esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Discussion of the Results
To avoid the consideration of wear facets during best fit calculation a threshold value should be defined so that the algorithms exclude surfaces from the registration process if they differ too much. If excluding the worn regions, the remaining areas of the scans should fit perfectly. However, every scan differs depending on the accuracy (trueness and precision) of the scanning system. [PERSON] et al. measured local accuracy values. The term local referred to analyses that were limited to a single tooth preparation. The authors found trueness values of 36.7 \(\upmu\)m for OC and 23.3 \(\upmu\)m for Tr3. In terms of precision, 14.0 \(\upmu\)m Tr3 and 20.3 \(\upmu\)m OC were reported [21]. According to [PERSON] et al., Tr3 showed a precision of 15.5 \(\upmu\)m \(\pm\) 1.7 \(\upmu\)m and trueness of 27.5 \(\upmu\)m \(\pm\) 1.8 \(\upmu\)m when capturing a posterior tooth segment (13-17). OC exhibited a precision of 18.8 \(\upmu\)m \(\pm\) 4.1 \(\upmu\)m and trueness of 28.9 \(\upmu\)m \(\pm\) 3.2 \(\upmu\)m [22]. The mean trueness of a single-tooth acquisition by [PERSON] et al. was 13.8 \(\pm\) 1.4 \(\upmu\)m (max. 16.3 \(\upmu\)m, min. 12.0 \(\upmu\)m) when OC was used. The precision was 12.5 \(\pm\) 1.4 \(\upmu\)m (max. 18.8 \(\upmu\)m, min. 7.7 \(\upmu\)m) [23]. Deviations of trueness and precision depend on the applied scanning system and the surface material to be scanned. [PERSON] et al. examined the impact of different materials on the scanner's accuracy. Scanning a zirconia crown, OC exhibited a mean trueness of 29.3 \(\upmu\)m \(\pm\) 5.7 \(\upmu\)m and Tr3 22.1 \(\upmu\)m \(\pm\) 5.7 \(\upmu\)m. Whereas Tr3 showed no statistically significant influence on the accuracy due to the surface material. IOS applying the optical principle of active triangulation, like OC are more prone to errors [38]. However, [PERSON] et al. compared metallic and non-metallic materials. Concerning the applied IOS, Tr3 revealed significantly worse results concerning the metallic materials. Additionally, the impact of surface conditioning (application of powder) was focused on. Comparable to the findings  
Figure 9: The variety of the triangular meshes of the different acquisition systems is shown. of the present study, no significant differences were identified [29]. It has to be emphasized as a limitation of this investigation that the present results refer to scanned zirconia surfaces, but no additional material beyond that. The literature reports an average accuracy of about \(+/-20\)\(\upmu\)m in relation to a single tooth measurement, which correlates with the present accuracy values of this study. In particular, OC non-p exhibited the highest values for trueness (22.5 \(\upmu\)m-19.2 \(\upmu\)m) and the highest deviation in wear measurement (45 \(\upmu\)m) at the same time. Therefore, a mean maximum error of approximately \(+/-40\)\(\upmu\)m could be postulated if two digital impressions of different wear statuses are compared with each other. This corresponds to the maximum deviation of 45 \(\upmu\)m of the IOS compared to WLP.
## 3 Results
### Surrounding Rock Excavatability Identification Based on Deep Forest
#### 3.2.1 Data Cleaning, Data Normalization, and Model Feature Selection  
The data used to train, validate, and test the model in this section were from the KS Tunnel project, containing 12,382 datasets of Classes II, IIIa, IIIb, IV, and V surrounding rocks.  
According to Section 2.2, the data from the stable phase of TBM tunneling were identified and extracted, and the outliers were rejected. Due to the influence of adverse geological conditions, there were many short-cycle data of the excavation cycle in TBM tunneling. Therefore, this paper eliminated excavation cycle data with a duration of fewer than 600 s to improve the accuracy of tunneling parameters. To improve the prediction performance and convergence speed, the data need to be normalized by  
\[x_{n}=\frac{x\ -\ x_{min}}{x_{max}\ -\ x_{min}} \tag{18}\]  
Figure 22: Comparison of the surrounding rock excavatability and actual surrounding rock classification.  
where \(x_{n}\) is the normalized data; \(x_{min}\) and \(x_{max}\) represent the minimum and maximum values of the parameters, respectively.  
