esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Probabilistic Model (PM) for the Distributed Approach
### Lindeberg Theorem  
Suppose for each \(n\),  
\[(X_{n1},X_{n2},\ldots,X_{nr_{n}})\]  
is an independent random vector. The probability space may change with \(n\), and the set of these vectors is called a triangular array of random variables. Put \(S_{n}=X_{n1}+\cdots+X_{nr_{n}}\). In the network application, let \(X_{ni}\) be \(X_{i}\), and let \(X_{i}\) take the values one and zero with probability \(p_{i}\) and \(q_{i}=1-p_{i}\), respectively. We may interpret \(X_{i}\) as an indicator that sensor \(i\) is chosen to be a clusterhead with probability \(p_{i}\) and \(S_{n}\) is the number of clusterheads in a network.  
Denote \(Y_{i}=X_{i}-p_{i}\). Hence,  
\[S_{n}^{Y}\equiv\sum_{i=1}^{n}Y_{i}=S_{n}-\sum_{i=1}^{n}p_{i} \tag{12}\]  
with \(E[Y_{i}]=0\), \(\sigma_{Y_{i}}^{2}=\sigma_{X_{i}}^{2}=p_{i}(1-p_{i})\), and \(\sigma_{s_{n}}^{2}=\sum_{i=1}^{n}\sigma_{Y_{i}}^{2}=\sum_{i=1}^{n}\sigma_{X_{ i}}^{2}\). For our case, the Lindeberg condition [32] reduces to:  
\[\lim_{n\to\infty}\sum_{i=1}^{n}\frac{1}{s_{n}^{2}}\int_{|Y_{i}|\geq\epsilon s_ {n}}Y_{i}^{2}dP\leq\lim_{n\to\infty}\sum_{i=1}^{n}\frac{1}{s_{n}^{2}}\int_{|Y_ {i}|\geq\epsilon s_{n}}dP=0 \tag{13}\]  
which holds, because all the random variables are bounded by one and \([|Y_{i}|\geq\epsilon s_{n}]\to 0\) as \(n\to\infty\).  
**Theorem 1**.: _Suppose that \(Y_{i}\) is an independent sequence of random variables and satisfies \(E[Y_{i}]=0\), \(\sigma_{Y_{i}}^{2}=E[Y_{i}^{2}]\), \(S_{n}^{Y}=\sum_{i=1}^{n}Y_{i}\) and \(s_{n}^{2}=\sum_{i=1}^{n}\sigma_{Y_{i}}^{2}\). If the Lindeberg condition (13) holds, then \(S_{n}^{Y}/s_{n}\to\mathcal{N}(0,1)\)._  
Consequently, referring to Equations (10) and (11), and Theorem 1, the distribution of the number of group members of vehicle \(u\) can be approximated by \(\mathcal{N}(\mu_{u},\sigma_{u}^{2})\) with \(\mu_{u}=\sum_{i=1}^{m_{u}}p_{i}^{(u)}\) and \(\sigma_{u}^{2}=\sum_{i=1}^{m_{u}}p_{i}^{(u)}(1-p_{i}^{(u)})\), where \(p_{i}^{(u)}\) is the probability for being a group member of vehicle \(i\) with respect to vehicle \(u\).
