esa-sceva/satcom-chunk-collection huggingface dataset:
# Compound Heuristic Information Guided Policy Improvement for Robot Motor Skill Acquisition
## 1 Introduction  
Imitation learning (IL) and reinforcement learning (RL) [1] have always been a hot topic in the field of robot skill acquisition. Imitation learning can be divided into two categories: behavioral cloning (BC) and inverse reinforcement learning (IRL). BC is the method of learning expected policy directly from expert teaching information, while IRL learns policy indirectly using reward function. Hidden Markov Model (HMM) [2], Dynamic Movement Primitives (DMPs), Probabilistic Movement Primitives (ProMPs) [3], Dynamic Systems (DS) [4], and Cross Entropy Regression (CER) are popular Behavior Cloning methods. The most frequently used methods of IRL are Maximum Margin Planning (MMP) [5] and Markov Process (MP).  
Imitation learning is limited because it requires the robot to learn only from demonstrated trajectories. When the reproduction environment is different from the demonstration environment or there is a big deviation, such as placing an obstacle on the path of the robot, the imitation learning method may fail. Instead, RL allows the robot to find a new control policy by exploring the state-action space freely. The combination of IL and RL aims to use the advantages of two methods to overcome their respective shortcomings, so that the robot can adapt to the deviation from the demonstration behavior, so as to improve the performance of the robot.  
The classical RL methods include SARSA [6], Natural Actor-Critic (NAC) [7], Policy Learning by Weighting Exploration with the Returns (PoWER) [8], Relative Entropy Policy Search (REPS) [9], and Path Integral Policy Improvement (PI\({}^{2}\)). We believe that PI\({}^{2}\)[10] is one of the most effective, numerically robust, and easy reinforcement algorithms. However, classical PI\({}^{2}\) searches the whole parameter space, so it is less efficient to complete the task. Kernel Canonical Correlation Analysis (KCCA) can infer the implicit nonlinear heuristic information between the joints of the robot when facing the new task, leading PI\({}^{2}\) to find a solution. Based on the works of Covariance Matrix Adaptation (CMA) [11] together with our previous research on KCCA [12], we propose a new algorithm PI\({}^{2}\)-CMA-KCCA in this paper, where KCCA and CMA are integrated as compound heuristic information to speed up the learning procedure from the demonstration to a new task.  
This paper is structured into several major sections: Section 2 investigates the DMP used in the imitation learning phase. In this paper, we use Dynamic Movement Primitives as the underlying policy representation. Section 3 briefly introduces the algorithm PI\({}^{2}\)-CMA which aims at discovering the hidden relationship between the components of the weight. In Section 4, we introduce the algorithm KCCA, and finally derive the algorithm PI\({}^{2}\)-CMA-KCCA. In Section 5, based on SCARA and Swayer robots, we validate our algorithm through the classical via-point task and analyze the experimental results. Finally, the conclusions are given in Section 6.
## 4 Discussion
PLA is highly biocompatible, which minimizes the risk of adverse reactions when in prolonged contact with humanskin, making it a safe option for prosthetic users. PLA offers adequate mechanical strength and rigidity necessary for the structural support of prosthetic sockets, ensuring durability and stability. Additionally, PLA's compatibility with 3D printing technologies facilitates the production of customized, anatomically precise sockets, enhancing comfort and fit for individual users. Its cost-effectiveness further supports its selection, especially in contexts where affordability is crucial. In this study, a prosthetic socket printed with PLA material demonstrated a maximum Von Mises stress of 0.14 MPa, significantly lower than PLA's yield strength of 70 MPa [53]. This indicates that the prosthetic socket would not undergo permanent deformation under loading. PLA material can elongate up to 7% before reaching its breaking point [54], and the maximum deformation observed in the prosthetic socket during loading was 0.008 mm. This study has limitations. It was conducted under static ground reaction forces without considering dynamic loads throughout the full gait cycle. Future research should address this to gain a more comprehensive understanding of socket performance under real-world conditions. Additionally, the study focused on a single material for socket printing, highlighting the need to explore alternative materials that could improve customization and durability. Furthermore, as the study was limited to a single subject, increasing the sample size is necessary to further validate the methodology and ensure broader applicability in developing prosthetic sockets.
