esa-sceva/satcom-chunk-collection huggingface dataset:
# Free Vibration of a Taut Cable with Two Discrete Inertial Mass Dampers
## 1 Introduction
Many inerter-based absorber layouts have been proposed, and their control performance advantages have been proven for civil engineering structures [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59]. As for the vibration suppression of cables, typical inertial mass dampers (IMDs) [60; 61; 62; 63; 64; 65] and tuned inerter dampers [66; 67] were well developed, and their significant improvement on the achievable modal damping ratio of the cable was verified via both theoretical and experimental investigations. With the increasing cable length, it may be difficult to attain a desired level of supplemental modal damping with a single damper or a pair of dampers installed near the deck anchorage. Hence, some hybrid techniques have been further proposed. The idea of combining external dampers with cross-ties for cable vibration control was considered, which not only addresses the deficiencies of these two commonly used countermeasures but also still retains their respective merits [68; 69; 70; 71; 72; 73]. A hybrid damper system, combining a viscous damper and a tuned mass damper, can overcome the shortcomings of single type of dampers and improve effectiveness and robustness in suppressing cable vibration [74]. In addition, application of two viscous dampers or two high-damping rubber dampers at different locations of a cable was proposed [75; 76; 77]. The results have shown that when two viscous dampers are installed at opposite ends of a cable, their damping effects are approximately the sum of the contributions from each damper [77]. However, when they are at the same cable end, the maximum modal damping ratio of the cable is determined by a single damper at the further distance, indicating no benefits over a single damper configuration [77]. Inspired by the potential advantages of attaching two external discrete viscous dampers (VDs) on a cable, this study aimed to evaluate the feasibility of a cable with two discrete IMDs, either on the opposite end or on the same end of the cable, to improve the vibration mitigation performance of the cable in each mode. Complex modal analysis based on the taut-string model was employed and extended to allow for the existence of two external discrete IMDs. The formulation for free vibration of a taut cable with two discrete IMDs was established, and corresponding complex wavenumber equations of free damped vibration were derived. The asymptotic and numerical solutions of the wavenumber equation were obtained, and the applicability of asymptotic solutions was then evaluated. Finally, parametric studies were performed to investigate the effects of damper positions and damper properties on the control performance of the cable with two discrete IMDs.
# Active Optics and Aberration Correction Technology for Sparse Aperture Segmented Mirrors
## 2 Sparse Aperture Segmented Mirror Aberration Evaluation and Active Optical Control
### Segmented Mirror Active Optical Control Mode
#### 2.2.2 Control Model Based on Aberration Evaluation Model  
The current co-phase maintenance technology is mainly implemented by edge sensors and displacement actuators, which are overly dependent on the measurement accuracy of the edge sensor. In addition, when the segment diameter thickness is relatively large, and the number of support points is small, the mirror body at the edge sensor installation position undergoes large deformation. At the same time, due to the edge sensor temperature, zero drift and other errors exist, which will lead to aberration correction transfer efficiency being reduced. This reduces the segmented mirror co-phase maintenance accuracy.  
It is assumed that under ideal co-phase detection conditions, the mirror aberration \(U_{k}\) can be used as an evaluation criterion, mainly when active optics are utilized to adjust the segmented mirror from out of phase to a co-phase state. Suppose a hard spot \(P_{i}\) in the segmented mirror goes up along \(Z-axis\), then the specular aberration changes to \(\Delta U_{ki}\). According to Section 2.2.1, when the segmented mirror is subject to gravity and temperature until the hard spot position in the segment becomes \(z_{i}^{\prime},i=1,\cdots,21\), the aberration of the out-of-phase segmented mirror is \(U_{k}^{\prime}\) The change of height \(\Delta z\) can be expressed in terms of out-of-phase aberration of the segmented mirror \(U_{k}^{\prime}\) as follows.  
\[\Delta z\cdot\Delta U_{k}=U_{k}^{\prime} \tag{18}\]In Equation (18), \(\Delta U_{k}\) is the stiffness matrix between the hard spot displacement and the aberration variation.  
