esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Quantitative Metrics for Assessment of Surface Reconstruction  
In most papers about 3D surface reconstruction, the quality of the reconstructed surface is assessed by visual inspection, rather than quantitative assessment, for two reasons. First, the original 3D surface model of VOI is unavailable before the reconstruction. Second, there is a lack of proper quantitative metrics for assessing surface reconstruction.  
In this paper, we prepare two types of original surface models to assess the quality of surface reconstruction. The first type is synthetic data (such as an acorn model). The second type is original surface models gained by other image modalities of the same VOI, e.g., we prepare a kidney surface model from dense and parallel contours of CT imaging, and then compare it with its reconstructed ultrasound surface model.  
For the quantitative metrics, we compute several distances between the original surface and the reconstructed surface. For each vertex of a surface, we compute its distance to the closest point on another surface. From the histogram of these values the following metrics are computed:  
* mean distance (mean)
* standard deviation from the mean distance (std)* root mean square distance (rms)
* maximum distance (Hausdorff)
* medial distance (median).  
These metrics measure the shape difference between two triangulated surfaces, and their meanings are self-explanatory and intuitive. For example, the Hausdorff distance measures the maximum difference between two triangulated surfaces, and std and rms detail statistical information about the difference between the two triangulated surfaces. Moreover, we also compute the area difference and volume difference between the original and the reconstructed surfaces. For these metrics, a greater value indicates a greater difference between the original and the reconstructed surface.  
For a set \(P\subseteq R^{k}\) and a point \(x\in R^{k}\), let \(d\left(x,P\right)\) denote the Euclidean distance of \(x\) from \(P\), _i.e._,  
\[d(x,P)=\inf_{p\in P}\left\{\parallel p-x\parallel\right\} \tag{10}\]  
The Hausdorff distance [48] between two sets \(X,Y\subseteq R^{k}\) is given by  
\[\max\left\{\sup_{x\in X}d(x,Y),\sup_{y\in Y}d(y,X)\right\} \tag{11}\]
# Efficient Re-Parameterization Residual Attention Network for Nonhomogeneous Image Dehazing
## 3 Materials and Methods
### Experimental Validation
#### 3.3.4 Comparative Experiments and Ablation Studies  
Comparison with State-of-the-Art Methods  
We compare our RRA-Net with 7 single-image dehazing methods, including the traditional DCP [9] and SOTA deep-learning-based methods. Among the latter type of methods, AOD-Net [25], FFA-Net [13], MSBDN [14], DMPHN [15], and TBNN [19] are based on CNNs, and the Dehamer [20] is based on a transformer. The evaluations are conducted on the NH-Haze (2021) and Dense-Haze datasets.  
For each compared model, the PSNR and SSIM metrics are calculated between the dehazed results and the haze-free images of the testing set. A higher PSNR score or a higher SSIM score indicates a better dehazing performance.  
To validate the efficiency of the proposed RRA-Net, we also conduct a comparative runtime test. Specifically, images of the size \(1600\times 1200\) are passed into each compared model. Additionally, then, the average processing FPS of each model is calculated. A higher FPS score indicates a better dehazing speed and, hence, a higher efficiency.
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
###### Abstract  
Urban road collapse is a common disaster in modern cities, posing a severe threat to the safety of life and property of urban residents. Effective risk assessment is crucial for preventing collapses. This study proposes a novel method for assessing the risk of urban road collapse by integrating Interferometric Synthetic Aperture Radar (InSAR) technology with Long Short-Term Memory (LSTM) networks. We conduct experimental analysis using collapse incidents and corresponding subsidence maps from five areas in Guangzhou. The results demonstrate that the proposed model successfully establishes a mapping relationship between the temporal features of InSAR data and the risk of road collapse, enabling a risk assessment of specific areas solely based on InSAR data. This model overcomes the difficulty of obtaining numerous assessment indicators in traditional risk assessment methods, providing strong support for the prevention and control of regional road collapse risks.  
