esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theoretical Framework and Methods
### Uncertainty Measurement Principle
#### 2.2.2 Uncertainty Measurement
The space \(C\) is a closed measurable space that satisfies the basic operations of set intersection, union, and complement, and can be represented as follows:  
\[C=\left\{B|B=\underset{i=1}{\overset{k}{\cup}}b_{i},b_{i}\in\{\varnothing,1,c _{1},c_{2},\ldots c_{z}\},1\leq i\leq k\right\} \tag{9}\]  
Assume \(\mu_{ijk}=\mu\big{(}x_{ij}\in c_{k}\big{)}\) is the measurement value \(x_{ij}\) belonging to the evaluation level \(c_{k}\), and it is required that \(\mu\) meets the following conditions:  
\[0\leq\mu(x_{ij}\in c_{k})\leq 1 \tag{10}\]  
\[\mu(x_{ij}\in Y)=1 \tag{11}\]  
\[\underset{i=1}{\cup}F_{i}=F \tag{3}\]0  
\[\underset{i=1}{\cup}F_{i}=F \tag{3}\]1  
\[\underset{i=1}{\cup}F_{i}=F \tag{3}\]2  
When Equations (10) to (13) are simultaneously satisfied, \(\mu\) is referred to as the uncertain measure. Based on the defined construction of the uncertain measurement single index measurement function \(\mu\big{(}x_{ij}\in c_{k}\big{)}\), where \(j=1,2,\ldots,m\); \(i=1,2,\ldots,n\); \(k=1,2,\ldots,z\), the measurement values \(\mu_{ijk}\) of each index of the influencing factor \(x_{i}\) are obtained. The matrix composed of \(\mu_{ijk}\) is called the single index measurement matrix, denoted as follows:  
\[\Big{(}u_{ijk}\Big{)}_{m\times z}= \begin{bmatrix}\mu_{i11}&\mu_{i12}&\cdots& \mu_{i1z}\\ \mu_{i21}&\mu_{i22}&\cdots&\mu_{i2z}\\ \vdots&\vdots&\vdots&\vdots\\ \mu_{im1}&\mu_{im2}&\cdots&\mu_{imz}\end{bmatrix}  \tag{14}\]  
4. Multi-Indicator Comprehensive Measurement  
Let \(\omega_{j}\) be the importance of evaluation index \(P_{j}\) of evaluation factor \(x_{i}\) compared to other evaluation indices, and it satisfies the following:  
\[0\leq\omega_{j}\leq 1 \tag{15}\]  
\[\sum_{j=1}^{m}\omega_{j}=1 \tag{16}\]  
\(\omega_{j}\) is the weight of evaluation index \(P_{j}\). When there exists \(\mu_{ik}\) such that \(0\leq\mu_{ik}\leq 1\) and  
\[\mu_{ik}=\sum_{j=1}^{m}\omega_{j}\mu_{ijk} \tag{17}\]\(\mu_{ik}\) is an uncertain measurement, and the matrix \(\left(\mu_{ij}\right)n\times z\) is referred to as the multi-index comprehensive evaluation matrix, represented as follows:  
\[\left(\mu_{ik}\right)_{n\times z}= \begin{bmatrix}\mu_{11}&\mu_{12}&\cdots&\mu_{ 1z}\\ \mu_{21}&\mu_{22}&\cdots&\mu_{2z}\\ \vdots&\vdots&\vdots&\vdots\\ \mu_{z1}&\mu_{z2}&\cdots&\mu_{nz}\end{bmatrix}  \tag{18}\]  
When \(\mu_{k}=\mu(x_{ik}\in c)\) indicates the degree to which evaluation factor \(x_{i}\) belongs to level \(k\), then  
\[\mu_{k}=\sum_{i=1}^{n}\omega_{i}\mu_{ik}(k=1,2,\cdots,z) \tag{19}\]  
In Equation (19), \(\omega_{i}\) is the weight of evaluation factor \(x_{i}\), \(0\leq\mu_{k}\leq 1\), \(\sum_{k=1}^{z}\mu_{k}=1\), so \(\mu_{k}\) is determined as an uncertain measure, and the vector \(\mu_{k}=\left(\mu_{1},\mu_{2},\ldots,\mu_{2}\right)\) is referred to as the multi-criteria comprehensive evaluation vector.
