esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Limitations and Potential Improvements  
The proposed method can automatically extract the extent of water and meet the accuracy requirements for water extraction. It also gives an excellent performance in small water bodies. Radar signals over still water are close to zero due to specular reflection. While the VV backscatter intensities appear corrugated over the windy surface, since the Ji'An site is an inland basin, there is no ripple on the SAR images. There are also no obvious waves on the image of the WeiFang site. Therefore, we do not discuss the influence of windy surfaces in terms of our method, which might prove an issue elsewhere. There are also improvements in using historical water-body data to determine the type of histogram. Specifically, the determination of parameters \(\alpha\) and \(\beta\) was obtained from our calculations for the WeiFeng site. Although the adaptation of the proposed thresholds at the Ji'An site was demonstrated, we have not tested it in other, larger areas. On the other hand, the cDSWE product itself also shows improvements in accuracy.  
In future experiments, we will continue to investigate the relationship between historical water and image histograms. Specifically, the value of the parameters \(\alpha\) and \(\beta\) needs to be determined by more experiments. Other datasets, such as GSW and G3 WBM, will be analyzed and compared to address the shortcomings of thresholds judged using a single dataset. Experiments will be conducted from the following three aspects.  
First, a longer period of Landsat data will be used to calculate persistent open-water extent. A total of 16 years of Landsat data were used for the composite cDSWE products to calculate the area of persistent open-water extent. In the future, we will try to use longer-term data for experiments to improve the accuracy of our method. Longer-term data may lead to more suitable coefficients, thus improving the accuracy of the method.  
Second, other datasets, such as GSW and G3 WBM, will be tested, and it will be considered whether these datasets can be combined to improve accuracy. Only cDSWE products were used to calculate the probability of water bodies in our method. However, a variety of global water datasets are provided to scholars for research. It is possible that multiple water datasets, when combined, could improve the coefficients and algorithm.  
Third, the proposed method (AT-EWC) will be applied to a larger area. In this paper, the determination of parameters \(\alpha\) and \(\beta\) was obtained from our calculations for the WeiFeng site. Although the adaptation of the proposed thresholds at the Ji'An site was demonstrated, whether our method still performs well for a larger area needs to be verified. We will also conduct algorithm tests in other research areas in the future.
# Assessment of Self-Supervised Denoising Methods for
## 2 Materials and Methods
### VoiceFixer  
VoiceFixer (VF) is a proposed system [21] that aims to mimic speech analysis and comprehension of the human auditory system. VF uses advanced algorithms to simultaneously eliminate noise, distortions, and other issues in speech signals. Additionally, it can improve speech's intelligibility and clarity by enhancing the signal's spectral and temporal features. This restoration system consists of an analysis stage and a synthesis stage. The analysis stage was modeled by a ResUNet model (Mel spectrograms were utilized during this stage), and neural vocoder, which is a generative adversarial network (GAN) that operates in both time and frequency domains (TFGAN), was used to model the synthesis stage. VoiceFixer generalizes well to severely degraded real speech recordings. Figure 4 presents an overview of its architecture. VF was trained (as described in another paper [21]) on two speech databases (CSTR VCTK corpus and AISHELL-3) and two noise databases (VCTK-Demand and TUT).
## 2 Materials and Methods
### Performance Evaluation Metrics  
We have evaluated a set of statistical measures, such as specificity, sensitivity, and accuracy, for each model. The following formulations were used to calculate the statistics:  
Accuracy = (True Positive + True Negative)/# of all examples;  
Specificity = True Negative/(True Negative + False Positive);  
Sensitivity (Recall) = True Positive/(True Positive + False Negative).  
Moreover, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) has been employed to estimate the likelihood of a classifier scoring a randomly chosen positive instance higher than a randomly selected negative instance. All performance metrics reported in this study represent the average values derived from 10-fold Monte Carlo Cross-Validation.
