esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study of Vibration Isolation Characteristics of a Geometric Anti-Spring Isolator
###### Abstract  
In order to realize low-frequency vibration isolation, a novel geometric anti-spring isolator consisting of several cantilever blade springs are developed in this paper. The optimal design parameters of the geometric anti-spring isolator for different nonlinear geometric parameters are theoretically obtained. The transmissibility characteristic of the geometric anti-spring isolator is investigated through mathematical simulation. A geometric anti-spring isolator with a nonlinear geometric parameter of 0.92 is designed and its vibration isolation performance and nonlinearity characteristic is experimentally studied. The experiment results show that the designed isolator has good low-frequency vibration isolation performance, of which the initial isolation frequency is less than 3.6 Hz when the load weight is 21 kg. The jump phenomena of the response of the isolator under linear frequency sweep excitation are observed, and this result demonstrates that the geometric anti-spring isolator has a complex nonlinearity characteristics with the increment of excitation amplitude. This research work provides a theoretical and experimental basis for the application of the nonlinear geometric anti-spring low-frequency passive vibration isolation technology in engineering practice.  
low-frequency vibration isolation; geometric anti-spring; nonlinearity; initial isolation frequency +
Footnote †: journal: Applied
# Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review
## 3 Reviewing Relevant Technologies and DT Components
### Technologies Involved in DTs
#### 3.1.3 Communication Technologies and Schemes
WWAN technologies, such as 4G Long-Term Evolution (LTE), 5G, and the evolving 6G, enable high-speed mobile internet access and support a wide range of services, including voice communication, video streaming, and data transfer. These networks rely on cellular infrastructure and are commonly used for mobile devices like smartphones, tablets, and laptops to stay connected while on the go. * Low Power Wide Area Networks (LPWAN) are wireless network technologies ideal for IoT devices with long battery life, extensive coverage, and low cost. Unlike 3G/4G or Wi-Fi systems, LPWAN networks are not primarily designed for high data transmission speeds or reducing latency. Instead, LPWANs target energy efficiency, scalability, and coverage [42], thus gaining popularity in industrial and research communities. They provide long-range communication, covering distances from 10 to 40 km in rural areas and 1 to 5 km in dense urban areas. Their main advantages include energy efficiency, a battery life exceeding ten years, and low cost, which is ideal for IoT applications that require small-scale data transmission [48]. Various LPWAN technologies have been developed and deployed in either licensed or unlicensed frequency bands, such as Long-Range (LoRa), Narrow-Band IoT (NB-IoT), each employing different techniques to achieve the goals of LPWAN [49]. * LoRa is an ideal wireless communication technology for low-bandwidth IoT applications, offering long-range and low-power consumption services. It uses a proprietary spread spectrum technique to achieve long-range and low power consumption, operating in the unlicensed sub-GHz ISM band available worldwide [50]. * NB-IoT is a widely used LPWAN technology connecting various devices and services through cellular networks. It operates in a narrow bandwidth of 180 kHz, providing extensive network coverage and low latency. Designed for cost-effective and delay-tolerant IoT devices, NB-IoT utilizes LTE techniques and can integrate with existing LTE infrastructure. Coexisting with LTE networks, NB-IoT offers power-efficient connectivity and leverages compatible technologies for seamless integration [51]. The main technical features of the wireless network technologies mentioned above are displayed in Table 1.  
Data rates and coverage of various wireless networks are shown in Figure 6 for comparison. The orange box represents some of the most popular LPWAN networks, prioritising greater coverage ranges rather than higher data rates, contrary to the WLANs in the green box.
