esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Parameter Tuning  
Machine Learning algorithms operate depending on certain fundamental hyperparameters (henceforth just parameters), whose values govern the way the algorithm performs. To find a suitable model, the initial step is to find the combination of parameter values that provide the best performance for the generated model--an operation referred to as \"parameter tuning\" [39; 51]. Here, parameter tuning was performed using a \"brute force\" approach (also known as \"grid search\") by training the algorithm multiple times, each with different combinations of the parameters, and then validating the performance of the models thus generated.  
The neural network algorithm depends on several parameters, and the most important ones account for the network's architecture and are the number of hidden layers and the number of nodes per layer; the larger these values, the more complex the output model can be, and this can allow more subtle differences to be identified. Nevertheless, model complexity can lead to overfitting when the model learns to classify the data in the training set but has poor predictive abilities on new data [39]. Overfitting is particularly common in applications using limited amounts of observations, such as this work and several applications in the archaeological and anthropological fields, where data are often scarce. A limited number of features (a characteristic of our dataset) can help lower the chance of overfitting by reducing redundant information [52]--this is the case when using cranial measurements because they are correlated with each other. To further reduce the chance of overfitting, regularisation parameters can be tuned. Regularisation is a set of techniques used to avoid overfitting by reducing complexity through the penalisation of the features' influence [39]. In the case of a Neural Network, regularisation can assign zero to part of the weights (L1-regularisation), or it can make those weights smaller (L2-regularisation).  
The training was tuned over two values for the number of hidden layers (1 and 2) and over seven values for the number of nodes (3, 7, 11, 15, 19, 23, and 27--the same number of nodes was used in all hidden layers). Additionally, a sequence of seven values for the L2-regularisation parameter (0, 1 \(\times\) 10\({}^{-4}\), 5 \(\times\) 10\({}^{-4}\), 1 \(\times\) 10\({}^{-3}\), 5 \(\times\) 10\({}^{-3}\), 1 \(\times\) 10\({}^{-2}\) and 5 \(\times\) 10\({}^{-2}\)) was used in the tuning. The overall number of different models trained during the tuning phase was 98 (2 \(\times\) 7 \(\times\) 7). The values of other parameters (see Table 3) were estimated using a trial-and-error manual tuning, thus reducing the computational time needed for the \"brute force\" approach.
## 4 Results and Discussion
### Numerical Model Results  
Figure 13 illustrates the lateral displacement of the wall facing and the tensile load distribution within the reinforcement layers during the construction of the wall (load level of 12 kN/m\({}^{2}\)). The numerical model results were similar to the report of [PERSON] et al. [38].  
Figure 11: The trend line of the failure surface in the reinforced soil.  
Figure 12: Lateral displacement of the wall facing.  
At a maximum load level of 300 \(\mathrm{kN/m^{2}}\), the model results showed that the highest lateral displacement of the wall facing was 4270 mm, as shown in Figure 14. In addition, the maximum tensile load in the fourth reinforcement layer (near the top of the wall) was similar to the full-scale model and was located approximately 0.95 m further away from the facing panel. This value decreased close to the wall facing of the lower reinforcement layers, as shown in Figure 10.  
Figure 14: The numerical model results at a load level of 300 \(\mathrm{kN/m^{2}}\): (**a**) stress distribution in the retaining wall; (**b**) displacement of the wall facing; (**c**) the distribution of the tensile force in the reinforcement layers.  
Figure 13: The numerical model results during the construction of the wall: (**a**) displacement of the wall facing; (**b**) distribution of tensile force in the reinforcement layers.  
Figures 15 and 16 demonstrate that the simulation results were in good agreement with the measurements from the full-scale model. Thus, the findings from the study were reliable and could be used in practice. They also indicated that the local backfill material and self-fabricated steel reinforcements could widely be used in the Central Region of Vietnam.
## 4 Discussion  
### Gain Change Effects on LE Patterns  
Gain parameter changes clearly affect LE patterns because they alter the parameters of the system. As discussed in Section 3.1 and shown in Figure 5a, the gain is decreased from the canonical \(C\) = _3.0_ to \(C\) = _2.7_; here the symmetric-like bubble is eliminated leaving a pattern similarly matching the canonical AV and AVI. In Figure 5b,c the gain is increased from \(C\) = _3.0_ to \(C\) = _3.1_, respectively. Due to this slight increase, a symmetric-like bubble changes to an asymmetric bubble supporting the hypothesis that symmetric-like bubbles represent an imminent bifurcation in the system. In Figure 5d-f the gain parameter  
Figure 11: Power-shift method applying a linear ramp with \(T_{\text{lag}}\) = _15_ and noise with amplitude = \(\pm\)_0.0015_, \(Y\) = _1.63_ for base AIII with the _Y_\({}_{PS}\) = _1.84_.  
