esa-sceva/satcom-chunk-collection huggingface dataset:
# Contactless Palm Vein Recognition Based on Attention-Gated Residual U-Net and ECA-ResNet
## 3 Attention-Gated Residual U-Net and ECA-ResNet
### Palm Vein Segmentation
#### 3.2.2 Attention Mechanism  
We incorporated the attention gate mechanism, as referenced in [44], into our segmentation model. This mechanism is applied after a series of convolutions, serving as the enhancement module diminishing irrelevant regions and amplifying crucial features. The combination of the attention mechanism and U-Net model in the skip connections can achieve better results while maintaining a minimal computation. The detailed architecture of the attention mechanism is shown in Figure 5. \(g_{i}\) is the gating signal that provides contextual information that can be used to define focus areas for a given feature map \(x_{i}\). The attention coefficient, \(\alpha\), detects the salient regions while refining feature responses to retain only the activation that is relevant for segmentation. Finally, the final feature map is obtained by multiplying \(x_{i}\) and \(\alpha\) element-wise, which can be defined as Equation (6):  
\[x_{out}=x_{i}*\alpha \tag{6}\]  
To compute the gating coefficient \(\alpha\), additive attention is utilized for achieving accurate segmentation results. The additive attention is defined as Equation (7):  
\[\alpha=\sigma_{2}\Big{(}\psi^{T}\Big{(}\sigma_{1}\Big{(}W_{x}^{T}x_{i}+W_{g}^ {T}g_{i}+b_{g}\Big{)}\Big{)}+b_{\psi}\Big{)} \tag{7}\]  
where \(\sigma_{1}\) and \(\sigma_{2}\) are ReLU and Sigmoid functions, respectively, \(W_{x},W_{g}\), and \(\psi\) are all linear transformations, and \(b_{g}\) and \(b_{\psi}\) are biases.
## 2 Materials and Methods
### Statistical Analysis  
Both partial least squares discriminant analysis (PLS-DA) and orthogonal PLS-DA (OPLS-DA) were successfully implemented by employing the SIMCA-P 13.0 software (Umetrics, Umea, Sweden). Multiple comparisons were performed utilizing one-way analysis of variance (ANOVA), employing SPSS statistics 20.0 (SPSS Inc., Chicago, IL, USA). The column was accomplished via the utilization of Origin 9.1 software (OriginLab Corporation, Northampton, MA, USA). Data visualization was facilitated through the creation of heat maps using MultiExperiment Viewer 4.9.0 (Oracle Corporation, Redwood Shores, CA, USA).
## 3 Results and Discussion  
### Influence on the Pounding Stiffness  
In previous studies [43], a nonlinear bounding force model was proposed and validated experimentally for simulating the collision of an object on viscoelastic material. The bounding force model is based on a Hertz contact element in conjunction with a damper that is active only during the approach period of the impact. Its expression is described as follows:  
\[F\ =\ \left\{\begin{array}{rl}\beta\delta^{3/2}+c\dot{\delta}&\ \ \ (\text{approach period of impact})\\ \beta\delta^{3/2}&\ \ \ (\text{restitution period of impact})\end{array}\right. \tag{1}\]  
where \(\delta\) describes the deformation of the colliding bodies; \(\dot{\delta}\) denotes the relative velocity between them; \(\beta\) is the bounding stiffness; and \(c\) is the bounding damping, which at any instant of time can be obtained by:  
\[c\ =\ 2\xi\sqrt{\beta\delta\frac{m_{1}m_{2}}{m_{1}+m_{2}}} \tag{2}\]  
\[\xi\ =\ \frac{9\sqrt{5}}{2}\frac{1-e^{2}}{e(e(9\pi-16)+16)} \tag{3}\]  
where \(m_{1}\) and \(m_{2}\) are the masses of the two colliding bodies; and \(\xi\) is the impact damping ratio correlated with the coefficient of restitution \(e\), which is defined as the relation between the post-impact (final) relative velocity, \(\dot{\delta}_{f}\) and the prior-impact (initial) relative velocity, \(\dot{\delta}_{0}\).  