# Structural Correlation Based Method for Image Forgery Classification and Localization
## 6 Experimental Results
### Evaluation Metrics
#### 6.2.1 Metrics for Image Retrieval  
To evaluate the performance of the proposed image forgery clustering algorithm, we use the mean average precision (\(MAP\)) metric used in image retrieval problem. For a query \(q\), let us denote \(N_{q}\) the number of retrieved images, \(M_{q}\) the number of relevant images, and \(\text{Rel}_{q}(k)\) the number of relevant images in top \(k\) retrieved results. The precision and recall of query \(q\) at cut-off \(k\), denoted by \(P_{q}(k)\) and \(R_{q}(k)\), are calculated as follows:  
\[P_{q}(k) =\frac{\text{Rel}_{q}(k)}{k}. \tag{14}\] \[R_{q}(k) =\frac{\text{Rel}_{q}(k)}{M_{q}}. \tag{15}\]  
Then, the average precision for query \(q\) is computed as follows:  
\[AP_{q}=\sum_{k=1}^{N_{q}}P_{q}(k)\Delta R_{q}(k), \tag{16}\]  
where \(\Delta R_{q}(k)=R_{q}(k)-R_{q}(k-1)\) is the change in recall from items \(k-1\) to \(k\). Note that \(R_{q}(0)=0\). Finally, \(MAP\) for all the queries is defined as follows:  
\[MAP=\frac{\sum_{q=1}^{Q}AP_{q}}{Q}, \tag{17}\]  
Figure 5: Examples of CMFL in MICC-600 dataset. First row: original images, second row: copy-move images, third row: ground truth images, and fourth row: source regions (green) and target regions (white) detected by the proposed method.  
where \(Q\) is the number of queries.
## 3 Methodology
### System Design
#### 3.3.1 Stress Detection  
The researchers used a proven photoplethysmogram (PPG) sensor (MDL0025, 3.3 V, 10 mA) [56] to detect the heart rate of children during the task. The PPG sensor was placed tightly against the child's left-hand index finger and secured with skin-friendly paper tape. The PPG sensor was connected to the Arduino UNO R3 mainboard via Dupont wires. The detected heart rate data were transmitted to the Arduino mainboard, where an algorithm was processed to derive Inter-Beat Interval (IBI) and Heart Rate Variability (HRV) data. We chose the Root Mean Square of Successive Differences (RMSSD), a statistical measure used in HRV analysis, as the HRV parameter for its widespread use in short-term stress assessment [57]. It reflects the child's stress levels during the process. Higher RMSSD values indicate lower stress, while lower RMSSD values indicate higher stress.
## 4 Discussion
### The Effects of ALS Data Characteristics
#### 4.1.2 Optimal Scan Angle Range  
The scan angle range greatly affected the estimation accuracy of the four forest component proportions. We compared the four forest component proportions obtained using the ALS- and 3 DSMax-based methods in plot WPA-P3 with multiple scan angle ranges of -26\({}^{\circ}\)--16\({}^{\circ}\), -10\({}^{\circ}\)-10\({}^{\circ}\), and 18\({}^{\circ}\)-28, shown in Figure 11. It was found that the estimations of four forest component proportions obtained from both positive and negative sides (hereafter referred to two-side) scan angle ranges were better than the ones estimated from only a single half range, reported in Table 5. This was verified by the fact that the total RMSEs of 10.4% obtained from the one-side scan angle range of -26\({}^{\circ}\)--16\({}^{\circ}\) and -10\({}^{\circ}\)-0\({}^{\circ}\) was bigger than the one (i.e., 9.0%) from two-side scan angle range of -10\({}^{\circ}\)-10\({}^{\circ}\). Furthermore, the RMSE of ALS-based, the four forest component proportions (i.e., 9.0%) obtained from two-side small scan angle range of -10\({}^{\circ}\)-10\({}^{\circ}\) were better than the one (i.e., 9.7%) from two-side broad scan angle range of 18\({}^{\circ}\)-28\({}^{\circ}\)and -26\({}^{\circ}\)--16\({}^{\circ}\). A more comprehensive forest canopy ALS data with multiple scan angle ranges of -26\({}^{\circ}\)--16\({}^{\circ}\), -10\({}^{\circ}\)-10\({}^{\circ}\), and 18\({}^{\circ}\)-28\({}^{\circ}\) held the smallest RMSE of 7.7% of the four forest components. Then we recommend obtaining a relative comprehensive forest canopy ALS data with perpendicular or multiple crossed flight paths and a two-side scan angle range for estimating the four forest component proportions. While there might be the trade-off between the flight cost and comprehensiveness of forest ALS data, at least two-side scan angle range around nadir direction of -15\({}^{\circ}\)-15\({}^{\circ}\) is recommended to acquire the forest canopy ALS data.  