\[\Delta U_{k}=[\Delta U_{k1},\Delta U_{k2},\cdots,\Delta U_{k21}] \tag{19}\]  
Let the solved residuals of \(U_{k}\) are represented as \(\Delta\), and it is given in the following equation.  
\[\Delta=\Delta\hat{z}\cdot\Delta U_{k}-U_{k}^{\prime} \tag{20}\]  
where \(\Delta\hat{z}\) is the estimated value of \(\Delta z\). Suppose \(\hat{U}_{k}^{\prime}=\Delta\hat{z}\Delta U_{k}\) is the estimated value of \(U_{k}^{\prime}\), then the evaluation function of the correction quantity is \(MSE(\hat{U}_{k}^{\prime})\).
## 3 Results and Discussion
### Experimental Results and Analysis
#### 3.2.1 Results of Cyclic Triaxial Tests
[56] indicated that the bentonite content in the constituted specimens increased the excess pore water pressure at a slower rate, and the total excess pore water pressure decreased at the end of the cyclic loadings. One of the reasons for this was that the specimen with the higher plasticity index had higher compressibility, resulting in less excess pore water pressure under cyclic loading in undrained conditions. The difference between the MRV silt and specimens modified with bentonite might be related to soil permeability and drainage. The results of the tests performed on the specimens containing the three types of fines were examined separately. The specimens with Liugui FCs of 0%, 15%, and 30% were analyzed first. The PWP curves of Sand-01, Sand-02, and Sand-03 with a CSR of 0.14-0.22 are presented in Figure 13a. The WP curves of the pure sand specimens were sometimes higher or lower than each other. The normalized relationship between the PWP ratios and cyclic numbers of the specimens with a Liugui FC of 15% is illustrated in Figure 13b. The increasing trends of the nine curves were similar. The curves of K-FC15%-01, K-FC15%-02, and K-FC15%-03 increased the fastest. The remaining six curves of the specimens containing mudstone and Liugui fines were comparable. Figure 13c illustrates the normalized relationship between the PWP ratios and cyclic numbers of the specimens with plastic FCs of 30%. The curves were denser; however, the curves of the specimens containing kaolinite fines increased the fastest. Figure 13d illustrates the normalized relationship between the PWP ratios and cyclic numbers of the clean sands and specimens containing fines with PI values >5.7. The data of the cleans sand specimens differed significantly from those of the specimens with PI values >5.7. Figure 12: The process of liquefaction as the PWPs of the specimens increased during cyclic triaxial testing for non-plastic and plastic soils.
## 1 Introduction
Some other alternatives to dissipate heat have focused on the development of GaN devices on substrates with very high thermal conductivity, such as diamond, but it is not a favorable strategy, due to the large GaN-diamond lattice mismatch, the lack of compatibility of diamond with other substrate materials, and the high expense of the process [15]. Up to now, the temperature measurement of HEMTs could be made with infrared cameras [16], liquid crystal thermography [17], micro-Raman thermography [18], photoluminescence [19], transient thermo-reflectance [20], and pulsed current versus voltage measurements methods. These methods can be expensive and limited by the spatial resolution that may cause problems in measurement precision of the hotspot temperature. Since heat generation is concentrated within a small volume at the drain-side edge of the gate contact, high spatial resolution is required for the measurement of the temperature in the active region of the device. To overcome some of these issues, efficient simulations are necessary [21]. Previous works on self-heating effect in GaN HEMT growth on SiC substrates has been made [22; 23]. Numerical solutions are on one hand more commonly followed and a number of accurate simulators have been developed based on finite-volume, finite-difference,and finite-element methods [24]. On the other hand, as alternative and powerful numerical tool, transmission line modeling (TLM) has been used to model the thermal behavior of punch-through diodes, TRAPATT diodes, and conducting films on insulating substrates [25; 26; 27]. The TLM has already been used successfully to model the thermal behavior of power devices [28]. This method is numerically stable and compares favorably with other numerical techniques in computational expenditure and convenience in modeling complex geometries. In this paper, we analyzed the thermal characteristics of AlGaN/GaN HEMT's single gate finger, multi-gate finger, and multi-material. We focused in this analysis on a 3-D distribution of the temperature, thermal behavior, self-heating and channel temperature. Furthermore, Micro-Raman Spectroscopy experience method performed by [PERSON] et al. [18; 22] was used to compare the detailed temperature distribution of the device, and the measurements are carried out for the verification of the 3D TLM model. We selected Micro-Raman spectroscopy as the Infrared image technique because of the spatial resolution on the order of 1 \(\upmu\)m, which is required to accurately measure and explore the peak temperature of AlGaN/GaN HEMT with their narrow source-drain opening.
