esa-sceva/satcom-chunk-collection huggingface dataset:
# A Model-Free Online Learning Control for Attitude Tracking of Quadrotors
## 5 Conclusions  
Although many nonlinear controllers can be used for quadrotor attitude tracking, none of them have addressed external disturbances, uncertain or even unknown model  
Figure 4: Photo of (**a**) the tested quadrotor platform; (**b**) Photo of the flight controller; and (**c**) the outdoor flight experiment.  
Figure 5: Evolution of (**a**) attitude tracking and (**b**) angular velocity tracking in a real-world experiment.  
parameters, and low computational consumption simultaneously. In this paper, we developed a novel model-free online learning control scheme to achieve attitude tracking in the presence of time-varying external disturbances, uncertainties in the inertia matrix, and angular velocity sensor noises. The proposed approach guaranteed the closed-loop attitude error system to be uniformly ultimately bounded stable. An accurate inertia matrix and expensive computational consumption were not needed to implement the control law. Thus, the proposed MFOLC is quite suited for small quadrotors with compact arithmetic, which is illustrated by a real-world experiment. A remaining problem of our method is that it is not specifically designed for the input saturation limit case, especially when the quadrotor needs to maintain enough vertical thrust to resist gravity when performing attitude control tasks. Moreover, the control margin that can be allocated to attitude control is limited. Hence, in future work, we intend to further investigate the MFOLC algorithm for the input saturation limit case.  
Investigation, L.T. and S.Z.; Methodology, L.T.; Project administration, G.J.; Resources, L.W.; Software, G.J.; Writing--original draft, L.T.; Writing--review & editing, G.J. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Natural Science Foundation of China (No. 61673017).  
Not applicable.  
Data Availability Statement: Data is contained within the article.  
The authors declare no conflict of interest.
## 4 Simulation Results
### Simulation Results for the Control Approach Based on the Bounded Disturbance  
First, a disturbance torque of vibration \(\left[\tau_{v1}\ \ \tau_{v2}\right]^{T}\) by a Gaussian noise is composed taking into account that it should be bounded. Therefore, a signal with a mean value \(=\left[0\ \ 0\right]^{T}\) Nm, a variance \(=\left[0.01\ \ 0.015\right]^{T}\) Nm, and a sampling time of 0.01 s is set. Then, the effect of the disturbance torque of the payload variation on the first and second joints is represented as follows:  
\[\tau_{\Omega}=\begin{cases}0.65\ \text{Nm},&4\ \text{s}\leq t\leq 8\ \text{s}\\ 0.15\ \ \text{Nm},&8\ \text{s}\leq t\leq 10\ \text{s}\\ 0,&\text{otherwise}\end{cases}\]  
\[\tau_{\Omega}=\begin{cases}0.75\ \text{Nm},&4\ \text{s}\leq t\leq 8\ \text{s}\\ 0.25\ \ \text{Nm},&8\ \text{s}\leq t\leq 10\ \text{s}\\ 0,&\text{otherwise}.\end{cases}\]To provide further explanation, the effects of both disturbances on the first and second joints are plotted in Figure 2.  
The combination of the disturbances is bounded as follows:  
\[\begin{bmatrix}-0.2873\text{ Nm}\\ -0.3518\text{ Nm}\end{bmatrix}\leq\begin{bmatrix}\tau_{v1}+\tau_{1}\\ \tau_{v2}+\tau_{2}\end{bmatrix}\leq\begin{bmatrix}0.9446\text{ Nm}\\ 1.1108\text{ Nm}\end{bmatrix}.\]  
The control gains and design parameters in (12) are selected as \(K_{1}=\text{diag}[40\quad 60]\), \(\sigma_{1}=\text{diag}[10\quad 12]\), \(v_{b}+l_{b}=1.50\text{ Nm}\) and \(d=0.73\). The proportional and derivative gains for the PD control input in (38) are chosen to yield the best performance as \(K_{p}=\text{diag}[225\ \ 100]\), \(K_{d}=\text{diag}[30\ \ 20]\). The tracking errors and input torques of this approach are shown in Figure 3, where they are compared with the torques and errors obtained from the PD controller in (38). Although the PD control can reject the disturbances, the tracking errors are still large. By contrast, the proposed controller damps the same disturbances smoothly with less control efforts. Furthermore, the proposed controller performs rather well in terms of drifts and systematic errors as well. To show this, we run the simulation for 140 s and depict the actual displacements with their desired references and the errors between them in Figure 4.  
