esa-sceva/satcom-chunk-collection huggingface dataset:
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 5 Experimental Results and Analyses
### Process and Quality Analyses Using Probability Density Function
The pdf between 100 A and 210 A was about 0.1%, which denoted that the signal was stable, and the possibility of current between peak stage and base stage was low. All of these denoted that the welding process of experiment 13 was stable. The corresponding welding seam of experiment 13 is shown in Figure 10, and it can be observed that the quality of the welding seam was very high with smooth surface, and the compact fish-scale grain distributed on the seam. Moreover, no splash appeared during the process. According to the experimental results and corresponding analyses, the proposed multi-frequency waveform control method can obtain satisfactory welding quality under proper parameter combinations. However, the effect of each parameter on the welding quality cannot be clearly shown, and can be further explored by means of other experiments and analyses. Figure 10: Welding seam of experiment 13. Figure 9: Pdf figure of experiment 13.  
in Figure 11 denoted that the value of \(I_{pT}\) should not be too large, and the medium value can obtain better result. According to the above experiments and corresponding analyses, in the orthogonal experiments, higher scores denoted that more satisfactory welding quality could be obtained. Hence, the best operational parameter selection should be a combination which can increase the scores of welding quality in each level. As shown in Table 3 and Figure 11, the average scores of \(N_{fTH}\), \(DC_{TH}\), \(T_{BL}\) and \(I_{pT}\) achieved the highest values at levels 3, 3, 4 and 2, respectively. Under this circumstance, the optimal parameter combination can be obtained according to the following parameter values:  
1. The number of high frequency modulation pulses \(N_{fTH}\) at level 3:3;
2. Duty cycle \(DC_{TH}\) at level 3:0.29;
3. Duration of leading wire current in base value \(T_{bl}\) at level 4.21 ms; and
4. Peak current of trailing wire \(I_{pT}\) at level 2:240 A. It is anticipated that the chosen values are the optimal combination for the experiments and offer the welding seam with maximum quality.
## 7 Conclusions and Future Work  
When graphical representations of modeling formalisms are in use for the description of controller behaviors, relying on events to encapsulate the detection of a specific evolution  
Figure 11: Comparing (**a**) the unfolded model using the conditions on the signals with (**b**) the model using events.  
Figure 10: Packing controller.  
in the controller's input signals can contribute to achieving higher levels of compactness of the model, improving its expressiveness and having a positive impact on its readability.  
The approach proposed in this paper, supported by (hidden) sub-models generating events associated with the specific evolution of input signals, is in line with this strategy and is entirely supported by a web-based tools framework (IOPT-Tools), which is freely available and covers all the steps of controller development, including the automatic generation of the execution code. This approach results in a more compact controller model that enhances its comprehensibility and readability. To our knowledge, no other Petri net-based tool frameworks exist that utilize events to encapsulate signal evolution behavior while maintaining the ability to automatically generate executable code for a wide range of platforms, including software implementations (C, with Python planned), hardware implementations (VHDL), and PLC platforms, benefiting from the translation of the underlying Petri net model into ladder diagrams.  
It is important to note that the proposed approach, even presented within a Petri net-based development framework, can also be used in cooperation with other discrete-event state-based modeling formalisms, namely, concurrent state machines, statecharts, and other classes of non-autonomous Petri nets. As a matter of fact, all the presented sub-models can be seen as state diagrams represented using a Petri net notation.  
In future work, the definition of additional composed events based on the analysis of several signals (such as the ones suggested in this paper) is worth analyzing with a possible significant impact on the compactness of the behavioral models related to embedded controllers.  
Conceptualization, L.G.; methodology, L.G.; software, D.N.; validation, L.G., D.N., A.C., J.-P.B. and R.C.-R.; writing--original draft preparation, L.G., A.C. and J.-P.B.; writing--review and editing, D.N., A.C., J.-P.B. and R.C.-R. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the Portuguese Agency \"Fundacao para a Ciencia e a Tecnologia\" (FCT) program, in the framework of project Center of Technology and Systems (CTS) UIDB/00066/2020 / UIDP/00066/2020.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Mineral Content Analysis of Prickly Pear Juice Samples  
As in the case of soil samples, the respective mineral content of prickly pear juice samples varied significantly (\(p<0.05\)) according to geographical origin. The dominant minerals (mg/kg) were K, P, Ca, Mg, and Na, followed by B, Mn Zn, Sn, and Si. Total mineral content of prickly pear juice samples (average values, mg/kg) was obtained from the sum of each individual mineral and followed the order: Lakonia (2827.98 \(\pm\) 310.65 mg/kg) > Eastern Messinia (2602.66 \(\pm\) 203.51 mg/kg) > Western Messinia (2206.07 \(\pm\) 214.77 mg/kg) (Table 1).  
