esa-sceva/satcom-chunk-collection huggingface dataset:
# Triangular-Shaped 5-DOF Piezoelectric Robot for Optical Lens Positioning
## 5 Conclusions  
A novel design of a 5-DOF piezoelectric robot for angular-planar positioning was introduced and investigated. The robot has a simple scalable design and consists of a piezo ceramic ring and a triangle-shaped passive layer. The robot operates at two resonant frequencies, and angular and planar motions are obtained using the vibroimpact operation principle. Two harmonic signals controlled by two electronically controlled switch boxes allow achieving 5-DOF motion of the payload.  
A numerical investigation of the robot showed that the first bending mode of the trapezoidal cantilever and the third radial mode of the piezoceramic ring can be employed to obtain planar and angular motion. The results of the numerical study showed that the coupling ratio between vibrations of active segments of the piezo ceramic ring is greater than four times, while the coupling ratio between vibrations of active and passive segments is more than 20 times. Therefore, the coupling between vibrations of different contacts has a minor influence on the particular direction of motion. On the other hand, in order to achieve precise motion control of the robot, a robot control algorithm must be developed.  
A prototype of the robot was made, and experimental investigation showed that the difference between calculated and measured resonance frequencies does not exceed 8.2%. Experimental investigations of dynamic characteristics showed that the highest planar and angular motions speeds reached 22.3 mm/s and 29.3 RPM when the voltage of 200 V\({}_{\text{P-P}}\) was applied and a payload of 25.1 g is used. Analysis of experimental results also showed that a notable difference of motion speed exists while planar and angular motions are generated in different directions, i.e., for planar motion the difference is up to 8.96%, while for angular motion is up to 4.94%. When the payload of 12.1 g is used, the difference does not exceed 2.82% for planar motion and 4.29% for angular motion.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app12052498/s1](https://www.mdpi.com/article/10.3390/app12052498/s1)]([https://www.mdpi.com/article/10.3390/app12052498/s1](https://www.mdpi.com/article/10.3390/app12052498/s1)); Video S1: represents the planar motion of robot under payload; Video S2: represents the angular motion of payload generated by the robot.  
Conceptualization, D.M.; data curation, A.C.; Investigation, A.C.; supervision, D.M.; validation, V.J.; visualization, V.J.; writing--original draft, A.C. and V.J.; writing--review and editing, D.M. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the European Social Fund according to the activity \"Promotion of postdoctoral fellowships studies\" of Measure No. 09.3.3-LMT-K-712-19-0082.  
The authors confirm that all data of this study are available within the article.  
The authors declare no conflict of interest.
## 4 Stator-Side External Retrofit Techniques
### Series Grid-Side Converter  
The SGSC is a bidirectional power converter connected across the DC-link, as shown in Figure 18. The SGSC has the capability to regulate DC-link voltage by controlling the active power injection or absorption. Another function of the SGSC is to compensate for stator harmonic voltages by synthesizing a harmonic voltage content. In order to offset the influence of negative-sequence grid voltage, the SGSC is capable of injecting series compensation voltage to balance the stator voltage. The stator flux can also be controlled by the series voltage injection through the SGSC [55; 56; 57; 58].  
To improve the control effect of the SGSC, an adaptive resonant controller was proposed in Reference [59]. Through voltage distortion and unbalance compensation, the hybrid controller regulates the stator voltage at its rated value even under unbalanced grid voltage conditions. The simulation results proved that this scheme could keep the stator voltage balanced and sinusoidal to enhance the generated power quality. A unified DFIG-WT architecture comprising parallel grid-side rectifiers (PGSRs) and an SGSC was proposed in Reference [60]. In this scheme, both the PGSR and the SGSC are connected to the DC-link and share the same DC bus. During sub-synchronous operation, the PGSR maintains the DC-link voltage by injecting power into the rotor circuit. The SGSC maintains the stator flux magnitude in a desirable range to meet steady-state power requirements. The hybrid control scheme equips the DFIG with the capability of unencumbered power processing and enhances the LVRT capability.  
Figure 17: DFIG system with SCFCL.  
Figure 18: Series grid-side converter protection circuit.
