esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results and Discussion
### Degradation of Cross-Modal Retrieval for Seen Class Visual Data  
When UCE considers all input data as unseen classes, the performance of generalized zero-shot cross-modal retrieval is increasing but the performance of cross-modal retrieval for seen class input query may be decreasing. The performance evaluation on seen class data can measure the influence of UCE. So, the performance of cross-modal retrieval for seen class visual data is evaluated by using the CIFAR-100 dataset (seen classes) with the same model used in the previous generalized zero-shot cross-modal retrieval experiments. Table 4 shows the performance evaluation results for visual-semantic cross-modal retrieval for 10k CIFAR-100 test dataset (seen classes).  
In this setting, the performance of HSL+UCE model in retrieving correct label for seen class visual data among 110 seen and unseen classes is somewhat lower than that of HSL model. The reason is that the unseen confidence score is applied so that it prefers the unseen semantic vectors. Performance improvements on the seen data are future research topics for generalized zero-shot learning. Nevertheless, considering the trade-off with the performance improvement of zero-shot learning obtained in Table 2, it can be considered an acceptable performance degradation. In terms of performance change ratio, the performance on unseen data increases by about 200-300% due to the addition of UCE, while the performance on seen data decreases by about 10-20%.
# Incisors Extrusion with Clear Aligners Technique: A Finite Element Analysis Study
## 4 Discussion
In open-bite FE simulations, the area of the lateral incisors experienced the greatest deformation, particularly at the gingival margin. Therefore, it may make sense to consider that the lack of adherence between aligner and tooth surface could lead to undesired forces and displacement. Regarding aligner pressure, the analyzed configurations showed better results when posterior and anterior attachments were used in combination. Particularly when rectangular and vertical attachments are planned on canines, bicuspids, and molars, and rectangular horizontal attachments are planned on the buccal or the palatal surface of the upper incisors, the resulting force system is more efficient, with minimal aligner deformation. This attachments' configuration allows for better anchorage control and more efficient control of the lateral incisors. The observed strong tendency for posterior teeth buccal tipping and intrusion should be considered as favorable from a clinical perspective--upper arch expansion and posterior intrusion represents treatment plan aspects that should be considered in adults' open-bite treatments [10]. When using fixed appliances to treat non-growing open-bite patients, it generally results in extrusion of the molars, leading to an increase of the mandibular plane angle [10, 11, 12, 13]. Therefore, a greater amount of incisor extrusion should be planned, reducing the efficiency of the treatment modality [11, 14]. This study demonstrated that clear aligners may produce posterior intrusion and therefore, counter-clockwise rotation of the mandible, facilitating anterior open-bite correction. A similar result was obtained in the retrospective study by [PERSON] et al. [5]. From a clinical perspective, we should remember that a small amount of posterior intrusion can be obtained with aligners, even if not planned [15]. The well-known \"bite block\" effect of the aligners has been previously described and attributed to the thickness of aligners covering the occlusal surfaces and to the bite force exerted by patients [5, 16]. This effect has been quantified in previous studies, ranging from 0.47 mm [5] to 0.6 mm [6, 16]. In conclusion, biomechanical results match the observed clinical outcomes for open-bite treatment with orthodontic aligners. The efficiency and accuracy of treatment should be improved on the basis of biomechanical designed solutions, to improve control and understanding of the technique in the daily clinical setting.
## 3 Results  
### Groove Formation Behaviour  
First, laser irradiation was performed on CVD diamond and the irradiated surfaces were examined. Irradiating the CVD samples with a fluence of around 14.0 J/cm\({}^{2}\) caused sample breakage, so the fluence was limited to below 11.5 J/cm\({}^{2}\). Grooves were found only on the surface on which the laser was incident (upper surface) and the bottom of the grooves was smooth, as presented in Figure 2a-e.  
