esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work, the elastic, thermodynamic and electronic properties of intermetallic compounds Co\({}_{2}\)Zr and Co\({}_{2}\)Ti in cubic structure are investigated and compared by first principles calculations. Detailed structural information of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti are presented, and the optimized lattice constant, crystal volume, is in accordance with the available experimental value. The elastic constants, bulk modulus \(B\), shear modulus \(G\), anisotropy, ductility, heat capacity \(C_{V}\), Debye temperature, thermal expansion coefficient \(\alpha\) are functions of pressure and temperature. Co\({}_{2}\)Zr is always stable while Co\({}_{2}\)Ti is unstable when the pressure is more than 80 GPa, and it is possible that a transition phase exists under the pressure of 85 GPa. Co\({}_{2}\)Zr crystals are brittle at 10 and 30 GPa, whereas the toughness of Co\({}_{2}\)Zr increases with the increment of pressure except at 10 and 30 GPa. The toughness of Co\({}_{2}\)Ti is weaker than that of Co\({}_{2}\)Zr and reaches the highest point at 60 and 70 GPa. It is also found that the anisotropy of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti reaches its highest degree at 30 and 70 GPa. Moreover, the anisotropy of Co\({}_{2}\)Zr is larger than that of Co\({}_{2}\)Ti only at 10 and 30 GPa. The thermodynamic properties are also predicted successfully. Debye temperature is a function of pressure and increases monotonically with  
Figure 8: (color online) The electron density difference in the (110) plane of Co\({}_{2}\)Zr at (**a**) 0, (**b**) 10, and (c) 30 GPa; and that of Co\({}_{2}\)Ti at (**d**) 0, (**e**) 10a, and (**f**) 30 GPa, isosurface value: 0.1 e/Å\({}^{3}\).  
increasing pressure. The Debye temperature of Co\({}_{2}\)Zr is always larger than that of Co\({}_{2}\)Ti at various temperatures under the same pressure. Both the heat capacity \(C_{V}\) and thermal expansion coefficient \(\alpha\) of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti increase non-linearly with increasing temperature and decrease monotonically with increasing pressure, showing that the effect of increasing temperature is just the same as that of decreasing pressure. In addition, Co\({}_{2}\)Zr always has larger heat capacity \(C_{V}\) than Co\({}_{2}\)Ti under same condition. The thermal expansion coefficient \(\alpha\) of Co\({}_{2}\)Zr is larger than that of Co\({}_{2}\)Ti at low temperature, while it is the opposite at high temperature under same pressure. Finally, the electron density difference and density of states of Co\({}_{2}\)Zr and Co\({}_{2}\)Ti are investigated. What is more, the total density of states and partial density of states are calculated to find origins of band structures. Co\({}_{2}\)Zr and Co\({}_{2}\)Ti are all metal crystals, but Co\({}_{2}\)Zr has greater covalence than Co\({}_{2}\)Ti.  
Conceptualization, M.-A.X.; Supervision, X.Y.; Writing--original draft, C.Z. and P.W.; Writing--review & editing, X.Y. and M.X. All authors have read and agreed to the published version of the manuscript. This research is supported by the Fundamental Research Funds for the Central Universities (2019B19314). The authors declare no conflict of interest.
## 1 Introduction
### Image Matching Applications in the Construction Industry  
In the construction industry, especially in recent years, image matching techniques have shown capabilities for addressing different issues regarding information management. There is abundant research regarding applications of image matching techniques through AEC/FM (architecture, engineering and construction and facilities management). For instance, to solve issues related to difficulties in updating as-built and as-is information on jobsites, some researchers have utilized image matching techniques to create a building information model of the scenes. They have taken images from different angles, stitched them, and attached the data to these models [12]. Others such as [PERSON] et al. [18] reported that in a large-scale indoor environment full of self-repetitive visual patterns, recognizing the location of images captured from different scenes can be confusing. To address this issue, they applied image matching techniques, which analyzed unique features in captured images, to retrieve the location. [PERSON] et al. [19] used image matching techniques to compare virtual images of a construction site with the real construction photographs for the purpose of detecting differences between the actual and planned conditions of the jobsite. Another application of using image matching techniques is to detect changes in a scene by comparing features of pictures captured at different times [20] to estimate the rough progress of a project.  