## Nomenclature  
\begin{tabular}{l l} _COP_ & coefficient of performance \\ \(c_{p}\) & specific heat (kJ/kg \({}^{\circ}\)C) \\ \(e\) & specific exergy (kJ/kg) \\ E & exergy (kW) \\ \(h\) & specific enthalpy (kJ/kg) \\ \(LMTD\) & logarithmic mean temperature difference \\ \(m\) & mass flow rate (kg/s) \\ \(n\) & speed of compressor (r/s) \\ \(Q\) & energy (kW) \\ \(T\) & temperature (\({}^{\circ}\)C) \\ \(\Delta T\) & temperature difference (\({}^{\circ}\)C) \\ \(\Delta\Delta T\) & double difference of temperature (\({}^{\circ}\)C) \\ \(\mathit{UIA}\) & multiplication of heat transfer coefficient and area (W/\({}^{\circ}\)C) \\ \(V\) & volume per revolution (cm\({}^{3}\)/r) \\ \(W\) & work of the compressor (kW) \\ \(z\) & ratio of logarithmic mean temperature difference to arithmetic mean \\ _Greek symbols_ \\ \(\rho\) & density (kg/m\({}^{3}\)) \\ \(\eta\) & efficiency \\ _Subscripts_ \\ \(a\) & absorber \\ _as_ & absorption subsystem \\ _avg_ & average \\ \(c\) & condenser \\ _chtw_ & chilled water \\ _cw_ & cooling water \\ \(d\) & design \\ _dis_ & discharge \\ \(e\) & evaporator \\ _ef_ & external fluid \\ \(f\) & fuel \\ \(g\) & generator, global \\ _he_ & heat exchanger \\ _hw_ & hot water \\ \(i\) & inlet \\ _if_ & internal fluid \\ _loss_ & loss \\ _min_ & minimum \\ \(o\) & outlet \\ \(p\) & product \\ \(s\) & isentropic \\ _sub_ & subcooler \\ _suc_ & suction \\ \(p\) & volumetric \\ \end{tabular}
## 2 Methodology
### Refrigeration Cycle Measurement Method

Table 1: Description of the measurement variables. Figure 3: Process of the refrigeration cycle measurement method: Step 1 (**a**), Step 2 (**b**), Step 3 (**c**), Step 4 (**d**), Step 5 (**e**). The difference between the measurement method for air-source heat pump systems proposed in this study and existing methods is the input and measurement variables. The existing method measures the refrigeration cycle by measuring the pressure and enthalpy of the refrigerant. If it is difficult to measure the enthalpy of a refrigerant, the enthalpy can be estimated by measuring the temperature and pressure of the refrigerant. The proposed measurement method uses the temperature and humidity of the inlet and outlet air of the evaporator and condenser, the supply airflow rate, and the evaporator refrigerant temperature, as the measurement variables. The evaporator refrigerant temperature is measured by the surface temperature of the refrigerant pipe of the evaporator. Figure 5 presents a schematic diagram of the difference between the existing and proposed methods. Figure 4: Schematic diagram of the measurement points. Figure 5: Comparison of existing (**a**) and proposed method (**b**). The proposed refrigeration cycle measurement method was used to predict the COP and power consumption, which are the key operating data of the air-source heat pump system. The power consumption predicted by the refrigeration cycle method is the enthalpy change in the evaporator and condenser and can be expressed as Equation (10). The COP predicted using the refrigeration cycle is the enthalpy change in the evaporator and condenser, expressed as Equation (11). \[W=\dot{m}_{air}\big{(}\Delta h_{Cond}-\Delta h_{Equ}\big{)} \tag{10}\]  
\[COP=\frac{1}{\frac{\Delta h_{Cond}}{\Delta h_{Equ}}-1} \tag{11}\]  
where \(\Delta h_{Cond}\) is the enthalpy change in air in a condenser in kJ/kg, \(\Delta h_{Equ}\) is the enthalpy change in air in an evaporator in kJ/kg.
## 1 Introduction
Overall, there seems to be a reasonable amount of data about the principles and mechanisms of laser drilling, with many studies focusing on the effects of processing parameters on the quality and speed-related properties of the drilled hole. However, there are no studies conducted specifically on drilling laser additively manufactured materials. This is understandable, since the technology is still new and the differences between metallic materials produced conventionally and from melted powder seem to be quite small. This study aims to find out whether there are any significant differences between laser drilling a one-millimeter thick cold rolled AISI 316L stainless steel and a similar sheet produced by selective laser melting--one of the techniques of additive manufacturing. The results provided will support more fundamental research into the differences between conventionally produced and additively manufactured steel parts, as well as help the industry utilize laser engraving or drilling technology to better optimize their laser parameters. In this study, the laser drilling is performed by engraving a circular hole through the material, because that method works best for the laser used in the study. While this is an unusual process, in this case the specific method of drilling is not important as the study is about the differences in processing two different materials. Laser engraving shares many similarities with laser drilling, because they are both processes that essentially require the removal of material until a desired shape is achieved. The major differences are that in engraving, the material is not usually fully penetrated, and the desired shapes are more complex.
