esa-sceva/satcom-chunk-collection huggingface dataset:
# Volatile Metabolome and Aroma Differences of Six Cultivars of _Prunus mume_ Blossoms
## 2 Results and Discussion  
### Analysis of P. mume Blossom Volatile Metabolites by HS-SPME-GC-MS  
Deferent sample pretreatment methods have been employed in previous studies; both static headspace adsorption and head space-solid phase microextraction (HS-SPME) were used to collect headspace volatiles emitted from whole blooming flowers [4; 6; 7; 9], while organic solvent extraction of the ground powder of flowers in liquid nitrogen was applied for endogenous component sampling [5; 7]. HS-SPME was selected to collect volatile constituents from the powder of flowers ground in liquid nitrogen for the easy addition of internal standards and preparation of quality control samples. The same strategy was also used in the analysis of volatile metabolites in dark tea during the fermentation process [10]. Extraction efficiency was evaluated by comparing the compound numbers and the total peak areas between whole blooming flowers and the ground powder of flowers in the same amounts, as well as under the same analytical conditions as HS-SPME-GC-MS. The RSD were 2.7% and 2.5% for their compound numbers and the total peak areas, respectively. Except for the slight variation in the relative peak area of certain constituents, no significant difference between the two sampling methods was observed. Meanwhile, the stability of the sample during short storage (within 6 days) at \(-40\)\({}^{\circ}\)C before analysis was examined. The RSD of the areas of 15 main components were in the range of 2.9-17.5%, the same level as the HS-SPME-GC-MS analytical method. Thus, the sampling method adopted in this work is reliable and feasible. The TICs of volatile metabolites in _P. mume_ blossoms are shown in Figure S1. All TIC were processed by Agilent MassHunter Unknowns Analysis software and peaks were manually aligned. A total of 89 compounds were detected in all _P. mume_ blossom samples (Table S1), and 65 of them were identified by varying degrees by comparing their mass spectra with NIST 11 and their RIs with NIST Chemistry WebBook ([[https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/)]([https://webbook.nist.gov/chemistry/](https://webbook.nist.gov/chemistry/))) (accessed on 6 April 2022) [11]; 24 unidentified compounds were labeled as unknown. On this basis, some significant compounds were also compared with the standards. The sixty-five identified compounds are shown in Table 1, including thirty-seven phenylpropanoids/benzenes, seventeen fatty acid derivatives, ten terpenoids, and one other compound.
## 3 Results
### Long Short-Term Memory Neural Network Model Results
#### 3.4.2 UVE-LSTM Established by Sensitive Variables  
Similarly, in the development of UVE-LSTM, this study also conducts training experiments under different parameters, and the results are presented in Table 9. The pattern change in the parameters of the UVE-LSTM and LSTM models is more consistent. The optimal parameters of the whole network are finally obtained by setting the batch size to 30, the learning rate to 0.01, and the number of iterations to 100, under which the model runs at a speed of about 2 s.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Batch Size**} & **Initial Learn** & \multirow{2}{*}{**Iterations**} & **RMSE** & **RMSE** & **Training** \\  & & & **(Training Set)/\%** & **(Validation Set)/\%** & **Duration/s** \\ \hline \multirow{4}{*}{30} & & 50 & 0.983 & 1.022 & 2 \\  & & 100 & 0.865 & 0.876 & 3 \\  & & 500 & 0.883 & 0.861 & 9 \\  & & 1000 & 0.852 & 0.946 & 18 \\  & & 50 & 1.002 & 0.961 & 1 \\  & & 100 & 0.875 & 0.896 & 2 \\
50 & 0.01 & 500 & 0.892 & 0.905 & 5 \\  & & 1000 & 0.887 & 0.922 & 9 \\  & & 50 & 1.057 & 1.131 & 1 \\
100 & 0.01 & 100 & 1.201 & 1.242 & 2 \\  & & 500 & 0.892 & 0.935 & 5 \\  & & 1000 & 0.912 & 0.960 & 9 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of LSTM network model with varying training parameters.  
