esa-sceva/satcom-chunk-collection huggingface dataset:
# Facilitating Robot Learning in Virtual Environments: A Deep Reinforcement Learning Framework
## 2 Related Work
A key distinction between environments like Gymnasium and simulation platforms like Webots is the fidelity of actuator and sensor modeling. In Webots, actuators and sensors, such as robotic arms and LiDAR, are designed to closely resemble their physical counterparts. Webots allows robot programming via controllers in multiple languages, including C, Python, and MATLAB. For DRL implementations, Python is preferred due to its ease of use and the straightforward translation of C examples. Several projects aim to facilitate RL in robotic simulators. Gym-Ignition [24] provides an OpenAI Gym interface for Gazebo, supporting reproducible robot environments, external software integration, multiple physics and rendering engines, and ROS compatibility. Zamora [25] extends the Gym interface with ROS compatibility for Gazebo. Lopez [26] offers ROS 2 compatibility and is applied in real-world scenarios. NVIDIA Isaac ROS [27] provides a comprehensive framework for DRL and robotics, featuring photorealistic rendering and parallelization. While Deepbots [28] represented an earlier effort to facilitate DRL in Webots, its implementation presents several limitations for current research. Firstly, it relies on the now-deprecated OpenAI Gym standard, leading to compatibility issues with modern RL libraries and environments. Furthermore, integrating Deepbots requires the installation of a dedicated Python package, which itself carries dependencies often requiring older versions of core libraries, potentially causing conflicts within contemporary development setups. To our knowledge, other generic wrappers interfacing Webots with the current OpenAI Gymnasium are not implemented. Most significantly, existing approaches like Deepbots often necessitate reliance on the Webots 'Supervisor' concept for managing the simulation state and facilitating the RL interaction loop (observation, action, reward). Our proposed framework introduces a key architectural improvement by largely removing this dependency on the Supervisor for the core RL agent-environment interaction. This presents the first generic interface for Webots built directly upon the updated OpenAI Gymnasium standard. By circumventing the older dependencies and the mandatory use of the Supervisor for basic interaction, our framework standardizes and significantly simplifies the process of applying modern RL algorithms within the Webots simulation environment.
## 4 Conclusions  
In the present paper, the microstructural and mechanical properties of welding joined by AC-GMAW for 5 mm thick steel plate was investigated on square groove butt joints at different settings of current and electrode negative (EN) ratios. The following conclusions were drawn:  
1. At lower values of currents, the penetration depth decreased with the increase in EN ratio. The penetration depth is less at a 50% EN ratio as compared to 0% and 30%.
2. At higher values of currents, an increase in EN ratio resulted in an increase in penetration depth. At high currents of 225 A and 250 A with 50% EN ratios, the highest penetration was found. Moreover, it was concluded that by changing the EN ratio, the depth of penetration can be controlled.
3. The mechanical properties were greatly influenced by the current and EN ratio settings. The tensile strength improved with increasing current, whereas the percentage elongation increased with increasing EN ratio. The maximum UTS, i.e., 477.77, was achieved at welding current of 250 A and 0% EN ratio, whereas high percentage elongation was obtained at 225 A and a 50% EN ratio.
4. Under the settings of 250 A and varying EN ratios, micro-hardness distribution was not strikingly affected. Moreover, low hardness values were found in the HAZ as compared to the FZ.
5. The AC-GMAW process at high currents and EN ratios can be a solution to join thick steel plates with a minimum number of passes. It is recommended that, under the current settings of 250 A and 50% EN ratios, sound welds can be obtained for thick plates.
6. A detailed study for understanding the gap bridging capability of the AC-GMAW process at different joint gaps, welding speed and thickness of plates are required to analyze optimal filling of molten metal in the joint gap and the effect of these parameters on mechanical properties.  
Figure 18: Microstructures (**a**) BM (base metal); (**b**) FZ (fusion zone); (**c**) HAZ (heat affected zone); and (**d**) boundary differentiating HAZ and FZ.  
