esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Study on Vibrated Grout-Enriched CSGR Material of Tangba Protection Dam
### The Vibrated Grout-Enriched CSGR
#### 3.4.2 Determination of the Amount of Grouting  
Utilizing the slurry compositions listed in Table 6 as the grouting material and incorporating the \(A1\) and \(A2\) CSGR mixtures from Table 4, six distinct slurry addition rates were established: 5%, 6%, 8%, 10%, 12%, and 14%, respectively. The prepared vibrated grout-enriched CSGR is marked as \(GA1\) and \(GA2\). Following the guidelines outlined in \"Test code for hydraulic concrete\" (SL/T 352-2020) ([PERSON] et al., 2020), the 'indoor mixing and forming method of GERCC', was executed using a vibrating table. Various volumes of the specified slurry, as detailed in Table 6, were added to the machine-mixed CSGR. After undergoing three rounds of manual mixing, specimens were formed. Wet sieving ensured particles larger than 40 mm were not retained, and subsequent tests were conducted to measure the slump and air content of the resulting specimens.  
The test results are depicted in Figure 5. Figure 5 indicates that the slump of the mixture post slurry addition ranges between 10 and 60 mm, while the indoor air content falls within the 4% to 5.8% range, signifying improved workability of the mixture. In Figure 5, it is evident that the compressive strength of vibrated grout-enriched CSGR escalates with increasing slurry rate. At an 8% slurry rate, with a total cement content of 176 kg/m\({}^{3}\), the 90-day compressive strength exceeds 15 MPa, meeting design specifications (where \(GA1\) surpasses a design strength of 15 MPa at an 80% strength guarantee rate, and \(GA2\) exceeds a preparation strength of 17.9 MPa). Concurrently, the impermeability grade reaches W8 and the frost resistance grade reaches F100, as illustrated in Figure 5,d, satisfying the requirements for impervious layer materials for dams with heights below 30 m (with a minimum allowable impermeability grade of W4). However, at a 12% slurry addition rate, the resulting vibrated grout-enriched CSGR contains a total cementitious material of 223 kg/m\({}^{3}\), achieving a 90-day compressive strength surpassing 20 MPa, with an impermeability grade of W11 and a frost resistance grade of F125. This meets the design criteria for impervious layer materials for dams, with heights ranging from 30 to less than 70 m (with a minimum allowable impermeability grade of W6). Notably, a 14% slurry addition rate increases the cementitious material content to 247 kg/m\({}^{3}\), significantly impacting economic feasibility and raising the risk of cracking, hence not recommended.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multicolumn{4}{c}{**1 m\({}^{3}\) Slurry Material Dosage (kg\({}^{3}\))**} & \multirow{2}{*}{**Marsh Fluidity (s)**} & \multirow{2}{*}{**Density (kg/L)**} \\ \cline{2-2} \cline{6-6}  & **Water** & **Cement** & **Water Reducing Admixture** & & **Air Entraining Agent** \\ \hline \(A580\) & 540 & 1270 & 12.9 & 1.29 & 11 & 1.884 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mix proportion of slurry.
## 4 The Como Test
### Analysis of the Time Series between Como EPN and Rover
#### 4.2.1 Significance Analysis  
The significance analysis aims at assessing how many imposed displacements are identified by a significance test run on the estimated displacements. The Fisher test is adopted [33]. Given two sessions \(i\) and \(j\), the null hypothesis is:  
\[H_{0}:\mathbf{x}_{i}=\mathbf{x}_{j} \tag{4}\]  
If \(H_{0}\) is true, then:  
\[\frac{\delta\mathbf{x}_{ij}^{T}\mathbf{C}_{\delta\delta_{ij}}^{-1}\delta \mathbf{x}_{ij}}{m}=F_{\text{exp}}\sim F_{m,r_{i}+r_{j}} \tag{5}\]  
where \(\mathbf{C}_{\delta\delta_{ij}}\) is the covariance matrix of the differences, \(r_{i}\) and \(r_{j}\) are the redundancies of the two data adjustments, and \(m\) is the dimension of the coordinates vector.  