# Augmented Reality for PCB Component Identification and Localization
## 5 Results
### Experiment 1
#### 5.1.3 Impact of User Expertise on Localization Times (Run 3)  
The third phase of the experiment analyzed localization times based on participants' experience levels, categorizing them into experts (\(\geq\)8 years) and non-experts (<8 years). Each group performed localization tasks using both the traditional and HoloLens(tm) methods.  
* Comparison of Methods  
Figure 11: Comparison of localization times for SD card and resistor across methods.  
As summarized in Table 4, the traditional method consistently outperformed the HoloLens(tm) across the entire population and among expert participants. The difference was statistically significant (\(p\) = 0.0013 for the overall population and \(p\) = 1.30 \(\times\) 10\({}^{-6}\) for experts), confirming the superior efficiency of the traditional method for experienced users. Experts achieved a median localization time of 8 s with the traditional method, compared to 16.5 s with the HoloLens(tm).  
For non-experts, performance was slightly better using HoloLens(tm), which had a median time of 10 s, compared to 16.5 s with the traditional method. However, this difference was not statistically significant (\(p\) = 0.4908), indicating that user preference or familiarity may have influenced outcomes rather than inherent methodological advantages.  
Additionally, variability in performance, reflected in broader interquartile ranges, was more pronounced with the HoloLens(tm) method across all groups, suggesting that individual user interactions with AR interfaces varied more widely compared to the traditional setup.
## 3 Results and Discussion  
Figure 10 illustrates the experimental procedure for checking the lateral overturning phenomenon when the inclination angle of the slope plate is gradually increased. As the inclination angle is increased, the proximity sensor is utilized to check whether the front and rear wheels are still making contact with the slope plate. Moreover, the angle at which the wheels start to lift off the slope plate is measured. As the inclination angle is increased, one of the two wheels located in the upper part of the slope plate first lifts off the plate. If the inclination angle is further increased, the remaining wheel lifts off the slope plate as well, and finally lateral overturning occurs rapidly.  
Table 6 presents the results of the inclination angles at which the miniature model's front and rear wheels in the upper part of the slope start to lift off the ground. The inclination angle of the rear wheel is small compared to the front wheel, which indicates that the rear wheel lifts off the ground before the front wheel. When both wheels in the upper part of the slope lift off the ground, lateral overturning occurs rapidly. hence, the inclination angle of the front wheel becomes the static falling down sidelong angle in this case. When measuring, the data could not be quantified through the sensor. The test was repeated 5 times to check the reproducibility and to obtain reliable average value.  
The result of the miniature model test indicates an average static falling down sidelong angle of 44.3\({}^{\circ}\). The standard deviation is 0.5\({}^{\circ}\) which is about 1.1% of the average value. Therefore, the test has high reproducibility.  
