esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### HF-Based Access Control  
The authors of [25] present the development of an attribute-based access control (ABAC) mechanism using Hyperledger Fabric components for IoT devices in a blockchain network. The article uses Raspberry Pi 4 Model B based on ARM64 architecture as the IoT device and evaluates the HF blockchain implementation and access control mechanism on the ARM64 architecture. The article demonstrates a real-world IoT-blockchain integration scenario, and through effective chaincode execution and testing, the authors successfully assess the ABAC mechanism using HF components.  
[PERSON] et al. [26] present the design and implementation of Fabric-IoT, a blockchain-based access control system for IoT that aims to solve the access control problem in IoT by providing dynamic access control management and record tracing using distributed architecture. The system is based on the HF platform and the Attribute-Based Access Control (ABAC) model.  
[PERSON] et al. in [27], propose an access control system for the Industrial Internet of Things (IIoT) based on blockchain technology and attribute-based access control (ABAC). The proposed access control system aims to provide decentralized, fine-grained, and dynamic access control management for IIoT to address security issues and ensure secure transactions between manufacturers.  
The authors of [28] propose an Attribute-Based Access Control Model for Internet of Things using Hyperledger Fabric Blockchain (ABAC-HLFBC). By using smart contracts, they have implemented fine-grained and expressive access control. The ABAC model extracts the attributes of the subject, object, permission, and environment and transforms permission management into attribute management. The proposed model can provide a distributed and lightweight secure access control solution for IoT that overcomes traditional centralized access control issues.  
[PERSON] et al. in [29], propose and implement a lightweight group management (H-LGM) model for IoT devices based on HF. The contribution of the proposed research work includes the proposal of lightweight rekeying to reduce the update overhead of group key (GK) and H-LGM, the use of GK to organize and manage groups, the use of an agent for lightweight rekeying, and the demonstration of H-LGM's effectiveness.
# Calibration and Analysis of Seeding Parameters of Soaked _Cyperus esculentus_ L. Seeds
## 2 Materials and Methods
### Determination of Physical Parameters of soaked C. esculentus Seeds
#### 2.2.3 Shear Modulus  
The magnitude of the shear modulus is a physical quantity that describes the ability of a seed to resist shear deformation [21]. In this paper, an FTC texture meter (TMS-Touch) was used for the extrusion test. During the testing process, soaked _C. esculentus_ seeds were selected and measured using electronic vernier calipers (accuracy of 0.01 mm) to determine their three-axis dimensions. The seeds were placed on the test platform with an initial loading force of 0.3 N and a loading speed of 1.5 mm/min. The computer automatically recorded the pressure change over time during the test. The test was repeated 10 times. The modulus of elasticity of soaked _C. esculentus_ seeds was calculated using Equation (4), and the test setup is shown in Figure 5.  
\[E=K_{\mathrm{a}}\frac{T_{\mathrm{C}}}{A_{S}}=K_{a}\frac{4T_{\mathrm{C}}}{\pi D ^{2}} \tag{4}\]  
\[G_{\mathrm{j}}=\frac{E}{2(1+\mu)} \tag{5}\]  
where E is the modulus of elasticity of the _C. esculentus_ seeds; k\({}_{\mathrm{a}}\) is the slope of the elastic phase in the curve of pressure with time on the seeds of _C. esculentus_; T\({}_{\mathrm{c}}\) is the initial thickness of the extruded _C. esculentus_ seeds, mm; A\({}_{\mathrm{s}}\) is the equivalent cross-sectional area of the extruded surface of the _C. esculentus_ seeds, mm \({}^{2}\); and G\({}_{\mathrm{j}}\) is the shear modulus of the _C. esculentus_ seeds.  
Figure 4: Measurement of Poisson’s ratio of soaked _C. esculentus_ seeds.  
Combined with the Poisson's ratio, the shear modulus of the _C. esculentus_ seeds was calculated using Equation (5). The average value of the shear modulus of the _C. esculentus_ seeds was 33 MPa.
