esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
### Background  
Medical diagnosis is the process of finding the disease from the symptoms the patient is experiencing. However, medical data is often uncertain and difficult to retrieve. Moreover, the relationship between symptoms and disease depends on uncertain information that influences the decision-making process [1]. Fuzzy sets by [PERSON] have been applied in the past for many medical applications. This framework provides a way to overcome problems in which imprecision, absence of sharply defined criteria, and incomplete or uncertain data, are involved [2]. Fuzzy logic has a significant role in medical science due to its complexity. Applications like cardiology, endocrinology, urology, ophthalmology, and dentistry are some of the numerous medical fields in which fuzzy logic is applied [3]. Also, the concept of fuzzy logic deals with problems including pattern recognition, medical diagnosis, and decision-making [4]. Thus, the ambition of using fuzzy logic in medicine is to generate results like an expert and diagnose the patients at an early stage [3]. Nevertheless, the theory of [PERSON] about fuzzy sets is limited in the sense that it is defined by membership grade only [4]. So, the inevitable presence of uncertainty in the real world as well as the limitation of fuzzy set theory by [PERSON], led to extensions of fuzzy sets (advanced fuzzy sets), which cope with imprecision and vagueness more successfully and accurately than fuzzy sets by [PERSON] [5]. The extensions of fuzzy sets are defined by the degree of membership, degree of non-membership, and degree of indeterminacy, and include intuitionistic fuzzy sets (IFSs), Pythagorean fuzzy sets (PFSs), and Fermatean fuzzy sets (FFSs). The concepts of these fuzzy sets are all generalized as q-rung orthopair fuzzy sets (q-ROFS) [4]. Fuzzy logic also uses similarity measures for disease detection.
## 3 Experimental Results and Discussion  
In our experiment, the influence of different output transmittances on M2 of diode-pumped Ho: GdVO\({}_{4}\) laser has been investigated, as shown in Figure 7. Through the  
Figure 5: Polarized emission cross sections of Ho: GdVO\({}_{4}\) crystal.  
Figure 6: The experimental setup of diode-pumped Ho: GdVO\({}_{4}\) laser.  
comparison of different groups of the slope efficiency, an optimal result was reached by using the output couple with the output transmittance of 50%. When the pump power was 15 W, the laser obtained the maximum output power of 1.8 W, with a slope efficiency of 39.5%. It is worth noting that the slope efficiency here is for the absorbed pump power. In the case of transmittance of 30 and 10%, the output power dropped to 1.6 and 1.1 W, and the slope efficiencies at this time were 33.8 and 19.7%, respectively. With optical spectrum analyzer, the output spectra of diode-pumped Ho: GdVO4 laser were recorded under different transmittances. It can be seen from Figure 8 that, when the transmittance was 10%, the center wavelength of the laser was located at 2048.2 nm. As the transmittance increases, the center of the spectrum moves to the shortwave direction to 2047.9 nm. The blue shift of output wavelength is mainly caused by absorption loss in quasi-three-level system of Ho laser. No obvious wavelength fluctuation was observed with the three output couplers used in the above experiment.  
To further determine the beam quality factor M\({}^{2}\), we used a lens (focal length: 150 mm) to converge the laser beam. With the maximum output power, the output beam radii were measured at several positions through the 90/10 knife-edge technique. Then, the data were fitted by the Gaussian beam standard expression. The fitting results are shown in Figure 9. It can be seen from Figure 9 that the beam quality M\({}^{2}\) factors in x- and y-directions are 1.4 and 1.3, respectively.  
Figure 8: Output spectra of the diode-pumped Ho: GdVO4 laser.  
Figure 7: The output performance versus absorbed pump power for diode-pumped Ho: GdVO4 laser.
