esa-sceva/satcom-chunk-collection huggingface dataset:
# QoS-Based Pattern Recognition Approach for Web Service Discovery: Ar_WSDS
## 4 Overall Design of Ar_WSDS
: _The quality thresholds, represented as \(\Delta\), are discrete values that must be met by the composition (composed service) and each \(WS_{S_{i}}\) (atomic service) to be a candidate service._  
The value of \(\Delta\) depends on the QoS parameter; for these values, its optimum will be given as a maximum or minimum. The characterization of attributes has been described in [27], and analyzed using a data set approach in [15]. There are three different types of \(\Delta\):  
1. \(\Delta\)WSC is a value (percentage) that represents the quality condition that must be met by the composed service through the process of analyzing the \(WS_{c}\) and its respective \(\varphi\), in order to be considered within an SP. 2. A key threshold (AKS) is a value of a quality parameter for a \(WS_{S_{i}}\) that must be in the acceptance interval of its respective \(WS_{S_{i},i}\). As previously mentioned, it depends on the nature of the parameter and can be expressed as a minimum or maximum. The delta must be fulfilled. 3. A partial threshold (\(\Delta\)PS) is a quality parameter value for a \(WS_{S_{i}}\) which is above or below the average of its QoS of the same type. These values may be below the quality condition. The \(\varphi_{ij}\) value is subjected to a penalty function, denoted by \(\beta(\varphi)\), which presents the ranking of the \(QoS_{i,j}\) value and whose ranking values are calculated from the difference between the best service and the service furthest away from the quality condition. Thus, for a service to be considered a candidate, it must meet the thresholds defined in \(\Delta\)KS or \(\Delta\)PS. **Definition 7**. : _A recognition type is a value between 0 and 1 associated with a \(QoS_{i,j}\) to use a \(\Delta\); if the value is 1, it will use \(\Delta\)KS, while 0 indicates the use of \(\Delta\)PS. No \(\Delta\) value means the QoS parameter will not be considered by the recognition modules._  
In Figure 2, a service is extracted, while Figure 6 presents an example of a service specification of the \(WS_{T}\) set; the values and type are presented. The latency and throughput value are processed by key thresholds, at runtime, using a partial threshold (average value). The following section defines the recognition strategies developed in Ar_WSDS, based on the definitions mentioned above.
## 3 Test Results
### Pore Water Pressure  
The results of pore water pressure dissipation (i.e., the reduction from hydrostatic pressure to vacuum pressure) over time, measured at distances of 30 mm, 60 mm, 90 mm, and 120 mm from the PVD, are presented in Figure 6. The pore water pressure in the slurry gradually dissipates following the initiation of the vacuum preloading test. The closer the measurement point is to the PVD, the faster the dissipation rate. This phenomenon occurs because a clogging zone progressively develops near the PVD during vacuum preloading, impeding the transmission of vacuum pressure to more distant regions, ultimately reducing the pore water pressure dissipation rate.  
At the end of the vacuum preloading test, the pore water pressures measured at R = 30 mm were \(-\)21 kPa for untreated slurry, \(-\)46 kPa for APAM-flocculated slurry, \(-\)72 kPa for dual use of PAC and APAM-flocculated slurry, and \(-\)80 kPa for lime-flocculated slurry. The untreated slurry exhibited the slowest pore water pressure dissipation rate at R = 30 mm, indicating the highest degree of clogging. Flocculation treatment enhanced pore water pressure dissipation. Among the tested flocculants, APAM alone improved the dissipation rate only at R = 30 mm, with a relatively limited effect on mitigating the clogging of slurry. However, pretreating the slurry with PAC before adding APAM significantly expanded the effective influence range of vacuum preloading and improved pore water pressure dissipation. At R = 120 mm, the pore water pressure in the dual PAC and APAM-treated slurry decreased to \(-\)8 kPa, whereas both the untreated slurry and the APAM-flocculated  
Figure 6: Pore water pressure dissipation versus time. (**a**) Untreated slurry. (**b**) Time-treated slurry. (**c**) APAM-treated slurry. (**d**) Dual use of PAC and APAM-treated slurry.  
slurry remained at 0 kPa, indicating a more effective vacuum pressure transmission in the dual-treatment case.  