Trace minerals such as Be, Co, Mo, Sb, and Ti were not identified, whereas Cr was identified in two samples (no. 9 and 11 from Lakonia) at 0.03 mg/kg. The same holds for Tl, which was identified in two samples (no. 2) from Western Messinia and Lakonia at 0.04 and 0.20 mg/kg, respectively. Even though considerable amounts of Cr and Ti were identified in the soil samples analyzed (Supplementary Table S9), these elements were identified in minor amounts or were completely absent in the fruit juice samples analyzed. This is probably owed to a specific mineral accumulation mechanism that exists in prickly pear fruit of wild cultivars. On the other hand, despite the fact that Tl was not identified in the soil samples analyzed, this element was identified in prickly pear juice samples (no. 2) from Western Messinia and Lakonia at 0.04 and 0.20 mg/kg, respectively. This \"paradox\" may be justified by the fact that Tl exists in two oxidation states (+3) and (+1), as ionic salts. The +1 state is more prominent in Tl than the elements above it. Thallium somehow recalls the chemistry of alkali metals. In that sense, thallium (1) ions are found geologically in potassium-based ores, transmitted then to specific fruits, and are probably released through the preparation of prickly pear juice.  
Finally, [PERSON] recorded non-significant differences among the different geographical origins, ranging from 0.03-0.05 mg/kg (average value). Full data regarding the mineral content of minerals determined are given in the supplementary material (Supplementary Table S7). [PERSON] et al. (2019) reported higher contents of Ca, Mg, P, and Na in prickly pear juice prepared from Moroccan prickly pear cultivars, whereas those of K, Zn, and Cu were lower compared to results of the present study. Higher contents of Ca, K, P, Fe, and Mg were also reported by [PERSON] et al. (2019) involving Algerian prickly pear juice. However, the respective Na content was much lower compared to present results. The impact of geographical origin is of great importance to realize the differences between previous, even though limited, studies in the literature and results of the present study. Finally, there was also a perfect [PERSON]'s correlation (\(r=1\)) (\(p<0.05\)) between total mineral content of soil samples with those of prickly pear juice samples.
# Automatic Modulation Classification Using Hybrid Data Augmentation and Lightweight Neural Network
## 2 Signal Model and Problem Formulation  
### Signal Model  
The signal data in this paper take into account the effects of the phase shift and additive Gaussian noise; the signal model can be expressed as  
\[x(n)=he^{i\theta(n)}s(n)+w(n),n\in 0,1,2,\cdots,N-1, \tag{1}\]  
where \(s(n)\) denotes the transmitted baseband signal sequence, \(h\) denotes the channel gain, \(\theta(n)\) denotes the time-varying phase offset, and \(w(n)\) represents the additive white Gaussian noise. \(N\) is the number of sampling points of the signal. The radio signal \(x(n)\) received by the receiver consists of in-phase (I) and quadrature (Q) components, which can be seen as the real and imaginary parts of \(x(n)\), respectively. \(x(n)\) can be expressed as  
\[x(n)=\left[\begin{array}{c}I\\ Q\end{array}\right]=\left[\begin{array}{c}real\left(x(n)\right)\\ imag(x(n))\end{array}\right]. \tag{2}\]
# Study of the Applicability of Thermochemical Processes for Solid Recovered Fuel
## 4 Conclusions  
This paper analyses the possible application of thermochemical processes (combustion and gasification) for valorisation of the SRF obtained from screening waste from a WWTP. After studying these alternatives, this paper makes the following conclusions:  
* Valorisation of SRF derived from screening wastes by means of gasification would be feasible. Experimental tests at the laboratory scale have demonstrated the technical feasibility of the process. The gasification gas obtained reached up to 4.4 MJ/m\({}^{3}\)STFP, but its high tar content makes it suitable only for direct use in boilers. Air/steam gasification, under the conditions used in this study, resulted in a gas with lower tar content than air gasification, but its LHV would not allow burning it without the aid of an auxiliary fuel.