## 1 Introduction
However, the requirement for force sensing in the MRI environment is particularly burdensome because the MRI-compatible force sensor is not available commercially and its development cost is high. Recently, the backstepping design method has been proven to be a very effective way to develop nonlinear robust controllers for pneumatic servo systems. However, this method has the problem of \"explosion of complexity\" since the requirement of repeated differentiation of virtual controls. Thus, a practical implementation is difficult. To solve this problem, [PERSON] et al. [28] proposed the dynamic surface control (DSC) method, in which the calculation of the virtual control variable's derivative was prevented by introducing a filter at each design step. Since then, the DSC method has been the topic of significant research efforts and a number of excellent theoretical contributions have been made. The applications of DSC can be found in many engineering fields, for example, hydraulic servo systems [29], underwater/autonomous surface vehicles [30], mobile wheeled inverted pendulum [31], pneumatic artificial muscle [32], and servo motor [33]. In this study, the needle insertion motion control of the MRI compatible robot developed in our lab is considered. The robot is actuated by a pneumatic cylinder with long transmission lines. The focus of this paper is dealing with the issue of long transmission lines and realizing a high accuracy control of a pneumatic actuator with a pressure observer. Therefore, the long transmission line is assumed to be an intermediate chamber connected between the control valve and the actuator in series, and a pressure observer based adaptive dynamic surface control is proposed. The main contributions are: (1) The long transmission line dynamic is approximated as a nonlinear first order system, which can estimate the pressure losses and time delay through the long transmission line precisely in real time. (2) A pressure observer, which is proven to be globally stable, is developed to estimate the chamber pressure in a pneumatic actuator with a long transmission line. (3) In contrast to most of the existing nonlinear robust controllers synthesized by the backstepping method, by using the dynamic surface control (DSC) technique, the proposed controller can cope with the problem of \"explosion of complexity\", since the repeated differentiation of virtual controls is no longer required. The rest of this paper is organized as follows: Section 2 presents the dynamic models and problem statement; Section 3 gives the design and stability proof of the pressure observer based adaptive dynamic surface controller; Section 4 presents the experimental results to demonstrate the performance of the proposed controller; and Section 5 draws the conclusions.
# Second-Harmonic Generation of Blue Light in GaN Waveguides
## 2 Materials and Methods
The efficiency of coupling was constantly monitored with two CCD detectors as shown in Figures 2b and 3a-c. The out-coupled light was collimated and filtered, and the SHG signal was directed to the detection system. The camera was operated using the gated regime and was synchronized to the femtosecond system repetition rate. Figure 1: (**a**) Cross-sectional scanning electron microscopy (SEM) image of a III-polar GaN waveguide. The image is taken at an oblique angle of 52\({}^{\circ}\). A platinum layer has been deposited prior to making the cross-section in order to protect the uppermost part of the waveguide during the focused ion beam (FIB) etching. The measured thickness of the GaN waveguides used in the experiment is around 1150 nm. (**b**) Comparison of the side roughness of GaN waveguides which are 3 \(\upmu\)m (**left**) and 20 \(\upmu\)m (**right**) wide. A higher sidewall roughness is observed for narrower waveguides. As a result, the GaN waveguides used for second-harmonic generation (SHG) were at least 10 \(\upmu\)m wide.
# Cartesian Aerial Manipulator with Compliant Arm
## 5 Experimental Results
### Compensation of Mass Displacement  
The positioning accuracy of the end effector is also affected by the dynamic coupling between the aerial platform and the manipulator, as the movement of the Cartesian base causes a variation of the center of mass and a reaction torque over the multirotor. The X-axis linear guide in Figure 4 incorporates a counterweight mechanism that moves the manipulator battery synchronized with the Y-axis guide, so the mass distribution is more symmetric, but at expenses of reducing the range of \(d_{1}\) by half. The goal of this experiment is to evaluate the effectiveness of this mechanism in a flight test, showing the evolution of the position and velocity of the multirotor when the Y-axis structure and the compliant arm move in the forward direction while the multirotor is controlled in stabilize mode. The position controller is disabled in order to observe more clearly the desired effect, using a laser tracker to measure the multirotor position. Figure 11 illustrates the execution of the experiment, representing the signals of interest in Figure 12.  
The manipulator is initially at rest position, \(d_{1}=0\), \(d_{2}=0\), \(q_{3}=0\). Between \(t=0\) and \(t=1\) [s], the Cartesian base is displaced to \(d_{1}=95\) [mm], and between \(t=4\) and \(t=5\) [s] \(q_{3}\) is rotated from 0 to \(90^{\circ}\). Note that the rotation of the joint cannot be compensated with the counter-weight mass. As can be seen on the lower part of Figure 12, the perturbation in the position and velocity of the multirotor along the X-axis (blue line) is more evident when the counterweight mechanism is disabled.