road collapse; regional risk evaluation; time-series; InSAR
# Fuzzy-Based Multivariate Analysis for Input Modeling of Risk Assessment in Wind Farm Projects
###### Abstract  
Currently, input modeling for Monte Carlo simulation (MSC) is performed either by fitting a probability distribution to historical data or using expert elicitation methods when historical data are limited. These approaches, however, are not suitable for wind farm construction, where--although lacking in historical data--large amounts of subjective knowledge describing the impacts of risk factors are available. Existing approaches are also limited by their inability to consider a risk factor's impact on cost and schedule as dependent. This paper is proposing a methodology to enhance input modeling in Monte Carlo risk assessment of wind farm projects based on fuzzy set theory and multivariate modeling. In the proposed method, subjective expert knowledge is quantified using fuzzy logic and is used to determine the parameters of a marginal generalized Beta distribution. Then, the correlation between the cost and schedule impact is determined and fit jointly into a bivariate distribution using copulas. To evaluate the feasibility of the proposed methodology and to demonstrate its main features, the method was applied to an illustrative case study, and sensitivity analysis and face validation were used to evaluate the method. The results demonstrated that the proposed approach provides a reliable method for enhancing input modeling in Monte Carlo simulation (MCS).  
Keywords:Monte Carlo simulation; input modeling; fuzzy logic; risk analysis and assessment; multivariate distribution; marginal Beta; copula +
Footnote †: journal:
# Switchable Filter with Four Operating Modes Implemented on a 50-Ohm Microstrip Line
###### Abstract  
In this paper, we implement a compact switchable bandpass filter on a 50 \(\Omega\) microstrip line. The proposed structure consists of an input/output stage with one end terminated at 50 \(\Omega\), a C-shaped-open loop resonator, and two L-shaped-open loop resonators. The proposed filter operates in four different modes depending on the on/off combination of the five PIN diodes. Each mode includes a dual-band pass filter (DB-BPF) designed for the 1.4 GHz and 5.1 GHz bands, another DB-BPF covering the 2.4 GHz and 4.2 GHz bands, a wideband BPF with a bandwidth ranging from 2 to 4.5 GHz, and an all-pass filter (APF) that allows all frequencies to pass through. The proposed structure is extremely compact because it is implemented on a 50 \(\Omega\) line without any additional space.  
all-pass; band pass; dual-band pass; switchable filter +
Footnote †: journal: Electronics
## 1 Introduction
However, RNNs have issues with gradient vanishing and gradient exploding when dealing with long time sequences. An RNN-based LSTM model was created to address the issue, outperforming existing neural networks and significantly enhancing prediction accuracy [21; 22]. The above discoveries also provide crucial guidance to the research ideas in this paper. This study is significant due to the creation of a scheme framework (IM-NN) that seamlessly integrates the adaptive CKF algorithm with LSTM neural networks for INS/MNS. The contributions of this paper are summarized as follows:  
1. The IMU utilizes the EKF to fuse data from the gyroscope and accelerometer using the quaternion solution method. This process enhances the accuracy and stability of the pitch and roll angles in the attitude information. According to the attitude information obtained by the above method, an adaptive-assisted CKF algorithm is proposed based on innovation and residuals to fuse INS and MNS information, resulting in more accurate heading angle information with less computational effort. The adverse effects of \(Q\) and \(R\) on the filtering estimation in nonlinear systems are addressed to improve the robustness of the system. 2. A seamless IM-NN fusion strategy is established to map the nonlinear relationship between sensor measurements during MNS interruption and the fused INS/MNS heading angle. The adaptive-assisted CKF algorithm is used to fuse the INS and MNS data when MNS is accessible to achieve accurate and reliable heading angle information. Simultaneously, the LSTM network is applied to train the INS data and MNS data in order to construct the IM-NN model. When MNS is unavailable,the trained IM-NN model is used to forecast the target output to maintain high navigation accuracy. The remainder of this paper is organized as follows: In Section 2, the details are provided for the INS/MNS navigation system and the proposed mathematical model using the adaptive-assisted CKF algorithm. And,the process of building a seamless navigation model for IM-NN when faced with MNS disruption is described. In Section 3, validation experiments and simulations are described. Finally, conclusions are presented in Section 4.