Compared to the accredited certification test result of the original tractor which is 45.3\({}^{\circ}\), the average miniature model test indicates a difference of 1.0%. Because the difference is small, and the time and cost required to run the test is relatively smaller than the certification test, it is concluded that a miniature model test can be utilized to derive the static falling down sidelong angle quickly and conveniently.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Test No.**} & \multicolumn{2}{c}{**Instability Angle (\({}^{\circ}\))**} \\ \cline{2-3}  & **Upslope Front Wheel** & **Upslope Rear Wheel** \\ \hline
1 & 45.0 & 41.0 \\
2 & 44.1 & 40.7 \\
3 & 44.1 & 41.9 \\
4 & 44.6 & 40.4 \\
5 & 43.8 & 39.9 \\ Avg. \(\pm\) Std. & 44.3 \(\pm\) 0.5 & 40.8 \(\pm\) 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Instability angle for upslope front and rear wheels.  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{**Items**} & **Specification** \\ \hline Model & Advanced digital level \\ Manufacturer, Nation & YATO, Korea \\ Measuring range (\({}^{\circ}\)) & 360 \\ Resolution (\({}^{\circ}\)) & 0.01 \\ Accuracy (\({}^{\circ}\)) & \(\pm\)0.1 \\ Precision (\({}^{\circ}\)) & \(\pm\)0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Specifications of the digital level.
# Federated Pseudo-Sample Clustering Algorithm:
## 5 Experiments
### Parameter Tuning of Clustering Algorithm  
The goal of the clustering algorithm is to generate pseudo-samples generated by the adversarial network, not the original data. Therefore, the existing algorithm parameters cannot be directly applied to this experiment and need to be further adapted. Since the clustering target is not the original data and the generated samples are random, conventional evaluation indices cannot be used to evaluate the clustering algorithm in this experiment. Therefore, the focus of parameter tuning is mainly concentrated on two aspects: 1. the number of clusters produced by clustering; and 2. whether the samples contained in each cluster belong to the same category.  
Using the Fashion-MNIST dataset as an example, a generator is used to generate a set of samples for debugging the parameters of the DBSCAN algorithm. The sample set consists of 500 images, including all 10 categories. Since the image is generated by the adversarial network, the quality of the sample will necessarily be lower than the quality of the original image. Therefore, the common parameters of the DBSCAN algorithm cannot cluster this group of samples. It is found that the default parameter Eps is 0.5, and when Eps is greater than 0.3, all samples will converge into a cluster. The adjustment range  
Figure 6: Additional comparative experimental results for the MNIST dataset. (**a**) IID Setting. (**b**) Non-IID Setting.  
Figure 7: Additional comparative experimental results for the Fashion-MNIST dataset. (**a**) IID Setting. (**b**) Non-IID Setting.  
of Eps parameters is 0.05-0.3, and the change amount of each debugging is 0.05. The MINPTS parameter cannot be set too high or too low. The default value is 5. In addition to the default values, this experiment also tests the case of 10 and 15. The corresponding classification results of Eps and MINPTS parameter selection are shown in Table 3. Refer to the number of categories in Table 3 and observe the corresponding classification results. Finally, Eps = 0.1 and MINPTS = 5 were selected as experimental parameters.
## 3 Results and Discussion
### Metamodel and Multi-Objective Optimization
#### 3.2.2 Metamodel Accuracy  
In establishing the metamodel, 90% of the DoE samples were used as the train set, while 10% were reserved as the test set. Additionally, 10% of the training set was employed as the validation set during the training process. This ratio was chosen to ensure model  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Geometry** & **Design Constraint** \\ \hline REH & \(d>4\) \\ DAH & \(\theta^{\prime}>10\) \\ SH & \(d_{1},d_{2}>4\) \\ CH & \(r<\min((w-4)/2,(h-4)/2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Design constraints for each auxetic model in the multi-objective optimization problem.  
Figure 10: Design space of (**a**) REH, (**b**) DAH, (**c**) SH, and (**d**) CH for multi-objective optimization problem (MOOP). The color represents the normalized relative density (\(\rho_{r}\)).  
accuracy given the limited number of sampling points generated in this study. The Adam optimizer was used due to its efficiency in accelerating the gradient descent algorithm. A learning rate of 0.001 and an early stopping algorithm were also employed to monitor the loss of the validation set, preventing overfitting. All hidden layers used the ReLU (rectified linear unit) activation function to capture model nonlinearity, while the output layer used a linear activation function to produce continuous values.  
