esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of Ti/Ni Nanolayered Structures to
## 3 Results and Discussion  
### Powder X-ray Diffraction  
Figure 1 showed the X-ray diffraction pattern of Ni-Ti-4 LDHs after reaction time 6 h. It revealed non-crystalline structure. By continuing the reaction time to be 8-14 h, one clear peak started to grow at 0.73 nm. Furthermore, weak peaks were observed at 0.37 nm and 0.27 nm as shown in Figure 1.  
These peaks are similar to the main peaks of the conventional layered double hydroxides and the natural hydrotalcite. After the reaction time 19-26 h, the X-ray diffraction patterns showed clear peaks at 0.73 nm, 0.37 nm, 0.26 nm and 0.24 nm. These peaks agree with the reflections of 003, 006, 009, and 012 planes of LDH structure. The good agreement between the values corresponding to successive diffractions by basal planes, i.e., d003=2d006=3d009, reveals nanolayered structures with highly packed stacks of brucite-like layers ordered along axis c. Dimension c is calculated as three times the spacing for planes (003), i.e., 2.19 nm. This c dimension is lower than that reported for natural and synthetic hydrotalcite, 2.31 nm [28].  
Comparing with the XRD results of the carbonate-LDHs, the interlayered spacing of the prepared product shifted to lower value from 0.76 nm to 0.73 nm indicting that the interlayered anions are different from carbonate anions. This value is related to the thickness of the brucite-like layers (0.48 nm for hydrotalcite), as well as the size of interlayered anions (and, in some cases, its orientation) and the number of water molecules existing in the interlayer.  
Figure 1: X-ray diffraction of Ni-Ti-4 LDHs at different reaction times.  
To study the effect of water molecules on the interlayered spacing of Ni-Ti-4 LDHs, the samples were dried at room temperature and measured by XRD. Figure 2 showed the X-ray diffraction patterns of the prepared products after different reaction times. They showed that the interlayered spacing shifted from 0.73 nm to 0.75 nm because of presence of more water molecules inside the interlayered spacing. However, the nanolayered structure of the prepared product did not change indicating successive reflections at 0.75 nm, 0.38 nm and 0.26 nm.  
The effect of Ni/Ti molar ratio on formation of Ni-Ti LDHs was also investigated as shown in Figure 3. Figure 3 showed that Ni-Ti LDHs are formed with different molar ratios of Ni/Ti; 5, 4 and 3. The XRD patterns revealed the basal reflections of (003), (006), and (009) planes, which are characteristic of LDHs structure. Also, it can be noted that the nanolayered structures are observed for all samples.  
Figure 3: X-ray diffraction of (**a**) Ni-Ti-5 LDHs, (**b**) Ni-Ti-4 LDHs and (**c**) Ni-Ti-3 LDHs.  
Figure 2: X-ray diffraction of Ni-Ti-4 LDHs at different reaction times and dried at room temperature.
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 5 Discussion
### Model Acceleration
#### 5.1.4 Pure Linear Operator Model  
From Table 6 and Section 5.1.3, it can be seen that reducing the communication cost can effectively reduce the model latency; Section 5.1.3 uses parallelisation to reduce the loss of a single communication, instead of reducing the number of communications to reduce the model latency in this section. We modify the operator of the test algorithm by replacing the nonlinear operator with a linear operator. The experimental results are shown in Table 9.  
From the above table, it can be seen that by replacing the nonlinear arithmetic, the number of communications can be greatly reduced, and the delay of this method can be reduced by 44.3%.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 4 Simulation Results and Discussion
### Compared Algorithms  
Several optimization algorithms are employed and compared to the proposed TFWO (Turbulent Flow of Water Optimizer) for the same purpose. These algorithms are the backtracking search optimization algorithm (BSA) [47], gray wolf optimizer (GWO) [48], crow search optimization algorithm (CSO) [49], equilibrium optimizer (EO) [50], marine predators algorithm (MPA) [51], Bernstein-Levy search differential evolution algorithm (BSDE) [52] and manta ray foraging optimization (MRFO) [53]. The BSA, GWO and CSO have different successive applications, while the EO, MPA, BSDE, and MRFO are very recent algorithms from 2020. Table 2 represents examples of their recent applications.  
All these algorithms have the merit of utilizing adaptive internal control parameters. For all algorithms, the population size is specified as 100, where the maximum number of iterations is taken as 1000 and 2000 for an R.T.C. France solar cell and KC200 GT module, respectively. The compared algorithms in Table 3 are employed for optimal extraction of the PV parameters with the SDM, DDM, and TDM. The convergence performance, robustness, and accuracy for all algorithms used in this work are found based on 30 separate runs for each algorithm.