## 4 Progress in the Understanding of Illuminated B-O LID Mitigation Processes
### Alternative Theory for Permanent Deactivation and Confusion Related to the Involvement of Hydrogen  
Despite evidence from numerous authors on the involvement of hydrogen [87; 101; 107; 116], there has been some debate over the role of hydrogen in the regeneration process [80]. In particular, it has been suggested that thermal effects during fast firing and the impact of plasma processing are responsible for the permanent deactivation of the defects, rather than the introduction of hydrogen [72; 121]. One theory was proposed, suggesting that the fast cooling rates reduce the concentration of interstitial boron-oxygen pairs (B\({}_{\text{i}}\)-O\({}_{\text{2}}\)) and increase the sinking efficiency of boron nano-precipitates, which then determines the rate constant for regeneration [80]. However, subsequent studies have confirmed the critical role of the presence of a hydrogen-containing dielectric layer during firing and that the firing process alone cannot lead to complete suppression of B-O defect formation, nor is it purely caused by exposure to plasma processing [87; 122; 123].  
Subsequent attempts have also been made to prepare samples containing no hydrogen, using lean hydrogen dielectrics deposited at temperatures below 200 \({}^{\circ}\)C, with no following high-temperature process to introduce hydrogen into the silicon bulk [124]. These samples were observed to undergo permanent deactivation of the B-O defect, albeit at a rate many orders of magnitude slower than that with the intentional introduction of hydrogen. The authors argued that this demonstrated that hydrogen is not required for regeneration to take place but instead passivates other defects leading to higher lifetimes and hence carrier concentrations. It is, however, extremely challenging and perhaps impossible, to prepare samples containing _no_ hydrogen. Hydrogen can be unintentionally introduced into silicon during a variety of processing steps such as ion-beam or chemical processing [125; 126]. Even a dilute HF dip at room temperature can result in significant amounts of atomic hydrogen diffusing several microns into the silicon [126]. With appropriate processing conditions and sufficient time, hydrogen can diffuse throughout a silicon wafer even at temperatures below 100 \({}^{\circ}\)C [127]. While high temperatures are often thought to be critical for introducing hydrogen to silicon solar cells, this is much more likely a function of the temperature required to release hydrogen from current commercial dielectrics than due to the limited diffusivity of the hydrogen within the silicon. The most logical conclusion to draw from this study [124] is then that samples containing much less hydrogen demonstrated a much slower regeneration rate when compared to samples with higher hydrogen concentrations.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 5 Discussion
### The Impact of GMDP  
We perform a series of ablation experiments on various stacking numbers of distribution propagation on CUB-200-2011 in 5-way 1-shot and 5-way 5-shot configurations to confirm the viability of GMDP. Table 3 demonstrates that when the degree of distribution propagation rises, the classification accuracy of MDPN steadily improves and peaks with a configuration of three levels.
## 5 Experiment
As another measure that compensates for the unbalanced datasets issue, MCC measures the correlation between an actual class and a prediction class. MCC for \(G\) classes is defined as in [36,38]  
Figure 7.0  
where the total number of correctly predicted elements is denoted by \(c=\sum_{g}^{G}C_{gg}\), the total number of elements \(s=\sum_{i}^{G}\sum_{j}^{G}C_{ij}\), the number of times that class \(g\) was predicted (column total) \(p_{g}=\sum_{i}^{G}C_{gi}\), and the number of times that class \(g\) truly occurred (row  
Figure 7.1 Average confusion matrix of the 50 SVM models. (**a**) In number and (**b**) in percentage. total) \(t_{g}=\sum_{i}^{G}C_{ig}\). It ranges in \([-1,+1]\), where the value \(-1\) represents the case of perfect misclassification, the value \(+1\) does the case of perfect classification, and \(MCC=0\) does the coin tossing classifier. MCC can also be used to evaluate the performance of a model with an unbalanced ratio of data between classes, such as F1-score [39; 40]. The F1 score depends on which class is defined as positive in Equation (6). However, MCC is an indicator that has the advantage of preventing a positive class from being incorrectly defined over an F1 score because it does not depend on which class is positive. Table 3 summarizes the performance of the CNN and SVM models in terms of precision, recall, accuracy, F1 score, and MCC, respectively. In each evaluation, precision, recall, and F1 score were calculated for each class, and then the macro-averaged values were reported as each model's final values. The values shown in Table 3 are obtained by averaging over 50 runs. According to Table 3, the trained CNN model exhibited better recall performance than precision, whereas the SVM model showed better precision performance than recall. This means that when the trained CNN model classifies a data sample, it is essential not to classify it incorrectly. Conversely, SVM models focus on classifying data into actual categories. The CNN model performed better than the SVM model in terms of accuracy. As mentioned, accuracy may be biased due to an imbalanced dataset issue. Precision and recall have relatively little meaning when viewed alone due to differences in perspective; hence, the performance was evaluated using the F1 score and MCC. These indices comprehensively look at precision and recall at once. The difference between the accuracy and the F1 score in the CNN model is about 1%, whereas the difference in the SVM model is 3.8%. This means that compared to the SVM model, the bias in classifying the CNN model is less. Furthermore, the MCC of the CNN model is 0.9447, and that of the SVM model is 0.7986, indicating that the CNN model can be classified similarly to the actual correct answer compared to the SVM model. As a result of using various indicators to evaluate the model, the CNN model shows excellent classification performance without being biased to a specific class in leak detection classification prediction.