Figure 8: Prediction model of moisture content of corn outlet based on LSTM.  
The UVE-LSTM model has an R\({}^{2}\) of 0.931 and an RMSE of 0.711% for the training set and an R\({}^{2}\) of 0.934 and an RMSE of 0.697% for the validation set. Combined with Figures 8 and 9, the gap between predicted and actual values is significantly reduced in Figure 9. The UVE-LSTM model increases the R\({}^{2}\) of predictions by nearly 0.05 compared with the LSTM model, and the prediction error is reduced by more than 0.2%. This also reaffirms the effectiveness and advantage of sensitive variables in predictive model building.
# Sunspot Detection Using YOLOv5 in Spectroheliograph H-Alpha Images
## 3 Related Work  
Many researchers have been working on automatic sunspot analysis through different methods and sources. Some use images taken from the earth, while others use images taken from space. Traditional approaches are based on image processing techniques, while more modern techniques in general use deep learning neural networks.
# A Novel Energy Optimization Control Strategy for Electric Drive System Based on Current Angle
## 3 Efficiency Optimization Control Based on Current Angle Look-Up Table  
\(\beta\) is the angle between stator current \(i_{s}\) and d-axis, as shown in Figure 5, and there are few researches on the optimization of EDS efficiency based on it at present. As described in Section 1, conventional motor efficiency optimization control (such as MTPA control) improves motor efficiency by optimizing dq-axis current \(i_{d}\)/\(i_{q}\), but it should be noted that two factors need to be considered: The current limit and voltage limit. Therefore, the conventional \(i_{d}\)/\(i_{q}\) optimization usually adds a current amplitude calculation module to reach the limit of current limit value, which will lead to the increase of calculation amount. The constraints of motor stator current and voltage are as follows.  
\[i_{s}=\sqrt{{i_{d}}^{2}+{i_{q}}^{2}}\leq i_{\text{lim}} \tag{24}\]  
\[U_{s}=\sqrt{{U_{d}}^{2}+{U_{d}}^{2}}\leq U_{dc,lim} \tag{25}\]  
where \(i_{\text{lim}}\) and \(U_{dc,lim}\) are the current limit and DC bus voltage limit, respectively.  
However, optimization on \(\beta\) will break through this limitation, which means that this research has potential.
# Estimate of Passive Time Reversal Communication Performance in Shallow Water
## 1 Introduction
In this paper, we propose a new parameter, \(E_{q}\), which describes the performance of passive time reversal communication using the \(q\)-function. The parameter \(E_{q}\) can be used to quickly assess the symbol rate of a coherent underwater communication system using the time reversal technique, and it can be used to determine the optimal number of receiver elements required to meet the BER requirement. Here, we estimate \(E_{q}\) and compare it to other communication parameters such as the BER and output SNR using the communication data acquired at two experimental sites. These sites have different bottom sediment compositions, which is a main factor that influences CIR in shallow water when the experimental geometries are the same. The paper is organized as follows. The parameter \(E_{q}\) is introduced in Section 2. Section 3 provides a description of the underwater acoustic communication experiments. The estimates of \(E_{q}\) for two different sites and comparisons with other communication parameters are made in Section 4. Finally, a summary and discussion are given in Section 5.
# Information-Centric Network-Based Vehicular Communications: Overview and Research Opportunities
## 5 ICN-Based VANET Research Opportunities
### ICN-Based VANET Reliability  
The deployment of ICN schemes in VANETs confronts the most vibrant and heterogeneous kind of problems, when considering the goals and requirements of such integration [5]. The difference in techniques (with respect to actuators, sensors, end-to-end diversity, and their functionalities, and in the collected and consumed data in such scenarios, would certainly lead to various concerns [5]. For example, vehicular nodes share similarities with the restrictions and prerequisites. One of the most vital problems is the use of technologies that may deploy resourceful connectivity in an unreliable network, i.e., VANET.  