In this nonlinear bounding force model, the bounding stiffness \(\beta\) is the only unknown parameter, which can be estimated based on the recorded bounding force and the displacement of the colliding bodies. In this study, the bounding force model explained in Equations (1)-(3) is established in MATLAB/Simulink environment and the bounding stiffness can be attained by the embedded Curve Fitting Toolbox. The estimation method is the nonlinear least-squares method.  
At the beginning of this study, three pieces of viscoelastic material samples were tested. After this, the specimens were tested every month and the estimated bounding stiffness is shown in Table 2. Initially, the bounding stiffness varied from 48,258 N/m\({}^{1.5}\) to 53,417 N/m\({}^{1.5}\). After submersion in seawater for up to 5 years, the bounding stiffness increased from 45619 N/m\({}^{1.5}\) to 53,943 N/m\({}^{1.5}\). This is still very close to the initial value, which suggests that the seawater has little influence on the behavior of the viscoelastic material.
## 6 Discussion and Limitations  
The case study shows that the environmental impacts of CRC demolition and grinding are significantly influenced by the size of the grains. For GWP, a reduction of about 86% is possible comparing the best with the worst scenario (range: 2-14 kg CO\({}_{2}\)e). Moreover, the use of renewable energy plays an important role in CRC as the use of renewable energy can  
Figure 10: Using renewable energy mix for mechanical recycling and pyrolysis.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **EoL SRC** & **ADPe** & **ADPf** & **AP** & **EP** & **FAETP** & **GWP** & **HTP** & **MAETP** & **ODP** & **POCP** & **TETP** \\ \hline Steel & 0.0 & 25.2 & 0.0 & 0.0 & 0.0 & **1.9** & 0.0 & 30.98 & 0.0 & 0.0 & 0.0 \\ \hline A & Concrete & & & & & & & & & & & \\ fraction—mobile plant & 0.0 & 700.3 & 0.0 & 0.0 & 0.3 & **6.9** & 0.9 & 860.2 & 0.0 & 0.0 & 0.1 \\ \hline  & A: Steel + mobile plant & 0.0 & 725.5 & 0.0 & 0.0 & 0.3 & **8.8** & 0.9 & 891.2 & 0.0 & 0.0 & 0.1 \\ \hline B1 & Concrete & & & & & & & & & & & \\ fraction—stationary & 0.0 & 412.9 & 0.0 & 0.0 & 0.2 & **9.5** & 0.6 & 769.6 & 0.0 & 0.0 & 0.1 \\ conventional & & & & & & & & & & & & \\ \hline  & B1: Steel + stat. plant con. & 0.0 & 438.1 & 0.0 & 0.0 & 0.2 & **11.4** & 0.6 & 800.6 & 0.0 & 0.0 & 0.1 \\ \hline B2 & Concrete & & & & & & & & & & & \\ fraction—stationary & 0.0 & 475.5 & 0.0 & 0.0 & 0.2 & **12.8** & 0.8 & 1270 & 0.0 & 0.0 & 0.1 \\ innovative & & & & & & & & & & & & \\ \hline  & B2: Steel + stat. plant & & 0.0 & 500.7 & 0.0 & 0.0 & 0.2 & **14.7** & 0.9 & 1301.0 & 0.0 & 0.0 & 0.1 \\  & inno. & & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: LCA SRC-EoL CML2001.  
reduce over 50% of environmental impacts. For SRC, wet processing (scenario B2) provides the largest emissions but offers the smallest particle size for further processing.  
Although the CRC double walls consist of less material and might have a longer lifespan, the EoL emissions are partly higher than those for SRC. This is due to energy-intensive processes of pyrolysis. Thus, purely renewable energy can already help to reduce the environmental impacts. However, the further life of the reinforcements is hardly comparable. While steel can be recycled nearly without any losses or downcycling, a closed cycle of carbon fibers is, despite high emissions, realistically not possible today. Hence, fiber properties or EoL processes need to be greatly improved for an efficiently improved loop that provides more environmental benefits.  