Providing easier access to construction information on a jobsite is another reason to use image matching techniques. For this purpose, some researchers suggested using augmented reality technology to superimpose a layer of data (e.g., text, voice, 3D model, image, etc.) over the locations where access to information is required [21; 22]. Marker-based augmented reality (AR) and markerless AR, which both use image matching techniques, can be used for this purpose. For both methods, the image matching algorithms need to detect distinct features between live video frames that are captured from the environment, and a reference image that is already available. In the marker-based approaches, since the algorithms need to detect the features of a label (e.g., Quick Response Code/QR code), the results are very robust [1] in contrast with markerless AR, which needs to use the natural features of the environment that can vary [23; 24] (more information regarding AR is presented in Appendix A).
## 4 Numerical Examples
### Example 3  
The 3 rd example is the modified Camassa-Holm equations [1; 2]. The exact solution is given as follows:  
\[u^{ext}(x,t)=-2\text{sech}^{2}\Big{(}\frac{x}{2}-t\Big{)}, \tag{27}\]  
The following are applied in this numerical example: \(a=-10\), \(b=10\), dt = 0.5, \(T=2\), \(N_{D}=4\), \(N_{T}=37\),231, and \(n_{s}=20\). The initial condition and boundary conditions are obtained by introducing the above parameters into Equation (27). The numerical solutions are illustrated in Figure 7. In these plots, the obtained numerical results are well compared  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multicolumn{2}{c}{\(x=2.5\)} & \multicolumn{4}{c}{\(x=5\)} \\ \hline \(t\) & **MVIA [5]** & **VIM [8]** & **ST-GFDM** & **MVIA [5]** & **VIM [8]** & **ST-GFDM** \\ \hline
0.02 & \(3.780\times 10^{-5}\) & \(1.180\times 10^{-4}\) & \(1.883\times 10^{-7}\) & \(9.966\times 10^{-6}\) & \(2.124\times 10^{-5}\) & \(4.272\times 10^{-8}\) \\
0.04 & \(7.240\times 10^{-5}\) & \(2.363\times 10^{-4}\) & \(1.949\times 10^{-7}\) & \(1.778\times 10^{-5}\) & \(4.797\times 10^{-5}\) & \(4.077\times 10^{-8}\) \\
0.06 & \(1.036\times 10^{-4}\) & \(3.547\times 10^{-4}\) & \(1.996\times 10^{-7}\) & \(2.333\times 10^{-5}\) & \(8.029\times 10^{-5}\) & \(3.867\times 10^{-8}\) \\
0.08 & \(1.313\times 10^{-4}\) & \(4.731\times 10^{-4}\) & \(2.042\times 10^{-7}\) & \(2.653\times 10^{-5}\) & \(1.183\times 10^{-4}\) & \(3.647\times 10^{-8}\) \\
0.1 & \(1.552\times 10^{-4}\) & \(5.914\times 10^{-4}\) & \(2.087\times 10^{-7}\) & \(2.727\times 10^{-5}\) & \(1.622\times 10^{-4}\) & \(3.419\times 10^{-8}\) \\ \hline \end{tabular}
\end{table}
Table 6: MAEs of Example 2 with the exact solution and the other numerical methods.  
Figure 6: (**a**) The comparisons of the \(u^{ext}\) and \(u^{num}\) at different \(t\) and (**b**) the surface plot of \(u^{num}\) in the entire \(x\)-\(t\) domain of Example 2.  
with the exact solution in Figure 7a. Moreover, Figure 7b demonstrates the physical behavior of the wave transmission. Additionally, Table 7 presents the numerical results at various locations, comparing them with the numerical results of the VIM [2]. The table shows that the proposed ST-GFDM successfully solves the Camassa-Holm equation and provides accurate numerical solutions.
# A 200-MS/s 10-Bit SAR ADC Applied in WLAN Systems
## 3 Circuit Implementation  
### Bootstrap  
Figure 7 shows a diagram of the conventional structure of the bootstrap. The cksb is the inverted signal of the sampling clock signal cks, and its working state mainly has two stages: charge storage and charge sharing. When the sampling clock cks is high, C3 is in the charge storage stage, and C3 is connected to VSS and _VDD_ through M3 and M4 to complete the charging process. After charging is complete, cks jumps to a low voltage, and the C3 capacitor enters the charge sharing stage. At this step, the bottom plate of C3 is connected to IN/OUT through M9/M10, and the top plate of C3 is connected to the gate voltage signal sw_ctrl through M5. In the charge storage stage, the stored charge on C3 is _VDD*_C3, and under the charge sharing stage, the \(V_{gs}\) of M10 is \(V_{boost}\) according to the law of charge conservation, which gives the expression:  
\[V_{boost}\times(C3+Cp)=VDD\times C3\]  
\(C_{p}\) is the \(C_{gs}\) of M9, M10, M5, and M6, where \(C_{gg}\) contains variable capacitance that reduces linearity.  