The error parameters of each metamodel are presented in Table 7. All MAX values are less than 5 mm for \(\delta_{p}\) and 0.5 J/g for the SEA, respectively. Generally, the \(R^{2}\) values of all metamodels are greater than 0.95, indicating that the predicted values from the ANN model are very close to the simulation values. The predicted values of the trained metamodel for each auxetic geometry are shown in Figure 11. The deviations between the predicted values and the simulation sampling points are very small. Based on the three evaluation parameters, all metamodels are confirmed to be accurate and can be used for the subsequent multi-objective optimization process.
# Asynchronous Hierarchical Federated Learning Based on Bandwidth Allocation and Client Scheduling
## 3 System Model
### Training Process
#### 3.1.2 Local Training on Clients  
At the beginning of the local training process, the participating clients receive the latest model parameters from the cloud server. Then, each client \(i\) utilizes its local dataset \(D_{i}\) to minimize the loss function \(f_{i}(x_{m},y_{m},\mathbf{\omega})\), which facilitates the representation of \(y_{m}\) with the optimal model parameters \(\mathbf{\omega}\). The loss function of local training on client \(i\) is defined as follows [12]:  
\[F_{i}(\mathbf{\omega})=\frac{1}{|D_{i}|}\sum_{m\in D_{i}}f_{i}(x_{m},y_{m},\mathbf{ \omega}), \tag{1}\]  
which aims to find the optimal model parameters that minimize the loss function, i.e., \(\mathbf{\omega}_{i}^{*}=\arg\min[F_{i}(\mathbf{\omega})]\).  
The stochastic gradient descent method is adopted in the local training process. To achieve the desired level of local accuracy \(\theta\in(0,1)\), the clients carry out several local iterations such as \(L(\theta)=\mu\log(1/\theta)\)[13], where the value of the constant \(\mu\) relies on the dimensions of the training task. Specifically, the update rule of the \(l\)-th local iteration is given as \(\mathbf{\omega}_{i}^{(l)}=\mathbf{\omega}_{i}^{(l-1)}-\eta\
abla F_{i}\Big{(}\mathbf{ \omega}_{i}^{(l-1)}\Big{)}\), where \(\eta\) represents the learning rate.  
Figure 2: Model uploading and aggregation.  
The time consumption of the local training is calculated as follows:  
\[\tau_{i}^{\text{comp}}=L(\theta)P_{i}\psi, \tag{2}\]  
where \(L(\theta)\) represents the number of local iterations, \(P_{i}\) represents the time consumed by processing a data sample on client \(i\), and \(\psi\) represents the batch size.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators
### Dielectric Electroactive Polymers (DEAPs)  
Dielectric electroactive polymer (DEAP) actuators are extensively studied because of their low cost, easy manufacturing, and fast response. The manufacturing costs of the DEAP actuator are low compared to the IPMC actuators discussed in the previous chapter. This applies to both VHB tape and the silicones most often used in the production of dielectric actuators (for example, Silicone Mold Start or Dragon Skin). The materials, as well as a thorough analysis of the methods used to produce dielectric electroactive performance membranes, are discussed in detail by the authors in [51] while for the production of IPMC, the expensive Nafion membrane is used [3]. DEAPs are composed of a soft, thin, dielectric elastomer membrane coated on both sides with conducting electrodes (Figure 4) [52]. This makes the DEAP actuators soft capacitors in terms of design features. When the electrodes are connected to the current, they accumulate the charge and cause the appearance of a Coulomb force between the electrodes. Because the dielectric membrane between electrodes is a highly soft and flexible elastomer, the appearance of the Coulomb force causes its deformation. After the voltage is released, the DEAP restores to the original size. The materials most commonly used as flexible membranes are silicone elastomers [19; 53; 54], and acrylate elastomers--mainly VHB tape produced by 3M [55; 56; 57]. Additionally, the literature reports on the use of other materials acting as elastomers in DEAPs could be found, but they are not very common [30; 58]. Due to the construction and operation of DEAPs, different directions of their modification can be distinguished. The first concerns the modification of the properties of the dielectric membrane, while the second concerns the modification of the conductive electrodes. Another direction of development of DEAPs is the construction of actuators of various, new shapes and the modeling of their behavior.  
Figure 3: The prototype of IPMC microrobot (source: [42]).