esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The icebreaking process of water-exiting vehicles involves complex nonlinear interactions as well as multi-physical field coupling effects among ice, solids, and fluids, which poses enormous challenges for numerical calculations. Addressing the low solution accuracy of traditional grid methods in simulating large deformation and destruction of ice layers, a numerical model was established based on the FEM-SPH-SALE coupling algorithm to study the dynamic characteristics of the water-exiting vehicle on the icebreaking process. The FEM-SPH adaptive algorithm was used to simulate the damage performance of ice, and its feasibility was verified through the four-point bending test and vehicle breaking ice experiment. The S-ALE algorithm was used to simulate the process of fluid/structure interaction, and its accuracy was verified through the wedge-body water-entry test and simulation. On this basis, numerical simulations were performed for different ice thicknesses and initial velocities of vehicles. The results show that the motion characteristics of the vehicle undergoes a sudden change during the ice-breaking. The head and middle section of the vehicle are subject to greater stress, which is related to the transmission of stress waves and inertial effect. The velocity loss rate of the vehicle and the maximum stress increase with the thickness of ice. The higher the initial velocity of the vehicle, the larger the acceleration and maximum stress in the process of the vehicle breaking ice. The acceleration peak is sensitive to the variation in the vehicle's initial velocity but insensitive to the thickness of the ice.  
water-exiting vehicle; ice-breaking; FEM-SPH adaptive algorithm; S-ALE algorithm; numerical simulation Article
## 1 Introduction
campestris_ biochemical synthesis pathway of 1-octen-3-ol suggests that linoleic acid is first oxidized by LOX to form 13-hydroperoxide (13-HPOD), and 13-HPOD is cleaved by HPL into 1-octen-3-one and 10-oxodecanoic acid (10-ODA). Then, 1-octene-3-one is reduced by oxidoreductase to 1-octene-3-ol [27]. However, the protein extract of button mushrooms cleaved only 10-HPOD, not 13-HPOD, to generate 1-octene-3-ol and 10-ODA [3; 28]. Further, 10-HPOD, not 13-HPOD, is the precursor of 1-octen-3-ol that is also observed in _Pleurotus pulmourius_[29; 30]. The stereochemical and volumetric correlations between 1-octen-3-ol and 10-HPOD have been confirmed in both _Lentiunula edodes_ and _T. matsutake_ mushrooms [31]. Importantly, 10-HPOD does not have a conjugated diene motif and thus is not a LOX product. Furthermore, the specific inhibitor of DOX inhibits the synthesis of 1-octen-3-ol and 3-octanone but does not inhibit the synthesis of 1-octene-3-one in the reaction system of button mushroom homogenate and linoleic acid, indicating that DOX, not LOX, is involved in the synthesis of 1-octen-3-ol for _A. bisporus_[32]. _Aspergillus nidulans_ is the first specimen in which the biosynthesis pathway of 1-octen-3-ol in fungi was identified. The _ppoC_ gene of _A. nidulans_ encodes a DOX-(CYP), which catalyzes the production of 10-HPOD from linoleic acid. Following this, 10-HPOD then decomposes into 10-ODA and 1-octene-3-ol [33]. Subsequently, this pathway was also found in _A. luchunensis_[34; 35]. Alternatively, the LOX and HPL from _T. matsutake_ co-expressed by yeast can catalyze the conversion of linoleic acid to produce 1-octene-3-ol [18]. However, the conserved domains in this LOX show that it is actually DOX-(CYP), and the precursor of 1-octene-3-ol was also not determined. The HPL from _T. matsutake_ contains a C-22 sterol desaturase subfamily domain encoded by fungal cytochrome P450 61 (CYP61) or a related plant cytochrome P450 710 (CYP710), similar to the yeast C-22 sterol desaturase ERG5, but this domain does not belong to the plant HPL encoded by CYP74. Plant HPL characteristically cleaves 9-HPOD and/or 13-HPOD and does not form 1-octen-3-ol [36]. In the cyanobacterium _Nostoc punctiforme_, a dioxygenase and catalase act as linoleic 10S-dioxygenase and 10S-hydroperoxide lyase for the oxidative cleavage of linoleic acid to 1-octen-3-ol [37]. In seaweed and moss, a multifunctional lipoxygenase is responsible for the biosynthesis of 1-octen-3-ol from the oxidative cleavage of linoleic acid [38; 39; 40]. In soybeans, 1-octen-3-ol is formed by the degradation of unsaturated fatty acid derivative glycosides [41; 42]. Thus, the biosynthesis pathways for 1-octen-3-ol are very diverse. In this study, we retrieved four genes from the _A. bisporus_ genome that may be involved in the oxidation and cleavage of linoleic acid to form 1-octene-3-ol. We cloned these genes and expressed them in prokaryotic systems. The products of the expressed enzymes that acted on linoleic acid were determined. The enzymatic pathway of _A. bisporus_ for the biosynthesis of 1-octene-3-ol was demonstrated.