Figure 3: Tracking errors and control inputs for the first proposed approach compared with the proportional derivative (PD) approach: (**a,b**) angular displacement errors of joint 1 and joint 2, respectively; (**c,d**) input torques of joint 1 and joint 2, respectively.  
Figure 2: Disturbance torques due to vibration and payload variation for the first approach: (**a,b**) vibration effect; (**c,d**) payload variation effect; and (**e,f**) effect of combination on joint 1 and joint 2, respectively.
## 2 Materials and Methods
### Effective Connectivity Analysis
#### 2.4.3 Bayesian Model Reduction and Bayesian Model Averaging  
Using PEB analysis, we could not make an intuitive view about which effective connectivity is highlighted because of significant group effects. To answer this, Bayesian model reduction (BMR) and Bayesian model averaging (BMA) were used to further evaluate the significance of the PEB results [28]. In this study, we used SPM12 toolbox to conduct BMR, where a greedy search was automatically applied to exclude parameters that do not affect the PEB model evidence. BMA was then used to give the average parameters of the best 256 reduced models. According to BMR and BMA analysis, some unimportant connections would be switched-off, and the remaining effective connectivity could be regarded as the significant group-level results.
# Scaling of Rain Attenuation Models: A Survey
## 5 Research Scopes and Challenges
### Application of Artificial Neural Network (AI) for Radio Link Scaling  
It has been recently shown that rain attenuation is more effectively determined with the help of AI-based techniques [4]. Recently, in [62] an artificial neural network was used to determine the polarization and frequency scaling for terrestrial and slant links. Typically, long-term rain attenuation, rainfall rate, and other climatic or infrastructural related parameters need to be studied for rain attenuation models. In recent years, it has been found that considering the microphysical phenomena of rainfall events can be helpful for accurate attenuation models for radio links [63; 64]. Consequently, the rainfall rate and other related physical parameters can to contribute to accurate attenuation prediction models. All these parameters can be helpful to develop rain attenuation scaling models for frequency, the elevation angle of the antenna, polarization, and pathlength scaling.
## 3 Results
### Technical Analysis
#### 3.2.1 Control System
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Control System** & **Description** & **Efficacy** & **Efficiency** \\ \hline \multirow{3}{*}{[66]} & \multirow{3}{*}{_Type-1 fuzzy_} & Supercapacistors are proposed and & & \\  & controlled to reduce THD and & & & \\  & regulate reactive power for wind & & & \\  & & farms & & \\  & & & & \\ \hline \multirow{3}{*}{[67]} & \multirow{3}{*}{_Fuzzy-PI_} & Multi-feeder UPQC fuzzy & & & \\  & controlled to reduce voltage and & & & \\  & current imperfections & & & \\  & & & & \\ \hline \multirow{3}{*}{[68]} & \multirow{3}{*}{_Fuzzy-PID_} & Frequency and power control in & & & \\  & insolated distribution generation & & & \\  & units, considering also & & & \\  & superconducting magnetic energy & & & \\  & storage & & & \\ \hline \multirow{3}{*}{[69]} & \multirow{3}{*}{_Hysteresis fuzzy_} & Hysteresis controller bands & & & \\  & adapted by a fuzzy controller for & & & \\  & harmonics elimination & & & \\  & & & & \\  & & & & \\ \hline \multirow{3}{*}{[70]} & \multirow{3}{*}{_Adaptive fuzzy_} & Inverters controlled for power & & & \\  & factor tracking changes and power & & & \\  & fluctuation decrease & & & \\  & & & & \\ \hline \multirow{3}{*}{[71]} & \multirow{3}{*}{_Type-2 fuzzy_} & Load frequency control and & & & \\  & frequency stability considering the & & & \\ \cline{1-1}  & high penetration of distributed & & & \\ \cline{1-1}  & generation & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Relevant papers considering the type of fuzzy-based control system. Figure 1: The numbers of published articles based on control system type. T1F—type-1 fuzzy; FPI—fuzzy-PI; FPID—fuzzy-PID; HF—hysteresis fuzzy; AF—adaptive fuzzy; TF—tuned fuzzy; FA—fuzzy-active; DF—decoupled fuzzy; type-2 fuzzy; FOF—fractional-order fuzzy; HyF—hybrid fuzzy; FPD—fuzzy-PD; FFF—feed-forward fuzzy; EAF—evolutionary algorithm fuzzy; STF—self-tuned fuzzy; RF—recursive fuzzy; UF—unified fuzzy.