* From an energy point of view, combustion, with energy benefits of up to 1.6 MJ per kg of wet raw SRF (with 77.3% of moisture), proved to be a more efficient process than gasification, which achieved a maximum benefit of 1.4 MJ per kg of wet raw SRF when gasifying the totally dried SRF with only air as a gasifying agent.  
**Author Contributions:** Conceptualisation, M.Z. and G.G.; methodology, M.Z. and J.J.d.l.T.-B.; software, M.Z.; validation, J.M.-P. and I.F.; formal analysis, J.J.d.l.T.-B.; investigation, J.J.d.l.T.-B.; resources, N.G.-L.; data curation, J.M.-P.; writing--original draft preparation, J.J.d.l.T.-B. and J.M.-P.; writing--review and editing, J.J.d.l.T.-B. and J.M.-P.; visualisation, M.Z. and G.G.; supervision, G.G. and J.M.-P.; project administration, M.Z. and J.C.T.-R; funding acquisition, J.C.T.-R. and I.F. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by EMASAGRA S.A. (research project No. 4325 Valorizacion energetica de residuos de desbaste como combustible solido recuperado para lograr el residuo cero en EDAR).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Data is contained within the article.  
**Acknowledgments:** The authors express gratitude for providing frame support for this work to the Project PID2022-137016 OB-I00 financed by Ministerio de Ciencia, Innovacion y Universidades and Agencia Estatal de Investigacion (MICIU/AEI)/10.13039/501100011033, Spain and by FEDER, UE. The Aragon Government has also given frame support (Research Group Ref. T22_23R). [PERSON] acknowledges the post-doctoral fellowship (RYC2020-030593-I) financed by MICIU/AEI/10.13039/501100011033 and by EI FSE invierte en tu futuro.  
**Conflicts of Interest:** The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. [PERSON] is employee of Emasagra S.A., who provided funding and teachmist support for the work.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Co-Simulation Time Coordination and Unified Data Logging  
A key innovation of the developed multimodal simulation system is its ability to maintain real-time coordination across multiple independent components--PTV Vissim for background traffic, CARLA for high-fidelity 3D simulation, human-operated driving, bicycle, and pedestrian simulators, and physiological sensors--ensuring consistent simulation timing and unified data output.  
To achieve this, all simulation subsystems operate on a shared fixed timestep of 0.05 s. CARLA's simulation loop is modified via the _run_synchronization.py_ script to match Vissim's update frequency, creating a lockstep execution pattern where simulation steps only proceed after all modules complete their operations. This design prevents drift between platforms and ensures accurate, time-aligned interactions between human participants and dynamic traffic environments.  
Each simulation step follows a structured pipeline:  
1. Traffic Update: PTV Vissim advances background traffic conditions, including vehicle movements and signal phase changes.
2. State Synchronization: The current vehicle and signal states are transmitted to CARLA via the _DrivingSimulator.dll_ interface using Python bindings (_ctypes_).
3. Human-Agent Update: CARLA uses these inputs to update the virtual environment and synchronize agent locations and animations with the driving, bicycle, and pedestrian simulators.
4. Sensor Polling and Input: Control inputs from the hardware simulators (e.g., steering, speed, walking) are captured and transmitted back to CARLA.
5. Physiological Data Acquisition: Simultaneously, physiological sensors stream data into the system through Lab Streaming Layer (LSL), maintaining precise alignment using shared global timestamps.  
The coordination of these steps ensures that all agents--both human and simulated--respond in real-time to evolving traffic scenarios. The temporal synchronization guarantees that physiological signals such as heart rate, electrodermal activity, and EEG data can be directly mapped to behavioral events such as braking, lane changes, or pedestrian crossing decisions.  
All behavioral and physiological data streams are logged into a unified dataset. LabRecorder aggregates the LSL-based streams into time-synchronized files, while behavioral data from CARLA and Vissim are exported with corresponding timestamps.  
This design enables post-processing tools to analyze multimodal responses across agents and modalities with high temporal precision.  
The connections and data flow among all simulation components are illustrated in Figure 5. This tightly coordinated framework not only supports high-fidelity analysis of human factors in traffic simulation but also provides a scalable foundation for integrating new modalities, sensor types, or simulation platforms. The same architecture can be adapted for use with SUMO, enabling researchers to deploy a fully open-source multimodal co-simulation system while retaining physiological data synchronization capabilities.
