esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Practical Formula for Bearing Capacity and Reliability Analysis
### Formula Validation  
Figure 12a-c present a comparison between the collected experimental results \(N_{\text{u,Exp}}\), the FE results \(N_{\text{u,FE}}\), and calculations based on Equation (10) \(N_{u,r}\). The average ratios of actual ultimate bearing capacity \(N_{\text{u,Exp}}\) to \(N_{u,r}\), \(N_{\text{u,FE}}\), and \(N_{\text{u,FE}}\) to \(N_{u,r}\) are 0.972, 0.993, and 0.961, respectively, with CV of 0.054, 0.040, and 0.026, respectively. Additionally, Figure 12d illustrates a comparison between FE analysis results from parametric analysis  
Figure 11: Relationship between the FE models’ ultimate strength and the steel tube stress ratio. (**a**) Longitudinal stress in the steel tube; (**b**) Lateral stress in the steel tube.  
and calculations based on Equation (10). The mean ratio of \(N_{\text{u,FE}}\) to \(N_{u,r}\) is 1.006, with a CV of 0.046. The evidence clearly indicates that calculated values from Equation (10) align well with experimental results and closely correspond to FE analysis outcomes, thereby affirming the accuracy of Equation (10). Consequently, the proposed equation offers an accurate estimation of the ultimate bearing capacity of DS-CFCST stub columns.
# Compressible Diagnosis of Membrane Fouling Based on Transfer Entropy
###### Abstract  
Membrane fouling caused by many direct and indirect triggering factors has become an obstacle to the application of membrane bioreactors (MBRs). The nonlinear relationship between those factors is subject to complex causality or affiliation, which is difficult to clarify for the diagnosis of membrane fouling. To solve this problem, this paper proposes a compressible diagnosis model (CDM) based on transfer entropy to facilitate the fault diagnosis of the root cause for membrane fouling. The novelty of this model includes the following points: Firstly, a framework of a CDM between membrane fouling and causal variables is built based on a feature extraction algorithm and mechanism analysis. The framework can identify fault transfer scenarios following the changes in operating conditions. Secondly, the fault transfer topology of a CDM based on transfer entropy is constructed to describe the causal relationship between variables dynamically. Thirdly, an information compressible strategy is designed to simplify the fault transfer topology. This strategy can eliminate the repetitious affiliation relationship, which contributes to diagnosing the root causal variables speedily and accurately. Finally, the effectiveness of the proposed CDM is verified by the measured data from an actual MBR. The results of experiments demonstrate that the proposed CDM fulfills the diagnosis of membrane fouling.  
membrane fouling; diagnosis; causal relationship; root causal variables; transfer entropy
# Semantic Segmentation Method for Road Intersection Point Clouds Based on Lightweight LiDAR
## 2 Materials and Methods
### Point Cloud Registration and Stitching
#### 2.2.2 Iterative Closest Point (ICP) Registration Algorithm  
The iterative closest point (ICP) algorithm [30] seeks to find a solution to aligning two point clouds. Specifically, it aims to determine the optimal rigid transformation (including both rotation and translation) that minimizes the distance between corresponding points in the two point clouds.  
The process involves (1) identifying the closest point in the target point set for each point in the source point set and forming feature point pairs and (2) solving for the optimal mapping transformation matrix between the source and target point clouds to ensure the best match under the constraint conditions.  
The main steps of the ICP algorithm are as follows:  
* Step 1: In the KD-tree data indexing structure, create feature point pairs \(p_{i}\) and \(q_{i}\) in source point set P and target point set Q.
* Step 2: Based on the three-dimensional spatial information of the feature point pairs, solve for the corresponding rotation parameters R and translation parameters T.
* Step 3: Perform coordinate transformation on the source point set using these parameters. If the root mean square (RMS) distance between the transformed point set and the target point set is less than the pre-set threshold, the iteration terminates; otherwise, repeat the iteration until the result meets the threshold or the maximum number of computations is reached.
* Step 4: Calculate the optimal transformation parameters and use the mapping transformation matrix to stitch the point cloud data.
# Improved MUSIC Method against Range Dimension Deceptive Jamming Based on FDA-MIMO
## 5 Simulation and Analysis
### Performance Analysis  
Figure 12 shows the variation trend of the mean square error (MSE) of several spectrum estimation algorithms with SNR. As can be seen from Figure 12, the MSE of several algorithms gradually decreases as the SNR increases. The ESPRIT algorithm has the highest MSE, the Capon algorithm and MUSIC algorithm are in the middle, and the proposed improved MUSIC algorithm has the lowest MSE. The performance of the proposed improved MUSIC algorithm is better than the other algorithms.  