###### Abstract  
Data-driven models have been successfully applied to flood prediction. However, the nonlinearity and uncertainty of the prediction process and the possible noise or outliers in the data set will lead to incorrect results. In addition, data-driven models are only trained from available datasets and do not involve scientific principles or laws during the model training process, which may lead to predictions that do not conform to physical laws. To this end, we propose a flood prediction method based on data-driven and knowledge-guided heterogeneous graphs and temporal convolutional networks (DK-HTAN). In the data preprocessing stage, a low-rank approximate decomposition algorithm based on a time tensor was designed to interpolate the input data. Adding an attention mechanism to the heterogeneous graph module is beneficial for introducing prior knowledge. A self-attention mechanism with temporal convolutional network was introduced to dynamically calculate spatiotemporal correlation characteristics of flood data. Finally, we propose physical mechanism constraints for flood processes, adjusted and optimized data-driven models, corrected predictions that did not conform to physical mechanisms, and quantified the uncertainty of predictions. The experimental results on the Qjiang River Basin dataset show that the model has good predictive performance in terms of interval prediction index (PI), RMSE, and MAPE.  
data-driven and knowledge-guided; tensor decomposition; heterogeneous graph; temporal convolution +
Footnote †: journal:  
0000-0001-999-227-2811
# An Accelerated Method for Protecting Data Privacy in Financial Scenarios Based on Linear Operation
## 3 Proposed Methods
### Experiment Settings
#### 3.4.2 Models and Algorithms for Experiment
Depthwise convolution is different from standard convolution in that the convolution kernel is used for all input channels, while depthwise convolution uses different convolution kernels for each input channel, that is, one convolution kernel corresponds to one input channel, so depthwise convolution is a depth-level operation. The pointwise convolution is actually a normal convolution, except that it uses a \(1\times 1\) convolution kernel. For depthwise separable convolution, the depthwise convolution is used to convolve the different input channels separately, and then the pointwise convolution is used to combine the above outputs, which is actually similar to a standard convolution. The overall effect is similar to that of a standard convolution, but the computational effort and the number of model parameters are greatly reduced. The main purpose of using this network is to test the performance of this method in scenarios with low linearity. The above three models are used to test the execution efficiency of the methods in this paper. Another encryption algorithm that can be compared with this method is homomorphic encryption, which provides the same level of security protection. With the help of homomorphic encryption (HE), operating directly on the ciphertext is the same as operating on the plaintext and then encrypting it. A typical application scenario is that the data holder wants to perform computation on the large amount of data it holds, but the computing resources it has are not enough and it wants to use the computing power of the cloud server to complete the computation. If the data is transferred directly to the cloud server, then a pre-written program is run to perform the computation. But then, the sensitive data is exposed on the cloud server. Homomorphic encryption solves this problem by encrypting the data before the data holder transmits it, and the cloud server computes the data as usual after receiving it, except that this time it is done on the ciphertext, and the cloud server cannot access any original information. When the result is obtained, the ciphertext of the result is returned to the data holder, and the final result is obtained when the data holder decrypts it.
# Operationalizing Dyadic Urban Traffic Interaction Studies: From Theory to Practice
## 4 Results: Aspects of Traffic Interaction Study Design Warranting Trade-Offs
### Choice of Scenarios
#### 4.1.3 Sample Size and Participant Diversity  
Determining an appropriate sample size depends heavily on the specific research question and the effect sizes of the dependent variables under investigation. While larger sample sizes enhance statistical power, smaller, more focused samples may provide greater depth and richer qualitative insights, particularly in exploratory or user experience-focused studies. We emphasize the importance of balancing breadth with depth, depending on the study's objectives. For instance, when investigating nuanced behaviors or capturing outlier interactions, qualitative methods such as in-depth interviews, longitudinal observations, and reflective exercises may yield more actionable insights than statistical analyses alone. These approaches are particularly valuable for understanding the subjective experiences and motivations that underpin traffic interactions, which may not be fully captured through objective behavioral measures.  