Figure 11: Graphs showing the result of four forest component proportions including shaded forest background (inset **a**), sunlit forest background (inset **b**), shaded overstory (inset **c**) and sunlit overstory (inset **d**) with the view zenith angles varying from -60\({}^{\circ}\) to 60\({}^{\circ}\) in solar principal plane under different scan angles for forest plot WPA-P3. The solid black line represents the 3 DSmax-based result and the various colored solid lines represent the ALS-based four forest component results with different scan angles. The solar zenith and azimuth angles are 5\({}^{\circ}\) and 100\({}^{\circ}\), respectively.
# An Improved Few-Shot Object Detection via Feature Reweighting Method for Insulator Identification
## 2 Improved Feature Reweighting Model  
In this paper, the few-shot object detection via feature reweighting model is improved to deal with insulator identification. The improved model structure is shown in Figure 2, mainly including the feature extraction module, reweighting module, and prediction module, respectively, in improving the corresponding feature extractor, reweighting module, and prediction layer. Among them, a feature extractor is used to extract image meta-features. In the part of the feature extraction network of the model where a self-calibrated convolutional feature extraction network is introduced and improved, the model can obtain multi-scale and finer meta-feature information. The convolutional neural network in the reweighting module consists of six convolutional layers, five maximum pooling layers, and a global pooling layer. The input of this module is the splicing of a support set image composed of few-shot insulator images and its mask, among which the mask is a detailed mask generated after processing based on the SKNet attentional mechanism module. After the convolution integration of the meta-feature reweighting module, the reweighted meta-feature vector is generated, and the channel convolution integration is carried out. The prediction layer uses the reweighting meta-feature information to get the classification and positioning score of the target. To reduce false detection, the non-maximum suppression algorithm is improved in the prediction layer. The training strategy of the network uses the data set with abundant sample space to train the whole model initially and then quickly transfer the trained meta-knowledge to the recognition of few-shot insulator images.  
Figure 1: Example insulator image. (**a**) Examples of small-scale insulators; (**b**) Examples of defective insulators; (**c**) Examples of blocking insulators.
# Implementation of the HHL Algorithm for Solving the Poisson Equation on Quantum Simulators
## 1 Introduction
This makes it possible to calculate the distribution of pressure potential in underground reservoirs and determine the optimal points for drilling wells. However, the Poisson equation also has its limitations and problems. Most practical situations require the use of numerical methods to solve the Poisson equation. This can be resource intensive and requires significant computing power. The use of quantum algorithms to solve the Poisson equation can offer the promise of speeding up computations and improving the accuracy of simulations. An example of a quantum algorithm that can be applied to solve the Poisson equation is the HHL algorithm. HHL can be used to find an approximate solution to the Poisson equation with a given accuracy. The HHL algorithm was developed by [PERSON], [PERSON], and [PERSON] in 2009 and is a quantum algorithm that allows you to solve systems of linear equations faster than classical algorithms. The main idea of the algorithm is to use the modulo Fourier transform, as well as the inverse Fourier transform, to transform the state vector encoding the solution of the system of equations into a superposition of states corresponding to the significant components of this solution. This makes it possible to efficiently process information about the solution of a system of equations and obtain results much faster than in classical calculations. When applying the HHL algorithm to the solution of the Poisson equation in the two-dimensional case, the grid is divided into nodes, and the corresponding matrix \(A\) becomes sparse. In the classical case, solving such systems requires a lot of computing resources, while the HHL algorithm allows you to obtain a solution using a much smaller number of operations.