## 2 Materials and Methods
### Distributed Hydrological-Hydraulic Modelling
#### 2.2.1 Computational Mesh  
We built a computational mesh across the whole drainage area of 7 km\({}^{2}\) subdividing it into three regions. The whole system model requires a high spatial resolution to capture with accuracy the topographic flow control exerted across the catchment pathways and urban areas. First, we discretised the man-made channel of 1 km length and 3 m width (brown line in Figure 1) with 60,000 structured elements of 0.1 m \(\times\) 0.5 m size. Elevations of the channel thanweg and lateral walls were measured in situ with a Leica Zeno 20 Global Positioning System (GPS) combined with laser Leica DISTO(tm) and AS11 multi-frequency Global Navigation Satellite System (GNSS) antenna. Subsequently, a digital elevation model of the channel area was constructed, and the elevations were assigned to the computational grid. Second, the urban area (black squares in Figure 1) was discretized with triangular cells with a characteristic size of 1 m. The nodes elevation were obtained from a home-made DEM based on Light Detection and Ranging elevations acquired on May 2021 by the Spanish Geographical Institute ([[https://www.ign.es/](https://www.ign.es/)]([https://www.ign.es/](https://www.ign.es/)), accessed on 1 July 2022). The average distance of a point from its neighbours in the LiDAR dataset is 0.82 m (corresponding to the point density of 1.5 points per square meter) with a root mean squared error between the ground reference and estimated position of 0.3 m and 0.15 m in the horizontal and vertical coordinates, respectively. Third, the computational grid is coarser in the greatest rural area of the Manillas catchment (green region in Figure 1) because the unstructured grid has a characteristic edge length of 1.5 m. Elevations were also obtained from the LiDAR data that had to be filtered to delete the vegetation and olive trees, keeping only the ground points. The elevations of the gullies and streams are accurately represented in the LiDAR data thanks to the seasonal behaviour of the precipitation. The computational mesh amounts to 7 million cells and replicates real topography in the simulated region.  
Figure 2 shows details of the geometrical model in a zoomed area that includes the channel, buildings and olive groves. The elevations (Figure 2a) accurately represent the civil infrastructures visible in the orthophoto (Figure 2b). The man-made channel that surrounds the town can be better observed in the shadowgraph (Figure 2c). Lastly, Figure 2d shows a zoom of the computation grid near the channel. We ensured that the surface in the olive-grove floodplains was smooth by removing the olive trees when constructing the DEM from LiDAR, as described above.