Rational model input features are a key step in identifying SRE. It has been shown that the total thrust can reflect the surrounding rock hardness and the thrust required for the current excavation. The pressure of the top shield can reflect the contact pressure generated when different lithologies gather outside the top shield structure. The gripper force state can reflect the reaction force of TBMs during tunneling in different lithologies. In addition, the torque applied to the rolling disc cutter can reflect the torque produced by the disc cutter rock breaking on the cutter rotational axis. The cutterhead rotational speed, advance rate, and penetration can reflect the overall rock-breaking efficiency of the disc cutter [28; 43]. Therefore, the selected input features included the total thrust (\(K1\)), top shield cylinder rod  
Figure 23: Five surrounding rock classes at construction sites: (**a**) Class V; (**b**) Class IV (**c**) Class IIIb; (**d**) Class IIIa; (**e**) Class II.  
chamber pressure (\(K2\)), top shield cylinder rodless chamber pressure (\(K3\)), gripping force (\(K4\)), gripper pump pressure (\(K5\)), cutterhead torque (\(K6\)), cutterhead rotational speed (\(K7\)), penetration (\(K8\)), advance rate (\(K9\)), and propulsion pump pressure (\(K10\)).
## 2 Related Works
### Wafer Map Defect Pattern Classification  
To effectively increase productivity, various studies have been conducted to minimize unexpected process abnormalities that occur during the manufacturing process [16; 17; 18]. In particular, as mentioned in Section 1, the spatial defect pattern shown on the wafer map provides information to help identify errors in the related processes. Therefore, several studies have been conducted to improve yield by identifying the root causes of the related processes through the spatial patterns shown in WBMs. [PERSON] et al. proposed a spatial analysis spectrum method by which to characterize spatial variations in wafers without prior knowledge of wafer patterns [19]. [PERSON] et al. proposed an analytical methodology that predicts the results of package testing from the properties obtained by analyzing the defect patterns of wafer maps at the chip level [20].  
In recent studies, ML and DL approaches to analysis have been actively examined. [PERSON] et al. proposed a WBM defect pattern detection method that can simultaneously detect single defect patterns and mixed defect patterns of WBMs using the density-based spatial clustering of applications with noise (DBSCAN) [8; 21]. DL-based classifiers using CNN methods [22] in image classification tasks have exhibited particularly high performance. Since CNNs achieved state-of-the-art performance in image classification in 2012, DL has shown excellent performance in image classification and computer vision to date [23; 24]. [PERSON] et al. experimented with WBM pattern classifiers through various ML structures (SVM/AdaBoost/XGBoost), including CNNs. They showed that the proposed CNN model performed better in the WBM classification task than other ML-based classifiers [25]. As such, various methods have recently been studied to create better-performing WBM pattern classifiers using various ML/DL approaches. However, achieving a high-performance defect pattern classification accuracy with ML/DL approaches typically involves a large amount of defect pattern data.
