esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Unmanned Aerial Vehicle Swarm Communication Architectures and Routing Protocols
###### Abstract  
Over the past decades, Unmanned Air Vehicles (UAVs) have achieved outstanding performance in military, commercial and civilian applications. UAVs are increasingly appearing in the form of swarms or formations to meet higher mission requirements. Communication plays an important role in UAV swarm control and coordination. The communication architecture defines how information is exchanged between UAVs or between UAVs and the central control center. Routing protocols help provide reliable end-to-end data transmission. Therefore, it is particularly important to design UAV swarm communication architectures and routing protocols with high performance and stability. This review article details four communication architectures including the advantages and disadvantages. Applicable scenarios are also discussed. In addition, a systematic overview and feasibility research of routing protocols are presented in this paper. To spur further research, the open research issues of UAV swarm communication architectures and routing protocols are also investigated.  
Ad hoc Network; communication architecture; routing protocol; systematic review; UAV Swarm; Unmanned Aerial Vehicle +
Footnote †: journal: Applied
## 3 Results
### Corticospinal Excitability Outcomes  
Regarding CSE changes (Figure 2, Supplementary Table S1), the rmANCOVA results revealed no intervention effect (intervention \(\times\) MEP\({}_{\text{Pre\_S1}}\): F\({}_{1,24,13,66}\) = 1.64, \(p\) = 0.226, \(\eta_{\text{p}}\) = 0.13) and no intervention \(\times\) TOA effect (intervention \(\times\) TOA \(\times\) MEP\({}_{\text{Pre\_S1}}\):F\({}_{2,17,23,87}\) = 0.54, \(p\) = 0.0603, \(\eta_{\text{p}}\) = 0.05). The results, however, reveal a significant TOA effect (TOA \(\times\) MEP\({}_{\text{Pre\_S1}}\): F\({}_{1.63,\,17.95}\) = 4.33, \(p\) = 0.036, \(\eta_{\text{p}}\) = 0.28), with the MEP measured at Post\({}_{5}\) and Post\({}_{35}\) being higher than that measured at the pre-intervention (\(p\) = 0.032 and \(p\) = 0.05, respectively). Figure 2 shows the absolute values of the mean \(\Delta\)MEP areas adjusted to the pre-intervention values at the different TOA. The ShamPAS/MI intervention appears to induce less variable, gradual, and larger MEP area increases postintervention (significant at 35 min postintervention, mean change: +1.813 \(\times\) 10\({}^{-3}\) mV\(\cdot\)ms; Cl\({}_{95}\) not including the null value: 0.296-3.331 \(\times\) 10\({}^{-3}\) mV\(\cdot\)ms; \(p\) = 0.02, Supplementary Table S1).  
Figure 2: Mean MEP area changes (\(\Delta\)MEP) over time for the three interventions. \(\Delta\)MEP area values, in microvolt \(\times\) milliseconds (mV\(\cdot\)ms), adjusted to the prestimulation values and across different time points of assessment (TOA at 5, 15, 25, and 35 min postintervention) for the three interventions (PAS/MI (green squares), PAS (blue squares), and ShamPAS/MI (orange squares). * Only the ShamPAS/MI intervention induced a significant change in the MEP area at 35 min poststimulation compared to the prestimulation value (mean change: +1.813 \(\times\) 10\({}^{-3}\) mV\(\cdot\)ms; Cl\({}_{95}\) not including the null value: 0.296–3.331 \(\times\) 10\({}^{-3}\) mV\(\cdot\)ms; \(p\) = 0.02).
## 5 Discussion
### Ablation Studies
#### 5.5.2 Impact of the ResBiGRU Block  
We conducted another ablation experiment to further assess the convolutional block's ability to capture spatial features. We compared our full model with a modified version that lacked the residual BiGRU block. This modified version served as the baseline.  