## 4 Interference-Aware Cooperative Anti-Jamming Distributed Channel Selection Algorithm
### Complexity Analysis  
Inspired by [35], we analyze the complexity cost of the proposed algorithm in the following part. As is shown in Table 1, the complexity is divided into computational complexity, storage size and information sharing size. The details are shown as follows.  
Firstly, we consider the computation complexity for users. In one slot, if multi-agent Q-learning is adopted, each user chooses the joint action according to Equation (16); the complexity is \(O\left(C_{1}\right)\), where \(C_{1}\) is a small constant; and each user updates its joint Q table according to Equation (13); the complexity is \(O\left(C_{2}\right)\), where \(C_{2}\) is a constant. Thus, the computation complexity for one user in multi-agent Q learning is \(O\left(C_{1}\right)+O\left(C_{2}\right)\). If single Q-learning is adopted, each user chooses its action independently according to Equation (12); the complexity is \(O\left(C_{3}\right)\); and then updates its single Q table according to Equation (9); the complexity is \(O\left(C_{4}\right)\). \(C_{3}\) and \(C_{4}\) are small constants. Assuming there are \(N\) users in the UAV group, for user \(n\), the time slot number of multi-agent Q-learning is \(Tm_{n}\), and the time slot number for single-agent Q-learning is \(Ts_{n}\), then the computation complexity for the UAV group in the whole time slot is:  
\[C_{comp}=\sum\limits_{n=1}^{N}{\{Ts_{n}\left[O\left(C_{1}\right)+O\left(C_{2} \right)\right]+Tm_{n}\left[O\left(C_{1}\right)+O\left(C_{2}\right)\right]\}}. \tag{17}\]  
Secondly, for every user \(n\), it has to store one single Q-table and \(N-1\) joint Q tables. Then, the storage size of the UAV group is \(N\left(O\left(S_{1}\right)+\left(N-1\right)O\left(S_{2}\right)\right)\), where \(O\left(S_{1}\right)\) and \(O\left(S_{2}\right)\) are the storage sizes of single Q-table and joint Q table, respectively.  
Thirdly, information sharing is needed in multi-agent Q learning to share the joint Q-tables. Thus, the information sharing size of the UAV group is \(\sum\limits_{n=1}^{N}{Tm_{n}O\left(S_{1}\right)}\). Hence, the total complexity of the UAV group is expressed as:  
\[\begin{array}{l}C_{total}=\sum\limits_{n=1}^{N}{\{Ts_{n}\left[O\left(C_{1} \right)+O\left(C_{2}\right)\right]+Tm_{n}\left[O\left(C_{1}\right)+O\left(C_{ 2}\right)\right]\}}+\\ \sum\limits_{n=1}^{N}{Tm_{n}O\left(S_{1}\right)+N\left(O\left(S_{1}\right)+ \left(N-1\right)O\left(S_{2}\right)\right)}.\end{array} \tag{18}\]  
In a word, we can find that the computational complexity of each user in each time slot is a small constant, and the storage size in each time slot is not too large. Moreover, the data of information sharing in each time slot is also a constant, which means information sharing is realistic for users. In the simulation setting part, the settings for the Q table size also indicate that the data size of information  
Figure 4: Anti-jamming transmission slot structure for UAVs.  
sharing and the storage size are acceptable. Thus, we think the total complexity is affordable for users in the UAV group.