**Acknowledgments:** This research was supported by the Ministry of Trade, Industry & Energy (MOTIE, Korea) under Industrial Technology Innovation Program No. 10067156, 'Development of heavy structure dimensional accuracy measurement and management software system for improving productivity in shipbuilding company', and No. 10050495, 'Development of simulation based production management systems for middle-sized shipbuilding companies'.  
**Author Contributions:** [PERSON] conceived the research. He designed and performed the experiments and analyzed the data. [PERSON] academically supervised the research. Both authors contributed to the manuscript preparation and have given approval to the final version of the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Towards a Standardized Framework for Digital TPM Implementation
### Organizational Readiness and Workforce Involvement  
While technology is a key enabler of TPM 4.0, it cannot, on its own, guarantee successful implementation. To achieve a meaningful and lasting impact, the human and organizational dimensions must be fully integrated into the standardized framework. This integration reinforces the foundational values of TPM, namely, operator involvement, shared ownership, and structured discipline.  
First, digital training programs must go beyond traditional instruction by incorporating tools such as augmented reality for procedural guidance, interpreting predictive maintenance outputs, and navigating performance dashboards. This ensures that operators are equipped to engage with digital tools confidently and effectively.  
In parallel, the concept of autonomous maintenance must evolve. Rather than serving as passive executors, operators should be empowered as data-informed decision-makers, capable of interpreting system feedback and acting proactively.  
Equally important is the establishment of collaborative structures. Cross-functional TPM teams, comprising maintenance, IT, and production personnel, should work together to co-design and evaluate digital workflows. Such collaboration fosters alignment and breaks down operational silos.  
To support this shift, change management must be actively led by organizational leadership. Clear communication, a focus on incremental successes, and the empowerment of frontline teams are essential to driving cultural transformation and ensuring buy-in at all levels.  
Finally, practical success stories highlight the effectiveness of tools such as standardized digital One-Point Lessons (OPLs), real-time alert systems, and gamified dashboards in boosting operator engagement, reinforcing learning, and sustaining motivation.
## 2 Materials and Methods
### Classification Model  
#### 2.4.1 Network Architecture  
The graphical convolution network (GCN) [31] was chosen as the classification model. The GCN employs layer-wise propagation, enabling a first-order approximation of spectral convolutions on graphs [31]. This approach facilitates encoding both the graph structure and node features, leading to improved modeling of relational data. By effectively capturing dependencies between nodes and linkages, GCNs extract robust, hierarchical features from graph-structured data.  
The proposed model takes two inputs, the adjacency matrix, and the feature vector. The model was designed with four multiplication layers. These layers capture and refine the information from both the graph structure and node features. The first layer (32-feature output) aggregates the data from neighboring nodes while the second (32-feature output) and third (478-feature output) layers update the node representation based on the aggregated information, refining the features to capture high-level representations. The last multiplication layer (8-feature output) is followed by the SoftMax activation function, which provides the output of the architecture and is equal to the number of classes. Figure 3 represents the model architecture.
# An Augmented Warning System for Pedestrians: User Interface Design and Algorithm Development
## 2 Materials and Methods
### Evaluation  
The interface was evaluated in the previous study, and the results showed that the participants could understand the user interface well and could react correctly to avoid an accident [27]. To test the performance and the accuracy of the developed projection algorithm, two experimenters simulated a vehicle-pedestrian conflict scenario at a campus parking lot, and data from both simulated pedestrian and vehicle were collected to evaluate the performance of the developed algorithm. In detail, an experimenter simulating a pedestrian walked toward the footpath. The experimenter stopped when a signal was received and turned to look at the vehicle driven by another experimenter. A smartphone fixed at the experimenter's eye level was used to collect the pedestrian's GPS data, IMU data, and video of the instant view of the pedestrian. A vehicle was driven by another experimenter, and the vehicle data were collected using the same type of device fixed on the dashboard of the vehicle. The vehicle was located by the simulated pedestrian's left side of the road. When the simulated pedestrian approached the crosswalk, the vehicle received a signal and started driving toward the crosswalk to simulate the vehicle-pedestrian conflict.  
