esa-sceva/satcom-chunk-collection huggingface dataset:
# An Approach to Data Analysis in 5G Networks
## 3 SELFNET Analyzer Module Design
### Design Principles
To assume a centralized approach lead us to pose a general purpose scheme where the onboarding of new use cases is completely configurable by specification, and which does not requires updating the implementation (see Figure 2). Therefore the centralized approach is not dependent on the characteristics of the use case, so it is highly scalable and allows performing tasks efficiently (avoiding redundancy). However, its design and the description of use cases is complex. On the other hand, the distributed approach includes an additional component for each use case in which specific pattern recognition and prediction methods are implemented via plugin. The preprocessing, selection and symptom discovery mechanisms have general purpose. In essence, this second approach is easy of design, but completely use case dependent; each time a new use case is onboarded, the Analyzer implementation must be updated. Due to the large impact on the scalability that this entails, the centralized approach is considered hereinafter. * _Data Encapsulation_. The greatest challenge in designing the SELFNET Analyzer approach is the requirement of dealing with unknown data. It is possible to assume that use cases do not provide clear enough information about the characteristics of the information to be analyzed (in fact, several future use cases are completely unknown). At the specification stage, use cases operators tend to provide good qualitative information about the metrics to consider, but may overlook details about their quantitative nature: data type, domain, range, restrictions, etc., which is what in the first instance, will be considered in the analysis tasks. Furthermore, quantitative information is much use case dependent. In order to subtract relevance to quantitative details (which are the backbone  
Figure 2: Centralized and Distributed Architectures. of the aggregation/correlation tasks), and thus facilitate the incorporation of new use cases by definition of general purpose descriptors, the SELFNET Analyzer is driven by data encapsulated in two levels of abstraction: quantitative and qualitative parameters (see Figure 3). The first one is independent of the use cases, and allows designing a centralized analysis framework valid for any type of data specification. On the other hand the qualitative parameters gather information directly related with SELFNET and the use case to which they belong (metric name, source, location, tenant, etc.). This data is mainly required for aggregation/correlation, diagnosis and decision making.
# Generating Process Models by Interacting with Chatbots--A Literature Review
## 5 Reporting of the Systematic Literature Review
### Generating Natural Language Text Input from Process Models and Backwards (RQ3)  
Research Question RQ3 delves into studies that encompass bidirectional transformations, addressing both the conversion of natural language text to process models (RQ1) and the generation of natural language text output from process models (RQ2). This comprehensive inquiry seeks to explore the interplay between textual input and graphical representations within the context of process modeling. While RQ1 and RQ2 focus on unidirectional processes individually, RQ3 endeavors to examine the holistic integration of these processes, thereby elucidating the synergistic effects and challenges inherent in bidirectional transformation endeavors. As aforementioned, concerning RQ2, we have identified a study (S18) [29] by [PERSON] et al. that indeed addresses both directions of transformation--converting natural language text to process models and generating natural language text output from process models. Remarkably, similarly to the findings for RQ1, this study prominently employs NLP as its primary technology. During the conversion from Text to Model, the study uses natural language analysis involving tokenization, part-of-speech tagging, and semantic role labeling to parse textual descriptions. It then identifies process-related concepts and maps them into structured formats, like BPMN process models. For the conversion from Model to Text, study S18 details methods to analyze existing process models to extract their components and relationships. Using template-based or generative NLP techniques, it then generates textual descriptions while ensuring semantic consistency to accurately reflect the original models. The study also presents practical applications and case studies demonstrating the efficiency and scalability of these techniques. By addressing both directions of conversion, the study aims to bridge the gap between human-readable descriptions and machine-interpretable process models, facilitating better communication, documentation, and optimization of business processes. In Table 9 the distribution of the studies for generating natural language text input from business process models and backwards is presented.
# A Low-Overhead Countermeasure against Differential Power Analysis for AES Block Cipher
## 3 DPA Attack and Our Proposed Countermeasure
### Our Proposed Countermeasure
#### 3.2.1 Multiplicative Masking  
_Original concept._ In masking, the intention is to protect sensitive information while providing a functional alternative when real information is not needed. Generally, masking is the different representation of real information. It can distinguish using the splitting of a function (i.e., \(f(x)\)) into \(n+1\) shares, as shown in Equation (4). The Boolean sharing is computed using a bit multiplication operation over the split shares.  
\[x=({q_{0}}^{x},....,{q_{d}}^{x})\Longleftrightarrow x=\bigotimes_{i=0}^{d}{q_ {i}}^{x} \tag{4}\]  
In Equation (4), we use \({q_{i}}^{x}\) to present Boolean shares of a function defined in terms of \(x\). The \({q_{0}}^{x}\)...\({q_{d}}^{x}\) are the \(n+1\) Boolean shares of \(x\), where \(n\) is the natural number.  
