esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictability of ClinCheck in Overbite Correction with Aligners: A Systematic Review
## 1 Introduction
reported that anterior open bite correction primarily results from incisor extrusion, whereas other studies have concluded that molar intrusion combined with mandibular counterclockwise rotation plays a significant role in overbite correction, with molar intrusion measurements of approximately 1 mm [18; 22; 23]. [PERSON] et al. suggested that the inherent thickness of the aligner contributes to promoting intrusion in the posterior region [24]. Based on these findings, some authors have recommended incorporating thickened areas on the chewing surfaces of posterior teeth--commonly described as \"occlusal posterior bite blocks\"--into the ClinCheck digital treatment plan [25]. Current evidence indicates that the average accuracy of extrusion is approximately 29.6%, whereas [PERSON] et al. reported a higher accuracy of 56% [26]. According to [PERSON] et al., only 38% of the programmed posterior intrusion was successfully achieved [27]. In clinical practice, for both anterior open bite and deep bite cases, the Invaslign system offers clinicians various tools for vertical control, including the option to select attachment designs (optimized or conventional), adjust treatment staging, and apply overcorrection of tooth movements according to the clinician's biomechanical strategy. The accuracy of the biomechanical mechanisms underlying overbite correction with aligners has not been extensively investigated in the literature. Furthermore, most studies tend to evaluate overbite correction only after the initial series of aligners or following the first refinement phase. The heterogeneous data available in the literature on vertical control suggests that the accuracy of dental movements does not necessarily equate to clinical effectiveness. As with conventional orthodontic treatment, clear aligner therapy does not ensure outcomes that precisely replicate the initial virtual treatment plan. A variety of factors may influence the effectiveness of orthodontic treatment, including the interindividual biological variability, patient adherence to prescribed protocol, the clinician's level of expertise and the technical limitations associated with the precise execution of planned dental movement [28; 29]. The ClinCheck software 3.0 simulates dental biomechanics and generates a digital preview of the anticipated final outcome. Therefore, this systematic review investigates how accurately overbite correction predicted by ClinCheck aligns with clinically achieved results following planned aligner therapy.
## 2 Proposed Metrology System
### Calculation Method of Rotation Axis Center  
First, physical objects must be converted into coordinates of the virtual system. Therefore, the conversion coordinates of the rotation axis center and the measurement of the LS-7030 light band must be obtained. These two factors form the basis for the subsequent software calculation. We calculated the center coordinate C by using the area method.  
As shown in Figure 2, the CCD was used to take photos of the correction block at three different positions; the positions of the three correction blocks do not overlap, and the total rotation angle is greater than 180\({}^{\circ}\). Then, image processing is used to identify the coordinates of the large and small circles on the calibration block, and the center of platform is C (\(x_{c},y_{c}\)). A triangle (denoted in red) is formed from the center points of two circles and platform, as shown in the Figure 2. The area of the triangle at the three rotation positions is equal because the distance between the big circle and the small circle is fixed, as well as the distance to the center of point C.  
As shown in Figure 2, the positions of the big circles are numbered 1, 3, and 5, and the positions of the small circles are numbered 2, 4, and 6; the coordinates of the 6 points are written as (\(x_{i},y_{i}\)), \(i=1\)-6. Equation (1) shows the area calculation formulas for the three triangles, \(\Delta\)12C, \(\Delta\)34C, and \(\Delta\)56C; the areas of the three triangles are equal (A\({}_{0}=\) A\({}_{1}=\) A\({}_{2}\)) to solve C.  
The three triangle-area formulas are as follows:  
\[\text{A0}=\frac{1}{2}\left|\begin{array}{ccc}x_{1}&y_{1}&1\\ x_{2}&y_{2}&1\\ x_{c}&y_{c}&1\end{array}\right|;\text{ A1}=\frac{1}{2}\left|\begin{array}{ cccc}x_{3}&y_{3}&1\\ x_{4}&y_{4}&1\\ x_{c}&y_{c}&1\end{array}\right|;\text{ A2}=\frac{1}{2}\left|\begin{array}{ cccc}x_{5}&y_{5}&1\\ x_{6}&y_{6}&1\\ x_{c}&y_{c}&1\end{array}\right| \tag{1}\]  
Figure 2: Determining the rotation center by the triangular areas. The big circle locations are at 1, 3, and 5. The small circle locations are at 2, 4, and 6. C is the rotary table center with coordinates (\(x_{c},y_{c}\)).  