Some limitations of the study shall be addressed. For instance, the results are based on literature data and assumptions, as few primary data related to EoL of CRC currently exist. Furthermore, only the EoL is considered and no upstream and downstream processes are included. Downstream processes are not considered as steel and carbon fibers are not comparable if not used for the same purpose again. Moreover, the melting of steel is not accounted for, as a direct further use is assumed. Thus, the comparability can certainly be criticized depending on the use and should be considered accordingly in future studies.
## 4 Experimental Results and Dataset
### Performance Metric
#### 4.2.2 Prediction Accuracy  
The goal of our research was to identify the clearest image in a group of images, so the accuracy of judging the clearest image was an important evaluation indicator. In each image group, we defined the capture order of each image in the autofocus process as \(i(i\in[1,n])\), where n is the number of images in this group. Furthermore, we defined the capture order of the image with the maximum predicted score as \(i^{p}\), and the capture order of the image with the maximum annotated score as \(i^{m}\). When \(i^{p}\) is equal to \(i^{m}\), the prediction of the IQA method is consistent with HVS in this image group, and we defined the corresponding group as type \"top-0\". When \(i^{p}\) is not equal to \(i^{m}\) but the absolute difference between them is 1, the prediction of the IQA method is slightly different from HVS, and we defined the corresponding image group as type \"top-1\". In [3], we proposed a super depth of field (SDoF) network to detect cells by an SDoF feature aggregation module. The inputs of SDoF-Net are three microscopic images (the clearest image and its preceding and succeeding image), which are captured in one autofocus process. Therefore, image groups with type \"top-0\" and \"top-1\" are acceptable for our research. We defined \(t_{0}\) and \(t_{1}\) to represent the number of image groups with type \"top-0\" and \"top-1\", respectively. Furthermore, we defined \"acc\" to represent the proportion of the sum of \(t_{0}\) and \(t_{1}\) in the number of evaluation image groups.
# CMOS Fixed Pattern Noise Removal Based on Low Rank Sparse Variational Method
## 2 Existing Methods  
In order to obtain better CIS image quality and reduce the impact of FPN on image quality, many scholars have carried out many studies in this field in recent years. In summary, the FPN removal methods of CIS can be summarized into two categories: 1. The calibration-based method; 2. the scene-based method. As shown in Figure 3.
## 4 Results & Discussion
### Comparison of Case 2
#### 4.2.1 Reflection on Temperature  
Based on the results of the numerical simulation, it can be concluded that the flow rate temperature on the coolant inlet is almost not changed as 50 K\(\cdot\)kg/s. But, the outlet flow rate temperature is various with different groups, which is shown as Table 3. According to the conservation of quality, it is obviously that the larger the absolute value of \(\lambda_{out}\) is, the more heat can be taken away. So, as is shown in Table 3, the simulation results indicate that the cooling efficiencies on the bionic surfaces are all clearly improved. In detail, when \(W\) = 5 mm and 10 mm, the best cooling efficiency shows on \(H\) = 10 mm. However, when \(W\) = 15 mm, the case of \(H\) = 10 mm presents the worst. So, the height and width of the rib are considered that can both strongly influence the cooling performance. That is the reason why the third group has the best thermal protection effect, while the effect of the sixth group is very closed to the third. When compared with the result of the smooth thermal surface, the best cooling efficiency can be improved to 32.5%.  
In order to study the thermal protection on the transition piece, the comparisons of temperature distribution on thermal surfaces are shown as Figure 5. Temperature distributed on the thermal surface without rib turbulators below the injections is low, but other place is very high; however, the whole temperature that was distributed on the bionic thermal surfaces with rib turbulators is exhibited uniformly. In addition, the minimum temperature can be found on the bionic heat transfer surface and  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**N** & \(W\) **(mm)** & \(H\) **(mm)** & \(S\) **(mm)** & \(\lambda_{out}\) **(K\(\cdot\)kg/s)** \\ \hline
1 & 10 & 10 & 20 & \(-110.3\) \\
2 & 10 & 10 & 40 & \(-110.3\) \\
3 & 10 & 10 & 60 & \(-110.6\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data of the outlet flow rate temperature on biomimetic thermal surface in case 1.  
