esa-sceva/satcom-chunk-collection huggingface dataset:
# The Mechanism of Tendentious Information Dissemination in Cyberspace
## 4 Results
### Comparative Experiments
#### 4.3.1 Comparison of Different Modeling Theories  
To compare different modeling theories, we compared the TIDM model constructed in this study and a model based solely on the TPB using the same sample data. Figure 6 presents the two models.  
As Table 6 shows, after comparing the fitting degree of this study's TIDM model and the model based on the TPB, both models showed a good degree of fit to the sample data across all indices, indicating that the research conclusions are relatively reliable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Fit Index** & **Reference Value** & **Model Value** & **Overall Model Fit** \\ \hline \hline  & \multicolumn{3}{c}{Absolute fit index} \\ \(\chi^{2}/df\) & 1–3 & 1.474 & Yes \\ _GFI_ & \textgreater{}0.80 & 0.861 & Yes \\ _AGFI_ & \textgreater{}0.80 & 0.836 & Yes \\ _RMSEA_ & \textless{}0.08 & 0.046 & Yes \\  & \multicolumn{3}{c}{Relative fit index} \\ _CFI_ & \textgreater{}0.90 & 0.951 & Yes \\ _NNFI_ & \textgreater{}0.90 & 0.946 & Yes \\  & \multicolumn{3}{c}{Parsimony fit index} \\ _PGFI_ & \textgreater{}0.50 & 0.729 & Yes \\ _PNFI_ & \textgreater{}0.50 & 0.782 & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Fitting index of the TIDM model.  
Table 7 presents the results of comparing the explainability of core variables in both models. As Table 7 shows, the TIDM model's interpretation of ATD, IDI, and IDB is significantly higher than that of the model built based on TPB, exhibiting increases of 0.319, 0.117, and 0.112, respectively. These results suggest that the introduction of the two variables representing communication characteristics, namely, ISO and DTR, better fit the actual tendentious information dissemination process and objectively reflect a real communication scenario. As such, the modeling basis of the TIDM proposed in this study, which is based on the TPB and the characteristics of tendentious information dissemination, is theoretically sound.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Model Fit Index** & **Reference Value** & **Model Value (TIDM)** & **Model Value (TPB)** \\ \hline \multicolumn{4}{c}{Absolute fit index} \\ \(\chi^{2}/df\) & 1\(\sim\)3 & 1.474 (Yes) & 1.420 (Yes) \\ _GFI_ & \(>\)0.80 & 0.861 (Yes) & 0.899 (Yes) \\ _AGFI_ & \(>\)0.80 & 0.836 (Yes) & 0.875 (Yes) \\ _RMSEA_ & \(<\)0.08 & 0.046 (Yes) & 0.043 (Yes) \\ \multicolumn{4}{c}{Relative fit index} \\ _CFI_ & \(>\)0.90 & 0.951 (Yes) & 0.970 (Yes) \\ _NNFI_ & \(>\)0.90 & 0.946 (Yes) & 0.966 (Yes) \\ \multicolumn{4}{c}{Parsimony fit index} \\ _PGFI_ & \(>\)0.50 & 0.729 (Yes) & 0.725 (Yes) \\ _PNFI_ & \(>\)0.50 & 0.782 (Yes) & 0.801 (Yes) \\ \hline \end{tabular}
\end{table}
Table 6: Model fitting comparison based on different modeling theories.  
Figure 6: Model comparison based on different modeling theories: (**a**) based on TIDM and (**b**) based on TPB.