Table 10 summarizes the results. The model with the residual BiGRU block achieved significantly better performance, demonstrating the effectiveness of this block in processing temporal information. The F1-score improvements were substantial across all datasets: Opportunity (1.66%), PAMAP2 (1.55%), and REALWORLD16 (2.07%). These findings highlight the importance of the residual BiGRU block in enhancing the model's ability to recognize activities from time-series data.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Performance**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Opportunity**} & \multicolumn{2}{c}{**PAMAP2**} & \multicolumn{2}{c}{**REALWORLD16**} \\ \cline{2-7}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Att-ResBiGRU without the ResBiGRU block & \(\begin{subarray}{c}89.13\%\\ (\pm 1.12\%)\end{subarray}\) & \(\begin{subarray}{c}85.00\%\\ (\pm 1.37\%)\end{subarray}\) & \(\begin{subarray}{c}94.04\%\\ (\pm 0.15\%)\end{subarray}\) & \(\begin{subarray}{c}94.89\%\\ (\pm 0.13\%)\end{subarray}\) & \(\begin{subarray}{c}94.03\%\\ (\pm 0.15\%)\end{subarray}\) & \(\begin{subarray}{c}94.16\%\\ (\pm 0.15\%)\end{subarray}\) \\ Att-ResBiGRU with the ResBiGRU block & \(\begin{subarray}{c}90.27\%\\ (\pm 0.23\%)\end{subarray}\) & \(\begin{subarray}{c}86.69\%\\ (\pm 0.30\%)\end{subarray}\) & \(\begin{subarray}{c}96.61\%\\ (\pm 0.26\%)\end{subarray}\) & \(\begin{subarray}{c}96.44\%\\ (\pm 0.25\%)\end{subarray}\) & \(\begin{subarray}{c}96.11\%\\ (\pm 0.17\%)\end{subarray}\) & \(\begin{subarray}{c}96.23\%\\ (\pm 0.19\%)\end{subarray}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Impact of the ResBiGRU block.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{6}{c}{**Performance**} \\ \cline{2-7}  & \multicolumn{2}{c}{**Opportunity**} & \multicolumn{2}{c}{**PAMAP2**} & \multicolumn{2}{c}{**REALWORLD16**} \\ \cline{2-7}  & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline Att-ResBiGRU without convolution blocks & \(\begin{subarray}{c}89.34\%\\ (\pm 0.45\%)\end{subarray}\) & \(\begin{subarray}{c}86.56\%\\ (\pm 0.61\%)\end{subarray}\) & \(\begin{subarray}{c}95.34\%\\ (\pm 0.16\%)\end{subarray}\) & \(\begin{subarray}{c
# Evolutionary Algorithms for Optimization Sequence of Cut in the Laser Cutting Path Problem
## 4 Overview of Application of ABRKGA to LCPP  
### Introduction  
Metaheuristics serve as practical tools for acquiring high-quality solutions to combinatorial optimization problems. Nevertheless, during the design phase, developers must carefully consider various aspects, including solution representation, objective function calculation, constraints handling, neighborhood structures, initial solution selection, and parameter configuration. These decisions present numerous alternatives, and selecting the most suitable options becomes essential in attaining reasonable solutions for a given optimization problem.  
The biased random-key genetic algorithm (BRKGA), put forward by [PERSON] and [PERSON] [35], presents an evolutionary algorithm that streamlines several choices mentioned above. It relies on the notion of random keys to depict a solution and employs a decoder function to transform the random-key solution into an optimized solution for the problem. Consequently, developers must implement the problem-specific decoder and adjust the method's parameters.  
Nonetheless, a crucial aspect of metaheuristics is the impact of parameters on the efficiency and effectiveness of exploring the solution space. These parameters necessitate configuration in every metaheuristic. However, the values are only sometimes optimal for specific problems or instances, and they may not be suitable considering the available computational time for execution. Consequently, conducting an efficient configuration of parameter values utilized in each specific application is essential.  
The execution of evolutionary algorithms can involve configuration parameters as constants at the beginning of the execution or allowing flexibility in adjusting them as the process unfolds. In [36], the authors present a classification of parameter configuration techniques for evolutionary algorithms. Parameter values can be predetermined before the execution and remain fixed throughout the search (offline), or the parameter values can be dynamically modified during the execution (online).  
In this section, we present a self-adapted params BRKGA, [8] (ABRKGA), aiming to balance exploration and exploitation considering the context of LCPP representation. The parameter values are modified according to deterministic rules (e.g., varying over the number of iterations) or adapted based on information obtained during the search (e.g., the quality of the solution found). Parameters can also be included in the solution representation and evolve during the search process (self-adaptive control). We know this approach does not necessarily reduce the number of parameters; however, the authors proposed simplifying the parameter configuration. Figure 2 depicts the evolutionary progression of the proposed ABRKGA.
