esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Classic Atmospheric Optical Turbulence: Review
## 3 Theoretical Modeling of Optical Turbulence
### Coherent Turbulence  
A number of theoretical models have been proposed for accounting for turbulence containing coherence structures, which may be present in the close proximity of hard boundaries (see References [76; 77] and references wherein). Such coherent structures appear because of the temperature or pressure gradients, and are formed as spatial vortices. They have a longer lifespan as compared with classic turbulence eddies. The coherent structures also undergo the direct energy cascade process, being deterministic, unlike the classic turbulent cascade. A typical situation in which a solitary coherent eddy would be generated is an obstacle placed across the air flow. The associated power spectrum has the following form:  
\[\Phi_{n}(\kappa) =0.033C_{n}^{2}(6.6\kappa_{0})^{2(\
u-1/3)}\kappa^{-2(\
u+3/2)} \tag{38}\] \[\times\exp\left(-\frac{\kappa^{2}}{\kappa_{m}^{2}}\right)\Bigg{[} 1-\exp\left(-\frac{\kappa^{2}}{\kappa_{0}^{2}}\right)\Bigg{]}.\]  
Here, \(\
u=1/3\) and \(\
u=5/6\) correspond to Kolmogorov and coherent turbulence.
## 4 Conclusions  
This work investigated the enhancement of the optical clearing effect by laser irradiation in conjunction with a chemical penetration enhancer by using VIS-NIR spectrometer in vivo experiments. The conclusions are as follows:  
1. Mono-treatment of laser irradiation is less effective than combined treatment, and insignificant optical clearing effect is observed with the mono-treatment of thiazone.
2. Combined treatment with a lower dose of 1064 nm Nd:YAG laser irradiation achieves improved optical clearing effect. Under the effect of 0.5 J/cm\({}^{2}\) laser irradiation dose, the reflectance decreases by 25.2%, 23.7%, and 17.6% at 475, 575, and 615 nm, respectively.
3. The optimum laser irradiation dose exists in the combined treatment. The combined penetration enhancement at 0.7 J/cm\({}^{2}\) dose achieves the best optical clearing effect in only 10 min. The final optical clearing effect is also the best, decreasing by approximately 30% of the original value.  
Conceptualization, X.L. and B.C.; Methodology, B.C., and X.L.; Formal Analysis, X.L.; Investigation, X.L.; Data Curation, X.L.; Writing-Original Draft Preparation, X.L.; Writing-Review & Editing, B.C.; Supervision, B.C.; Project Administration, B.C.; Funding Acquisition, B.C.  
This research was funded by the National Natural Science Foundation of China, grant number 51727811.  
Thanks for the technical support of the Second Affiliated Hospital of Xi'an Jiaotong University.  
The authors declare no conflict of interest.
# Experimental Investigation of the BIPV System under Sanluurfa Meteorological Conditions
## 4 Conclusions  
This study investigated the enhancement of BIPV system efficiency through new technologies and various parameters under Sanluurfa conditions. The results of the experimental setup conducted between February and July were found to be consistent with the experiment results under the same conditions in the PVsyst software--PVsyst 7.4.  
One of the most significant findings in the study is that the bifacial panel on the south facade always operated at a higher efficiency compared with the monofacial panel on the same facade. In the PVsyst analysis, when comparing the total annual electrical energies produced by the bifacial panel and the monofacial panel on the south facade, the bifacial panel produced 5.86% more electrical energy. However, in the experimental setup over 6 months, the bifacial panel provided 15.1% higher production than the monofacial panel. When compared with the PVsyst analysis, the gain of 9.24% in the experimental setup was attributed to the reflective surface used in the experimental setup, which increased the amount of radiation reflected from the building wall, thus resulting in increased efficiency. Furthermore, in the study conducted on 5 May 2023, considering the amount of incoming radiation, it was found that among the four panels, the bifacial panel had the highest efficiency.  
During the winter months, solar radiation coming from a more southern direction is expected to increase both electrical energy production and thermal energy production. For this reason, it was determined that it is appropriate to install the BIPV/T system on the south facade during winter under the conditions of Sanlhurfa. For the BIPV system, the direction with the best production was observed to be the horizontally oriented roof, both in the experimental setup and in the PVsyst analysis. Considering that the bifacial panel, in conjunction with its reflective wall surface, achieves a higher electrical efficiency compared with the monofacial panel, it was concluded that the best outcome under the conditions of Sanlhurfa would be a BIPV system implemented on the roof using a bifacial panel.  