The initial sampling rate of GPS and IMU data was 600 Hz. Once the data were cleaned, GPS and IMU were down-sampled to 1 Hz to feed in the projection algorithm. As mentioned in Equation (2), the projected point (\(\text{P}_{\text{x}}/\text{P}_{\text{w}}\), \(\text{P}_{\text{y}}/\text{P}_{\text{w}}\)) was calculated for every input point. If the projected point was outside of the warning window, the intersection of the vehicle path and the warning window boundary was used as the projected point. An ideal vehicle path was visually identified and manually drawn on the video image based on the location of the vehicle and the direction of the road. To check the accuracy of the algorithm, the average distance between the projected points and the ideal vehicle path was calculated. The smaller the distance between the projected points and the ideal vehicle path was, the more accurate the algorithm was. The vehicle path was formed by connecting the vehicle point and the conflict point. The slope of the projected and the ideal  
Figure 10: Example of scenario 4: Conflict position could not be projected.  
Figure 9: Example of scenario 3: Only the turning point could be projected in the warning window.  
vehicle path was calculated based on these two points to test the vehicle path direction. The closer the projected slope was to the ideal slope, the more accurate the vehicle path direction was.
# Thermographic Stall Detection by Model-Inspired Evaluation of the Dynamic Temperature Behaviour
###### Abstract
Note that the common aim of both recent approaches is to identify characteristics in the behaviour of the spatiotemporal surface temperature. However, the understanding of the signal measurement chain is incomplete, since a distinct consideration of the different influences of the boundary layer flow characteristics (_desired sensitivity_) and the incoming flow temperature fluctuation as influencing quantity (_cross-sensitivity_) is missing. Additionally, a separate evaluation of the harmonic and transient components of the surface temperature is not conducted. For this reason, the question of how to design a signal processing schema that is robust and effectively visualises regions of flow separation cannot be answered. As a result, the thermographic stall visualisation by evaluating the dynamic behaviour of the thermodynamic fluid-structure interaction seems promising, but designing a signal-processing scheme that provides a high degree of interpretability and maximum contrast is an open task. For this reason, the aim of the article is at first to model the rotor blade surface temperature response with respect to boundary layer flow characteristics _and_ temporal temperature fluctuations of the external flow using system theory. Finally, two measurement principles for different measurement conditions are derived on the basis of the system modelling to provide thermographic stall visualisation with enhanced interpretability and contrast. In particular, a harmonic analysis of temporal temperature fluctuations around predefined operating points is investigated, considering the amplitude information. In addition, a temporal gradient-based evaluation of the transient thermodynamic blade behaviour is studied. First, the measurement chain of thermographic flow visualisation is analysed, and the measurement principles based on the dynamic response of the surface temperature around an operating point (small-signal behaviour) and for a transient input signal (large-signal behaviour), respectively, are derived in Section 2. The experimental setup for the wind tunnel experiments with measurements for different flow velocities on a wind turbine airfoil is presented in Section 3. Note that the chosen flow conditions are similar to the conditions of in-process wind turbines. The experimental results regarding interpretability and distinguishability of the different flow regions are presented for both dynamic thermographic flow visualisation principles in Section 4. In particular, the achievable enhancement of image interpretability and contrast for stall detection is discussed. The article closes with a summary and outlook in Section 5.
## 6 Non-Linear Methods
### Ingredient Mixing and Food Formulation  
Ingredient formulation in the food industry often involves non-linear interactions among components, where changes in one ingredient affect the properties of the entire product. Non-linear methods enable precise optimization of these interactions, ensuring that products meet nutritional, sensory, and economic criteria.  
For example, in the development of plant-based meat alternatives, non-linear optimization models have been used to balance protein content, texture, and flavor. A case study in this domain reported a 10% improvement in product texture and a 5% reduction in production costs by optimizing ingredient proportions using NLP. Similarly, in bread-making, non-linear models have been applied to optimize dough rheology, resulting in improved load volume and reduced waste [59].  