Furthermore, vacuum pressure loss was minimal in the lime-floculated slurry. The pore water pressure at R = 120 mm in the lime-floculated slurry dissipated to \(-\)52 kPa, suggesting an extremely low clogging degree. This can be attributed to the flocculation of slurry particles into clusters, forming a relatively stable soil structure [43], which enhances the permeability of lime-floculated slurry [44]. As a result, vacuum pressure can propagate over a greater distance without significant attenuation.
## 5 Experiments and Results
### Ablation Study
#### 5.1.4 The Effect of Fusion  
Fusing features from both CC and MLO views enhances mammography classification, as they capture different breast tissue aspects. CC views offer a lateral perspective, while MLO views include additional tissue through an oblique angle. This fusion provides a more comprehensive tissue analysis, potentially improving model performance. Our experiments using SOTA backbone models compared the efficacy of single- and dual-view approaches for multi-label classification. The results in Table 5 show that dual-view fusion surpasses single-view classifiers in performance on the CBIS-DDSM dataset.
# Horizontal Polarized DC Grounded Omnidirectional Antenna for UAV Ground Control Station
## 1 Introduction
It also encloses the feeding topology, so antenna symmetry is not disturbed. The device's compactness, ruggedness, and direct-current grounding are further important features of this antenna design. The proposed technique has improved polarization stability since the cross-pols are very weak relative to the co-pols. The antenna structure is exhibited and explained in Section 2. Section 3 elaborates on the slot-feed mechanism. In Section 4, simulation verification is performed. Section 6 describes the manufacturing and measurements of the antenna prototype. Section 6 presents a comparison of the proposed work with those published. Finally, Section 7 details our conclusions.
# OPMSS: Optimal Producer Mobility Support Solution for Named Data Networking
## 5 Performance Evaluations  
Numerical evaluation is used, as in [49; 50; 51], for the handoff performance evaluation. However, the numerical evaluation only is not enough to cover the peculiarities of producer mobility. Hence, both numerical and simulation-based investigations are used for the evaluation of handoff performance, as in [7; 25]. Therefore, the performance of OPMSS is evaluated using both numerical and simulation investigation, specifically for the evolution of unnecessary interest packet loss, the provision of data path optimization, reduced handoff latency, signaling cost, and good data delivery. The numerical evaluation considers handoff performance, such as handoff latency, signaling cost, and data delivery via the optimal path.
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 4 Discussion
### 3D Bioprinting in Regenerative Dentistry  
Bioprinting as a process plays an important role in tissue regeneration. The selection of materials (including bioinks), form, methods, and design of the future product are some of the elements which need to be considered to obtain scaffolds with the potential to replace living tissues [132,133].  
The main goal is to approximate the anatomy of living tissues as precisely as possible [134]. Bioprinting nowadays can provide different components for the whole construction, thus helping substitute conventional treatment methods [8]. 3D bioprinting finds its place in the complex regeneration of teeth and its structures, including bioprinting of cells, matrix materials, and thus controlling the external and internal properties of regenerated modules [36]. Various fabrication approaches have been explored in different fields of regenerative dentistry, including the use of hydrogels, 3D scaffolds, and thin films. For example, skin tissue substitutes and wound dressings have been used in the maxillofacial region [135]. Bioink development is also very crucial for scaffold bioprinting. A study by [PERSON] et al. presented novel bioink, containing dental epithelial cells in an Alginate-Carboxymethyl Chitosan mixture for enamel tissue regeneration with good printability, porosity, integration, and high cell differentiation ability and viability [136].  
Bioreactors also take part in the bioprinting strategy selection. Several types of bioreactors can be used, such as flow perfusion, spinner flask, or the rotational ones. Their purpose is to mimic in vitro conditions for scaffolds and stem cells, modulate their behavior, and assess their biological properties [137].  
Bioprinting success relies on careful material selection, biological studies, and cell choices. It holds promise for revolutionizing regenerative dentistry and tissue engineering [138]. Further clinical studies are needed to determine the optimal filaments, bioinks, and bioreactors for 3D bioprinting of teeth structures.