## 1 Introduction  
Since [PERSON] proposed the concept of reinforced soil, various types of reinforced soil retaining walls have been developed [1]. Initially, long reinforcements with lengths 0.8-1.2 times the height of the wall were primarily used. Therefore, the behavior of the reinforced soil retaining wall was mainly studied with respect to the load acting on the reinforced area. [PERSON] and [PERSON] observed that the stiffness, vertical spacing, and length of the reinforcements are important parameters that influence wall displacement [2]. [PERSON] et al. collected useful data for numerical analysis models using a full-scale test [3]. [PERSON] et al. studied the bearing capacity of a strip foundation on geogrid-reinforced sand using laboratory model tests [4]. Until recently, studies on reinforced soil retaining walls using long reinforcements have been conducted in various fields, such as backfill materials [5; 6; 7] and analysis methods [8; 9; 10].  
However, as the application of reinforced soil retaining walls increased, interest in the use of short reinforcements also increased. [PERSON] et al. presented studies on the applicability of short reinforcements with a length of 35% of the height of the wall (0.35 H) integrated with a rigid wall [11; 12; 13; 14]. In South Korea, a reinforced subgrade for railways (RSR) was developed, and related studies have been conducted [15; 16]. It was recently applied to real scenarios, and its long-term application has been verified [17]. RSR is a reinforced rigid wall construction method, in which the backfill is pre-constructed using nets of welded rebars and short geogrids of length 0.35 H. The wall is then integrated with the geogrid using connectors. RSR using short geogrids has a relatively narrow reinforced area compared with that using long reinforcements, as shown in Figure 1, and the overburden load is frequently worked outside the reinforced area. In this case, the behavior of the RSR is expected to be different in terms of the settlement and horizontal displacement of the wall because there is a difference in the tensile force of the geogrids inside the reinforced soil, which is caused by the overburden load. [PERSON] et al. investigated the behavior of a reinforced rigid wall for a vertical load applied outside the reinforced area using model tests [18]; however, related studies are scarce.  
In this study, the behavior of a reinforced rigid wall with short reinforcement was investigated with respect to the load acting outside the reinforcement area by constructing a full-scale RSR and applying repetitive and static loads. Four different sections were constructed with different vertical spacings and lengths of reinforcement. Each section had three different types of wall-reinforcement connections. Using the load-displacement relationship, the effect of each parameter was evaluated when a vertical load was applied outside the reinforcement area of the RSR.
###### Abstract  
In this paper, the structural characteristics of electromagnetic suspension (EMS) inertial stabilizers are analyzed firstly, and then a mechanical analysis of a single mass block and double mass block is carried out. The relationship model between the inertial anti-rolling mass block and inertial force transmitted to the ship is established. The inertial force is determined by the number of coil turns, coil current, mass block, mass of the ship, electromagnet current, rate of change of the electromagnet current, air gap between the electromagnet and inertial mass block, and rotational angular speed. Through theoretical analysis, it is found that the response speed of inertia force is directly related to the electromagnetic coil current, the voltage at both ends of the electromagnetic coil, the coil resistance and the air gap. It is concluded that the response speed of the inertia force can be controlled by controlling the coil current, adjusting the voltage at both ends of the coil and adjusting the air gap. The inductance of the electromagnetic coil will also increase the nonlinearity of the inertial anti-roll system. On the basis of theoretical analysis, a digital simulation of EMS inertial stabilizer is carried out by MATLAB and ANSYS MAXWELL2D. Finally, a single mass block system of EMS inertial stabilizer is designed and tested. During the test, a 15 V sinusoidal excitation voltage is added to the electromagnetic coil after the mass block is suspended stably, and the maximum acceleration values of the inertial anti-rolling mass block and hull are 10.29 m/s\({}^{2}\) and 1.27 m/s\({}^{2}\). Finally, the theoretical analysis results, digital simulation results and experimental results are analyzed, which verifies the correctness of the acceleration and inertia force performance analysis of the EMS inertial stabilizer.  
