esa-sceva/satcom-chunk-collection huggingface dataset:
# Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
###### Abstract  
In this study, we used machine learning (ML) to estimate time-lapse velocity variations in a reservoir region using seismic data. To accomplish this task, we needed an adequate training set that could map seismic data to velocity perturbation. We generated a synthetic seismic database by simulating reservoirs of varying velocities using a 2D velocity model typical of the Brazilian pre-salt ocean bottom node (OBN) acquisition, located in the Santos basin, Brazil. The largest velocity change in the injector well was around 3% of the empirical velocity model, which mimicked a realistic scenario. The acquisition geometry was formed by the geometry of 1 shot and 49 receivers. For each synthetic reservoir, the corresponding seismic data were obtained by estimating a one-shot forward-wave propagation using acoustic approximation. We studied the reservoir illumination to optimize the input data of the ML inversion. We split the set of synthetic reservoirs into two subsets: training (80%) and testing (20%) sets. We point out that the ML inversion was restricted to the reservoir zone, which means that it was inversion-oriented to a target. We obtained a good similarity between true and ML-inverted reservoir anomalies. The similarity diminished for a situation with non-repeatability noise.  
four-dimensional seismic; OBN acquisition; seismic inversion; reservoir anomaly; neural networks +
Footnote †: journal: Applied  
_Article_  
Single-Shot Time-Lapse Target-Oriented Velocity Inversion Using Machine Learning
## 3 Results and Discussions
### Volatile Profile of RAPT with Various Years of Storage
Overall, the volatile profile of RAPT may experience a significant shift after 3-5 years of storage, representing the first turning point, followed by another notable transformation after 10 years of storage, potentially signifying the second turning point. The loading plot (Figure S2) indicated that volatile compounds underwent significant changesduring the storage period, particularly in linalool, \(\alpha\)-terpineol, 3,7-dimethyl-1,5,7-ocatrien-3-ol, geraniol, cis-5-ethenyltetrahydro-\(\alpha\),\(\alpha\),5-trimethyl-2-furanmethanol, and (R)-5,6,7,7a-tetrahydro-4,4,7a-trimethyl-2(4H)-benzofuranone. These results further demonstrated the non-negligible influence of storage on the volatile composition of RAPT.
## 3 Structure of MRAS
### State Space Equation of the Error  
The state space equation of the error can be obtained by calculating the difference between the reference model (see Equation (2)) and the adjustable model (see Equation (3)). Then:  
\[\left\{\begin{array}{l}\dot{\mathbf{e}}=\dot{I}-\dot{\hat{I}}=A\mathbf{e}-\mathbf{w}\\ \mathbf{w}=\left(-\Delta A\hat{I}-\Delta BU-\Delta C\right)\end{array}\right. \tag{4}\]  
where \(\mathbf{e}=\begin{bmatrix}e_{d}&e_{q}\end{bmatrix}^{T}=\begin{bmatrix}I_{d}-\hat{ I}_{d}&I_{q}-\hat{I}_{q}\end{bmatrix}^{T}\), \(\Delta A=A-\hat{A}\), \(\Delta B=B-\hat{B}\), \(\Delta C=C-\hat{C}\). The system described by Equation (4) can be regarded as a feedback system, and its system  
Figure 1: MRAS system block diagram.  
block diagram is shown in Figure 2. In Figure 2, the dark blue part is a linear time-invariant forward channel \(\dot{\mathbf{e}}=A\mathbf{e}-\mathbf{w}\), and the rest is a non-linear time-varying system that is used to calculate the variable \(\mathbf{w}\).  
To make the system Equation (4) stable, i.e., \(\mathbf{e}\) converge to zero, [PERSON]'s hyperstability theory is employed here. According to [PERSON]'s hyperstability theory, two prerequisites are required to make Equation (4) asymptotically stable.  
The first condition is the transfer function matrix of the linear time-invariant forward channel needs to be strictly positive and real. It is known from [8] that condition #1 holds when the inductances of \(d\) and \(q\) axes are equal.  