## 5 Experiments and Results
### Performance Evaluation of Models  
In this section, we carry out model training to accurately categorize network traffic either as an \"attack\" or \"benign\". To determine which model is best, we analyze the efficacy of several models, such as Logistic Regression, XGBoost, Random Forest, KNN, and CNN (the architecture includes dense layers with 120, 80, 40, and 20 neurons, followed by an output layer with a single neuron), using the performance criteria shown in Table 3.  
Figure 5: Visualization of class categories and their counts, before and after using SMOTE.  
Figure 6: Class label distribution—post-SMOTE.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline Logistic regression & 70.47\% & 0.7058 & 0.7037 & 0.7036 \\ K-nearest neighbor & 87.70\% & 0.8780 & 0.8727 & 0.8739 \\ XGBoost & 95.23\% & 0.9525 & 0.9524 & 0.9523 \\ Random forest & 94.40\% & 0.9448 & 0.9432 & 0.9439 \\ CNN & 65.36\% & 0.6536 & 0.6536 & 0.6536 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation metric.  
Based on the analysis, XGBoost outperforms Logistic Regression, Random Forest, KNN, and CNN in terms of accuracy and other evaluation metrics. This suggests that XGBoost is more adept at handling the complexities of the dataset, making it a superior choice for classifying network traffic. While more improvement and fine-tuning of these models may be investigated to get higher accuracy, our primary focus is still on making sure that the models produce understandable outputs using XAI approaches such as SHAP and LIME. This not only aids in model validation but also builds trust in the system, especially when dealing with critical security decisions in future networks like 6G.
# Smart Sensors and Smart Data for Precision Agriculture: A Review
## 5 Challenges and Future Directions  
Precision agriculture, enabled by the integration of intelligent sensors and data analysis, has made significant advances in the optimization of farming practices. However, today's path of transformation presents several challenges and constraints. An important obstacle lies in the deployment of smart sensors, where elements such as calibration, maintenance, and sensor durability influence the reliability of the data collected. Achieving extensive interoperability between various sensor networks also represents a challenge, hindering the seamless integration of data into agricultural systems. In addition, concerns about data accuracy and security require careful attention to establish trust between stakeholders. Constraints include reliance on historical data for predictive analysis, often at the expense of real-time environmental variations. Another limitation is that environmental factors such as soil composition and topography can influence the effectiveness of smart sensors, resulting in variations in data accuracy. The need for sustainable energy sources in remote areas remains a critical constraint, impacting the continuous monitoring of agricultural parameters.  
Looking ahead, PA holds promising developments that can respond to these challenges and reshape the landscape. The integration of IoT technologies promises to revolutionize data collection, offering real-time information on crop health, soil conditions, and environmental changes. The use of big data analytics will enable more sophisticated predictive modeling, enabling farmers to make informed decisions. In addition, AI will play a central role in automated decision-making processes, offering advanced solutions for crop management, disease detection, and resource optimization. Furthermore, the emerging trends also suggest the potential for ML algorithms to be adapted in response to continuous data streams, thereby enhancing the adaptability of precision farming systems. Despite persistent challenges, the future of precision farming is promising. Future advances, driven by IoT, big data, and AI, promise to overcome existing limitations and position PA at the heart of sustainable and efficient farming practices. As research progresses and technologies evolve, the synergy between smart sensors and advanced analytics will pave the way for a new era of PA, further enhancing worldwide food security and environmental sustainability.
## 6 Results and Discussion
### Comparative Analysis of FOPI-FOPI and PI-PI Controllers
#### 6.2.2 Case 1: Performance Evaluation under Balanced Three-Phase Sag  
Balanced three-phase voltage sags are typically triggered by events such as short circuits, the start-up of large motors, or sudden spikes in EV charging demand. Figure 12 illustrates the performance of the PI-PI controller and the FOPI-FOPI controller optimized using the WSO-WOA algorithm. Between 0.3 and 0.5 s, the microgrid experiences a sharp increase in charging demand, leading to a significant voltage drop. The load demand then returns to normal conditions at 0.5 s. The figure clearly demonstrates the superiority of the FOPI-FOPI controller over the PI-PI controller in regulating voltage magnitude. The control performance assessment, as summarized in Table 6, reveals notable differences between the PI-PI and FOPI-FOPI controllers. The FOPI-FOPI controller consistently achieved shorter settling times, with the quickest being 0.075 s compared to the PI-PI controller's best time of 0.145 s. In terms of maximum overshoot, both controllers exhibited similar performance, with the PI-PI controller recording a maximum overshoot of 25.85%, while the FOPI-FOPI controller had a marginally higher maximum overshoot of 25.89%.  