Next, laser irradiation was performed on the HPHT sample. The HPHT diamond could not be machined with a laser fluence below 7.0 J/cm\({}^{2}\). Interestingly, at fluences below 10.3 J/cm\({}^{2}\), the irradiation-induced grooves, as shown in Figure 2f-h, were found on the reverse side of the sample surface that was being irradiated (lower surface), whereas no marks were found on the upper surface. This phenomenon will be referred to as reverse irradiation in this paper. At fluences equal to or greater  
Figure 1: Schematic of the experimental set-up.  
than 10.3 J/cm\({}^{2}\), a groove was machined onto the upper surface on which the laser was incident (referred to as forward irradiation), as presented in Figure 2i,j while no grooves were found on the lower surface.  
Figure 2: SEM images of the grooves obtained by irradiating with varying laser fluences on the CVD sample (**a**) 7.8 J/cm\({}^{2}\); (**b**) 9.1 J/cm\({}^{2}\); (**c**) 9.9 J/cm\({}^{2}\); (**d**) 10.3 J/cm\({}^{2}\); (**e**) 11.0 J/cm\({}^{2}\); and on the HPHT sample (**f**) 7.8 J/cm\({}^{2}\); (**g**) 9.1 J/cm\({}^{2}\); (**h**) 9.9 J/cm\({}^{2}\); (**i**) 10.3 J/cm\({}^{2}\); (**j**) 11.0 J/cm\({}^{2}\).  
The surfaces of the grooves were further compared in detail. The SEM images of Figure 3 indicate similarities between the grooves formed on the upper surface of HPHT diamond and the grooves on CVD diamond. They both possess a crack-free groove centre. On the other hand, Figure 4 shows the rough, jagged surface of the grooves found on the lower surface of HPHT diamond. Thus, by comparing Figures 3 and 4, the grooves obtained by forward and reverse irradiation are shown to be clearly different.
## 4 Description of the Test Bench and Experimental Approach  
To validate the theoretical model, an experimental setup is developed using a miniaturized Bently Nevada rotor-kit 4. The test rig consists of a flexible vertical shaft with an imbalanced disc attached to it and connected to a variable-speed motor. The system is installed inside an 84.5 \(\times\) 25 \(\times\) 25 cm\({}^{3}\) rectangular container filled with non-viscous water. Engine speed is measured with a laser tachometer and the response is captured with devices such as probes and stainless-steel pressure sensors inside the water. The test bench set up for the experiment is described in the following section. The proposed rig design is focused on studying the rotational motion of a small-scale drill string, where axial and lateral vibrations are not recorded.
# A Novel Technique for High-Efficiency Characterization of Complex Cracks with Visual Artifacts
## 5 Applications
### Inspection of Roads and Buildings  
To demonstrate the application of this method, two scenarios are presented: (1) inspection of buildings and (2) inspection of roads. To achieve this, the proposed method is applied. The following two datasets were selected:  
1. DeepCrack: This is an online dataset showcasing the cracks on asphalt and concrete road surfaces with many different surface textures. The dataset was collected by [PERSON] et al. (2020).
2. Concrete Crack Dataset (CCD): This dataset was collected in China, showcasing cracks in different sections of buildings. The data were collected by [PERSON] et al. (2019).  
For these datasets, the performance indicators from the segmentation results are reported in Table 6 Apart from our method, this was compared with other methods from the literature. A deep convolutional network based on convolutional layers of VGG16 (similar to [PERSON] et al. (2019)) was used for comparison. This network is referred to as Typical-RC-AI. CrackUnet, which was developed from Unet, has shown superior performance over other deep learning models, such as the fully convolutional network (FCN) ([PERSON] et al., 2019) and Sarmiento's Model (Sar's Model). The results consistently show that our proposed method performs with high accuracy for datasets showcasing real engineering infrastructure. Not only this, but when comparing the results with other models in the literature, our model requires a significantly smaller number of parameters to achieve this. These results validate the potential of the proposed technique in field applications.  