## 5 Results and Discussion
### Text Prompted Verification Results  
The text prompted verification results are shown in Table 5. Average EER values were below 4%. It can be noticed that the noise level in speech signals does not considerably affect the system performance, however similarly to the identification evaluation, image transformations involving rotation and combinations yielded the worst results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Image**} & \multirow{2}{*}{\begin{tabular}{c} **Combination** \\ **Transformations** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **No** \\ **Transformation** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Brightness** \\ **Flip** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Horizontal** \\ **Flip** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Rotation** \\ **of Transformations** \\ \end{tabular} } \\ \hline Noiseless & 99.93 \(\pm\) 0.13 & 100 & 99.66 \(\pm\) 0.26 & 92.66 \(\pm\) 1.98 & 88.79 \(\pm\) 2.02 \\
0 & 100 & 99.73 \(\pm\) 0.32 & 99.79 \(\pm\) 0.16 & 94.66 \(\pm\) 1.67 & 88.59 \(\pm\) 1.79 \\
5 & 100 & 99.79 \(\pm\) 0.26 & 99.86 \(\pm\) 0.16 & 94.46 \(\pm\) 1.96 & 87.86 \(\pm\) 2.39 \\
10 & 99.93 \(\pm\) 0.13 & 100 & 99.73 \(\pm\) 0.25 & 95.06 \(\pm\) 0.97 & 87.53 \(\pm\) 1.4 \\
15 & 99.93 \(\pm\) 0.13 & 99.86 \(\pm\) 0.26 & 99.93 \(\pm\) 0.13 & 93.46 \(\pm\) 0.85 & 89.53 \(\pm\) 1.93 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Bimodal multitask network identification results in terms of accuracy (%).
## 2 Topological Analysis of Mega-Constellations
The fixed links among specific satellites within the orbits, spanning across different orbital tracks, are depicted by green lines (Figure 5). Thus far, the examination of the parameters pertaining to fixed links between neighboring orbits within the satellite constellation has been concluded. Three such fixed links have been thoroughly analyzed and established, providing a foundational framework for subsequent investigations.
## 3 Verification of Numerical Simulation Method
### Convergence Test of the Water-Exiting Vehicle Breaking Ice Computation Model  
We adopted hexahedral meshing for the vehicle and local mesh refinement for ice. The mesh size of ice in the middle area near the vehicle was refined, while the outer mesh size was larger. The local mesh refinement was also employed for air and water with the refined mesh size in the middle and larger mesh size on both sides.  
Mesh size has a high influence on the numerical computation results. In order to test the convergence of mesh, three sets of meshes were used with the mesh size gradually refined. The mesh quantity was 2.31 \(\times\) 10\({}^{6}\), 4.93 \(\times\) 10\({}^{6}\), and 9.55 \(\times\) 10\({}^{6}\), which were denominated as scheme 1, scheme 2, and scheme 3. The three sets of meshes were used in the numerical computation, and the initial velocity of the vehicle was set to 40 m/s. This paper used a 128-core simulation server for numerical simulation and adopted an MPP parallelization solution strategy.  
The results of the mesh convergence test are given in Figure 20. It is evidently revealed that the calculation results for velocity, acceleration, and stress gradually converge with the increasing mesh quantity, and the computation results of schemes 2 and 3 are highly consistent. It can be regarded that the computation results converge when the computation model adopts the set of meshes in scheme 2. The model for the water-exiting vehicle breaking ice after meshing in scheme 2 is shown in Figure 21.  
Figure 19: Comparison of numerical simulation phenomena and the experimental phenomenon of the vehicle breaking ice under the ejection pressure of 3 MPa.  
Figure 18: Comparison of the experimental result and the simulation result of the vehicle breaking ice under the ejection pressure of 3 MPa.
# Coenzyme Q at the Hinge of Health and Metabolic Diseases
## 1 Introduction  
Coenzyme Q (CoQ) or ubiquinone is an essential element for mitochondrial function and provides antioxidant protection of cell membranes and plasma lipids [1]. CoQ was isolated and identified by [PERSON] et al. in 1955 [2], and validated by [PERSON] et al. in 1957, who demonstrated its main function in the mitochondrial electron transport chain (ETC) [3]. CoQ also shows a potent antioxidant function in plasma membranes, endomembranes, and lipoproteins [4].  
The structure of CoQ is essentially the same in all organisms, composed of a benzoquinone ring bound to a polyisoprenoid side chain that preserve the molecule inserted into a lipid bilayer. Isoprene units of the chain are species-specific: human isoform mainly encloses ten isoporene units (CoO\({}_{10}\)). In rodents, this molecule presents mainly nine units (CoO\({}_{9}\)), and around a 10% has ten units (CoO\({}_{10}\)). The yeast _Saccharomyces cerevisiae_ holds six units (CoO\({}_{6}\)), whereas _Schizosaccharomyces pombe_ contains ten units (CoO\({}_{10}\)). Bacteria such as _Escherichia coli_ contains eight isoprene units (CoO\({}_{8}\)) [5].  
