kotlin, targeting API level 30 (Android 11) with backward compatibility to API level 26 (Android 8.0). The application architecture follows the Model-View-View-Model (MVVM) pattern, separating concerns between data management, user interface, and business logic.  
Real-time audio visualization presented a significant challenge, as rendering high-frequency audio data can overwhelm mobile device rendering capabilities. To address this, the following steps were taken:  
1. Downsampling of visualization data to 5 Hz update rate while maintaining full-resolution audio recording, applying techniques similar to those used by [PERSON] et al. [19] for audio-visual processing.
2. Efficient line chart rendering using the MPAdroidChart library.
3. Batched UI updates to minimize main thread overhead, following UI optimization approaches established by [PERSON] and [PERSON] [26].
4. Background processing of audio data using Kotlin coroutines, implementing concurrency patterns recommended by [PERSON], [PERSON] et al. [18] for processing-intensive applications.
5. Optimized WAV file generation using direct buffer writes, applying memory management techniques described by [PERSON] et al. [21] for multimodal data handling.  
The user interface was designed for single-handed operation during teleoperation, with movement controls positioned for thumb access while maintaining the visibility of the audio visualization components. This design enables effective simultaneous control and monitoring during field operations, incorporating principles of human-robot interactions established by [PERSON] et al. [22].  
The communication layer implements robust error handling and automatic reconnection capabilities based on Bluetooth connection state monitoring. This approach to resilient networking draws on methodologies described by [PERSON] et al. [10] for IoT applications in challenging environments.
# Research on Nanoparticle-Enhanced Cooling Technology for Oil-Based Drilling Fluids
## 1 Introduction
explored the effects of multi-walled carbon nanotubes (MWCNTs), ZnO nanoparticles, and SiO\({}_{2}\) nanoparticles in drilling fluids, revealing that thermal conductivity and rheological properties were significantly enhanced with the addition of these nanoparticles [30]. [PERSON] et al. reported that the inclusion of Al\({}_{2}\)O\({}_{3}\) and TiO\({}_{2}\) nanoparticles in drilling fluids significantly improved thermal conductivity and cooling efficiency, with Al\({}_{2}\)O\({}_{3}\) providing higher thermal conductivity and thereby cooling the drill bit more effectively [31]. Additionally, [PERSON] et al. demonstrated that the incorporation of copper oxide/polyacrylamideamide (CuO/PAM) nanocomposites into bentonite-based drilling fluids led to an increase in thermal conductivity by 69.68% [32]. [PERSON] et al. showed that reducing the size of TiO\({}_{2}\) nanoparticles from 76 nm to 54 nm improved the heat transfer coefficient by approximately 22%, emphasizing the critical role of nanoparticle size [33]. The current body of literature discusses surface cooling methods for wellbore temperature management, focusing mainly on cooling equipment. However, there is still a large gap in the research of wellbore cooling techniques that enhance the specific heat capacity of drilling fluids through nanoparticles. Previous studies have focused on increasing thermal conductivity, often ignoring the important role of specific heat capacity in improving heat transfer efficiency. Although increasing thermal conductivity is crucial for effective heat conduction, thermal conductivity alone is not sufficient for efficient heat exchange. The specific heat capacity, which determines the rate at which the drilling fluid warms up or cools down, is particularly important. Therefore, when designing and optimizing drilling fluids, it is important to measure and evaluate the effect of nanomaterials on the specific heat capacity of drilling fluids. In this study, not only the thermal properties of oil-based drilling fluids (OBDFs) with different concentrations of nanoparticles were examined, but also their rheological properties and high temperature and high pressure (HTHP) filtration loss were evaluated. In addition, the effectiveness of these enhanced fluids in reducing the bottomhole temperature was also evaluated. The results of the study emphasize the potential of nanoparticles in improving the performance of drilling fluids and highlight the importance in terms of efficient cooling strategies for operations in high temperature environments.
# Diagenesis of Deep Low Permeability Reservoir in Huizhou Sag and Its Influence on Reservoirs
## 3 Database and Methods  
In this study, we conducted a comprehensive analysis of the Enping Formation using drilling and logging data from nine coring wells (H1-H9) in conjunction with laboratory data derived from 114 core samples obtained from the Huizhou Sag (Figure 1C). The experimental methods employed included thin-section petrography, scanning electron microscopy (SEM) imaging, X-ray diffraction (XRD) analyses, and porosity-permeability tests. All data pertaining to thin-section samples, as well as reservoir porosity and permeability, were sourced from the Shanghai Branch of CNOOC Ltd., Shanghai, China.  
Following the vacuum impregnation of each core sample with blue epoxy resin, thin sections were meticulously prepared for the comprehensive analysis of petrological characteristics, grain structure, pore morphology, diagenesis, and visual porosity. Selected sections underwent staining with Alizarin Red to facilitate the identification of carbonate minerals. The presence and nature of authigenic clay minerals, micropores, and microfractures were scrutinized utilizing a scanning electron microscope (SEM). A cathode luminescence (CL) analysis was conducted on 65 samples to assist in elucidating the paragenetic sequence. An automated permeameter-porosimeter, AP-608, was employed to investigate the porosity and permeability of samples derived from various structural zones and members.  