In nutritional optimization, non-linear methods allow manufacturers to design fortified foods tailored to specific dietary requirements. For instance, a non-linear programming model for infant formula optimization achieved a 15% reduction in production costs while ensuring compliance with stringent nutritional standards, contributing to SDG 2 (Zero Hunger) and SDG 3 [60].
## 4 Results and Application
### Validation of Coherent Integration Time Optimization Model
Black indicates the model result considering the correlation between the waveforms. Blue indicates the model result without considering the correlation between the waveforms. and antenna pointing, etc. These random factors can be used to optimize the model by obtaining recorded data of aircraft attitude and vibration in the future.
## 4 Discussion
Accuracies of 87.5% and 85.71% are shown for the combination of input variables for female and male voices, respectively. This result validates the input parameters selected to build our model. For females, a combination of jitter (Post-tx), shimmer (Pre-tx), coffee-drinking status, and jitter comparison was used. For males, a combination of jitter (Pre-tx), MPT (Pre-tx), and jitter (Post-tx) was found to be statistically significant. Therefore, jitter (Post-tx) and MPT (Pre-tx) were identified as the most important parameters in the MLP models for female and male voices, respectively. A notable finding in this study's results is that, among the acoustic variables, jitter (Post-tx) for females and MPT (Pre-tx) for males have been identified as factors capable of predicting the effectiveness of voice therapy. Furthermore, the methodology proposed in this paper is believed to be well suited to identifying factors predicting the effectiveness of voice therapy before and after treatment, centered around an effectiveness metric based on the G scale from the GRBAS scale, which exhibits a strong correlation with acoustic variables. Our study has certain limitations. First, our dataset consisted of 81 individuals, including 55 females and 26 males, with more than 10 different pathologies. This sample size may not be sufficient for training a deep neural network effectively. Consequently, we are in the process of planning a validation study, aiming to recruit a larger number of patients with long-term follow-up data. Furthermore, we did not take into account factors that might have influenced interpersonal phonetic outcomes, such as the patient's surgical range, method, or age. It is important to note that the variability in the GRBAS scores can vary depending on the individual conducting the assessment, and prediction of these scores may not fully encapsulate the participant's vocal condition.
# Correntropy-Based Constructive One Hidden Layer Neural Network
## 5 Experimental Results
### Discussion
#### 5.4.6 Discussion on Tables 5 and 6  
These tables compare C2N2 with state-of-the-art constructive networks on the regression and classification datasets. They show that the best results are for C2N2. For the Housing dataset, RMSEs for C2N2, OLSCN and OHLCN are 0.0966, 0.0988 and 0.0993, respectively. In the presence of impulsive noise, RMSEs are 0.0978, 0.2411 and 0.1824. Thus, the proposed method has the best stability among the other mentioned state-of-the-art constructive networks when data samples are contaminated with impulsive noise. From the architecture viewpoint, OLSCN has the most compact architecture. However, it has the  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**C2N2**} & \multicolumn{3}{c}{\(N_{1}\)} & \multicolumn{3}{c}{\(N_{2}\)} & \multicolumn{3}{c}{\(N_{3}\)} \\ \cline{2-11}  & **Testing** & **RMSE** & **\#N** & **Time** & **Testing** & **RMSE** & **\#N** & **Time** & **Testing** & **RMSE** & **\#N** & **Time** (s)** \\ \hline \(f_{0}^{(1)}\) & 0.1358 & **3.70** & 0.69 & 0.1555 & 4.85 & 1.48 & **0.1284** & 6.30 & 1.78 & 0.1536 & 7.05 & 0.81 \\ \hline \(f_{1}^{(1)}\) & 0.1587 & 6.25 & 0.91 & 0.3244 & **3.70** & 0.44 & 0.2460 & **3.20** & 0.72 & 0.2750 & 4.40 & 2.40 \\ \hline \(f_{0}^{(2)}\) & 0.1963 & **2.70** & 1.09 & **0.1953** & 5.90 & 1.91 & **0.1953** & 4.55 & 1.57 & **0.1954** & 5.45 & 2.18 \\ \hline \(f_{1}^{(2)}\) & 0.2487 & **3.55** & 3.98 & 0.3676 & 5.25 & 1.08 & 0.2790 & **3.25** & 0.89 & 0.3226 & 5.65 & 1.16 \\ \hline \(f_{0}^{(3)}\) & 0.0892 & **3.15** & 1.05 & 0.0940 & 6.80 & 0.12 & 0.0943 & **3.30** & 1.34 & 0.0935 & 6.40 & 0.34 \\ \hline \(f_{1}^{(3)}\) & 0.1416 & 4.15 & 1.12 & 0.2949 & 4.15 & 0.64 & 0.2309 & **2.35** & 0.51 & 0.2555 & 4.35 & 0.57 \\ \hline \(f_{0}^{(4)}\) & 0.1136 & **2.10** & 1.81 & **0.1141** & 4.55 & 1.34 & **0.1139** & 8.10 & 2.02 & 0.1963 & 4.70 & 0.87 \\ \hline \(f_{1}^{(4)}\) & 0.1360 & 7.70 & 3.01 & 0.2928 & **1.65** & 1.54 & 0.2569 & 4.20 & 0.98 & 0.2961 & 4.85 & 0.45 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Performance comparison of C2N2 and the networks \(N_{1},N_{2}\) and \(N_{3}\): synthetic regression dataset.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline Noisy Data & **\#3** & **\#10** & **\#36** & **\#55** & **\#100** \\ \hline \((\kappa_{i},\gamma_{i})\) & \((-0.2251,-3.29*10^{-22})\) & \(-0.2551,-4.3*10^{-22})\) & \((-0.2310,-1.04*10^{-24})\) & \((-0.2607,-8.0*10^{-22})\) & \((-0.2482,-6.12*10^{-23})\) \\ \hline Noise-free data & **\#4** & \#11 & **\#37** & **\#56** & **\#101** \\ \hline \(\kappa_{i},\gamma_{i}\) & \((0.3837,-0.3989)\) & \((-0.3983,-0.3989)\) & \((-0.3989,-0.3955)\) & \((-0.3989,-0.3982)\) & \((-0.3988,-0.3988)\) \\ \hline \hline \end{tabular}
\end{table}
Table 8: Amount of auxiliary variables for noisy and noise-free data. The experiment is performed on the \(f^{1}\) dataset.  
worst training time. Table 6 shows that C2N2 outperforms OLSCN and OHLCN in the presence of impulsive noise in the classification dataset.
## 1 Introduction
Apart from modeling by first physical principles, nonlinear models can also be obtained by nonlinear identification [16; 17; 18]. In the modeling, about 10-30 unknown parameters were selected for identification. Prediction error method (PEM) [17] or genetic convergent algorithm [16] can be adopted for searching the optimal values of the unknown parameters. However, these methods also need flight data of multiple operating conditions. In addition to the small-scale helicopter modeling, there is little research about the small-scale compound helicopters. Therefore, huge research potential can be explored in this domain. This study intends to establish a reliable nonlinear mathematical model for a small-scale compound helicopter. The modeling method is meaningful not only for this particular prototype model but also for the compound research on multiple small-scale helicopter platforms, such as Yamaha R-50 [11], AF25B [17], MIT X-cell [11; 12; 19], NUS Raptor 90 SE HeLion [15; 20], Raptor 50 [14] and so forth. In the study, three compound configurations were considered: (1) the general helicopter carried the ducted fan; (2) the general helicopter carried the ducted fan and free-rotate wing; (3) the general helicopter carried the ducted fan, free-rotate wing and horizontal stabilizer. In the modeling, components were established separately at first and were assembled together. The full model was modified by combining the system identification results. Flight data were obtained from hover condition to 32 m/s forward flight condition. The nonlinear model was validated using the trim results and the time-domain responses data. Furthermore, the validated model was expanded to calculate a coordinated turn route. The elevator and aileron work efficiently in the simulation. The study gives deep insight into small-scale helicopter dynamics over a wider speed range.