## 6 Result and Discussion
### Simulation Result
#### 6.2.2 Scenario 2  
In this scenario, the ability of a configuration with optimum design parameters to navigate via waypoints was evaluated in the event of a single actuator failure. The waypoints are positioned so that they reflect the tasks that are carried out to avoid static barriers that may be encountered in real-world applications. The B-spline trajectory generating technique established in [28] was used to combine the waypoints as shown on Figure 17.  
As shown in Figure 18, the possible environmental confrontation is depicted as windows at different heights, trees, and a house. The first window was placed in such a way that it allowed the UAV to pass at a lower altitude below two meters, whereas the second window was placed at the height of six meters. Following the mission profile, the UAV was ordered to take off to the altitude of four meters (Figure 18a) pitch forward about ten meters, and follow the curved path to the first and second window obstacles while rolling, pitching, and descending to the height of two meters simultaneously (Figure 18c). Then it had to ascend simultaneously to an altitude of six meters (Figure 18d) to pass through the opening, and finally land at the depicted landing pad (Figure 18b). Therefore, in this flight  
Figure 16: Hovering test result for fault injected at t = 20 s on propeller (**a**) Altitude of the vehicle (**b**) Attitude response (**c**) Propeller tilting angle (**d**) Propeller’s rotation rate.  
Figure 17: The path followed by the UAV.  
path, the performance of the optimized configuration during a single actuator failure was conducted to fulfill the specified operation.  
The UAV was reconfigured to the optimum propeller tilting and offset angle listed in Table 3 corresponding to the actuator-1 failure. As shown in Figure 19, the result showed that the desired operation is fulfilled while the actuator-1 failed with optimized parameters.
## 3 Results and Discussion
### Capsule Microstructure  
The cross-sectional ESEM images of capsules fabricated with A/R ratios of 60/40, 50/50, 40/60, 30/70, 20/80 and 10/90 are shown in Figure 5. In these images, the bright pixels are recognized as alginate, while the rest are rejuvenator. These ESEM images indicate an existence of alginate network inside of the capsules, which means the capsules have the rejuvenator encapsulated in the porous media instead of the traditional capsule' core-shell structure. From the image in Figure 5a to Figure 5f, an increasing rejuvenator volume and decreasing alginate network can be seen, which matches the applied amount of rejuvenator in each capsule.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Alginate/Rejuvenator Ratio (\%)** & **Diameter (\(\mu\)m)** & **Minor Axis (\(\mu\)m)** & **Morphology** \\ \hline
100/0 & 1500 & 980 & \\
60/40 & 1920 & 1670 & \\
50/50 & 1640 & 1310 & Ellipsoid \\
40/60 & 1740 & 1600 & \\
30/70 & 1950 & 1950 & \\
20/80 & 2170 & 2170 & Sphere \\
10/90 & 2450 & 2450 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calcium alginate capsules with different alginate/rejuvenator ratios.  
Figure 5: The cross-sectional ESEM images of calcium alginate capsules fabricated with A/R ratios of: (**a**) 60/40, (**b**) 50/50, (**c**) 40/60, (**d**) 30/70, (**e**) 20/80 and (**f**) 10/90.  
As stated in the previous research [16], this honeycomb-like structure provides a structural reinforcement that allows the capsule to possess higher thermal and mechanical resistance. Meanwhile, the compartmented rejuvenator storage in porous media could even have multi-healing function on micro cracks.
# Binary-Tree-Fed Mixnet: An Efficient Symmetric Encryption Solution
## 4 Security Analysis
### Analysis
Therefore, for this change to allow the size of \(B\) to be reduced, the  
Figure 4: Binary-tree cuts: (**a**) permutation of third level (node \(i\)) is not known for the cut, so they cannot build the key; (**b**) cut able to break privacy. level would have to be decreased, thus reducing the number of nodes necessary to know the permutations. However, reducing a level implies multiplying by two the minimum number of nodes necessary to know the key (those of the level) while only reducing those necessary to know the permutation by one unit. Therefore, \(B\) can never be less than \(A\). Its size, \(|A|=2^{r-1}+k-r\), reaches the minimum value for \(r=1\); that is, the root node is enough to know the key to which any sequence up to a leaf node must be added. Therefore, there is one node per level. This result can be applied to labels as they follow the same process as keys. At the second stage, the mixnet is constituted with a random sequence of all the nodes. Then, each node chooses a random permutation to apply to the batches. Users then send their encrypted labels to the mixnet. Nodes learn the position to apply each key through the labels. If a node knows the owner of a key (for example, when this node is the root node in a successful attack of \(k\) adversaries on stage 1), it can identify the owner of any message when it reaches that node and share that information with the following node. However, this node cannot disclose the message without the remaining keys. Therefore, any successful collusion in stage 2 will need the collaboration of all the existing nodes between this attacking node and the end of the mixnet. The easiest case is when the attacking node is precisely the last in the mixnet. In this case, no additional node is required for the attacker and therefore no more adversaries than \(k\) are needed. In stage 3, the same reasoning can be applied as in stage 2 since it is very similar (messages are encrypted instead of labels). Thus, the minimum number of adversaries to break privacy is, again, \(k\).