Figure 11: The spatial spectrum of four false targets and desired targets after passing through the adaptive beam. (**a**) Range-angle two-dimensional spatial-spectral distribution; (**b**) Range profile.  
Figure 10: Spatial spectra of new deceptive jamming and desired targets for the FDA-MIMO radar. (**a**) Spatial spectral distribution of range–angle two-dimensional; (**b**) Range profile.
# Applications of Cement-Based Smart Composites to Civil Structural Health Monitoring: A Review
## 2 Fabrication and Operational Issues
### Mixing, Moulding and Curing  
Depending on the morphology and nature of functional fillers, different mixing processes can be used: (i) the first admixing method, in which water is first mixed with surfactant and fillers, and cementitious material and aggregates (if present) are added afterwards; (ii) the synchronous admixing method, when all components are added to the mix one by one; (iii) the latter admixing method, when water-cement and filler-water-surfactant mixtures are separately prepared and mixed together afterwards [23]. Once the final admixture is obtained, it is molded in the desired shape and cured. Both these fabrication aspects are relevant, since they may affect the sensing capacity of the final CSC.  
Molding has an influence on the pore structure of CSC. Generally, the CSC admixture is casted into the framework and then vibrated in order to increase compaction. Nevertheless, in some cases pressing or extrusion have been applied, too [55].  
Curing regime and duration have an influence on the microstructure of CSCs since they determine the hydration process. Moist curing yields high strength CSCs due to the dense microstructure; however, larger resistivity values are also obtained with respect to air cured CSCs. Furthermore, moist cured CSCs tend to increase their resistivity over time [9]. Indeed, at early curing age, the matrix presents high porosity, and the resistivity is low due to ionic presence. Conversely, the longer the curing age, the higher the sensitivity, because the bond strength between filler and matrix increase [56; 57]. While a few studies are available in the literature about the curing effect on sensing properties of CSCs, the definition of an appropriate curing age is definitely a critical and under-investigated aspect in the development of CSCs.
## 1 Introduction  
Recent advances in the design of wirelessly powered, millimeter-scale sensor tags will allow for the construction of increasingly small sensors for a variety of applications [1; 2; 3; 4; 5]. In the case of wirelessly powered sensors like RFID tags, however, decreasing size leads to decreased power delivery due to reduced antenna area. All sensor components must therefore be designed for minimum power consumption. A common component of such sensors is a non-volatile memory used to store a unique identification (ID) number. However, for very small sensors, implementation of such a memory is non-trivial because of the low supply voltage and available energy, which can be insufficient to program a conventional non-volatile (NV) memory such as Flash or EEPROM. An alternative would be to program each sensor at its time of manufacture, for example by a physical electrical connection that could supply the energy required for a NV memory, or by an array of laser-blown fuses. These approaches require extra masks or post-manufacturing steps, which raise the individual cost of the supposedly low-cost devices.  
An alternative is an identification system based on the individual variation of ICs due to the CMOS manufacturing process [6]. A suitably designed circuit could be sensitive to these variations, and produce a string of bits that is random between chips, but temporally static, to be used as an ID number. This could be thought of as a particular type of NV memory that is \"programmed\" once at the time of manufacture with random data. Such variation sensitive circuits also have security and cryptographic applications, where they can be used to form Physical Unclonable Functions (PUFs) that allow for authentication and secret key generation [7, 8].  
In this paper, we present a circuit that generates random identification numbers based on manufacturing variation, with lower energy than reported in prior published work. We also present an addressing protocol that allows for reliable on-chip identification in the presence of unreliable bits. This reduces the amount of data that must be transmitted off-chip, lowering the energy consumption of the sensor. An analysis of the reliability of the system is also presented. While intended for the low-energy identification of wireless sensors, this same circuit could be used as part of a low-energy secret key generator for cryptographic applications [9].
# Time-Sensitive Networking Mechanism Aided by Multilevel Cyclic Queues in LEO Satellite Networks
## 5 Performance Evaluation  
We built a testbed for the LEO satellite network to validate the proposed TSN-MCQ mechanism. The testbed contained an LEO satellite network system where each satellite could randomly generate access user traffic. We controlled the bandwidth of the uplink traffic for each satellite, so that the load varied from 0.1 to 1. In addition, we compared this scheme with the CQF in IEEE 802.1 Qch [26] and the conventional Ethernet switch in terrestrial networks. The Ethernet switch handles time-sensitive traffic as regular traffic.