## 4 Conclusions and Future Perspectives
Each point is the average of three experimental replicates, Figure S3: Pressure (mV) increase in the assays amended with butyrate (first addition), in the presence of different MWCNT concentrations: 0 g L\({}^{-1}\) (**a**), 0.5 g L\({}^{-1}\) (\(\blacklozenge\)) and 1.0 g L\({}^{-1}\) (\(\blacklozenge\)). Blank assays (no added substrate) are also shown (open symbols). The results presented are the averages and standard deviations for triplicate assays, Figure S4: ORP values measured in the assays performed with river sediment in the absence of MWCNT (w/o MWCNT), and in the presence of MWCNT or MWCNT-Fe, Table S1: Reactions involved in ethanol and butyrate oxidation, and their corresponding standard Gibbs free energy changes. **Author Contributions:** Conceptualization, A.J.C., G.M., M.A.P. and M.M.A. ; methodology, A.J.C., A.F.S., G.M., M.A.P. and M.M.A. ; validation, A.J.C., A.F.S., G.M. and A.R.C. ; formal analysis, A.J.C., A.F.S., G.M., C.C.O. and V.R.M. ; investigation, C.C.O., Y.L., V.R.M. and A.R.C. ; resources, O.S.G.P.S., M.F.R.P., L.P. and M.M.A. ; writing--original draft preparation, A.J.C., A.F.S. and G.M. ; writing--review and editing, A.R.C., O.S.G.P.S., M.F.R.P., L.P., A.A.M.L., M.A.P. and M.M.A. ; visualization, A.J.C., A.F.S. and G.M. ; supervision, L.P., A.A.M.L., M.A.P. and M.M.A. ; project administration, A.J.C., A.F.S. and G.M. ; funding acquisition, A.J.C. and M.M.A. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Portuguese Foundation for Science and Technology(FCT) under the scope of project MORE (POCI-01-0145-FEDER-016575), of the strategic funding of UIDB/04469/2020 unit and BioTechorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020-Program Operational Regional do Norte, as well as FCT/MCTES trough national funds (PIDDAC) and Base Funding--UIDB/50020/0 of the Associate Laboratory LSRE-LCM--funded by national funds rough FCT/MCTES (PIDDAC). Research of O.S.G.P.S. was funded by FCT under the Scientific Employment Stimulus--Institutional Call EECINST/00049/2018. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems
## 2 Background
### Distributed Constraint Optimization Problems  
A _Distributed Constraint Optimization Problem_ (DCOP) can be defined as a tuple \(\langle\mathbf{A},\mathbf{X},\mathbf{D},\mathbf{F},\alpha\rangle\), where  
* \(\mathbf{A}=\{a_{1},a_{2},\ldots,a_{n}\}\) is a set of _agents_; an agent can control one or more variables.
* \(\mathbf{X}=\{x_{1},x_{2},\ldots,x_{m}\}\) is a set of discrete _variables_; each variable \(x_{j}\) is controlled by one of the agents \(a_{i}\in\mathbf{A}\).
* \(\mathbf{D}=\{D_{1},D_{2},\ldots,D_{m}\}\) is a set of discrete _domains_; each variable \(x_{i}\in\mathbf{X}\) takes value from the domain \(D_{i}\in\mathbf{D}\).
* \(\mathbf{F}=\{f_{1},f_{2},\ldots,f_{l}\}\) is a set of _utility functions_; each \(f_{i}\in\mathbf{F}:D_{i_{1}},D_{i_{2}},\ldots,D_{i_{k}}\to\mathbb{R}\) specifies the utility function assigned to each combination of \(x_{i_{1}},x_{i_{2}},\ldots,x_{i_{k}}\), where the arity of the utility function \(f_{i}\) is \(k\). We consider that all utility functions are binary (thus \(f_{ij}\in\mathbf{F}:D_{i}\times D_{j}\to\mathbb{R}\)) in this paper.
* \(\alpha:\) is a variable-to-agent _mapping function_ that associates each variable \(x_{j}\in\mathbf{X}\) to one agent \(a_{i}\in\mathbf{A}\). In this paper, we assume one agent controls only one variable (\(n=m\), thus the terms \"agent\" and \"variable\" could be used interchangeably).  