## 3 Results and Discussion
Thus, the DTxF method was clearly effective in improving the image quality, particularly where the point spread function was significantly degraded by the unfocused FT-XR. Figure 5: B-scan images acquired from unfocused FT-FR in (**a**) azimuthal and the (**b**) diagonal planes, and from unfocused FT-XR in (**c**) azimuthal and the (**d**) diagonal planes. For an \(N\times N\) element array, FT-XR can utilize \(2/N\) times less receive elements (i.e., \(2N\)) than FT-FR (i.e., \(N\times N\)), so an X-shaped array can have high noise susceptibility. Hence, it is necessary to estimate the SNR in order to employ the X-shaped array for imaging applications. As shown in Table 1, the SNR gain of FT-XR is \(\sqrt{N}/\sqrt{2}\) times lower than that of FT-FR for a single plane wave transmit. Figure 7 displays the relative imaging SNRs calculated for the FT-FR and the FT-XR as a function of the number of the elements in the 2-D array. Since the SNR gain was calculated relative to that of a 1-D array with 100 elements, 0 dB means the same SNR gain as that of the 1-D array. When 4096 (64 \(\times\) 64) elements were used, the SNR of the FT-XR was approximately 30 dB lower than that of the FT-FR, clearly indicating that the FT-XR was less robust to noise than the FT-FR. Thus, the FT-XR significantly compromised the SNR of the reconstructed images in comparison with the FT-FR. However, the SNR of the FT-XR became equivalent to that of the FT-FR when 31 \(\times\) 31 plane waves were used with the DTxF method (Figure 7). For \(N\times N\) plane waves, FT-XR employed with the DTxF achieved a 40 dB higher SNR than the FT-FR did, implicating that the limitation of image quality could be readily compensated by transmitting a higher number of beam angles. When the plane waves of \(\kappa\) angles were transmitted for the unfocused FT-XR, \(\kappa\)-overlapped plane-wave fields could be used for the coherent angular compounding to increase SNR by a factor of \(\sqrt{\kappa}\). In turn, for the FT-XR with unfocused ultrasound imaging, the DTxF method can improve the point spread function and compensate for the loss of SNR resulting from the reduced number of the receive elements. While the SNR can improve, the frame rate can be lowered by the number of transmitted beams (1/\(\kappa\) -1/\(\kappa^{2}\) where \(\kappa<N\)) as shown in Table 1. The number of transmitted beams can be controlled by adjusting the targeting SNR and frame rate of the image. In spite of a decrease in the frame rate, the implementation of the X-shaped sparse array with the unfocused ultrasound waves can be a feasible method for volumetric ultrasound imaging when a receive channel limitation exists. Furthermore, the proposed method can achieve an image quality equivalent to the full array of a single plane wave by employing the DTxF method. Figure 6: B-scan images acquired from the unfocused FT-XR with DTxF using 31 \(\times\) 31 plane waves in the (**a**) azimuthal and the (**b**) diagonal planes, and with DTxF using 11 plane waves in the (**c**) azimuthal and the (**d**) diagonal planes.
## 2 Dual-User Operation of Remote Robot System with Force Feedback  
This section presents both single-user and dual-user operations of the remote robot system with force feedback. In single-user operation, one user manipulates a haptic interface device to control the movement of a remote robot. In dual-user operation, each of the two users uses their own haptic interface device to collaboratively control the movement of a remote robot.
# A Study on the Properties of Recycled Aggregate Concrete and Its Production Facilities
## 2 Scope and Method of Research  
In this research, the quality improvement of the recycled fine aggregate and the characteristics of concrete using recycled aggregate have been studied through the following studies.  
(1) A study on the quality improvement of recycled fine aggregate  
Recycled fine aggregate contains significant amounts of organic and inorganic impurities with an inconsistent quality, creating concerns about quality decline of the concrete. Therefore, the production facilities for quality improvement of the recycled fine aggregate were reviewed, and the equipment used to remove impurities and strip the recycled fine aggregate was developed for stable production and quality improvement of the recycled fine aggregate.  
(2) A study on the quality improvement of mixed aggregate  
The recycled and natural aggregates have different specific gravities and are injected separately when producing concrete. If the mixer efficiency is low, this creates quality deviations in the concrete. Accordingly, to achieve a stable mixing of the recycled and natural aggregates, aggregate mixture facilities were developed. The densities, absorption rates, and other properties of the mixed aggregate, with various compositions were tested. In addition, the characteristics of the quality deviation of the recycled aggregate concrete, following premixing, were determined, and a premixing facility for the aggregate was developed.  
(3) An empirical study on the mechanical characteristics and durability of concrete using the recycled aggregate  
Before and after modification, the recycled fine aggregates, recycled coarse aggregates, and natural aggregates that were mixed in accordance with the replacement rate of the physical and mechanical properties of the concrete that used the recycled fine aggregate, were evaluated. The concrete using the recycled aggregate was mixed with natural aggregate and the target strength was 24 MPa (general strength) or 35 MPa (high strength). Slump, air volume, compressive strength, drying shrinkage, freezing, and thawing resistance and other properties were measured to analyze the physical, mechanical and durability characteristics of the concrete containing recycled fine aggregate, before and after modification.