# Deep Clustering of Student Code Strategies Using Multi-View Code Representation (CMVAE)
## 3 Methodology
### Pre-Training to Learn Code Representations
#### 3.3.2 Extraction of Structural Features
The calculation is shown in Equation (6): \[C_{K}(v)=\alpha\sum_{u\in N(v)}C_{K}(u)+\beta,\] (6) where \(\alpha\) is the attenuation factor, and \(\beta\) is the initial constant term. 7. **Clustering Coefficient**[34]: This measures the connectivity between the neighbors of a node, reflecting the local clustering characteristics of the network. The calculation formula is provided in Equation (7): \[C_{Clu}(v)=\frac{E(v)}{\text{deg}(v)(\text{deg}(v)-1)},\] (7) where \(E(v)\) represents the count of actual connections among the neighbors of node \(v\), and \(\text{deg}(v)\) denotes the node's degree. These seven centrality metrics measure the significance and importance of code structures at different levels of the graph, and are capable of extracting core information from the code structure, such as dependencies and call relationships. By concatenating them into a complete structural feature vector, it facilitates input into downstream tasks in subsequent deep learning models. The final structural feature embedding \(X_{1}\) can be represented as follows:  
\[X_{1}=\text{concat}(C_{H}(v),C_{E}(v),C_{C}(v),C_{B}(v),C_{D}(v),C_{K}(v),C_{ Clu}(v)), \tag{8}\]  
where concat denotes the concatenation operation of the vectors. The result is a vector of dimension \(7\times d\), where \(d\) denotes the total number of nodes. This representation primarily reflects the structural information of the code and has a weaker capability in capturing the semantic information of functions (such as variable meanings, function purposes, etc.). To address this limitation, the pre-trained model Code-BERT is used to extract semantic features of the code. Through contrastive learning, these semantic features are fused with the structural features, enhancing the code representation's understanding of both semantics and structure.
## 3 Results
### Pelvic Tilt  
The repeated ANOVA showed no effects for the measurement time or the interaction of the factors for the pelvic tilt, whereas significant differences were found between all postures (Table 3). The statistical analysis of these differences is shown for all postures parameters in Table 4, with the results of the paired \(t\)-tests after Bonferroni correction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Comparison** & **t** & \(p\) & **Cohen’s d** & **95\% Confidence Interval** \\ \hline \multirow{4}{*}{Pelvic tilt} & Hab–Act & 2.55 & 0.041 * & 0.35 & [0.11; 0.92] \\ \cline{2-6}  & Hab–Aec & \(-\)3.89 & \(<\)0.001 ** & 0.53 & [\(-\)1.07; \(-\)0.34] \\ \cline{2-6}  & Act–Aec & \(-\)8.16 & \(<\)0.001 ** & 1.11 & [\(-\)1.52; \(-\)0.92] \\ \hline \multirow{4}{*}{Body lean} & Hab–Act & \(-\)9.56 & \(<\)0.001 ** & 1.30 & [\(-\)0.85; \(-\)0.56] \\ \cline{2-6}  & Hab–Aec & 1.62 & 0.111 & 0.22 & [\(-\)0.04; 0.36] \\ \cline{2-6}  & Act–Aec & 10.4 & \(<\)0.001 ** & 1.41 & [0.70; 1.03] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Post hoc tests: results of paired \(t\)-tests (degrees of freedom, DF = 53) for all posture parameters, between postures after Bonferroni correction.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Posture** & \multicolumn{1}{c}{**T1**} & **T2** & **T3** \\ \cline{3-5}  & & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \cline{2-6}  & & **[95\% CI]** & **[95\% CI]** & **[95\% CI]** \\ \hline \multirow{4}{*}{Trunk lean} & Hab & 6.79 \(\pm\)1.86 & 7.66 \(\pm\) 1.69 & 7.56 \(\pm\) 2.04 \\  & & [5.86; 7.71] & [6.82; 8.50] & [6.55; 8.57] \\ \cline{2-6}  & Act & 7.52 \(\pm\)1.98 & 8.71 \(\pm\) 1.93 & 8.16 \(\pm\) 2.02 \\  & & [6.53; 8.50] & [7.75; 9.67] & [7.16; 9.17] \\ \cline{2-6}  & Aec & 6.50 \(\pm\)2.07 & 7.83 \(\pm\)1.97 & 7.14 \(\pm\) 1.77 \\  & & [5.47; 7.53] & [6.85; 8.82] & [6.26; 8.03] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Effect** & **F** & **DF1** & **DF2** & \(p\) \\ \hline \multirow{4}{*}{Pelvic tilt} & time & 1.05 & 2 & 34 & 0.363 \\ \cline{2-6}  & posture & 15.89 & 2 & 34 & \(<\)0.001 ** \\ \cline{2-6}  & interaction & 0.59 & 4 & 68 & 0.669 \\ \hline \multirow{4}{*}{Body lean} & time & 0.85 & 2 & 34 & 0.437 \\ \cline{2-6}  & posture & 32.70 & 2 & 34 & \(<\)0.001 ** \\ \cline{2-6}  & interaction & 2.11 & 2.87 & 48.83 & 0.113 \\ \hline \multirow{4}{*}{Pelvic displacement} & time & 3.28 & 2 & 34 & 0.05 * \\ \cline{2-6}  & posture & 17.05 & 1.19 & 20.29 & \(<\)0.001 ** \\ \cline{2-6}  & interaction & 1.54 & 4 & 68 & 0.20 \\ \hline \multirow{4}{*}{Trunk lean} & time & 8.00 & 2 & 34 & 0.001 * \\ \cline{2-6}  & posture & 13.03 & 1.51 & 25.71 & \(<\)0.001 ** \\ \cline{1-1} \cline{2-6}  & interaction & 1.48 & 4 & 68 & 0.217 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of repeated ANOVA for the posture parameters.  