electromagnetic suspension (EMS); inertial stabilizer; anti-rolling mass block 28 March 202  
achieved. Other scholars have studied the ship torque gyro anti-roll device [16]. The gyro rotor is controlled by a magnetic powder brake to realize the control of gyroscopic torque, and the goal of ship roll suppression is ultimately achieved. In the early stages of this study, research on the supporting characteristics of the permanent EMS active mass block [17] and on the anti-rolling mechanism of the EMS inertial anti-rolling mass block [18] was carried out by the authors. The anti-rolling effect of the EMS inertial stabilizer mainly depends on the accuracy of the acceleration control of the inertial anti-rolling mass block. In order to ensure the accurate control of the inertial mass block, it is necessary to study its acceleration and inertial force characteristics. The performance of the acceleration and inertial force of the maglev inertial anti-rolling mass block is studied.
## 4 Results and Discussion
### Hyperparameter Tuning  
Optimizing the architecture of neural networks represents a fundamental facet of deep learning methodologies to enhance model performance. The selection of hyperparameters within the deep learning model bears a direct influence on both the accuracy and efficiency of the ultimate model. In this study, a combination of grid search and the Particle Swarm Optimization (PSO) technique [39] was employed for the hyperparameter tuning within a 10-fold cross-validation framework. The aim was to balance prediction accuracy/convergence with potential overfitting.  
For the GRU network, varying the number of hidden layers was investigated first. Increasing the number of layers improved the predictive performance but came at an additional computational cost. Fewer than three hidden layers yielded unsatisfactory results, while more than three provided similar performances on the validation datasets. Consequently, three hidden layers were chosen. The PSO was then used to determine the number of neurons in each hidden layer, dropout rate, and batch size, with predefined ranges set at [50, 300], [0.1, 0.5], and [32, 128], respectively. The PSO algorithm converged to a set of hyperparameters that resulted in the lowest average validation loss. The specific values of these hyperparameters were then utilized to configure the final neural network model. Furthermore, a series of experiments were conducted to progressively increase the number of epochs from 100 to 500 while monitoring the convergence of both training and validation losses. The most optimal performance emerged at 100 epochs.  
In the MLP network, the number of hidden layers was selected through trial and error. Opting for more than one hidden layer produced subpar results, so a single hidden layer was chosen. The number of neurons within this layer was tested with 32, 64, and 128, and 32 neurons generated the best performance on the cross-validation. In addition, the choice of activation function favored ReLU due to its simpler derivative compared to Tanh, which facilitates the training process.  
The fine-tuned hyperparameters for the GRU-MLP model were itemized in Table 3. In light of the possibility of suboptimal configurations of the model, forthcoming research will delve into hyperparameter tuning techniques like Bayesian optimization and nondominated sorting genetic algorithms II [40,41]. This systematic exploration aims to achieve a balance between model convergence and the prevention of overfitting.
# High-Speed Photodetectors for Microwave Photonics
## 3 Integrated Photodiode-Antenna Emitters
### Photonic Emitter at 60 GHz  
Figure 3a shows the epitaxial layer structure of the MUTC PD that was designed for 60 GHz [19]. It includes a 400-nm-thick 1 \(\times\) 10\({}^{16}\) cm\({}^{-3}\) n-type doped drift layer and an InGaAs absorbing region with a total thickness of 500 nm. The transit-time limited component of the bandwidth was estimated to be 85 GHz. The measured bandwidth and saturation power of a 10-\(\upmu\)m diameter PD (no antenna) are shown in Figure 3b,c. The PD had a 3-dB bandwidth of 60 GHz while the 1-dB RF saturation power reached 16.7 and 14.3 dBm at 50 and 60 GHz, respectively.  
A coplanar patch antenna was designed to be about half a wavelength long to resonate at 60 GHz [20]. While the CPW antenna feed was designed to be 50 \(\Omega\), a 100 \(\upmu\)m-long high impedance (85 \(\Omega\)) contact pad was integrated on the PD chip to compensate for parasitic capacitance. After the antenna was fabricated on an AlN submount, the MUTC-PD was integrated by flip-chip bonding (Figure 4).  
The radiated power of the integrated photodiode-antenna emitters was measured using the setup shown in Figure 5. The optical RF signal was generated using an optical heterodyne setup. The radiation power was received in the far field using a commercial horn antenna and measured by a RF power meter.  