A threshold \(F_{i}(\alpha)\) is computed for the Fisher function, such that \(P(F_{m,r_{i}+r_{j}}>F_{i}(\alpha))<\alpha\). If \(F_{\text{exp}}\leq F_{i}\) the null hypothesis is accepted as true. In the contrary case, the null hypothesis is not accepted.  
In our test, \(\alpha\) was put equal to 0.05. Let \(i\) and \(j\) be two sessions, let \(n\) be the number of movements between \(i\) and \(j\). Firstly, the test is performed for the horizontal (2D, \(m=2\)) and vertical displacements (\(m=1\)): in these cases, \(i\) and \(j\) are separated by imposed displacements of \(n\times 5\) mm. Then, the test is performed for the three dimensional displacements (3D, \(m=3\)): in this case, the imposed displacement between \(i\) and \(j\) is \(n\times\sqrt{2}\times 5\) mm (the composition of \(n\) horizontal and vertical displacements of 5 mm). The test was applied to couples of imposed displacements up to \(n=5\) movements.  
In Table 8 the percentages of right answers for the significance test are shown. In case of 0 movement, \(H_{0}\) hypothesis should be accepted (\"no displacement\"); for other movements \(H_{0}\) should be rejected (\"yes displacement\"). In horizontal, after three movements the majority of displacements is identified: after five movements, the displacement is identified almost in the whole dataset. Similar statistics are provided by the analysis of three dimensional displacements: in this case 3 and 5 movements correspond to displacements of about 2.1 cm and 3.5 cm, respectively. As expected, vertical results are worse. Firstly, this is due to the worse accuracy of GNSS vertical estimates; moreover, the significance test in Equation (4) strengthens at the increase of \(m\), therefore, the displacement test on one component is intrinsically weaker than the 2D and 3D cases.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **E (mm)** & **N (mm)** & **U (mm)** \\ \hline Mean & 1 & \(-2\) & 3 \\ RMSE & 4 & 6 & 13 \\ Min & \(-9\) & \(-18\) & \(-30\) \\ Max & 14 & 11 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Baseline between the primary reference and the rover. Hourly sessions: mean and RMSE of the residuals. E: east, N: north, and U: up.
## 2 Concepts and Methods
### Multiplicative Speckle Noise Behavioral Formulation and Modeling
On the contrary, and in the case of speckle PSD function modeling, the pixel statistics and distribution will be completely different from the reference image statistics. According to Equations (32)-(39), the results of PSD function modeling for embedded speckle within the reference image are presented in Figure 2e. Figure 2e shows that the speckle behavior, as an overlapping structure which occupies approximately the entire spatial area of the SAR image, has several high-frequency peaks which are close in terms of their spatial locations. Figure 2e also shows that, in comparison to the main texture of the reference image, the speckle has a replication structure on the basis of its distribution and statistics. In other words, speckle with significant effects on the amplitude, phase and the intensity can be modeled as a high-frequency replication structure of the PSD function. As a result, multiplicative speckle noise can be named as high-frequency multiplicative speckle (HMS) noise hereafter. From Figure 2 it is also deduced that HMS noise not only affects the maximum value of the PSD function, but also affects the uniformity of the texture. The results show that real reference SAR imagery and noise-free conditions have a total 4 dB difference in PSD function magnitude, while the HMS has a maximum 0.4 dB adverse effect on the PSD magnitude.