For the three postures (i.e., habitual, activated, and activated with eyes closed), ICCs of 0.95, 0.97, and 0.95 were calculated for the three measurement points.
## 4 Experiments and Analysis
### Comparative Analysis of Single-Source Domain and Multi-Source Domain Transfer Learning Task Results
#### 4.3.3 Comparative Analysis of Diagnostic Accuracy and Change Curve  
Figure 11 shows the change curves of three different target domains on single-source domain and multi-source domain transfer learning tasks. The gray and red curves in the figure represent the single-source domain tasks. The blue curve represents the multi-source domain task. The final fault diagnosis accuracy results are shown in Table 5.  
Figure 11: Comparison of single-source domain and multi-source domain transfer learning tasks in different target domains: (**a**) the target domain is dataset A; (**b**) the target domain is dataset B; (**c**) the target domain is dataset C.  
It can be seen from Figure 11 and Table 5 that the accuracy of the multi-source domain fault diagnosis method is significantly higher than the corresponding single-source domain fault diagnosis method, which can better classify the target domain samples. For example, the accuracy of the multi-source domain method, B-C\(\rightarrow\)A, can reach 97.78%, whereas the diagnostic accuracy of the single-source domain methods, B\(\rightarrow\)A and C\(\rightarrow\)A, can only reach 95.09% and 96.13%, and the classification accuracy is improved by 1.97% and 1.56%, respectively. To a certain extent, this can be explained that under complex working conditions, a multi-source domain can learn richer domain-invariant feature information more effectively than a single-source domain.
# Setonix: Blockchain-Based Hierarchy Domain Name System for Web3
## 2 Challenges with Conventional and Decentralized DNS in Relation to Web3  
The previously mentioned issues regarding Web3, Web3 service access methods, DNS, and existing blockchain-based DNS can be summarized as follows:  
* **Structural incompatibility between the centralized DNS architecture and the decentralized nature of Web3:** The traditional centralized server-client DNS operates using fixed-location servers in a hierarchical structure. This does not align with the philosophy of Web3, which aims for decentralization and a serverless topology, and thus cannot effectively support Web3 services.
* **Stability threats due to a Single Point of Failure:** Web3 services, which operate on a decentralized basis, rely on centralized Web2 access paths due to the lack of a system that directly supports access to these services, which introduces the Single Point of Failure (SPoF) issue.
* **Scalability limitations of DNS in response to the exponential growth of Web3 services:** Unlike Web2 services, which inherently carry the burden of operating servers at fixed physical locations, Web3 services, with reduced operational burdens, are likely to increase exponentially. As a result, conventional DNS, designed for Web2, will struggle to accommodate this growth, which will not only hinder user experience but also introduce management difficulties from the perspective of DNS operations.
* **Limitations of existing blockchain-based DNS in providing compatibility between Web2 and Web3:** Blockchain-based DNSs designed to provide Web3 services or replace conventional DNSs often fail to offer compatibility between Web2 and Web3. Even when compatibility is provided, poor performance can cause users to face significant resistance in utilizing such services. To address these issues, this study aims to answer the following questions: 1. How can we provide a platform that supports Web3 service access in a fully decentralized manner, addressing the stability threats posed by current methods? 2. How can Setonix DNS overcome the scalability issues inherent in conventional DNS? 3. How can Setonix effectively provide compatibility between Web2 and Web3?