## 1 Introduction
More specifically, they consist of the following modules: (1) GIS subsystem for seismic early warning, risk assessment and emergency management tasks; (2) knowledge discovery database (KDD) and data warehouse (DW) subsystems for discovering decision-relevant knowledge from the very large amount of available seismic parameters observations; (3) spatiotemporal modeling subsystems for simulating the seismic activities; (4) specialized geo-visualization modules for supporting the operator in the process of deciding whether a warning has to be generated. Most of these prototypes earthquake EWS currently available, develop one or more of the components above described for predicting, managing, mining and visualizing (see [16, 17, 19, 20, 21], among others). In this research work, we have designed and developed a web-oriented GIS application for predicting and mapping seismic vulnerability, assessing potential impacts of possible seismic events and to support the disaster response planning. This GIS application has been designed as an integrated component of the data-processing unit of the earthquake early warning system developed in the national SIT_MEW Project ([[http://www.consorziotre.com/index.php?option=com_content&view=article&id=73&Itemid=62](http://www.consorziotre.com/index.php?option=com_content&view=article&id=73&Itemid=62)]([http://www.consorziotre.com/index.php?option=com_content&view=article&id=73&Itemid=62](http://www.consorziotre.com/index.php?option=com_content&view=article&id=73&Itemid=62))). The novel SIT_MEW EWS architecture combines our GIS system with an advanced SDMMS. The proposed GIS subsystem for earthquake EWSs includes a geospatial database system; a local GIS application for predicting and mapping the seismic hazard vulnerability of regional areas; a WebGIS module for collecting, synthesizing and sharing the geo-information among private and public stakeholders, emergency managers and common citizens involved in disaster response [22].
# An OCARI-Based Wireless Sensor Network for Heat Measurements during Outdoor Fire Experiments
## 2 Wireless Sensors Networks (WSN) in the IEEE 802.15.4: Zigbee and OCARI Protocols
### General Information  
OCARI and ZigBee Technologies are designed for use in a short-range wireless personal area network (WPAN) context. These technologies are typically used to deploy dedicated wireless networks for data transmission between home devices or in industrial environments. They have shared a growing interest since the 2000s, with the home automation market, which has evolved significantly since 2003, leading to the Internet of Things (IoT) market, which is still moving today. These kind of networks, particularly WSNs, are of great interest to the scientific community [17]. Specifically, WSN devices are based on WPAN technologies in which a few standards are commonly used in daily life (for example, Bluetooth). A few others (HomeRF, ZigBee, LiFi, etc.) are increasingly used for the collection of environmental data, especially given their many advantages in terms of costs, performance, and usability. The performance of these devices is closely related to protocols in charge of physical and MAC layers of the wireless network and the deepest layers of the hardware-software architecture of the network elements.  
Figure 1: _IEEE 802.15.4_ standard networking topologies.
## 1 Introduction
[PERSON] [22] suggested an algorithm for the conversion of the encounter-frequency spectrum to the original wave spectrum by allocating the energy based on the parametric spectrum with proper sea state parameters, such as significant wave height and zero-upcrossing period, obtained from the spectral moments of the encounter wave spectrum. Further research [23] enhances the algorithm by applying an optimization scheme under reasonably given constraints. This method recovered the original wave spectrum well in many cases; however, its performance tended to decrease with high forward speeds. As mentioned above, this underdetermined inverse problem has non-unique solutions with a given encounter wave spectrum, making it challenging to find the original wave spectrum without well-defined constraints. Additionally, the real ocean environment is more complex, including multi-modal and multi-directional characteristics, which makes the problem more difficult to solve. Therefore, alternative database-driven ML approaches may be more attractive in solving this kind of challenging inverse problem, which is the methodology adopted in the present paper. ML models are capable of learning complex nonlinear mappings from data without requiring explicit physical assumptions. By training on a diverse dataset, these models can generalize across varying sea states and vessel speeds, offering a flexible and robust solution to the spectrum conversion task. As far as the authors know, this kind of approach for the given mission has never been used in the open literature. In this study, artificial neural network (ANN) models were used to recover the original wave frequency spectrum from a given encounter wave frequency spectrum that is supposed to be measured on board. The model was trained with a large, synthesized dataset, which was generated by using a very large number of pairs of the spectra produced by using their mathematical relations for various combinations of vessel speed, wave-vessel relative headings, and spectral types. For illustration in this paper, the original random wave spectrum was assumed to be unidirectional, described by the JONSWAP spectrum [24], which sufficiently represents the majority of realistic sea states. However, a more general wave database encompassing a wider range of wave patterns could be constructed and utilized for the same ML approach.
