esa-sceva/satcom-chunk-collection huggingface dataset:
# Characteristics of Low-Frequency Acoustic Wave Propagation in Ice-Covered Shallow Water Environment
## 2 Elastic Model
### Boundary Condition  
In this model, the ice layer and seafloor are regarded as an elastic medium. When the sound wave enters the elastic medium, it will produce two kinds of waves: compressional wave and shear wave [17]. In certain conditions, three complex interface waves may be excited at the interface [18], such as the Rayleigh wave (air-elastic interface) [19] and Scholte wave (fluid-elastic interface) [20]. Therefore, the boundary conditions of the elastic parabolic equation are considered from the above two aspects.  
Generally, the air-elastic interface is regarded as the pressure release interface, which meets the zero traction condition, it is expressed as  
\[\lambda_{1}\Delta_{1}=0 \tag{9}\]  
\[\lambda_{2}u_{r2}+(\lambda_{2}+2\mu_{2})\frac{\partial w_{2}}{\partial z}=0 \tag{10}\]  
\[\frac{\partial}{\partial z}(\lambda_{2}u_{r2})+\frac{\partial}{\partial z}( \lambda_{2}+2\mu_{2})\frac{\partial w_{2}}{\partial z}+\rho_{2}\omega^{2}w_{2 }=0 \tag{11}\]  
where subscript 1 and subscript 2 represent the air layer and the elastic layer, respectively.  
The boundary conditions of the fluid-elastic interface should satisfy that the vertical stress is continuous, the displacement is continuous, and the tangential stress is zero [21], which is written as  
\[\frac{\partial}{\partial z}(\lambda_{2}\Delta_{2})+\rho_{2}\omega^{2}w_{2}=0 \tag{12}\]  
\[\lambda_{2}\Delta_{2}=\lambda_{3}\Delta_{3}+2\mu_{3}\frac{\partial w_{3}}{ \partial z} \tag{13}\]  
\[\frac{\partial}{\partial z}(\lambda_{2}u_{r2})+\frac{\partial}{\partial z}( \lambda_{2}+2\mu_{2})\frac{\partial w_{2}}{\partial z}+\rho_{2}\omega^{2}w_{2 }=0 \tag{14}\]  
where the subscript 3 represents the fluid layer.  
In the discrete process of parabolic equation, the interface conditions may be handled by introducing artificial grid points; we placed the interface midway between real and artificial grid points in both layers [22]. The interface condition is discretized by the central difference method and substituted into the discrete parabolic equation. The artificial quantity can be eliminated at the interface.
# Comprehensive LCA of Biobased Sustainable Aviation Fuels and JET A-1 Multiblend
## 2 Materials and Methods
### Environmental Assessment
During the allocation, the determined environmental impacts are partitioned between the different manufactured products according to a predefined parameter (e.g., lower heating value, product prices, or mass). Because the by-products generated during the HEFA production process are used internally, they were not further considered methodologically. In the case of SIP production, the surplus electricity was taken into account using a credit system to replace the emission factor for the same type of fossil fuel power generation. For example, it was assumed that electricity from a biogas CHP replaced the same amount of electricity from a natural gas CHP. Accordingly, an emission factor for electricity from a natural gas CHP was used for the credit. The method of crediting is based on the assumption that by-products from the manufacturing process can replace other products, which therefore no longer require manufacturing. The emissions thus avoided are credited to the product system. The LCA was conducted as a first step for the considered biobased sustainable aviation fuels (SAF), followed by an assessment of the two multiblend concepts within defined system boundaries. Setting the system boundaries is one of the most important aspects of carrying out a life cycle assessment. The system boundaries define the framework in which the assessment takes place and determine which material and energy flows are taken into account in the assessment. The system boundaries of biobased SAF comprise the entire supply chain. This includes the process of biomass production, transport and conversion, and transport of SAF to the fuel depot where the multiblend is mixed (Figure 1). Biomass production comprises both the cultivation and the supply processes required for the use of cultivated biomass. If residues and waste are used for biofuel production, the analysis starts with transport of the feedstock to the conversion plant; upstream processes are not included. The system boundaries of the investigated multiblend concepts included the supply chain of the SAF as well as the supply chain of the conventional JET A-1 fuel, transport of the multiblend from the fuel depot to the airport, and use of the multiblend in an aircraft (Figure 1). CO\({}_{2}\) emissions from biobased SAF use were not included in the calculation of GHG emissions; according to the IPCC, biogenic CO\({}_{2}\) emissions are considered to be offset by the CO\({}_{2}\) sequestration during plant growth [30]. The mass and energy flows for the SAF components and the JET A-1 fossil fuel were based on actual data and information from the literature. The functional unit, which provided a reference to which all the inputs and outputs were related, was set for 1 MJ aviation fuel.
