esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Control Optimization for Large-Load Plant-Protection Quadrotor
## 3 Results
### Training Details  
The rotor dynamic response constant of this large-load quadrotor is \(T_{m}=0.035\) s, and we calculate that this constant for a common small quadrotor is \(T_{m}=0.0157\) s. During the training process, \(T_{m}\) is generated randomly between 0.015 s and 0.045 s for agents.  
Using the data in Table 1, we can calculate the maximum and minimum MOIs in three axes using Equation (7), and the results are listed in Table 2:  
As a result, the MOIs in the x and y axes range between 0.8 and 1.7 kg\(\cdot\)m\({}^{2}\), and the MOI in the z axes ranges between 1.8 and 2.1 kg\(\cdot\)m\({}^{2}\) during the training process.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Mass \(m\) & 8 kg \\ Moment of inertia \(J_{xx}\) & 1.066 kg\(\cdot\)m\({}^{2}\) \\ Moment of inertia \(J_{yy}\) & 0.961 kg\(\cdot\)m\({}^{2}\) \\ Moment of inertia \(J_{zz}\) & 1.843 kg\(\cdot\)m\({}^{2}\) \\ Arm length \(d\) & 0.65 m \\ Lift coefficient \(C_{T}\) & \(5.7894\times 10^{-4}\) N/(rad/s)\({}^{2}\) \\ Torque coefficient \(C_{M}\) & \(2.283\times 10^{-5}\) N\(\cdot\)m/(rad/s)\({}^{2}\) \\ Rotor control constant \(C_{R}\) & 316 rad/s \\ Rotor dynamic response constant \(T_{m}\) & 0.035 s \\ Volume of the tank \(V_{tank,max}\) & 10 L \\ Maximum mass of water \(M_{wa,max}\) & 10 kg \\ Density of water \(\rho_{ua}\) & \(1.0\times 10^{3}\) kg/m\({}^{3}\) \\ Distance between the quadrotor and water \(d_{ogg}\) & 0.3 m \\ Length and width of the water cube \(l_{uu}\) & 0.28 m \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical parameters of the quadrotor with an empty tank.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Maximum MOI in the \(x\)-axis & 1.540 kg\(\cdot\)m\({}^{2}\) \\ Minimum MOI in the \(x\)-axis & 1.066 kg\(\cdot\)m\({}^{2}\) \\ Maximum MOI in the \(y\)-axis & 1.435 kg\(\cdot\)m\({}^{2}\) \\ Minimum MOI in the \(y\)-axis & 0.961 kg\(\cdot\)m\({}^{2}\) \\ Maximum MOI in the \(z\)-axis & 1.973 kg\(\cdot\)m\({}^{2}\) \\ Minimum MOI in the \(z\)-axis & 1.843 kg\(\cdot\)m\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Range of MOIs in three axes.  
In this research, sensor noise is added during the training process to improve the robustness of the controller. The attitude and heading reference system (AHRS) used in our flight controller is a mature product, so the attitude angle data and angular velocity data can be read directly from the module. The AHRS module claims that the errors of attitude angle in the roll and pitch axes are between \(\pm 0.1^{\circ}\) and the errors of attitude angle in the yaw axis are between \(\pm 1^{\circ}\). As a result, we add noise conforming to uniform distributions between \(\pm 1^{\circ}\) to attitude angle data in the three axes during the training process.  
Table 3 presents the training hyperparameters. The total training process takes approximately 3 h.
## 3 Results and Discussion
#### Sensitivity Analysis  
In order to figure out the effect of each variable in the output values, correlation sensitivity analysis (CSA) and regression sensitivity analysis (RSA) were conducted for different purposes. It should be noted that both types of sensitivity analyses were performed using the Risk Solver Platform. In the CSA, the ranges of correlations were between \(-1\) and \(+1\). In CSA, MC simulation uses rank order correlation, rather than linear correlation, to calculate the relationship between two data sets by comparing the rank of each value in a data set. Rank order correlation has an acceptable performance if uncertain variables are involved in the simulation, or the distribution function of the variables are not specified precisely [96]. As shown in Table 6, the level of effectiveness of each variable in the final output of the simulation can be obtained from the results of CSA.  