Regarding its redox activity, CoQ can be found in a reduced form (CoO\({}_{2}\) or ubiquinol), in a completely oxidized state (CoO or ubiquinone), or in a semi-reduced state known as a semi-ubiquinone (CoOH\({}^{\bullet}\)). Through this redox cycle, CoQ plays an essential role in the ETC maintaining the electron flux from complexes I and II to complex III, leading a chemiosmotic gradient that is used by the ATP synthase to produce ATP by oxidative phosphorylation (OXPHOS) [6].  
The CoQ-dependent oxidation/reduction cycle also confers to this molecule the property to transfer electrons in different biological processes and to act as an antioxidant at cell membranes preserving lipids, protein, and nucleic acids from oxidative damage [7; 8]. By this mechanism, CoQ is the major antioxidant preventing lipid peroxidation in cell membranes and lipoproteins [9; 10]. Further, CoQ also plays a key role in the maintenance of vitamin C and \(\alpha\)-tocopherol antioxidant activities at the plasma membrane, leading the regulation of cellular redox homeostasis [11; 12].  
CoQ is involved in essential physiological processes in cells and tissues, and its homeostasis is maintained through highly conserved regulatory mechanisms of the biosynthesis pathway [13]. Defects in either the biosynthesis pathway or in the mitochondrial functions that affect CoQ homeostasis in tissues and organs imply an activation of metabolic diseases and aging, in which mitochondrial dysfunction is involved [1; 14]. This review highlights the importance of CoQ in the maintenance of health and in the evolution of metabolic diseases and aging.
# Bus Travel Time Prediction Based on the Similarity in Drivers' Driving Styles
## 3 Methodology
### Bus Travel Time Prediction Method  
The objective of predicting bus travel time is to predict bus travel times between two locations (e.g., two bus stops) [3]. The travel time of a bus between two adjacent stops includes the time spent waiting before passing through an intersection, the time spent waiting on the way, and the time spent waiting at the departure stop, as shown in Equation (1).  
\[T_{travel(i\to i+1)}=T_{d,i+1}-T_{d,i} \tag{1}\]  
where:  
* \(T_{travel(i\to i+1)}\) is the predicted time for the bus to travel from stop \(i\) to stop \(i+1\), including the parking time at stop \(i\), as shown in Figure 3;
* \(T_{d,i}\) is the predicted arrival time of the bus at stop \(i\);
* \(T_{d,i+1}\) is the predicted arrival time of the bus at stop \(i+1\);  
Similarly, the bus travel time between two non-adjacent stops is the sum of the travel time of multiple adjacent stops.  
In this section, we will introduce the process of predicting bus travel time based on clustering drivers' driving styles. This process includes two steps: (1) the selection of training dataset based on the results of the driver's driving style classification in the previous  
Figure 3: The time it takes for the bus to travel from stop \(i\) to stop \(i+1\).  
section; and (2) the construction of the travel time prediction model. Corresponding travel time prediction models for drivers with different types of driving styles were constructed.  
Previous studies [7; 34] have indicated that the kernel of the radial basis function (RBF) is better at predicting bus travel time. Therefore, the RBF kernel function is used in the SVR model in this study, along with the parameters \(C\) and \(e\) of SVR. The selection of this parameter is based on the best combination in previous papers [31], where \((C,e)\) is set to (2, 0.1). \(C\) defines the cost of the penalty function. To accurately and quickly predict the travel time of buses, it is important to determine the appropriate factors for estimating the traffic conditions. The factors we consider in this work are as follows [38]:  
1. \(X_{1}=\) day of the week. In general, traffic flow is different during the five working days of the week;
2. \(X_{2}=\) number of road segments. The number of sections between two adjacent stops of the predicted bus line. Different road sections have different road condition characteristics;
3. \(X_{3}=\) departure time of the bus. At different times of the day, the bus journey time is different. For example, during peak hours and off-peak hours, the travel time of buses varies greatly.  
