esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
A method to bring about higher focusing energy in pipe, by controlling the delay time of each element in the piezoelectric transducer array and adjusting the amplitude coefficient of each excitation channel, was introduced elaborately in Ref. [23; 24; 25]. In a thick-walled pipe with large diameter, [PERSON] et al. utilized the multichannel time reversal focusing method (MTRF) to focus the circumferential Lamb wave from independent channels, and located the defect by the aid of direct waves which were partially compensated [26]. Up to now, time reversal technique is conducted extensively in plates and pipes, whereas the application in rod-shaped waveguide is rarely reported. [PERSON] et al. excited a single-mode of longitudinal guided waves in a steel rod to identify a notch, and used TRM to eliminate the dispersion to increase the resolution of the defect echo signal [27]. [PERSON] et al. came up with a reference-free method to detect the delamination in a rebar-reinforcement concrete beam. A damage index was also defined based on the correlation coefficient between the actuated and reconstructed signals which were derived from TRM [28]. Both previous literatures mentioned above limited TRM in a single mode in rod-shaped waveguides, and only dealt with removing the within-mode dispersion. In this paper, ultrasonic guided waves with multiple modes are excited intentionally in differently damaged galvanized steel wires used in bridge cables, and the coupling defect signals attributed to multimode and multipath natures are focused by TRM. Compared with the conventional method for damage detection, not only is the proposed method with higher resolution to the variation of the damage degree, but is proved to supply the entire historical information of the pitting corrosion development.
# Effect of Vinyl Flooring on the Modal Properties of a Steel Footbridge
## 1 Introduction
The main outcome of the present study is the experimental assessment of the damping ratios generated by vinyl flooring when this non-structural element is used as the pavement of steel footbridges. An average non-structural damping ratio of 2.069% has been estimated experimentally. The results show that this type of pavement may be used as a valuable tool to increase the damping ratios of steel footbridges. Thus, vinyl flooring may be taken into account as a passive control system in order to mitigate the pedestrian-induced vibrations in slender footbridges. However, further studies are needed to better characterize the structural behavior of this non-structural element, making possible the development of mathematical models which allow predicting more accurately the response of steel footbridges damped with vinyl flooring; and to analyze in detail some reported limitations about the widespread use of these type of flooring in outdoor scenarios, such as, its strength to heavy point loads and the effect of sunlight exposure, the extreme temperatures and the defects of the subfloor on its structural behavior [18]. This paper is organized as follows. The main motivation of this study, a case study about the unexpected high damping ratios of the Jorge Manrique footbridge, is described in the second section. Subsequently, the main mechanisms which govern the generation of damping in footbridges and the description of the comparative method adopted herein to estimate experimentally the non-structural damping ratios associated with the vinyl flooring are presented in the third section. Later, both the estimation of the numerical and experimental modal parameters of a laboratory steel footbridge without and with the vinyl flooring and the experimental estimation of the damping ratios associated with this type of pavement have been included in the fourth section. Finally, some concluding remarks are formulated to close the paper.
# Dynamic Workload Management System in the Public Sector: A Comparative Analysis
## 4 Experimental Results  
### Comparing Algorithms  
This chapter provides a detailed comparison of the performance metrics obtained from various algorithms applied to our predictive modeling problem. We analyze the results of all seven different algorithms. In all cases, the output was the Time Factor, the mean time of \(TF_{i}\) per node \(N_{i}\), for \(n\) samples.  
\[\overline{TF}=\frac{\sum_{i=1}^{n}}{n} \tag{23}\]  
The Capacity Factor, introduced by [PERSON] et al. (2022) [1], is the amount of \(TW_{1}\) tasks executed per time unit. \(TW_{x}\) is defined as the task weight in terms of time in minutes. Therefore, for every node \(N_{i}\) with profile \(P_{j}\) for Task \(T_{k}\),  
\[CF(P_{j})=\frac{\text{average time spent on number of accomplished }TW_{1}\text{ tasks of }P_{j}}{TU}=\frac{TF_{i}}{TU} \tag{24}\]  
where \(TU=1\) min.  