## 3 Results and Discussion
### The Use of Downscaled SIF to Estimate GPP
One explanation is that the approach may not work well when bare soil contributes significantly to the observed reflectance and/or because the wavelengths of the MODIS reflectances are not optimal for this purpose. While the SIF* results were noisy as compared with reflectance-based approaches, we were encouraged by these initial SIF* results given that only a single scaling factor was applied. We expect improvements in subsequent versions of SIF* that will use more recent datasets for SIF, as well as the high spatial resolution variables employed for downscaling. In addition, we expect progress to be made in developing methods to account for the effects of vegetation structure on SIF escape from the canopy globally. Figure 3: Two-dimensional histograms (density or heat map) of the monthly-averaged and scaled (**a**) SIF*, (**b**) NDVI\({}^{\prime}\times\) SW\({}_{\text{TOA}}\) and (**c**) NIR\({}_{V}\times\) SW\({}_{\text{TOA}}\), all at approximately 0.05\({}^{\circ}\) resolution, versus GPP from the FLUXNET 2015 dataset. The colors represent the number of individual data points in a particular bin. Single points within a bin are represented as a dot rather than a color-filled box. Data are from 47 individual sites with 2065 individual data points for years 2007–2013. The statistics for the fit are listed in the lower right corner with the linear fit at the top. The solid line is the 1:1 line. Units of GPP are g C m\({}^{-2}\) d\({}^{-1}\). MEF, model efficiency factor.
# A Mechanism for Reliable Mobility Management for Internet of Things Using CoAP
## 5 Conclusions  
In this paper, we have presented a mechanism for reliable IoT mobility management using CoAP. In particular, the detailed algorithm and performance analysis are presented for achieving the reliability of mobility management in Internet of Things environments. Most standard mobility management protocols at the network layer such as MIPv6 and its variants do not consider packet retransmissions during handovers because the network layer does not support packet retransmission. In this paper, we have extended the CON message of the CoAP protocol for reliable transmission of the signaling message under an unreliable IoT network environment. A detailed mathematical analysis of the retransmission model for supporting the reliable mobility management has been presented. Finally, performance is evaluated by both mathematical analysis and simulation. The performance evaluation confirmed that the proposed mechanism can effectively support reliable mobility management. Further study may be required to provide the security aspect of the proposed mechanism.  
This study was supported by the BK21 Plus project funded by the Ministry of Education, Korea (21A20131600011) and by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) Ministry of Science, ICT and future Planning (NRF-2016R1A2B4010551). This work was also supported by Institute for Information & communications Technology Promotion (IITP) grants funded by the Korea government (MSIP) (No. R0126-16-1002, Development of agro-livestock cloud and application service for balanced production, transparent distribution and safe consumption based on GS1) and (No. R7115-16-0002, Wise-IoT).  
[PERSON] contributed in the design of the experiments, data acquisition, experimental work, data analysis, and manuscript writing. [PERSON] contributed in the design of the protocol, data analysis, and manuscript writing.  
The authors declare no conflict of interest.
## 2 Tension-Shear Coupling Model
### Dissipation Energy of in-plane Transverse Compression  
As shown in Figure 7a, parallel yams start to contact each other at the shear-locking angle. As the shear angle increases, the yams are transversely compressed under the in-plane transverse compression force \(F_{com}\). The points C and D move to C' and D', and the total width reduction of a yarn is \(\Delta w\), as illustrated in Figure 7b. Since no yarn sliding is considered in this work, the side length of the parallelogram ABCD remains constant, which is \((w_{0}+g_{0})\).  
Figure 5: The contact force produced by the tension force of a yarn (plain-woven fabric).  
Figure 6: The contact force produced by the tension force of a yarn (different weave pattern).  