## 4 Results
### Influencing Factors of the Change in Life Expectancy
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variables**} & \multicolumn{4}{c}{**Standardized Coefficient**} \\ \cline{2-5}  & **Nationwide** & **Low-Income Cities** & **Middle-Income Cities** & **High-Income Cities** \\ \hline Change rate of & 0.061 & \(-\)0.041 & 0.021 & 0.288 *** \\ per capita GDP & (1.231) & (\(-\)0.398) & (0.225) & (3.006) \\ (base year 2000) & (1.231) & (\(-\)0.398) & (0.225) & (3.006) \\ Change in health & 0.086 & 0.123 & 0.085 & \(-\)0.064 \\ care & (1.609) & (1.224) & (0.951) & (\(-\)0.634) \\ Change rate of & \(-\)0.003 & \(-\)0.007 & 0.050 & \(-\)0.032 \\ road density & (\(-\)0.070) & (\(-\)0.076) & (0.502) & (\(-\)0.356) \\ Change in & 0.202 *** & 0.125 & 0.293 *** & 0.373 *** \\ average years of & (3.449) & (0.982) & (2.651) & (3.332) \\ education & (3.449) & (0.205 * & 0.148 & 0.282 ** \\ Change in aging & 0.174 *** & 0.205 * & 0.148 & 0.282 ** \\ rate & (2.820) & (1.982) & (1.193) & (2.105) \\ Change in birth & \(-\)0.184 *** & \(-\)0.263 ** & \(-\)0.131 & \(-\)0.128 \\ rate & (\(-\)3.230) & (\(-\)2.376) & (\(-\)1.191) & (\(-\)1.298) \\ Change in & 0.028 & 0.283 ** & \(-\)0.023 & \(-\)0.076 \\ gender ratio & (0.464) & (2.068) & (\(-\)0.230) & (\(-\)0.570) \\ Change in & 0.086 & 0.012 & 0.199 & \(-\)0.025 \\ family size & (1.559) & (0.116) & (1.594) & (\(-\)0.247) \\ Average level of & 0.083 & 0.213 & 0.109 & \(-\)0.102 \\ air quality for & (1.008) & (1.157) & (0.643) & (\(-\)0.774) \\ many years & & (1.028) *** & 0.183 & 0.477 *** & \(-\)0.012 \\ Average altitude & 0.328 *** & (0.814) & (2.702) & (\(-\)0.073) \\ (ln) & (3.382) & (0.814) & (2.702) & (\(-\)0.073) \\ Average slope & 0.124 & 0.323 ** & 0.016 & 0.234 * \\ Average slope & (1.610) & (2.120) & (0.115) & (1.861) \\ Perennial mean & 0.128 ** & 0.230 * & 0.036 & 0.078 \\ temperature & (2.077) & (1.860) & (0.276) & (0.721) \\ \hline constant & \(-\)1.174 & \(-\)0.761 & \(-\)2.766 & \(-\)1.052 \\ Adj-R2 & 0.243 & 0.244 & 0.228 & 0.239 \\ F & 9.732 & 3.899 & 3.559 & 3.937 \\ \hline \hline \end{tabular} Notes: T statistic of the regression coefficient listed in parentheses. *** \(<\) 0.01, *** \(>\) 0.05, * \(>\) 0.1.  
\end{table} 
Table 4: OLS regression results. the change in life expectancy is insignificant, thus, it is not included in the geographically weighted regression analysis and will not be discussed in detail.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 1 Introduction  
MicroRNAs (miRNAs) play crucial regulatory roles that cause mRNA degradation or translational repression [1], which are involved in the initiation and progression of cancer [2; 3]. Composite miRNA functional module (CMFM) comprises miRNAs and mRNAs in a module that is believed to participate cooperatively in specific biological processes [4; 5]. To date, several functional modules have been computationally extracted from the regulation networks [6; 7]. However, most of these studies mainly performed on a single network, and few considered the cooperation between different types of networks such as the transcriptional regulation networks, protein-protein interaction networks and metabolic networks.  
In recent years, scholars have put forward many related computer methods to identify CMFM on the basis of multiple-types network [8; 9]. For example, SNMMNF (Sparse Network-regularized Multiple Non-negative Matrix Factorization) proposed by [PERSON] et al. [10] described a factorized matrix framework to identify composite miRNA functional modules by integrating miRNA and gene expression profiles, the protein-protein interactions (PPIs) and transcription factor binding sites. Another method called Mirsynergy, which was proposed by [PERSON] et al. [11], detected miRNA regulatory modules based on synergistic scores between two miRNAs. The method also took m/miRNA expression profiles, target site information and PPIs into consideration during the process of module identification. However, these two methods either require to presuppose the number of modules or generate many small-scale modules.  