## 4 Experimental Results
### The Reconstruction of CSNGAN-ASPP
#### 4.3.2 Reconstruction for Lithofacies-Fold Structure Model  
Referring to the parameter settings in Table 1, the 3D network was trained. After finishing training, 12 reference models were randomly selected from the testing set of the lithofacies-fold structure model, and the corresponding conditioning data were used as inputs to the network. The simulation results are presented in Figure 12.  
Moreover, Figure 13 shows the internal spatial distribution trend of folds between the four simulation results and the corresponding reference model.  
Combining Figures 12 and 13, it is evident that the folds are distributed continuously in the 3D reservoir domain, without a large amount of noise and artifacts. Compared with the reference model, the fold structures in both figures maintain a similar trend and overall boundary morphology, which demonstrates that CSNGAN-ASPP can effectively reproduce the 3D structural features of the folds.  
Figures 14 and 15 present the variogram function curves and connectivity function curves for the 50 realizations and their corresponding references.  
Figure 12: Reconstruction results for lithofacies-fold structure model.  
Figure 13: The internal distribution between the four simulation results of lithofacies-fold structure model and the corresponding reference.  
Meanwhile, Figures 16 and 17 display the boxplots and MDS visualization results for the 50 simulation results in comparison to the reference model, respectively.  
As shown in Figures 14 and 15, the spatial variogram function and connectivity function curves (gray solid lines) of simulations are uniformly distributed around the reference line (the black solid line). The direction and shape of the curves are largely consistent, with only minor differences at various lag distances. In Figure 16, the statistical features of the facies proportions between the two sets of simulation results show minimal differences, indicating that the simulation effectively captures the proportional characteristics of the fold properties. Figure 17 illustrates that the distance between the red and blue point clusters is smaller in the interval of [\(-\)50,50], which demonstrates that the simulation results maintain structural properties similar to the reference model. In conclusion, for the 3D lithofacies-fold structure model, CSNGAN-ASPP effectively recovers and reproduces the structural characteristics and distribution trends of folds.  
Figure 14: The variogram plots for the 50 realizations of lithofacies-fold structure model and the corresponding reference.  
Figure 15: The connectivity function distribution along x-directions and y-directions for the 50 realizations of lithofacies-fold structure model and the corresponding reference.
## 5 Accuracy of the Proposed Formulation
### Comparison with Other Existing Formulae
\[P=0.15\frac{E_{a}}{\left(C_{S}C_{L}C_{\beta}R\right)^{1.41}}d^{0.41};\text{ Mean equation} \tag{34}\]  
\[P=0.14\frac{E_{a}}{\left(C_{S}C_{L}C_{\beta}R\right)^{1.41}}d^{0.41};\text{ Design equation} \tag{35}\]  
* Stringer-stiffened cylinder:  
The force-displacement response of the proposed formulation is expressed in Equations (27) and (28) for mean equation and characteristic equation, respectively. It can be quickly achieved by the derivation of Equations (36) and (37) with respect to \(d\). \[P=0.224\frac{E_{a}}{\left(C_{S}C_{L}C_{\beta}R\right)^{1.471}}d^{0.471};\text { Mean equation} \tag{36}\]  
\[P=0.18\frac{E_{a}}{\left(C_{S}C_{L}C_{\beta}R\right)^{1.471}}d^{0.471};\text { Design equation} \tag{37}\]  
The force-displacement responses obtained using proposed formulations were compared with that of the experimental results and other existing equations to assess the validity of the approach and any shortcomings, as illustrated in Figure 28. The proposed formulation from [PERSON] and [PERSON] (1991) is highly over-predicted, while that of [PERSON] et al. (2015) equation is under-predicted with experimental results. However, the initial part of the Cerik curve matched well with the test results. For the force-displacement response curve of the proposed formulation, when Equation (15) was equated to the impact energy in the experimental result, maximum dent depth at peak force can be obtained. In comparison with the force-displacement values, it predicted well with experimental results for the proposed mean Equation (34). When considering the safety design in risk conditions, the proposed design Equation (35) was under predicted. The proposed formulations were compared with the numerical results and existing formulae of the two stringer-stiffened cylinder models (SS-C-1 and SS-C-2), as shown in Figure 29. It is noted that the fluctuations in numerical results could not be captured for all models. Further, the initial resistance, which is the collapse resistance of the first stringer and attached plating, was a bit higher in the numerical results. This should be attributed to the strain rate effect and vibration during impact processes. Nevertheless, the overall tendencies of the numerical response are well captured by both models. It is clear that force-displacement relations of [PERSON] (1991) and [PERSON] et al. (2015) is over-predicted. However, the proposed formulation predicted well. The main difference of the proposed formulation was that the slope of the force-displacement curve did not decrease much as in the existing formulae. It is believed that for a simple tool this inaccuracy is acceptable. This is important when designing the stringer-stiffened cylinder based on strength design principles. Figure 28: Comparison of predicted force-displacement relations using proposed formulations with other existing formulations for ring-stiffener cylinders: (**a**) Model RS-C-1; (**b**) Model RS-C-2.