_Zero value problem._ Initially, the significant security flaw of multiplicative masking was highlighted in [38]. It does not mask the value 0. The mean power consumption of a single share \({q_{1}}^{x}\) reveals whether the underlying secret is zero or non-zero since \(\mathrm{E}[{q_{1}}^{x}|x=0]=\mathrm{E}[{q_{1}}^{x}|x\
eq 0]\) for any share index i [39]. This means that for any number of shares, the original multiplicative masking scheme is vulnerable to first-order DPA.  
_Solution._ Our proposed solution to avoid the zero value problem is illustrated in Figure 2. It realizes the fact that if we invert zero, we obtain zero, while if we invert one, we obtain one. To achieve this, we used only the Kronecker delta function (see \(\delta(x)\) in Figure 2). The corresponding mathematical formulations are shown in Equations (5)-(7), respectively.  
\[x^{-1}=x\text{ for }x\in\{0,1\} \tag{5}\]  
\[\delta(x)=\left\{\begin{array}{ll}1&\text{ if }x=0\\ 0&\text{ if }x\
eq 0\end{array}\right. \tag{6}\]  
\[x^{-1}=(x\oplus\delta(x))^{-1}\oplus\delta(x) \tag{7}\]  
For any bit value (0 or 1), Equation (5) determines the inverse of a Boolean share. The Kronecker delta function is provided in Equation (6). Finally, Equation (7) reflects the implementation of Figure 2. It is obtained with the substitution of Equations (5) and (6).
# State of the Art and Trends Review of _Smart Metering_ in Electricity Grids
## 4 Development of _Smart Metering_ Worldwide
### Other Regions  
Other regions of the Middle East, such as Libya and the United Arab Emirates, are also starting their path into _Smart Metering_; _Dubai Smart City_ can be cited as a model. Regarding Africa, utilities from South Africa and Zimbabwe lead the _Smart Metering_ introduction initiative, which is still very poor.
## 1 Introduction
### Contributions of This Study  
To overcome the limitations identified above and tackle the unique challenges of IoV-based federated learning, we propose Fed-DTB, a Federated Dynamic Trust-Based framework designed explicitly for vehicular environments. Fed-DTB fuses internal indicators (IDS alerts, energy consumption, FL participation history) with external context (regulatory compliance, network stability). Unlike static models, it employs adaptive weighting that recalibrates factor importance in real time. Based on the foregoing limitations, Fed-DTB advances the state of the art as follows:  
* We introduce a multi-factor trust evaluation system that integrates diverse IoV-specific factors, including network status, vehicle state, energy efficiency, compliance with traffic rules, federated learning participation, user feedback, and IDS alerts. By combining these factors in a cohesive and context-aware manner, our approach provides a richer and more dynamic measure of trustworthiness compared with existing single-metric or static trust mechanisms.
* We propose a context-aware adaptive weighting mechanism that dynamically adjusts the priority of trust factors (e.g., prioritising intrusion detection system (IDS) [35] alerts during attacks or energy efficiency in low-resource scenarios) to reflect real-time IoV conditions.
* We design a trust-driven client selection framework that optimises the choice of FL participants based on contextual trust scores, enhancing global model accuracy while minimising training latency.
* We generate a custom synthetic dataset that simulates realistic IoV scenarios, including adversarial attacks, dynamic network states, and vehicular mobility, to address the lack of public benchmarks for IoV-FL research. Experimental validation using standard benchmarks (e.g., MNIST, CIFAR-10) confirms the framework's generalisation across diverse scenarios.
* Through extensive experiments, we validate the framework's robustness against poisoning attacks, improved convergence behaviour, and enhanced accuracy compared with single-factor trust baselines.  
Previous work on trust or security in federated learning typically relies on a single static factor (e.g., IDS or user feedback). By contrast, Fed-DTB offers the first lightweight, dynamic multi-factor trust mechanism validated under realistic IoV mobility.  