In this work, we propose a novel model called MBCFM that integrates mRNA/miRNA expression profiles, mRNA interaction information as well as network topology characteristics. We apply MBCFMto ovarian (OV) cancer dataset. Comparing with two existing prominent methods, Mirsynergy and SNMNMF, we find that CMFM plays an important role in cooperation between regulation and implementation of protein function and our model is more computationally efficient. Moreover, the miRNAs and mRNAs involving CMFMs detected by MBCFM are strongly implicated in cancer.
# Resveratrol: A Review on the Biological Activity and Applications
## 3 Antitumor Properties
### Lung Cancer  
The antitumor effect of RSV has been connected to transformations in the sphingolipid metabolism. The analysis performed on the enzymes accountable for the aggregation of the two most relevant sphingolipids, apoptosis-promoting ceramide and proliferation-stimulating sphingosine-1-phosphate (S1P), in human lung adenocarcinoma cells, revealed that RSV administration enhanced apoptosis-promoting ceramide and sphingosine and reduced sphingomyelin and S1P [93].  
Studies show that RSV generated apoptosis in lung cancer cells by precisely targeting phosphoAkt (pAkt) and cellular FLICE-like inhibitory protein (c-FLIP) downregulation as a result of proteasomal degradation contingent to epidermal growth factor receptor (EGFR) [94]. Other data determined that it inhibits lung tumor cell proliferation by suppressing COX-2 expression [95]. New mechanisms of RSV tumor suppression have been discovered, implying that this phytochemical has the ability to reprogram M2-like tumor-associated macrophages and tumor-infiltrating CD8T effector cells [96].  
Further investigations reported the implication of autophagy in RSV-induced apoptosis and its possible molecular mechanisms via Ca\({}^{2+}\)/adenosine monophosphate-activated protein kinase (AMPK)-mammalian target of rapamycin (mTOR) signaling pathway in human lung adenocarcinoma cells [97]. In a non-small-cell lung cancer assay, RSV-induced autophagy and apoptosis at levels higher than 55 uM were mediated by the nerve growth factor receptor-AMPK-mTOR signaling pathway [98].  
There are numerous indications that cancer stem cells are a crucial conductor of carcinogenesis and tumor reoccurrence, involving cytokines and chemokines--the cellular and soluble constituents of the tumor microenvironment. The expression level of interleukin (IL)-6 is interconnected to the existence of lung cancer stem-like cells in human lung carcinoma. RSV has the ability to multi-target lung cancer stem-like cells and IL-6 in the tumor microenvironment, and it is an innovative approach to the therapy and prevention of lung cancer [99].  
Regarding the lung cancer metastasis of melanoma, the cytotoxicity of RSV was assessed in immunocompetent mice. The in vitro development of melanoma cells was substantially inhibited by RSV. The average survival rate of mice was improved, and the lung tumor proliferation was suppressed through in vivo intraperitoneal injection of 40 mg/kg RSV. The lung tumors exhibited enhanced interferon-gamma (IFN-\(\gamma\)) and C-X-C motif chemokine ligand 10 (CXCL10) levels and reduced angiogenesis and infiltration [100].
## 1 Introduction
We analyze the disadvantages of PCAWF- and ARPCAWF-based OMA for LTV structures, such as the high time complexity, space complexity, time required for matrix decomposition, modal exchange, and low accuracy. 3. Integrating the idea of \"forgetting factor weighting\" and the technology of ERPCA, we propose an ERPCAWF-based OMA method to identify the transient natural frequencies and transient modal shapes of LTV structures using only nonstationary response signals under unmeasured stationary ambient loads. This method is suitable for both SLTV and FLTV structures. 4. Through a theoretical comparison among different adaptive PCA-based OMA methods, we show that ERPCAWF-based OMA offers a faster runtime, lower memory consumption, and comparable accuracy while avoiding the ill-posed, singular values of matrix decomposition and modal exchange problems. By enhancing the efficiency and stability of modal parameter identification for LTV structures, this method is more suitable for online, real-time health monitoring and fault diagnosis. 5. We analyze the limitations and application scope of ERPCAWF-based OMA for LTV structures. 6. We design a three degree-of-freedom (DOF) weakly damped LTV structure to verify the operational modal identification ability of ERPCAWF-based OMA. A comparison of different methods using nonstationary vibration response simulation data shows that ERPCAWF-based OMA is faster, less memory-intensive, and more precise than the ARPCAWF-based OMA method. This paper is organized as follows. In Section 2, we introduce the transient representation of LTV structures. In Section 3, we describe PCAWF- and ARPCAWF-based OMA for SLTV structures and analyze their limitations. The proposed ERPCAWF-based OMA technique is illustrated in Section 4, and the operational modal identification ability of ERPCAWF-based OMA is verified in Section 5. Finally, we conclude this paper in Section 6.