Conceptualization, M.A.A.; Methodology, Y.C.D.; Validation, Y.C.D.; Formal analysis, Y.C.D.; Investigation, Y.C.D.; Resources, Y.C.D.; Writing--original draft, Y.C.D.; Writing--review & editing, Y.C.D. and M.A.A.; Visualization, Y.C.D.; Supervision, M.A.A.; Project administration, M.A.A.; Funding acquisition, M.A.A. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Scientific Research Projects unit of Harran University (Project number 22187).  
Not applicable.  
The data used in this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 5 Summary  
To address the problems of multiclass imbalance and concept drift in data streams, this paper proposed a multi-class imbalanced data stream classification algorithm based on hybrid sampling with dynamic weighting (HSDW-MI). HSDW-MI processes data streams in two phases. In the hybrid sampling phase, adaptive spectral clustering is used to maintain the original data distribution after clustering and sampling. By calculating the safety level of each sample using the sample safety factor, the classifier's ability to identify both minority and majority classes is effectively improved. To handle extremely imbalanced and variable data streams, a sample storage pool is created to store samples with high safety factor during the training process. Samples are extracted from the pool and added to the data stream when the number of minority class samples is insufficient. In the dynamic weighting phase, a dynamic weighting method based on G-mean values is proposed, with G-mean values serving as the weights of the base classifier. The ensemble is dynamically updated during data stream processing by adding new base classifiers and timely removing old, unqualified ones, which allows the algorithm to adapt to concept drift. Experiments verify that the proposed HSDW-MI exhibits superior classification capabilities and more Stable performance than other algorithms.  
Despite its merits, the algorithm still has some limitations due to the complexity of multi-class imbalanced data streams. Although HSDW-MI can recover quickly after a concept drift occurrence, its performance degrades more at the concept drift point compared to other algorithms. Additionally, calculating the sample safety factor is time-consuming. Future research will incorporate a drift detector into the algorithm, enabling it to address  
Figure 5: Comparison algorithm of macro-Recall on the stationary stream. (**a**) ImbSta_Extreme_SG stream; (**b**) ImbSta_Extreme_SIG.  
Figure 6: Comparison algorithm of macro-Recall on the variable stream. (**a**) ImbVar_Extreme_SG stream; (**b**) ImbVar_Extreme_SIG.  
concept drift upon detection, and refine the sample safety factor calculation formula to reduce time complexity.  
Conceptualization, M.H.; methodology, A.L.; software, A.L.; validation, Z.G. and S.L.; formal analysis, Z.G.; investigation, A.L.; resources, D.M.; data curation, A.L.; writing--original draft preparation, A.L.; writing--review and editing, M.H.; visualization, D.M.; supervision, S.L.; project administration, M.H.; funding acquisition, M.H. and A.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Nature Science Foundation of China (62062004), the Ningxia Natural Science Foundation Project (2022 AAC03279) and the Graduate Innovation Project of North Minzu University (YCX22191).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.  
The authors declare no conflict of interest.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 2 Design Specifications
### Miniaturization
#### 2.2.1 High Permittivity Dielectric Substrate or Superstrate  
High permittivity substrate/superstrate material can shift the antenna resonant frequency near to the lower frequency band, which shortens the operation wavelength [2]. This simple technique can provide a higher degree of miniaturization. Some of the materials, which are generally used as a dielectric substrate for in-body antennas, include alumina ceramic (relative permittivity, \(\varepsilon_{\mathrm{r}}=9.4\)) [26; 27] and Rogers 3210 [28], 3010 [29], 6002 [30] with \(\varepsilon_{\mathrm{r}}=10.2\). A substantial reduction in effective antenna length is realized by using MgTa\({}_{1.5}\)Nb\({}_{0.5}\)O\({}_{6}\) as a dielectric substrate, with the dielectric constant \(\varepsilon_{\mathrm{r}}=28\)[31]. However, this method results in a significant level of surface wave excitation within the substrate. This results in lower bandwidth and a decrease in overall radiation efficiency [32]. The higher cost of these materials is another issue.