## 4 Results  
### Dataset and Preprocessing  
We utilize two abdominal datasets for verifying the effectiveness of our method, the Learn2 Reg dataset [35] and the CHAOS dataset [36]. The Learn2 Reg dataset consisted of 16 MRI and CT datasets and each scan included the liver, spleen, and left and right kidneys, four organs, which were labeled with manual and automatic organ segmentation. Each scan had a 3D volume of size 192 \(\times\) 160 \(\times\) 192 with a voxel spacing of 2 mm. For the experiment, we use Elastix [37] for coarse affine registration and set the train/validation/test datasets in 10/2/4 pairs randomly. The healthy abdominal organs were from 80 patients, where 40 of them went through CT scans and 40 of them went through MRI scans in the CHAOS dataset. We use 20 volumes of CT and T1-DUAL MRI images in the CHAOS dataset and randomly divide them into 14/2/4 pairs for train/validation/test datasets. For the preprocessing of both datasets, we select 70 central slices of each piece of data for our experiments. The input size of the slice is 256 \(\times\) 256. Figure 4 illustrates examples from both the Learn2 Reg dataset and the CHAOS dataset. Table 1 shows a concise description of the two datasets utilized in our method.
## 2 Methods
### Elastic Full-Waveform Inversion (EFWI)
\begin{array}{rl}\frac{\partial\sigma_{2}}{\partial\mathbf{v}_{s}}&=4\rho \mathbf{v}_{s}\int_{0}^{T}\bigg{(}\frac{\partial\mathbf{u}_{x}}{\partial x}+ \frac{\partial\mathbf{u}_{z}}{\partial z}\bigg{)}\bigg{(}\frac{\partial \mathbf{u}_{x}^{+}}{\partial x}+\frac{\partial\mathbf{u}_{z}^{+}}{\partial z} \bigg{)}dt\\ &-2\rho\mathbf{v}_{s}\int_{0}^{T}\bigg{[}2\bigg{(}\frac{\partial\mathbf{u}_{x}} {\partial x}\frac{\partial\mathbf{u}_{x}^{+}}{\partial x}+\frac{\partial \mathbf{u}_{z}}{\partial z}\frac{\partial\mathbf{u}_{z}^{+}}{\partial z} \bigg{)}+\bigg{(}\frac{\partial\mathbf{u}_{x}}{\partial x}+\frac{\partial \mathbf{u}_{z}}{\partial z}\bigg{)}\bigg{(}\frac{\partial\mathbf{u}_{x}^{+}}{ \partial x}+\frac{\partial\mathbf{u}_{z}^{+}}{\partial z}\bigg{)}\bigg{]}dt. \end{array}  \tag{14}\]  
There are many factors that can affect the EFWI effects, among which the source wavelet accuracy is important. The source wavelet is the initial condition of the forward simulation of the seismic wavefield, and the forward simulation is an important part of the FWI algorithm. Source wavelet errors can easily cause the inversion to fall into a local minimum. Therefore, accurate source wavelet estimation is very important for EFWI.
## 5 Conclusions  
The performance of a Roots pump for hydrogen recirculation in PEM fuel cell system is simulated based on three-dimensional CFD modeling. The effects of the working fluids on the performance of the Roots pump are presented. Here is the summary of the major findings.  
(1) The CFD model can be used to predict the performance of the Roots pump based on the validation with the experimental data measured with working gas of air and helium, respectively. The maximum deviation of the simulation results from the experiments is 9.4% for air and 9.8% for helium within the main operating speed range between 5000 rpm and 9000 rpm.  
(2) The volumetric efficiency and isentropic efficiency of the Roots pump are improved separately by 9% and 6% with the increasing of suction temperature from 60 \({}^{\circ}\)C to 80 \({}^{\circ}\)C under the condition of pressure difference of 15 KPa and speed of 9000 rpm. With the increasing of nitrogen fraction from 5% to 25%, the volumetric efficiency and isentropic efficiency are improved by 10% and 3% under the same condition. This effect would clearly benefit the recirculation of hydrogen in the anode loop of the stack.  
(3) The performance of the Roots pump integrated in the PEM fuel cell system is between the performance of the pump working with air and helium. The absolute volume flowrate with mixture containing nitrogen fraction of 15% is less 158 L/min than that of with air, but is more 118 L/min than that of with helium for the Roots pump studied. This implies that the performance of the Roots pump can be evaluated on the test rig with air or helium.  
(4) The correlations of the volumetric efficiency and isentropic efficiency based on the CFD results are presented to reflect the general pattern of this kind of hydrogen pump. It is believed that these equations are very helpful to the design and operation control of the PEM fuel cell system.  