## 3 Experiments and Results
For the vibration applied to PZT2, its phase-recovered time-domain signal is illustrated in Figure 10c, accompanied by the power spectrum shown in Figure 10d, which boasts an SNR of roughly 35 dB. These representations in Figure 10a-d collectively validate the system's capability to identify and accurately reconstruct the waveforms of multiple-point vibrations.
## 3 Dynamic Characterization of High Damping Alloy Mounted Structures Calculations
### Harmonic Response Analysis  
1 g excitation force is applied to the bottom of the mounting structure along the Y-axis direction (perpendicular to the casing direction), and the excitation force sweeps in the range of 0-1500 Hz to compare the acceleration responses of mounting structures made of different materials at the response pickup point. According to the relevant Ref. [23], 1 g is one of the common vibration excitation amplitudes of aerospace power equipment structures, and subsequently, our experimental results also show that this excitation amplitude can obtain the required modal responses. The position of the response pick-up point is shown in Figure 16a, and the position of the excitation force is shown in Figure 16b. Figure 16a shows that the mounting structure has two sets of threaded holes at the position of connection with the base; each set includes three threaded holes; one set of threaded holes at the position directly under the mass block is in the vertical direction, and the other set of threaded holes at the other side of the mounting structure is in the horizontal direction, and the mounting structure is fixed completely by these two sets of bolts in both vertical and horizontal directions. In the simulation, we simulate the axial connection force of the actual bolts by applying a certain pressure on the mapping surface between the bolt spacer and the two connecting surfaces, which greatly simplifies the simulation and saves computation time. Since the focus of this paper is on the overall vibration level of the mounting structure with the mounting mass block installed, it can be assumed that this simplification has a negligible effect on the results. A comparison of the swept response obtained from the finite element calculation is shown in Figure 17, and a comparison of the results is shown in Table 5.  
Figure 16: The position of the response pickup point and the application position of the excitation force (**a**) respond to the pickup point location, (**b**) position of the excitation force.  
Figure 17: The comparison of sweep response comparison.  
From Table 5, it can be seen that in the range of 0-1500 Hz, the modal frequency of the high damping alloy material mounting structure is comparable to that of the carbon steel material mounting structure, but there is a significant reduction in the vibration response amplitude, and the reduction rate of the vibration amplitude of the corresponding order can reach 60%, respectively.