Nevertheless, one of the most significant points in vehicular communications is how different semantics of the shared and stored contents would affect content distributions. In reality, this is a challenging issue which is not covered in the ICN scope [45]. Considering the facility of in-network nodes to store forwarded contents to increase upcoming requests, an issue appears concerning the ICN framework that whether it should participate in any type of process that enables the association or the analysis of various suppliers of information [5].  
In VANETs, the traffic on wireless medium that results from periodic packet exchange needs to be wisely controlled so as to avoid decline in the quality of safety-related data at the time of receiving [46]. For this reason, various strategies have been proposed in the literature such as D-FFAV [47], ATC [48], and PULSAR [49], which regulate traffic congestion with a stringent fairness measure that needs to be achieved for security purposes as well as emergency messages. However, the exchange of control messages in vehicular ICN is yet-to-resolve issue and needs careful considerations for designing adaptive strategies to achieve fair and reliable transmissions.
# A Cyber-Physical Approach for Residential Energy Management: Current State and Future Directions
## 6 Data Analytics
### Understanding HEMS as a CPS  
According to the above discussion, in the Data Analytics stage, a cyber-infrastructure analyzes the data following a model and/or operating mechanism, and sends instructions back to the physical devices. In [9], the authors defined CPSs as systems which embed the sensing, communication, computing, and control abilities into physical devices, allowing to achieve distributed sensing, reliable data transmission, and comprehensive information processing of the external environment. This way, real-time control is offered through an external loop. Based on this definition of CPS, a HEMS can be understood as a CPS which relates physical devices such as appliances and meters, with energy data. Measurements are collected, processed, and send to the cyber-system through a communication network, and then, control commands will be sent back once analyzed in the cyber-entity, as shown in Figure 11.  
In more details, a control mechanism which can be either rule-based or RL-based will benefit from the data coming from different campaigns (energy monitoring, forecasting, comfort analysis), and signals originated in the grid (utility) to successfully carry out the decision-making process. Figure 12 depicts this idea of the Data Analytics scheme in a HEMS.  
Figure 11: CPS framework for HEMS.  
The authors of [13] claim that compared to traditional HEMS, CPS-based solutions improved essential parameters such as efficiency and planning, which are very low in conventional HEMS mainly motivated by the inadequate collection of information on energy consumption and analysis, which leads to high energy cost. By integrating CPS strategies in the system, the HEMS acquires powerful capacities for awareness and automatic control. This is achieved through an advanced network infrastructure and Machine Learning techniques, becoming smart HEMS.
## 5 Conclusions and Future Works
### Main Findings and Implications for Practice  
This work demonstrates that FTP has significantly enhanced Moodle's assessment capabilities by simplifying the creation and integration of diverse question types and multimedia elements. The positive response from educators, 94% of them finding the use of images in quizzes via FTP interesting, underscores the tool's effectiveness in improving Moodle-based assessments.  
Concerning the statistical analysis of the results, it can be concluded that the training has been highly effective for STEM teachers in promoting the use of specific types of questions (especially Matching and Missing Word). However, it has had little impact on non-STEM teachers. Future research should focus on understanding why these differences occurred and develop tailored interventions to support non-STEM educators. Additionally, the larger sample size of STEM teachers should be considered when interpreting these results. Also, the results suggest that the training intervention has had a positive impact on the shift towards using certain types of questions, particularly Matching and Missing Word. This indicates that FTP facilitated the adoption of these types of questions.  
As a result of this research, the primary recommendation for Moodle quiz users, based on the findings of this study, is that while the use of Cloze-type questions (nested responses) has significantly increased, it still represents a very low percentage of overall usage relative to its capabilities and versatility. All Moodle quiz users (both STEM and non-STEM) are encouraged to experiment with creating this type of question using FTP, as no coding knowledge is required.  