The solution of a DCOP is an assignment combination \(X^{*}\) to all variables that minimize (we are going to consider the minimization in this paper) the aggregate utility, as shown in Equation (1).  
\[X^{*}=\underset{d_{i}\in D_{i},d_{j}\in D_{j}}{\arg\min}\sum_{f_{ij}\in \mathbf{F}}f_{ij}(x_{i}=d_{i},x_{j}=d_{j}) \tag{1}\]
## 3 Results and Discussion
### Results Analysis of Single-Factor Experiments
#### 3.1.4 Effect of Sterilisation Time on NFC Goji Juice Quality  
The impact of sterilisation time on the quality of NFC goji juice is depicted in Figure 5. As sterilisation time increased, the turbidity of the juice also increased. When the sterilisation time was 90 s, the juice achieved a higher TSS/TA ratio and sensory score, measuring 12.48 and 79%, respectively. At 120 s, the TSS/TA ratio and sensory evaluation score decreased by 7.6% and 3.2%, respectively, compared to the 90-s mark. Significant differences (\(p<0.05\)) were observed in TSS/TA ratio, turbidity, and sensory evaluation values across different sterilisation times. When the sterilisation time was extended to 150 s, the TSS/TA ratio decreased to 12.1. This is attributed to longer medium-to-high-temperature heat treatments, which alter the content of TSS and organic acids in the juice system, thereby affecting the taste and overall quality of the juice [33].  
Figure 4: Effect of sterilisation temperature on fruit juice quality.  
The sensory scores of the processed juice samples were calculated using matrix multiplication to ensure accuracy and avoid algorithmic errors [37]. Given the weight set X = {0.30, 0.20, 0.25, 0.25} for NFC goji juice, the sensory evaluation results for each juice sample were obtained by applying the fuzzy principle Y1 = X \(\times\) T1:  
\[\mathrm{Y_{1}=X\ \times\ T_{1}=|0.30,\ 0.20,\ 0.25,\ 0.25|\ \times\ \begin{bmatrix}0.6&0.3&0&0.1\\ 0.1&0.5&0.1&0.3\\ 0.6&0&0&0.4\\ 0.3&0.5&0.2&0\end{bmatrix}=\{0.425,0.315,0.07,0.19\} \tag{1}\]  
Similarly, Y2-Y29 were calculated. The fuzzy comprehensive evaluation score was determined by W = Y \(\times\) K with the evaluation set K = {95, 85, 75, 30} and Y1 = {0.425, 0.315, 0.07, 0.19}. The comprehensive score for the first combination was calculated as:  
\[\mathrm{W_{1}=Y_{1}\times K=\{0.425,\ 0.315,\ 0.07,\ 0.19\}\ \times\ \begin{bmatrix} 95\\ 85\\ 75\\ 30\end{bmatrix}=78.1 \tag{2}\]  
Similarly, the results for W2-W29 were computed and are listed in Table 6.
# Secure Cognitive Radio Vehicular Ad Hoc Networks Using Blockchain Technology in Smart Cities
## 5 Conclusions  
This paper proposes a novel cognitive scheme for securing vehicular communications using blockchain technology. This is a type of cognitive communication in which PU announces its free channel for use by secondary users, and any authentic secondary user can use it upon payment. All vehicles confirm their authenticity by registering with the RSU. A PU provides a channel to the source node only when a source node has some information to forward, and it pays a certain amount for using the channel. The message which the source intends to deliver to the destination is verified only if at least one miner validates it. Miners are vehicles that volunteer to assist the destination in conveying the source message. Miners validate this message by paying a small amount from their wallets to receive a large reward from the destination. This message enables the destination to save fuel and time by rerouting its travel. The destination pays both the sender and the miner when it receives this beneficial message. The belief propagation algorithm is used by every secondary user, including source, miners, and destination, to make final decisions about the channel state at their respective current locations. Our findings indicate that execution time, delivery time, and overhead are reduced compared to other reference schemes. We will extend this work to multiple accounts by examining both highway and city scenarios. Furthermore, by considering a real-time foggy weather scenario, we will examine how this idea can secure information and ensure the safe movement of vehicles on roads. In addition, we plan to incorporate machine learning algorithms to identify free channels and stable routes in the near future. In such a highly dynamic cognitive vehicular network, scaling can be a challenge, but it can be improved by incorporating machine learning algorithms.  
Conceptualization, H.G.; methodology, F.A. and H.G.; software, F.A.; validation, H.G.; formal analysis, F.A.; investigation, H.G.; resources, H.G. and I.K.; data creation, F.A.; writing--original draft preparation, H.G. and F.A.; writing--review and editing, H.G.; visualization, H.G.; supervision, H.G.; project administration, H.G. and I.K.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Regional Innovation Strategy (RIS) through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (MOE) under Grant 2021 RIS-003.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.