## 2 Related Work  
There are several simulators that model the underwater environment and enable the simulation and study of underwater sensor networks. Aqua-Sim [1] is a simulation package for the ns-2. This package is configured alongside the CMU wireless package for the ns-2 and has various MAC and routing protocols designed for UWSN that are ready for use.  
Another simulation package for the ns-2 that enables the simulation of underwater sensor networks is ns-miracle [2]. This package also includes several protocols designed for UWSN, and when used alongside the WOSS project, can accurately model channel characteristics by including bathymetry data, salinity and sound speed profile.  
In this work, the ns-3 simulator is used. The ns-3 is a network simulator written from scratch that enables the easy extension and development of new models for underwater simulation. It is designed to overcome the main drawbacks of the ns-2 simulator--such as the use of split objects (OTcl and C++), which do not scale well and complicate the debugging process. Moreover, ns-3 includes flexible tracing and logging systems, libraries to facilitate memory management, _etc_.  
The ns-3 simulator includes an underwater framework that accurately models channel characteristics using, in the same way as the ns-miracle, the bellhop propagation model and environmental data, such as bathymetry or sound speed profiles. Similarly to the previously introduced simulators, the ns-3 includes a model of an underwater modem. To the best of our knowledge, there is no available underwater wake-up modem model for any of these simulators.  
The ns-3 also includes an easily extensible energy framework that models various batteries and supports a range of devices; however, no model of a solar energy-harvesting module is included.  
Figure 1: UWSN simulation ecosystem.  
Moreover, to the best of our knowledge, the currently available simulators do not include an energy-harvesting module, except for theoretical approaches [3]. For example, in [4], the authors use stationary energy levels for a one-hour period or, in [5], the authors attempt to adapt node duty cycles assuming static radiation values.  
To accurately study the effects on energy consumption of using different protocols in an underwater network with energy-harvesting capabilities, it is desirable to be able to include real radiation values for days or months and analyze their impact.
# One-Step Enhancer: Deblurring and Denoising of OCT Images
## 2 Related Work  
### GAN-Based Speckle Removal  
OCT images are known to suffer from speckle noise, which are artifacts produced mostly by the coherent nature of the image formation process. Recently, various GAN-based models have been developed to remove such noise from OCT images based on knowledge extracted from unpaired training data. These include SNR-GAN [10], ARM-SRGAN [18], nonlocal-GAN [19], and DRGAN [16].  
SNR-GAN was proposed by [PERSON] et al. to establish an end-to-end structure-aware noise reduction GAN that uses cycle GAN to translate data between noisy and clean domains [20]. In order to preserve subtle features during denoising, they used regional structural similarity index (SSIM) loss of image patches instead of the entire image. This method enabled promising improvements in terms of signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and SSIM index with a processing speed of 0.3 s per image.  
The ARM-SRGAN is a GAN-based method developed for fast and reliable generation of super-resolution (SR) images without relying on a paired training dataset of low- and super-resolution images [18].  
The nonlocal-GAN method, unlike cycle-GAN based methods that include two generators and two discriminators, is based on only one generator and one discriminator [19].  
Figure 2: Framework of proposed image enhancement method. X and Y are inputs, where subscripts b, c, r, n, bn and cycle are blurred, clean, reconstructed, noisy, blurred and noisy, and cycled, respectively. The encoder and generator’s superscripts c, b, n, and bn is content, blur, noise, and blur-noise, respectively.  
The discriminator can learn the features of noise in noisy OCT images and then direct the denoising generator without reference images.  
Finally, DRGAN was proposed by [PERSON] et al. as an unsupervised denoising method that disentangles the noisy image into content and noise spaces by using corresponding encoders. It then predicts the denoised OCT image based on the extracted content features [16]. According to the published results, DRGAN outperforms the methods mentioned above in noise reduction and detail preservation.