In conclusion, FTP has been well-received by participants, who appreciate its ability to simplify quiz creation, automate processes, and organize question banks effectively. While a few participants noted technical issues, the majority found the tool highly valuable and expressed their intention to recommend and continue using it in their teaching practices.  
The FTP course for Moodle has as its main expectations the improvement of efficiency, speed, and ease in the creation and management of quizzes. Participants are looking for a tool that allows them to automate the generation of questions and question banks in order to optimize their time and effort in developing exams and continuous assessments while minimizing errors in Moodle and improving their teaching experience.  
Additionally, since FTP facilitates the incorporation of images into questions--an aspect that is particularly appealing to today's students--it is recommended to make greater use of images in quiz questions.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
###### Abstract  
In this work, we present a complete hybrid navigation system for a two-wheel differential drive mobile robot that includes static-environment- global-path planning and dynamic environment obstacle-avoidance tasks. By the given map, we propose a multi-agent A-heuristic algorithm for finding the optimal obstacle-free path. The result is less time-consuming and involves fewer changes in path length when dealing with multiple agents than the ordinary A-heuristic algorithm. The obtained path was smoothed based on curvature-continuous piecewise cubic Bezier curve (C\({}^{2}\) PCBC) before being used as a trajectory by the robot. In the second task of the robot, we supposed any unforeseen obstacles were recognized and their moving frames were estimated by the sensors when the robot tracked on the trajectory. In order to adapt to the dynamic environment with the presence of constant velocity obstacles, a weighted-sum model (WSM) was employed. The 2D LiDAR data, the robot's frame and the detected moving obstacle's frame were collected and fed to the WSM during the movement of the robot. Through this information, the WSM chose a temporary target and a C\({}^{2}\) PCBC-based subtrajectory was generated that led the robot to avoid the presented obstacle. Experimentally, the proposed model responded well in existing feasible solution cases with fine-tuned model parameters. We further provide the re-path algorithm that helped the robot track on the initial trajectory. The experimental results show the real-time performance of the system applied in our robot.  
Keywords:hybrid navigation system; weighted-sum model; a heuristic algorithm; piecewise cubic Bezier curve; mobile robot +
Footnote †: journal: Applied Mechanics and Mechanics of the
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
###### Abstract  
Powder bed fusion using a laser beam (PBF-LB) offers a suitable alternative to manufacturing Ti65 with intricate geometries and internal structures in hypersonic aerospace applications. However, issues such as undesirable surface roughness, defect formation, and microstructural inhomogeneity remain critical barriers to its wide application. In this study, a coupled discrete element method-computational fluid dynamics (DEM-CFD) model was utilized to investigate the spreading behavior of Ti65 powder in a multi-layer PBF-LB process. The macro- and microscopic characteristics of the powder beds were systematically analyzed across different layers and regions under various spreading velocities. The results show that the packing density and uniformity of the powder beds in multi-layer PBF-LB of Ti65 powder improves as the number of solidified layers increases. Poor bed quality is observed in the first two layers due to a strong boundary effect, while a stable and denser powder bed emerges from the fourth layer. The presence of a previously solidified region strongly influences its neighboring unsolidified areas, enhancing density in the upstream region and causing looser packing downstream. Additionally, due to the existence of a solidified region, the height of the powder bed progressively decreases along the spreading direction.  