## 3 Results and Discussion
### Oxidation
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Alcalase** & \multicolumn{2}{c}{**Neutrase**} & \multicolumn{2}{c}{**Protamex**} \\  & **35 min** & **70 min** & **35 min** & **70 min** & **35 min** & **70 min** \\ \hline \multicolumn{2}{c}{**Whole fish**} & & & & \\ AV & 11.10 \(\pm\) 0.61 \({}^{\text{a}}\) & 16.87 \(\pm\) 2.77 \({}^{\text{b,c}}\) & 16.91 \(\pm\) 0.78 \({}^{\text{b,c}}\) & 15.25 \(\pm\) 0.03 \({}^{\text{a,b}}\) & 21.08 \(\pm\) 1.31 \({}^{\text{c,d}}\) & 25.84 \(\pm\) 0.54 \({}^{\text{d}}\) \\ PV & 22.95 \(\pm\) 1.92 \({}^{\text{a}}\) & 53.66 \(\pm\) 6.90 \({}^{\text{b}}\) & 54.44 \(\pm\) 7.27 \({}^{\text{b}}\) & 43.65 \(\pm\) 2.89 \({}^{\text{b}}\) & 57.58 \(\pm\) 6.50 \({}^{\text{b}}\) & 75.65 \(\pm\) 7.40 \({}^{\text{c}}\) \\ TOTOX & 54.88 & 124.19 & 125.80 & 102.56 & 136.23 & 177.14 \\ \hline \multicolumn{2}{c}{**By-products**} & & & & \\ AV & 16.34 \(\pm\) 0.37 \({}^{\text{b,c}}\) & 25.88 \(\pm\) 5.80 \({}^{\text{c}}\) & 5.60 \(\pm\) 1.14 \({}^{\text{a}}\) & 13.35 \(\pm\) 0.29 \({}^{\text{a,b}}\) & 12.05 \(\pm\) 0.30 \({}^{\text{a,b}}\) & 9.94 \(\pm\) 0.03 \({}^{\text{a,b}}\) \\ PV & 49.67 \(\pm\) 1.53 \({}^{\text{c}}\) & 68.43 \(\pm\) 1.01 \({}^{\text{d}}\) & 23.56 \(\pm\) 0.19 \({}^{\text{a}}\) & 52.05 \(\pm\) 2.00 \({}^{\text{c}}\) & 35.21 \(\pm\) 0.68 \({}^{\text{b}}\) & 36.77 \(\pm\) 0.64 \({}^{\text{b}}\) \\ TOTOX & 115.69 & 162.74 & 52.72 & 117.44 & 82.46 & 83.48 \\ \hline \end{tabular}
\end{table} 
Table 4: \(p\)-Anisidine values (AV), peroxide values (PV) and total oxidation product (TOTOX) values of different enzymatically extracted oils. The results for AV and PV are shown as mean value \(\pm\) standard deviation. Significant differences (\(p<0.05\)) were determined by one-way ANOVA and Tukey HSD. both AV and PV, although the EPA, DHA and PUFA contents increased with extraction time. For oils extracted from by-products, an increased extraction time decreased DHA with all enzymes and EPA with Alcalase and Neutrase; however, the differences were not significant. Furthermore, PUFA showed a decreasing trend with increasing extraction time with Alcalase and Neutrase, but again, the differences were not statistically significant. The AVs increased significantly with Alcalase- and Neutrase-extracted oil with longer extraction times, but there were no statistically significant differences in PVs.
## 3 The Proposed Routing Protocol--CRD
### Underlying Technology--Necessary Algorithms
#### 3.3.2 The Proposed Connected Dominating Set Size Reduction Algorithm: CDS Size Reduction  
Based on rules in [11], we use the proposed weighted function (Equation (8)) and the list of node's neighbors to propose two new rules which are used to reduce number of intermediate nodes of a CDS. Let \(v\) be an intermediate node. We denote \(N(v)\) as the set of node \(v\)'s neighbors and \(N[v]\) as \(N(v)\) including \(v\).  
**Rule 1.** Let \(v\) and \(u\) be two nodes in the CDS. If \(\{N[v]\subsetneq N[u]\}\)**or**\(\{N[v]=N[u]\)**and**\(W(v)<W(u)\}\)**or**\(\{N[v]=N[u]\)**and**\(W(v)=W(u)\)**and**\(id(v)<id(u)\}\), node \(v\) is removed from the CDS.  
**Rule 2.** Let \(u\) and \(w\) be two nodes which are intermediate neighbors of node \(v\) in the CDS and they can communicate with each other. If \(\{N(v)\subsetneq N(u)\cup N(w)\}\)**or**\(\{N(v)=N(u)\cup N(w)\)**and**\(W(v)<W(u)\)**and**\(W(v)<W(w)\}\)**or**\(\{N(v)=N(u)\cup N(w)\)**and**\(W(v)=W(w)\}\)**and**\(id(v)=\min\{id(v),id(u),id(w)\}\), node \(v\) is removed from the CDS.  