Figure 12: The percentage chance that AIII survives the gain shift as a function of \(Y\).  
increases from the canonical \(C=3.0\) to \(C=3.2\), where there is a first transition to a symmetric-like bubble, and second to an asymmetric bubble, confirming the thesis that symmetric-like bubbles are predictors of an imminent bifurcation provided there is a small change to one parameter of the system. Note, this result is consistent with the studies in References [13; 44] who confirm these types of dynamic changes. It is interesting that a new symmetric-like bubble emerges in Figure 6d forecasting the bifurcation shown in Figure 6e.
## 2 System Model and Problem Definition  
In this paper, we consider a scenario where each UAV is equipped with two directional antennas: a switched beam antenna and a phased array antenna. The switching delay for antenna sectors is negligible. We assume that the software-defined radio (SDR) technology is adopted so that all UAVs can work on different channels and switch channels flexibly. We assume that there is no interference among channels. The network and antenna models are described in Section 2.1, the multichannel model of the directional FANET is described in Section 2.2, and the formulation of the problem based on the model is found in Section 2.3.
## 4 Data Treatment
### Impact Assessment  
Overall, the data protection impact assessment (DPIA) should be completed in case of high risks highlighted during the risk assessment procedure. However, according to the European board, the DPIA must be filled in nine cases (extended to twelve in case of the Italian Privacy Authority), as mentioned below: (1) evaluation or assignment of a score, including profiling and forecasting, in particular in consideration of \"aspects concerning professional performance, economic situation, health, personal preferences or interests, reliability or behavior, location or movement of the interested party\"; (2) automated decision-making process that has legal effect or similarly significantly affects people; (3) systematic monitoring of the interested parties; (4) sensitive data or data of a highly personal nature; (5) large-scale data processing; (6) creation of matches or combination of datasets; (7) data relating to vulnerable interested parties; (8) innovative use or application of new technological or organizational solutions; (9) when the processing itself \"prevents data subjects from exercising a right or making use of a service or contract\".  
In the specific case of the Health360 platform, the collection of health-related data is included within those criteria, therefore the DPIA must be performed, thus identifying not only the risks concerning data, but also the impact on the freedom and rights of the interested party.  
The choice concerning the measures to be implemented should be the natural consequence of this process, with the aim to mitigate the risk level. In case such measures are not sufficient to decrease the risk level, or adequate measures could not be implemented, the Supervisory Authority should be contacted for authorization to conduct the treatment.
## 3 Simulation Results
### Changes in Patient Parameters to Demonstrate an Exercise Test  
Figure 7 illustrates the waveforms of the model simulation during the changes in parameters for the CVS in the exercise test. In this test, \(E_{\mathit{es,lv}}\) and \(E_{\mathit{es,rv}}\) were increased by 20% and \(R_{\mathit{sa}}\) was reduced by 50% at 60 s over a period of 10 s. During this period, the closed-loop control system automatically activated to linearly track the increase in \(Q_{r}\) from (\(3+4 sin(2\pi t/T)\)) to (\(3+4 sin(2\pi t/T)\)).  
During the exercise test, the controller responded to changes in the systematic flow from the initial value of 3.5 L/m to 2.8 L/min (Figure 7a) by increasing the pump speed from 2800 rpm to 3500 rpm (Figure 7b). These changes were associated with a decrease in the size of the stroke volume and shifted to the left by 210 mL (Figure 7c). As a result, the IRBP successfully reduced \(P_{la}\) and increased \(P_{ao}\) and \(P_{lv}\) as fluctuation signals (Figure 7d, Figure 7e and Figure 7f, respectively).  
The results also demonstrated that \(\hat{Q}_{\mathit{est}}\) accurately tracked \(Q_{r}\) within an error of 0.25 L/min (Figure 7g). Moreover, the correlation between \(\hat{Q}_{\mathit{est}}\) and \(Q_{p}\) was significantly high; the slope was 1.12 for the linear regression, the mean absolute error \(error_{mean}=0.2430\), and \(R^{2}=0.981\).  
The hemodynamical results in healthy humans and the simulation of both scenarios (rest and exercise) are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameters**} & \multirow{2}{*}{**Healthy**} & \multicolumn{2}{c}{**HF and IRBP**} \\ \cline{3-4}  & & **Rest** & **Exercise** \\ \hline \(P_{la}\) (mmHg) & 25 & 15 & 10 \\ \(P_{ao}\) (mmHg) & 120 & 100 & 80 \\ \(P_{lv}\) (mmHg) & 120 & 95 & 80 \\ \(Q_{p}\) (L/min) & 5.5 & 2 & 2.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: CVS variables from the simulation results.  