Regarding participant diversity, we highlight the importance of ensuring representative samples that reflect the demographic and cultural variability of road users. This includes accounting for not only differences in age, gender, driving experience, and familiarity with autonomous vehicle systems, but also cultural norms that influence interaction behaviors. Studies aimed at exploring cross-cultural differences or generalizing findings across regions should prioritize participant diversity to ensure ecological validity and inclusivity. In some cases, smaller sample sizes (e.g., 5-10 participants) may be appropriate for generating deep, qualitative insights, particularly in the early stages of research or when piloting new frameworks. Conversely, larger sample sizes (e.g., 30+ participants) may be warranted for studies requiring robust statistical comparisons or generalizability. Ultimately, the choice between breadth and depth should align with the research question and the study's goals. Incorporating iterative study designs that combine both approaches--e.g., qualitative insights from smaller, exploratory studies that inform the design of larger-scale quantitative studies--can ensure that both depth and representativeness are achieved over the course of investigating a research question.
# Comparison of Various Factors Affected TID Tolerance in FinFET and Nanowire FET
## 1 Introduction  
The effects of the total ionizing dose (TID) on electronic devices are critical issues in various fields such as space and nuclear applications. To reduce the radiation effects on the electronic components, three radiation hardening methods have been widely considered: radiation hardening by process (RHBP), radiation hardening by shielding (RHBS), and radiation hardening by design (RHBD) [1; 2; 3; 4]. The international thermonuclear experimental reactor (ITER) studies the TID effect for the development of a precise remote control system [5]. Electronic equipment operating in a radiation environment is subject to radiations that lead to defects in transistors. The TID effects, single event effects (SEEs), and displacement damage (DD) can lead to disturbances in the reliable operation of semiconductor devices due to radiation [6; 7; 8; 9; 10]. In particular, in the TID effect, the trapped holes in the oxide of transistor in the electron hole pairs (EHP) are caused by radiation and change in the threshold voltage (V\({}_{\text{T}}\)) [11]. The hole trapped in the oxide results in the inversion charge of the channel region and leads to change in V\({}_{\text{T}}\). In particular, nanoscale devices can affect other static and dynamic parameters [12]. Therefore, the reliability of the device is degraded. The devices with a 3D gate structure can solve the SCE problem and enable continuous scaling [13; 14; 15]. In particular, FinFET, which has a tri-gate structure, and NW, which has a gate all around (GAA) structure, are being studied as effective solutions for device miniaturization [16]. Therefore, both structures devices must be studied for mitigating the radiation effects. In this paper, the TID simulation of NW and FinFET was used to analyze that received more of the TID effect, depending on the structural part of the device. The experiments were performed considering the oxide area and gate controllability, which are the structural elements affected by the TID effect. For a more accurate comparison, the TID simulation was performed by calibrating the sizeand current level of the NW and FinFET. Through these experiments, compared to the FinFET, we observed that the V\({}_{\text{T}}\) of the NW changed insignificantly and that this device was more resistant in the radiation environment. This result shows that gate controllability is more effective for causing radiation tolerance than the oxide area. However, the oxide area of the hole trap is a key factor in the TID effect. As the oxide area increases, the number of trapped holes also increases and consequently, the reliability of the device is further degraded. However, by introducing the GAA structure, the gate controllability can be improved to suppressing the TID effect in the oxide area. In addition, the TID characteristics were confirmed by changing the V\({}_{\text{DD}}\) and channel doping, which affect the resistance to the TID effect. Therefore, the importance of improving gate controllability, which is a method of suppressing the TID effect, is herein presented.