Figure 13 illustrates the improvement in load voltage at bus B4 after applying the DSTATCOM with the FOPI-FOPI controller. In Figure 13a, the load voltage at phase (a) is shown with no enhancement, while Figure 13b displays the voltage after enhancement. Figure 13c depicts the injected signal using the controller.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameters** & **Controller** & **Case 1** & **Case 2** & **Case 3** \\ \hline \multirow{3}{*}{Settling time (sec)} & PI-PI & 0.145 & 0.155 & Not less than 0.105 \\  & FOPI-FOPI & 0.075 & 0.068 & 0.05–0.073 \\ \cline{1-1}  & PI-PI & 25.85 & 12.35 & 18.1 \\ \cline{1-1}  & FOPI-FOPI & 25.89 & 12.35 & 18.19 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Control performance assessment.  
Figure 12: Regulation of the load voltage at B4 under a three-phase balanced sag.
## 2 Materials and Methods
### Materials  
Data collection occurred at two time points, both before the intervention and after the intervention. Cognitive performance was assessed using a variety of task-based measures, including the number of correct answers, total training attempts, success rate, and time to highest level. Cognitive and emotional functions were assessed using the Mini-Mental State Examination (MMSE) to evaluate global cognitive function and the Geriatric Depression Scale (GDS) to assess depressive symptoms. Both the experimental and control groups underwent fNIRS (NIRSIT Lite Analysis Tool v3.1.0; OBELAB, Inc., Seoul, Republic of Korea) measurements during their first and final visits to assess the hemodynamic responses of cerebral blood volume during the intervention by detecting oxyhemoglobin levels in the brain. The fNIRS signals were recorded using the NIRSIT Lite system with a sampling rate of 8.138 Hz and dual wavelengths (780 nm and 850 nm). Measurements were performed under controlled indoor LED lighting conditions. The raw data were converted into \(\Delta\)HbO and \(\Delta\)HbR concentrations using the modified Beer-Lambert law. A band-pass filter (0.01-0.1 Hz) was applied, and motion artifacts were corrected using the CBSI method available in the NIRSIT Lite software.  
The final output of each optode was then reported as mean total oxygenated hemoglobin (HbO \(\mu\)Mol). A total of 15 channels were located on both sides of the dorsolateral prefrontal cortex (DLPFC) (channels 2, 3, 12, 14), orbitofrontal cortex (OFC) (channels 1, 4, 7, 10, 13, 15), and frontopolar prefrontal cortex (FPPFL) (channels 5, 6, 8, 9, 11) [13]. Positions of these channels were: channel 1, right lateral OFC; channels 4 and 7, right medial OFC; channels 2 and 3, right DLPFC; channel 15, left lateral OFC; channels 10 and 13, left medial OFC; channels 12 and 14, left DLPFC; channels 5, 6, 8, 9, and 11, frontopolar prefrontal cortex. Figure 1 shows the positions of these channels.  
Figure 1: Schematic layout of the 15 fNIRS channels over the prefrontal cortex regions, including dorsolateral prefrontal cortex (DLPFC), orbitofrontal cortex (OFC), and frontopolar prefrontal cortex (FPPFC).  
Channels 1 through 15 corresponded to specific areas of the prefrontal cortex that are known to be involved in these higher-order cognitive processes. Statistical analysis was performed using paired \(t\)-tests to compare pre- and post-intervention results within each group and independent \(t\)-tests to compare the post-intervention differences between the experimental and control groups. A significance level of \(p<0.05\) was used to determine statistical significance, and effect sizes were calculated to assess the magnitude of the intervention's impact.