The second one is the non-linear time-varying feedback link \(\mathbf{w}\) needs to satisfy [PERSON]'s integral inequality, that is,  
\[\eta(0,t_{1})=\int_{0}^{t_{1}}\mathbf{e}^{T}\mathbf{w}dt\geq-{r_{0}}^{2} \tag{5}\]  
where \(\mathbf{e}^{T}\) and \(\mathbf{w}\) are the output and feedback quantity of the error state equation in Equation (4), respectively; \(r_{0}\) is an arbitrary finite constant independent of time \(t\).  
Substituting Equation (4) into Equation (5), we have  
\[\left\{\begin{array}{l}\int_{0}^{t_{1}}\left(b-\hat{b}\right)\left[e_{d} \left(R_{s}\hat{I}_{d}-U_{d}\right)+e_{q}\left(R_{s}\hat{I}_{q}-U_{q}\right) \right]\geq-{r_{1}}^{2}\\ \int_{0}^{t_{1}}(c-\hat{c})e_{q}\omega_{c}dt\geq-{r_{2}}^{2}\end{array}\right. \tag{6}\]  
where \(b=\frac{1}{L}\), \(\hat{b}=\frac{1}{L}\), \(c=\frac{\hat{\mathbf{v}}_{f}}{L}\), \(\hat{c}=\frac{\hat{\mathbf{v}}_{f}}{L}\); \(r_{1}\) and \(r_{2}\) are arbitrary finite constants independent of time \(t\), respectively.
# Long-Wavelength VCSELs: Status and Prospects
## 6 VCSELs with Hybrid (Metal-Dielectric) Distributed Bragg Reflectors
The active region was based on six In\({}_{0.63}\)Ga\({}_{0.31}\)Al\({}_{0.06}\)As QWs with a thickness of 6-7 nm In\({}_{0.50}\)Ga\({}_{0.24}\)Al\({}_{0.26}\)As barriers. As discussed above, the large aluminum content in the active region caused an increase of the PL half-width at low temperatures from 4.5 meV (for 1550 nm VCSELs) to 8 meV [30]. Moreover, due to the lower refraction index contrast as compared to 1550 nm spectral range lasers, the authors had to increase the number of pairs in the semiconductor DBR to 42, which limited the output power to 0.4 mW [30]. A switch to a design with semiconductor top DBRs replaced by dielectric ones [31; 69; 123; 124; 125] increased the output power to 4.6 mW for 4 \(\upmu\)m BTJ VCSELs. Increasing the aperture diameter to 6.5 \(\upmu\)m increased the output power to 4.8 mW [126], and a record modulation bandwidth of 15 GHz for 4 \(\upmu\)m VCSELs [123; 124] was reached. Later, the authors increased this value to 17 GHz [127; 128], showing a SM output power of 4 mW. Instead of using InAlGaAs QWs, GaInAsP QWs and AlGaInAsP barriers were considered [75]. AlGaInAs has a larger conduction band and a lower valence band offset compared to GaInAsP. As a result, InAlGaAs lasers show improved temperature stability. However, until now, GaInAsP has demonstrated a larger radiative recombination rate as compared to InAlGaAs, probably due to deeper impurity levels. Consequently, the advantages of both systems using GaInAsP QWs and AlGaInAsP were combined. The half-width of the low-temperature PL of test structures in the active region increased to 12 meV as compared to 8 meV for InAlGaAs QWs [30], indicating growing alloy disorder. The lasers showed modest characteristics: single-mode output power of about 1 mW and a maximum modulation bandwidth of about 7.5 GHz.
## Characterization of Transition Metal Oxide/Silicon Heterojunctions for Solar Cell Applications  
**[PERSON] *, [PERSON] \({}^{\dagger}\), [PERSON], [PERSON] and [PERSON]**  
Electronic Engineering Department, Polytechnic University of Catalonia, Barcelona 08034, Spain;  
E-Mails: [EMAIL_ADDRESS] (S.M.); [EMAIL_ADDRESS] (C.V.);  
[EMAIL_ADDRESS] (R.A.); [EMAIL_ADDRESS] (J.P.)  