Based on Rules 1 and 2, we propose the CDS size reduction algorithm which is described as follows. First, we use Rule 1 to remove some intermediate nodes from the CDS. Each removed node uses common channel to send a new status message to all neighbors. If a node \(v\) receives a status message, it updates the list of node \(v\)'s neighbors. Second, Rule 2 continues to be applied to removing some intermediate nodes from the CDS. Each removed node also uses common channel to send a new
# Notch Fatigue Life Research Based on Critical Distance Theory
## 4 Finite Element Analysis  
### Finite Element Model of Notched Specim  
To apply the point and line method in the three-dimensional case, a line for calculation needs to be defined in advance, starting from the fatigue hot spot and extending in the expected direction of fatigue crack expansion, and this line is called the hot spot path. The theoretical path is chosen in the direction of the highest stress gradient. In order to facilitate the determination of the hot spot path and fatigue test, a simplified specimen of the curved cutout of the cross-section is established and marked with the hot spot path as shown in Figure 7, by combining the stress characteristics of the curved cutout of the cross-section.  
Using ABAQUS finite element software, a 3D finite element model of the rectangular plate was created based on the geometry and details shown in Figure 3. A 20-node solid-cell C3D20R simulation was used to make the finite element model identical to the actual geometric configuration. In the fatigue test, the numerical study mainly focuses on the stress distribution around the cutout, and the local sub-model cell of the curved cutout of the cross-sectional plate is refined to 0.5 mm, and the mesh was studied to meet the accuracy and convergence of the simulation, and the whole model mesh is divided as shown in Figure 8. In the finite element analysis, the bottom end of the rectangular plate is completely fixed, and the top end is allowed to be displaced in the y-direction. For static loading, a 50 kN tensile force is applied at the top center of the specimen with material parameters of [PERSON]'s modulus E = 206 GPa and [PERSON]'s ratio \(\
u=0.3\). After that, the result file of the FEA is imported into Fe-safe, and a cyclic tensile force of 1-50 kN is applied at the top center of the specimen with a sinusoidal curve loading at a frequency of 3 Hz, representing the fatigue load in the test, fatigue analysis by applying Eurocode3 [17] correlation S-N curves.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Notch Radius/mm** & **Fatigue Life/Cycles** & **Mean Value** \\ \hline
10 & 1,029,010 & 1,246,527 & 1,153,097 & 1,142,878 \\
20 & 1,346,250 & 1,395,468 & 1,566,734 & 1,436,151 \\
30 & 1,658,972 & 1,695,423 & 1,812,280 & 1,722,225 \\ \hline \end{tabular}
\end{table}
Table 3: Fatigue life table of specimens under cyclic loads.  
Figure 7: Hotspot path diagram (r = 10 mm).
# Experimental and Numerical Dynamic Identification of an Aerostatic Electro-Spindle
## 2 Experimental Setup
### Experimental Characterization at Null Rotational Speed
#### 2.4.1 Single-DOF Modal-Identification Analysis through LDM and HPM  
In view of these considerations, as a first approach, two single-DOF methods were employed to identify the modal parameters of the system: the logarithmic decrement method (LDM) [29] and the half-power method (HPM) [30]. Table 3 lists the mean values of the estimations of the damped frequencies, \(\omega_{d}\), and the damping ratios, \(\zeta\), computed through HPM and LDM. The values of the damped natural frequencies increase with the supply pressure, whereas the damping ratios exhibit an opposite trend. The accuracy of these estimations was verified by comparing the experimental signals with those reconstructed through the identified modal parameters (see Figure 4) by making use of the following  
As can be seen, in this case, the accuracy of the method is satisfactory. The results obtained with the LSCEM method are summed up in Table 4, where the damped natural frequencies and damping ratios are reported, together with their phase, \(\phi\), and modal constant, A. Only the first rigid mode was considered, as the second mode exhibited a natural frequency of about 220 krpm, which was almost constant with the supply pressure and had modal constants much smaller than the first mode. The evaluation of the phase shifts, \(\Delta\phi=\ |\phi(y_{1})\ -\ \phi(y_{2})|\), confirms that the mode identified is of the tilting type.