Figure 7: Results in exercise test; (**a**) systematic flow (\(Q_{s}\)); (**b**) IRBP speed (\(\omega(t)\)); (**c**) stroke volume (SV); (**d**) left atrium pressure (\(P_{la}\)); (**e**) aortic pressure (\(P_{ao}\)); (**f**) left ventricle pressure (\(P_{lo}\)), (**g**) estimated flow (\(Q_{est}\)) vs. desired reference flow (\(Q_{r}\)); (**h**) correlation between estimated flow (\(Q_{est}\)) and actual flow (\(Q_{p}\)).
# Mechanical Performance of Concrete Made with the Addition of Recycled Macro Plastic Fibres
## 4 Results and Discussion  
### Physical Properties  
The results of the dry density and water absorption in concrete samples are depicted in Table 2. The dry density values ranged between 2.21 g/cm\({}^{3}\) and 2.25 g/cm\({}^{3}\) for mixtures with recycled fibres, and between 2.26 and 2.27 g/cm\({}^{3}\) for reference concretes. Thus, owing to the small volume fraction of the plastic fibre added to the concrete, there was no significant reduction in the density of plastic FRC as compared to the density of conventional concrete, in accordance with the findings of [PERSON] and [PERSON] [36], [PERSON] and [PERSON] [18], [PERSON] et al. [37], [PERSON] and [PERSON] [38], and [PERSON] [39].  
The open porosity (Table 2) of concretes with recycled fibres presented values between 12.17% and 13.34%, compared to between 11.84% and 14.15% for reference concretes. In all cases, the concretes made with fibres presented higher porosity values than the concrete made without them (C-REF). However, mixtures with recycled fibres presented lower values than those obtained for concrete made with commercial polypropylene fibres (PRFC-REF).  
Even the effective porosity of concrete, with values ranging from 5.48 \(\times\) 10\({}^{-2}\) cm\({}^{3}\)/cm\({}^{3}\) to 8.72 \(\times\) 10\({}^{-2}\) cm\({}^{3}\)/cm\({}^{3}\) (corresponding to the reference mixture), did not undergo significant variation.  
The resistance to water penetration by capillarity absorption, and the capillary absorption coefficient--with values ranging between 24.98 min/cm\({}^{2}\) and 34.64 min/cm\({}^{2}\) and between 9.51 \(\times\) 10\({}^{-2}\) kg/(m\({}^{2}\cdot\)min\({}^{1/2}\)) and 14.81 \(\times\) 10\({}^{-2}\) kg/(m\({}^{2}\cdot\)min\({}^{1/2}\)), respectively--highlighted a slight decline in resistance to water penetration by capillarity absorption, and a slight increase in the capillary absorption coefficient compared to the reference values. This is consistent with the studies carried out by other researchers [40; 41], who reported  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Test Methods\(\backslash\)Mixes** & **C-REF** & **PFRC-REF** & **PFRC-2** & **PFRC-4** & **PFRC-6** \\ \hline Dry density (g/cm\({}^{3}\)) & 2.27 (0.01) & 2.26 (0.01) & 2.24 (0.01) & 2.25 (0.01) & 2.21 (0.02) \\ Open porosity (\%) & 11.84 (0.24) & 14.15 (0.31) & 12.21 (0.20) & 12.17 (0.44) & 13.34 (0.49) \\ Effective porosity of concrete, \(\epsilon_{\mathrm{e}}\) & 5.48 (0.38) & 8.72 (0.65) & 6.30 (0.20) & 6.55 (0.08) & 6.53 (0.07) \\ (\(\times\)10\({}^{-2}\) cm\({}^{3}\)  
that the addition of plastic fibres to concrete does not affect the properties studied, since the values obtained differ little from those of the fibre-free concrete mix.  
[PERSON] and [PERSON] [38], on the other hand, indicated that the water porosity and the water absorption capacity of concrete containing PP fibres (with concentrations lower than 0.20%) increased compared to the corresponding value for the control mix.
## 4 Conclusions  
Three experimental parameters were evaluated: sample weight, adsorption temperature, and extraction time (min). No significant differences were observed for PA and PDMS/DVB fibers. However, for the analysis with DVB/CAR/PDMS, there was interference regarding the sample weight and extraction time. Thus, it can be inferred that the method was efficient. Furthermore, it is noteworthy that the best experimental condition for extracting volatile organic compounds in grumixama was 75 \({}^{\circ}\)C, 2.0 g of pulp, and 20 min.  