\({}^{\dagger}\) Current Address: Department of Applied Physics, Indian School of Mines, Dhanbad 826004, India  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-93-401-1002; Fax: +34-93-401-6756.  
Academic Editor: [PERSON]  
_Received: 15 September 2015 / Accepted: 1 October 2015 / Published: 10 October 2015_  
**Abstract:** During the last decade, transition metal oxides have been actively investigated as hole- and electron-selective materials in organic electronics due to their low-cost processing. In this study, four transition metal oxides (V\({}_{2}\)O\({}_{5}\), MoO\({}_{3}\), WO\({}_{3}\), and ReO\({}_{3}\)) with high work functions (\(>\)5 eV) were thermally evaporated as front p-type contacts in planar n-type crystalline silicon heterojunction solar cells. The concentration of oxygen vacancies in MoO\({}_{3-x}\) was found to be dependent on film thickness and redox conditions, as determined by X-ray Photoelectron Spectroscopy. Transfer length method measurements of oxide films deposited on glass yielded high sheet resistances (\(\sim\)10\({}^{9}\)\(\Omega\)/sq), although lower values (\(\sim\)10\({}^{4}\)\(\Omega\)/sq) were measured for oxides deposited on silicon, indicating the presence of an inversion (hole rich) layer. Of the four oxide/silicon solar cells, ReO\({}_{3}\) was found to be unstable upon air exposure, while V\({}_{2}\)O\({}_{5}\) achieved the highest open-circuit voltage (593 mV) and conversion efficiency (12.7%), followed by MoO\({}_{3}\) (581 mV, 12.6%) and WO\({}_{3}\) (570 mV, 11.8%). A short-circuit current gain of \(\sim\)0.5 mA/cm\({}^{2}\) was obtained when compared to a reference amorphous silicon contact, as expected from a wider energy bandgap. Overall, these results support the viability of a simplified solar cell design, processed at low temperature and without dopants.  
keywords:transition metal oxides; silicon heterojunction solar cells; vanadium oxide; molybdenum oxide; tungsten oxide;henium oxide +
Footnote †: journal: Journal of the American Chemical Society
## Appendix C  
As shown in Figure A2, according to Equation (A1), the compliance matrix of the point \(o_{p1}\) is \(C_{o_{p1}}\). Then, after being converted to point \(o_{p2}\), it is given as:  
\[C_{o_{p1-2}}=P(r_{1})C_{o_{p1}}P(r_{1})^{T},\] (A5)  
wherein \(r_{1}=(-v_{1},0,0)\) and \(v_{1}\) is the distance between point \(o_{p1}\) and point \(o_{p2}\).  
Figure A2: Double parallel rectangular beam hinges (local).  