## 2 Informative Frequency Band Selectors
### Gini Index  
Gini index was originally proposed in economics [59] to measure the inequality of wealth and it has been recognized as a sparsity measure. It was also applied in others area, e.g., in the speech signal processing [60]. Recently [PERSON] et al. [47] replaced kurtosis in the framework of a \(1/3\)-binary tree filter bank (i.e., kurtogram) with the Gini index used to quantify the envelope of filtered signals for extraction of bearing fault signals from a vibration mixture. The results experimentally demonstrated the effectiveness of the spectral Gini index for characterizing repetitive transients caused by bearing defects. Another application of the spectral Gini index for bearing fault diagnosis can be found in the recent work of [PERSON] et al. [61]. The Gini index of signal \(x=(x_{1},\ldots,x_{N})\) is defined as:  
\[\hat{G}(x)=1-2\sum_{i=1}^{N}\frac{x_{(i)}}{\left\|x\right\|_{1}}\Big{(}\frac{N -i+\frac{1}{2}}{N}\Big{)}, \tag{3}\]  
where numbers \((1)\), \((2)\), \((3)\), \(\ldots\), \((N)\) are the new indices after the sorting operation of vector \(x\) and \(\left\|\cdot\right\|_{p}\) is the \(p\)-norm of vector \(x\): \(\left\|x\right\|_{p}=\left(\left|x_{1}\right|^{p}+\left|x_{2}\right|^{p}+\ldots+ \left|x_{N}\right|^{p}\right)^{1/p}\).  
In general, the Gini index equal to 0 represents ideal energy equality, that is, each component has the same share, while the Gini index equal to 1 reflects the maximum energy inequality, which means that all energy belongs to one component. The characteristics of this inequality indicator are discussed in [62]. In the recent work of [PERSON] [17] one may find that the Gini index is less sensitive to outlier values than the spectral kurtosis. The IFB selector based on the Gini index is obtained by simply replacing the kurtosis statistic in the kurtogram with the Gini index.
## 1 Introduction
Similarly, poly(\(n\)-butyl methacrylate) has a lower \(T_{\text{g}}\) and a higher \(<\)k\({}^{\text{Mrf}}\)\(>\)blob value (27 \({}^{\circ}\)C and 0.27 ns\({}^{-1}\), respectively) than poly(_tert_-butyl methacrylate) (118 \({}^{\circ}\)C and 0.19 ns\({}^{-1}\), respectively) [10]. More recently, the ability of PEF to probe extremely rigid polymeric backbones was applied to study the LRPCD of poly(isobutylene-_alt_-maleic anhydride) (PIMA) [11]. With its rigid succinin anhydride or succinimide ring (depending on the nature of the derivatization) being part of the main chain, PIMA yielded extremely slow LRPCD associated with a very small \(<\)k\({}^{\text{Mrf}}\)\(>\)blob value of 0.047 ns\({}^{-1}\) in THF at room temperature; that value is one order of magnitude lower than for PS and PMMA. While PIMA could be viewed as an example of polymeric backbone at the low end of the LRPCD scale, polydimethylsiloxane (PDMS) with a \(T_{\text{g}}\) of \(-\)120 \({}^{\circ}\)C [10] would certainly be a good backbone example at the higher end of the LRPCD scale. Polysiloxanes are also among the most important industrial polymers due to their wide utility in applications such as high-performance fluids, elastomers, coatings, surface modifiers and separation membranes [12]. To further explore the PEF response on flexible backbones, a series of poly(dimethylsiloxane-_co_-(3-aminopropyl)methylsiloxane), where 20.3 mol % of the structural units were (3-aminopropyl)methyl siloxane, was randomly labeled with 1-pyreneacetic acid (PAA) to yield a series of PyLLPs referred to as Py-PDMS since these polymers have a high dimethylsiloxane content. The steady-state (SSF) and time-resolved (TRF) fluorescence experiments typically conducted on PyLLPs were applied to the Py-PDMS solutions, but their analysis was complicated by the presence of a short oligomeric PDMS species (ODMS). Gel permeation chromatography (GPC) analysis indicated that 11 \(\pm\) 1 mol % of the pyrene labels were covalently attached onto ODMSs. Since the ODMSs were too short to accommodate more than one pyrene label at the low pyrene-labeling level used in these experiments, all fluorescence spectra showed strong pyrene monomer contributions that prevented the use of the SSF spectra to characterize the LRPCD of the Py-PDMS samples. Fortunately, MFA of the TRF decays acquired with the Py-PDMSsamples was able to isolate the contributions of the different pyrene species in solution, so that the fraction of pyrene labels actually attached onto the polymeric PDMS sample could be determined and their behaviour quantified. The \(<\)_k\({}^{\text{MF}}\)_>blob value retrieved for the Py-PDMS samples in THF equaled 1.16 (\(\pm\)0.13) \(\times\) 10\({}^{9}\) s\({}^{-1}\) which makes Py-PDMS the most flexible polymeric construct ever reported from a MFA of TRF decays acquired with PyLLPs. How this conclusion was reached is described hereafter.