Figure 9: Scatter results of each input against PPV obtained by Monte Carlo simulation.  
According to Table 6, D has the most impact on the results of PPV in an MC simulation, followed by ST, MC, PF and BS. It is worth mentioning that D and PF had negative impacts on the amount of PPV, while ST, MC and BS had positive impacts.  
In RSA, multiple regression analyses were conducted by varying one of the variable ranges (in this paper from \(-50\%\) to \(+50\%\)), while keeping the other variables constant [97]. It is noteworthy that these same processes were conducted for all variables to determine the influence of each variable on the output. The results of RSA indicated that the PPV result is highly sensitive to changes in D followed by ST and MC (see Figure 10). On the other hand, changes in the amount of BS have the lowest influence on the results. Results of the sensitivity analysis (especially regarding D, which is the most effective parameter on PPV) are in good agreement with studies carried out by [7; 8; 60]. For example, [PERSON] et al. [60] proposed that the distance from the blast face is the most influential parameter on PPV among other used parameters (i.e., hole depth, stemming, maximum charge per delay). In addition, as discussed earlier, the distance from the blast face is one of the most effective parameters in empirical equations [15; 17].
## 4 Solution Methodology  
This paper focused on automated techniques that maximize the coverage and quality of service (RSRP, SINR, and per user downlink throughput) of modern 5G capacity layers using optimum azimuth steering. The high-level concept and context of our solution when implemented in a mobile operators' network is shown in high level in Figure 2 and can be briefly described as follows.  
The cell network was analyzed with respect to its coverage and capacity needs and with respect to its traffic intensity in different areas. A set of cells was identified as the 'candidate' to be reconfigured in terms of their azimuth in order to cover these coverage and capacity needs. Measurements from these cells are collected in the form of radio access network (RAN) KPIs including RSRP, SINR, and throughput in software systems that are traditionally responsible for the operation and management of RANs, namely, RAN operations support systems (OSS). Location information is also collected either through the OSS or through third party geo-location systems. Algorithms responsible for parameter configuration and optimization then use these data in order to derive parameter settings (e.g., azimuth angles). These algorithms are typically hosted in an intelligent (data-driven) optimization server, as depicted in Figure 2, which can be either a sub-part of a RAN OSS or a separate software application. The derived azimuth settings are then applied to the selected cell(s) and the newly produced KPIs are expected to increase. The above should be a continuous and (as much as possible) automated process to ensure that the network always operates with the optimal parameters in all different contexts (i.e., time and space variations).  
In this paper, we opted to evaluate three data-driven techniques as part of the intelligent optimization server in Figure 2, namely, two from the literature and one proposed scheme. The selected techniques include a combination of unsupervised learning and statistical analysis, which have been shown to have a robust performance in diverse geographical, temporal, and operational contexts. Independently of its algorithmic specificities, each technique was evaluated by following a set of general steps as follows: (a) an initial measurement phase of the network in an initial configuration; (b) processing of the measured dataset to derive insights for the underlying cellular network; (c) conversion of these insights into reconfiguration parameters; (d) application of the reconfiguration on the network; and (e) evaluation of the impact of the actions with respect to the initial scenario. These are described in detail in the next sub-sections, followed by an algorithm complexity analysis section.
## 4 Results
### Experimental Results and Analysis
#### 4.3.2 License Plate Corner Detection Performance  
Table 4 presents the ablation study results to demonstrate the effectiveness of the parallel ensemble processing approach through multi-aspect ratio transformations and the CEM module proposed in this paper for accurate corner detection in license plate images with severe distortion.  
