esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Security Threats and Side-Channel Attacks for Digital Agriculture
## 3 Threats to Digital Agriculture
### Internet of Things, Robotics, and Aerial Systems  
The Internet of Things, robotics, drones, and aerial systems are the enablers of digital agriculture. Sensors and agricultural robots are remotely controlled. The compromise of sensors, actuators, and robots can disrupt their normal operation or, in the worst case, be used in agri-terrorism. Heavy tractors or drones can be used to destroy fields, transport illegal goods, conduct a crime, or make physical attacks by crashing into the target. GPS spoofing and wireless communication vulnerabilities can be exploited to conduct destructive attacks.
# Advantages and Limitations of Current Microgravity Platforms for Space Biology Research
## 3 Ground Microgravity Analogues
### The Rodent Hindlimb Unloading (HU)  
The rodent hindlimb unloading (HU) consists of the selective unloading of the hindquarters of rats or mice using tail traction, surgical pins, or body harnesses (Figure 2d) [51]. Since its development in the 1980s, HU has become a widely used and generally accepted method to simulate spaceflight-like conditions in rats and mice [52]. HU causes removal of ground reaction forces from the hindlimbs and cephalad fluid shift, similarly to what happens to astronauts in Space [5].  
The validity of HU as an analogue was confirmed in the Cosmos 2044 mission. HU was used as a 1-g on-ground control for a 14-day long flight onboard of an unmanned biosatellite [53,54]. Despite differences in tissue collection after unloading between the two models (15-60 min for HU rats vs. 8-11 h for spaceflight rats), the results showed that HU and spaceflight induced similar atrophic changes in bone and muscle tissues [54,55]. Subsequently, other investigators confirmed that HU induces similar, but not always identical physiological changes to those observed in spaceflight [56].  
Data should be interpreted cautiously when comparing the HU rodent model and spaceflight. Unlike spaceflight, in the HU model, the gravity force is still acting on the internal organs of the rodent, meaning that they exert pressure against each other, instead of free floating. Moreover, animals do not experience the stress of launch and transportation [57]. The time between animal 1-g reloading and sacrifice spans from minutes in HU to many hours for spaceflight (Table 2), which could either mitigate or mask some of the spaceflight-induced effects [51].  
Environmental and physiological parameters can influence the ability of the HU to effectively induce spaceflight-like physiological responses. Animal housing, handling, sex, age (growing vs. adult), species (mouse vs. rat), and strain are important factors to consider when designing HU studies [56]. The extrapolation of conclusions from data gained in animals of different age or species should be carried out carefully. Indeed, HU could be a suitable microgravity analogue for the study of the processes responsible for growth, but not for maintenance of adult tissues. Similarly, features found in a species might not widely apply to other species (e.g., contractile properties of skeletal muscles). Another critical variable for HU studies is the length of exposure, as acute responses to short exposure and chronic adaptation after longer exposures can differ dramatically [51].  
Despite its limitations, HU has several strengths compared with spaceflight. Experiments can be performed without the crew time and safety constraints arising onboard of Space vehicles. Furthermore, during the experiment, adjustments can be made without an excessive impact on cost and tissues can be sampled from sedated animals at any time. These operations, which are routine on the ground, cannot be carried out with ease in Space [56].