The rest of this paper is organised as follows. Section 2 describes the system model and problem formulation. Section 3 presents the proposed Fed-DTB trust management framework and vehicle selection mechanism. Section 4 discusses experimental results and analysis. Section 5 provides discussion and implications of the findings. Finally, Section 6 concludes the paper and outlines future research directions.
## 5 Experiments
### PI Assessment Indexes  
In related research [14; 16], the most important feature of PIs is their coverage probability. PI coverage probability (_PICP_) is measured by calculating the number of covered target values [16]:  
\[PICP=\frac{1}{N}\sum_{i=1}^{N}C_{i} \tag{11}\]  
where:  
\[C_{i}=\begin{cases}1,\;\;if\;\;t_{i}\in[L_{i},U_{i}]\\ 0,\;\;if\;\;t_{i}\
otin[L_{i},U_{i}]\end{cases} \tag{12}\]  
where \(N\) is the number of samples. \(L_{i}\) and \(U_{i}\) represent the upper and lower bounds of the PI corresponding to the \(i\)-th sample.  
Another important indicator is the mean prediction interval width (_MPIW_), which is used to quantify the width of PI [14]. The definition is as follows:  
\[MPIW=\frac{1}{n}\sum_{i=1}^{n}(U(X_{i})-L(X_{i})) \tag{13}\]  
Meanwhile, in some literature [14; 42], two additional evaluation indicators are also used to supplement the evaluation of PI, namely _NMPIW_ and _CWC_. Assuming that the target range \(R\) is known, normalized _MPIW_ (_NMPIW_) can be calculated as follows [14]:  
\[NMPIW=\frac{MPIL}{R} \tag{14}\]  
_NMPIW_ represents the percentage of the average width of PIs in the underlying target.  
Reference [42] developed a coverage width criterion (_CWC_) consisting of _PICP_ and _NMPIW_, which simultaneously evaluates PIs from both coverage probability and width perspectives:  
\[CWC=NMPIW*\left(1+\gamma(PICP)e^{(-\eta(PICP-\mu))}\right) \tag{15}\]  
where \(\gamma(PICP)\) is given by:  
\[\gamma=\begin{cases}0,&PICP\geq\mu\\ 1,&PICP<\mu\end{cases} \tag{16}\]  
In Equation (15), \(\eta\) and \(\mu\) are the two hyperparameters that control _CWC_, as suggested in reference [18]. The \(\eta\) And \(\mu\) values are set to 50 and 0.9, respectively. _CWC_ provides an effective compromise between the information and the precision of the PI. It is worth noting that the smaller the _CWC_, the better [16].
# Dynamical Pseudo-Random Number Generator Using Reinforcement Learning
## 3 Experiment and Results
### Different Sequences Even in the Same with 800 Bits of Random Numbers  
In the study of multiplexed quantum random number generation [11], the correlation of signals was examined to verify each signal's independence from seven homodyne detector channels for quantum random number generation. Likewise, this study uses a correlation matrix to verify whether multiple sequences out of the same seed maintain independence in disposition and pattern. The correlation matrix is close to one when the disposition of two sequences is the same. Thus, all start in the condition of '0 seed.' This condition proves that different sequences can be generated even from the same seed. A total of 10,000 sequences of 800 bits are generated, and 2 out of these 10,000 are selected to measure the correlation. Ten thousand values are compared in each sequence, and the maximum value is measured. As such, each of 10,000 sequences is measured.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Maximum** & **Maximum Test** & **Average Test** \\  & **Validation Value** & **Value** & **Value** \\ \hline Proposed method & 0.532 & 0.522 & 0.509 \\ Target method & 0.486 & 0.486 & 0.470 \\ \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of the maximum and average values between the proposed method and target method when there are 800 bits of random numbers.  
Figure 3: Comparative analysis of learning and verification between the proposed method and target method when there are 800 bits of random numbers.  
As shown in Figure 4 the red dotted line indicates the value of correlation max in 10,000 sequences. The blue line is the number of sequences with the same correlation value. The maximum correlation value among all of the sequences was 19%. It turned out that 10,000 sequences generated at different times were not similar. The maximum value of the correlation in most sequences was about 13%. The fact that no other sequence existed suggests that dynamical pseudo-random numbers are all different when the reinforcement learning mechanism is applied, even if the same seed is used. As the dimension of random numbers is low, the same pattern will most likely be repeated. Thus, the similarity of correlation scores is likely to be relatively high. However, the values indicate that the similarity was not high, and as the test was conducted on 100 sequences of 100,000 bits, the maximum value decreased to 1.1%.