The effectiveness of the parallel ensemble processing technique can be quantitatively confirmed through ablation studies. The process of each ablation study is shown in Figures 7 and 8. When comparing the baseline CSPNext model (AP: 0.608) with the CSPNext + Ensemble model incorporating ensemble techniques (AP: 0.692), ensemble processing alone achieved approximately a 13.8% performance improvement. This demonstrates that parallel processing through various aspect ratio transformations significantly enhances robustness against geometric deformations of license plates. The ablation study results confirm the critical importance of the DCN (Deformable Convolution Network). The CEM module with a DCN applied (CSPNext + CEM) achieved an AP of 0.685, while the CEM module without a DCN (CSPNext + CEM(w/o DCN)) showed an AP of 0.652, representing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **AP** & **AP.5** & **AP.75** & **AR** & **Latency (ms)** \\ \hline CSPNext + CEM + Ensemble (Proposed) & 0.721 & 0.789 & 0.737 & 0.778 & 32.6 \\ CSPNext + CEM & 0.685 & 0.750 & 0.700 & 0.739 & 21.3 \\ (Proposed) & & & & & \\ CSPNext + Ensemble & 0.692 & 0.759 & 0.708 & 0.745 & 29.8 \\ CSPNext + CEM(w/o DCN) & 0.652 & 0.735 & 0.686 & 0.729 & 19.8 \\ CSPNext & 0.608 & 0.769 & 0.680 & 0.812 & 17.0 \\ RTM Pose & 0.615 & 0.731 & 0.636 & 0.729 & 21.0 \\ Uniformer & 0.608 & 0.713 & 0.710 & 0.658 & 18.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: License plate corner detection performance comparison.  
Figure 6: The training loss curves: (**a**) YOLOv7-based Vehicle Detector; (**b**) YOLOv8-based Vehicle Detector; (**c**) YOLOv7-based License Plate Detector; (**d**) YOLOv8-based License Plate Detector.  
approximately a 4.8% performance degradation. This indicates that DCNs can effectively handle geometric deformations and distortions in license plates. In terms of processing time, the final proposed model of CSPNext + CEM + Ensemble exhibits a latency of 32.6 ms, representing approximately a 91.8% increase compared to the baseline CSPNext model's 17.0 ms. This corresponds to a processing speed of approximately 30.7 FPS, maintaining performance suitable for real-time processing while achieving significant accuracy improvement (approximately an 18.6% improvement from AP 0.608 to 0.721).  
Figure 8: Corner detection model COCO/AP validation curve.  
Figure 7: Corner detection model training loss curve.
# A Thorough Evaluation of GaN HEMT Degradation under Realistic Power Amplifier Operation
## 5 Experimental Results
### Class-E Stress at 100 \({}^{\circ}\)C  
The same experiment at 100 \({}^{\circ}\)C was carried out on the second device sample, already stressed under class-E operation at 30 \({}^{\circ}\)C. The results are reported in Figures 14 and 15. Looking at the waveforms reported in Figure 14, it is immediately evident that the status of the device changed less when compared to the previous measurement. The maximum drain voltage along the load line decreases from 84.7 V to 84.2 V (\(-0.6\%\)). The output power goes from 3.45 W/mm before the stress to 3.36 W/mm after the stress (\(-0.3\%\)), and, consequently, the drain efficiency reduces from 68.5% to 67.9%, as shown in Figure 14i,j, respectively.  
No significant change was observed on the gate waveforms, where the gate current remained negligible throughout the entire experiment (Figure 14d-f). Looking at the small average gate current over the stress time (Figure 14g), we note very noisy data which became even noisier towards the end of the experiment, although on average they did not highlight any significant variation. The drain current instead has a behavior similar to the one observed at a lower temperature, with a sudden change around 0.1 h, a stable phase up to 5 h and then a more gradual decrease till the end of the experiment.  
Figure 15 shows the results from the 100 \({}^{\circ}\)C DC control measurements, which have a similar trend with respect to the ones achieved at 30 \({}^{\circ}\)C, although the entity of the variations of the drain current (from 722 mA/mm to 705 mA/mm, \(-2.4\%\)) and of the device threshold are more limited.  