Figure 9: Performance of SHSnet in segmenting complex (micro)cracks on different surfaces, textures, and crack densities in the presence of visual artefacts such as loose fibers, experimental equipment, surface markings, and loose edges on images from laboratories and the literature. To summarize, in all such cases, even when the surface is unprepared and dark with high density (which even includes thin cracks propagating through surface defects as in Figure 6), the segmented cracks, i.e., crack pattern, match well with manual observation. (Left: Original Image; Right: The segmentation result).
## 5 Implementation and Evaluation
### Success Rate and Time Efficiency Compared with Other Work  
5.3.1. Success Rate Performance  
Table 3 compares our approach with another approach based on protocol popularity. The results show that we can identify device fingerprints at a finer granularity (device model and firmware version) than [36]. In addition, we use no more than five probe methods, which reduces the communication overhead significantly.
## 3 Results and Discussion
### Aroma Markers Differentiating between HY EO and GF EO  
To screen differential aroma compounds between HY EO and GF EO, we performed OPLS-DA, a supervised classification method [42], based on the content of 59 aromadactive compounds in two CP EOs. As shown in Figure 5A, there was a clear separation between HY EO and GF EO, with HY samples distributed in the left area (second and third quadrants) and GF EO in the right (first and fourth quadrants). The OPLS-DA model was established with good fitting parameters (R\({}^{2}\)Y = 1, Q\({}^{2}\) = 0.999), indicating strong explanatory validity and predictive ability. For the permutation test (\(n\) = 200), the intercepts of R\({}^{2}\) and Q\({}^{2}\) were \(-\)0.02 and \(-\)1.36, respectively, which further demonstrated the OPLS-DA model was statistically significance and not over-fitting (Figure 5C).  
Figure 4: Correlation between aroma-active compounds and sensory attributes. Sensory flavor wheel based on GC-O analysis (**A**); PLSR model (**B**). Numbers of compounds correspond to Table 2.  
The OPLS-DA loading plot and VIP (variable importance in the projection) values were used to screen out aroma markers that showed great contribution in differentiating HY EO from GF EO. The loading plot (Figure 5B) revealed that \(\gamma\)-terpinene, germacrene D, germacrene B and nootkatone were mainly related to HY EO, while \(D\)-limonene, \(\beta\)-myrcene, octanal and decanal were mainly related to GF EO. Among them, germacrene D and \(D\)-limonene, which are located furthest from the center of the loading plot, contributed most greatly to the model. Furthermore, the VIP values measure the importance of each variable in the model, with larger VIP values indicating a greater contribution to the differentiation of samples [43]. Typically, VIP \(>\) 1 suggests the variables play an essential role in distinguishing different samples [43]. As shown in Figure 5D, six aroma-active compounds with VIP \(>\) 1 were filtered as aroma markers for differentiating HY EO and GF EO, including \(\gamma\)-terpinene (VIP = 4.84), \(D\)-limonene (VIP = 3.54), germacrene D (VIP = 3.36), nootkatone (VIP = 1.62), germacrene B (VIP = 1.44) and terpinolene (VIP = 1.02).
# A High-Efficiency K-band MMIC Linear Amplifier Using Diode Compensation
## 1 Introduction  
GaAs MMIC is regarded as the premier power device for the microwave communication system [1] and phase array radar system [2] witnessed in recent decades. However, when facing high peak-to-average ratio (PAR) modulation schemes such as QPSK and OFDM, the nonlinearity of the power amplifier causes spectral reproduction and intermodulation distortion. When multi-signals are amplified within a single channel, the beat between carriers generates amplitude modulation [3].  