# Niching-Driven Divide-and-Conquer Hill Exploration
## 4 Experiments
### Effect of NDDCHE in Complex Cases
As for LRDE, its niching technique is markedly distinct from the niching techniques employed by the other four niching methods. This distinction is so pronounced that the heat map of LRDE bears little to no resemblance to those of the latter methods. In addition to irregular fitness landscapes, the increase in the dimensionality of the problem's decision space also escalates the search difficulty. To investigate the effect of NDDCHE when the number of decision variables is much higher than two, 100 independent runs of the five selected niching methods on a 10-D test problem are conducted. As was the case in previously tested problems, there also exists an exponential difference in the size of BoA between the seven optima of the 10-D test problem as described in Equation (7). The maximum numbers of evaluations for LRDE, kNPSO, NSDE, NEA2, and HillVallEA are set to \(5\times 10^{6}\), \(5\times 10^{6}\), \(1\times 10^{7}\), \(5\times 10^{5}\), and \(2\times 10^{5}\), respectively. The population size for LRDE, NSDE, or NEA2 is set to 20. As for kNPSO, the speed of convergence decreases drastically with the growth of the number of decision variables when its number of neighbors \(k\) remains to be 2, so \(k\) is set to 5 and the population size is set to 100. The results are shown in Figure 14, where (a) demonstrates the heat map of the average improvement in error for each optimum made by NDDCHE on LRDE with different radii of repulsion, and (b-e) show the variation in the average improvement in error for each optimum made by NDDCHE on the remaining four EC methods. According to the results, NDDCHE decreased the errors for optima with smaller BoAs for LRDE in most cases, especially when the radius of repulsion was set to 0.35. As for the remaining four EC methods, the incorporation of NDDCHE resulted in at least one order of magnitude enhancement in the rate of reduction in errors for optima with smaller BoAs. Above all, the above experimental results demonstrate the effectiveness of NDDCHE in complex cases, i.e., when the problem has a higher number of decision variables or an irregular fitness landscape in each BoA, a hierarchical divide-and-conquer framework can still enhance the abilities of niching methods in locating optima with lower attractiveness.
## 3 Results and Discussion
### Relationship Between Deformation and HSV Values  
Figure 7 shows the changes in the intensities of H, S, and V as functions of the applied displacement. HSV is a digital color format in which a specific color is defined by its position coordinates in a cylindrical coordinate, where H is the angle, S is the radius, and V is the height. This allows intuitive color changes, particularly for visualizing sensitive changes in hue and saturation [20]. H, S, and V were normalized to the ratios of the intensities changed by deformation to the initial intensities.  
In stage I (0-1 mm), S showed the most sensitive discoloration compared to H and V; that is, a decrease in saturation was clearly observed. The H and V values showed minor decreases, and their behaviors were similar during this stage. The high sensitivity of S in this stage suggests that initial structural deformation induced increased scattering, which lowered the purity of color. Beyond stage I, S still showed a large decrease, and H started to increase significantly beyond 1.5 mm, indicating a shift in the reflected wavelength toward longer (redder) one due to structural elongation. However, no significant change was observed in V in stage II. In stage III, H and S appeared to converge along with a decrease in their slopes, and V still showed no significant change, except for a slight increase toward the end of the experiment. These results suggest that H, S, and V values reflect the periodic deformation of the sensor's internal structure, governed by Bragg's diffraction equation, and align with the RGB discoloration trend of B \(\rightarrow\) G \(\rightarrow\) R. Furthermore, the polynomial relationships (each dotted line in Figure 7) between H, S, and V and the applied displacement (with \(R^{2}>\) 0.84) highlight the non-linear deformation behavior of the sensor, indicating the influence of nanoscale structural reconfiguration at higher displacements.
## 4 Discussion  
Endermologie is a versatile treatment that is utilized in the medical and aesthetic treatment of numerous skin disorders. It is often used to strengthen, complement, prepare, and support surgical or other medical or aesthetic procedures. Endermologie stimulates microcirculation, drains fluid flow, and accelerates revascularization. This reduces edema, cellulite, and other dysfunctional changes in subcutaneous tissue [3; 14; 24; 29; 39; 40]. Treatments favor dermal remodeling, enhance skin elasticity, and reduce fibrosis and scarring [5; 6; 21; 31]. Moreover, the treatment affects muscle recovery [22].  
Many studies have been conducted on endermologie and reveal extracellular matrix remodeling that can be interpreted as a cell activation process: fibroblasts acquire a \"secretory\" phenotype, adipocytes become more sensitive to fat release, and venolymphatic circulation improves [5; 26; 30]. Endermologie has documented circulatory, trophic, and anti-fibrosis properties and can be used to improve the integrity of the integumentary system, treat most types of scars, stimulate circulation, drain fluids, soft tissue, improve fibrosis and adhesions, relieve pain, and improve range of motion [3; 6; 14; 31; 32; 39].  