In an infinitesimal load increment, \(F_{com}\) can be seen as constant. Since the fill and warp yarns are consistent in geometry and mechanical properties, the dissipation energy of the in-plane transverse compression of a fabric RVE is  
\[\mathrm{d}W_{com}=2N_{y}^{2}F_{com}dw, \tag{20}\]  
where \(dw\) is the differentiation of \(\Delta w\). \(\Delta w\) and \(dw\) are given by  
\[\Delta w=w_{0}\varepsilon_{com}, \tag{21}\]  
\[dw=w_{0}d\varepsilon_{com}, \tag{22}\]  
where \(\varepsilon_{com}\) is the in-plane transverse compressive strain of a yarn. From the view point of simplicity, it is reasonable to assume that the yarn compression is linear. Then, the compression force \(F_{com}\) is calculated with the following equation:  
\[F_{com}=t(w_{0}+g_{0})E_{com}\varepsilon_{com}, \tag{23}\]  
where \(E_{com}\) is the transverse compressive modulus of a yarn.  
Please remember that our goal is to obtain the constitutive relation between the shear stress and shear angle of a fabric RVE. Thus, \(\varepsilon_{com}\) must be written as a function of the shear angle. \(\varphi_{L}\) is the shear-locking angle given by Equation (14). Obviously, when \(\varphi\leq\varphi_{L}\), the width is not compressed, and both \(\varepsilon_{com}\) and \(d\varepsilon_{com}\) are zero. When \(\varphi>\varphi_{L}\), is the following equation is given as:  
\[\Delta w=w_{0}-w^{\prime}=(w_{0}+g_{0})(cos\varphi_{L}-cos\varphi),\ \ \ \varphi> \varphi_{L}. \tag{24}\]  
Then, \(\varepsilon_{com}\) is obtained as  
\[\varepsilon_{com}=\frac{\Delta w}{w_{0}}=\frac{w_{0}-w^{\prime}}{w_{0}}=\frac {(w_{0}+g_{0})(cos\varphi_{L}-cos\varphi)}{w_{0}},\ \varphi>\varphi_{L}. \tag{25}\]  
Furthermore, \(d\varepsilon_{com}\) is reached:  
\[d\varepsilon_{com}=\frac{d(\varepsilon_{com})}{d\varphi}\mathrm{d}\varphi= \frac{(w_{0}+g_{0})sin\varphi}{w_{0}}d\varphi,\varphi>\varphi_{L}. \tag{26}\]  
Obviously, when \(\varphi\leq\varphi_{L}\), the dissipation energy is zero. When \(\varphi>\varphi_{L}\), \(\mathrm{d}W_{com}\) can be achieved by substituting Equations (25) and (26) into Equations (22) and (23) and then into Equation (20). Finally, the expression of \(\mathrm{d}W_{com}\) is achieved:  
\[\mathrm{d}W_{com}=2N_{y}^{2}E_{com}\frac{t(w_{0}+g_{0})^{3}}{w_{0}}(cos\varphi _{L}-cos\varphi)sin\varphi d\varphi,\ \ \varphi>\varphi_{L}. \tag{27}\]  
Figure 7: In-plane transverse compression of an RVE.
# From Transactions to Transformations: A Bibliometric Study on Technology Convergence in E-Payments
## 5 Discussion and Conclusions
### Theoretical Contributions and Conceptual Integration  
This study makes several key contributions to the theory of digital finance and bibliometric analysis: it demonstrates how topic evolution analysis can track not only emerging technologies but also the integration of once-separate research streams (e.g., TAM \(\times\) trust \(\times\) security); it reveals how macro-level trends (e.g., publication growth, institutional expansion) intersect with micro-level conceptual shifts, particularly around user experience and system design; and it confirms the interdisciplinary convergence of behavioral theories and technological innovations, offering a more integrated lens for understanding the complexities of digital transaction systems.  
This study contributes to the theory of digital finance by proposing the Convergent Adoption-Trust-Security (CATS) Framework, which synthesizes key findings from the bibliometric and thematic analyses to explain how technological convergence shapes user adoption, trust, and platform security in e-payment ecosystems.  
