esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Forward Models for GNSS Radio Occultation Data Processing and Assimilation
## 2 Experiments
### Data, Collocation, and Quality Control
#### 2.1.3 Quality Control  
Since we used coarse resolution EC4 Dvar to collocate with FY3D data, some FY3D's collocated profile may be far from it. To ensure that the refractivity of FY3D is close to that of ECMWF and to make sure the results have statistical representativeness, we set four steps of quality control (hereafter 4 QC). These four steps were initially a scheme for quality control used by [30] to compare MetOp RO data from two different RO data processing centers, which is concise and effective [31]. The details of 4 QC can be found in Appendix C. After 4 QC, 368 out of 5914 FY3D soundings remained for experiment 1. The high rate of discard is attributed to the low resolution (\(2.8^{\circ}\times 2.8^{\circ}\)) EC4 Dvar.  
In experiment 1, EC4 Dvar's refractivity was transformed to BA. The forward-modeled BA contained not only the bias introduced by FMs and interpolations but also the inherent bias of the inputs when they were refractivity. Experiment 2 was a simulation experiment, in which only FY3D was used. Therefore, the data quality was not taken into consideration.
# A Damage Model for Concrete under Fatigue Loading
## 3 Thermodynamics Based Continuum Damage Mechanics Model
### Thermodynamics Interpretation  
In this section, we briefly recall here the main steps of the methodology followed by [52] for the thermodynamics interpretation of the damage variable. This work developed the method from [52] for interpreting the damage variable under fatigue loading.  
The infinite deformation behavior of concrete material with damage can be viewed within the framework of thermodynamics with internal state variables. The Helmholtz free energy per unit mass,  
Figure 6: Thermodynamics interpretation of micro-scale damage behaviors. Specifically, the elastic deformation damage (mode-I cracks) produces the stiffness reduction \(E_{\text{d}}\), and the irreversible deformation damage (both mode-II microcracks and irreversible friction sliding) causes the development of irreversible strains \(\varepsilon^{\text{I}}\).  
in an isothermal deformation process at the current state of the deformation and material damage, is assumed as follows:  
\[\Psi_{n}=\psi_{n}+\gamma_{n} \tag{1}\]  
where the subscript \(n\) denotes the cyclic number of the fatigue loading (\(n=1,2,3,\,...,\,N\)), \(\psi\) denotes the strain energy or a purely reversible stored energy, while \(\gamma\) represents the irreversible energy associated with specific micro structural changes produced by damage (i.e., elastic deformation damage induced by mode-I fractures, and irreversible deformation damage due to both mode-II micro-cracks and irreversible fictional sliding, see Figures 4 and 6). An explicit presentation of the irreversible energy and its rate is generally limited by the complexities of the internal micro structural changes discussed in the recent section; however, only one internal variable damage (contains two components) is considered in this work. The damage contains two components, that is, elastic deformation damage induced by mode-I fractures, and irreversible deformation damage due to both mode-II micro-cracks and irreversible fictional sliding, contribute to stiffness degradation and irreversible strains development, respectively (see Figure 6). For the purpose of developing a schematic description of the concepts based on the proposed micro damage model, the uniaxial stress-strain curves are used in Figure 7. In Figure 7a, \(E_{0}\) denotes the initial undamaged stiffness (relates to loading line OA\({}_{0}\)). The strain and the stress at point A\({}_{0}\) are denoted by \(\varepsilon_{0,1}\) and \(\sigma_{\text{max}}\), respectively.
## 2 Basic Models
### Illustration: Necessary Charging-Discharging Complementarity Constraint  
For a storage-concerned economic dispatch problem, the complementarity constraints on charging and discharging are, in most cases, naturally satisfied without using binary variables. However, in this paper, such binary variables are necessary because, in the renewable energy curtailment problem, simultaneous charging and discharging can act as a load, which consumes a certain amount of renewable energy and thus reduces the curtailment. Here, we give a simple example for illustration: The system consists of one bus, one ESU, and one wind turbine. Let \(T=1\), \(P^{s}=1\) MW, \(E^{s}=2\) MWh, \(e_{0}=2\) MWh, \(\eta^{c}=\eta^{d}=0.9\), \(p^{d}=0.6\)MW, \(p^{rm}=1\)MW; for brevity, the bus and time subscripts are omitted.  
If we use the binary variable as (4), simultaneous charging and discharging is not permitted; meanwhile, the SoC level has reached its ceiling. Consequently, 0.6 MWh of wind energy is consumed by the load, and the rest 0.4 MWh is curtailed.  