# Stable Rules Definition for Fuzzy TS Speed Controller Implemented for BLDC Motor
## 2 Description of the Speed Control Algorithm  
### Fuzzy Controller Applied in the Control Structure  
In the presented research, a BLDC drive was used. BLDC motors are very popular in various industrial and electromobility applications. They are characterized by a noticeably higher power and relatively small size in comparison to standard brushed DC motors. In most industrial implementations, the high speed of BLDC motors is reduced through gearboxes. However, in a large number of applications, a high rotational speed is demanded. In devices such as drones, RC models, and electric scooters, the active load is applied directly to the rotor. For instance, the propellers of a drone are mounted on motor shafts, and their speed and the produced thrust are proportional to the motor speed [48; 49]. There are also applications in which the rotor is the external part of the motor--such a design is very popular in electric scooters--and the rotor cooperates directly with a wheel [48; 50]. For the abovementioned applications and many others, the mechanical part of the drive can be modeled with a simple transfer function defined as follows (1):  
\[G_{m}(s)=\frac{1}{T_{m}\cdot s} \tag{1}\]  
where \(T_{m}\) is the mechanical time constant of the motor.  
In fact, the speed control structure can be considered as a cascade-connected system. It consists of the mechanical part (described above), the loop related to the electromagnetic torque, and the external speed control path (Figure 2). The main purpose of this work was the hardware construction of the drive, algorithm implementation, and tests of the fuzzy controller. However, for initial feature analysis, numerical tests are also presented. Thus, for the definition of the simplified mathematical model of the speed control structure, some conditions (simplifications) must be predefined:  
* The mechanical part does not include nonlinearities (e.g., friction torque and backlash).
* The elasticity of the shaft is omitted.  
The optimal tuning of the internal part is ensured (the dynamic response of the electromagnetic torque after the reference signal from the speed controller).
* The fast processing and measurement of the state variables.
* The relation between the total time constant of the torque control loop \(T_{e}\) and \(T_{m}\), due to the modern switches used in specialized power electronic devices and modern control techniques [51], can be defined as follows: \[T_{e}<<T_{m}\] (2)
* The omission of the influence of disturbances in the power electronic devices.  
Considering the assumptions/relations mentioned above, the representation of the torque shaping loop can be described with transmittance:  
\[G_{e}(s)=\frac{1}{T_{e}\cdot s+1} \tag{3}\]  
where \(T_{e}\) is the electrical time constant of power electronics (fulfilling Relation (2)).
## 3 Results and Discussion
### SCB Test
#### 3.2.2 Effect of Gradation Type on the SCB Test Result  
As shown in Figure 17, the \(G_{\rm f}\) value of sample 1 is bigger than that of sample 3 which means that the cracking resistance of SMA-13 mixture is better than that of SUP-13 mixture. As shown in Figure 18, the FI value of sample 1 is bigger than that of sample 3 which indicates that SMA-13 asphalt mixture has slower crack propagation speed than the SUP-13 mixture. Therefore, it could conclude that the crack resistance of asphalt mixtures could be affected by the gradation type and the asphalt mixtures having better skeleton structures show better anti-crack ability. The test characteristics of SCB test is similar to that of the IDEAL-CT test, therefore, the test result and the conclusions of these two test are also very similar. The cracks are mainly caused by the tensile stress inside the asphalt mixtures during the test loading process. The structure of SMA is a kind of dense skeleton structure while the structure of SUP is a kind of suspended dense structure. The dense skeleton structure could have a more solid structure than the SUP mixtures because the coarse aggregate inside the SMA asphalt mixtures could form a solid skeleton and the consistent structure of SUP does not have a similar solid skeleton structure as the SMA asphalt mixtures.  
Figure 16: Effect of basalt fiber on FI value.  
Figure 17: Effect of gradation type on \(G_{\rm f}\).
###### Abstract  
The paper presents a methodology for determining the minimum split DC link capacitance for a family of three-phase, three-level grid-connected bidirectional AC-DC converters operating under arbitrary power factor under restriction of DC-only zero-sequence injection. The approach is based on the recently revealed generalized behavior of split DC link voltages in the above-mentioned converters family while distinguishing between leading and lagging power factors in order to highlight different impacts on split DC link capacitor voltages pulsating components. The minimum capacitance value is derived from the boundary condition, ensuring the mains voltage remains below or equal to the capacitor voltage at all times. It is revealed that operation with the lowest expected leading power factor should be employed as the design operating point. The accuracy of the proposed methodology is validated by simulations and experiments carried out employing a 10 kVA grid-connected T-type converter prototype. The results demonstrate close agreement between theoretical predictions and experiments, confirming the practical applicability of the proposed method.  