## 4 Results and Discussion
### Results of Single Parameter Evaluation
#### 4.1.6 Impact of Vadose Zone  
The unsaturated zone, also known as the vadose zone, refers to the area between the Earth's surface and the water table. The type of vadose zone medium plays a crucial role in controlling water exchange between the soil layer and aquifer, the migration and transformation of pollutants, as well as various physicochemical and biological processes [43]. Additionally, the vadose zone medium determines the length and path of percolation. Therefore, it serves as an important indicator for evaluating groundwater vulnerability.  
In the plain area of the study region, the vadose zone mainly consists of Quaternary sediments, which can be categorized into nine types. When assessing the self-purification capacity of the vadose zone, factors such as particle size and permeability of the Quaternary sediments are primarily considered. The assigned values range from 1 to 10. In the mountain aquifers, the vadose zone comprises regolith with varying degrees of metamorphic rocks and igneous rocks, as well as dissolution layers of dolomites and limestone in karst areas. Based on vulnerability assessments conducted in different mountainous regions in China, the medium score for the vadose zone in bedrock regolith is determined to be 4 points.  
This determination takes into account the extent of fracturing and dissolution observed in the surface regolith of the Tangshan mountain area, finally showed in Figure 3f. For dolomite or limestone vadose zones, the medium score is set at 8 points.
## 6 Simulation Results and Discussion  
Once the neural network was trained, the receiver performance was verified using ten million of new random bit triplets with different SNRs (from \(-20\) dB to \(+20\) dB), and a Rayleigh fading channel was considered between the transmitter and the receiver. Figure 5 shows the precision in the predictions for SVD-SCMA and phase rotation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Codewords (M)** & **8** \\ \hline Users per subcarrier (\(d_{f}\)) & 3 \\ SNR range & From \(-20\) dB up to \(+20\) dB \\ Channel & Rayleigh fading channel \\ Total training data set & 50 times the data set shown in Equation (12) \\ Iterations & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Configuration parameters of the training stage.  
Figure 4: Neural network learning precision.  
To achieve 98% accuracy, SVD-SCMA requires an SNR of +5 dB, while phase rotation requires an SNR of +8 dB. Figure 4 shows that SVD-SCMA performs better under adverse SNR conditions (between \(-15\) and +8 dB).  
Figure 6 shows the BER performance comparison for SVD-SCMA and phase rotation; additionally, the SVD-SCMA performance using MPA (red line) is shown. It can be seen, from Figure 6, that SVD-SCMA has a better performance under adverse noise conditions (between \(-15\) dB and +8 dB SNR); to achieve 2% errors, SVD-SCMA needs a SNR equal to +5 dB while phase rotation requires +8 dB. For SNRs close to +20 dB, both methods have a similar performance. On the other hand, it is appreciated that MPA begins to decode from an SNR of +3 dB; this is due to the high complexity of the IQ constellation since it could exist up to 512 different values.  
SVD-SCMA and phase rotation methods rely on the design of codebooks using only one transmitting antenna and one receiving antenna. On the other hand, the authors in [14; 15; 16] improved the spectral efficiency using SM and MIMO SCMA, which requires multiple antennas for the transmission and reception of the signals. In these methods the user bit stream is divided into two parts; one of them selects the antenna and the other part is mapped to codewords. Figure 7 shows the comparison of the method proposed in this work (SVD-SCMA) and the results shown in [16] for SM-SCMA and RGSM-SCMA using different numbers of antennas.  
Figure 5: Precision of predictions.  
Figure 6: Bit error rate (BER) performance comparison.  
As shown in this figure, SVD-SCMA performs better than SM-SCMA and RGSM-SCMA with one and two receiving antennas. In the range from 0 to +9 dB, SVD-SCMA has a better performance than SM-SCMA and RGSM-SCMA, and for better channel conditions (above 8 dB SNR) the methods with four antennas have a better BER. It should be noted that SVD-SCMA uses a neural network-based receiver while SM-SCMA and RGSM-SCMA use MPA and ML for signal decoding.