# Long-Term Effects of External Sulfate Attack on Low-Carbon Cementitious Materials at Early Age
## 3 Results
### Fourier Transform Infrared Spectroscopy  
FTIR analysis was conducted on the whole six mixes after 8 (Ti1) and 80 (Ti2) weeks of immersion in the sulfate solution at 1, 2, 5, and 15 mm depths.  
In accordance with findings from previous studies [52; 53; 54], various chemical compounds and phases within the cementitious samples can be identified through FTIR. Port  
Figure 5: TG and DTG curves of the reference mix (P1) and of Q1 at 1 mm and 15 mm depths after 80 weeks of immersion in sulfate solution.  
landite, characterized by O-H stretching, is identified with a peak at 3642 cm\({}^{-1}\). The compounds AFm, ettrinigite, and gypsum, exhibiting S-O vibration, manifest by the peak of 1102 cm\({}^{-1}\). The presence of water is indicated by a broad band at the peak of 3373 cm\({}^{-1}\) attributed to the stretch mode of the O-H bond. Ettringite, distinguished by S-O vibration, can be detected at the peak of 619 cm\({}^{-1}\), while gypsum shows up at the band of 1685 cm\({}^{-1}\). These distinctive peaks and bands provide valuable insights into the chemical composition and phases present in the cementitious samples, contributing to a comprehensive understanding of their behavior under ESA.  
The analysis of the FTIR spectra presented in Figure 6a, which depicts the chemical composition of the reference mix P1 at 1, 2, and 5 mm after 80 weeks of exposure to sulfate, reveals a significant attack at 1 mm and a diminishing impact with increasing depth. This observation is distinguished by examining the intensity of peaks corresponding to gypsum (at 1685 cm\({}^{-1}\)), ettrinigite, gypsum, and AFm (AFm's presence is questionable based on TGA results discussed in Section 3.3) at 1102 cm\({}^{-1}\), as well as ettringite at 619 cm\({}^{-1}\). Figure 6c, representing the same mix at 1 mm depth but comparing 8 weeks of exposure to 80 weeks, demonstrates that the attack has progressed and become more pronounced after 80 weeks of exposure. To assess the impact of SCM on enhancing durability against ESA, Figure 6d illustrates P1 and the quaternary mix Q1 at 1 mm depth after 80 weeks of exposure, highlighting a significant difference. Notably, the formation of ettrinigite and gypsum in the reference mix is substantial, showcasing the effectiveness of substituting clinker with SCM at specific percentages. This aligns with the overarching findings of the study. Further insights can be gained by examining Figure 6b, which compares the degree of formation of expansive products at different depths of the ternary mix T1 and the mix P1 shown in Figure 6a.  
Figure 6: FTIR spectra, showcasing (**a**) the reference mix (P1) and (**b**) mix T1 at various depths after 80 weeks of exposure to sulfate. Additionally, it includes (**c**) mix P1 at 1 mm depth after 8 and 80 weeks of sulfate exposure, and (**d**) mix P1 and mix Q1 at 1 mm depth after 80 weeks of contact with sulfate.
# Sub-Terahertz Computer Generated Hologram with Two Image Planes
## 4 Discussion  
Due to the high coherence of the source and properties of terahertz radiation, adding any unnecessary object to the optical path resulted in undesirable interferences. In order to resolve this problem, some simplifications in the experimental system were done. For this reason, the classical collimator was not used in this setup.  
The prepared structure was tested in two setups. The first one (Setup A in Figure 2) consisted only of the hologram illuminated directly by the source. Due to the size of the image and the long registration time for the second setup, the hologram was illuminated with the beam close to the plane wave (lens \(f=300\) mm \(d=300\) mm placed 600 mm from the source). Scanned positions were chosen after performing simulations of reconstruction in such a setup. To reduce the stray beams, an additional isolator was necessary.  
Incredible correspondence between simulations and obtained experimental measurements stood out and is shown in Figure 6. In the case of Setup A, the images were larger than designed, and thus, the whole area shown has a size of 21 mm. All presented images have the same size, and due to the fact that experimental scans had smaller dimensions, the additional white square were added to keep proper magnification. Experimental results allowed clearly identifying the features of defined objects despite a large amount of interferences (particularly visible in Figure 4), usually present in such THz setups. The presence of the parallel image plane is visible, although it does not disturb the image significantly. It should be noted that reconstruction from such a small hologram area introduced a large depth of focus due to the small aperture size. Moreover, the size of speckles was larger in such case.  
Figure 6: Comparison of the simulation with realistic illumination and experimental results for reconstructing two images from a single hologram. Represented values for the experimental evaluation are normalized.