## 1 Introduction
Concentrating on the Burr XII distribution, this study covers the maximum-likelihood estimation (MLE), uniformly minimum-variance unbiased estimation (UMVUE), and Bayesian estimations of the shape parameter and the reliability function. For Bayesian estimations, the squared error loss (SEL), general entropy loss (GEL), and Q-symmetric entropy loss (QEL) functions, together with the new loss function proposed in this paper named the weighted Q-symmetric entropy loss (WQEL), were applied. Our innovation lies in providing UMVUE and Bayesian estimations under the new loss function for the reliability of the Burr XII distribution. The evaluation of the given estimators, especially the Bayesian estimators under the new WQEL function, is also discussed. The remainder of this paper is structured as follows. In Section 2, the frequentist methods of the MLE and UMVUE under the Burr XII distribution are investigated. Section 3 deals with the Bayesian estimators under the three pre-existing loss functions, namely the SEL, GEL, and QEL functions. In Section 4, a novel loss function named the weighted Q-symmetric entropy loss function is proposed and applied. The Monte Carlo simulation is established in Section 5, which is conducted to assess the performance of the given estimators. In Section 6, a remission time data set is used to demonstrate the utility of the discussed estimators.
# Acoustic Scattering Models from Rough Surfaces: A Brief Review and Recent Advances
## 3 Historical Approximations
### The Kirchhoff Approximation
#### 3.2.1 Theory and History
In fact, to model the impedance surfaces used in their experimental validations, they simply add a reflection coefficient as a factor inside the Kirchhoff integral, and for backscattering the local reflection coefficient is the one at normal incidence for a plane surface: this formalism does not take into account any mode conversion in the elastic medium and is unable to predict any perturbation at the different critical angles. Following [PERSON]'s works which studied the Kirchhoff approximation using monochromatic plane waves, [PERSON] [73] has proposed a Kirchhoff model in time domain for 2D rough profiles and Neumann conditions (rigid interface). We can sum up the main steps of [PERSON] approximation application as follows:  
1. Use of the Helmholtz equation: the scattered field is: \[\psi(r)=\psi^{inc}(r)+\int_{S_{0}}\!\left(\psi(r_{0})\frac{\partial G(r,r_{0} )}{\partial n_{0}}-G(r,r_{0})\frac{\partial\psi(r_{0})}{\partial n_{0}}\right) \!dS_{0}\ \.\] (13)  
The equation is also called Green theorem and \(G(r,r_{0})=\frac{e^{|\psi(r_{0})|}}{4\pi|r-r_{0}|}\) is called the Green function, where \(r_{0}\) is a current point on the scattering rough surface \(S_{0}\) and \(r\) the observation point. The field in the fluid medium can be obtained thanks to the knowledge of its value on the surface \(S_{0}\). 2. Boundary Conditions on the rough surface: [PERSON] [27] considered for instance Dirichlet boundary conditions (soft case) so that \(\psi(r_{0})=0\). So one term in the previous Green integral disappears: \[\psi(r)=\psi^{inc}(r)+\int_{S_{0}}\!\left(-G(r,r_{0})\frac{\partial\psi(r_{0 })}{\partial n_{0}}\right)\!dS_{0}\] (14)
3. Kirchhoff approximation: the tangential plane approximation enables to approximate the value of \(\frac{\partial\psi(r_{0})}{\partial n_{0}}\) inside the previous integral. 4. Far field approximation: the integrand value can be simplified by assuming \(kr>>1\), \(k\) being the wave number. 5. Projection of integration on the rough surface on the smooth surface: this projection involves the gradients of the roughness. 6. Incorporation of a shadow function to approach auto-shadowing: [PERSON] [74] has proposed to incorporate a shadow function inside the Kirchhoff integral to take into account shadowing effects. Note that the Kirchhoff approximation is mainly used to predict far-field scattering; a model has been proposed to evaluate the scattering pattern in near-field [75] after the interaction of an acoustic wave with a rough interface.