# \(\beta\)-Glucans from Yeast--Immunomodulators from Novel Waste Resources
## 5 Recognition Receptors for \(\beta\)-Glucans  
Dectin-1 is often referred to as the \(\beta\)-glucan receptor. Dectin-1 is expressed on monocytes, macrophages, neutrophils, dendritic cells, and T lymphocytes, activated by the binding of \(\beta\)-glucans [55; 56]. The receptor is also present in mucosal immune cells where pathogens invade. By regulating the inflammasome and transcription factor activation, this binding generates cytokines, chemokines, and reactive oxygen species (ROS) [46; 57; 58]. This recognition relies on the 1,3 backbone [59]. Several other receptors react to \(\beta\)-glucans, including lactoseylceramide, scavenger, and Toll-like receptors [55; 60]. Toll-like receptor (TLR2) binding causes ROS, pro-inflammatory indicators, and pathogen clearance phagocytosis [61]. The pathway activated after binding can either stimulate the immune response and initiate a cascade of inflammatory mediators or, in contrast, dampen down inflammation through modulatory processes [16].  
When \(\beta\)-glucans bind to Dectin-1, it increases phosphorylation of its intracellular immunoreceptor tyrosine-based activation motif (ITAM) and Syk and activates the PI3K/Akt pathway. This finally results in phagocytosis, the creation of ROS, microbial death, and cytokine release [62; 63; 64]. A more detailed graphical representation of this process can be found at [65; 66].  
Neutrophils, monocytes, and natural killer (NK) cells express the CR3 receptor. CR3 is distinctive in that it contains two different binding sites for ligands. A carbohydratebinding lectin-like domain, which can bind \(\beta\)-glucans, serves as the second ligand-binding site. Binding will enhance cytotoxicity against \(\mathrm{iC3b}\)-opsonized target cells such as tumour cells, phagocytosis, and degranulation [67]. The CR3 produced by innate cells such as macrophages, dendritic cells, natural killer cells, and neutrophils binds to yeast-derived low molecular weight soluble \(\beta\)-glucans. The CR3 receptor is activated by the binding of soluble \(\beta\)-glucans and \(\mathrm{iC3b}\), and this leads to the destruction of tumour cells coated with \(\mathrm{iC3b}\) through CR3-dependent cellular cytotoxicity (DCC) [68].
# Determining the Best Dressing Parameters for External Cylindrical Grinding Using MABAC Method
## 4 Results and Discussions
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Trial.**} & \multicolumn{2}{c}{**MABAC**} & \multicolumn{2}{c}{**TOPSIS**} & \multicolumn{2}{c}{**MARCOS**} \\ \cline{2-7}  & **Si** & **Rank** & **Ri** & **Rank** & **f(Ki)** & **Rank** \\ \hline
1 & \(-\)0.3097 & 16 & 0.0494 & 16 & 0.0204 & 16 \\
2 & \(-\)0.1599 & 15 & 0.3368 & 13 & 0.0228 & 15 \\
3 & 0.4209 & 1 & 0.8882 & 1 & 0.0435 & 1 \\
4 & \(-\)0.0194 & 9 & 0.5878 & 7 & 0.0273 & 9 \\
5 & 0.0172 & 8 & 0.3952 & 11 & 0.0307 & 6 \\
6 & \(-\)0.0566 & 11 & 0.3592 & 12 & 0.0266 & 11 \\
7 & 0.1804 & 3 & 0.7214 & 2 & 0.0425 & 2 \\
8 & 0.0724 & 5 & 0.6530 & 4 & 0.0312 & 5 \\
9 & 0.0639 & 6 & 0.3954 & 10 & 0.0297 & 8 \\
10 & 0.1541 & 4 & 0.6325 & 5 & 0.0353 & 4 \\
11 & \(-\)0.0326 & 10 & 0.4276 & 8 & 0.0269 & 10 \\
12 & 0.0504 & 7 & 0.6166 & 6 & 0.0302 & 7 \\
13 & \(-\)0.1200 & 14 & 0.2461 & 14 & 0.0254 & 14 \\
14 & \(-\)0.1012 & 13 & 0.2290 & 15 & 0.0255 & 13 \\
15 & 0.1866 & 2 & 0.6821 & 3 & 0.0365 & 3 \\