## 4 Results and Discussion
### Numerical Analysis of Characteristic of SOC and Current Balance with IM240 Mode
The observed differences in SOC drop between the two modes were less than those observed when engine torque was reduced. When the battery capacity decreases by 20%, the difference in the SOC drop between the IM240 and FTP-75 modes is approximately 2.66%. In other words, because the test modes correspond to shorter distances than would be driven in practice, this difference in SOC drop may be remarkable. The differences in SOC drop converge with  
Figure 9: SOC drop in IM240 mode for Phase 1 and Phase 2: (**a**) Reduction in engine torque. (**b**) Reduction in battery capacity. reduced battery capacity, which suggests the capacity of the LIBs mounted in the test vehicle could be reduced. Additionally, by considering the battery life, if the battery SOC can be higher than 50%, then performance equal to that achieved currently can be realized with lower battery capacity. Figure 11 shows the relationship between the current balance and SOC according to the change in engine torque between the IM240 and FTP-75 modes. In the IM240 mode, which corresponds to a relatively short driving period, the results are located in the first quadrant and gradually move closer to the origin as the engine torque decreases. The electrical energy consumption tends to increase gradually. In the FTP-75 mode, the motor torque rapidly decreases and locates in the third quadrant. The maximum decrease in SOC is approximately \(-11\%\) relative to the initial SOC because the driving time is relatively long and the frequency of motor use at medium and low speeds increases. Also, because the engine torque decreases as the drive-motor ratio increases, the amount of charge during power generation is reduced, which tends to move in three quarters compared with the IM240 mode significantly. As mentioned above, an intermittent operation is required at the optimum efficiency of the internal combustion engine to maintain a constant SOC. Figure 10: Difference in the SOC drop between IM240 and FTP-75 modes: (**a**) reduced engine torque; (**b**) reduced battery capacity. Figure 12 shows the relationship between the current balance and SOC according to changes in the battery capacity during IM240 and FTP-75 modes. For both driving modes, the SOC values are located in the first quadrant, but the trend in current-balance values as the battery capacity changes shows that that the IM240 mode affords a more gradual change in both SOC and current balance. However, we observe that the SOC values are no relatively big difference between modes when the battery capacity is reduced by 20%, and the difference is less than 3%, which corresponds to a relatively low sensitivity to battery capacity. By using a variety of strategies (e.g., downsizing, engine operation mode, limiting, SOC increase) to compensate for the SOC drop, HEVs can be designed to operate at lower battery capacities. Figure 11: Current balance during IM240 and FTP-75 modes under conditions of reduced engine-torque. Figure 12: Current balance during IM240 and FTP-75 modes under conditions of reduced battery capacity.
# Coordinated Dispatch of Multi-Energy Microgrids and Distribution Network with a Flexible Structure
## 2 Mathematical Model Formulation
### Multi-Energy Microgrids
#### 2.1.3 Waste-Heat Boiler (WH)  
The waste-heat boiler (WH) is used to recollect the waste heat to supply cooling and heating load and realize the cascade utilization of energy. The mathematical model of the waste heat boiler can be expressed as follows:  
\[\begin{array}{l}H_{WH}(t)=H_{GT}(t)\eta_{WH}\\ H_{WH}^{\min}\leq H_{WH}(t)\leq H_{WH}^{\max}\end{array} \tag{6}\]  
where \(H_{WH}(t)\) is the output heating power of WH at time slot \(t\). \(H_{GT}(t)\) is the heat generated by the combined supply equipment at time slot \(t\). \(\eta_{WH}\) is the efficiency of WH. \(H_{WH}^{\min}\), \(H_{WH}^{\max}\) are minimal and maximal output heating power of WH, respectively.
# Theoretical and Numerical Research on High-Speed Small Refrigeration Twin-Screw Compressor
## 3 Theoretical Model
### Thermodynamic Model  
The dynamic model of the screw compressor working process is developed based on variable-mass thermodynamics, incorporating the following fundamental assumptions [17; 18]:  
1. Steady-state pressure conditions are present at the suction and discharge ports;
2. Complete thermal equilibrium exists between the refrigerant and lubricant;
3. The working fluid (gas) and lubricant (oil) remain single-phase states;
4. The gas within the working chamber maintains uniform temperature and pressure distributions;
5. Adiabatic boundary conditions are present at the chamber walls.  
Figure 4 presents a schematic representation of the control volume configuration.  