## 6 Conclusion  
A new pre-supporting system, Steel Tube Slab (STS) for construction of large-span underground spaces is presented. The conclusions are as follows.  
(1) Steel Tube Slab (STS) structure used in large span underground excavation with shallow depth can effectively control and reduce the surface settlement and can prevent the impact of soil excavation on adjacent buildings. The STS structure acts as the initial supporting system during the excavation stages and can increase the stiffness of the whole construction system. So, it reduces the final ground surface settlement and the deformation of the existing building.  
(2) Choosing the reasonable step length can be an effective way to control the ground surface settlement. It will not only control the slipping of excavation face and longitudinal displacement, but also can close into a ring as soon as possible, which will control the ground settlement effectively.  
(3) STS structure is a permanent supporting system, so it is more dependable and serviceable during its service period. STS structure simplifies the force mechanism in the initial supporting system which causes less construction sequences and reduces the engineering cost.  
(4) It can be concluded that STS structure can be a very dependable and effectively method in controlling and reducing the surface settlement and the influence of the construction sequence on the existing building, especially as for the large span and ultra-shallow underground structures.  
Concepts and designs (P.-j.J. and W.Z.); Monitoring analysis (Y.C.); Data were analyzed and simulation results (P.-j.J. and S.-g.L.); Numerical simulation (J.-y.H. and J.-c.D.).  
The authors would like to thank Northeastern University and The University of New South Wales in providing the access to the software and other facilities, and thank [PERSON] for his kind help in the research contents, analyses and the English language and style of the manuscript. The research described in this paper was supported by the Fundamental Research Funds for the Central Universities (Grant No. N160106006), the National Natural Science Foundation of China (Grant No. 51578116) and China Scholarship Council. These supports are gratefully acknowledged. And the data is available for the journal.  
The authors declare no conflict of interest.  
Figure 36: Crown settlement profile for tunnel Z1.
## 2 R-SpaceFibre Design: Features and Trade-Offs  
To understand the design choices made in the R-SpaceFibre development, it is necessary to briefly introduce the SpaceFibre protocol stack.  
The stack is composed of five layers, as shown in Figure 1:  
* The **Network layer** is responsible for transferring both data and broadcast messages through the SpaceFibre network.
* The **Data Link layer** is responsible for the FDIR system that implements a retry mechanism to resend corrupted frames. Moreover, it handles independent flow of information through Virtual Channel Buffers (VCBs), and it manages the QoS, broadcast service and data scrambling.
* The **Multi lane layer** is responsible for synchronising a link composed by more than 1 lane (up to 16). This is an optional layer.
* The **Lane layer** is responsible for establishing the communication between two ends.
* The **Physical layer** is responsible for serialising, transmitting, receiving and de-serialising data.  
The Network layer has not been included in R-SpaceFibre design as it is realised as an optional upper layer, and it is not needed for point-to-point links. R-SpaceFibre indeed has been designed for satellites with stringent requirements in terms of size and volume. These satellites often mount a reduced number of devices and usually a single payload, thus point-to-point high-speed connections are definitively preferred against more complex network topologies. The Multi lane layer is not included as well: it allows increasing the maximum achievable data-rate and system reliability adding redundancy but requiring considerably more hardware resources [18].  
For this reason, the reference SpaceFibre IP cell of this study is composed only by the core layers: Data link, Lane and Physical Layer. Most of the architectural modifications aim at reducing the hardware resources in the Data link layer, which is the most complex layer in terms of area occupation [19]. Some of its features are not essential to the core communication process, thus are removable from the design, accepting some performance degradation. Indeed, the Lane layer, which is responsible for initialising the link, and the Physical layer are fully compliant with the standard, as no significant optimisations are possible.  
Schematic descriptions of both SpaceFibre and R-SpaceFibre Data link layers are presented in Sections 2.1 and 2.2, in order to point out architectural innovations and shared features. The internal architectural description of each building block is not described as the aim of this work is to propose a high level architecture which saves resources employing a subset of the building block already present  
Figure 1: SpaceFibre protocol stack.  
in a regular SpaceFibre interface. For a full description of the layer, please refer to the SpaceFibre standard [2].