On the point \(o_{p8}\), point \(o_{p6}\), point \(o_{p7}\), and point \(o_{p8}\) are connected in series, as follows:  
\[C_{o_{p8}}=C_{o_{p6-8}}+C_{o_{p7-8}}+C_{o_{p6}}. \tag{14}\]  
As shown in Figure 15, the point \(o_{p8}\) is converted to point \(o_{w2}\), as follows:  
\[C_{o_{w2}}=R_{x}\bigg{(}\frac{\pi}{2}\bigg{)}R_{z}\bigg{(}\frac{\pi}{2}\bigg{)} C_{o_{p7}}R_{z}\bigg{(}\frac{\pi}{2}\bigg{)}^{T}R_{x}\bigg{(}\frac{\pi}{2} \bigg{)}^{T}. \tag{15}\]  
The point \(o_{w2}\) and point \(o_{w2}\) are converted to point \(o_{w3}\) via a translation transformation and are connected in parallel. The point \(o_{w1}\) is converted to point \(o_{w3}\) and connected with two displacement transmission units in series, as follows:  
\[C_{o_{w2-3}}=P(r_{6})C_{o_{w2}}P(r_{6})^{T}, \tag{16}\]  
\[C_{o_{w2^{\prime}-3}}=P(r_{7})C_{o_{w3}}P(r_{7})^{T}, \tag{17}\]  
\[C_{o_{w1-3}}=P(r_{8})C_{o_{w3}}P(r_{8})^{T}, \tag{18}\]  
\[C_{1}=\bigg{[}(C_{o_{w2-3}})^{-1}+\Big{(}C_{o_{w2^{\prime}-3}}\Big{)}^{-1} \bigg{]}^{-1}+C_{o_{w1-3}}, \tag{19}\]  
wherein \(r_{6}=(0,\ -h_{4}\,\ 0)\), and \(h_{4}\) is the distance between point \(o_{w2}\) and point \(o_{w3}\); \(r_{7}=(0,h_{5},\ 0)\) and \(h_{5}\) is the distance between point \(o_{w2^{\prime}}\) and point \(o_{w3}\); \(r_{8}=(v_{4},-h_{6},\ 0)\), \(v_{4}\) is the distance between point \(o_{w1^{\prime}}\) and point \(o_{w3}\), and \(h_{6}\) is the distance between point \(o_{w1}\) and point \(o_{w1^{\prime}}\).  
Figure 15: Global coordinate conversion diagram of \(C_{1}\).  
\(C_{w}\) is the compliance matrix of the device, given as:  
\[C_{w}=\Big{[}(C_{w_{1}})^{-1}+(C_{w_{2}})^{-1}+(C_{w_{3}})^{-1}+(C_{w_{4}})^{-1} \Big{]}^{-1}. \tag{10}\]
# A Hybrid Metaheuristic for Multi-Objective Scientific Workflow Scheduling in a Cloud Environment
## 5 Performance Evaluation
### Evaluation Metrics
#### 5.2.2 Hypervolume (\(HV\))  
This metric indicates the volume of the objective space covered between the obtained Pareto front \(X\) and a reference point [45]. For calculating hypervolume, first a vector of non-dominated solutions is generated as an approximation of the actual Pareto front, while the solutions dominated by this vector are discarded. A hypercube \(hc_{i}\) is created for each non-dominated solution \(i\in X\) obtained by the algorithms. Then, a union of all hypercubes is taken. \(HV\) is mathematically given in Equation (29).  
\[HV=\cup_{i=1}^{|X|}\{hc_{i}\} \tag{29}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Workflow** & **Number of Nodes** & **Number of Edges** & **Mean Data Size (MB)** \\ \hline Montage\_25 & 25 & 95 & 3.43 \\ Montage\_50 & 50 & 206 & 3.36 \\ Montage\_100 & 100 & 433 & 3.23 \\ Montage\_1000 & 1000 & 4485 & 3.21 \\ CyberShake\_30 & 30 & 112 & 747.48 \\ CyberShake\_50 & 50 & 188 & 864.74 \\ CyberShake\_100 & 100 & 380 & 849.60 \\ CyberShake\_100 & 1000 & 3988 & 102.29 \\ Epigenomics\_24 & 24 & 75 & 116.20 \\ Epigenomics\_46 & 46 & 148 & 104.81 \\ Epigenomics\_100 & 100 & 322 & 395.10 \\ Epigenomics\_997 & 997 & 3228 & 388.59 \\ LIGO\_30 & 30 & 95 & 9.00 \\ LIGO\_50 & 50 & 160 & 9.16 \\ LIGO\_100 & 100 & 319 & 8.93 \\ LIGO\_1000 & 1000 & 3246 & 8.90 \\ SIPHT\_30 & 30 & 91 & 7.73 \\ SIPHT\_60 & 60 & 198 & 6.95 \\ SIPHT\_100 & 100 & 335 & 6.27 \\ SIPHT\_1000 & 1000 & 3528 & 5.91 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Characteristics of the benchmark workflows.  