## 2 Materials and Methods
### Numerical Simulation Method  
#### 2.2.1 Governing Equation  
To capture the aeroacoustic sound, pressure and density fluctuations were calculated using the 3D Navier-Stokes equations while considering compressibility:  
\[\frac{\partial U}{\partial t}+\frac{\partial F_{1}}{\partial x_{1}}+\frac{ \partial F_{2}}{\partial x_{2}}+\frac{\partial F_{3}}{\partial x_{3}}=0, \tag{1}\]  
where \(U\) is the conservative form and \(F_{i}\) is the flux term. Denoting the density of the fluid by \(\rho\), \(U\) and \(F_{i}\) are expressed as  
\[U=\begin{pmatrix}\rho\\ \rho u_{1}\\ \rho u_{2}\\ \rho u_{3}\\ \rho e\end{pmatrix} \tag{2}\]  
and  
\[F_{i}=\left(\begin{array}{l}\rho u_{i}\\ \rho u_{i}u_{1}+p\delta_{i1}-\mu A_{i1}\\ \rho u_{i}u_{2}+p\delta_{i2}-\mu A_{i2}\\ \rho u_{i}u_{3}+p\delta_{i3}-\mu A_{i3}\\ (\rho e+p)u_{i}-\mu A_{i}u_{j}-k\delta T/\partial x_{i}\end{array}\right), \forall i=1,2,3, \tag{3}\]  
where \(A_{ij}=\partial u_{i}/\partial x_{j}+\partial u_{j}/\partial x_{i}-2/3( \
abla\cdot u)\delta_{ij}\). The pressure \(p\) is given by the ideal gas equation:  
\[p=\rho RT. \tag{4}\]  
The dynamic viscosity and thermal conductivity of the fluid at temperature \(T\) are based on [PERSON]'s law:  
\[\mu(T)=\mu_{0}(T/T_{0})^{3/2}[(T_{0}+110)/(T+110)], \tag{5}\]  
\[k(T)=\mu(T)\gamma R/[(\gamma-1)Pr], \tag{6}\]  
where \(\rho_{0}=1.1842\) kg/m\({}^{3}\), \(\mu_{0}=1.85\times 10^{-5}\) N s/m\({}^{2}\), \(T_{0}=298.06\) K, \(\gamma=1.4\), \(R=287\) J/kg/K, and \(Pr=0.72\).
## 3 Results  
### Isolation of Collagen  
In this study, collagen was extracted from bone, skin, muscle, and general waste (a mixture of bone, muscle, and viscera in approximately equal proportions) obtained from the bluefin tuna farming industry. For each waste type, the yields for the ASC and PSC components were compared. The collagen yield was found to be lower for all ASC-tissue extracted, ranging from 1.2 g/100 g of collagen derived from general waste to 1.64 g/100 g from bone to 4.9 g/100 g from skin, compared to 3.3, 2.01, and 16.6 g/100 g, respectively, for the PSC components. The increased yield noted for PSC extraction occurred as the proteases used to cleave the telopeptide cross-linking regions of the collagen structure that represent the main sites of intra- and inter-molecular cross-linkage in the collagen structure, effectively exposing further collagen for extraction. The values reported in this study were consistent with those reported for other tuna, where the value obtained here for ASC skin (4.9%) was similar to that obtained for pacific bluefin tuna at 5.4% [33]. Similarly, for the PSC skin, the reported value is consistent with the 16.7% obtained by [PERSON] et al. [34] for bigeye tuna (_Thunnus obesus_).  
The sensorial qualities (appearance and odor) of the extracted PSC were found to be initially poor due to the presence of chromophores and as well as the use of acetic acid that left a residual odor. However, the introduction of a lengthier second salting-out step and slow addition of the crude collagen solution to the sodium chloride residue allowed for an improved separation of collagen from the inclusions present. Visual quality was further improved by a decolorization and decolorization step implemented during the second salting-out step using a concurrent treatment with 4% hydrogen peroxide, commonly used in the food and pharmaceutical industry [35]. Furthermore, after filtration of the salted-out material, the dissolution of collagen was carried out in citric acid, which eliminated residual acetic acid odor in the final product (see the flow diagram in Figure 1).  
Figure 1: Flow diagram indicating steps involved in the pre-treatment, extraction, retrieval, and purification of Atlantic bluefin tuna collagen.  