Ti65 alloy; multi-layer powder spreading; DEM-CFD; powder bed quality +
Footnote †: journal: Applied sciences
## 1 Introduction
To achieve this, we extend an important centralized FL algorithm called FedAvg [9] to operate in a P2P environment. This extension draws inspiration from other works that explore decentralized model training [12, 13, 14]. The motivation for this research is to evaluate the feasibility of implementing a deep learning-based classification of DFU using a peer-to-peer federated learning approach and to compare its efficacy with a centralized federated learning framework. Furthermore, while there has been significant research focused on the classification of DFU and the application of federated learning to publicly available datasets by developers, to the best of our knowledge, this study represents the first attempt to apply a federated learning approach specifically to DFU classification using a dataset obtained from the DFUC2021 [15] challenge organized by the Medical Image Computing and Computer-Assisted Intervention (MICCAI) society [16]. The novelty of this work lies in the exploration and adaptation of the FedAVG algorithm [9] for a peer-to-peer (P2P) setting, which we term FedAVGP2P, and the introduction of its counterpart, FedSGDP2P. Furthermore, this study is among the first to investigate the practicality of applying federated learning algorithms within a P2P architecture to the meticulously labeled DFU dataset. We explore its application for DFU in a novel architecture DFU-SIAM [17], which has yielded very promising results for DFU classification compared to other research in this field. We study two algorithms, which are FedAVGP2P and FedSGDP2P. A comparative evaluation is conducted with an accuracy threshold of 90%. We focus on empirical performance metrics, such as model convergence and communication overhead in both IID and non-IID data distributions across local clients. Moreover, this research is pioneering in proposing and integrating novel heuristics to enhance the performance and efficiency of FedAVGP2P and FedSGDP2P, paving the way for experimenting with other heuristics for future federated P2P learning frameworks. The subsequent sections of this paper are organized as follows: Section 3 provides a comprehensive analysis of relevant previous works in the field. In Section 4, we present a detailed description of our proposed solution. This is followed by the presentation of our experimental results in Section 5. Finally, we conclude the paper with a summary of our findings and suggestions for future research, which are presented in Section 6.
## 3 Results
### Polytetrafluoroethylene
The increased loss coefficient for pellets produced at lower compaction pressures with the compaction simulator is driven by increased scattering from pores within the sample. The hydraulic press without a vacuum performed well at 139 MPa, with both refractive index and loss coefficient values either matching or slightly above literature values. However, the samples produced using a compaction pressure of 159 MPa have a significantly lower refractive index, suggesting either an increase in sample porosity or the inclusion of structural defects in the sample. These structural defects are also seen in the increased loss coefficient in Figure 2.  
A sample porosity for PTFE pellets produced using the compaction simulator and the hydraulic press at all compaction pressures is shown in Figure 3a. The ZPA was employed to calculate sample porosity from refractive index. An additional investigation into the sample porosity of PTFE pellets involved THz-TDS analysis of dry and paraffin oil soaked pellets. The paraffin oil will fill surface connected pores and increase the refractive index due to a decrease in sample porosity. This investigation suggested minimal open (surface connected) pores (Figure A3) [14]. As PTFE is opaque to visible light, microscope imaging can be deployed to identify pores close to the sample surface. Microscope imaging of PTFE samples compacted at >350 MPa showed minimal porosity (Figure A2). Furthermore, due to the refractive index values matching or exceeding that of solid PTFE, it is clear that these pellets have close to zero porosity, indicating that PTFE is a suitable material for fabricating compressed pellets with minimal porosity. PTFE pellets fabricated in the compaction simulator and hydraulic press without a vacuum do not undergo significant axial relaxation one week after compaction (Figure 3b). Pellets fabricated in the hydraulic press with a vacuum (HPV) exhibit more significant axial relaxation between zero and one week after compaction. Therefore, due to the axial relaxation of PTFE pellets being negligible for those produced with the compaction simulator and the hydraulic press without a vacuum, it is clear that THz-TDS analysis can be carried out one week after compaction allowing for repeated analysis. Figure 2: Refractive index (**a**) and loss coefficient (**b**) of PTFE pellets compacted at various pressures. Optical parameters were extracted at 2.45–2.55 THz. Three compaction methods were used: hydraulic press with a vacuum (HPV) and without a vacuum (HPNV) as well as a compaction simulator (CS). Literature values for both RI and loss coefficient are shown as a grey band. The literature is presented as a band reflecting the range of values quoted in the literature [8,28,29,30,31]. Values plotted are the mean, with the error bars depicting \(\pm\) one standard deviation (\(n\) = 10).