# PadGAN: An End-to-End dMRI Data Augmentation Method for Macaque Brain
## 3 Results
### Ablation Experiments and Results  
We conducted three ablation experiments to further investigate the role and effectiveness of the auxiliary generator in our proposed method. The details of the experiments are as follows: (1) removing the auxiliary network and retaining only the encoder part of the auxiliary network to encode the peak information map, to verify the role of the auxiliary network; (2) replacing the latent space features extracted by the auxiliary generator with random Gaussian noise to explore the role of latent space features; and (3) directly reusing the weights of the main generator in the auxiliary network to verify whether the auxiliary network needs to be trained separately.  
The results are shown in Table 6. (1) After removing the auxiliary network, PSNR decreased by 5.1256, SSIM decreased by 0.1225, and MI decreased by 0.0736. This indicates that the auxiliary generator plays an important role in improving the network performance. (2) When replacing the auxiliary generator with noise, PSNR decreased by 4.2291, SSIM decreased by 0.0649, and MI decreased by 0.0445. This suggests that the auxiliary generator can effectively extract latent space features from the peak information map. (3) When reusing the main network's network weights in the auxiliary network, PSNR decreased by 1.8627, SSIM decreased by 0.0385, and MI decreased by 0.0371, fully demonstrating that the latent space learned by the auxiliary generator is different from that of the main generator, and the auxiliary generator has a necessary existence.  
Figure 5: Visualization of 3 site datasets. These are 3 randomly selected data samples from the 3 datasets. The first column represents the source image, the last column represents the target image, and the middle column represents the comparative result. The red box highlights some details.
## 4 Performance Evaluation of Void Slab with Shear Reinforcement  
### Shear Capacity Evaluation Based on Standard Formulae in Different Codes  
#### 4.1.1.1. _Ac1_ 318-14  
The expected shear strengths for all test specimens were calculated following the American Concrete Institute (ACI) design code procedures. Table 5 summarises the results, categorised by the type of shear reinforcement (unreinforced, 45-degree, and 90-degree) and the hollow core ratio:  
* The expected shear strength of unreinforced specimens varied. It ranged from a minimum value of 1.95 for HC-35 (likely indicating a void ratio of 35%) to a maximum of 2.14 for HC-40 (likely indicating a hollow core ratio of 40%). On average, the expected shear strength for this group was 2.07. The data also showed some variation within the group, with a variance of 0.011 and a standard deviation of 0.107.
* Specimens with a 45-degree shear reinforcement exhibited a clear benefit in terms of consistency compared to unreinforced specimens. This group displayed a noticeably narrower range of expected shear strengths, varying from a minimum of 1.55 to a maximum of 1.64. Additionally, the average expected shear strength for the 45-degree group was 1.60.
* This improved consistency is further supported by the lower variance (0.002) and standard deviation (0.046) observed in the data. These reduced values suggest that a 45-degree shear reinforcement helps to achieve more predictable results in terms of expected shear strength.
* Similar to the unreinforced specimens, the expected shear strength of specimens with a 90-degree reinforcement also varied based on void ratio. The minimum value observed was 1.44 for both HC-35-90 and HC-40-90 (likely indicating void ratios of 35% and 40%, respectively). This value increased to a maximum of 1.62 for HC-30-90 (likely indicating a void ratio of 30%).
* The average expected shear strength for the 90-degree group was 1.50, with a variance of 0.011 and a standard deviation of 0.104. While a 90-degree reinforcement may not entirely eliminate the influence of the void ratio, these findings suggest it can offer some improvement in achieving more consistent shear strength compared to unreinforced specimens.
# Continuous Variable Quantum Secret Sharing with Fairness
## 2 CVQSS Scheme with Fairness
### \((2,2)\)-CVQSS Scheme with Fairness
#### 2.3.1 Initialization  
**I1** Dealer selects any two integers \(m^{a},m^{b}\geq 2\), satisfying \(gcd(m^{a},m^{b})=1\), as moduli for CRT, for example, \(m^{a}=2,m^{b}=3\). Then, generates the secret \(S\in\{0,1,...,M\}^{L}\), a checking sequence \(R\in\{0,1,...,M\}^{L_{1}}\) and a determine pointer \(P^{*}\in\{0,1,...,M\}^{L_{2}}\), where \(M=m^{a}\times m^{b}-1\) and \(L,L_{1},L_{2}\) are the lengths of \(S,R,P^{*}\) respectively.  