16 & \(-\)0.0876 & 12 & 0.4086 & 9 & 0.0255 & 12 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Priority index and ranking when the weight calculation by entropy method. Figure 4: Graph to compare MABAC, TOPSIS, and MARCOS results. (**a**) Entropy weight. (**b**) MEREC weight. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Trial.**} & \multicolumn{2}{c}{**MABAC**} & \multicolumn{2}{c}{**TOPSIS**} & \multicolumn{2}{c}{**MARCOS**} \\ \cline{2-7}  & **Si** & **Rank** & **Ri** & **Rank** & **f(Ki)** & **Rank** \\ \hline
1 & \(-\)0.27647 & 16 & 0.0367 & 16 & 0.0211 & 16 \\
2 & \(-\)0.14691 & 15 & 0.3405 & 13 & 0.0229 & 15 \\
3 & 0.49609 & 1 & 0.8889 & 1 & 0.0438 & 1 \\
4 & \(-\)0.03072 & 9 & 0.6180 & 7 & 0.0266 & 9 \\
5 & \(-\)0.00262 & 8 & 0.4585 & 9 & 0.0290 & 8 \\
6 & \(-\)0.06243 & 11 & 0.4011 & 12 & 0.0259 & 11 \\
7 & 0.144716 & 4 & 0.7842 & 2 & 0.0398 & 2 \\
8 & 0.061298 & 6 & 0.6963 & 4 & 0.0302 & 5 \\
9 & 0.104289 & 5 & 0.4131 & 11 & 0.0302 & 6 \\
10 & 0.145346 & 3 & 0.6815 & 5 & 0.0335 & 4 \\
11 & \(-\)0.03564 & 10 & 0.4623 & 8 & 0.0263 & 10 \\
12 & 0.035771 & 7 & 0.6609 & 6 & 0.0291 & 7 \\
13 & \(-\)0.13474 & 14 & 0.3034 & 14 & 0.0245 & 13 \\
14 & \(-\)0.08992 & 12 & 0.2681 & 15 & 0.0255 & 12 \\
15 & 0.183893 & 2 & 0.7283 & 3 & 0.0348 & 3 \\
16 & \(-\)0.11064 & 13 & 0.4500 & 10 & 0.0244 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Priority index and ranking when the weight calculation by MEREC method.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Comparison with Existing Algorithms
#### 5.2.3 Impacts of the Packet Generation Frequency  
Figure 9 shows the resulting impact of the packet generation probability on the network lifetime. In this experiment, the number of sensors and targets is set to 300 and 150, respectively. The location of the targets are generated randomly. In all algorithms, the network lifetime tends to decrease when the packet generation probability increases. When the probability is too large (i.e., being more than 0.25), all sensors' energy consumption rate (especially sensors in the base station's vicinity) becomes fast. Therefore, the sensors' batteries exhaust quickly. In such a critical case, the difference between the algorithms is minor. We can see the improvement of Fuzzy Q-charging over the existing algorithms clearly under the condition of small packet generation probability. Notably, when the probability is 0.1, Fuzzy Q-charging prolongs the network lifetime infinitely, while the others cannot. When the probability is greater than 0.1, Fuzzy Q-charging's network lifetime is 1.4 times more than Q-learning, 5.0 times more than INMA's, 5.1 times more than GSA's, and 21.1 times more than RMP-RL, on average. The performance gaps between Fuzzy Q-charging and the other algorithms decrease when the packet generation probability increases. Even when the probability is 0.25, the network lifetime's ratio achieved by  
Figure 8: Network lifetime vs. the number of targets.  
Fuzzy Q-charging is 1.4, 4.5, 4.5, and 10.8 times better than Q-charging, INMA, GSA, and RMP-RL, respectively.  
In summary, we can conclude Fuzzy Q-charging outperforms the existing algorithms. Moreover, the performance gaps between Fuzzy Q-charging and the others increase when the number of sensors increases, the number of targets decreases, or the packet generation probability decreases.