# Optimal Design of One-Sided Exponential Adaptive EWMA Scheme Based on Median Run Length
## 1 Introduction
They found that the proposed scheme exhibited a superior average run length (ARL) compared to existing monitoring schemes across various shift sizes. Most studies on exponential schemes focus on the ARL as the primary performance metric. However, the ARL alone can be misleading because the shape of run length (RL) distribution alters depending on the size of the shift. Notably, the RL distribution for the exponential EWMA scheme tends to be skewed, particularly under in-control conditions [23]. This skewness can affect the interpretation of the scheme's performance, making it important to consider additional metrics or the entire distribution of RL when evaluating monitoring scheme effectiveness. In such cases, the median run length (MRL) offers a more insightful measure of scheme performance [24]. The optimization of monitoring schemes involves selecting the best parameters to achieve desired performance metrics. For traditional monitoring schemes, this often involves minimizing the ARL for a specific shift size. However, as discussed earlier, the ARL alone may not provide a complete picture of scheme performance, especially for skewed RL distributions. Therefore, optimizing monitoring schemes based on MRL has gained increasing attention in recent years. [PERSON] et al. (see [25]) studied the RL properties of run rules EWMA schemes by employing integral equations, providing a theoretical foundation for optimizing these schemes. [PERSON] et al. (see [26]) proposed an optimal design procedure for the AEWMA scheme based on MRL, showing that the resulting schemes performed uniformly better than traditional EWMA schemes. Motivated by the fact that AEWMA schemes can provide more balanced monitoring performance compared to traditional EWMA schemes [22], and that MRL is a more rea sonable performance metric for exponential monitoring schemes [24], this paper aims to develop an optimized design for one-sided exponential AEWMA schemes using MRL as the performance metric. The RL properties are investigated using a Markov chain approach, in considering the two cases of upper-sided and lower-sided schemes. The optimal design procedures, in terms of MRL, are proposed for a range of mean parameter shift. The performance of the proposed schemes is assessed and compared to existing monitoring schemes using MRL. The remainder of this paper is structured as follows: In Section 2, the proposed one-sided exponential AEWMA scheme is introduced. The Markov chain models are used to investigate the RL properties of the proposed schemes in Section 3. The optimal design procedures for the MRL-based one-sided exponential AEWMA schemes are presented in Section 4. In Section 5, the performance of the proposed schemes with existing ones is compared. Two practical case studies are shown in Section 6. Finally, the conclusion and the suggested directions for future study are given in Section 7.
## 3 Sources of Stochastic Noises
### Clustering
AP proceeds by alternating 2 message passing steps to update the '_responsibility_, \(R(i,k)\)' and '_availability_, \(A(i,k)\)' matrices, where \(R\) quantifies how \"well-suited\" \(x_{k}\) is to serve as the exemplar for \(x_{i}\) compared to other candidate exemplars, while A shows how \"appropriate\" it would be for \(x_{i}\) to pick \(x_{k}\) as its exemplar, taking into account other points' preference. \(R\) and \(A\), are initialized to zero being viewed as log-probability tables and then AP is iteratively updated for \(R\) and \(A\) by:  
\[R(i,k)=s(i,k)-\max_{x^{\prime}\
eq k}\bigl{\{}a\bigl{(}i,k^{\prime}\bigr{)}+s \bigl{(}i,k^{\prime}\bigr{)}\bigr{\}}\]  
\[A(i,k)=\min\left(0,r(k,k)+\sum\limits_{i^{\prime}\
eq\{i,k\}}\max(0,r\bigl{(} i^{\prime},k\bigr{)}\right)_{\text{for }i\
eq k}\]  
Figure 7: Conceptual dendrogram for fuzzy-logic (FL) [35]. The exemplars are extracted from the final updated matrices where'responsibility + availability' is positive. Figure 8 shows the AP iterative result of R and A for a QPSK middle-channel in WDM CO-OFDM at 3200 km for an optimum launched optical power (LOP) per channel of -5 dBm, where 13 iterations are required for convergence [36]. AP is considered as an advanced soft-clustering algorithm.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 4 Double-Scale Channel State Prediction
### Small-Scale Channel State Prediction  
To accurately regulate transmission power in time-varying acoustic channels, a decomposition-based prediction algorithm is proposed.
## 4 The Concept of BIM
### Benefits of BIM Implementation for Value Engineering  
In recent years, leading firms in the Architecture, Engineering, and Construction (AEC) industry have recognized the benefits of BIM, transitioning from traditional Computer-Aided Design (CAD) systems to BIM technology [18]. BIM's collaborative capabilities across multiple disciplines have enhanced its value as a tool for optimizing construction projects. When combined with Value Engineering (VE), BIM provides significant benefits by improving project efficiency, enhancing decision making, and optimizing costs. By offering a comprehensive representation of a building's physical and functional characteristics, BIM aligns with VE's objectives of maximizing project value through cost reduction and performance improvement [19].  