The CATS framework builds upon and extends established adoption theories such as TAM and UTAUT by introducing multi-technology integration, trust mechanisms, and layered security architectures as central constructs. This integrated model helps explain how digital trust (e.g., blockchain transparency), AI-driven personalization, and secure mobile/NFC systems jointly influence user perceptions and technological acceptance. It also recognizes regulatory responsiveness and governance mechanisms as external systemic enablers of sustained adoption.  
This framework is directly grounded in empirical findings from Section 4:  
Convergent Adoption: Keyword co-occurrence (Table 5, Figure 8) and country-affiliation-keyword linkages (Figure 2) reveal high-frequency and centrality connections among blockchain, AI, NFC, and mobile technologies, highlighting convergence as a structural theme in the research domain.  
Adoption Models Enhanced: The top-cited literature (Table 7) shows how traditional models such as TAM and UTAUT are increasingly applied in convergent contexts, emphasizing interoperability, real-time service, and system-level coordination.  
Trust Mechanisms: Cluster 2 in Table 5 and centrality measures in Figure 5a,b underscore the importance of trust, privacy, and usability, confirming their role as conceptual bridges in multi-technology systems.  
Security Architecture: Co-word networks (Figure 9a,b) and the motor theme quadrant in the thematic map emphasize the integration of security, authentication, biometrics, and blockchain, validating the security pillar of the CATS model.  
By embedding these components within a unified theoretical framework, this study offers a novel lens to understand both the evolutionary trajectory of digital finance research and the practical architecture of converged e-payment platforms. The CATS model provides a foundation for future empirical testing and cross-national comparison in fintech ecosystems.
# Strength Analysis of Cylindrical Shells with Tangential Nozzles under Internal Pressure
## 1 Introduction
[PERSON] [11], by utilizing machine learning technology and redeveloping an ABAQUS plug-in toolset, developed a prediction model for nozzle connections in pressure vessels. This model enables rapid prediction of local stress at the nozzle joint by inputting parameters such as radius and wall thickness of both cylinder and nozzle (\(R\), \(r\), \(T\), \(t\); mm), as well as nozzle inclination (\(a\); \({}^{\circ}\)). It provides a reliable and convenient platform for swift evaluation and optimization of pressure vessel designs. As for the pressure vessel design by analysis, there are three methods based on numerical simulations for protection against collapse: (a) Elastic Stress Analysis Method--Stresses are computed using an elastic analysis and classified into categories. (b) Limit-Load Method--A calculation is performed to determine a lower bound to the limit load of a component. (c) Elastic-Plastic Stress Analysis Method--A collapse load is derived from an elastic-plastic analysis, considering both the applied loading and deformation characteristics of the component. [PERSON] [12] compared the difference between the Elastic Stress Method and the plastic Limit-Load Method in evaluating static strength and shakedown. [PERSON] [13] discussed the principles of Elasto-Plastic shakedown analysis and limit analysis and emphasized the greater difficulty in the practical application of shakedown analysis. Compared with the Elastic Stress Analysis Method, the Limit-Load Method is more straightforward without setting stress classification lines and performing stress categories. And compared with the Elastic-Plastic Stress Analysis Method, the Limit-Load Method may be more conservative without considering the strain-strength property of the material. Of course, as a nonlinear analysis, computation based on the Limit-Load Method is hard to converge, especially for a large structure. In this paper, a limit-load analysis was performed to evaluate the weakening effects of the tangential nozzles on the strength of the cylindrical shells under internal pressure. The strength-weakening coefficient was defined to reflect the weakening degree of the load-bearing capacity of the cylindrical shells by the tangential nozzles or specifically by the three dimensionless structural parameters \(d_{o}/D_{i}\), \(D_{i}/T\) and \(t/T\). Regression equations for the strength-weakening coefficient were obtained, which provides a reference for the strength design of cylindrical shells with tangential nozzles under the internal pressure.