**I2** For security, \(S\) is hidden in sequence \(R\) to form a new sequence named \(X\), which is shown in Figure 1 and described as following steps. (1) Add \(P^{*}\) to the end of \(S\). (2) Insert \(S\) and \(P^{*}\) into \(R\) at one random place. The sequence \(P^{*}\) has to satisfy its uniqueness in message \(X\), which means \(P^{*}\) meets the constraint i.e., define \(T_{i}=X_{i},X_{i+1},\ldots,X_{i+L_{2}-1}\), if \(T_{j}=P^{*}\), \(T_{k}\
eq P^{*}\), for all \(k\
eq j\), where \(i,j,k\in[1,L+L_{1}]\).  
**I3** Dealer calculates the shadows \(X^{A}=X\bmod m^{a}\) and \(X^{B}=X\bmod m^{b}\), generates the verification information \(V\) of \(X\) according to Equation (4) and publishes the parameters i.e., \(m^{a},m^{b},L,L_{1},L_{2},P^{*},V\) and the selected Hash function \(H()\).
## 5 Performance Analysis
### Numerical Simulation
#### 5.2.3 Tracking Performance  
The code tracking for the SDR relies on a discriminator based on noncoherent early-late processing (NELP) [31]. For an early-late spacing of 10.2 ns for ACE-BOC(12,1), 8.15 ns for ACE-BOC(15,10) and ACE-FHBOC(24:1:6,1), 9.8 ns for ACE-FHBOC(24:1:1,1), a coherent integration time of 1 ms, a one-side equivalent rectangular bandwidth of the code tracking loop of 1 Hz, and interference power of zero, Figure 14 shows the simulated code tracking errors versus \(C/N_{0}\) for the example signals. The tracking error for ACE-BOC(15,10) is the smallest among the example signals. The tracking error for ACE-FHBOC(24:1:1,1) is larger than ACE-FHBOC(24:1:6,1) but smaller than ACE-BOC(12,1).
## 3 Results
### Results from the Selected Section(s)  
Obtained results for pavement SMA 11S and EACC 8 seem to be very similar. A correlation analysis of five sections was performed to determine whether the acoustic aging of both types of pavements is similar. The sections have been monitored for six years. Correlations reach values of 0.9666-0.9955 (see Table 3), e.g., the value 0.9955 means that the noise increasing of the surface SMA 11S of section 1 between years 1 and 6 is similar to the increase in surface noise of EACC 8 during years 1-6. The acoustic behaviour/aging of both types of surfaces is very similar according to the obtained results.  
One section from the previous five sections was chosen for frequency spectra comparison. The measured noise values in the frequency range 315-5000 Hz are shown in Figure 11. Equivalent sound pressure levels are as follows SMA 11S age 1 year = 98.6 dB(A), SMA 11S age 6 years = 99.7 dB(A), EACC 8 age 1 year = 98.4 dB(A) and EACC 8 age 6 years = 99.5 dB(A). The highest values are reached for a frequency of 1000 Hz.  
Measured results of the SMA 11S frequency spectrum in the sixth year after laying are comparable with the results obtained within the ROSATNE [37] which states 99.6 dB(A) as the average noise value of SMA 11 surface aged 2 months to 8 years. Changes in the frequency spectra are evident in Figure 11. Surfaces show an increase in noise for spectra higher than 1000 Hz (especially for SMA 11S) probably in connection with the degradation of the surface.
## 1 Introduction
By shifting to the visible spectrum to achieve sub-micrometer axial resolution, extended-focus OCM has been used to investigate plaques and small fiber tracts in ex-vivo cerebral mouse tissue [35]. A PS-OCM setup in the near infrared around 800 nm enabled the visualization of neuritic amyloid-beta plaques in brain tissue of human AD patients. These measurements provided information about the polarization characteristics of these pathological structures [19]. Finally, a spectral-domain visible light OCM system with sub-micrometer axial resolution has been used to investigate the inherent scattering contrast and the spectroscopic properties of amyloid-beta plaques in ex-vivo human and mouse brain tissue [32; 33]. To the best of our knowledge, no previous study has evaluated the capabilities of OCT and PS-OCT for amyloid-beta imaging in a large human sample cohort. In this article, we present a comparison of conventional OCT and PS-OCT for the visualization of amyloid-beta plaques in human brain tissue of 11 patients. We relate the plaque load observed in both of the OCT contrast channels to that revealed by quantitative histology and evaluate the advantages of multi-contrast imaging.