Figure 4: Contours of turbulence kinetic energy for different distances.  
the data are tabulated in Table 3. The position of the lowest temperature point is mainly influenced by the height of the ribs. When \(H\) = 5 mm, the lowest temperature appears below the impingement cooling hole, which has no difference with the smooth thermal surface. But, when \(H\) = 15 mm, the lowest temperature is located on the upper rib facet and is closer to the hole. As shown in Table 3, the value of the lowest temperature declines with \(H\) increasing. It is considered that the upper rib facet is closer to the cooling hole, with \(H\) increasing and it can be rushed directly by the air from cooling holes.
## 6 Summary and Conclusions
* In general, there is a good correlation of the \(A\) vs. \(D_{\rm max}\) relationship for the shape groups, with correlation coefficients varying from 0.73 for shape group (2) Crossed needles and crossed columns to over 0.9 for shape groups (5) Plates, (11) Spatial stellar crystals, (12) Graupel, (13) Ice and melting or sublimating particles, (14) Irregulars and aggregates, and (15) Spherical particles (see Table 9). * In most shape groups, area ratio decreases with increasing particle size. This is strongest for the shape groups with the lowest area ratios: (1) Needles and thin or long columns, (2) Crossed needles and crossed columns, and (3) Thick columns and bullets, which consequently also have the lowest values of \(C_{2}\). The shape groups (15) Spherical particles, (14) Graupel, and (6) Stellar crystals are exceptions and have area ratios that are almost constant. * Shape groups (1) Needles and thin or long columns, (3) Thick columns and bullets, (5) Plates, and (15) Spherical particles have the highest correlation in the \(A_{\rm r}\) vs. \(A_{\rm s}\) relationship, with \(R_{b}^{2}\) larger than 0.8 (see Table 9). On the other hand, shape groups (2) Crossed needles and crossed columns, (6) Stellar crystals, (7) Bullet rosettes, and (9) Side planes have correlation coefficients lower than 0.5. Shape groups (6) Stellar crystals and (7) Bullet rosettes have a particularly low \(A_{\rm r}\) vs. \(A_{\rm s}\) correlation of 0.25 despite a \(A\) vs. \(D_{\rm max}\) correlation larger than 0.82. For instance, in the case of the shape group (6) Stellar crystals, this might be due to several factors; the empty space between their branches resulting in low \(A_{\rm r}\) values and/or variations in \(A_{\rm s}\) due to particle orientation. This shows that, with the D-ICI and the classification in shapes and shape groups, particle characteristics can be studied for groups of similar shapes. Since fall speed can be measured by analyzing side-view images taken by the D-ICI [22], the shape dependence of fall speed can also be studied. The resulting parameterizations of the snow microphysical properties may be useful for improving our understanding of precipitation in cold climates in addition to helping improve the cloud microphysical parameterizations in the climate and forecast models. Methodology, S.V.-M. and T.K. ; instrument, T.K. ; formal analysis, S.V.-M. and T.K. ; investigation, S.V.-M., T.K., and S.E. ; data curation, S.V.-M. and T.K. ; writing--original draft preparation, S.V.-M.; writing--review and editing, S.V.-M., T.K., and S.E. ; visualization, S.V.-M.; supervision, T.K. and S.E. All authors have read and agreed to the published version of the manuscript. This research received no external funding. We like to thank the Graduate School of Space Technology at Lulea University of Technology for financial support and the Swedish Institute of Space Physics (IRF) at Kiruna for offering its facilities for our instrument. The authors declare no conflict of interest.