Four hundred and fifty employee records were utilized for the training dataset, an additional one hundred records served as the testing dataset, and eight records were deployed for validation. The data samples were collected over three years. Approximately 11% of the initial data were deemed inaccurate due to continuous or unassigned tasks, introducing time-related issues and invalid value ranges (infinite values).  
The data in terms of Mean Squared Error (MSE) and Regression Analysis yielded 2781.07, while ANFIS outperformed all other models with an MSE of 1284.38. This indicates that ANFIS achieved the best accuracy in predicting our target. Similarly, ANFIS achieved the lowest Root Mean Squared Error (RMSE) at 35.83, demonstrating its superiority in minimizing prediction errors. XGBOOST reported the worst RMSE value of 81.63, suggesting that it cannot be selected for our purposes.  
Comparing other metrics, ANFIS consistently outperforms the other algorithms in Mean Absolute Error (MAE), Median Absolute Error (MedAE), Mean Squared LogarithmicError (MSLE), Root Mean Squared Logarithmic Error (RMSLE), Mean Bias Deviation (MBD), MAPE, and Huber Loss. The accompanying graphs present these results visually, highlighting ANFIS as the top-performing algorithm in accuracy and predictive power.
# A Systematic Literature Review on the Revolutionary Impact of Blockchain in Modern Business
## Appendix B Model Record Data Management
### Recent Advances and Emerging Clusters
#### 4.4.3 Virtual Environments: Emerging Focus on the Metaverse and Digital Twins  
In recent years, research has increasingly focused on blockchain's potential in the metaverse and digital twins. The metaverse is a virtual environment in which users engage with computer-generated surroundings and other participants to create immersive digital experiences [86]. [PERSON] et al. [87] highlight that blockchain technology is essential in the metaverse, facilitating compatibility across different platforms and enabling economic systems through digital goods and services. Furthermore, the incorporation of blockchain empowers users to safely possess and exchange virtual assets within a metaverse ecosystem [88]. This functionality allows individuals to acquire digital real estate, participate in community activities, and conduct business transactions in a virtual environment. Nevertheless, according to [PERSON] et al. [89], the technology behind the trusted collaborative network that supports the metaverse is still in its early stages. They argued that using blockchain as the underlying trusted collaborative network for the metaverse poses significant security and privacy risks, making it susceptible to breaches.  
A digital twin is a virtual representation of a physical entity, system, or procedure. It utilizes real-time sensor data to accurately reflect the real-world equivalence. Blockchain technology enhances digital twins by safeguarding data exchanges, maintaining data integrity, and generating unalterable records, which is especially crucial in scenarios where trust and verification are paramount. Recent studies, such as those by [PERSON] et al. [90], have highlighted the significance of blockchain in predictive asset management (PAM) for building facilities through the creation of a data provenance framework for blockchain-enabled digital twins. This framework offers a transparent and immutable log of data origin, ownership, and history, thereby bolstering the dependability and credibility of data in PAM.
# Correlation of Optical Constants and Morphologies with Mueller Matrix for Micro-Rough Surfaces
## 2 Theoretical Background
### Simulation Method and Jones-Mueller Matrix Conversion  
FDTD is a robust numerical method, which can be used to directly solve the differential Maxwell equations in the time domain to accurately calculate the electromagnetic response [1]. The FDTD method scales with high efficiency on a parallel-processing CPU-based computer and performs especially well for three-dimensional geometrical structures. The detailed algorithm of FDTD has been published [28; 29].  