where \(hc_{i}\) is the hypercube for each solution \(X_{i}\). This metric is useful for providing combined information about the convergence and diversity of the Pareto optimal solutions. Algorithms that result in solutions with a large value of \(HV\) are desirable, because a larger value signifies that the solution set is close to the Pareto front and also has a good distribution. A result of \(HV=0\) indicates that there is no solution close to the true Pareto front and the corresponding algorithm fails to produce the optimal solution set. For the purpose of comparison between algorithms, the objective values of the obtained solutions are separately normalized between the interval [0, 1], with 1 representing the optimal value, before calculating the \(HV\). In this study, a reference point (1, 1, 1) is selected in the calculations of \(HV\).
# Double-Peaked Mid-Infrared Generation Based on Intracavity Difference Frequency Generation
## 4 Conclusions  
The TFBG was chosen to suppress the intensity at the central wavelength of the ASE fiber source to generate a double-peaked pump source. The intracavity DFG of the double-peaked pump source generated the tunable double-peaked mid-infrared laser. The  
Figure 6: Intracavity DFG generates a double-peaked idler beam with the primary peak at (**a**) 3725 nm and (**b**) 3753 nm.  
primary peak of the mid-infrared generation was located at 3695-3747 nm. Under the phase-matching condition, the spacing of the two peaks was 23-37 nm, and the FWHM of each peak was 20-30 nm. The power of the double-peaked idler beam was 0.18 W. The pump-to-idler conversion efficiency was 8.5%. With the increase in phase-mismatching during the intracavity DFG, the FWHM of the double-peaked idler spectrum gradually decreased. The intensity of the secondary peak in the short wavelength decreased during the tuning in the short wavelength.  
Similarly, the intensity of the long wavelength's secondary peaks decreased during the extended wavelength tuning. This was demonstrated by modulating the pump spectrum and combining it with the intracavity DFG. The double-peaked mid-infrared laser was generated with a tunable central wavelength, controllable peak spacing, and controllable peak FWHM. It was shown to be promising in realizing the absorption and analysis of gas molecules by DIAL.  
Conceptualization, X.L. and P.W.; methodology, X.L.; software, K.W. and P.W.; validation, X.L., W.H., and K.W.; formal analysis, W.H.; investigation, Z.W.; resources, W.H.; data curation, X.L.; writing--original draft preparation, K.W.; writing--review and editing, K.W., P.W., W.H., and X.L.; visualization, Z.W.; supervision, W.H.; project administration, K.H.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 61975236), Project 2019-JCJCJ-JJ-202 and the Open Research Fund of State Key Laboratory of Pulsed Power Laser Technology, Electronic Countermeasure Institute, National University of Defense Technology (SKL2020 ZR03). Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors thank Nanjing University, Zhejiang University, and Fujian Institute of Research on the structure for offering nonlinear crystals. The authors declare no conflict of interest.
## 2 Unsaturated Subgrade Filler Triaxial Testing System
### Analysis of Experimental Results
#### 3.4.1 Sample Volume Change
In Figure 10a, under the lower confining pressure of 5 kPa, deviatoric stress increases significantly with axial strain, peaking at around 3% axial strain before decreasing, which indicates sample softening. As the moisture content increases, the peak deviatoric stress decreases slightly, and the sample shows varying degrees of softening during subsequent strain, potentially due to deformation and shear expansion under high moisture conditions and low confining pressures. As shown in Figure 10b, under the higher 200 kPa net confining pressure, deviatoric stress follows a different trend with increasing axial strain. For samples with initial moisture content below 13.42%, deviatoric stress initially increases and then slightly decreases, exhibiting shear compression and softening. Under high confining pressure, samples with lower moisture content show more pronounced shear compression. For samples with moisture content above 13.42%, deviatoric stress continuously increases with axial strain, demonstrating strong shear resistance with no noticeable softening, indicating that higher moisture content enhances shear strength and prevents softening. In general, under both 5 kPa and 200 kPa net confining pressures, shear strength increases as moisture content decreases. Low moisture content increases the sample's suction, enhancing shear strength through the effect of confining pressure. Particularly under the higher 200 kPa confining pressure, samples with lower moisture content exhibit higher shear strength, while those with higher moisture content show lower shear strength, mainly due to stronger plastic deformation. Figure 10: _Cont._
## 2 Related Work  
Since deep learning was proposed, it has been considered to be highly suitable for various image processing tasks because of its remarkable capability to extract rich features from images [13]. In recent years, deep learning has made great progress in medical image processing, and DR lesion segmentation based on deep learning has become the mainstream research direction in this field.  