## 2 The TASIPPS Conceptual Framework
### The TASIPPS Framework Components
#### 2.1.4 The Security Module  
This module manages the security aspect of the framework, which is critical in the sharing and exchanging of EHRs. The GDPR in (Art 5(1) (f)) states that personal data must be \"processed in a manner that ensures appropriate security of the personal data, including protection against unauthorized or unlawful processing and accidental loss, destruction or damage, using appropriate technical or organizational measures\" This informs some of the actions undertaken to achieve security within the proposed research. Figure 7 shows the three components of the security module of the framework. They are application security, network security, and database security.  
Figure 7: Components of the security module.  
As illustrated in Figure 7, these three components are part of the security provisions needed to protect against attacks on the EHR systems, and form part of the interoperability system. The components are discussed further below.
## 4 Primary Findings
### Holistic Analysis of Findings  
#### 4.1.1 I4.0 Awareness  
The \"I4.0 awareness\" category considers the awareness of companies from two perspectives. User companies indicated the average awareness level among their employees, while I4.0 providers and consultants--among their clients. In addition, 33% of respondents described the knowledge level as decisively low, and 13% as generally good (Figure 2). Furthermore, 53% of interviewees reported mixed awareness levels within the companies. In this condition, only employees implementing the technologies understand the I4.0 concepts, and the rest of the employees possess a low level of knowledge. Surprisingly, even some provider companies have a mixed level of I4.0 awareness.  
Based on the interview analysis, several possible reasons for the low level of I4.0 awareness can be underlined. First, the variety of definitions of I4.0 and the complexity of the concept makes it difficult for implementers to identify the real scope and aims of I4.0 [51]. Another reason could be the wide misconception of the term within the country.  
The term I4.0 is not so popular in Kazakhstan. This term is usually wrongly understood by clients. (Interviewee 1)  
Another reason for misconception might be the wording of governmental policies to increase the digitalization of industries. They solely concentrate on that term with no references to I4.0. Thus, the I4.0 concept remains unusual and unknown for most citizens of the country [62].  
Further investigation showed that cultural perception by the workforce also might be a serious obstacle. Respondents reported that employees fear automation, robots, and other technologies because of the associated massive workplace reduction.  
People think that, after the implementation of I4.0, they will lose their job. Thus, they start quiet sabotage. (Interviewee 03)  
Figure 2: Industry 4.0 awareness level in Kazakhstan companies.  
Fear of unemployment due to I4.0 is an important factor, investigated by many researchers in worldwide practices [4; 44]. However, there is a contradictory opinion that I4.0 will create more job opportunities [63]. Nevertheless, continuous acquisition of new skills is a requirement of the modern world.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 2 Principle and Design of the Sharpness Evaluation Method
### Sharpness Evaluation Model  
After acquiring distance factors, the final sharpness evaluation value can be obtained by introducing them into Equation (6).  
\[\text{Value}=\sum_{\omega_{minE}}^{\omega_{maxE}}d(\omega_{i})P(\omega_{i}) \omega_{i} \tag{6}\]  
In the above equation, \(\omega_{minE}\) and \(\omega_{maxE}\) are the minimum and maximum edge widths, respectively.  
Finally, we summarize the sharpness evaluation model proposed in this paper. The edge information of the measured image can be obtained after the edge detection. Then, the edge direction of the edge point can be determined by calculating the eight-neighborhood grayscale difference of the extracted edge point and the edge width can be calculated along the edge direction of the edge point. With the edge width, the histogram of edge width can be established. Then, the distance factor of each edge width can be acquired according to the distance factor calculation equation. Afterwards, the distance factor is introduced into the evaluation index to obtain the sharpness evaluation model of this paper. The above process is shown in Figure 15.  