## 7 Our Approach--Hierarchical Weighted Attention Network
#### Comparison of HAN an HWAN  
To evaluate our approach, we performed experiments aiming to compare the HAN method with different variants of the HWAN method. It was observed that limiting the length of text processed by the model degraded the performance of the HWAN model to a lesser extent. We also noted that the Reuters dataset, for almost all configurations of the HWAN model, performed better for both restricted and not-restricted document length. The worst results came from the operation of multiplying statistical features and neural network excitation, as can be seen in Tables 3 and 4. For the limited length of the text processed, the best results were obtained by adding statistical features whose results did not vary much. For the unlimited length of processed text, the best results were achieved for concatenation and addition TFIDF features, with a maximum difference of 0.71 percentage points in their results, and for the addition of TF features. As shown in Tables 3 and 4, the multiplication operation degraded the resulting representations, which affected the quality of further processing, resulting in a drastic decrease in the performance of these representations in the classification task, especially for the BBCSport dataset. For further comparison, the TFIDF + _add_ variant was denoted as HWAN, as it produced the best performance for the majority of datasets. For the Reuters dataset, the best performance was achieved by the baseline model BoW (83.42%). The other models performed worse: TFIDF (\(-\)7.84 percentage point. ), LSA (\(-\)19.30 percentage point. ), LDA (\(-\)30.51 percentage point. ), P-SIF (\(-\)20.81 percentage point. ), Doc2 Vec-DM (\(-\)3.61 percentage point. ), Doc2 Vec-DBoW (\(-\)0.77 percentage point. ), HAN (\(-\)2.09 percentage point. ), HWAN (\(-\)0.57 percentage point. ), Longformer (\(-\)3.52 percentage point.). For the Ohsumed dataset, the best performance was achieved by the BoW (60.25%) model. The other models performed worse: TFIDF (\(-\)3.81 percentage point. ), LSA (\(-\)24.53 percentage point. ), LDA (\(-\)40.18 percentage point. ), P-SIF (\(-\)25.92 percentage point. ), Doc2 Vec-DM (\(-\)1.59 percentage point. ), Doc2 Vec-DBoW (\(-\)0.77 percentage point. ), HAN (\(-\)5.86 percentage point. ), HWAN (\(-\)4.61 percentage point. ), Longformer (\(-\)6.37 percentage point.). For the 20 Newsgroups dataset, the best performance was achieved by the HAN (89.57%) model. TFIDF (\(-\)4.02 percentage point.) performed slightly worse. The other models performed worse: BoW (\(-\)17.85 percentage point. ), LSA (\(-\)17.81 percentage point. ), LDA (\(-\)28.67 percentage point. ), P-SIF (\(-\)26.16 percentage point. ), Doc2 Vec-DM (\(-\)14.32 percentage point. ), Doc2 Vec-DBoW (\(-\)7.05 percentage point. ), HWAN (\(-\)0.89 percentage point. ), Longformer (\(-\)8.24 percentage point. ).
# Enhanced LDM for Next-Generation Digital Broadcasting Transmission
## 3 Proposed En-LDM Scheme
### Channel Capacity Analysis  
#### 3.2.1 Analysis for LDM  
The maximum achievable data rate under a communication link (in bits/s/Hz) is also known as the Shannon limit. The Shannon limit for the additive white Gaussian noise (AWGN) channel is given in [28]:  
\[C_{S}=\log_{2}(1+\frac{S}{N}) \tag{9}\]  
where \(S\) and \(N\) are the signal average transmit power and noise power respectively.  
Assuming that the total transmission power of the En-LDM system is normalized (that is, \(S=1\)) and the injection level is \(\Delta\) dB, then the transmission power of the two layers can be calculated as  
\[\begin{array}{l}P_{U}=\frac{10^{\Delta/10}}{1+10^{\Delta/10}}\\ P_{L}=\frac{1}{1+10^{\Delta/10}}\end{array} \tag{10}\]  
The CL signal power is regarded as the transmission power of the desired signal, and the EL signal power is regarded as the noise power when calculating the CL channel capacity. Since the working points of CL service and EL service are obviously different, the CL signal can be regarded as error-free elimination when calculating the EL channel capacity.  