The profile of volatile compounds in the grumixama pulp is mainly composed of sesquiterpene compounds, representing 85.7%, 83.3%, and 85.7% for DVB/CAR/PDMS, PA, and PDMS/DVB fiber, respectively, of its composition. Fourteen volatile organic compounds (VOCs) were identified through the DVB/CAR/PDMS, six through the PA, and seven through the PDMS/DVB fiber. By analyzing the relative areas of the chromatographic peaks isolated for each SPME fiber, DVB/CAR/PDMS presented better results.  
In this way, the present study presented new information about a fruit species of great importance in the Atlantic Forest, contributing to the deepening of knowledge about the sensory characteristics of grumixama pulp.  
Conceptualization, J.O.F.M., A.L.C.C.R., I.C.A.L., R.L.B.d.A. and R.A.; methodology, A.L.C.C.R., J.O.F.M., R.A. and M.R.S.; software, A.L.C.C.R., M.R.S., A.P.X.M. and T.N.d.A.R.; validation, A.L.C.C.R. and M.R.S.; formal analysis, A.L.C.C.R., A.P.X.M. and T.N.d.A.R.; investigation, A.L.C.C.R., L.A.N. and A.C.d.C.M.; resources, R.A., A.C.C.F.d.P., A.C.d.M. and J.O.F.M.; data curation, A.L.C.C.R., L.A.N. and A.C.d.M.; writing--original draft preparation, A.L.C.C.R. and L.A.N.; writing--review and editing, A.L.C.C.R. and M.R.S.; visualization, A.C.C.F.d.P., A.C.d.M., R.L.B.d.A. and I.C.A.L.; supervision, R.L.B.d.A., I.C.A.L. and J.O.F.M.; project administration, R.L.B.d.A., I.C.A.L., J.O.F.M. and A.L.C.C.R.; funding acquisition, R.A., J.O.F.M., I.C.A.L., R.L.B.d.A., A.C.C.F.d.P. and A.C.d.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** All data are contained within the article.  
**Acknowledgments:** The authors would like to thank Universidade Federal de Minas Gerais-UFMG, Pro-Reitoria de Pesquisa-PRPq-UFMG, Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior-CAPES (88882.380829/2019-01), Instituto Federal de Educacao Ciencia e Tecnologia de Minas Gerais (IFMG), and Universidade Federal de Sao Joao del-Rei (UFS) for the equipment loaned to carry out the analyzes and financial support.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
**Sample Availability:** Samples of grumixama are available from the authors.
## 4 System Model
### DMPF Scheduler
#### 4.2.1 DMPF Scheduler Processing Modes  
In Figure 6, we have two client requests \(\text{R}_{\text{i}}\) and \(\text{R}_{\text{j}}\). Each client request demands multiple SVC layers. The clients decide the requirement of multiple layers after looking at their playback and network bandwidth capacity. The client \(\text{R}_{\text{i}}\) requests two SVC layers \(\text{BL}_{0}\) and \(\text{EL}_{1}\), while client \(\text{R}_{\text{j}}\) requests three SVC layers, \(\text{BL}_{0}\), \(\text{EL}_{1}\), and \(\text{EL}_{2}\). If the DS is configured to be in _allLayer_ processing mode, then it takes the request \(\text{R}_{\text{i}}\) and accommodates all of its layers and then processes the next request \(\text{R}_{\text{j}}\). If the DS is configured to be in _layerWise_ request processing mode, then the DS processes the base layer (\(\text{BL}_{0}\)) of \(\text{R}_{\text{i}}\) and \(\text{R}_{\text{j}}\) first. Next, the DS processes the \(\text{EL}_{1}\) of \(\text{R}_{\text{i}}\) and \(\text{R}_{\text{j}}\). Since \(\text{R}_{\text{i}}\) does not contain \(\text{EL}_{2}\) the DS in _layerWise_ processing mode processes request \(\text{R}_{\text{j}}\)\(\text{EL}_{2}\).