Further experimental test should consider the performance measurements of the Roots pump with working gas of the mixture same as the discharge from the stack outlet to correct the correlation and also the low temperature start test is worth to be carried out to research the effects of water vapor and liquid on the reliability of the Roots pump.  
Conceptualization, J.F., L.X., B.W. and H.W.; validation, L.X., B.W. and H.W.; investigation, J.F. and L.X.; resources, Z.X.; writing--original draft preparation, J.F. and L.X; writing--review and editing, J.F. All authors contributed to the final manuscript. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Key Research and Development Program of China (Grant No.2018 YFB0105303) and the Technology Innovation and Application Development Program of Chongqing [Grant No. cstc2019 scx-zdzdzrox0035]. The authors declare no conflict of interest.  
Figure 9: Comparison between the simulated and fitted value: (a) volumetric efficiency; (b) isentropic efficiency.
# Optical Bullets and Their Modulational Instability Analysis
## 3 Mathematical Analysis of the Model  
The investigation of the optical bullet solutions to the 3D-NLSS (2) demands reducing it to NLODE in order to apply the proposed solution techniques. To start, we consider the transformation of the traveling wave form given by  
\[\begin{split}\Upsilon(x,y,z,t)&=u(\xi)e^{i\Phi(x,y,z, t)},\\ \Gamma(x,y,z,t)&=v(\xi).\end{split} \tag{19}\]  
Here, the variable \(\xi\) is the traveling coordinate defined as  
\[\xi=\alpha x+\beta y+\gamma z+\
u t, \tag{20}\]  
and the function \(\Phi(x,y,z,t)\) stands for the phase component introduced as  
\[\Phi(x,y,t)=\lambda x+\eta y+\sigma z+\omega t, \tag{21}\]  
where \(\alpha,\beta,\gamma,\
u,\lambda,\eta,\sigma\), and \(\omega\) are arbitrary constants.  
Making use of the transformation (19), Equation (2) reduces to  
\[i(\
u-\alpha\eta-\beta\lambda-\alpha\sigma-\lambda\gamma)u^{\prime}-\alpha( \beta+\gamma)u^{\prime\prime}-(\omega-\lambda\eta-\lambda\sigma)u-uv=0, \tag{22}\]  
\[\alpha v^{\prime}=4(\beta+\gamma)uu^{\prime}, \tag{23}\]  
where \(u=u(\xi)\), and \(v=v(\xi)\). We deduce from integrating Equation (23) an equation of the form  
\[v=2\frac{(\beta+\gamma)}{\alpha}u^{2}, \tag{24}\]  
where the constant of integration is considered to be zero. The substitution of Equation (24) into Equation (22) yields  
\[i(\
u-\alpha\eta-\beta\lambda-\alpha\sigma-\lambda\gamma)u^{\prime}-\alpha( \beta+\gamma)u^{\prime\prime}-(\omega-\lambda\eta-\lambda\sigma)u-2\frac{( \beta+\gamma)}{\alpha}u^{3}=0. \tag{25}\]  
Separating the last equation into the real and imaginary parts results in  
\[\alpha(\beta+\gamma)u^{\prime\prime}+(\omega-\lambda\eta-\lambda \sigma)u+2\frac{(\beta+\gamma)}{\alpha}u^{3}=0, \tag{26}\] \[(\
u-\alpha\eta-\beta\lambda-\alpha\sigma-\lambda\gamma)u^{ \prime}=0. \tag{27}\]  
One can readily induce from Equation (27) the constraint condition given by  
\[\
u=\alpha\eta+\beta\lambda+\alpha\sigma+\lambda\gamma. \tag{28}\]  
Hence, the optical soliton solutions of the 3D-NLSS are retrieved by dealing with the elliptic-like equation presented as  
\[A_{0}u^{\prime\prime}+A_{1}u+A_{3}u^{3}=0, \tag{29}\]  
where the constants \(A_{0},A_{1},\) and \(A_{3}\) are defined by  
\[A_{0} =\alpha(\beta+\gamma), \tag{30}\] \[A_{1} =\omega-\lambda\eta-\lambda\sigma,\] (31) \[A_{3} =2\frac{(\beta+\gamma)}{\alpha}. \tag{32}\]
# Performance of Time Reversal Based Underwater Target Detection in Shallow Water
###### Abstract  
In this paper, a scheme based on the time reversal technique is proposed to improve the detection performance for detecting a cylindrical object bottoming at the seafloor in shallow water. When the time reversal technique is applied to the response of the clutter with the strong time-varying characteristic of shallow water, it is difficult to obtain a high peak response. However, in the case where a cylindrical object is placed on the seafloor because the time-invariant property of the target response is stronger than the time-varying property of the reverberation by the clutters, the time reversal technique can be applied to enhance the target signal. In this paper, it is demonstrated that the peak due to the target that is contacted at the seabed becomes higher when applying the time reversal technique. The performance is investigated by using numerical computation of the probability of detection for various probabilities of false alarm and computer simulation.  