# Enhancing Propaganda Detection in Arabic News Context Through Multi-Task Learning
## 5 Results and Discussion
### Experimental Results on the Proposed Task-Weighting  
As Section 3.6.3 outlines, several task-weighting schemes were proposed. The primary objective was to analyze their impact on the proposed MTL models' performance. The evaluation results, presented in Table 9, demonstrated that MTL-UW offers a distinct advantage over all other task-weighting methods, achieving an accuracy of 79% and a Macro-F1 score of 0.776. This Macro-F1 score was 0.13% higher than MTL-HW, which achieved a Macro-F1 score of 0.775, 0.52% higher than MTL-DDW, which achieved a Macro-F1 score of 0.772. In contrast, the three MTL-PGRW variants employing normal, Bernoulli, and uniform sampling achieved Macro-F1 scores of 0.767, 0.765, and 0.764, respectively. These results corresponded to relative decreases of 1.17%, 1.44%, and 1.57 percentage points, respectively, compared with the MTL-UW scheme. Furthermore, the top three schemes (MTL-UW, MTL-HW, and MTL-DDW) achieved the highest accuracy of 79%, outperforming the MTL-PGRW methods (77-78%) by 1-2%.  
Overall, the experimental results consistently highlighted the positive impact of task-weighting on MTL performance. Additionally, as Table 9 shows, certain weighting schemes are more effective than others. To test whether the most promising weighting rule based on Macro-F1, namely UW, could further benefit related task MTL settings, experiments were conducted using MTL models with the best proposed task-weighting. The MTL-Sent-UW and MTL-Emo-UW models, as detailed in Table 9, both achieved an accuracy score of 79% but differed in overall Macro-F1. Specifically, MTL-Sent-UW achieved a Macro-F1 of 0.778, whereas MTL-Emo-UW remained at 0.777. In general, the MTL-UW framework significantly enhanced the efficacy of propaganda detection through the integration of auxiliary tasks. Notably, the incorporation of sentiment analysis yielded a marginally higher Macro-F1 score (0.778) than emotion detection (0.777), although this difference was statistically small. This subtle variation suggests that sentiment features may provide a slight advantage in optimizing class-level performance metrics, potentially owing to their broader contextual relevance within textual data. Nonetheless, both auxiliary tasks contributed comparably to the primary objective of propaganda detection, highlighting their utility in improving model generalizability.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 3 Experimentation and Results
### Example Analysis
#### 3.3.3 Comparison of Experimental Segmentation Results
The traditional U-Net and CloudSegNet can capture the main parts of the clouds, but they lack the ability to segment cloud details. The improved U-Net performed best in segmentation tasks, accurately capturing the edges and shapes of the clouds while reducing false positives and negatives, successfully achieving more precise segmentation results.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 4 Hybrid-Precision Circuit Simulation Validation and Stress Analysis  
### Development of Behavioral Simulation Models for Integrated Devices Using Verilog  
To determine the electrical stress on key components within the communication module, a circuit simulation model needs to be constructed. Compared to board-level circuits composed of discrete components, digital integrated devices have more complex structures and functionalities, making it difficult to establish precise SPICE models directly.  
Given the characteristics of mixed-signal circuits, this article adopts a hybrid-precision modeling method, achieving high-precision modeling for critical paths while applying approximate calculations for non-critical areas. The board-level circuit composed of discrete components is modeled using SPICE, whereas the digital control logic is simulated using a Verilog real-number model. Interface components are employed to synchronize simulation time steps. Hybrid-precision simulation combines the high accuracy of SPICE with the computational efficiency of Verilog, making it suitable for the precise analysis of critical circuit paths while reducing computational costs. This method is ideal for applications involving digital-analog interactions that require a balance between accuracy and efficiency.  
The behavioral model of digital integrated devices primarily focuses on their logical behavior, abstracting input and output signals as logical '0' or '1' and describing their relationships. Verilog and VHDL are hardware description languages used to model digitally integrated devices, and in this article, Verilog is chosen to develop the behavioral simulation model for digitally integrated devices.  
Based on the communication module's circuit structure, the digital integrated devices include DS26C31, DS26C32, SRAM, and FPGA. The following sections establish behavioral simulation models for each device using Verilog and design validation circuits to verify their functionality.
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 4 System Model Description of FBMC OFDM-OQAM and GFDM-OQAM
### Gfdm-OQAM Transceiver
#### 4.2.2 Channel Estimation for GFDM-OQAM  
To avoid harping on, the linear CE techniques for the described filter bank GFDM-OQAM based transceiver is only presented in this section. The adaptive schemes can be derived in similar procedures as mentioned in Section 4.1.3.  