## 4 PM-DQfD-Based Energy-Efficient Joint Design Scheme
### Prioritized Memories Deep Q-Learning from Demonstrations
```
1:Initialize:
2: Input replay memory \(D^{mplay}=\phi\), demonstration data set \(D^{demo}\), replay period \(P\), demonstration ratio \(v\), number of pre-training steps \(\kappa\) and memory size \(R\). 3: Initialize the Q-function and the target Q-function. 4:Pre-training:
5:For steps \(t\in[1,2,\ldots,\kappa]\)Do
6: Sample \(n\) transitions randomly from \(D^{demo}\). 7: Compute the overall loss function. 8: Perform gradient descent to update \(\theta\). 9:End For
10:Self-learning:
11:For episode \(\in[1,2,\ldots,T]\)Do
12: Initialize state \(s_{1}\). 13:For steps \(t\in[1,2,\ldots]\)Do
14: Choose action \(a_{t}\) based on policy \(\pi^{\in\mathbb{Q}_{\theta}}\). 15: Execute action \(a_{t}\) and observe reward \(r_{t}\), state \(s_{t+1}\). 16: Store transition \((s_{t},a_{t},r_{t},s_{t+1},\delta_{t}=\infty)\) in \(D^{mplay}\). 17: Sample \(n\) transitions randomly from \(D^{demo}\cup D^{mplay}\) with a fraction \(v\) of the samples from \(D^{demo}\). 18: Compute the overall loss function. 19: Perform gradient descent to update \(\theta\). 20: Calculate MET according to (18). 21:If\((R_{t}>a\%*N)\) or \(((R_{t}<R_{t-1})\) and \((R_{t}\geq b\%*N))\)
22:For\(transition=1,N\)Do
23:If\(b_{transition}<MET_{t}\)
24:Erase the transition from \(D^{mplay}\). 25:End For
26: Every \(P\) steps reset \(\tilde{Q}=Q\). 27:End For
28:If\(\left|D^{demo}\right|>S\)
29: Erase \(q\) transitions with the lowest \(\delta_{t}\) from \(D^{demo}\). 30:End For
```  
**Algorithm 1** Prioritized Memories Deep Q-learning from Demonstrations  
Figure 3: Implementation of memory optimization for the replay buffer.
# Novel Predictive Model of the Debonding Strength for Masonry Members Retrofitted with FRP
###### Abstract  
Strengthening of masonry members using externally bonded (EB) fiber-reinforced polymer (FRP) composites has become a famous structural strengthening method over the past decade due to the popular advantages of FRP composites, including their high strength-to-weight ratio and excellent corrosion resistance. In this study, gene expression programming (GEP), as a novel tool, has been used to predict the debonding strength of retrofitted masonry members. The predictions of the new debonding resistance model, as well as several other models, are evaluated by comparing their estimates with experimental results of a large test database. The results indicate that the new model has the best efficiency among the models examined and represents an improvement to other models. The root mean square errors (RMSE) of the best empirical Kashyap model in training and test data were, respectively, reduced by 51.7% and 41.3% using the GEP model in estimating debonding strength.  
debonding strength; FRP; masonry; gene expression programming; formulation +
Footnote †: journal: Applied Sciences
## 4 Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Techniques** & **BER** & **SSIM** & **NCC** & **Training Accuracy** & **Validation Accuracy** \\ \hline \multirow{4}{*}{**TCIA**} & **ASMA** & 55 & 42 & 52 & 85 & 81 \\ \cline{2-7}  & **DDWT** & 56 & 43 & 54 & 88 & 83 \\ \cline{2-7}  & **IW\_DH\_DWTQ\_CGA** & 59 & 45 & 56 & 89 & 85 \\ \hline \multirow{4}{*}{**REMBRANDT**} & **ASMA** & 61 & 51 & 58 & 92 & 82 \\ \cline{2-7}  & **DDWT** & 63 & 53 & 61 & 93 & 85 \\ \cline{2-7}  & **IW\_DH\_DWTQ\_CGA** & 65 & 55 & 63 & 95 & 89 \\ \hline \multirow{4}{*}{**MIAS**} & **ASMA** & 67 & 52 & 65 & 94 & 91 \\ \cline{2-7}  & **DDWT** & 68 & 54 & 68 & 96 & 93 \\ \cline{1-1} \cline{2-7}  & **IW\_DH\_DWTQ\_CGA** & 71 & 56 & 71 & 98 & 95 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparative analysis between proposed and existing techniques based on various medical images. Figure 6: Comparative analysis between proposed and existing technique for TCIA dataset in terms of (**a**) bit error rate, (**b**) SSIM, (**c**) NCC, (**d**) training accuracy, (**e**) validation accuracy. Figure 7: Comparative analysis between proposed and existing techniques for the REMBRANDT dataset in terms of (**a**) bit error rate, (**b**) SSIM, (**c**) NCC, (**d**) training accuracy, (**e**) validation accuracy. Figure 8: Comparative analysis between proposed and existing techniques for the MIAS dataset in terms of (**a**) bit error rate, (**b**) SSIM, (**c**) NCC, (**d**) training accuracy, (**e**) validation accuracy.