# Estimating the Volume of the Solution Space of SMT(LIA) Constraints by a Flat Histogram Method
## 2 Backgrounds
### SMT(LIA) Formulas and Their Solution Spaces  
**Definition 1**.: _A linear integer arithmetic (LIA) constraint is an expression of the form \(a_{1}x_{1}+a_{2}x_{2}+\ldots+a_{n}x_{n}\)\(\infty\)\(a_{0}\), where {x\({}_{1}\), x\({}_{2}\), \(\ldots\), x\({}_{n}\)} are numeric variables that take integer values, {a\({}_{1}\), \(a\)\({}_{2}\), \(\ldots\), \(a\)\({}_{n}\)} are constant coefficients (reals or integers), and the operator \(\infty\)\(\in\){\(\geq\), \(>\), \(=\), \(\
eq\), \(<\), \(\leq\)}. A LIA constraint is also called an SMT(LIA) atom._  
**Definition 2**.: _A SMT(LIA) formula \(\phi\)(\(b_{1}\), \(b_{2}\), \(\ldots\), \(b\)\({}_{m}\)) is a Boolean formula over a set of SMT(LIA) atoms {\(b_{1}\), \(b_{2}\), \(\ldots\), \(b\)\({}_{m}\)}._  
**Example 1**.: _A SMT(LIA) formula on a set of SMT(LIA) atoms: \(b_{1}\)\(\equiv\)\(x_{1}\)\(>\) 49, \(b_{2}\)\(\equiv\)\(x_{2}\)\(<\) 36, and \(b_{3}\)\(\equiv\)\(x_{3}\)\(>\) 11, is: \(\phi\): (\(x_{1}\)\(>\) 49) \(\wedge\) (\(x_{2}\)\(<\) 36 \(\vee\)\(x_{3}\)\(>\) 11)._  
**Definition 3**.: _The propositional skeleton (PS) of a SMT(LIA) formula \(\phi\)(\(b_{1}\), \(b_{2}\), \(\ldots\), \(b\)\({}_{m}\)), denoted as PS(\(\phi\)), is the Boolean structure over {\(b_{1}\), \(b_{2}\), \(\ldots\), \(b\)\({}_{m}\)}._  
For example, the SMT(LIA) formula \(\phi\) in Example 1 has a propositional skeleton \(PS(\phi)\): \(b_{1}\)\(\wedge\) (\(b_{2}\)\(\vee\)\(b_{3}\)), which contains two clauses: \(b_{1}\), and \(b_{2}\)\(\vee\)\(b_{3}\). To avoid confusion, we call \(x_{i}\) numeric variables, and call \(b_{i}\) Boolean variables.  
**Definition 4**.: _The solution (model) of a SMT(LIA) formula \(\phi\) is a valuation on numeric variables in \(\phi\) that evaluates \(\phi\) to true. And the volume of \(\phi\) is the number of its solutions, denoted by Vol(\(\phi\)). The problem of computing the volume of a SMT(LIA) formula is denoted as #SMT(LIA)._
## 4 Simulation Results
### Experimental Results
#### 4.2.1 Energy Consumption  
Since energy consumption is the core issue in WSNs, we discuss the impact of our protocol on energy consumption by comparing the performance of the proposed protocol with existing protocols.  
Figure 6 illustrates the difference of the energy consumed per round for different number of nodes in the network and different error estimation range \(\varepsilon\) in the proposed, EECDA, and LEACH-Hetero protocols. It is noticed that the energy consumption increases as the number of nodes increases in all protocols and EECDA performance is better than the performance of LEACH-Hetero, whereas EECDA maintains efficiently the energy consumption of sensor nodes by involving them in a single-hop communication within a cluster. Our proposed protocol outperforms EECDA, and LEACH-Hetero protocols in reducing the energy consumption. The reason is ECADP-CS uses the weighted election probabilities and the distances of sensor nodes to the BS in electing CHs, therefore, the energy efficiency is enhanced. From these results, it can be seen that the effect of the error estimation is not high in our proposed protocol.  
Figure 7 shows that ECADP-CS conserves lower energy comparing with CS data collection protocol. This is because in ECADP-CS all the sensor nodes organize themselves into clusters with one node elected as a CH according to their weighted election probabilities and their distance to the BS leading to reducing the energy consumption of sensor nodes.
# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 1 Introduction
Although Transformer-based methods obtain substantial improvements for MDE tasks, these models are difficult to train compared with CNN models due to the fact that the Transformer lacks spatial inductive bias [10]. To mitigate this drawback, an intuitive motivation is to combine the global receptive field of Transformer and the inductive bias of CNN into an integrated framework. In 2022, [PERSON] et al. proposed Lite-Mono, which effectively integrates CNN and Transformer to capture local features and global context information [18]. Compared to the Transformer-based methods, it maintains strong performance on MDEs with a significantly reduced number of parameters. Herein, we call such CNN-Transformer architecture hybrid methods. Soon after, several hybrid models were proposed in the field of MDE [19; 20; 21]. The existing MDE methods can be categorized into supervised and self-supervised ways from the perspective of learning strategy. The training of supervised-based methods needs ground truth, which is expensive to label. Leveraging the inherent geometric consistency of stereo images or video sequences, the self-supervised-based methods remove requirements of ground truth, and use photometric error to train the depth estimation networks. Consequently, we summarize the existing attention-based approaches into six categories: supervised CNN-based, self-supervised CNN-based, supervised Transformer-based, supervised hybrid, and self-supervised hybrid methods. Hereafter, we follow this taxonomy to review the literature. In this work, we provide a comprehensive survey on the attention mechanism used in MDE methods. There were a few overviews on monocular depth estimation [22; 23; 24]. However, to our best knowledge, this is the first time that the focus is on the attention mechanism used in MDE. Our contributions lie in providing a detailed review on how attention is integrated into the MDE methods, as well as why attention can benefit the performance of dense prediction tasks. We hope that our contributions can not only clarify the concept of attention in MDE, but also facilitate the related researchers to determine the potential directions. The remainders of the paper are structured as follows: Section 2 introduces the backgrounds of MDE and attention. Section 3 analyzes the existing MDE methods. Section 4 presents performance comparison of typical algorithms. Section 5 discusses the challenges and trends of MDE algorithms. Section 6 concludes the review.