The proposed new structure of bootstrap is shown in Figure 8. It can be seen that in the charge sharing stage, the non-linear capacitance between sw_ctrl and IN terminals is only the switch transistors M9 and M10. In addition, the newly added M13 acts as a protective transistor, mainly to protect the \(V_{ds}\) of M7 from exceeding 2 \(\times\)_VDD_ in the charge sharing stage, thereby improving the lifetime of the transistor. Furthermore, when in the charge sharing stage, M13 prevents the charge from leaking C3 into the drain of M8, further improving the linearity of the bootstrap.  
Figure 7: The conventional bootstrap.  
During the SA phase, the M10 switch is closed, and the input signal (IN/IP) is coupled to the top plate of the CDAC through the \(C_{ds}\) of M10. This will directly lead to a degradation of the ADC's performance, which can be cancelled by cross-coupling [21]. A dummy capacitor scheme similar to that of ref [1] is also used to cancel the coupling effect. Nevertheless, in ref [1], the gate voltage of M1 is directly grounded, but in this design, the gate position of M1 is increased to include M2, M11, and M12, which are the same size as M7, M8, and M13. Their main function is to make the environment around M1 and M10 consistent in the layout to achieve better matching and, thus, better cancellation of the coupling effect. In the layout of this circuit, all the transistors in the red part of Figure 8 are placed in the deep n-well (DNW), which can better reduce the \(V_{bs}\) of M10 and make the on resistance (\(R_{un}\)) more stable. In fact, the adoption of DWN and dummy transistors not only improves linearity but also leads to an increase in layout area.
## 4 Conclusions  
In summary, PCL fibers functionalized with plant extract _I. gravoelens_ were successfully obtained through the electrospinninging process using PCL as polymer, in acetic/formic acid (1:2 w/w) as an organic solvent to prepare the polymeric solutions. The fibers produced by electrospun from the spinning _I. gravoelens_/PCL were nanofibers with ribbon-like twisted forms and have no toxic effect on cell cultures. Our study has demonstrated that electrospinning is an encouraging progress for the manufacture of scaffolds consisting of nanofibers containing a potent natural polymer such as polymer derived from _I. gravoelens_ that has medicinal applications with synthetic biodegradable PCL polymer with a suitable choice for fibroblast cell culture at a 7/3 (V:V) ratio. Evaluating the Tensile strength hydrophilicity test, as well as, cell toxicity and biocompatibility, indicated that _I. gravoelens_/PCL scaffold could be a potential candidate for tissue regeneration and wound healing applications.  
Conceptualization and methodology, W.J.A.-K., S.A., A.J.M.A.-F.; formal analysis, H.K.A.-K., S.M.A.-M. and G.M.S.; investigation and data curation, W.J.A.-K., S.A., A.J.M.A.-F., S.M.A.-M., and G.M.S.; validation Y.H.D., M.S.A. and D.A.S.; visualization, all authors; writing--original draft preparation, W.J.A.-K., S.A., A.J.M.A.-F., S.M.A.-M., and G.M.S.; writing--review and editing, Y.H.D., M.S.A., and D.A.S.; supervision, S.A. and G.M.S.; project administration, S.A. All authors gave approval to the final version of the manuscript.  
The authors extend their appreciation to the Deanship of Scientific Research at King Saud University for funding this work through research group NO (RGP-1435-086).  
Not applicable.  
The authors extend their appreciation to the University of Misan, University of Basrah, Al Qasim Green University, University of Technology, and Researchers Support & Services Unit (RSSUF) at King Saud University for their technical support.  
The authors declare no conflict of interest.  
Figure 4: Cytotoxicity results of PCL and _I. gravoelens_/PCL nanofibers on fibroblast cell culture by MTT assay, after 24, 48, and 72 h of cell seeding. (\(p<0.05\)); represent significant differences between the same sample at a different time (\(p<0.05\)).