The Jones vector basically describes the electric fields which are simulated by FDTD [1; 30] in this work. A Jones matrix is defined as a 2 \(\times\) 2 matrix, which is associated with incident and exit electric vectors in the p and s directions. Consequently, if we aim to acquire the 4 \(\times\) 4 Mueller matrix of rough surfaces via the simulation method, it is necessary to derive the formula for the conversion from the Jones matrix to the Mueller matrix. The specific steps to perform the matrix conversion can be found in ref. [31]. If the sample is non-depolarizing, the 4 \(\times\) 4 Mueller matrix \(\mathbf{M}\) takes the following form [32]:  
\[\mathbf{M}= \tag{3}\] \[\left(\begin{array}{cccc}\frac{1}{2}(\left|r_{pp}\right|^{2}+ \left|r_{sp}\right|^{2}+\left|r_{ps}\right|^{2}+\left|r_{ss}\right|^{2})&\frac{ 1}{2}(\left|r_{pp}\right|^{2}+\left|r_{sp}\right|^{2}-\left|r_{ps}\right|^{2}- \left|r_{ss}\right|^{2})&Re(r_{pp}r_{ps}^{*}+r_{sp}r_{ss}^{*})&Im(r_{pp}r_{ps}^{ *}+r_{sp}r_{ss}^{*})\\ \frac{1}{2}(\left|r_{pp}\right|^{2}+\left|r_{sp}\right|^{2}+\left|r_{ps}\right| ^{2}+\left|r_{ss}\right|^{2})&\frac{1}{2}(\left|r_{pp}\right|^{2}-\left|r_{ps} \right|^{2}-\left|r_{ps}\right|^{2}+\left|r_{ss}\right|^{2})&Re(r_{pp}r_{ps}^{ *}-r_{sp}r_{ss}^{*})&Im(r_{pp}r_{ps}^{*}-r_{sp}r_{ss}^{*})\\ Re(r_{pp}r_{sp}^{*}+r_{ps}r_{ss}^{*})&Re(r_{pp}r_{ss}^{*}+r_{ps}r_{ss}^{*})&Re( r_{pp}r_{ss}^{*}+r_{ps}r_{ss}^{*})&Re(r_{pp}r_{ss}^{*}-r_{ps}r_{ss}^{*})\\ -Im(r_{pp}r_{sp}^{*}+r_{ps}r_{ss}^{*})&-Im(r_{pp}r_{ss}^{*}+r_{ps}r_{sp}^{*})& Im(r_{pp}r_{ss}^{*}-r_{ps}r_{sp}^{*})\end{array}\right)\]  
where \(r_{\text{xy}}\) (x, y = p, s) represents the amplitude reflection coefficient of x-polarized light induced by the incident y-polarization and \(r_{xy}^{*}\) is the complex conjugate number of \(r_{\text{xy}}\). Re (\(\cdot\)) and Im (\(\cdot\)) show the real and imaginary parts of a complex number, respectively.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 6 Methods of Reducing Collisions
### Decreased Probability Reducing (DPR) Method  
The \(PPR\) method can increase the discovery probability, but it is independent of the schedule itself. We present the \(DPR\) method, which tries to coordinate any discovery schedule with the method. For the generated discovery schedule \(S_{i}\) by any algorithm \(f\), node \(u_{i}\) should turn on the radio at time \(t_{1}\) when \(s_{i}(t_{1})=1\). Denote the next time slot that \(u_{i}\) turns on the radio by schedule \(S_{i}\) as \(t_{2}\), i.e., \(s_{i}(t_{2})=1,t_{2}>t_{1}\). Modify the schedule of time slots \([t_{1},t_{2})\) as:  
* Change \(s_{i}(t)=0\) for \(t\in[t_{1},t_{2})\);
* increase \(t^{*}\) from \(t_{1}\) to \(t_{2}-1\), if \(\hat{s}_{i}(t)=0\) for all \(t\in[t_{1},t^{*})\), set \(s_{i}(t^{*})=1\) with probability \(p_{2}\cdot\frac{t_{2}-t^{*}}{t_{2}-t_{1}+1}\) where \(p_{2}\) is a constant value in \((0,1)\).  