# Influence of Fatigue Crack Formation and Propagation on Reliability of Steel Members
## 8 Conclusions  
The paper is focused on the influence of fatigue damage (fatigue cracks) of the steel welded I-girders on change of moment resistance and reliability in time. Methods such as analytical calculation and probabilistic calculation (numerical simulations) were used. The crack due to fatigue in tension flange of welded I-girder changes the cross-sectional characteristics as cross-sectional area, second moment of area and plastic or elastic section modulus. The analytical calculation and also probabilistic approach showed the following conclusions:  
* In the case of independent crack growth in directions \"a\" and \"c\", the fatigue crack can cause unavoidable decline of moment resistance (deterministic and stochastic) from the viewpoint of the Ultimate Limit States, what is going to decrease the member  
Figure 15: Dependence of reliability index \(\beta\) on crack depth “a”.  
reliability from \(\beta\) = 3.652 to \(\beta\) = 2.754 (I400) and to \(\beta\) = 2.200 (I800) within bridge lifetime of 100 years.
* The derivation of the model of dependent crack growth in both directions represents a major contribution of this work. Using this approach, it is possible to directly determine the bending resistance and fatigue stressing from the viewpoint of the Ultimate Limit States.
* In the case of dependent crack growth, the reliability index \(\beta\) of the member at the maximum dimension of the considered crack a = 0.8-t takes the values of \(\beta\) = 3.443 (I 400) and \(\beta\) = 3.400 (I 800) respecting strength criteria of the Ultimate Limit States.
* The results of the study showed more favourable and real (lower changes) interconnection between fatigue stressing and bending resistance. However, it still tends to decrease reliability of the member and to shorten the remaining lifetime.  
Conceptualization, P.K. and J.V.; data curation, P.K.; software, P.K.; validation, P.K. and J.V.; formal analysis, P.K. and J.V.; visualization, P.K.; writing--original draft preparation, P.K.; writing--review & editing, J.V.; All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Slovak Research and Development Agency under contract No. APVV-14-0772, and by Research Project No. 1/0306/21 and Project No. 1/0623/21 of the Slovak Grant Agency, and under the project of Operational Programme Interreg V-A Slovak Republic--Czech Republic: Assessment of the impact of the environmental load on the bridge structures condition of the cross-border transportation network, No. 304011Y277. The project is co-funding by European Regional Development Fund.  
Not applicable.  
The authors declare no conflict of interest.
## 3 Analysis of Influencing Factors  
In this section, the influences of basement geometry and tunnel location relative to the basement on the vertical deformation of the tunnel, \(\mu\), are investigated. Figure 7 shows the basement length, \(L\), along the tunnel direction, the basement width, \(B\), perpendicular to the tunnel direction, the basement depth, \(H_{\rm e}\), the tunnel cover depth, \(H_{\rm c}\), the tunnel diameter, \(D\), and the distance between the tunnel axis and the basement center, \(F\).  