Figure 14: The distance factor variation relationship in Equation (5).  
Figure 13: The distance factor variation relationship in Equation (4).
# Multi-Source Cooperative Data Collection with a Mobile Sink for the Wireless Sensor Network
## 3 Directed Graphs and Distributed Low-Density Parity-Check Codes for Data Collection
### Directed Graphs for Multi-Source Sparse Cooperation  
In the following, we represent the cooperation relation using the directed graphs for the two models.
# Mitigating Digital Ageism in Skin Lesion Detection with Adversarial Learning
## 3 Methods
### Protecting Continuous Sensitive Attributes with Mutual Information Penalty  
To address bias related to continuous sensitive attributes such as age, we propose incorporating a mutual information loss term in the objective function of the learning algorithm. The core idea is that, by minimizing the mutual information between the sensitive attribute (age) and the latent representation, we can reduce the model's dependence on age when making predictions. This ensures that the model focuses more on relevant skin lesion features rather than age, thus mitigating the impact of age-related bias in skin lesion detection. Given the input features, which include the skin image and metadata \(X\), the target label \(Y\), and the continuous sensitive attribute \(A\) (in this case, age), we can calculate the mutual information between \(X\) and \(A\) as  
\[I(X;A)=H(X)-H(X|A) \tag{5}\]  
where \(H(X)\) is the entropy of \(X\) and \(H(X|A)\) is the conditional entropy of \(X\) given \(A\). To mitigate the bias, we wish to minimize the mutual information between \(A\) and the hidden representation \(Z\), while preserving the mutual information between \(X\) and \(Z\), \(Y\), and \(Z\). This can be formulated as the following optimization problem:  
\[\min I(Z;A),\text{ s.t. }I(Z;X)\geq C_{1}\text{ and }I(Z;Y)\geq C_{2} \tag{6}\]  
where \(I(Z;A)\) is the mutual information between the latent representation \(Z\) and the sensitive attribute \(A\). Minimizing this term helps to reduce age bias. \(I(Z;X)\) is the mutual information between the latent representation \(Z\) and the input \(X\). This term should be maintained above a certain threshold \(C_{1}\), ensuring that the representation \(Z\) retains enough information from the input \(X\) for accurate classification. \(I(Z;Y)\) is the mutual information between the latent representation \(Z\) and the target label \(Y\), and it should also be above a threshold \(C_{2}\) to ensure that \(Z\) contains enough information to make correct predictions. \(C_{1}\) and \(C_{2}\) are constants that define the minimum acceptable levels of mutual information, balancing the model's ability to use the input and target label information.  
As a result, we can formulate the adversarial loss term; this is achieved by minimizing the following objective function:  
\[L_{Adv}(h(f(X,A)),A)=I(Z;A)-\sigma_{1}I(Z;X)-\sigma_{2}I(Z;Y) \tag{7}\]  
where \(\sigma_{1}\) and \(\sigma_{1}\) are hyperparameters that balance the trade-off between the classification and reconstruction accuracy and the fairness of the model from the level of mutual information. Finally, we can integrate Equation (7) into Equation (2).
## 2 Data and Methods
### Construction of the Sea Surface  
The Gaussian sea surface is envisaged as a series of superpositions of cosine waves with different wavelengths, amplitudes, and phases by the linear superposition method [33; 34]. The surface elevation can be written as follows:  
\[z_{1}(x_{m},y_{n},t)=\sum_{x=1}^{M}\sum_{y=1}^{N}\sqrt{2S(k,\varphi)dk}\cos \left(k_{x}x_{m}+k_{y}y_{n}-\omega_{k_{x},k_{y}}t+\epsilon_{k_{x},k_{y}}\right) \tag{4}\]  
where \(M\) and \(N\) are the numbers of facets in ground range and azimuth direction, respectively. \(S(\cdot)\) is the ocean wave spectrum, in which the spreading function of the cosine form [16] is used. \(\omega_{k_{x},k_{y}}\) is the angular frequency, and \(\epsilon_{k_{x},k_{y}}\) is uniformly distributed over (0, 2\(\pi\)). The coordinate system can be expressed as follows:  
\[\begin{array}{ll}x_{m}=&\Delta x(m-M/2)|_{m=1,2,\ldots,M}\\ y_{n}=&\Delta y(n-N/2)|_{n=1,2,\ldots,N}\end{array} \tag{5}\]  
in which \(\Delta x\) and \(\Delta y\) are the size of the facet in ground range and azimuth direction.  