# Modeling and Compensation of Motion Errors for 6-DOF Robotic Manipulators
## 3 Trajectory Compensation Based on Stiffness Modeling
### Flexibility Matrix  
External loading will cause deformation of the manipulator due to the lack of stiffness. Joint deformation basically accounts for a large proportion of deformations, while other deformations account for a very small proportion.  
The spring coefficient \(K_{qi}\) was assumed to represent the stiffness of the entire drive system:  
\[\tau_{i}=K_{qi}dq_{i}\ \ \ \ \ i=1,2,\ldots,n \tag{11}\]  
where \(\tau_{i}\) is the joint torque, \(dq_{i}\) is the additional deformation caused by the action of joint variables \(q_{i}\) from \(\tau_{i}\), and the total relation is  
\[\tau=K_{q}dq \tag{12}\]  
\[K_{q}=diag\left(k_{q1},k_{q2},\ldots,k_{qn}\right) \tag{13}\]  
where \(diag\left(k_{q1},k_{q2},\ldots,k_{qn}\right)\) represents the matrix whose diagonal elements are \(k_{q1},k_{q2},\ldots,k_{qn}\).  
Based on the error \(D=J(q)dq\) derived from the differential motion relation and Jacobian matrix \(\tau=J^{T}(q)F\) of the forces, the following relation can be obtained:  
\[D=J(q)dq=J(q)K_{q}^{-1}\tau=J(q)K_{q}^{-1}J^{T}(q)F \tag{14}\]  
The inverse of \(K_{q}\) is \(K_{q}^{-1}\), which is often referred to as joint flexibility.  
Let the end flexibility matrix be \(C(q)\), then  
\[C(q)=J(q)K_{q}^{-1}J^{T}(q) \tag{15}\]  
Equation (15) is used to represent the relationship between the error \(D\) (or \(\delta_{\theta}\)) of the working space and the generalized force \(F\), which can be expressed as  
\[D=C(q)F \tag{16}\]  
where \(D\) is proportional to \(C(q)\), so the deformation at the end of the manipulator will change as the Jacobian matrix changes.
## 3 Contextual Received Signal Strength Approach  
### Assumptions  
In developing the CRSSA for WSNs, our methodology is predicated on a set of foundational assumptions. These assumptions are designed to mirror realistic deployment scenarios and signal propagation behavior in WSN environments, ensuring that our approach is both practically relevant and scientifically rigorous. Key among these assumptions are:  
* Random 2D Deployment with Unknown Initial Positions: All sensors, unaware of their own positions initially, are randomly deployed across a two-dimensional plane, mirroring diverse real-world deployment scenarios. After deployment, they are assumed to remain stationary. This static nature simplifies the analysis by eliminating the variable of sensor movement, focusing our study on the efficacy of localization under stable conditions.
* Adoption of Nakagami-Lognormal Model for Real-World Scenarios: The adoption of the composite Nakagami-lognormal (NL) distribution model is pivotal in accurately representing the signal fading and shadowing effects in various environments. By utilizing this model, the CRSSA effectively adapts to realistic signal propagation scenarios, enhancing the robustness and reliability of the localization process in diverse WSN settings. The composite NL distribution is employed to model the mixture of small-scale fading and shadowing.
* Varied and Dynamic Communication Range Based on Propagation Model: While each sensor operates within a communication range \(R\), this range is influenced by the specific propagation model in use, particularly the composite NL distribution. This approach acknowledges the variability in signal strength and range due to environmental factors, ensuring a more accurate representation of real-world WSN conditions.