Following Equation (9), the achievable capacities for the two transmission layers are calculated as follows:  
\[\begin{array}{l}C_{U}=\log_{2}(1+\frac{P_{U}}{P_{U}+N})\\ C_{L}=\log_{2}(1+\frac{P_{U}}{N})\end{array} \tag{11}\]  
Therefore, by substituting Equation (10) into Equation (11), the Shannon limit for the two services of the traditional LDM system are calculated as  
\[\begin{array}{l}C_{M}^{LDM}=\log_{2}\left(1+\frac{10^{\Delta/10}}{1+N\cdot(1 +10^{\Delta/10})}\right)\\ C_{F}^{LDM}=\log_{2}\left(1+\frac{1}{N\cdot(1+10^{\Delta/10})}\right)\end{array} \tag{12}\]
## 2 Variational Mode Decomposition
 \tag{4}\]  
In the frequency domain, the solution to Equation (4) can be found as [19]  
\[\hat{u}_{k}(\omega)=\frac{\hat{x}(\omega)-\underset{i\
eq k}{\sum}\hat{u}_{i}( \omega)+\frac{1}{2}\hat{\lambda}(\omega)}{1+2\alpha(\omega-\omega_{k})^{2}}. \tag{5}\]  
Then, the IMF in the time domain \(u_{k}(t)\) can be obtained by inverse Fourier transforming Equation (5) and taking the real part. The center frequency \(\omega_{k}\) associated with each IMF \(u_{k}(t)\) can also be updated as a solution to a minimization problem equivalent to Equation (3)  
\[\omega_{k}=\underset{\omega_{k}}{\operatorname{argmin}}L(\{u_{k}(t)\},\{ \omega_{k}\},\lambda(t)]=\underset{\omega_{k}}{\operatorname{argmin}}\sum_{k} \|\frac{\partial}{\partial t}[\delta(t)+j\frac{1}{\pi t}]*u_{k}(t)\}\exp(-j \omega_{k}t)\|_{2}^{2}. \tag{6}\]  
It can be found as the center of gravity of the associated IMF's power spectrum [21]  
\[\omega_{k}=\frac{\int_{0}^{\infty}\omega|\hat{u}_{k}(\omega)|^{2}d\omega}{\int _{0}^{\infty}|\hat{u}_{k}(\omega)|^{2}d\omega}. \tag{7}\]  
The complete algorithm of VMD is summarized as follows:  
1. Initialization: Let \(\{\hat{u}_{k}^{0}(t)\}\), \(\{\hat{\omega}_{k}^{0}\}\), \(\hat{\lambda}^{0}(t)\), \(n\) be 0, and predefine convergence threshold \(\varepsilon\) and the number of IMFs \(K\) to be separated. 2. Update each IMF \(u_{k}(t)\) and its associated center frequency \(\omega_{k}\), for \(k=1:K\) and all \(\omega\geq 0\)  
\[\hat{u}_{k}^{n+1}(\omega)=\frac{\hat{x}(\omega)-\underset{i<k}{\sum}\hat{u}_{i }^{n+1}(\omega)-\underset{i>k}{\sum}\hat{u}_{i}^{n}(\omega)+\frac{1}{2}\hat{ \lambda}^{n}(\omega)}{1+2\alpha(\omega-\omega_{k}^{n})^{2}}, \tag{8}\]  
\[\omega_{k}^{n+1}=\frac{\int_{0}^{\infty}\omega\left|\hat{u}_{k}^{n+1}(\omega) \right|^{2}d\omega}{\int_{0}^{\infty}\left|\hat{u}_{k}^{n+1}(\omega)\right|^{2 }d\omega}. \tag{9}\]3. Update the Lagrangian multiplier, for all \(\omega\geq 0\)  
\[\hat{\lambda}^{n+1}(\omega)=\hat{\lambda}^{n}(\omega)+\tau[\hat{x}(\omega)-\sum \limits_{k}\hat{u}_{k}^{n+1}(\omega)], \tag{10}\]  
where \(\tau\) is the Lagrangian multiplier update parameter. 4. Check the convergence condition  
\[\sum\limits_{k}\frac{\left\|\hat{u}_{k}^{n+1}(t)-\hat{u}_{k}^{n}(t)\right\|_{2 }^{2}}{\left\|\hat{u}_{k}^{n}(t)\right\|_{2}^{2}}<\varepsilon. \tag{11}\]  
If it is met, let \(u_{k}(t)=\hat{u}_{k}^{n+1}(t)\) and \(\omega_{k}=\omega_{k}^{n+1}\), and terminate the decomposition. Otherwise, let \(n=n+1\), return to Step 2. VMD decomposes a complex signal into a specific number of IMFs. These IMFs are AM-FM in nature, thus we can estimate their amplitude envelope and instantaneous frequency. More importantly, the non-recursive and concurrent decomposition nature of VMD effectively avoids the shortcoming of recursive decomposition algorithms, such as sensitivity to noise and sampling, over-shooting or under-shooting of upper and lower envelope via interpolation to extrema.
# Research on Springback Compensation Method of Roll Forming Based on Improved Fuzzy PID Control
## 5 Conclusions  
This paper addresses the issue of metal sheet springback affecting the accuracy of circular roll forming and proposes a springback compensation control strategy. Through theoretical analysis and experimental research, the following conclusions are drawn:  
1. Based on the mechanical principle of sheet springback during the circular roll forming process, a kinematic model for circular rolling springback compensation is established. This model, relying on the principle of the linkage between motor drive and mechanical structure, adjusts the gap between the working roll and the driven roll by controlling the rotation angle of the motor, thereby adjusting the rolling angle of the bending roll to achieve springback compensation and real-time adjustment of the forming radian of the sheet.