However, if we do not use the binary variable, consider the following strategy: \(p^{sc}=1\) MW, \(p^{sd}=0.81\) MW; then, \(e_{1}=e_{0}+h\left(p^{sc}\eta^{c}-p^{sd}/\eta^{d}\right)=2\) MWh. According to the power balance condition in (1), the dispatched wind power \(p^{r}=0.79\) MW, and thus the curtailed wind energy, is 0.21 MWh. There is no doubt such a strategy is feasible while the curtailment is lower than that in the case with binary variable.  
This example verifies that the binary complementarity indicator is necessary in this paper; otherwise, simultaneous charging and discharging will happen.
## 3 Relationship between the Flanging Lip Deformation and Quality Inspection Indexes  
### Five States of the Flanging Lip Deformation History  
In the roller staking process, the staking load makes the flanging lip produce a certain amount of deformation. The rotation flexibility of the bearing will be affected under some strict deformation, and even cause the bearing to work abnormally [12; 13]. According to the failure analysis of the bearing in service, improper process quality is an important reason for the bearing failure. For example, during the installation process of the bearing on the ARJ21-700 aircraft, failures such as bearing \"locking\" have occurred. The main reason for this is that the flanging lip is excessively squeezed during the roller staking process, and then the liner inside the bearing is severely deformed.  
The existing quality inspection indexes after roller staking mainly include the minimum push-out load, the maximum starting torque, and the maximum gap between the flanging lip and the housing chamfer. However, the influence between these indexes and process control methods, process control parameters, and the deformation degree of the flanging lip has not been analyzed in depth. The application requirements cannot be met if only one index is met. Combined with the quality inspection indexes, to study the influence of the deformation degree of the flanging lip on the staking quality, the deformation degree of the flanging lip is divided into five states in this paper. Figure 2 shows the schematic diagram of the five limit states of the deformation degree of the flanging lip during the roller staking process [14; 15].  
Figure 2 shows that the \"State 1\" is the initial state, i.e., the state in which the flanging lip of the V groove just comes into contact with the roller, the flanging lip is not deformed, and the feeding displacement and the staking load are zero. \"State 2\" is the state in which the staking process has started, until the maximum feeding limit where the starting torque is not significantly affected; at this stage, the push-out load may not meet the quality requirements. \"State 3\" is the state in which the push-out load and the gap between the flanging lip and the housing chamfer meet the requirements after the roller staking, and the starting torque reaches the maximum value specified in the requirements [11]. \"State 4\" is the state in which the push-out load meets the requirements; however, the starting torque exceeds the requirements, and the damage degree of the flanging lip reaches the maximum allowed. \"State 5\" is where the push-out load and the gap meet the requirements, but the starting torque is too large and the flanging lip is severely damaged. At this stage, the bearing cannot be used.  
Figure 2: Schematic diagram of the five limit states of the deformation degree of the flanging lip: 1—Initial state; 2—Limit state of “State 2”; 3—Limit state of “State 3”; 4—Limit state of “State 4”; 5—Limit state of “State 5”; 6—Comparison of the five states.
## 2 Materials and Methods
### Continuous Relative Phase  
The continuous relative phase is derived from the phase diagram of two segments in a motion pattern. As the segments may have different amplitudes and velocities, it has been suggested that the components of the phase diagram be normalized to avoid one segment dominating the continuous relative phase pattern [22; 25; 36] and make the trajectory of the phase plot smoother.  
Figure 1: Equipment layout diagram regarding camera set-up.  
Figure 2: Diagram of swimmer’s segmental angle.  
The angle and angle velocity were normalized to the interval [\(-\)1; +1] according to Equations (1) and (2)  
\[\theta_{norm}=\frac{2\theta}{\theta_{max}-\theta_{min}}-\frac{\theta_{max}+\theta _{min}}{\theta_{max}-\theta_{min}} \tag{1}\]  
where \(\theta_{max}\) is the maximum angular within start time and \(\theta_{max}\) is the minimum angular within start time.  
\[\omega_{norm}=\frac{2\omega}{\omega_{max}-\omega_{min}}-\frac{\omega_{max}+ \omega_{min}}{\omega_{max}-\omega_{min}} \tag{2}\]  
where \(\omega_{max}\) is the maximum angular velocity within start time and \(\omega_{max}\) is the minimum angular velocity within start time.  