The received radiation power versus frequency is shown in Figure 6a. The power at 15 mA average photocurrent amounts to \(-\)15 to \(-\)35 dBm between 50 to 75 GHz. Also shown in Figure 6a is the simulated radiation power using the circuit model shown in the inset of Figure 6a. The received  
Figure 4: Schematic and optical image of the integrated photodiode-antenna emitter. A 50-\(\Omega\) transmission line connects the patch antenna (829 \(\upmu\)m \(\times\) 2668 \(\upmu\)m) and the flip-chip bonded MUTC PD chip.  
Figure 3: (**a**) Epitaxial layer design of MUTC-PD. (**b**) Measured bandwidth of 10-\(\upmu\)m-diameter MUTC-PD. The inset shows a microscopic image of the fabricated device. (**c**) RF output power measurement of the MUTC-PD.  
RF power was calculated using Friis' Equation [21] based on the radiated power, gain of transmitting (4.5 dBi) and receiving (15 dBi) antennas, and free-space loss (\(-43\) dB). The simulation and experimental results agree with each other. The radiation power at 60 GHz versus the photocurrent is shown in Figure 6b. The 1-dB RF saturation power is \(-6.5\) dBm at \(-5\) V and 45 mA photocurrent. Using Friis' equation, the effective radiated power is 20 dBm. According to the IEEE 802.16 standard [22], a minimum power of \(-46.2\) dBm is required at the receiver for a QPSK-modulated signal at 60 GHz, which should be possible once a 25-dBi receiving antenna in 15 m distance from the integrated photodiode-antenna emitter is used.
## 3 Results
### Use of FFoV Array Synthesis Methods
#### 3.2.1 Example B.1  
In the first example, we combine two identical Dolph-Chebyshev linear arrays placed in \(x\)- and \(y\)-axes and, using pattern multiplication [23], we produce a square planar array. This results in a pattern that has lower sidelobes than those specified in all planes, except  
Figure 5: Power pattern: (**a**) red: without element pattern, green: with element pattern, and (**b**) layout of the final array.  
the principal ones along the axes. Furthermore, the planar array design is limited from 2D to 1D.  
The limited-field-of-view indices are \(SLL_{\text{L}}<-30\) dB, \(HPBW_{\text{L}}\)\(\sim\)0.4\({}^{\circ}\), and \(D_{\text{L}}>50\) dBi and the range of view is \(\sim\)10\({}^{\circ}\). Let us now follow the Steps 1 to 4:  
Step 1: The scaling factor is found to be approximately equal to 5.76.  
Step 2: The FFoV indices are \(SLL\) < \(-30\) dB, \(HPBW_{\text{F}}\)\(\sim\)2.3\({}^{\circ}\), and \(D_{\text{F}}>35\) dBi.  
Step 3: To design the FFoV array, we need the interelement distance, the number of the elements, and the excitation. Since the excitations are separable, then with good approximation the following holds [1]:  
\[D_{F}=\pi\cdot D_{x}\cdot D_{y}=\pi\cdot D^{2}\Rightarrow D=\sqrt{D_{F}/\pi} \Rightarrow D=15 dBi, \tag{24}\]  
where \(D_{\text{x}}=D_{y}=D\) is the directivity of a single FFoV linear array. From the beam-broadening factor of a Chebychev array with \(SLL=-30\) dB, a directivity of 15 dBi is achieved by 37 elements (\(d=\lambda/2\)). This gives \(D_{\text{IF}}\) = 35.1 dBi and \(HPBW_{\text{F}}\) = 2 \(\times\) 1.685\({}^{\circ}\). We scale the inter-distance \(d\) of the elements to achieve the prescribed \(HPBW\). Then the directivity becomes \(D_{\text{2F}}\) = 38.34 dBi.  
Step 4: The scaling transformation provides the final array, where we use elements of side-length equal to \(\mu\times d\) to achieve a near brick-wall pattern [26]. The achieved power pattern is given in Figure 6. The directivity of the array is 53.72 dBi. Note here that we obtain 3 dB above the prescribed lower bound of \(D_{\text{L}}\) due to \(D_{\text{2F}}-D_{\text{1F}}\)\(\sim\)3 dB (this is a result of the effort to achieve the prescribed \(HPBW_{\text{F}}\)).