Least Square  
The GFDM-OQAM LS channel estimate is given as:  
\[\begin{split}\hat{\textbf{h}}_{LS}[\hat{n},\hat{k}]=\bigg{[} \hat{h}_{LS}[\hat{n},0],\hat{h}_{LS}[\hat{n},1],\ldots,\hat{h}_{LS}[\hat{n},(L_ {ch}-1)]\bigg{]}^{T}\\ =\bigg{[}\frac{L_{ch}}{M}\bigg{(}\sum\limits_{k=0}^{\frac{M}{L_{ ch}}-1}\frac{\vec{x}_{R}[\hat{n},\hat{k}]}{\vec{x}[\hat{n},\hat{k}]},\sum \limits_{t_{ch}=\frac{M}{L_{ch}}}^{\frac{2M}{L_{ch}}-1}\frac{\vec{x}_{R}[\hat{ n},\hat{k}]}{\vec{x}[\hat{n},\hat{k}]},\ldots,\sum\limits_{\frac{k\sim M(L_{ch}-1)}{L_{ ch}}}^{M-1}\frac{\vec{x}_{R}[\hat{n},\hat{k}]}{\vec{x}[\hat{n},\hat{k}]}\bigg{)} \bigg{]}^{T}.\end{split} \tag{55}\]  
LMMSE  
The simplified time-domain LMMSE channel estimate \(\hat{\textbf{h}}_{LMMSE}[\hat{n},\hat{k}]\) for GFDM-OQAM is given as [73; 86; 137]:  
\[\hat{\textbf{h}}_{LMMSE}[\hat{n},\hat{k}]=\frac{\textbf{R}_{HH}[\hat{k}]\vec{ x}_{R}[\hat{n},\hat{k}]}{\textbf{X}[\hat{n},\hat{k}]\left[\textbf{R}_{HH}[\hat{k}]+( \textbf{X}^{H}[\hat{n},\hat{k}]\textbf{X}[\hat{n},\hat{k}])^{-1}\sigma_{\eta}^ {2}\textbf{I}[\hat{n},\hat{k}]\right]}, \tag{56}\]where \(\textbf{R}_{HH}[\hat{k}]\) is the autocorrelation matrix of the channel impulse response given by Equation (11), \(\sigma_{\eta}^{2}\) denotes the noise variance \(E|\eta[n,k]|^{2}\) defined in Equation (21), \(\textbf{I}[n,k]\) is an identity matrix and \(E\) is the expectation notation.
## 4 Proposed Metrics
### Results and Discussion of the Proposed Metrics
#### 4.2.1 MAC Layer Metrics
However, the MPHprotocol does not exhibit this latter peak, because nodes periodically reconfirm their neighbors, and the information in their neighbor tables is reliable, as it is constantly updated (due to the fact that MPH is a proactive protocol). Besides, MPH allows any child node to have several parents, so if an upward route is lost, there may be other backup routes that can be used, so preventing packet retransmissions. ZTR has the largest number of retransmissions and CSMA/CA retries of all of the protocols, because when a node loses connection with their neighbors, all of the child nodes remain without communication to send packets to the coordinator. Therefore, the nodes attempt to retransmit packets; this may also cause the channel to be busy for more time, and the CSMA/CA retries to become higher. ZTR shows no peak, because there is no route expiration. Approximately at 80 s, AODV and DSR have a new retransmissions peak value of 2.5 and 2.4 respectively. Moreover, there are also peak values of 3.1 for AODV and three for DSR regarding CSMA retries. Therefore, we can establish that the MPH protocol shows a reduction on retransmissions of 19.3%, 17.4% and 17.8% compared to AODV, DSR and ZTR respectively. Furthermore, MPH presents a reduction on CSMA/CA retries of 30.3%, 27.3% and 28.9% with respect to AODV, DSR and ZTR correspondingly.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Swift-Heavy Ion Irradiation
#### 2.1.1 Uranium-238 Ion Irradiation  
Exfoliated MoS\({}_{2}\) mono-layers were irradiated by 1.14 GeV uranium (\({}^{238}\)U) ions [83]. It was found that its electrical properties were significantly changed and the MoS\({}_{2}\)-based transistor was destroyed at a fluence of \(4\times 10^{11}\) ions/cm\({}^{2}\) (4000 ions/\(\upmu\)m\({}^{2}\)).