The relationship between the vertical deformation of the tunnel, \(\mu\), and the various influencing factors is expressed by the function  
\[(\mu)=f\{B,L,H_{\rm e},F,H_{\rm c},D\}, \tag{1}\]  
where the parameters in the equation are all length parameters. The dimensionless processing of each parameter is carried out to analyze the general relationship between these parameters and tunnel deformation. The dimensionless group is obtained as follows:  
\[\left(\frac{\mu}{H_{\rm c}}\right)=f\bigg{\{}\frac{B}{H_{\rm c}},\frac{L}{H_{ \rm c}},\frac{F}{B},\frac{H_{\rm e}}{H_{\rm c}},\frac{D}{H_{\rm c}}\bigg{\}}, \tag{2}\]
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 3 UWB Positioning System
### UWB Positioning Techniques and Algorithms  
Several techniques can be used with ultra-wideband waves to determine the indoor position of movable objects. In general, the UWB location tracking of the unknown movable tag can be determined using one of the following basic techniques: triangulation, or trilateration [10]. These techniques have been derived from ancient mathematical calculations to determine the locations of things.
# Hand Gesture Recognition on Edge Devices:
## 5 Pre-Processing/Signal Processing
### Distance Calculation and Multilateration  
Distance calculation uses the knowledge of the speed of propagation of either sound at 343 m/s or electromagnetic waves such as radar, WiFi, and light of 299,792,458 m/s.  
High pass filter, cross-correlation, time gain compensation, and summation of several pulses are common algorithms to find the echo in the received signal. An especially positive effect of cross-correlation is, that it lowers the length of the echo while the center points of the echo are elevated. In radar processing these steps are usually carried out inside the radar unit. For ultrasound the sensor hardware usually outputs the amplitude of the sound pressure level directly. Due to the lower frequency of ultrasound the distance calculation steps require less processing power. Beamforming in combination with distance measurement results in knowledge about the angle of arrival (AoA) and distance.  
While one sensor suffices for distance calculation, at least two are required for multilateration. The receivers should be placed at a distance significantly higher than \(\frac{\lambda}{2}\). For omnidirectional sensors each distance measurement of a sensor results in a sphere of that radius of possible locations for the detected object. The object of interest must be located, where the spheres of possible distances measured by several sensors intersect. In case of several intersections usually some can be excluded due to the FoV of the sensor or other given restrictions, like for example the movement range of a hand. Theoretically distance measurements to three sensors allows the exact localization of a point object. But in reality distance measurement is not perfect and a hand is not a perfect point object.  
Figure 14: Raw ultrasound data sample in 2D.  
Figure 15: Range-doppler/velocity map, calculated by 2D Fourier transform (in fast - first and then in slow-time direction), of the segmented gesture.  
Additional measurements can compensate for noise in combination with statistical methods like maximum likelihood estimation.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
###### Abstract
To accomplish this, an analysis of the impact of various CWT configurations on the performance of popular CNN architectures was conducted, followed by an evaluation of the best-performing model on a target dataset with varying numbers of frozen layers to determine how it performs on new data. Hence, the main contributions of this study can be summarized as follows:  
* A novel deep-learning model pre-trained on CWT-generated scalograms was proposed, which is targeted specifically for sensor-based HAR classification problems. The suggested model outperformed the majority of state-of-the-art studies where the KU-HAR dataset was employed;
* It was experimentally established that the usage of the proposed pre-trained model, especially with layer freezing, results in a more stable gradient descent, faster training, and improved performance on small datasets;
* The impact of different CWT configurations on the performance of well-known neural network architectures was analyzed, which resulted in 60 combinations and over 300 models being trained and evaluated;
* The potential of the CNN/CWT-based approach for addressing wearable sensor-based HAR classification problems was demonstrated, and the directions for future works employing the scalogram-based pre-training technique were proposed. The paper is structured as follows: Section 2 reviews contemporary state-of-the-art studies and approaches in the sensor-based HAR domain and outlines the research gaps in this field. In Section 3, we explain the workflow of this study and describe the datasets, methods, and techniques employed. In Section 4, we provide the results obtained from the experiments. Section 5 includes analyses and discussions of the acquired results and their implications. Finally, Section 6 outlines the conclusion and proposes directions for future work employing the scalogram-based pre-training approach in the wearable-based HAR domain.