## 5 Experimental Settings  
### Dataset  
To validate the effectiveness of SACT, the authors of this paper followed previous research and evaluated SACT on two commonly used FSRE datasets, FewRel 1.0 [42] and FewRel 2.0 [43]. FewRel 1.0, made available by Tsinghua University, is an expansive dataset designed for FSRE and is sourced from Wikipedia. It encompasses 100 distinct relations, each with 700 annotated instances. The dataset comprises training, testing, and validation subsets, featuring 65, 16, and 20 unique relations, respectively. Notably, there is no overlap in the relations between the training, testing, and validation sets. Please be aware that the validation set is not publicly accessible, but researchers can obtain the test scores by submitting their models via official testing scripts. However, it is worth noting that all the instances in the FewRel 1.0 dataset originate from a single domain's corpus. In real-world scenarios, data often span various domains such as medicine, education, biology, and cybersecurity, each with distinct syntax and content. These differences can potentially influence the performance of the model. To assess the performance of SACT in cross-domain scenarios, this paper also utilizes the FewRel 2.0 dataset. FewRel 2.0 extends upon FewRel 1.0 by including 25 additional relations from the biomedical domain, with each relation consisting of 100 instances as its validation set. The details of the dataset are provided in Table 1.  
The authors of this paper evaluated the model's accuracy in the following four FSL task settings: 5-way-1-shot, 5-way-5-shot, 10-way-1-shot, and 10-way-5-shot.  
Figure 6: Diagram of the contrastive–center loss. In the upper-left corner, we have the basic prototype representations. Through the influence of the center loss function, you can observe a significant reduction in the distance between positive samples. After being affected by the contrastive loss function, there is some increase in the distance between class centers and negative samples. However, the contrast-center loss function used by SACT not only reduces the distance between positive samples but also increases the distance between centers and negative samples.
## 7 Classification of Slope Instability Hazard Levels and Description of the Calculation Method  
The stability condition of the slopes was assessed using the following classification of landslide hazard levels [55]:  
* FS \(\geq\) 1.5--landslide occurrence is highly unlikely.
* 1.3 \(\leq\) FS \(\leq\) 1.5--landslide occurrence is unlikely.
* 1.0 \(\leq\) FS \(\leq\) 1.3--landslide occurrence is probable.
* FS \(<\) 1.0--landslide occurrence is highly probable.  
In engineering practice, a factor of safety (FS) greater than 1.5 is generally considered acceptable. However, since the analysed area constitutes a public space used by numerous city residents, a minimum FS of 1.5 was adopted as the threshold for safe conditions. A significantly higher safety level is recommended for large public gatherings and mass events, with a suggested FS exceeding 2.0.  
Stability calculations were performed using a finite element method (FEM)-based programme dedicated to two-dimensional (2D) slope stability analysis. The factor of safety was determined using the shear strength reduction (SSR) technique [56; 57; 58; 59; 60]. This method modifies the soil or rock mass as an elastic-plastic medium ruled by an associated or non-associated flow rule. The Coulomb-Mohr failure criterion was applied to define the yield surface for the cases analysed. In this context, the SSR method involves simultaneously reducing the internal friction angle (through tangent reduction) and cohesion. The SSR method enables the identification of the potential slip surface at the location where the limit state is first reached. Additional assumptions are introduced into the analysis to capture the progression of failure mechanisms and trace the subsequent development of slip surfaces.  
For all calculation scenarios, displacement boundary conditions were applied by restricting horizontal displacements along the model's sides and both horizontal and vertical displacements along the model's base. Based on the analyses conducted, the slope factor of safety (FS) values were determined.