To meet the linearity requirements in the point-to-point radio or satellite communications which usually operate with a high PAR and inconstant enveloped input signal, conventional designs have to work at a back-off output point compared to their saturated power level. Thus, several techniques have been employed to improve the efficiency in the low power region, such as the linear Doherty design, feed-forward technique, and envelop feedback. Some linear Doherty amplifier [4] and feed-forward designs [5] show a high linearity at an acceptable efficiency; however the complexity and cost of chips are not low. Class-J [6] was also reported to achieve a high linearity in the back-off region. Those technologies usually generate a high circuit complexity. Some literature has also reported on the possibility of inner chip nonlinear compensation methods based on diodes [7]. However, no concrete MMIC design has been proposed.  
This paper presents a high-efficiency K-band MMIC linear power amplifier fabricated with a 0.15 \(\upmu\)m GaAs pHEMT processing technology. A kind of linearizer circuit of diode nonlinear compensation was accomplished. The Y-parameter matrix method was used to analyze and deduce the dynamic characteristic of the parallel diode and FET network. Both simulation and measurement results show the linearity improvement of the circuit. As a result, the proposed linear amplifier achieved an excellent performance with more than a 1 W output power and 33% power-added efficiency at the 1-dB compression point while maintaining an IM3 better than 20 dBc at an output power of 27 dBm per carrier over a 20-22 GHz band.
# Antennas and Propagation: A Sensor Approach
### Metamaterial, Metasurface and Other Periodic Structures  
In [9], a new design method for a planar and compact dual-band dipole antenna is proposed. The antenna has a hybrid CRLH (composite right- and left-handed) structure with lumped elements for a dual-band operation. A design for 2.4 and 5.2 GHz mobile applications is presented.  
A printed edge-fed counterpart of the Bruce wire antenna array for frequency scanning applications is presented in [10]. The unit cell of the proposed antenna consists of bowtie and semi-circular elements that cover a bandwidth between 22 GHz and 38 GHz.  
The work presented in [11] focuses on design issues in the implementation of holey glide-symmetric periodic structures for waveguide-based components. An analysis of realistic hole structures is performed by using an effective hole depth method that can be used as a tool for designing electrically large waveguide-based components.  
An integration of a microstrip slot antenna array for 5.8 GHz with dye-sensitized solar cells is proposed in [12]. It is shown that the antenna array has a slight influence on the solar cell performance, and the interference of the solar cells with the antenna feeding system is also negligible.  
A new slot-based antenna system with horizontal polarization for unmanned aerial vehicle (UAV) ground control stations (GCS) is outlined in [13]. The proposed antenna system consists of coaxial cylinders and slots. The vertical slots are periodically placed around the outer cylinder and generate in-phase, horizontally polarized beams, resulting in an omnidirectional radiation pattern.
## 5 Experimental Verification
### Performance Comparison Under Different Operating Conditions  
Comprehensive performance evaluation of both original and optimized high-speed magnetic-field-modulated motors under diverse operating conditions reveals substantial enhancements across critical parameters that significantly influence flywheel energy storage system viability. As shown in Figure 15a, systematic comparison of steady-state performance characteristics demonstrates that the optimized design achieves a 3.9 percentage-point higher peak efficiency at rated load while maintaining over 91% efficiency down to 20% load--offering superior performance across the entire operational range.  
As illustrated in Figure 15b, thermal measurements under vacuum operation confirm that the optimized motor achieves a 23.7% lower temperature rise at rated load and remains below critical thresholds even at 120% overload, thanks to the improved thermal management structure. Meanwhile, Figure 15c shows that the optimized design simultaneously reduces acoustic noise by 7.2 dB (A) and vibration amplitude by 64.5% under rated conditions, enhancing mechanical robustness and operational quietness.  
Partial-load performance evaluation, as shown in Figure 15d, demonstrates that the optimized design sustains a power factor above 0.92 down to 20% load, while the original motor experiences degradation below 0.85 under 40% load--substantially improving grid interaction characteristics for energy storage scenarios. Environmental robustness assessments further confirm that the optimized design maintains consistent efficiency across ambient temperature variations from -20 \({}^{\circ}\)C to +60 \({}^{\circ}\)C, with variations of only 1.3 percentage points, compared to 4.8 in the original design.  