* Dynamic Communication Ranges: Given the reliance on the composite NL propagation model, the communication ranges between sensors are dynamic, reflecting the variability in signal propagation. This dynamic range adds realism to our model, acknowledging the fluctuating nature of signal strength in different environmental conditions.
* Ensuring Comprehensive Network Connectivity: In our model, we assume that the network is fully connected without any disconnected sub-networks, which is crucial for the effective functioning of the CRSSA method. This complete connectivity is essential for preventing information isolation and ensuring that data can flow seamlessly throughout the entire network. To ascertain and maintain this level of network integrity, we utilize [PERSON]'s theorem in our simulations. This helps in identifying the most efficient paths and avoiding scenarios where parts of the network become isolated, thereby safeguarding the reliability and effectiveness of the localization process across the entire network.  
These assumptions form the backbone of our approach, allowing us to develop and evaluate the CRSSA in a controlled yet realistic manner, simulating conditions representative of typical WSN applications.
## 3 Proposed Method
### Efficient Channel Residual Block  
In this paper, we expect the generator to be able to generate FPM color images similar to the domain Y. The ResNet-based [32] generator is widely used in the field of image-to-image translation. However, CUT failed to accurately identify the unstained part of the pathological section when staining the single-channel FPM input image, resulting in unnecessary artifacts and producing low-quality color images. The weight of each channel  
Figure 3: The overall architecture of the network. The generator G is broken down into two components: an encoder \(\mathrm{G_{enc}}\) and a decoder \(\mathrm{G_{dec}}\). We use \(\mathrm{G_{enc}}\) and the MLP (\(\mathrm{H_{I}}\)) as the embedding of domain X and domain Y. We expect the PatchNCE loss to make the red patch in the fake image generated by generator G more consistent with the yellow patch in the real input image while minimizing the similarity with other blue patches.  
in a deep neural network can be recalibrated by using channel attention. This allows for a more adaptive selection process for determining the object of focus in different feature maps, as each channel can represent a different object. However, most of the methods proposed in recent years have focused on designing more complex attention modules. It undoubtedly increases the complexity of the model while delivering better performance. For this purpose, we designed an ECR block to replace the residual block in the CUT.  
As shown in Figure 5, we introduce ECANet [33] into the residual block of the generator. Concretely speaking, the ECR block consists of two parts by the shortcut connection. One part is the original residual block, which includes two convolutional layers and one RELU layer, and the other is the efficient channel attention (ECA) block, which includes a global average pooling layer, a one-dimensional convolutional layer with an adaptive convolutional kernel, and a sigmoid layer. These two parts each have a shortcut connection between the input and output.  
ResNet can truly deepen the depth of convolutional neural networks without reducing network accuracy, and its powerful representation ability can significantly improve network performance in many computer vision fields. The ECA block improves network performance by avoiding reducing channel dimensions to learn effective channel attention while obtaining cross-channel interaction information in an extremely lightweight manner. We add identity maps to the ECR block, which not only ensures that there is no gradient vanishing problem during backpropagation but also shifts the network to learning residual  
Figure 4: The specific structure of generator and discriminator networks. The generator adopts an encoder-decoder architecture with nine ECR blocks. The discriminator utilizes the classic PatchGAN network, which has five convolutional layers.  
functions, ensuring a smoother decision function for the entire network and improving generalization performance.
## 3 Proposed Algorithm
### Properties of ISOD
#### 3.4.2 Interpretability  
Interpretability is an important aspect of the practical applications of anomaly detection. In network attack detection, for example, finding an anomaly is as important as identifying the cause of the anomaly. An algorithm with high interpretability has greater reliability, which not only means that it can provide a result but also the reason(s) behind such a result, which is good for improving the performance of the system and assisting in decision making. Therefore, interpretability is very important in the application of anomaly detection.  
As can be seen from Formula (5), the ISOD algorithm aggregates the anomalies on each dimension to determine the final anomaly score. Where necessary, we can give the anomalies of the anomalous data in each dimension, which helps the expert to further identify the dimension in which the anomaly occurs. This involves taking anomaly detection from a \"black box\" to a \"white box\".