# Performance of CRTS-II Ballastless Track-Bridge Structural System Rebars under Fatigue Loading Test
## 4 Finite Element Analysis Based on Material Fatigue Damage Model
### Constitutive Model of Material
#### 4.1.1 Constitutive Model of Steel Rebar
According to the Miner linear cumulative damage criterion, the area of damage \(\Delta A^{\text{CF}}_{\text{s}}(N)\) after \(N\) fatigue cycles can be expressed as follows:  
\[\Delta A_{S,Cr}=A_{s}\big{(}1-\sigma_{s,max}/f_{y}\big{)} \tag{8}\]  
where \(N\) is the number of loading cycles, \(N_{f}\) is fatigue life under constant amplitude fatigue load. The effective area \(A^{\text{CF}}_{\text{s}}(N)\) after N fatigue loading cycles is as follows:  
\[A^{\text{CF}}_{\text{s}}(N)=A_{s}\Bigg{[}1-\frac{N}{N_{f}}\bigg{(}1-\frac{ \sigma_{s,max}}{f_{y}}\bigg{)}\Bigg{]} \tag{9}\]  
As a damage characterization parameter of longitudinal steel rebars, the effective area can be used as a representative of the deformation parameter in the multi-stage cumulative damage criterion, i.e., when the fatigue load changes to the next constant-amplitude fatigue load, it is considered that the damage of the effective area at this time is only related to this level of constant-amplitude fatigue load, but not to the previous fatigue damage history. The residual effective area under the variable-amplitude fatigue stress \(A^{VF}_{s}(N)\) is as follows:  
\[A^{VF}_{s}(N)=A_{s}\prod_{i=1}^{m}\Bigg{[}1-\frac{N_{i}}{N_{f,i}}\bigg{(}1- \frac{\sigma_{s,\max,i}}{f_{y}}\bigg{)}\Bigg{]} \tag{10}\]  
where \(\sigma_{s,\max,i}\) is the maximum fatigue stress at the level of the first fatigue loading stage, and \(N_{i}\) and \(N_{f,i}\) are the number of real-time fatigue loading cycles and the fatigue life at the i-th fatigue loading stage, respectively. The residual area of damage is used as the damage characterization parameter of steel rebar under longitudinal stress:  
\[D^{VF}_{s}(N)=\frac{A_{S}-A^{VF}_{s}(N)}{A_{S}} \tag{11}\]  
\[D^{VF}_{s}(N)=1-\prod_{i=1}^{m}\Bigg{[}1-\frac{N_{i}}{N_{f,i}}\bigg{(}1-\frac {\sigma_{s,\max,i}}{f_{y}}\bigg{)}\Bigg{]} \tag{12}\]  
where \(D^{VF}_{s}(N)\) is the amount of damage of steel rebar under variable amplitude fatigue loading for N loading cycles. The fatigue life of steel rebars is given as follows [36]:  
\[\text{lg}N_{f}=9.75-1.51\,\text{lg}\sigma \tag{13}\]
## 4 Numerical Experiments
### Experimental Results
Overall, POGA-DP demonstrates a superior performance in handling complex scheduling problems, effectively increasing utilization while reducing the number of classrooms used, especially in multiple complex instances. Although the GA can achieve a higher classroom utilization in certain specific scenarios, it typically requires more classrooms, particularly in complex problems, where this gap is particularly pronounced. For instance, in Instance 15, which represents the most complex scheduling problem, POGA-DP's classroom utilization is slightly higher than that of the GA, while the number of classrooms used is significantly lower, further proving POGA-DP's significant advantages in resource optimization.