Acceleration/deceleration performance analysis, as depicted in Figure 15e, indicates that the optimized design achieves 24.8% faster response times during charging transients, reducing the time to reach 90% target speed from 2.76 s to 2.07 s, while maintaining 41.7% smaller speed oscillations during sudden load changes due to better electromagnetic damping.  
The performance advantages become particularly pronounced during continuous cycling operations, where the optimized motor maintains thermal stability within \(\pm\)3.5 \({}^{\circ}\)C after reaching steady-state conditions--compared to the greater temperature  
Figure 15: Performance comparison under different operating conditions.  
swings observed in the original design. Figure 15f further validates this robustness through endurance testing: the optimized motor maintains stable thermal and electromagnetic performance after 72 h of continuous operation under rated load, with losses increasing by only 2.1%, significantly lower than the 8.7% rise in the original system.  
Together, these comprehensive performance improvements across the entire operational spectrum substantially enhance the practical viability of high-speed magnetic-field-modulated motors for advanced flywheel energy storage applications.
## 2 Materials and Methods  
Studying the Effect of Moisture Content on the Waveguide's Efficiency and on the Distribution of Microwave Electromagnetic Field in a Grain Layer  
The studies were performed by the computer-assisted modeling of the distribution of the microwave electromagnetic field emitted from a semicircular waveguide with slot radiators into a grain layer (see Figure 2). To achieve this purpose, the software package CST Studio Suite 2022.05 SP5 [19; 20; 21] was applied. The inner diameter of the semicircular waveguide was 50 mm.  
This diameter value was selected to allow for the entire range of H-type waves to propagate in the waveguide. The wall thickness of the waveguide was 2 mm. The dielectric properties of wheat with various moisture contents were as follows: 14% (\(\epsilon^{\prime}\) = 4.55, \(\epsilon^{\prime\prime}\) = 0.43) at 16%, (\(\epsilon^{\prime}\) = 4.9, \(\epsilon^{\prime\prime}\) = 0.55) at 20%, (\(\epsilon^{\prime}\) = 5.87, \(\epsilon^{\prime\prime}\) = 0.89) at 24%, and (\(\epsilon^{\prime}\) = 7.33, \(\epsilon^{\prime\prime}\) = 1.32) at 26%, where \(\epsilon^{\prime}\) represents the real part of the grain's dielectric permittivity, known as the dielectric constant, and \(\epsilon^{\prime\prime}\) represents the imaginary part of the grain's dielectric permittivity, known as the dielectric loss factor.  
This range of moisture content variation falls within the most probable limits of its change during harvesting and storage [7].  
Figure 2: Configuration of the semicircular waveguide and grain layer selected for modelling: (1) semicircular waveguide with slot radiators; (2) grain layer.  
The depth of the grain layer was assumed to be 100 mm from the waveguide's walls. Therefore, the size of the grain layer was 254 mm vertically and 152 mm horizontally. Modelling was carried out without a metal casing of the microwave-active zone.  
During the process of modelling, radiation directional diagrams were plotted for various values of the moisture content in the grain. These directional diagrams were studied from the following angles: a side elevation perpendicular to the upper wall of the semicircular waveguide, angle Phi = 0\({}^{\circ}\) (see Figure 3a), and an end face view of the waveguide, angle Theta = 90\({}^{\circ}\) (Figure 3b). The directional diagram for Phi = 0\({}^{\circ}\) allows for the evaluation of the distribution of the electromagnetic field along the waveguide and the microwave field penetration distance into the grain layer. The directional diagram for Theta = 90\({}^{\circ}\) enables the analysis of the microwave electromagnetic field distribution along the waveguide's vertical axis. Moreover, each directional diagram shows the areas of maximum radiation density and maximum radiation vector in the grain layer (Figure 3a,b).