Nevertheless, the actual ocean environment is extremely complicated and usually has characteristics of non-Gaussianity. In this paper, we used the Tayfun model [35] to construct a non-Gaussian large-scale sea surface. The Tayfun model can be expressed as follows:  
\[z(x)=z_{1}+\frac{1}{2}\widetilde{k}\left[\left(z_{l}^{2}-\widetilde{z}^{2} \right)+2z_{l}\widetilde{z}\right] \tag{6}\]  
where \(\widetilde{k}\) is the mean wavenumber, \(\widetilde{k}=m_{1}/m_{0}\), \(m_{j}=\int k^{j}S(k)dk\), and  
\[\widetilde{z}(x_{m},y_{n},t)=\sum_{x=1}^{M}\sum_{y=1}^{N}\sqrt{2S(k,\varphi) dk}\sin\left(k_{x}x_{m}+k_{y}y_{n}-\omega_{k_{x},k_{y}}t+\epsilon_{k_{x},k_{y}}\right) \tag{7}\]
# Facile Preparation of Graphene Oxide-MIL-101(Fe) Composite for the Efficient Capture of Uranium
## 1 Introduction
One step or the in situ fabrication of MOF-based composite is a more facile and environmentally benign synthetic method to achieve novel adsorbents with a satisfying performance. Several previous studies have provided excellent examples for GO-MOF composites as adsorbents [41; 42]. Herein, we presented the fabrication of the GO-MIL-101(Fe) composite through in situ growth method. Different ratio of GO and MIL-101(Fe) in the composite was tuned through changing the addition amount of GO nanosheets. The size and distribution density of MOF polyhedrons varied due to the interference of the functional groups on GO nanosheets towards the nucleation and growth of MIL-101 seed. Morphology imaging based on scanning electron microscopy (SEM) and a composition analysis from powder X-ray diffraction (XRD), Attenuated total internal reflection Fourier (ATR-FTIR) and X-ray photoelectron spectroscopy (XPS) measurements eVidenced the successful synthesis of the composite material. Batch adsorption experiments further confirmed the much better adsorption performance of the GO15-MIL-101(Fe) composite than pristine MIL-101(Fe) with larger U(VI) adsorption capacity and superior reusing ability. Mechanism analysis further illustrated the importance of optimizing synergistic effects between individual components in improving the adsorption performance. This work would benefit the design and development of composite adsorbents with a versatile functionalization for the potential application in the effective recovery of U(VI) pollution.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 2 Lip-Reading Model Architecture
### ShuffleNet
#### 2.1.2 Channel Shuffle  
However, group convolution also leads to the problem that different groups can no longer share information. Therefore, ShuffleNet performs a channel shuffle operation on the output features so that information can circulate through different groups without increasing computing costs. The process of the channel shuffle is shown in Figure 3.  
Figure 3: The process of the Channel Shuffle. The GConv2 layer is allowed to obtain features of different groups from the GConv1 layer due to the Channel Shuffle layer. That means the input and output channels will be fully related. (GConv: Group Convolution).  
Figure 2: (**a**) Conventional Convolution (**b**) Group Convolution. C, N, G, and O correspond to the numbers of the channel, kernel, group, and output, respectively.
## 2 Theory and Method
### Reasons and Analysis of Missing Data
#### 2.1.2 Types of Missing Data  
According to the existing studies, different patterns of missing data occur in different time series. Considering the actual situation of landslide monitoring, the types of missing data in landslide monitoring time series can be usually classified into the following two types.  
1. Random missing (RM): Randomization can occur at any time during the monitoring process. Such data loss has no regularity or symptoms. The time of data loss is random and occasional, as shown in Figure 1. (2) Non-random missing (NM): In the monitoring process, regular or periodic phenomena such as cloudy weather and signal interruptions may occur with the same probability in a specific period of time, and such missing data has a certain regularity, as shown in Figure 2. (2) Non-random missing. The curves in the green area represent missing data.