Keywords:three-phase three-level converters; split DC link; minimum capacitance; power factor +
Footnote †: journal: Applied Sciences
# Joint Trajectory and Communication Design for Buffer-Aided Multi-UAV Relaying Networks
## 3 The Proposed Algorithm
### Sub-Problem 2: Optimizing the UAV Trajectory a Given Relay Selection and Power Control  
For a given relay selection and transmit power, by defining \(P_{n}=\frac{p_{k}\left[n\right]\left(\sqrt{\frac{C\delta}{C+1}}\varphi^{lb} \beta_{k,D}\left[n\right]\right)+\sqrt{\frac{\beta}{C+1}}g_{k,D}\left[n\right] \right)^{2}}{\sigma^{2}}\) and \(P_{S}^{f}=\frac{p_{S}\left(\sqrt{\frac{C\delta}{C+1}}\varphi^{lb}\beta_{k,S} \left[n\right]+\sqrt{\frac{\beta}{C+1}}g_{k,S}\left[n\right]\right)^{2}}{ \sigma^{2}}\), we can obtain Sub-problem 2 as [25]:  
\[\max_{Q}\frac{1}{N}\sum_{n=2}^{N}\sum_{k=1}^{K}a_{k,D}\left[n \right]\log_{2}\left(1+\frac{P_{n}}{\left\|q_{k}\left[n\right]-w_{D}\right\|^ {2}+H^{2}}\right) \tag{26a}\] \[s.t.\sum_{i=2}^{n}\log_{2}\left(1+\frac{P_{n}}{\left\|q_{k} \left[n\right]-w_{D}\right\|^{2}+H^{2}}\right)\leq\sum_{i=1}^{n}\log_{2}\left( 1+\frac{P_{S}^{f}}{\left\|q_{k}\left[n\right]-w_{S}\right\|^{2}+H^{2}}\right)\] \[\forall k,n=2,\ldots,N\] (26b) \[\left(1\right)-\left(3\right)\]  
Note that Sub-problem 2 is concave with respect to \(q_{k}\left[n\right]\). To make Problem (26) more tractable, slack variables \(\left\{t_{k,m}\left[n\right]\right\},m\in\left\{S,D\right\}\) are introduced. Thus, Problem (26) can be transformed as:  
\[\max_{Q,\left\{t_{k,m}\left[n\right]\right\}}\frac{1}{N}\sum_{n=2}^{N}\sum_{k=1 }^{K}a_{k,D}\left[n\right]\log_{2}\left(1+\frac{P_{n}}{t_{k,D}\left[n\right]}\right) \tag{27a}\]\[s.t. \sum_{i=2}^{n}\log_{2}\left(1+\frac{P_{n}}{t_{k,D}\left[n\right]} \right)\leq\sum_{i=1}^{n}\log_{2}\left(1+\frac{P_{S}^{f}}{t_{k,S}\left[n\right]}\right)\] \[\forall k,n=2,\ldots,N \tag{27b}\] \[\left\|q_{k}\left[n\right]-w_{m}\right\|^{2}+H^{2}-t_{k,m}\left[n \right]\leq 0,m\in\left\{S,D\right\}\] (27c) \[(1)-(3)\]  
It can be shown that when the constraint (27c) takes the equal sign, there is an optimal solution for Problem (27), because we can decrease \(t_{k,m}\left[n\right],m\in\left\{S,D\right\}\) to improve the objective value. Consequently, Problems (26) and (27) have the same optimal value and optimal solution of \(Q\). Next, we concentrate on handling Problem (27). It is easy to see that \(t_{k,m}\left[n\right]\) and \(\log_{2}\left(1+\frac{P_{n}}{t_{k,m}\left[n\right]}\right)\) are convex. However, the objective function is non-concave, and the constraints in (3) and (27b) are non-convex, so Problem (27) is still non-convex. In order to make the problem (27) solvable, we obtain the lower bound of the objective function (27) by taking the first-order [PERSON] expansion of \(\log_{2}\left(1+\frac{P_{n}}{t_{k,m}\left[n\right]}\right)\) at the points \(t_{k,m}^{f}\left[n\right]\) and then maximizing the concave bound to obtain the optimal solution of Sub-problem 2.