# Unsupervised Domain Adaptation with Coupled Generative Adversarial Autoencoders
## 2 Related Work  
The goal of unsupervised domain adaptation is to transfer knowledge from a labeled source dataset to a target dataset where labeled data is not available. Recent studies have tried to learn transferable features with deep neural networks. The DDC method [11] learned domain invariant representations by introducing an adaptation layer and a Maximum Mean Discrepancy (MMD) domain confusion loss. The work in [22] extended the MMD to jointly mitigate the gaps of marginal and conditional distributions between source and target domain. The DAN method [9] embedded task-specific layers in a reproducing kernel Hilbert space to enhance the feature transferability. The DANN method [8; 23] suggested that the features suitable for domain adaptation should be both discriminative and domain-invariant and added a domain classifier at the end of the feature extractor to learn domain invariant features. CAN [24] suggested that some characteristic information from target domain data may be lost after learning domain-invariant features with DANN. Therefore, CAN introduced a set of domain classifiers into multiple blocks to learn domain-informative representations at lower blocks and domain-uninformative representations at higher blocks. The work of [25] proposed to learn a representation that transfered the semantic structure from a well labeled source domain to the sparsely labeled target domain by adding a domain classifier and a domain confusion loss. The DRCN [12] proposed a model which had two pipelines: The first was label prediction for the source domain and the second was data reconstruction for the target domain. ADDA [26] learnsthe representation of the source domain and then maps the target data to the same space through a domain-adversarial loss.  
Other works have attempted to use GANs [14] into image-to-image translation and domain adaptation. The \"pix2 pix\" framework [27] used a conditional generative adversarial network to learn a mapping from input to output images with paired images. CycleGAN [28] learned the mapping without paired training examples using a cycle consistency loss. The method in [29] used GAN to translate unpaired images between domains while remain high level semantic information aligned by introducing attention consistent loss. CoGAN [18] learned a joint distribution of images without corresponding supervision by training two GANs to generate the source and target images respectively given the same noise input and tying the high-level layer parameters of the two GANs. Instead of generating images from noise vectors, PixelDA [30] generated style-transferred images conditioned on the source images. CoGASA [31] integrated a stacked autoencoder with the CoGAN, and UNIT [19] proposed an image-to-image translation framework based on CoGAN and VAE [32].
# Simultaneous Instance and Attribute Selection for Noise Filtering
## 2 Materials and Methods
### Datasets  
We selected 15 well-known hybrid and incomplete datasets (Table 1) from the Machine Learning Repository of the University of California at Irvine (UCI) [25], having multiple decision classes, to show that the proposed method can handle diverse datasets, such as hybrid, incomplete, and multiclass.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Instances**} & **Numerical** & **Categorical** & \multirow{2}{*}{**Classes**} & **Missing** \\  & & **Features** & **Features** & & **Values** \\ \hline autos & 205 & 15 & 10 & 7 & x \\ breast-w & 699 & 9 & 0 & 2 & \\ credit-a & 690 & 6 & 9 & 2 & x \\ diabetes & 768 & 8 & 0 & 2 & \\ heart-c & 303 & 6 & 7 & 5 & \\ hepatitis & 155 & 6 & 13 & 2 & x \\ iris & 150 & 4 & 0 & 3 & \\ labor & 57 & 8 & 8 & 2 & x \\ lymph & 148 & 3 & 15 & 4 & \\ post-operative & 90 & 0 & 8 & 3 & x \\ primary-tumor & 339 & 1 & 16 & 22 & x \\ vehicle & 946 & 18 & 0 & 4 & \\ vote & 435 & 0 & 16 & 2 & x \\ wine & 178 & 13 & 0 & 3 & \\ zoo & 101 & 1 & 16 & 7 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of the datasets.  
We use 10-fold cross-validation and inject mislabeled instances into each dataset by randomly changing the class of a percent of objects: 5%, 10%, and 15% in the training set. By using cross-validation, we ensure that the training and testing sets are disjoint sets and that no testing data were used for training during the experiments.
# Patient-Tailored Dementia Diagnosis with CNN-Based Brain MRI Classification
## 4 Discussion
### Added Value  
Early detection of neurodegenerative patterns within the brain is critical for the management of all dementia types, as the available pharmacological and non-pharmacological interventions are typically most effective precisely during either the preclinical or prodromal stage of the disease. If a model can accurately identify and highlight the early pathological changes, it could speed up or refine the diagnostic process, enabling patients to receive the most appropriate treatment much sooner.  
In this investigation, a strong focus was placed on how the models approached the inputs representative of the MCI class. While this class posed a challenge for image classification due to its internal diversity, the models still demonstrated objectively strong performance, which can be deemed promising for future research.  