Figure 13: DC control measurements for the 2 MHz tuned-load class-AB condition at 100 \({}^{\circ}\)C: (**a**) \(I_{D}\)-\(V_{G}\) trans-characteristic measured before (black dashed line) and after (red continuous line) the stress, (**b**) saturated drain current at \(V_{G}\) = 2 V versus stress time, and (**c**) gate voltage for \(I_{D}\) = 100 mA/mm versus stress time.  
Figure 14: Stress measurement results for the 2 MHz class-E condition at 100 \({}^{\circ}\)C: (**a**) load lines vs. initial DC I/V characteristics (grey lines), (**b**) drain voltage waveforms, (**c**) drain current waveforms, (**d**) input loci, (**e**) gate voltage waveforms, (**f**) gate current waveforms, (**g**) average gate current, (**h**) average drain current, (**i**) output power, and (**j**) drain efficiency. From (**a–f**), the black dashed lines represent the initial state of the DUT, whereas the red continuous lines represent the final state of the DUT.  
Figure 15: DC control measurements for the 2 MHz class-E condition at 100 \({}^{\circ}\)C: (**a**) \(I_{D}\)-\(V_{G}\) trans-characteristic measured before (black dashed line) and after (red continuous line) the stress, (**b**) saturated drain current at \(V_{G}\) = 2 V versus stress time, and (**c**) gate voltage for \(I_{D}\) = 100 mA/mm versus stress time.
# Risk-Based Virtual Power Plant Implementation Strategy for Smart Energy Communities
## 5 Discussion
### Brief Summary  
Through these results, lessons can be suggested for VPP implementation:  
* Temporal correlation is an important factor in VPP implementation. There should be a high correlation coefficient between demand and resources and low correlation among resources;
* An efficient VPP implementation strategy can be suggested using risk factors based on correlation;* The demand of commercial SECs has a high correlation coefficient with PV. Accordingly, it is possible to implement VPPs in this form, using PV as the main resource, supplemented by wind;
* In the case of residential SECs, there is a negative correlation coefficient with PV. Therefore, it is effective to configure the VPP using wind resources. This paper presents a risk-based VPP implementation strategy. By extending this, the following future research directions can be suggested:
* This paper focused on the VPP implementation problem. A co-optimization problem can be formulated considering both the risk of the VPP implementation and the energy balance during the VPP's operation;
* Flexible resources such as electric vehicles and energy storage can be considered as resources. Operations including energy storage charging and discharging should be considered flexible resources. Therefore, the problem with flexible resources can be formulated as a co-optimization problem.
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 2 Harmonic Detection Principles and Methods
### Neural Network (NN) Based Method  
With the noticeable growth of research in artificial intelligence, the neural network theory has rapidly developed. The non-linear and adaptive signal processing system composed of many interconnected processing units constitutes the neural network [20]. In recent years, researchers have used various neural network techniques for harmonic detection and harmonic parameter estimation [21; 22; 23]. Neural network-based detection methods have their own advantages but also have their own insuperable limitations, so they are combined with other algorithms to compensate for these shortcomings [24]. For example, in [24], a new method for accurate harmonic detection is proposed by combining  
Figure 2: Adaptive detection method.  
the Fast Fourier Transform algorithm with the improved artificial neural network. Table 1 presents a comparative analysis of different harmonic detection methods.  
In summary, various harmonic detection methods address specific challenges in identifying harmonic content in electrical signals. The Fourier transform is highly effective for stationary signals but faces limitations with time-varying ones, which wavelet transform can better handle, offering detailed frequency information. Discrete Fourier Transform (DFT) is widely used for its convenience, though it requires improvements like interpolation to enhance accuracy. The reactive power filter methods are advantageous for real-time applications but depend heavily on circuit components, which complicates tuning. Finally, adaptive detection and neural network-based approaches demonstrate robustness under dynamic conditions but are often integrated with other algorithms to compensate for limitations. Hence, various harmonic detection techniques each have their own strengths and weaknesses. These methods enhance their effectiveness by combining the benefits of different theoretical approaches, which greatly improves the overall performance of harmonic detection. The choice of method will depend on the characteristics of the signal being analyzed and the specific information that is desired.