## 4 Experiment Results
### Classification Performance  
We evaluate the classification performance on five affective datasets. Table 3 shows that the result of depth feature is higher than that of the hand-crafted feature and CNNs outperform the traditional methods. The VGGNet achieves significant performance improvements over the traditional methods such as DeepSentibank and PCNN on FI datasets of good quality and size. Simultaneously, due to the weak in annotation reliability, VGGNet does not make such significant progress on the Flickr dataset, indicating the dependence of the depth model on high-quality data annotation. Furthermore, our proposed method performs well compared with single model methods. For example, we achieve about 1.7% improvement on FI and 2.4% on EmotionROI dataset, which means that the sentimental interaction features extracted by us can effectively complete the image sentiment classification task. Besides, we adopt a simple ensemble strategy and achieve a better performance than state-of-the-art method.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 4 Discussion
### Land Cover/Band Correlations  
The finding that NDVI and NBAI are highly correlated with certain Sentinel-2 bands is neither novel nor groundbreaking; however, it is illustrative for our study. Commonly, studies use such indices for the classification of specific LULCs (e.g., 'impervious surface','vegetation') and proceed to perform analyses. By including all pertinent (i.e., removing the 60 m bands) Sentinel-2 bands in our study, we are providing the RF machine learning model with the information often used to classify LULC [39; 40; 41], and also the information necessary to continue one step further and model temperatures. In summary, the results suggest that RF offers a direct means for accurately classify LULCs from Sentinel-2 bands, and to predict ambient temperatures.
## 4 Analysis of Crack Width
### Estimation of Crack Widths
Although it is understood that crack width calculations are rather estimations than an exact calculation, a correction factor based on the probabilistic study is suggested in this study to minimize the variation in estimations observed in measurements. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Specimen** \\ **Name** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Thickness** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Rebar Stress** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Bottom** \\ **Concrete** \\ **Cover** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Rebar** \\ **Spacing** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Neutral Axis** \\ **Factor** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Calculated Crack** \\ **Width Using** \\ **Measured Load** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Calculated-To-** \\ **Measured Crack** \\ **Width Ratio** \\ \end{tabular} } \\ \cline{2-2} \cline{6-7}  & & \(H\) & \(f_{s}\) & \(d_{c}\) & \(s\) & \(\lg\) & \(w_{c}\) & \(w_{c}\)\(w_{m}\) \\ \hline
**Unit:** & **(mm)** & **(MPa)** & **(mm)** & **(mm)** & **-** & **(mm)** & **-** \\ \hline SA-H22 (PL)-1 & 300.3 & 344.3 & 59.5 & 200 & 1.35 & 0.54 & 1.08 \\ SA-H22 (PL)-2 & 297.8 & 334.4 & 59.2 & 200 & 1.35 & 0.52 & 1.05 \\ SA-H25 (PL)-1 & 298.7 & 289.8 & 58.3 & 200 & 1.36 & 0.46 & 0.91 \\ SA-H25 (PL)-2 & 303.8 & 308.3 & 58.8 & 200 & 1.35 & 0.48 & 0.97 \\ \hline SH-H19 (PL)-1 & 401.5 & 322.3 & 58.0 & 150 & 1.23 & 0.37 & 0.75 \\ SH-H19 (PR)-1 & 405.7 & 315.7 & 58.3 & 150 & 1.22 & 0.37 & 0.73 \\ SH-H19 (PL)-2 & 398.0 & 333.3 & 57.7 & 150 & 1.23 & 0.39 & 0.77 \\ SH-H19 (PR)-2 & 403.7 & 335.9 & 58.8 & 150 & 1.23 & 0.39 & 0.79 \\ \hline SK-H22 (PL)-1 & 507.3 & 298.7 & 58.3 & 150 & 1.17 & 0.33 & 0.67 \\ SK-H22 (PR)-1 & 505.3 & 306.2 & 59.0 & 150 & 1.18 & 0.34 & 0.69 \\ SK-H22 (PL)-2 & 504.8 & 287.7 & 57.8 & 150 & 1.17 & 0.32 & 0.64 \\ SK-H22 (PR)-2 & 505.0 & 315.4 & 58.5 & 150 & 1.17 & 0.35 & 0.70 \\ \hline Mean, \(\overline{\pi}\) & & & & & & 0.47 & 0.94 \\ \hline Standard & & & & & & & 0.089 & 0.179 \\ deviation, \(s_{x}\) & & & & & & & 0.190 \\ \hline cov, \(\delta\) & & & & & & & 0.190 & 0.190 \\ \hline Maximum & & & & & & 0.59 & 1.17 \\ \hline Minimum & & & & & & 0.32 & 0.64 \\ \hline Number of measurements & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._  
Figure 6: CDFs for the ratio of calculated-to-measured crack widths for (**a**) 0.3 mm and (**b**) 0.5 mm, crack widths (Equation (1)).