## 8 Discussion
In particular the forecast starting on 16 November at 12:00 UTC was unable to correctly predict the landfall over Apulia, while the forecast starting on 17 November at 12:00 UTC correctly predicted the rainfall amount and landfall over Apulia. The storm track simulated on 16 November is too far south from the real path, and rainfall was forecast mainly over the sea. The simulation starting on 17 November shows a good prediction of the precipitation field and was used to gain insight into the TLC phase of Numa. Wind speeds up to 20 m/s were simulated on 17 November at 19:00 UTC, in good agreement with ASCAT observations. The warm core extended up to 10 km showing a deep vertical extension of Numa, favored by the long-lasting TLC phase [18]. Other TLC characteristics as the small horizontal extension of Numa, the low vertical wind shear, the axisymmetric shape, and the calm eye of the Medicare, were well captured by the model. These results, in agreement with previous model-based studies on Medicanes (e.g., [PERSON] et al. [2], [PERSON] and [PERSON] [5], [PERSON] et al. [13], [PERSON] et al. [21]), support the passive MW observation analysis, showing that during the TLC phase warm rain processes occur in the area surrounding the eye, while weak convective activity is observed in small portions of the eyewall. We have analyzed how DPR measurements, available over the sea, can be also used in NWP data assimilation. However, because only one overpass of the DPR is available, the impact of the data assimilation on the Numa forecast was limited. Nevertheless, DPR reflectivity data assimilation caused an extension of the rainfall forecast toward the South, in better agreement with NASA GPROF-CLIM-SSMIS V05 product.
## 5 The Case Study
### The Device  
Ref. [PERSON] et al. (2015) analyzed three different devices based on different geometries and characteristics; the proposed experimental-numerical comparisons were based on the results obtained from the device that developed the most effective performances, the so-called Type I design.  
In detail, the studied device (see Figure 5) was structured as follows: a 20 \(\upmu\)m thick copper planar micro-coil was mounted over a glass-polylimide substrate. The coil presented an internal diameter of 2500 \(\upmu\)m and an external one of 5087 \(\upmu\)m, the in-planar spacing and track width were 80 \(\upmu\)m and 100 \(\upmu\)m, respectively. A PMMA plate covered the coil-substrate sandwich; at the center of the plate, a circular hole was obtained with a diameter of 4 mm that constituted the device's pumping chamber. On the plate's upper surface was glued the PDMS diaphragm-magnet coupled assembly. This assembly was constituted by a 1.5 \(\times\) 1.5 mm cylindrical NdFeB permanent magnet glued over a 100 \(\upmu\)m thick circular PDMS diaphragm with a diameter of 2000 \(\upmu\)m. Further and deeper constitutive information can be found in the reported article [PERSON] et al. (2015).  
Figure 4: ESRF Radia-Matlab/Simulink co-simulation algorithm.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
## 5 Conclusions  
This paper proposes a wind power forecast model that consists of a data processing and denoising module, a feature extract module and a bidirectional deep learning forecast module. The experiment is carried out on datasets collected from three different wind  
Figure 6: Comparison among different wind power short-term prediction models of Wind Farm #3 in April 2019.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Month** & **Algorithms** & **PA(\%)** & **MAE(MW)** & **MAPE(\%)** \\ \hline \multirow{4}{*}{\(1\)} & DWT\_AE\_BiLSTM & 82.23 & 15.17 & 12.01 \\  & AE\_BiLSTM & 81.47 & 15.53 & 12.30 \\  & LSTM & 81.02 & 18.81 & 14.89 \\  & BP & 79.65 & 19.58 & 15.50 \\ \hline \multirow{4}{*}{\(4\)} & DWT\_AE\_BiLSTM & 82.12 & 16.86 & 13.35 \\  & AE\_BiLSTM & 81.59 & 17.10 & 13.54 \\  & LSTM & 81.10 & 19.38 & 15.34 \\  & BP & 79.42 & 20.65 & 16.35 \\ \hline \multirow{4}{*}{\(7\)} & DWT\_AE\_BiLSTM & 82.00 & 16.37 & 12.96 \\  & AE\_BiLSTM & 81.48 & 17.30 & 13.70 \\ \cline{1-1}  & LSTM & 79.47 & 18.91 & 14.97 \\ \cline{1-1}  & BP & 78.45 & 21.25 & 16.83 \\ \hline \multirow{4}{*}{\(10\)} & DWT\_AE\_BiLSTM & 88.66 & 8.27 & 6.55 \\  & AE\_BiLSTM & 87.65 & 10.07 & 7.97 \\ \cline{1-1}  & LSTM & 84.12 & 14.03 & 11.11 \\ \cline{1-1}  & BP & 83.73 & 14.89 & 11.79 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance evaluation of each prediction algorithm in Wind Farm #3.  