# Privacy-Friendly Datasets of Synthetic Fingerprints for Evaluation of Biometric Algorithms
## 4 Experimental Studies
### Reconstruction Success  
Second, we report the success of fingerprint reconstruction. For that, the original fingerprints from the test sets are compared with their reconstructed counterparts by means of the Verifinger matching algorithm. Referring to the metrics introduced in Section 4.1.1, TAR Type 1 is used. The reconstruction rates are reported in Table 3 for both the idealistic Anguli fingerprints (an expected upper bound of reconstruction rates) and very challenging URU fingerprints taken from the FVC2004 DB2 A+B dataset (the estimation of pessimistic reconstruction rates). An important point to note is that URU samples can be quite challenging for minutiae extractors. Therefore, we have dropped the samples where Verifinger fails to extract even a single minutiae point.  
We can also observe that NFIQ2 score distributions of fingerprints reconstructed from URU samples have tails at the lower end. We suspect that this is due to the low quality of some URU samples, where the minutiae extractors may not be able to extract minutiae properly, resulting in incomplete or inaccurate patterns.  
The reason for the low reconstruction rates with the **aug39k_DL_BN_60+60** model and the URU fingerprints is the evaluation run in which zero-minutiae samples have not been dropped, causing a high portion of failed matching trials.  
Figure 7: NFIQ2 score distributions for Anguli (**left column**) and URU (**right column**) fingerprints and their reconstructed counterparts with the **aug39k_DL_IN (top row)** and **aug39k_PM_IN (bottom row)** models, i.e., their snapshots at 15, 30, and 55 training epochs. DL—“directed line” encoding, PM—”pointing minutiae” encoding.  
Based on the reconstruction rates, we arrive at the following statements, which apply to both test datasets: Anguli and FVC2004 DB2 A+B:  
* The \"pointing minutiae\" encoding outperforms the \"directed line\" encoding;
* The best reconstruction performance is achieved with the model snapshots at 15 training epochs;
* For the \"pointing minutiae\" encoding, there is almost no difference between snapshots at 15 epochs and 30 epochs, while there is a notable performance loss with the snapshot at 55 epochs;
* For \"directed line\" encoding, the additional training epochs worsen the reconstruction performance, i.e., the snapshot at 15 epochs is better than at 30 epochs, which is better than at 55 epochs.
# A Self-Consistent Physical Model of the Bubbles in a Gas Solid Two-Phase Flow
## 2 Theoretical Model
### Bubble Size and Velocity Equation  
For such a thermodynamic system with the adiabatic model, the pressure inside the bubbles is related to the initial state of the bubbles and the volume of the bubbles. Therefore, the relationship between the bubble pressure \(P\) and the volume \(V\) is [19]  
\[P=P_{c}+P_{0}(\frac{V_{0}}{V})^{\gamma}, \tag{14}\]  
where \(P_{0}\) and \(V_{0}\) are respectively the initial pressure and volume of bubbles, and \(P_{c}\) is the saturated pressure. The simulation model of the bubbles is approximately considered as the sphere model. The relationship of the bubble volume \(V\) with the rising velocity \(u\) and the height \(Z\) is obtained through the formula of sphere volume and diameter size, which is  
\[V=\frac{\pi}{6}[0.54(u-u_{f})^{0.4}(Z+4\sqrt{A_{0}})^{0.8}g^{0.2}]^{3}. \tag{15}\]  
Based on the above deductions, \(u\) as the function of \(Z\) follows, we have  
\[\begin{array}{c}P_{0}\Big{|}V_{0}^{\gamma}-(\frac{\pi}{6})^{\gamma}[0.54(u-u _{f})^{0.4}(Z+4\sqrt{A_{0}})^{0.8}g^{0.2}]^{3}\gamma\Big{|}=\\ Z(\frac{\pi}{6})^{\gamma}\Big{[}150\frac{(1-\varepsilon)^{2}u_{b}u}{\varepsilon^ {3}(\rho_{b}d_{p})^{2}}+1.75\frac{(1-\varepsilon)\rho_{f}u^{2}}{\varepsilon^ {3}\rho_{b}d_{p}}\Big{]}\times[0.54(u-u_{f})^{0.4}(Z+4\sqrt{A_{0}})^{0.8}g^{0. 2}]^{3\gamma}.\end{array} \tag{16}\]  
Through self-consistent solving Equations (11), (12) and (14)-(16), the change of the air velocity \(u\) with regard to the rising height \(Z\) in the gas-solid two-phase flow can be obtained as shown in Figure 1. Then, the distribution of the bubble diameter \(d_{b}(Z)\), as well as the distribution of the velocity of characteristic bubbles at the specific height \(Z\) in the gas-solid two-phase flow are as follows:  
\[u_{b}(Z)=0.711\sqrt{gd_{b}(Z)}+u-u_{f}. \tag{17}\]  
In the gas-solid two-phase flow system, the distributions and properties of the solid phase lead to the pressure and temperature variations of the bubble phase, and then result in the changes of the bubble heat capacity ratio \(\gamma\). Moreover, the changes of the heat capacity ratio \(\gamma\) affect the distributions and variations of the solid phases, and thus strongly influence the property of the gas-solid two-phase flow system. The solid and gaseous phases in the system represent a self-consistent system, and mutually affect and restrict each other.