Early deep learning-based image segmentation methods include fully convolutional networks (FCNs) [14], U-Net [15], SegNet [16], and U-Transformer [17], which incorporated the Transformer framework and introduced a self-attention mechanism. The emergence of these methods benefits from the rapid development of computer technology and AI (Artificial Intelligence), which effectively promotes the progress of medical image analysis technology and lays a foundation for researchers to propose new DR lesion segmentation methods. [PERSON] et al. [18] proposed an efficient hierarchical hybrid visual Transformer, named H2 Former, which combined the strengths of CNN, multi-scale channel attention, and Transformer for DR lesion segmentation. [PERSON] et al. [19] developed a Progressive Multi-scale Consistent Network (PMCNet) to achieve improved DR lesion segmentation. This network integrated Progressive Feature Fusion (PFF) and the Dynamic Attention Block (DAB) to facilitate progressive learning and the dynamic fusion of multi-scale features.  
Furthermore, the introduction of innovative approaches like RTNet by [PERSON] et al. [9], hyperbolic space-based Transformer by [PERSON] et al. [20], SSMD-Unet by [PERSON] et al. [21],and M2 MRF operator by [PERSON] et al. [22] has contributed to further advancements in DR lesion segmentation. These methods have leveraged self-attention, cross-attention, relation Transformer blocks, hyperbolic embeddings, and auxiliary reconstruction tasks to enhance segmentation accuracy and address specific challenges associated with scale discrepancy and optimal feature representation.  
However, despite the significant progress made in DR lesion segmentation using deep learning, there are still research gaps that need to be addressed. Particularly, there is a need to develop robust segmentation models that effectively capture multi-scale lesion features in fundus images while perceiving subtle lesions in depth. Current approaches have shown promising results; however, there is room for improvement in simultaneously leveraging multi-scale information and depth perception to enhance the accuracy and completeness of DR lesion segmentation.  
Furthermore, the influence of imbalanced datasets on the algorithm's performance remains a challenge. Existing methods often struggle with imbalanced data, where the number of positive lesion samples is significantly smaller than the number of negative samples. To overcome this issue, it is essential to design a novel loss function that incorporates tailored lesion weights. Such a loss function would effectively reduce the negative impact of imbalanced datasets on the segmentation algorithm, ensuring the accurate delineation of both common and rare DR lesions.
# Accuracy and Precision of Three-Dimensionally Printed Orthognathic Surgical Splints
### Variables in 3D Printing
#### 1.3.2 Print Layer Height  
Print layer height refers to the thickness of each layer of material added during the printing process, and it corresponds to the vertical resolution in the \(Z\)-axis. Although some CAD/CAM software allows the users to enter free values, the majority offer a couple of print layer height options that are pre-determined according to the manufacturer's recommendations.  
Despite the early suggestions that printing in thinner layers (or higher \(Z\)-resolution) would produce more accurate prints, several studies have concluded that there is no difference in the measurements between the objects 3D-printed in 25, 50, and 100 \(\upmu\)m layer heights [20; 21]. The overall printing speed and time taken for each print job are predominantly determined by the number of layers required to create an object, and therefore, it must be selected carefully.