Microbiological analysis of six common microbial contaminates (_Escherichia coli Enterobacteriaceae_ spp., _Klebsiella_ spp., _Aerobacter aerogenes_, and _Staphylococcus epidermidis_ and aureus) was also carried out; all tests were negative, and no evidence of these bacteria was found; furthermore, the total count for bacteria and fungi proved to be negative.
# The Vehicle Routing Problem: State-of-the-Art Classification and Review
## 4 Solutions for VRPs  
Because real-world problems involve complex constraints, advanced algorithms are required to solve VRPs in complicated and constantly changing environments. The number of customers and vehicle types is increasing and the use of optimization algorithms is a key component of effective customer service and efficient operations. A large variety of VRP solution strategies have been presented in the literature. These strategies range from exact methods to heuristics and meta-heuristics. Exact methods provide optimal solutions, whereas heuristics and meta-heuristics generally yield near-optimal solutions. Exact methods are typically only suitable for small-scale problems (up to 200 customers). Because the VRP and its variants are known to have NP-hard complexity, solving larger instances optimally is very time-consuming. However, there are no bounds on problem size when solving problems using heuristics and meta-heuristics that can efficiently handle large numbers of constraints and still output near-optimal solutions. Figure 2 presents various approaches to solving the VRPs and was adapted from content in [6,149]. Exact methods include a variety of approaches, mainly branch and X (X: cut, bound, price, and so on) approaches, as well as dynamic programming and column generation methods. In recent years, significant advances in the exact solution of VRPs have been achieved. A major milestone was the branch-and-price algorithm proposed by [PERSON] et al. [150]. The branch-and-bound (BB) method was developed to explore solution spaces implicitly. Because the performance of BB algorithms depends on the quality of bounds obtained throughout a tree, BB algorithms can be combined with the generation of cutting planes, forming so-called branch-and-cut algorithms, or with column generation, resulting in BAP algorithms [151]. Branch and X remain the dominant VRP approaches [150,152]. While branch and X approaches treat VRPs as integer linear programming (ILP) or mixed ILP (MILP), dynamic programming breaks complex problems into a number of simpler sub-problems. Constraint programming is a model that interrelates different variables using constraints. When the search space is reduced, relatively simple problems can be solved by various search algorithms [149]. Figure 2: Solutions for VRPs. Approximate methods called heuristics are designed to solve specific problems. Heuristics focus on systematically finding an acceptable solution within a limited number of iterations. A heuristic yields solutions faster than an exact method. A meta-heuristic may be referred to as an intelligent strategy combining subordinate heuristics for exploration and exploitation. For solution discussion, we classified exact methods, heuristic algorithms, and meta-heuristic algorithms in papers with the same numbers as those in Tables 11-13. The results are presented in the tables below.
###### Abstract
4. we evaluate our proposed opportunistic routing protocol in a simulated marine environment. Computer simulation experiments validate that the proposed opportunistic routing protocol can effectively increase the data packet delivery ratio, reduce time delay, and prolong the lifetime of MSR-WSNs. \begin{table}
\begin{tabular}{c c c c} \hline \hline  
In summary, in highly dynamic and communication harsh marine environments, the goals of the proposed opportunistic routing protocol are to achieve efficient collection and real-time transmission of the MSR data packets, and to improve the continuity, intelligence and robustness of MSR-WSNs based search and rescue systems to assist the MSRCC makes timely responses and correct decisions after the shipw. In addition, the proposed opportunistic routing protocol will play a positive role in promoting the construction of a deep-sea stereoscopic observation system for intelligent communication, networking and exploration, and at the same time have important scientific significance and academic value for the development of multidisciplinary and multi-level major frontier theories of sensor networks in deep-sea observation. The remainder of this paper is organized as follows. Section 2 presents the system model. The proposed opportunistic routing algorithm is illustrated in Section 3. Section 4 conducts our computer simulation results, and finally, this paper concludes in Section 5.