## 4 Conclusions  
The results from the eight schemes modeling the C\({}_{6}\) series show that incorporating rigorous chemical equilibrium information into the model leads to virtually identical predictions. The average prediction errors for molar flows range between 3.18% and 3.34%, while for temperature, the error is 1.44% across all schemes. These results confirm the good fit of the model and enable a detailed analysis of the phenomenon with any of the schemes, as long as the reactions considered are sufficient to define the reactor behavior.  
Although the linearly dependent schemes provide results similar to the independent ones, it is important to highlight that the independent schemes avoid redundancy in the model and allow for the representation of the C\({}_{6}\) series with fewer reactions, thereby facilitating its solution.  
Additionally, modeling the C\({}_{6}\) series using both linearly dependent and independent schemes resulted in a significant reduction in the number of chemical reactions, achieving a decrease of 72.2% and 77%, respectively, compared to the model reported in the literature [15], which considers 18 reactions.  
The results obtained indicate that the values of the kinetic constants for the reactions may be unique. That is, regardless of the scheme used among the eight studied for the C\({}_{6}\) series in the kinetic set of the isomerization process analyzed, the values of A\({}_{\text{j}}\) and E\({}_{\text{j}}\) for the reactions remain invariable.  
Conceptualization, D.K.D.-C. and A.E.-D.; Formal analysis, D.K.D.-C. and A.E.-D.; Investigation, D.K.D.-C., A.E.-D. and E.L.-L.; Methodology, D.K.D.-C. and A.E.-D.; Writing--original draft, D.K.D.-C., A.E.-D. and E.O.-d.-l.-R.; Software, D.K.D.-C. and A.E.-D.; Data curation, A.C.-A., E.L.-L., F.P.-V. and E.O.-d.-l.-R.; Validation, A.O.-A., E.L.-L. and F.L.-M.; Visualization, A.C.-A., A.O.-A., F.P.-V. and E.O.-d.-l.-R.; Writing--review and editing, A.C.-A., A.O.-A., F.P.-V. and F.L.-M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The datasets presented in this article are not readily available because they are detailed in [15].  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript
## 2 Results  
### Generating Lung Cancer Cell Lines with FAM13A Knockdown  
In our previous study, we have indicated that the expression of _FAM13A_ was significantly up-regulated under hypoxia conditions in two lung cancer cell lines (A549, CORL-105, \(p<0.001\)) and in lung cancer tissue fragments (\(p=0.0004\)) [6]. In this study we decided to analyze the impact of FAM13A knockdown in lung cancer cell lines. _FAM13A_ shRNAs (FAM13 Ash1, FAM13 Ash2) and control shRNAs (CtrNT2, CtrSCR) lentiviral particles were transduced into the A549 and CORL-105 cell lines (Figure 1A). Transduction efficiency was assessed with the GFP marker and it reached over 90% in both cell lines (Figure S1). Knockdown of _FAM13A_ mRNA and FAM13A protein was confirmed by real-time quantitative PCR and western-blot analysis (Figure 1B,C), respectively. For FAM13A sh1 RNA and FAM13A sh2 RNA cells stable FAM13A knockdown was obtained, measured after 72 h of culture in both oxygen concentrations. Induction of _FAM13A_ gene expression after hypoxia was observed among control cells (fold change FC, compared to normoxia): CtrNT2 (A549 FC = 2.2, CORL-105 FC = 2.1), CtrSCR (A549 FC = 2.5, CORL-105 FC = 2.8) and also wild-type cells (A549 FC = 2.2, CORL-105 FC = 2.1). Among shRNA treated cells this induction in hypoxia was strongly suppressed. During western blot analysis we detected the FAM13A isoform 1 with a molecular weight of about 117 kDa and the FAM13A isoform 2 (80 kDa). In A549 cells we observed the protein band with a molecular weight of about 50 kDa which could be the additional FAM13A isoform, as previously described [14]. Among shRNA treated cells specific downregulation of isoform 1 (117 kDa), containing the RhoGAP functional domain, was confirmed.