After normalization procedures, calculate the phase angle according to four different quadrant formulas (Figure 3).  
Finally, the continuous relative phase (CRP) of the arm-trunk and thigh-trunk was calculated using Equation (3)  
\[CRP=\varnothing_{Distal}-\varnothing_{proximal} \tag{3}\]  
The coordination patterns are distinguished by the in-phase (value of 0\({}^{\circ}\)) and anti-phase (value of 180\({}^{\circ}\) or \(-\)180\({}^{\circ}\)) behavior. In-phase mode means that two segments rotate or move in the same direction; conversely, two segments rotate or move in different directions in anti-phase. When the value is 0\({}^{\circ}\) or \(\pm\)180\({}^{\circ}\) the speed is identical [37]. However, the study by [PERSON] et al. [38; 39] accepted a lag of \(\pm\)30\({}^{\circ}\) to determine the coordination modes. Thus, it belongs to the in-phase mode in the case of 30\({}^{\circ}\) < CRP < 30\({}^{\circ}\), while the anti-phase mode is considered between \(-\)180 \({}^{\circ}\)< CRP < \(-\)150\({}^{\circ}\) or 150\({}^{\circ}\) < CRP < 180\({}^{\circ}\). In addition, [PERSON] [39] also added the concept of an intermediate coordination mode. The intermediate phase is a term used to describe the transitional phase between the anti-phase and in-phase coordination patterns. Analyzing the peak of CRP can provide a description of the dynamic changes (Figure 4), and analysis of CRP peaks can also provide a better understanding of the leading mechanisms between the segments. When CRP is positive, it means that the phase angle of the arm or thigh is ahead of the trunk; conversely, a negative CRP represents that trunk is leading the arm or thigh.  
Figure 3: Phase angle (\(\Phi\)) definition based on a phase plot of normalized angle (\(\theta\)) and normalized angular velocity (\(\omega\)).
# Research on Improved Bridge Surface Disease Detection Algorithm Based on YOLOv7-Tiny-DBB
## 3 Algorithm Enhancement
### Refinement of the Loss Function
Moreover, in the calculation process of the MPDIoU, the parameters \(d_{1}\) and \(d_{2}\) play a crucial role. The parameter \(d_{1}\) primarily influences the weight of the center point distance. By adjusting its value, it can alter the significance of this distance in the MPDIoU computation, thereby affecting both the accuracy and convergence speed of the bounding box regression. A larger value for \(d_{1}\) will elevate the importance of center point distance in calculations, facilitating greater emphasis on accuracy during bounding box localization. Conversely, the parameter \(d_{2}\) affects the weight assigned to deviations in width and height. By modifying the value of \(d_{2}\), it can modify the weight of width and height deviations in the calculation of MPDIoU. A higher value for \(d_{2}\) will result in increased significance being placed on width and height deviations during computations, which aids in more accurately regressing both the shape and size of bounding boxes. The loss function \(\mathrm{L_{MPDIoU}}\) is defined as shown in Equation (6). \[\mathrm{L_{MPDIoU}}=1-\mathrm{MPDIoU} \tag{6}\]  
In the detection of apparent diseases in bridges, the diversity of these diseases and the complexity of their backgrounds pose significant challenges. Specifically, exposed reinforcement-related diseases often occur concurrently with damage-related ailments, leading to overlapping conditions. When descriptions of the overlapping bounding boxes are insufficient, there is a heightened risk of missed detections. To address this issue, the original loss function in the YOLOv7-Tiny algorithm had been improved by replacing it with MPDIoU. It can effectively mitigate the failure of detection boxes caused by disease overlap, reduce the instances of missed detections, and improve the accuracy in detecting small target diseases.
###### Abstract  
Diffraction gratings are becoming a preferred option for waveguide head-mounted in-out coupling devices due to their flexible optical properties and small size and light weight. At present, diffraction waveguide coupling devices for AR head-mounted displays (HMD) have difficulties such as a long development cycle and complicated processing. In this paper, we first establish a set of two-dimensional (2D) grating ray tracing models, based on which we determine the initial architecture of the dual-region two-dimensional exit pupil expansion (2D-EPE) AR-HMD holographic waveguide diffraction system. Second, we propose a honeycomb coupled grating array and optimize the optical energy utilization and brightness uniformity of the holographic waveguide and use a custom dynamic linked library (DLL) function to implement the ray tracing of the 2D grating and add a probabilistic splitting function to the DLL, which reduces the single simulation time from 11.853 min to 1.77 min. We also propose a holographic lithography device composed of holographic optical elements (HOEs) and a method for preparing HOEs. Finally, in order to obtain the diffraction efficiency preoptimized by the above DLL for the uniformity of the exit pupil brightness and light energy utilization, we inverse design with the preparation process parameters as the optimization variables and develop the adaptable electromagnetic calculation program Holo-RCWA. Using Holo-RCWA with nondominated sorting genetic algorithm II (NSGA-II), we inverse design to obtain the process parameters satisfying the diffraction efficiency distribution, and the optimization time of the whole system is reduced from 2-3 days to 10 h. This work is of great significance for AR/VR applications.  