After derivation, the rotation center (\(x_{c}\), \(y_{c}\)) values are as follows, in Equations (2) and (3):  
\[x_{c} = \frac{(x_{2}y_{1}-x_{1}y_{2}+x_{3}y_{4}-x_{4}y_{3})(x_{2}-x_{1}+x_{5 }-x_{6})-(x_{2}y_{1}-x_{1}y_{2}+x_{3}y_{6}-x_{6}y_{5})(x_{2}-x_{1}+x_{3}-x_{4})} {(y_{1}-y_{2}-y_{3}+y_{4})(x_{2}-x_{1}+x_{5}-x_{6})-(y_{1}-y_{2}-y_{5}+y_{6})(x _{2}-x_{1}+x_{3}-x_{4})} \tag{2}\] \[y_{c} = \frac{(x_{2}y_{1}-x_{1}y_{2}+x_{3}y_{4}-x_{4}y_{3})(y_{1}-y_{2}-y_ {5}+y_{6})-(x_{2}y_{1}-x_{1}y_{2}+x_{3}y_{6}-x_{6}y_{5})(y_{1}-y_{2}-y_{3}+y_{4 })}{(y_{1}-y_{2}-y_{5}+y_{6})(x_{2}-x_{1}+x_{3}-x_{4})-(y_{1}-y_{2}-y_{3}+y_{4 })(x_{2}-x_{1}+x_{5}-x_{6})} \tag{3}\]
## 4 Proposed ms-SDRGN Solution For SAG-MCS
### Multi-Scale Convolutional Encoder  
Exploiting observations properly is essential for agents to perceive the current state of RL systems and make corresponding actions. Previous DRL methods (e.g., DQN, DGN, MAAC) apply multi-layer perceptron (MLP) as linear encoders to process raw observations, which is preferred for scenarios with smaller observation dimensions or less information, such as Cooperative Navigation [47]. However, in our SAG-MCS task, observations and environment states are more complicated and their input sizes are relatively larger.  
Our intuition lies that compared with MLP, convolutional neural network (CNN) is more capable of processing data that has spatial information and large receptive fields, such as images. CNN can integrate information from different input channels as well. So we treat the local observation \(\mathcal{O}_{local}\) and satellites' fuzzy global observation \(\mathcal{O}_{global}\) as simplified real images, and design two CNN to extract spatial feature representations of  
Figure 3: ms-SDRGN Model Architecture.  
local and global input states separately. Specifically, we construct the local CNN with two convolutional layers and two fully connected layers, which outputs local embedding \(\mathbf{e}_{u}^{local}\). The global CNN has a larger input scale, and we use five convolutional layers, which yields global embedding \(\mathbf{e}_{u}^{global}\). As for the auxiliary information in \(\mathcal{O}_{self}\), we simply use a fully connected layer and take \(\mathbf{e}_{u}^{self}\) as output from UAV self-owned information. Finally, we use concatenation operation to combine them as a multi-scale observation embedding \(\mathbf{e}_{u}\) for UAV \(u\):  
\[\mathbf{e}_{u}=\text{concatenate}(\mathbf{e}_{u}^{local}\mid\mathbf{e}_{u}^{ global}\mid\mathbf{e}_{u}^{self}),\;\forall u\in\mathcal{U}. \tag{9}\]  
Such multi-scale features can help UAVs better select actions, by taking full account of: (a) the relative position between current UAV and surrounding Pols, obstacles or other agents; (b) the correlation of current UAV's remaining battery and the distance to the closest charging station; (c) the distribution of Pols in the fuzzy global map for better exploration and coverage.