Despite the heterogeneous population and the wide diversity of designs used in studies, global results showed that endermologie has beneficial effects on the integumentary system and can be used in medical therapy, surgery, and aesthetic procedures as a holistic integrative treatment to help optimize the scarring process and final aesthetic and functional outcome [6; 28; 34]; prevent, reduce, or manage complications after surgery [15; 19; 21; 31]; and improve therapeutic care, satisfaction, and quality of life [3; 32; 39]. The role of therapists is of paramount importance.
# Tracking of Moving Targets Through Asynchronous Measures
## 4 Numerical Results
### Line of Sight Probability  
For this section, we have simulated a scenario according to the following assumptions:  
1. The altitude of the target is \(h=100\,\mathrm{m}\);
2. the velocity of the target is \(v=10\,\mathrm{m}/\mathrm{s}\);
3. the radius of the orbit is \(r=1000\,\mathrm{m}\);
4. the RMSE of range measurements is \(\sigma_{p}=0.1\,\mathrm{m}\).
5. For all the anchors the LoS probability is \(p=70\%\)  
The LoS probability is introduced in order to take into account impairments like shadowing, channel variability and moving obstacles. At each motion step, for each anchor the LoS availability is randomly generated according to the LoS probability \(p\).  
Figure 1: Hexagonal set of anchors with a flying target.  
The EKF needs at least 3 anchors to be visible simultaneously in order to compute a new 3D position and update the estimates, while the proposed algorithm employs all the visible anchors singularly and none of the range measurements gets wasted. Therefore, as shown in Figure 3, the performance gap between the algorithm and the benchmark (i.e., the EKF) is remarkable even when the line of sight probability is lowered.  
Now, we will assume that all the anchors are visible in order to explore the effect of other parameters on the positioning accuracy.  
Figure 3: Positioning root mean square error of the vertical (V) and horizontal (H) components with \(h=100\,\mathrm{m}\), \(v=10\,\mathrm{m}/\mathrm{s}\), \(\sigma_{p}=0.1\,\mathrm{m}\), \(r=1000\,\mathrm{m}\) and \(p=70\%\).  
Figure 2: Positioning root mean square error of the vertical (V) and horizontal (H) components with \(h=100\,\mathrm{m}\), \(v=10\,\mathrm{m}/\mathrm{s}\), \(\sigma_{p}=0.1\,\mathrm{m}\), \(r=1000\,\mathrm{m}\) and \(p=100\%\).
## 3 Theoretical Analysis and Programming of Zero-Thickness Interface Elements  
In geotechnical systems, contact surfaces, whether between the foundation and the soil or between different soil layers, serve as discontinuity surfaces or interfaces. Traditional analysis methods, such as the limit equilibrium method, often neglect these interfaces and their influence. However, the presence of discontinuities can significantly affect the overall engineering behavior and should not be ignored. Interface elements are generally categorized into two types: zero-thickness (thickness-free) interface elements [27; 28] and finite-thickness interface elements [32; 33]. This study adopts the four-node, zero-thickness interface element originally proposed by [PERSON] and [PERSON] (1994) [28] as the theoretical foundation. Building on this, a modified six-node isoparametric interface element is introduced, and its influence on the analysis results is examined and discussed.  
Zero-thickness interface elements are standard in geotechnical FEM for slip surfaces and joints, typically enforced with penalty or augmented-Lagrangian traction-separation laws. Our multi-segment Mohr-Coulomb interface follows the same paradigm. For context, PLAXIS uses zero-thickness interfaces with normal and shear stiffness and a Coulomb envelope [34]; we adopt identical stiffness definitions but embed the elements along predefined slip paths, assigning stiffness from the minimum Young's modulus of adjoining materials to avoid locking (a convention also used by PLAXIS). ABAQUS [35] cohesive/contact pairs likewise implement traction-separation behavior; we apply the zero-thickness ideal-plastic limit directly, which is well suited to upper-bound collapse analysis. Comparable formulations have proven accurate in prior studies on footing and slope stability [36; 37].