diffraction waveguide; holographic grating; two-dimensional exit pupil expansion +
Footnote †: journal: Physics Letters A
## 5 Simulation and Performance Analysis
### Migration Algorithm Performance Evaluation
algorithm, the PRT algorithm with its precomputation mechanism could effectively reduce its migration computation time, resulting in a reduction in migration costs. At the same time, because the number of migrations was the same as that of the RT algorithm, the cost of a single migration was also significantly lower than that of other algorithms. Figure 5: Comparison of total migration overhead. Figure 6: Comparison of the average number of migrations.
## 5 Result and Discussion
### Evaluation Result
#### 5.3.2 Comparative Analysis of YOLOv4 Model's Inference Performance in the PASCAL VOC Dataset  
Tables 4 and 5 show the results of measuring inference performance through sample video by transforming YOLOv4-Native.weight and YOLOv4-Tiny.weight according to each deep learning framework in the PASCAL VOC dataset.  
In the case of all deep learning frameworks in the PASCAL VOC dataset, when compared to the COCO dataset, the measured values are different, but show somewhat similar results.  
For TensorFlow in the PASCAL VOC dataset, the CPU utilization of YOLOv4-Native is slightly lower than that of YOLOv4-Tiny, and the GPU utilization is also higher than that of YOLOv4-Tiny. The latency of YOLOv4-Native is higher than that of YOLOv4-Tiny, so the image object detection accuracy of one frame is high, but the processing speed is slow. In particular, in the accuracy of the object, the person was measured 3-4% lower, and in the case of cars, they were measured 9-11% lower. For TF-Lite in the PASCAL VOC dataset, the delay time is very high. The reason is omitted because it is described in Section 5.3.1. For TF-TRT in the PASCAL VOC dataset, both YOLOv4-Native and YOLOv4-Tiny use the GPU efficiently with a GPU utilization rate of about 50%. For TRT in the PASCAL VOC dataset, CPU and GPU utilization rates are used appropriately among the four deep learning frameworks. Also, a low power consumption value was measured, and a result in which the accuracy of the object is maintained to some extent was measured.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Framework** & & **Average** & **Average** & **Average** & **Average** & **Average** \\
**(YOLOv4-Native in the VOC Dataset)** & & **CPU** & **GPU** & **Accuracy (\%)** & **Latency (ms)** & **Power (W)** \\ \hline \hline TensorFlow & Float32 & 22.43 & 70.35 & \begin{tabular}{c} Person: 38.25 \\ Car: 69.81 \\ \end{tabular} & 115.39 & 26.10 \\ \hline TF-Lite & & 20.44 & 1.87 & \begin{tabular}{c} Person: 38.74 \\ Car: 70.03 \\ \end{tabular} & 4983.97 & 10.73 \\ TF-TRT & Float32 & 23.62 & 68.92 & \begin{tabular}{c} Person: 39.01 \\ Car: 70.35 \\ \end{tabular} & 58.14 & 16.87 \\ TRT & & 27.98 & 55.30 & \begin{tabular}{c} Person: 39.26 \\ Car: 69.95 \\ \end{tabular} & 26.93 & 23.72 \\ \hline TF-Lite & & 18.98 & 1.24 & \begin{tabular}{c} Person: 37.13 \\ Car: 68.34 \\ \end{tabular} & 4810.34 & 10.29 \\ TF-TRT & Float16 & 30.46 & 49.50 & \
\begin{tabular}{c} Person: 38.13 \\ Car: 68.97 \\ \end{tabular} & 29.48 & 12.45 \\ TRT & & 29.71 & 56.32 &
\begin{tabular}{c} Person: 38.31 \\ Car: 70.22 \\ \end{tabular} & 26.70 & 23.72 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparative analysis of YOLOv4-Native.weights’s inference performance in the PASCAL VOC Dataset.