## 1 Introduction
In a recent study, ultrasonic welding was applied for bonding sailcloth using different types of anvil wheels. Under the welding conditions of this paper, the bonding strength of the bonding joints can be increased with suitable engraving, speed, and amplitude of the ultrasonic system [13]. Meanwhile, in the case of polyethylene terephthalate fabrics, the sealing time and welding pressure can also affect the bonding strength, as shown in [14]. For improving the quality of the ultrasonic welding process for fabrics, some of the key parameters have been investigated. In a recent paper, the welding time, holding time, mechanical force, and anvil gap were considered in detail [15]. Furthermore, because horn surface amplitude is one of the most important factors that greatly affects joint quality, the uniformity of the amplitude of the horn has been investigated by both simulations and experiments [16; 17]. The results presented that the maximum amplitude occurs at the nodal region, causing high stress levels and leading to horn crack. Thus, the amplitude was controlled at less than 77 \(\upmu\)m within the safety region to prevent damage to the horn and the welding apparatus [18]. One of the most frequently used polymer materials owing to its low price, good processability, and mechanical properties is polypropylene (PP), which is semi-crystalline thermoplastic. As indicated above, making nonwoven fabrics from PP materials requires to assemble staple fibers or infinitely continuous filament fibers on top of each other to be bonded via a mechanical, thermal, or chemical process [19]. Recently, ultrasonic welding technology has been considered as an ideal approach to bond PP fabrics. In a recent paper, [PERSON] et al. studied the ultrasonic bonding of various types of fibers, which, for example, included PP materials and N6. They found that an impressive feature of the many fabrics produced was their superior softness over comparable products produced by calendar bonding. They also found that high melting point fibers such as N6 were difficult to bond in comparison to PP [20]. In another study, it was reported that better bonding was achieved by using polypropylene-polypropylene and polyester-polyester fabrics [21]. Even though ultrasonic welding is a promising technology for bonding nonwoven fabrics, there are still very few research papers published in the open literature in this field, especially for PP nonwoven fabrics. Furthermore, the mechanism of this technology is not yet understood fully. In order to clarify these issues further, this experiment focused on determining the influence of the ultrasonic horn shape on the formation of welding joints in the case of PP nonwoven fabrics using a continuous ultrasonic welding approach. Scanning electron microscopy (SEM) images of the bonded joints at different positions and directions were taken to investigate the interfacial microstructure and the bond locations of the fabrics. Moreover, the effects of the roller engravings on the peel strength are also discussed in detail.
# Face Anti-Spoofing Based on Deep Learning: A Comprehensive Survey
## 2 Attack Types
### Obfuscation Attacks  
Obfuscation attacks involve disguising facial features using various methods to interfere with or deceive a FRS. For example, the SiW-M [28] dataset comprises 13 spoof types, including print attacks, replay attacks, 3D masks, makeup attacks, and partial attacks. Examples of obfuscation attacks are shown in Figure 3d. These attacks pose significant threats as they can effectively disrupt or mislead such systems, impairing their ability to accurately identify individuals. Typically, obfuscation attacks use visual disguises that force the FRS to retrain on standard features, thereby reducing system security and reliability.  
Figure 3: Various attack examples: (**a**) print attacks from CASIA-MFSD [25]; (**b**) replay attacks from CASIA-MFSD [25]; (**c**) 3D masks from HiFiMask [27]; (**d**) obfuscation attacks [28]; (**e**) physical adversarial attacks: Adv-glasses [29], Adv-makeup [30], Adv-hat [31], Adv-sticker [32].  
Tattoo attacks involve applying tattoos to the face or other visible areas to create disruptive patterns that affect the performance of a FRS.  
Funny glasses attacks involve wearing specially designed glasses with exaggerated patterns or colors to interfere with facial recognition algorithms.  
Wig attacks involve using wigs to change the appearance of hair, which affecting the system's assessment of hair and overall facial features.  
Makeup attacks refer to attackers using makeup to deceive to alter facial features in an attempt to deceive a FRS. For example, heavy makeup may cover key features like the eyes or nose.  
Physical adversarial attacks [21] refer to introducing subtle, carefully designed physical perturbations to face images in the real world, aiming to mislead the FRS. Unlike digital adversarial attacks, physical adversarial attacks involve disrupting the face in actual environments to cause the FRS to produce incorrect identity recognition results. Examples of physical adversarial attacks are shown in Figure 3e.
# Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization
## 2 Bayesian Network Model Construction
### Problem Description and Assumptions
In real life, consumers have a certain periodicity in their purchase behavior of commodities, whether online or in physical stores. In addition, some commodities have a certain service cycle and life, so we assume that the fluctuation factor \(\gamma_{t}\) has periodicity and the period is \(H\). That is, the relationship between each \(\gamma_{t}\) should satisfy the following: \[\gamma_{i+H}=\gamma_{i}\,(i=0,\,1,\,2,\,\ldots)\] (10) \[\gamma\sim N(0,\,{\sigma_{\gamma}}^{2})\] (11) According to this assumption, for any time \(\gamma_{t}\), it not only affects the demand data \(y_{t}\) at the same time, but also affects \(y_{t+H}\), \(\ldots\), \(y_{t+H}\) (\(n=1,\,2,\,\ldots\)). 4. The correction factor \(\beta\) as well as hyperparameters \(\mu\) and \(\phi\) obey normal distribution independently. \[\beta\sim N(0,\,{\sigma_{\beta}}^{2})\] (12) \[\mu\sim N(m_{0},\,{\sigma_{\mu}}^{2})\] (13) \[\phi\sim N(0,\,{\sigma_{\phi}}^{2})\] (14) where \(m_{0}\) is defined as the mean of customer demand for the target item at all historical points in time, and since \(\phi\)\(\in\)(\(-1\),\(1\)), according to the principle of \"\(\sigma\)\" and \"\(3\sigma\)\" of normal distribution, the value of \({\sigma_{\phi}}^{2}\) is limited to [0.5,1].