The prediction model of bus travel time can be summarized as the following relationship:  
\[T_{predicted}=f(X_{1},X_{2},X_{3}) \tag{2}\]
# Use of Phase Change Materials for Food Applications--State of the Art in 2022
## 2 Food Applications of PCM
### Food Storage and Transport
#### 2.2.8 \"Just\" Ice
The objective of the investigation was to reduce the chilling time for bigger catch and better use of the refrigeration system at low loads. The results showed that the addition of ice in the chilling tank and the use of a 4 \({}^{\circ}\)C PCM in a separate tank are most promising and should be investigated further. Using \"just\" ice is in many ways convenient. Ice has a phase change temperature which is very suitable to keep food cold, at temperatures as low as possible while at the same time avoiding freezing which could harm the quality of some foods. Being frozen water, ice can be used in direct contact with food to be cooled if the water used to produce ice is of food quality; otherwise, direct contact should be avoided. Also, as water is practically available everywhere, ice can be produced easily in any place, even homes. And last, but not least, it is environmentally friendly and practically at no cost, such that especially for transport it is not necessary to bring the \"PCM\" back from the destinations of transport to the origin. These issues are important in applications of storage of food, and even more for the transport of food. Another issue is the concept for cold (or heat) production and the use of PCM for cold (or heat) storage. For storage, in the previously described large cold storage and distribution facilities, smaller cold rooms, even smaller coolers and freezers, the cold production and the cold storage by the PCM was combined, meaning both were part of the application. But for transport this is often not feasible for various reasons. The most obvious is that cold production by electrically driven compression cooling requires electricity. During transportation, a connection to the electricity grid is not available, production of electricity by other means, like a combustion engine, is not very efficient, and storage of electricity, e.g., in batteries, is costly and adds weight. Nevertheless, the different options are always in competition with each other. Typically, the solution is to charge the PCM in an external facility, or if the PCM is fixed to connect it temporarily to an external facility for charging. The following discussions highlight this. Transport, not just of food but practically of anything, is done in mobile units ranging in size from small and medium sized bags and boxes to medium and larger containers. The largest ones can be an integral part of trucks or train wagons, while smaller ones are commonly independent and also used for storage.
## 2 Rock Sample and Test Scheme
### Test Procedure  
The carbonaceous slate samples were subjected to creep tests using the rock and concrete mechanical testing system (RMT-301 Figure 3a). Before performing the mechanical tests, longitudinal wave velocity tests were conducted on each sample. Five rock samples sharing the same foliation angle and comparable wave velocities constituted a set. A total of five sets, each comprising five samples (25 samples in aggregate), were utilized. In each set, three samples were dedicated to uniaxial compression tests for uniaxial compressive strength calculation, one for the creep test and the remaining one reserved as a backup. Subsequently, a group of four rock samples exhibiting comparable wave velocity values and the same foliation angle were selected. The loading ratio, \(k\)[27], was introduced as a means of representing the predefined ratio between loading stress and average uniaxial compressive strength. The equation of \(k\) is as follows:  
\[k=\frac{\sigma_{a}}{\sigma_{UCS}} \tag{1}\]  
where \(\sigma_{a}\) is the predefined loading stress, and \(\overline{\sigma}_{UCS}\) is the average uniaxial compressive strength.  
Figure 3: Experimental equipment and test plan. (**a**) RMT equipment (**b**) loading plan.  
Figure 2: Microscopic examination of rock samples. (**a**) Optical microscope image of carbonaceous slate [26]; (**b**) scanning electron microscope image of foliation surface; (**c**) XRD results.  
The creep test was conducted using a step-loading method, which is explained as follows:  
1. First, the rock samples were subjected to uniaxial compression testing. As shown in Figure 3a, the rock samples were firmly fastened and preloaded. The samples were thereafter loaded at a stress rate of 0.10 kN/s until they reached the point of failure. The \(\overline{\sigma}_{UCS}\) was determined by calculating the average of the peak strengths of three rock samples. The experimental data are presented in Table 1.
2. The creep test was performed by utilizing preset axial loading ratios of 0.45, 0.55, 0.65, 0.75, 0.85, and 0.95 (Figure 3b). The load level gradually increased at a rate of 0.10 kN/s until it reached the preset value and then it was maintained at that level for 48 h. The axial deformation was monitored using two axial linear variable differential transformers (LVDTs), while the lateral deformation was recorded using two circumferential LVDTs.
3. The aforementioned steps were repeated, while systematically increasing the axial loading pressure until the samples fractured.