## 1 Introduction
In this case, quadrature signals are used as the state variables. This type of observer can further enhance the dynamic tuning range of SOGI. In this paper, the focus is on comparative analysis of these recently proposed adaptive observers. Such a comparative comparison is not yet available in the literature. To this end, the observers are used for the purpose of grid-synchronization based sequence extraction for unbalanced and distorted grid. The comparison is facilitated by a systematic and fair gain tuning method. Extensive numerical simulations with adaptive notch filter-based frequency-locked loop [37; 38; 39] and experimental results are provided to highlight the advantages of the selected technique over the literature. Comprehensive and systemic presentation and extensive performance comparison of three recently proposed adaptive observer-based grid-synchronization techniques are the main contribution of this paper. The rest of this paper is organized as follows: Problem formulation is given in Section 2, overview of the selected techniques are given in Section 3, numerical simulation and experimental performance comparison are given in Section 4, and, finally, Section 5 concludes this paper.
## 3 Improved TSETMF Algorithm  
In order to address the balance between protecting the image detail and eliminating noise, a median filter can be improved using two-dimensional maximum Shannon entropy to realize the adaptive threshold. Under the calculated threshold, noise judgments can be achieved by using different-sized windows. A median can be replaced in a 5 \(\times\) 5 window to fit the noise level for increasing the filtering effects. Hence, the proposed improved TSETMF algorithm may achieve denoising while protecting the image detail as well.
## Appendix B Scaling Factor  
The scaling factor \(f(\lambda,\mathrm{SZA},\mathrm{Chl},\mathrm{Ozone})=E_{o}/E_{d}\) derived from Hydrolight computations allows us to estimate the scalar irradiance at the ocean surface provided the planar irradiance is known from an external source. In our case, the external source is satellite-derived planar irradiance at the surface. Then we can propagate the scalar irradiance into the ocean body using Hydrolight. The Hydrolight computations are carried out for a specified angular distribution of incoming radiance at the surface, which may differ from the actual angular distribution of incoming radiance. The below results refer to the default boundary conditions of Hydrolight.  
Figure 14 shows the scaling factor just above the ocean surface as a function of different input variables.  
The most notable dependence of the scaling factor on input parameters is its dependence on SZA (Figure 15). The scaling factor increases with SZA increasing up to SZA \(\approx 70^{\circ}\). Then, the scaling factor decreases with increasing SZA. This dependence of the scaling factor on SZA for high values of SZA is similar to the so-called Umkehr effect. Such a dependence of the scaling factor on SZA is explained by a different ozone absorption of the direct and diffuse solar light as a function of SZA in the atmosphere.
###### Abstract
However, no cooperative technology was used in [16]. This paper considers cognitive amplify and forward (AF) relay networks operating in OFDM scheme. A concise comparison between previous works and our paper is shown in Table 1. The differences between our paper and previous works mainly lie in three aspects. Firstly, different links: previous works often only considered either relaying link [6; 17] or only direct link [7], so there was no problem in choosing which link for the source to convey messages. However, the relaying link is not always better than the direct link and vice versa due to the fluctuating channel quality. In our paper, both relaying link and direct link are included and the selection criteria are provided according to the strength of both links. Secondly, different goals: although some work involves both links, their goal is to calculate accurate performance formulas rather than resource optimization [12; 18; 19]. However, what we are concerned about is the joint optimization of power allocation and subcarrier pairing. Thirdly, different system models: different from most works [18; 19; 20; 21; 22], where a primary user selected a secondary user acting as a relay to help the primary user forward the message. However, in our model, the secondary user doesn't participate in the communication process of the primary user as long as the secondary user meets the interference management condition. Through the above analysis, the cognitive radio technology based on relay assistance has not been fully explored. In this paper, we design a joint optimization algorithm in the cognitive relaying networks, where the secondary user can switch between direct link and relaying link depending on the channel conditions while maintaining an acceptable interference size at the primary user. Two types of typical problems are studied. One is to look for sum rate maximum of secondary network subject to total power budget of secondary network and tolerable interference level of primary network. The other one is to seek for sum rate maximum of secondary network subject to individual power budget of secondary network and tolerable interference level of primary network. In order to find the maximum value, we propose a joint optimization algorithm, in which power allocation and subcarrier pairing are decomposed into two subproblems. The first step is to find a closed form solution of power allocation without violating the interference constraint of the primary network. In particular, when the direct link is active, the power allocation reduces to a classical water-filling form. Then, the subcarrier pairing is found when the power has been solved at the first step.