# Real-Time FPGA-Based Balance Control Method for a Humanoid Robot Pushed by External Forces
## 3 FPGA-Based Balance Control Method  
A real-time FPGA-based balance control method is proposed for small-sized humanoid robots so that it can quickly respond to keep the robot in balance when it is pushed by external forces. The descriptions of the input/output of the executing process from sensors to actuators and the four modules of the proposed balance control method implemented on the FPGA chip are described in Figure 4. The procedure of these four modules for one-time at the moment of impact can be described as follows: (1) External force detection module: a gyroscope and an accelerometer are used to detect the moment of impact and an inclination angle \(\phi\) of the robot, which represent the strength of the external force; they are obtained from the external force detection module based on the measured sensor information \((w_{Gyro},a_{Acc})\). (2) Push recovery balance control module: When a large enough external force is measured, a capture point \(x_{Sup}\) is calculated from the push recovery balance control module based on the received inclination angle \(\phi\). The capture point \(x_{Sup}\) is defined as a position on the ground at which the robot can step to regain its balance when the humanoid robot is under the external force. (3) Trajectory planning module: the stepping trajectories \(P=(P_{RA},P_{LA})\) are determined from the trajectory planning module based on the obtained capture point \(x_{Sup}\) to let the robot can stretch out its foot. (4) Inverse kinematics module: The joint angles \(\theta\) are determined form the inverse kinematics module based on the planned stepping trajectories \(P\) until the robot steps on the capture point \(x_{Sup}\). The details of these four modules are described as follows.  
Figure 4: Description of the input/output of the executing process from sensors to actuators and the four modules of the proposed balance control method implemented on the field-programmable gate array (FPGA) chip.  
Figure 3: Diagram of the legs of TKU-X and its coordinate system.
# The MINLP Approach to Topology, Shape and Discrete Sizing Optimization of Trusses
## 5 Numerical Example: Synthesis of a Ten-Bar Truss Cantilever
### The Superstructure and Topological Alternatives
Equation (18c) calculates that all bars attached to joint 6 (i.e., bars 3\(\rightarrow\)6, 4\(\rightarrow\)6 and 5\(\rightarrow\)6) will be rejected in the case when joint 6 is rejected, while bar 4\(\rightarrow\)5 will be selected by the Equation (18b). Finally, bar 4\(\rightarrow\)6 will always be selected if joint 6 is selected; see Equation (18d). In the panel 3-4-5-6, 4 topological alternatives therefore exist when joint 6 is selected (equivalently to panel 1-2-3-4), plus one supplementary alternative when joint 6 is rejected, i.e., together, five different topological alternatives. The defined superstructure of the truss cantilever thus involves all the defined alternative combinations between the first and the second panel, i.e., 4 \(\times\) 5 = 20 topological alternatives in total. The superstructure is schematically presented in Figure 6, where solid lines represent fixed structural elements(joints and bars), while dashed lines denote alternative ones. It should be noted that eight topological binary variables are subjected to the defined eight fixed structural elements (five joints and three elements), while the other eight binary variables are attributed to the eight alternative structural elements. During the optimization, the values of the binary variables, which are subjected to the fixed elements, are fixed to 1. Thus, the total number of binary variables active in the optimization process is \(176\,-\,8\,=\,168\). It should be noted that in the case of seven alternative bars, the total number of all possible topological arrangements can be calculated as \(2^{7}\,=\,128\). By including heuristics and by using simple logical inter-connection equations mentioned before, a considerable reduction of topological alternatives is made possible and, thus, also the combinatorial expanse of the problem. The independent sizing variables are the cross-sectional areas of bars. Each cross-section area is defined by a single sizing design variable \(d_{ij}\), and the substituted function Equation (9) in the formulation MINLP-TS is simply \(A_{i,j}^{\text{sf}}\,=\,d_{ij}\).