# Coronary Centerline Extraction from CCTA Using 3D-UNet
###### Abstract  
The mesh-type coronary model, obtained from three-dimensional reconstruction using the sequence of images produced by computed tomography (CT), can be used to obtain useful diagnostic information, such as extracting the projection of the lumen (planar development along an artery). In this paper, we have focused on automated coronary centerline extraction from cardiac computed tomography angiography (CCTA) proposing a 3D version of U-Net architecture, trained with a novel loss function and with augmented patches. We have obtained promising results for accuracy (between 90-95%) and overlap (between 90-94%) with various network training configurations on the data from the Rotterdam Coronary Artery Centerline Extraction benchmark. We have also demonstrated the ability of the proposed network to learn despite the huge class imbalance and sparse annotation present in the training data.  
CCTA; centerline; coronary artery segmentation; deep learning; U-NET +
Footnote †: journal:  
0000-0001-9991-4000-0000-0000-0000-0000-00000-00000-00000-00000-00000-00000-00000-000000-000000-000000-00000000000000000000  
Assigning labels to voxels in biomedical applications has been a cornerstone in medical image processing, with most of the fully automated segmentation techniques being atlas based, in which a set of expert presegmented cases stand as a pillar for high-accuracy results [7].  
The proposed method presented in this paper is a machine learning centerline extraction algorithm that uses a proposed 3D adaptation of the U-NET architecture, which outputs the probability for each voxel of a full volume to be part of a vessel centerline. Additionally, based on an extensive state-of-the-art review, an adapted loss function was proposed to handle sparse annotation, meaning that not all the centerlines are part of the ground truth of the dataset and class imbalance since very few voxels from an entire volume belong to a centerline.  
The paper is organized as follows: Section 2 provides a brief description of the current state of the art methods in coronary centerline extraction, followed by Section 3 presenting our approach using a 3D-UNET neural network; based on data publicly available, the obtained results are illustrated in Section 4 and discussed in Section 5, together with the final conclusions presented in the last section.
## 1 Introduction
In the SCS method, in contrast to the CCS method and other older methods that use an evolving grid of CSs [33; 34; 35; 36], the CSs grid remains constant throughout the imaginary and real-time simulations. To simulate two-electron systems, the SCS method uses the same algorithm used in FCCS-II for symmetrizing the CSs grid [31; 32]. In this report, after reviewing CSs and their mathematical formulations, we introduce the SCS method. Importantly, we account for nuclear dynamics classically, by solving the Newtonian equations of motion. We apply SCS to compute the ground state of H\({}_{2}^{+}\) at a fixed initial inter-nuclear distance by propagating the TDSE in imaginary time. Implementing the Gram-Schmidt algorithm on the basis of SCS, the first excited state of the system at the same inter-nuclear distance is achieved. By treating the two nuclei dynamically, these two states are propagated in real time in the absence of an external laser field. Consequently, the potential energy curves of the first two electronic states of the system are achieved. Moreover, to investigate the charge migration between the ground state and the first excited state in a laser-induced H\({}_{2}^{+}\), we repeat the real-time propagation for the ground state at the initial inter-nuclear distance in the presence of attosecond laser pulses with different intensities.