###### Abstract  
Background: Extracted tooth material has been seen as a valuable alternative to synthetic biomaterials. Aims: A novel flapless alveolar socket preservation (ASP) procedure with demineralized extracted tooth dentin graft material covered by three reabsorbable membranes was histologically and clinically evaluated 4 months after bone healing. Methods: Ten patients were enrolled and separated into two groups. Five post-extractive sites without buccal and/or palatal bone walls were treated with the flapless ASP procedure with demineralized tooth dentin covered with three reabsorbable membranes (Group A--GA). Five patients were treated with primary wound closure covered only with a reabsorbable membrane (Group B--GB). Bone biopsies were performed for histologic and histomorphometric analyses. Results: In both procedures, all clinical outcomes showed good healing of hard and soft tissue and a good maintenance of maxillary architecture. The histological analysis showed no necrosis or inflammatory areas in either group. The histomorphometric analysis showed higher total bone volume in GA (62.78 \(\pm\) 7.97%) compared to GB (48.04 \(\pm\) 9.32%), higher vital new bone in GA (57.53 \(\pm\) 11.16%) compared to GB (42.41 \(\pm\) 13.06%) and similar values for residual graft in GA (5.24 \(\pm\) 5.82%) compared to GB (5.29 \(\pm\) 4.83%). Conclusions: The data obtained show how this novel technique, mixed with the dentin-derived graft material, seems to promote higher bone regeneration.  
ASP alveolar socket preservation; bone regeneration; demineralized tooth dentin; tooth transformer; bone
## 5 Conclusions  
This study comprehensively evaluated the applicability of multisource SIF satellite products for GPP estimation in arid regions using various indicators, adopted multiple methods to improve the accuracy of SIF satellite products that are most suitable for GPP estimation in arid areas, and comprehensively analyzed the spatial characteristics of GPP indirectly reflected by SIF data before and after improvement. The final research conclusions are as follows:  
1. The interannual variation of the monthly mean GPP in arid regions shows an inverted \"U\" shape, with peaks occurring in June and July. During the growing season (March to October), GPP first increases and then decreases, while in the nongrowing season (November to February), GPP fluctuations are not significant.
2. The overall suitability ranking of multisource SIF satellite products for GPP estimation in arid regions is as follows: RTSIF > CSIF > SIF_OCO2_005 > GOSIF. This has a profound significance for revealing the spatial and temporal patterns of the terrestrial ecosystem carbon cycle in arid regions by coupling multiple factors and provides new approaches for constructing carbon reduction policies in arid regions.
3. When improving the accuracy of SIF satellite products in arid regions, both the canopy improvement method and the linear improvement method need to be used in combination. This provides practical theory for achieving a more comprehensive and higher accuracy analysis of carbon source/sink spatial and temporal characteristics in arid region terrestrial ecosystems, which is of great significance for achieving \"carbon neutrality\" in arid regions.
4. Based on land use data, the spatial characteristics of SIF data in arid regions achieved through the two methods showed a high correlation with vegetation coverage, with the annual mean value of SIF data for each surface after improvement being approximately 0.13 mw/m\({}^{2}\)/nm/sr.
## 5 Methodologies
### Models
#### 5.1.5 RSQGAN Training Procedure  
The RSQGAN training procedure (Algorithm 1) extends the QGAN training procedure (see Appendix A.3) to the conditional case, as shown in Figure 7 below. This only involves minibatch sampling over all regime classes \(J\) and updating the class embedding parameters \(\theta_{Eg},\theta_{Eg}\). The base RSQGAN implementation applies the class-conditional encoding \(o_{j}\) for class \(\mathcal{C}_{j}\) into the conditional TCN by a learnable embedding representation \(e_{j}\) as an input into the second temporal block. This is a simplification to the 'z-skip layer' technique by [PERSON] et al. (2018), which suggested a joint noise and class encoding input at each hidden layer of the network. The effect of simplification is to allow for arbitrary size. Testing the efficacy of 'z-skip layers' at different time resolutions is expected to further improve the RSQGAN performance and is an area of future work. Finally, during training experiments, cosine annealing of the learning rate ([PERSON] et al., 2016) with a periodicity of eight epochs was implemented using the learning rate scheduler in pytorch. Figure 7: The training procedure for RSQGAN. Class regimes \(\mathcal{C}_{j},j\in J\) are input as onehot encoded vectors \(o_{j}\). Embeddings \(\theta_{Eg},\theta_{Eg}\) and network parameters \(\theta_{g},\theta_{\tilde{d}}\) are learned through training. The RSQGAN implementation concatenates the embedded representations \(e_{d},e_{g}\) with inputs in the first hidden layer and a fully connected network before forward propagation. This is a similar approach to [PERSON] et al. (2018) in the BigGAN architecture to allow for the generation of synthetic data of arbitrary size in the image domain. Adversarial training between \(cD(X;\theta_{d},\theta_{Eg})\) and \(cG(Z;\theta_{g},\theta_{Eg})\) continues until maximum epochs or stops early if synthetic data \(\tilde{X}|\mathcal{C}_{j}\) meet required synthetic standards in training for all classes \(j\in J\), e.g., when minibatch average test error metrics fall below certain thresholds.