## 5 Results and Experimental Discussion
### Analyzing Test Metrics of the Fine-Tuned Models
#### 5.2.2 Performance Benchmark with Related Approaches

Table 7: Comparing our approach to detecting fake news about politics in Spanish using Transformers with other relevant research on text classification across several languages. results also confirm that selecting a single fine-tuned RoBERTa-BNE model with the appropriate adjustments can lead to better outcomes compared to more intricate approaches, as illustrated in rows 3 to 5 of Table 7. These approaches involve the use of individual BERT models pre-trained on English corpora, fine-tuning models on Spanish data corpora (e.g., BETO), and even the combination of multiple BERT models tailored for the specific task of fake news detection, respectively. Furthermore, our approach attains the top performance with regards to other existing approaches not only in the specific domain of fake news detection in Spanish (and even in other languages like English, Portuguese, or Chinese) but also in the broader field of automated text classification. This extends beyond fake news detection to encompass various areas, including hotel and movie review classification or the identification of sexist language in Twitter posts, among others, as indicated in the works listed in the last rows of Table 7. These outstanding results can be attributed to the substantial size, diversity, and representativeness of our dataset, enhancing the limited corpora available for fake news detection published to date. As previously mentioned, the non-synthetic dataset used in our experiments, including only 30 authentic news articles from diverse web sources, introduces broader and more realistic fake news scenarios. The unpredictability of these real-world data highlights challenges in the model's capability to manage the inherent complexities of genuine news articles, as evidenced by a notable decrease in accuracy, to 71%. While this accuracy level may be considered acceptable, particularly in less-explored domains like detecting political news in minority languages such as Spanish, it underscores the necessity for further refinement to effectively address these complexities in practical applications.
# IoT Anomaly Detection to Strengthen Cybersecurity in the Critical Infrastructure of Smart Cities
## 4 Discussion
In previous research, Isolation Forest has been widely recognized for its ability to identify unusual patterns in nonlinear data and its effectiveness in anomaly detection applications in various domains [42]. Furthermore, RNNs have excelled at detecting temporal and sequential patterns in data, making them especially suitable for anomaly detection problems in time series [43]. Previous studies on VAEs have revealed their ability to capture the latent structure of high-dimensional data and detect significant deviations in these data [44]. These references support the choice of these algorithms and reinforce their applicability in the context of cybersecurity for critical infrastructure in a smart city [45]. Furthermore, these algorithms were chosen based on an exhaustive review of the existing literature on anomaly detection and its application in similar environments. Previous research has highlighted the importance of employing practical and versatile algorithms for detecting threats and anomalies in IoT-supported critical infrastructure systems [46]. The application of Isolation Forest, RNN, and VAE in our study aligns with the trend of using machine learning approaches to improve the safety and efficiency of smart cities [47, 48]. These choices are based on the solid theoretical foundation supported by previous research and the proven ability of these algorithms to address the challenges of anomaly detection in high-dimensional and time-series data. Therefore, we can confidently state that these algorithms represent a suitable and promising choice for anomaly detection in the critical infrastructure of an IoT-enabled smart city. The relevance of this work is reflected in its ability to address critical challenges in smart cities, such as IoT security, and the protection of crucial infrastructure, such as the electrical grid. The successful implementation of these models could significantly impact the safety and efficiency of smart cities around the world, improving the quality of life of citizens and the sustainability of urban systems.
## 7 Research Limitations and Implications  
VLC-based ITS is an emerging concept which will haveits associated challenges and socio-economic effects. The integration of the VLC components with existing transportation networks, and compatibility with the legacy communication networks, is a rising challenge for the standardization bodies. Apart from V2V communication, the telematics of transport [42] can play an important role in the development of smart-societies and lead to an increase in socio-economic benefits [43] through traffic management, upgrading of emergency services, administration of traffic enforcement and support to the logistics industry. Future work on the VLC-based ITS may include the environmental impact assessment and cost-benefit analysis to attract the automotive industry in the development and use of VLC components in the head and tail lights, as presented in the proposed framework.  
The research limitations related to VLC-based ITS include limited bandwidth of the LED devices, maintaining LOS connectivity on a bumpy road and flickering of LEDs due to continuous streams of ones and zeros. The implications of such a system can be anticipated, such as the provision of RF spectrum for other applications, provision of dual technology in parallel to existing RF communication systems, more efficient usage of the existing lighting infrastructure, and automobile lights and new opportunities in the area of photonics and transportation.  