## 3 Results and Discussion
### Thermodynamic Characteristics  
The impact of ambient temperature on the yttrium ions adsorption system was studied to identify not only the thermodynamic conditions but also the nature/feasibility of the adsorption process [45; 46]. Thermodynamic conditions for adsorption of the yttrium ions by AHIBC were evaluated from the Van't Hoff equations:  
\[\mathrm{logK_{d}}=\mathrm{\Delta S^{\circ}/2.303R}-\mathrm{\Delta H^{\circ}/2. 303 RT}, \tag{16}\]  
\[\mathrm{\Delta G^{\circ}}=\mathrm{\Delta H^{\circ}}-\mathrm{T\Delta S^{\circ}}, \tag{17}\]where K\({}_{\rm d}\) is the adsorption equilibrium constant (L g\({}^{-1}\)), \(\Delta\)G\({}^{\circ}\) is Gibbs free energy of the yttrium ions adsorption (kJ mol\({}^{-1}\)), \(\Delta\)S\({}^{\circ}\) is the changes in entropy of the yttrium ions adsorption (J mol\({}^{-1}\) K\({}^{-1}\)), \(\Delta\)H\({}^{\circ}\) is the changes in enthalpy of the yttrium ions adsorption (kJ mol\({}^{-1}\)), the universal gas constant R is 8.314 J mol\({}^{-1}\) K\({}^{-1}\), and T (K) refers to the absolute temperature. The thermodynamic conditions of the AHIBC adsorbent were evaluated by plotting logK\({}_{\rm d}\) versus 1/T (Figure 4). It gives a straight line; the value of both \(\Delta\)H\({}^{\circ}\) as well as the value of \(\Delta\)S\({}^{\circ}\) was attained from both the slope and intercept, respectively, and presented in Table 4.  
\begin{table}
\begin{tabular}{c c c} \hline
**Isotherm Models** & **Value** & **Parameters** \\ \hline \multirow{4}{*}{Langmuir isotherm} & 172.41 & q\({}_{\rm max}\) (mg\(\cdot\)g\({}^{-1}\)) \\  & 0.327 & b (L mg\({}^{-1}\)) \\  & 1.51 \(\times\) 10\({}^{-2}\) & R\({}_{\rm L}\) \\  & 0.99 & R\({}^{2}\) \\ \hline \multirow{4}{*}{Freundlich isotherm} & 49.52 & K\({}_{\rm f}\) (mg\(\cdot\)g\({}^{-1}\)) \\  & 0.241 & 1/n (mg min g\({}^{-1}\)) \\  & 0.68 & R\({}^{2}\) \\ \hline \multirow{4}{*}{D–R isotherm} & 140.26 & q\({}_{\rm D}\) (mg\(\cdot\)g\({}^{-1}\)) \\  & 0.561 & B\({}_{\rm D}\) (Mo\(1^{2}\) kJ\({}^{-2}\)) \\ \cline{1-1}  & 0.94 & E (kJ mol\({}^{-1}\)) \\ \cline{1-1}  & 0.78 & R\({}^{2}\) \\ \hline \multirow{4}{*}{Temkin isotherm} & 113.09 & b\({}_{\rm T}\) (kJ mol\({}^{-1}\)) \\  & 12.618 & K\({}_{\rm T}\) (L g\({}^{-1}\)) \\ \cline{1-1}  & 0.78 & R\({}^{2}\) \\ \hline Experimental capacity & 171.32 & q\({}_{\rm exp}\) (mg\(\cdot\)g\({}^{-1}\)) \\ \hline \end{tabular}
\end{table}
Table 3: Isotherm models for the Y(III) adsorbed on the AHIBC adsorbent.  
Figure 3: Isotherm models for the adsorption of Y(III) on AHIBC: (**a**) Langmuir isotherm, (**b**) [PERSON] isotherm, (**c**) Dubinin–Radushkevich isotherm, and (**d**) Temkin isotherm.  
In the attained result, the value of \(\Delta\)G\({}^{\circ}\) is negative, stating that the adsorption of Y(III) on the AHIBC adsorbent is feasible and spontaneous in nature. Additionally, the Gibbs free energy of the links reveals a favorable process for the chemical interaction between Y(III) and AHIBC.