# Evaluation of Different Control Algorithms for Carbon Dioxide Removal with Membrane Oxygenators
## 2 Modeling and Control
### Mathematical Model
#### 2.1.1 Gas Compartment  
In the membrane oxygenator, the dissolved CO\({}_{2}\) from the blood diffuses through the membrane into the gas phase and gets removed from the system with the sweep gas flow.
# Flexible-Link Multibody System Eigenvalue Analysis Parameterized with Respect to Rigid-Body Motion
## 5 Conclusions  
This paper has outlined the entire procedure to get an analytical polynomial expression of the eigenpairs of a flexible-link multibody system (FMBS) as function of its configuration, in terms of rigid-body motion coordinates and velocities. The method is suitable for FMBSs characterized by small deformations, whose dynamic model is analytically known and formulated by means of a minimum set of ordinary differential equations. Starting from a dynamic model linearized about a static or dynamic equilibrium configuration, the eigenvalue problem in such a configuration is computed. By iteratively expanding the eigenvalue problem in a Taylor series with respect to the rigid motion coordinates, their velocities, and, possibly, the parameters which the system model can depend on (e.g., payload mass), the eigenpair derivatives at the expansion points are inferred. These terms represent the coefficients of the polynomial functions describing the approximated eigenpairs.  
The correctness of the method in approximating the system eigenpairs under motion condition (while reducing the computational time), has been numerically proved with satisfactory results by employing a two-dof flexible planar manipulator forced to follow a given trajectory. An experimental validation of the method both in static and dynamic conditions will be addressed in future works by means of an impact analysis and an operational modal analysis, respectively.  
The availability of a polynomial function for the eigenpairs explicitly depending on the system configuration can result very helpful for model-based techniques that need an accurate knowledge of the system modal characteristics, such as motion planning and control strategies towards to zero residual vibration.  
The method allows to reach a desired accuracy in the eigenpair approximation by selecting the proper expansion order of the Taylor series. However, the selection of a high expansion order could compromise the computational benefits, as the number of addends of the polynomial functions that approximate the eigenpairs rapidly increases with the expansion order. Further research will focus on the development of an index that allows a priori estimation of the proper order of the truncated Taylor series to reach a desired accuracy on the approximated eigensolutions while preserving the computational efficiency.  
Conceptualization, I.P. and R.V.; formal analysis, I.P.; methodology, I.P. and R.V.; supervision, R.V.; validation, I.P.; writing--original draft, I.P.; writing--review and editing, R.V.  
The research leading to these results can be framed within the COVI project (TN200Y) Free University of Bozen-Bolzano.  
The authors declare no conflicts of interest.  
Figure 5: Fourth vibration mode along the planned trajectory.
## 7 Conclusions  
This communication proposes an isolation technique for closely spaced antenna elements in an MIMO system. A part of the presented approach is a novel wideband FSS unit cell, introduced to suppress the mutual coupling, with the specific realization being an FSS-based wall supplemented by a metallic strip, both inserted between the two antenna elements. Furthermore, a slot is etched in the common ground plane to reduce the surface current between the two antennas. A detailed study of the effects of the proposed approach on the mutual coupling level is carried out to corroborate its efficacy using two specific examples of MIMO systems, referred to as Design 1 and Design 2. It is demonstrated, both numerically and experimentally, that the proposed technique improves the isolation by about 23 dB and 30 dB for Designs 1 and 2, respectively. The presented mutual coupling suppression technique provides an attractive alternative to existing approaches and can be used in the design of compact platforms with high isolation for 5G NR massive MIMO system applications.  
Conceptualization, R.S.A.; methodology, R.S.A. and S.K.; software, R.S.A. and S.K.; validation, R.S.A. and L.L.; formal analysis, R.S.A.; investigation, R.S.A. and S.K.; resources, L.L. and S.S.; data curation, R.S.A. and S.K.; writing--original draft preparation, R.S.A. writing--review and editing, S.K. and L.L.; visualization, R.S.A. and S.K.; supervision, S.S.; project administration, L.L.; funding acquisition, S.S. All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
The authors would like to thank Dassault Systemes, France, for making CST Microwave Studio available. This work is partially supported by the Icelandic Centre for Research (RANNIS) Grant 174573 and by National Science Centre of Poland grant 2020/37/B/ST7/01448.  
All data generated or analyzed during this study are included in this published article.  
The authors thank Dassault Systemes, France, for making CST Microwave Studio available.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.