Conceptualization, U.H., and F.M.; methodology, M.I., F.A., U.H. and A.S.A.; software, F.A., U.H. and F.M.; validation, F.A. and U.H.; formal analysis, F.A. and U.H.; investigation, F.A., U.H., F.M., and W.G.; data curation, U.H.; writing--original draft preparation, U.H.; writing--review and editing, M.I., F.M., A.S.A., M.I, A.G., and S.U.; visualization, U.H.; resources, W.G.; supervision, U.H. and M.I.; project administration, F.M., M.I., A.S.A., A.G., S.U.; and W.G.; funding acquisition, M.I. and A.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the AGH University of Science and Technology, grant No. 16.16.120.773 to pay the open access article processing charges of the journal.  
This work was supported by the Ministry of Education and the Deanship of Scientific Research, Najran University, Kingdom of Saudi Arabia, under code number NU/ESCI/17/0114.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### MC Simulation
#### 2.3.2 Application of MC Simulation in PPV Estimation  
This section presents the modelling steps of MC techniques in the risk analysis of PPV. According to Figure 6, in this paper, six steps are considered for PPV estimation using MC simulation. First, the affecting variables (ST, BS, MC, PF and D), and their corresponding values which were gathered from quarry sites in Malaysia, were identified. Second, a predictive model which was carried out using GEP (Equations (1)-(6)) was developed. Third, the distribution function of each variable was considered. Because a large amount of data was gathered already, the distribution functions of all variables were calculated using the Risk Solver Platform [89]. The Risk Solver Platform is an MS-Excel plug-in feature which is used in this research for MC simulation. In this Risk Solver Platform, a 'best-fit' function is available to select the distribution function of the variables. The inputs for the MC simulation are presented in Table 4, together with their minimum and maximum amounts and the distribution functions. Notably, continuous probability distribution (CPD) was considered for all inputs during the use of this function.  
Figure 5: Operating principle of a Monte Carlo (MC) simulation [93].  
Fourth, because the current study is mainly aimed at the achievement of effective combinations in MC simulations, the input correlations were taken into consideration in the process of simulation. This program makes use of a correlation matrix between the model inputs and involves major rank-order correlations (Spearman's rho). In MC, the relations between model inputs have a deep impact on simulation results. Table 5 shows the mentioned relations obtained by Microsoft Excel Version 2013. Fifth, in developing the MC model, Latin hypercube sampling was used, due to its advantages compared with simple random sampling [94]. To ensure that every combination of variable values was analysed, 10,000 trials were performed. Then, the output ranges were generated as a sixth step.
## 2 Materials and Methods
### Testing Setup  
A four-point loading test was adopted for this study. A Magnus Test Frame with a maximum capacity of 300 kN was used to study the performance of the thirteen specimens. The specimens were preloaded to ensure that the instrument was functioning properly and to avoid any poor contact [17]. The specimens were loaded monotonically under displacement control at a loading rate of 0.01 mm/s [18]. The load was applied to the specimen through two loading steel plates at the top of each specimen [18]. The center-to-center distance between the two loading plates was set at 300 mm. All the specimens were simply supported by two bearing plates at the reaction base, with a clear span of 800 mm. Steel roller supports were placed at the soffits of both reaction bases to provide hinged support for the specimens. Three units of linear variable differential transducer (LVDT) were installed at the bottom of each specimen to measure the vertical displacement during the testing at L1, L2 and L3 positions. The typical setup of the deep beam testing is illustrated in Figure 4. All the LVDTs were connected to a data logger for displacement data collection throughout the entire testing program. White paint was applied to the surface of the specimens and 50 mm \(\times\) 50 mm grids were drawn on the front of each specimen to trace and map surface concrete cracking patterns. The test was terminated once the specimen showed significant deflection and failed to resist additional load.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Nominal Diameter (mm)** & **Yield Strength (MPa)** & **Ultimate Strength (MPa)** \\ \hline
8 & 487.3 & 610.1 \\
10 & 676.7 & 757.3 \\
12 & 672.7 & 760.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mechanical properties of high tensile steel reinforcement bars.  
Figure 4: _Cont._  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Plate Thickness (mm)** & **Yield Strength (MPa)** & **Ultimate Strength (MPa)** \\ \hline
1.5 & 349.90 & 363.40 \\
2.0 & 337.85 & 393.05 \\
2.5 & 303.70 & 351.35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Mechanical properties of TMS plates.