2. Comparative experimental results show that, after applying the springback compensation strategy, the errors between the circular roll forming radii and the desired radius for aluminum alloy, Q235 steel plate, and 20 steel plate are reduced to 1.60%, 1.55%, and 1.72%, respectively, effectively reducing the forming errors caused by sheet springback.  
Although certain achievements have been made in this study, there are still some limitations. Currently, this research only focuses on the relationship between springback and rolling angle. In the follow-up, it is possible to conduct an in-depth exploration of the influence laws of various factors such as the material, temperature, and rolling speed of the sheet on springback, providing a more comprehensive theoretical basis for the optimization of the control strategy.  
Conceptualization, T.W. and W.F.; methodology, W.F.; software, T.W. and W.F.; validation, T.W., W.F. and Y.J.; formal analysis, W.F.; investigation, T.W., W.F. and H.W.; resources, Y.Q.; data curation, W.F.; writing--original draft preparation, T.W. and W.F.; writing--review and editing, Y.Q. and D.Z.; project administration, Y.Q.; funding acquisition, Y.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Technical Innovation Project of Hubei Province, grant number 2022 BEC02; Major Projects of Hubei Provincial Science and Technology Department, grant number 2020 BAB037; and Hubei Provincial Department of Education Science and Technology Research Program Young Talents Project, grant number Q20201405.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.  
The following is a glossary of symbols employed in this study:  
\begin{tabular}{l l} Symbol & Description \\ \(R\) & Electrical resistance \\ \(R_{\rm s}\) & Actual forming radius \\ \(L\) & Inductance \\ \(L_{q}\) & Expected distance \\ \(L_{s}\) & Actual distance \\ \end{tabular}
## 3 Results
### AUROC Results  
The AUROC curves regarding biomarker levels in the dependence of time ratio (baseline--24 h--96 h--3 months--12 months) for the prediction of PH presence (sPAP \(\geq\) 40 mmHg) were figured out. Therefore, AUC, cut-off values with YI, and sensitivity and specificity were extracted in addition to ROC curves (Figure 2). This analysis identified sST2 plasma levels between 12,783.51 pg/mL (3 months) and 16,347.61 pg/mL (12 months) as an optimal cut-off value for PH prediction, with the 3-month cut-off as the best predicted value (AUC 0.746; 95% CI 0.634--0.859; \(p<\) 0.001; YI 0.48; sensitivity 0.55; specificity 0.93) (Figure 2A). For GDF-15 (Figure 2B), however, only the cut-off value of 1290.91 pg/mL was statistically significantly different after 24 h (AUC 0.666; 95% CI 0.539--0.792; \(p<\) 0.016; YI 0.31; sensitivity 0.53; specificity 0.78). From baseline to 12 months after TAVR, H-FABP and IGF-BP2 continuously showed significant cut-off values for the detection of sPAP \(\geq\) 40 mmHg (Figure 2C,D). Regarding the AUROC analyses of suPAR (Figure 2E), the best cut-off value with 3791.09 pg/mL was shown, with a solid significance of \(p=0.003\) at baseline (AUC 0.695; 95% CI 0.577--0.814; YI 0.42; sensitivity 0.70; specificity 0.72).
## 2 Results and Discussion
### Applied Voltage  
The electric field is a new emerging driving force in various analytical sample preparation methods. It may be coupled with an established separation technique or applied as the sole driving force in a sample preparation strategy. It accelerates the mass transfer of charged species, from the sample matrix into the receiving phase (sorbent). The sample and sorbent can be either in direct contact or separated by a solid or liquid barrier. Charged species in electrolyte solutions can be forced to migrate by the applied voltage, based on their electrophoretic mobility. Imposing this kind of control on the mass transfer of charged analytes has introduced a new strategy for conducting the selective sample preparation [27]. To evaluate the effect of voltage on the performance of the EE-SPME method, first \(-5\), \(-10\), \(-15\) and \(-20\) V were evaluated. The results showed that the extraction efficiency decreased after \(-5\) V. Therefore, the applied voltages were investigated over the range of 0--5 V. As shown in Figure 3, the highest extraction efficiency was at \(-1\) V and after that slightly decreased.  