farms in Hubei Province. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively. The comparison results show that: (a) by using a sparse autoencoder, the dimension of the original data is reduced, and it discovers more abstract hidden features; (b) all performance indexes of the proposed model outperform other machine learning models; and (c) the proposed model shows robustness and reliability in three wind farms.  
Conceptualization, Methodology, Writing--original draft, P.X.; Supervision, Writing--review and editing, M.Z.; Supervision, Project administration, Z.C.; Validation, Visualization, B.W.; Funding acquisition, Formal analysis, C.C.; Software, Resources, R.L. All authors have read and agreed to the published version of the manuscript.  
This study is jointly supported by the Hubei Provincial Natural Science Foundation of China under grant number 2022 CFD017, the Special Innovation and Development Program of the China Meteorological Administration under grant number CXFZ2023J044 and the Key Fund Project of Hubei Meteorological Bureau under grant number 2021Z08.  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data is contained within this article.  
The authors are grateful to [PERSON] of Hubei Energy Group New Energy Development Co., Ltd. for providing access to operational data on the three wind farms. We also wish to thank the anonymous referees who reviewed this paper, for their comments and recommendations.  
The authors declare no conflict of interest.
## 3 Time-Frequency Coded Cooperative Transmission Based on MC-CDMA and Power Allocation
#### 3.1.1 Phase 1  
In phase 1, each time-frequency coding frame \(\mathbf{d}_{k}^{1}\) of source sensor \(S_{k}\) is transmitted after MC-CDMA modulation. The transmitting signal vector is given by:  
\[\begin{split}\mathbf{T}_{S_{k}}=&[\underbrace{c_{k}(1)d_{ k}^{1}(1)e^{/2\eta_{i}},\cdots,c_{k}(G)d_{k}^{1}(1)e^{/2\eta_{G}}}_{G},\cdots, \underbrace{c_{k}(1)d_{k}^{1}(M)e^{/2\eta_{(M-1)-\alpha}},\cdots,c_{k}(G)d_{k }^{1}(M)e^{/2\eta_{(M-1)-\alpha}}}_{G}]\\ \in&(1\times Nc)\end{split} \tag{6}\]  
Therefore, the received signals at cooperative sensor \(P_{l}\) (\(l=1,\cdots,K\)) and the sink node \(D\) from \(K\) source sensors respectively are [27]:  
\[\mathbf{r}_{S,\mathbf{p}}=\sum_{k=1}^{K}\sqrt{E_{S_{k}}}\times\mathbf{T}_{S_{k}}\times\mathbf{ h}_{S_{k},\mathbf{p}}+\mathbf{N} \tag{7}\]  
\[\mathbf{r}_{S,\mathbf{p}}=\sum_{k=1}^{K}\sqrt{E_{S_{k}}}\times\mathbf{T}_{S_{k}}\times\mathbf{ h}_{S_{k},\mathbf{p}}+\mathbf{N} \tag{8}\]  
where \(E_{S_{k}}\) is the average transmit power of source sensor \(S_{k}\); \(\mathbf{h}_{S_{k},\mathbf{p}_{l}}\) and \(\mathbf{h}_{S_{k},\mathbf{p}}\) are the channel gain matrix between source sensor \(S_{k}\) and cooperative sensor \(P_{l}\) as well as between source sensor \(S_{k}\) and the sink node \(D\); \(\mathbf{N}\) is the noise vector of channel between sensors, \(\mathbf{N}=[n_{1},n_{2},\cdots,n_{Nc}]\in 1\times Nc\). Without loss of generality, the channel noise of each narrow subcarrier is assumed to be additive white Gaussian noise (AWGN) with zero-mean and \(\sigma^{2}\)-covariance.