Based on the first law of thermodynamics and the established hypotheses, the governing energy equation for the control volume is derived as follows:  
\[m\frac{du}{d\varphi}+u\frac{dm}{d\varphi}=\sum\frac{dm_{i}}{d\varphi}\left(h_{ i}+\frac{v_{i}^{2}}{2}\right)-\sum\frac{dm_{o}}{d\varphi}\left(h_{o}+\frac{{v_{o}}^{2}}{2} \right)-\frac{dQ}{d\varphi}-p\frac{dV_{\mathrm{sN}}}{d\varphi} \tag{5}\]  
Mass conservation equation:  
\[\frac{dm}{d\varphi}=\frac{dm_{i}}{d\varphi}-\frac{dm_{o}}{d\varphi} \tag{6}\]  
The numerical resolution of energy and mass conservation equations is bifurcated into two distinct formulations depending on the selection of the primary variables: the mass  
Figure 4: The control volume of the screw compressor.  
specific internal energy (_m-u_) model and the pressure-temperature (_p-T_) model [19, 20, 21, 22]. This study adopts the _m-u_ model as the governing framework, with the pressure and temperature parameters subsequently derived through the post-processing of the converged solutions.  
The leakage flow model employs the Lin two-phase nozzle model without considering the geometric boundary effects of leakage channels. For suction and discharge flows, a modified Lin two-phase nozzle model with upstream and downstream area corrections is implemented [23,24]. Thermodynamic properties are determined using the NIST REFPROP database, while the governing differential equations are numerically solved through the backward differentiation formula (BDF) method. The design condition parameters are specified in Table 3.  
The volumetric efficiency of the screw compressor:  
\[\eta_{V}=\frac{m_{\text{ac}}}{m_{\text{th}}} \tag{7}\]  
The adiabatic efficiency of the screw compressor:  
\[\eta_{\text{ad}}=\frac{m_{\text{ac}}\Delta h_{\text{s}}}{P_{\text{ind}}} \tag{8}\]
## 5 Case Study and Discussion
### Analysis of Results
(**a**) Departures of upward trips. (**b**) Departures of downward trips. We use the local schedule around the interval highlighted by the arrow in Figure 6b as an example to compare the effects of different algorithms, as shown in Figure 7. The local schedule covers part of the optimized schemes during both periods: 18:00-19:00 and 19:00-20:00. Because of the breakdown of bus_No_19 at 15:00, several trips need to be rearranged or canceled, such as trip 8 in Figure 7. Without backup buses, MA controllers cancel the above trips, as shown in Figure 7a, which reduces the capability of bus services. Therefore, the ISCR model adds trips to other buses to generate a certain number of trips. For example, trips are inserted behind trips 1, 4, and 6 to fill the gap in trip numbers during the periods 18:00-19:00 and 19:00-20:00. However, the greedy algorithm ignores the hysteresis effects of the solutions of previous stages, leading to additional rescheduling (e.g., the cancellation of trip 7 in Figure 7b). Thus, the interval exceeding 30 min occurs due to cancellations and reassignments following common methods, leading to fluctuating service frequencies. Therefore, we introduce the LNS algorithm and combine it with dynamic programming to design the DP-LNS algorithm. The heuristic operator, designed by combining domain knowledge, allows the improved LNS to identify the maximum departure interval bias and repair the irregular departure intervals by delaying trip 5 within an allowable time window, as shown in Figure 7c. This adjustment aims to offer a more consistent service for passengers. Compared to the greedy algorithm, dynamic programming comprehensively combines the sub-schemes of different stages to obtain a long-term optimal solution, preventing poor decisions in the early stages that could compromise subsequent plans. Therefore, the requirement of trip numbers in the period 18:00-19:00 can be satisfied by more reassignments, and bus_No_15 can perform trip 7 with a 9 min delay to avoid extra operational costs, as shown in Figure 7d. Finally, dynamic programming produces an optimized scheme that is more similar to the original schedule, making it easier to ensure successful operation and minimize variation in service frequency. Figure 6: Temporal variations in departure intervals. (**a**) Departure intervals for upward trips. (**b**) Departure intervals for downward trips. Finally, A visual representation of the optimization scheme for the DP-LNS algorithm is shown in Figure 8.