For MCI, the accuracy, F1 scores, and AUC values are consistently above 60%, 69%, and 0.76, respectively, indicating a valuable potential for refining MCI detection.  
When medical data are carefully curated and the study objectives are clearly defined, DL methods, specifically CNNs, can be effectively integrated into dementia management workflows. CNN-based models can speed up medical procedures, enhance patient comfort, and assist clinical personnel by reducing subjectivity and the risk of diagnostic errors. They provide physicians with filtered-out, clinically meaningful insights, thus flagging early pathological signs and supporting more accurate and timely decision-making.
## 3 Results and Discussion
Model 3 was adjusted per tower with EC-GPP as the dependent variable and GPPm and other tower variables as independent variables. For USAUD and USVAR (not normally distributed) the Spearman rho coefficient of correlation (\(\rho\)) was calculated instead (\({}^{\#}\rho^{2}\)). Notes: coefficient of determination (\(\mathbf{r^{2}}\)) (in bold: \(\mathbf{r^{2}}>0.5\), all are significant at the 95% confidence level), \({}^{\#}\) indicates that \(\rho^{2}\) has been calculated instead of \(\mathbf{r^{2}}\), standard error of the estimate (SEE) (g C\(\cdot\)m\({}^{-2}\cdot\)day\({}^{-1}\)), n: number of cases, Var: predictor variables in the model, Rg: global radiation (W\(\cdot\)m\({}^{-2}\)), SWC: soil water content (% volume), Ta: air temperature (\({}^{\circ}\)C), Ts: soil temperature (\({}^{\circ}\)C). temperature has a more important role [3]). For the forested site, the addition of variables related to water availability did not improve the GPP estimations, probably due to the fact that the C cycles did not have large variations (Figure 4f) due to water constraints. The models that characterised the temporal dynamics of the GPP in grasslands and shrublands located in water-limited areas were not able to explain more than 60% of the variance of the data (see Model 3 in Table 4). Although it was not possible to find a linear relationship to model GPPec for USVAR and USAUD, since the data were not normally distributed, GPPec showed a stronger correlation with SWC than with GPPm for USVAR, highlighting the importance of water availability to obtain accurate estimations of GPP. Along the same lines, the inclusion of SWC in addition to GPPm in the model adjusted for USFPe helped correct the overestimation of GPPm during the most water-limited period, overcoming the limitations of the MOD17 algorithm due to the underestimation of VDP [13, 43], and corrected the underestimation of GPPm during the beginning of the growing season, when VPD was probably overestimated. The poor performance of the model adjusted for USSO4 during the summer period of 2004 might have been due to the fact that water availability was not included as a predictor (probability of F to enter SWC in the model \(\geq\)0.05), and therefore the overestimation of GPPm could not be corrected. These results showed that soil water availability and its inclusion in the MOD17 algorithm is a critical factor in the estimation and monitoring of photosynthetic activity in non-forested water-limited sites, as reported by [13, 16, 32, 48].
# Joint Optimization of Data Transmission and Energy Harvesting in Relay Satellite Networks
## 5 Performance Analysis
### Maximum Storage of Buffer  
Since ISL has the characteristic of intermittent connection, data overflow can be solved by a large buffer.  
**Theorem 2**.: _Assume the data queue satisfies Formula (24) for any \(V>0\) at \(t=0\), that is_  
\[0\leq\mathcal{D}_{i}(0)\leq(1/\tau)V\mathcal{H}^{\prime}_{\max}+\tau\varphi_{ \max},\quad\forall i\in U \tag{24}\]  
_where \(\mathcal{H}^{\prime}_{\max}\) is the maximum value of the first order derivative of system utility \(\mathcal{H}(t)\), and the maximum of data queue \(\mathcal{D}_{\max}=(1/\tau)V\mathcal{H}^{\prime}_{\max}+\tau\varphi_{\max}\). Thus, we have_  
\[0\leq\mathcal{D}_{i}(t)\leq(1/\tau)V\mathcal{H}^{\prime}_{\max}+\tau\varphi_{ \max},\quad\forall i\in U,t\in\mathcal{T} \tag{25}\]  
Proof of Theorem 2.: See Appendix B.  
From (25), we can observe that the maximum data storage of each user satellite increases with the maximum data sampling rate. As defined in the optimization objective function (**MTASU**), a larger sampling rate brings a larger system utility, which indicates that increasing the system utility requires more storage capacity to ensure the system stability.