underwater target detection; time reversal; active sonar; shallow water +
Footnote †: journal: Applied Sciences
# Novel Research and Application on Swarm Optimization and Bioinspired Optimization Algorithms
## 1 Introduction  
Biological behaviors have inspired the development of numerous evolutionary optimization algorithms. Among these, swarm intelligence algorithms (SIAs) represent a significant category. SIAs are stochastic global optimization methods that rely on the collective intelligence of swarms. Unlike traditional evolutionary algorithms, which use operators such as mutation, crossover, and replication, SIAs achieve optimization through the interplay of antagonistic and synergistic interactions among particles. In SIAs, an initial population of particles--each representing a potential solution--is generated randomly. Through a straightforward yet effective iterative mechanism, the algorithm refines random solutions, ultimately converging on a global optimal solution by following the most efficient path and minimizing associated costs. SIAs have found applications across diverse domains, including engineering, evolutionary computing, biomedicine, navigation and communication systems, environmental sciences, cryptography, real-time traffic control, and many other fields. An overview of SIAs is provided in [1].
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 5 Results
### Box-Counting Test  
Some techniques used to estimate the fractional dimension of a geometric object were discussed by [PERSON] (1963). In this work, the box-counting method is applied because of its simplicity, automatically calculable with the use of computer and applicable for patterns with or without self-similarity. In this test, each image or geometric object is covered by a sequence of squares of descendant measurements and for each of the squares two values are stored: the number of boxes intersected by the image \(N(l)\) and the lateral length of each frame \(l\). The regression slope \(D\) of the straight line formed by the plot of \(\log N(l)\) against \(\log 1/l\) indicates the degree of complexity (or fractional dimension) between 1 and 2; \(1\leq D\leq 2\) [PERSON] (1963). An image with a fractional dimension of 1 or 2 is considered completely differentiable. The linear regression used to estimate the fractional dimension [PERSON] (1963) is given by  
\[\log N(l)=\log K+D\log 1/l, \tag{7}\]  
where \(K\) is a constant and \(N(l)\) is proportional to \((1/l)^{-D}\). Clearing \(D\) from Equation (7) we obtain the following expression:  
\[D=\frac{\log N}{\log 1/l}. \tag{8}\]  
To perform this test, we return to the grid used to measure the percentage of coverage of the test area performed by the trajectories of the mobile robot generated by the algorithm designed in this work. The size of the grid with \(l=1\) will be taken for the case when the length of the work area (the dimensions of the work area are 1400 \(\times\) 2400 mm) is equal to the length of a grid square. We start with squares of size \(l=0.111\), which will be reduced until we obtain very small squares (boxes) of size \(l=0.0200\). We take the path of the robot generated with the initial conditions \((x_{0},y_{0},\theta_{0})=(0,0,0)\). Figure 12 shows two different grid sizes for the proposed work area: (a) 135 boxes of \(l=0.0667\) and (b) 476 boxes of \(l=0.0357\).  
After making 25 reductions of the grid and plotting \(\log N(l)\) against \(\log 1/l\), the linear regression of the data results in Figure 13. We can see behavior very similar to the line given by the equation \(y=1.7762x+0.0028\). Using Equation (8) for every grid size reduction we obtain \(D=1.7762\), which is a fractional dimension and therefore the trajectories are chaotic.  
Figure 12: Different grid sizes for the box-counting test: (**a**) 135 boxes of \(l=0.0667\); (**b**) 476 boxes of \(l=0.0357\).