# Automatic Detection of Electrodermal Activity Events during Sleep
## 2 Materials and Methods
### Events Frequency Range  
As mentioned previously, EDA signals can be recorded either exosomatically or endosomatically. Most devices and wearables, such as the Empatica E4 watch and the WatchPATTM, record EDA exosomatically. The recorded signal is simpler, because it does not distinguish between monophasic, biphasic, and triphasic oscillations. However, these three different types of oscillations have different physiological meanings. Since endosomatic recording distinguishes between them [41], it is more valuable from an information theoretical point of view. The EDA signals considered in this work were recorded endosomatically. The procedure is described in the paper by [PERSON] and colleagues [5].  
For an EDA sleep signal oscillation to be considered an EDA event, it must have a certain duration. In the literature, different values are given for this duration. [PERSON] suggests 1.2 s to 4 s [42], while [PERSON] and [PERSON] suggest 1 s to 3 s [43]. We use these values to define the following EDA oscillation frequency range of interest:  
\[f_{s,min} = \frac{1}{\max(3\;\mathrm{s},4\;\mathrm{s})}=0.25\;\mathrm{Hz},\] \[f_{s,max} = \frac{1}{\min(1.2\;\mathrm{s},1\;\mathrm{s})}=3\;\mathrm{Hz}. \tag{2}\]  
This means that we consider triphasic oscillations that last for around 1 s and monophasic oscillations that last for around 4 s. We assume that significant signal fluctuations of higher frequencies are either motion artefacts, recording errors, or non-relevant EDA events.
# Full Real-Time Positioning and Attitude System Based on GNSS-RTK Technology
## 3 Material and Methods  
The protocols to transmit information and to communicate the different elements and devices of the prototype are introduced in this section. Afterwards, a description of the hardware selected and the software implemented is given.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 5 Results
### Compatibility of Different Arrays
Figure 15 depicts the time consumption of the conventional MIMO radar engineering system (DS-MRS), while Figure 16 illustrates the time consumption of the proposed system (ED-MRS). In these figures, the horizontal axis represents the frame number of the data, and the vertical axis represents the system time consumption for processing one frame of data. The blue color indicates the time consumption for processing data from antenna array I, and the orange color represents the time consumption for processing data from antenna array II. Figure 14: Antenna array II processing results. (**a**) DS-MRS antenna array II target trajectory. (**b**) ED-MRS antenna array II target trajectory. Figure 15: DS-MRS processing time. By comparing Figure 15 and Figure 16, it can be concluded that, compared to the proposed system, the conventional system exhibits relatively larger fluctuations in time consumption between antenna array I and antenna array II. The time consumption difference between the two arrays is in the order of several hundred microseconds. This discrepancy arises primarily because the conventional system involves extensive data sorting and filtering within a large volume of stored RDM data. Antenna array I, being a one-dimensional linear array, exhibits lower angular complexity and fewer effective antenna channels, simplifying the data retrieval process for the angle estimation module. Consequently, this results in a reduced processing time for each data frame. Conversely, in Figure 16, the time consumption for processing data from both antenna arrays is notably similar to the proposed system. This uniformity is attributed to the enhanced system architecture, which facilitates more flexible and compatible data sorting and screening. In this system, the extensive data processing tasks before the antenna data reordering and retrieval module do not necessitate specific adjustments for different antenna arrays, thus minimizing variations in processing time across different data volumes. Furthermore, during the antenna data reorganization stage, the input data have already been filtered through the Doppler FFT based on target distance and velocity information. Consequently, the volume of data requiring processing in the antenna data reorganization retrieval module is significantly reduced, equivalent to the total number of virtual antenna channels. Therefore, changes in the antenna array have a minimal impact on the array matchability data screening and sorting processes. This indicates that the proposed system exhibits superior compatibility with various antenna arrays.
## 5 Experimental Results and Discussions
### Results
#### 5.2.4 Discussions
Although randomness and dynamic changes introduce some fluctuations, the overall performance still showcases excellent scalability and adaptability. This lays a solid foundation for future applications in larger-scale, multi-task UAV scenarios.