Figure 3: Effect of applied voltage on the extraction efficiency of the EE-DI-SPME sampling strategy (condition: sample solution pH: 6; extraction temperature: 50 \({}^{\circ}\)C; extraction time: 20 min; stirring rate 500 rpm).  
Figure 2: Effect of sample solution pH on the efficiency of direct immersion (DI) and electroenhanced solid phase microextraction followed by gas chromatography flame ionization detector (EE)-DI-SPME-GC-FID methods (condition: extraction temperature: 50 \({}^{\circ}\)C; extraction time: 20 min; stirring rate: 500 rpm; applied voltage in EE-DI-SPME: \(-5\) V).
## 3 Control Algorithm Description
### Calculation of Acceleration Vector in Cartesian Coordinates  
According to Equation (16), the control acceleration vector in the curvilinear coordinates \(\mathbf{w}_{ij}=\begin{bmatrix}\ddot{\varphi}_{ij}^{w}&\ddot{\theta}_{ij}^{w} &\ddot{\varphi}_{ij}^{w}\end{bmatrix}^{\mathrm{T}}\) has to be transformed to Cartesian coordinates in order to calculate the required linear acceleration \(\mathbf{u}_{ij}\) in the LVLH reference frame. To this end, the Lame approach is adopted. The transition between the Cartesian and the curvilinear coordinates is provided by the following relations [30]:  
\[\left\{\begin{array}{l}x_{ij}=\begin{pmatrix}\rho_{ij}+a_{0}\end{pmatrix} \cos\theta_{ij}\sin\varphi_{ij},\\ y_{ij}=\begin{pmatrix}\rho_{ij}+a_{0}\end{pmatrix}\sin\theta_{ij},\\ z_{ij}=\begin{pmatrix}\rho_{ij}+a_{0}\end{pmatrix}\cos\theta_{ij}\cos\varphi_{ ij}.\end{array}\right. \tag{17}\]  
For the calculation of the components \(u_{k}\) of control acceleration vector \(\mathbf{u}\), the following formula is used:  
\[u_{k}=\frac{1}{H_{k}}\left[\frac{d}{dt}\left(\frac{\partial V^{2}/2}{\partial \dot{q}_{k}}\right)-\frac{\partial V^{2}/2}{\partial q_{k}}\right], \tag{18}\]  
where \(\mathbf{q}=\begin{bmatrix}\varphi_{ij}&\theta_{ij}&\rho_{ij}\end{bmatrix}^{ \mathrm{T}}\) is a vector of curvilinear coordinates, \(k=1,\ 2,\ 3\), and \(V\) is the absolute value of the velocity, which is given by:  
\[V^{2}=\sum_{k=1}^{3}\dot{q}_{k}^{2}H_{k}^{2}, \tag{19}\]\(H_{k}\) is the Lame coefficient defined by the formula:  
\[H_{k}^{2}=\left(\frac{\partial x_{ij}}{\partial q_{k}}\right)^{2}+\left(\frac{ \partial y_{ij}}{\partial q_{k}}\right)^{2}+\left(\frac{\partial z_{ij}}{ \partial q_{k}}\right)^{2}. \tag{20}\]  
Using relations (17)-(20), the transition of the control acceleration components calculated in the curvilinear coordinates to control acceleration components expressed in the Cartesian coordinates is as follows:  
\[\begin{array}{l}u_{x}^{ij}=2\dot{\varphi}_{ij}(-(\rho_{ij}+a_{0})\dot{\theta }_{ij}\sin\theta_{ij}+\dot{\rho}_{ij}\cos\theta_{ij})+\left(\dot{\rho}_{ij}+a_ {0}\right)^{2}\ddot{\varphi}_{ij}^{w}\cos\theta_{ij},\\ u_{y}^{ij}=(\rho_{ij}+a_{0})(\ddot{\theta}_{ij}^{w}+\dot{\varphi}_{ij}^{2} \cos\theta_{ij}\sin\theta_{ij})+2\dot{\rho}_{ij}\dot{\theta}_{ij},\\ u_{z}^{ij}=\ddot{\rho}_{ij}^{w}-(\rho_{ij}+a_{0})(\dot{\varphi}_{ij}^{2} \cos^{2}\theta_{ij}+\dot{\theta}_{ij}^{2}).\end{array} \tag{21}\]