The main benefit of BIM implementation is its ability to provide an accurate three-dimensional (3D) geometrical representation of a building within an integrated data environment [12]. Some of the key benefits of using BIM for Value Engineering include:  
* Enhanced collaboration and communication: BIM improves interaction among project stakeholders, ensuring that all team members work with the most current information [20].
* Increased efficiency and productivity: by reducing network during the construction phase, BIM enhances overall project productivity [21].
* Sustainability and waste reduction: BIM facilitates the selection of sustainable materials and reduces material waste, contributing to environmentally friendly practices [22].
* Integrated tracking systems: BIM enables the creation of tracking systems to analyze workplace behavior and improve efficiency [23].
* Dynamic data integration: by incorporating existing conditions, sensor measurements, and control signals, BIM enhances the analysis of building operations and maintenance [24].
* Improved decision making: BIM's ability to identify design and construction issues early in the process improves overall decision making and reduces risks.
# Wind Turbine Anomaly Detection Using Mahalanobis Distance and SCADA Alarm Data
### Mahalanobis Distance  
The Mahalanobis distance is the distance between two points in multivariate space. This is similar to the Euclidian distance, except there is also a correlation between variables that is involved for the MD approach [7]. One of the most common purposes of the MD is in locating outliers in the multivariate space. This is commonly used in applications such as fault detection in some measuring tools. The Mahalanobis distance between two objects can be defined as follows by Equation (1):  
\[d_{MD}=\sqrt{(x_{b}-x_{a})^{T}*C^{-1}*(x_{b}-x_{a})} \tag{1}\]  
where \(x_{a}\) and \(x_{b}\) refer to a pair of objects, while \(C\) is the sample covariance matrix.  
The advantage of using the Mahalanobis distance lies in its capacity to solve for the limitation of the Euclidian distance. Since using the Euclidian distance in a multivariate space would be inappropriate as it only deals with distances between points, the Mahalanobis distance solves this by measuring the distance between the point and the distribution itself [8]. This is achieved by using the covariance of the matrix distribution in the calculation, as shown in Equation (2):  
\[cov_{x,y}=\frac{\sum(x_{i}-\overline{x})(y_{i}-\overline{y})}{N-1} \tag{2}\]  
where \(x_{i}\) and \(y_{i}\) represent data values of the distributions, and \(\overline{x}\) and \(\overline{y}\) refer to the means of the distributions. Figure 1 is an example plot of the Mahalanobis distance in which a contour plot overlays the scatterplot of 100 random draws from a bivariate normal distribution with a mean of zero, unit variance, and 50% correlation. The centroid defined by the marginal means is noted by a blue square. It should be noted that the Mahalanobis distance is capable of detecting outliers in the bivariate space.
# Performance Evaluation Model of Landport and Seaport Collaboration Using the Support Vector Machine
## 2 Support Vector Machine (SVM)  
### Support Vector Machine  
The support vector machine (SVM) is a machine learning method which is a part of statistical learning theory. The SVM originally evolved from optimal segmentation surfaces in linearly separable cases. Through coefficient normalization, the linearly separable sample set \((x_{i},y_{i}),i=1,2,\ldots,n,x\in R^{d},y_{i}\in\{+1,-1\}\) satisfies the following constraint conditions:  
(1) Global optimal solution  
The SVM method transforms the quadratic programming problem under the original constraints into the dual quadratic optimization problem, and the solution it seeks must be the global optimal solution, thus avoiding the local optimal solution that may occur in the learning methods such as a neural network.  
(2) Dimension independence  
The problem studied in this paper belongs to nonlinear classification problems. When SVM is solving nonlinear classification problems, the kernel function is skillfully applied in the dual form of optimization problems, which overcomes the algorithm complexity.  
(3) Avoidance of ill-posed problems  
The SVM method is specifically proposed for learning problems in the case of a small number of samples. Coincidentally, the number of analysis samples used in this study is small, and the SVM method is suitable for the problem in this study.  
(4) Good self-adaptation  
The only freedom of the SVM method is the choice of kernel function, thus avoiding the dependence of methods like neural networks on techniques.  
(5) Diverse derivation methods  
In the SVM method, learning algorithms such as poly approximation and the RBF method can be implemented as long as different kernel functions are selected.