Point counts were performed on thin sections to quantify the content of detrital grains and carbonate cement, with at least 300 points counted. The scanning electron microscopy (SEM) experiment was conducted using an FEI Helios NanoLab 660 Dual Beam SEM equipped with an energy-dispersive X-ray spectrometer to identify the main types of authigenic minerals and different pore spaces. The samples were observed under SEM at an acceleration voltage of 20 keV to characterize the pore geometry and cement morphology and document the textural relationships between minerals. Core samples were dried and freshly broken, and their surfaces perpendicular to the bedding plane were coated with chromium before SEM and TIMA observations. Cathodoluminescence (CL) analysis was performed using a Technosyn cold cathode luminoscope to identify different generations of cement. This test was performed according to the Chinese trade standard for tight sandstone gas engineering (SY/T6832-2011) [45].  
Porosity and Permeability Assessment: The assessment of porosity and permeability was conducted utilizing an automatic porosimeter (AP-608) (Beijing Ordovician Technology Co., Ltd., Beijing, China) and a permeability analyzer (K02 GS006) (Best Instrument Technology Co., Ltd., Beijing, China). The detection limits for these instruments were not less than 0.1% and ranged between 0.05 and 10,000 mD, respectively.
# Kinematics and Statics of the Gough-Stewart Platform
## 3 Kinematics of a Multi-Rigid System  
As shown in Figure 2, suppose there are two single rigid linkages \(L_{i-1}\) and \(L_{i}\), which are connected by joint \(J_{i}\) in an open kinematic chain.  
The motion of a linkage can be described and determined according to the relative angular velocity and relative linear velocity. The relative angular velocity \(\varphi_{0}\)  
Figure 1: Geometry of a Stewart platform: (**a**) vertical view; (**b**) front view.  
Figure 2: Open kinematic chain.  
linear velocity \({}_{0}\mathbf{v}_{1}\) of link \(L_{1}\) are two three-dimensional vectors, and both of them could be defined as a dual vector to form the velocity screw [27]:  
\[{}_{0}\mathbf{\xi}_{1}=\begin{bmatrix}{}_{0}\mathbf{\omega}_{1}\\ {}_{0}\mathbf{v}_{1}\end{bmatrix} \tag{1}\]  
where \({}_{0}\mathbf{\omega}_{1}\) is relative angular velocity from joint \(J_{1}\) to the absolute coordinate 0, and \({}_{0}\mathbf{v}_{1}\) is the relative linear velocity from joint \(J_{1}\) to the coordinate origin 0.  
The three-dimensional vector \({}_{0}\mathbf{v}_{1}\) in Equation (1) can be expressed as \({}_{0}\mathbf{v}_{1}={}_{0}\mathbf{r}_{1}\times{}_{0}\mathbf{\omega}_{1}\), where \({}_{0}\mathbf{r}_{1}\) is the position vector of joint with respect to the origin of the absolute coordinate system.  
Suppose \(\|\mathbf{e}_{1}\|=1\). Equation (1) can be rewritten as:  
\[{}_{0}\mathbf{\xi}_{1}={}_{0}\mathbf{\omega}_{1}\begin{bmatrix}\mathbf{e}_{1}\\ {}_{0}\mathbf{r}_{1}\times\mathbf{e}_{1}\end{bmatrix} \tag{2}\]  
where \(\mathbf{e}_{1}\) is the unit direction vector of joint \(J_{1}\), and \({}_{0}\mathbf{\omega}_{1}\) is the amplitude of the relative angular velocity of joint \(J_{1}\). Let  
\[\mathbf{\xi}_{1u}=\begin{bmatrix}\mathbf{e}_{1}\\ {}_{0}\mathbf{r}_{1}\times\mathbf{e}_{1}\end{bmatrix} \tag{3}\]  
where \(\mathbf{\xi}_{1u}\) is the unit velocity screw of joint \(J_{1}\)[28].  
Substituting Equation (3) into Equation (2) yields:  
\[{}_{0}\mathbf{\xi}_{1}={}_{0}\mathbf{\omega}_{1}\mathbf{\xi}_{1u} \tag{4}\]  
Within the unit velocity screw, the first three-dimensional vector presents the posture of the rotational motion, and the last three-dimensional vector contains the position parameters of the rotational motion.
## 3 Results and Discussions
### Energy Capture Properties of C-TENG
#### 3.2.3 Influence of Friction Layer Distance  
The potential between the two electrodes varies with the distance between the friction layers. Based on the above analysis, the resistance of 5000 M\(\Omega\) is selected when the power is most reasonable. The friction layer distance is set to 0.1 mm, 0.15 mm, 0.2 mm, 0.25 mm and 0.3 mm, respectively, for simulation. It can be seen from the analysis (Figure 8) that the open-circuit voltage decreases with the increase of the distance between friction layers. According to the simulation conditions, the electric charge is evenly distributed on the inner surface of the polymer, and the electric field is formed inside the two insulators and the air gap. The transferred electric charge determines the electric field strength. When the distance of friction layers is relatively small, the electron transfer is more sufficient.  
Figure 5: Calculation model of energy capture properties.  
The change of the electric potential proves the electric principle of C-TENG that power generation of C-TENG relies on the electron transfer between the friction layers.  
Figure 6: Potential distribution in different stages. (**a**) The sliding electrode at the center (\(t=0\) s); (**b**) the sliding electrode at the top (\(t=0.35\) s); (**c**) the sliding electrode at the bottom (\(t=1.1\) s); (**d**) the sliding electrode back to the center (\(t=2\) s).  
Figure 7: Peak power, current and voltage vary with resistance.