# UVSQ-SAT, a Pathfinder CubeSat Mission for Observing Essential Climate Variables
## 4 Results
For a one-day observation period, the UVSQ-SAT expected performances were degraded (Figure 6, right middle panel). Earth's net radiation errors with only data processing could be greater than \(\pm 30\) Wm\({}^{-2}\) over large geographical areas. Indeed, with an LEO orbit (600 km, 98\({}^{\circ}\)), the satellite made just over 14 orbits in a day, and every point on the Earth was covered at least twice. It was clear that itwas insufficient with only one satellite in orbit to cover the entire surface of the Earth (spatial coverage). It took at least 15 days to have a good spatial coverage and to offer a satisfactory Earth net radiation map (Earth's net radiation errors could be greater than \(\pm 10\,\mathrm{Wm}^{-2}\) over very small geographical areas), as shown in Figure 6 (right bottom panel). In this case, the result was an average of 15 days. Consequently, we lost the information related to short temporal variations (albedo, clouds, etc.). Indeed, the outgoing shortwave radiation part of the Earth's net radiation is more challenging to measure since it has greater spatial and temporal variability, and it is distributed less evenly around the Earth. For example, sharp edges in albedo (e.g., clouds) are intricate and evolve quickly [8].
## 2 Preliminary Concepts
### Lamb Waves  
An LW is a type of elastic guided wave that moves through a solid medium, exhibiting pressure (P) and shear (S) propagation modes. When applied to a medium that has boundary conditions, reflection emerges and creates complex patterns. LWs present a particularly interesting property, which is their capacity to travel long distances and even pass through multiple materials with small attenuation [12; 13]. When LWs interact with a defect in the material, they alter their wave pattern, and thus, they are sensitive enough to detect, and even discern between, the damage type, size, and location [14]. When propagating in a thin plate, an LW presents two fundamental modes, namely the symmetric (Sn) and the anti-symmetric (An) modes. These modes occur in relation to the mid-plane of the plate. The vector of displacement of these waves can be analytically described by  
\[\mu\
abla^{2}\mathbf{u}+(\lambda+\mu)\
abla\
abla\bullet\mathbf{u}=\rho\frac{ \partial^{2}\mathbf{u}}{\partial t^{2}}, \tag{1}\]  
where \(\mu\) and \(\lambda\) stand for the Lame constants, which are two material-dependent quantities that come from the elastic stress-strain relationships, \(\rho\) is the material's mass density, and the vector \(\mathbf{u}\) represents the displacement, such that  
\[\mathbf{u}=\
abla\Phi+\
abla\times\Psi, \tag{2}\]  
with \(\Phi\) and \(\Psi\) denoting potentials.  
These equations can be divided into the transverse and longitudinal displacements and be written, in turn, by defining the longitudinal (L) wave speed \(c_{L}^{2}=(\lambda+2\mu)/\rho\) and the transverse (T) wave speed \(c_{T}^{2}=\mu/\rho\), yielding:  
\[\begin{split}\frac{\partial^{2}\Phi}{\partial x^{2}}+\frac{ \partial^{2}\Phi}{\partial y^{2}}+\frac{\omega^{2}}{c_{L}^{2}}\Phi& =0,\\ \frac{\partial^{2}\Psi}{\partial x^{2}}+\frac{\partial^{2}\Psi}{ \partial y^{2}}+\frac{\omega^{2}}{c_{T}^{2}}\Psi&=0,\end{split} \tag{3}\]  
where \(\omega\) stands for the angular frequency.  
One way to visualize LWs is through their dispersion curves, where it is possible to depict the phase alteration depending on the frequency [15; 16; 17; 18]. In the case of damage detection applications, the dispersion must be kept to a minimum, together with the attenuation, while still maximizing the sensitivity. This can be achieved by using a windowed tone burst as an excitation signal, as is the case of a Hann window. Several possible actuators can be employed, namely piezoelectric, due to their versatility, lightweight, and low cost. These elements have the advantage of being used also as sensors and possess a wide bandwidth, which can allow for multiple frequencies to be studied [14; 19].