## 5 Experiments and Results
### Conventional Beamforming of Oblique Backscatter Ionogram  
The comparison between the results of the oblique backscatter ionograms obtained by directly sum the signals of the six channels' and the conventional DBF can be shown in Figure 10, with Channel 1 as a reference. In the lower frequency band, in the vertical echoes' regions, the directly summed ionogram has the best SNR. At 5.2 MHz, 268.8 km as an example, the SNR of Channel 1 is 236.1 dB, the SNRs of the directly summed ionogram and the DBF result are 240.6 dB and 214.4 dB, respectively. This means that for the vertical echo signals, which are close to the vertical incidence, summing the six channels directly can enhance the signal strength, while DBF weakens it. It reflects the spatial filtering effect of DBF. But in the high frequency band, at 7 MHz, 691.2 km for example, with only oblique backscatter signal, the SNR of Channel 1, the directly summed ionogram and DBF result are 181.1 dB, 160.5 dB and 177.2 dB. Obviously, there is destructive interference in the way of directly summing while DBF retains the desired signal. Refer to Channel 1, it seems that DBF also weaken the SNR. This is because that under the effect of the spatial filtering, DBF not only retains the desired signal, but also suppresses the signals from other directions, resulting in a decrease of the total power.  
For further explanation, Figures 11 and 12 extract the signals of 5.2 MHz and 7 MHz respectively normalizing the SNR as examples to compare. At 5.2 MHz, the vertical echo is the strongest signal with a relatively strong 2-hop. For the three processing methods, the average background noise is \(-\)110.9 dB, \(-\)116 dB and \(-\)97.77 dB, respectively. The SNR of DBF's vertical echo is the lowest, which means that the vertical incidence signal is suppressed. However, at 7 MHz, the signal processed by DBF has the best normalized SNR and the steepest leading edge, the average background noise is \(-\)85.54 dB.  
Figure 10: (**a**) The oblique backscatter ionogram of Channel 1; (**b**) The oblique backscatter ionogram of directly summing; (**c**) The oblique backscatter ionogram of digital beamforming (DBF).  
Figure 9: Oblique backscatter ionograms of six channels.  
And since the incident direction is no longer vertical to the plane of the antenna array, the destructive interference is generated by the direct addition of the multi-channel signals, with the worst background noise of \(-62.9\) dB. The average background noise of Channel 1 is \(-76.6\) dB, in the middle.  
For the frequency points with RF interference signals, DBF also shows a certain suppression effect. Taking 6 MHz as an example, the signals of Channel 1, directly summing and DBF can be extracted as Figure 13. Comparing with Channel 1 and the directly summing method, the average background noise level is significantly reduced after DBF. For the area between vertical echo and oblique backscatter echoes, from about 300 km to 580 km, the interference signals are suppressed. Although this effect seems to be limited, it still means a steeper leading edge which would be easier to extract.
## 6 Conclusions  
In this study, the newly developed combined drought index, designed to more effectively monitor agricultural drought events, may offer more effective usage when used in conjunction with existing indices. The use of SHAP algorithm-determined weights and a mesoscale hydrologic model enhanced the reliability and accuracy of this new index. Notably, the index exhibited a higher correlation with wheat yields than widely used indices like SPEI and scPDSI, marking a significant contribution to agricultural drought monitoring. Furthermore, it is anticipated that the integration of increasing volumes of data from remote sensing systems with technological advancements will further enhance the efficiency of agricultural drought monitoring systems in the future. We anticipate that CDIs enhanced using ML techniques will continue to improve agricultural drought monitoring. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/rs16203799/s1](https://www.mdpi.com/article/10.3390/rs16203799/s1)]([https://www.mdpi.com/article/10.3390/rs16203799/s1](https://www.mdpi.com/article/10.3390/rs16203799/s1)), Figure S1. The mHM workflow. Figure S2. SHAP feature importance graph. Table S1. The mean [minimum: maximum] winter wheat yield (t/ha) per district in the study area from 2001 to 2020. Table S2. XGBoost hyperparameter range and optimum values. Table S3. XGBoost model performance evaluation results. Table S4. CDI weights obtained from PCA of the training set. Table S5. Cumulative variance explanation values of the principal components. Table S6. Correlation test results between drought indices and crop yield. **Author Contributions:** Conceptualization, E.E.B. ; formal analysis, E.E.B. ; investigation, E.E.B. ; methodology, E.E.B. ; software, E.E.B. ; supervision, M.C.D. ; validation, E.E.B. ; visualization, E.E.B. ; writing--original draft, E.E.B. and P.C.S. ; writing--review and editing, P.C.S., M.C.D., M.O. and J.A.O. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the Scientific and Technological Research Council of Turkriye (TUBITAK), BIDEB2214-A program, grant number 1059B142201613.
# Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions
## 2 Materials and Methods
Horizontally and vertically, this ROI only covered the target but included a region 1.5 m ahead and behind the target. In this way, we were able to capture the possible outliers and find out how much effect the adverse weather had on LiDAR's range measuring performance. From the selected echo points, a standard deviation of the distance was calculated. We primarily used the larger pair of targets to have more echo points for evaluations. The only exception to this was Ibeo's measurements at 20 m, where the smaller targets blocked the view to the larger ones, thus decreasing the number of echoes excessively. Turbulent snow tests took place outdoors in northern Finland during winter. Location and timing were chosen because of their high probability for snowy conditions. LiDARs were placed on the roof of VTT's test vehicle Martti (Figures 2 and 3) except for three Ibeo LiDARs, which were installed in the car's bumper. Tests were performed at the airport runway in Sodankyla. There was about 20 cm of sub-zero snow on the road surface and temperature was at around \(-4\)\({}^{\circ}\)C. At first, a reference drive was executed with calm conditions, i.e., no turbulent snow from a leading vehicle was present. Then, the same route was driven with another vehicle, which [PERSON] followed as closely as possible at distances of 15-25 m in order to be surrounded by the powder snow cloud that had risen from the road's surface. Both test drives were done for each LiDAR separately so that they would not disturb one another. Figure 1: LiDAR setup in Clermont-Ferrand fog chamber. The results have been saved to the hard drives of the vehicle with GPS time-stamp and coordinates. These are used for synchronizing data between different sensor devices. The LiDAR performance in turbulent snow and freezing conditions was visually evaluated for each sensor separately. The point cloud data are difficult to analyze with any feasible metrics since the aim is to support pattern recognition algorithms. Pattern recognition requires that there are good coverage points reflected from the surface even if there is material like snow in front of the sensor. Figure 3: Frozen sensors when the temperature is less than -15 \({}^{\circ}\)C.  
Figure 2: Sensor installation in the Marti test vehicle. The best method of analysis is to visually estimate whether the number of points is sufficient for estimating type and size of the object (e.g., passenger car, pedestrian, or building).
# Supporting Immersive Video Streaming via V2X Communication
## 2 Bandwidth Allocation for Diverse Streaming Services in V2X Communication
### Bandwidth Assignment Coordination
When the updated values of \(dub_{\bar{t}_{j}^{\sigma,v}}\) and \(ddb_{\bar{t}_{j}^{\sigma,v}}\) are less than zero, it indicates that the bandwidth reassigned from \(\varsigma\) to \(v\) still cannot meet the requirements of \(v\). * If the previous step shows that either \(burr_{\bar{t}_{j}^{\sigma,v}}^{\sigma,v}\) or \(bdr_{\bar{t}_{j}^{\sigma,v}}\) is greater than zero, it indicates that part of the bandwidth for a regular user's real-time application \(\varsigma\) has been reassigned to the premium user. In this case, initiate the \"Vehicle Fleet Bandwidth Coordination\" module to reallocate bandwidth for \(\varsigma\). * At this stage, if both \(dub_{\bar{t}_{j}^{\sigma,v}}\) and \(ddb_{\bar{t}_{j}^{\sigma,v}}\) are zero, it means that the expected bandwidth requirement of the requested application has been met. Notify the vehicle passenger and end the execution of this module. Otherwise, proceed to the next step. * Activate the \"Vehicle Fleet Bandwidth Coordination\" module to identify CF XL-MIMO access points in less congested road sections that can allocate bandwidth for application \(v\). * Notify the vehicle passenger of the results and end the execution of this module.