Node \(u_{i}\) turns on the radio in the initial slot with probability \(p_{2}\cdot(1-\frac{1}{t_{2}-t_{1}+1})\), and it could reduce the probability of collisions. If \(u_{i}\) does not turn on the radio at time \(t_{1}\), it decreases the probability and attempts to turn on the radio in the next slot, \(t_{1}+1\). This process does not finish until \(u_{i}\) turns on the radio in any slot within \([t_{1},t_{2})\), or it keeps the radio off for all of them.  
Overall, both PPR and DPR are designed to support deterministic neighbor discovery protocols. Unlike probabilistic-based protocols, the neighbor discovery schedules computed by both PPR and DPR are based on the schedules generated by deterministic protocols. Therefore, a deterministic protocol running with PPR or DPR can achieve a stable discovery latency (confirmed in Section 7.1).
# Hydrogenation of Adiponitrile to Hexamethylenediamine over Raney Ni and Co Catalysts
## 3 Results and Discussion
### Hydrogenation of ADN to HMDA  
Mass transport limitations for the Raney Ni and Co catalysts for the ADN hydrogenation reaction were estimated. No change in ADN conversion or product selectivity was observed with an increase in stirring speed, indicating that there is no interphase transfer limit for the reaction using Raney Ni and Co catalysts [30; 31]. For an estimation of the intraparticle transfer limit, the Weisz-Prater number was computed for the system (when the Weisz-Prater number is greater than 0.3, the intraparticle diffusion limit is a dominant factor). The calculated value was significantly lower than 0.3, which shows the absence of any limitations on intraparticle diffusion [32]. Therefore, the ADN hydrogenation reactions performed on the Raney Ni and Co catalysts in this study were conducted in the absence both of external and internal transport limitations.  
As shown in Figure 1, the ADN hydrogenation reactions over the Raney Ni and Co catalysts achieved almost complete conversions of ADN (>99%) within 30 min, yielding HMDA as the major product. Figures 1 and 2 show that, as the reaction proceeds, AHN is hydrogenated to HMDA via a highly reactive imine intermediate [33] that was not observed in the product samples. For the Raney Ni catalyst, a quantitative conversion of ADN was achieved within 47 min, with an AHN and HMDA selectivity of 1% and 90.5%, respectively (Figure 1a). However, for the Raney Co catalyst, the AHN and HMDA selectivities were 4.3% and 87.5%, respectively, with a nearly quantitative ADN conversion within 47 min (Figure 1b). Other than AHN and HMDA, small fractions of azacycloheptene and azepane were also formed (Figure 2). Intermolecular condensation of the imine intermediate  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Catalyst** & \begin{tabular}{c} **BET Surface** \\ **Area (m\({}^{2}\) g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Total Pore Volume** \\ **a (cm\({}^{3}\) g\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Average Pore** \\ **Diameter (nm)** \\ \end{tabular} & \
\begin{tabular}{c} **Irreversible H\({}_{2}\)** \\ **Uptake (mmol g\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} **Reversible H\({}_{2}\)** \\ **Uptake (mmol g\({}^{-1}\))** \\ \end{tabular} \\ \hline Raney Ni & 18.4 & 0.029 & 4.8 & 0.173 & 0.122 \\ Raney Co & 25.8 & 0.034 & 5.3 & 0.065 & 0.046 \\ \hline \hline \end{tabular}  
* Determined at a relative pressure (P/P\({}_{0}\)) of 0.99.  
\end{table}
Table 1: Physicochemical properties of the Raney Ni and Co catalysts.  
with the amine products (AHN and HMDA) and intramolecular cyclization lead to the formation of azacycloheptene and azepane [34]. The total carbon balance ranged from 85 to 100%, indicating small fractions of byproducts undetected by the GC-FID.
## 3 Results and Discussion
### Proof of Concept: Quantifying Real Noise
#### 3.3.3 Comparison of Genetic Regulatory Networks with Theoretical Models  
Finally, to assess the \(PQA\) methodology using systems biology data we clustered 210 networks according to their pairwise dissimilarity [9]. First, 42 curated biological networks were retrieved from Abasy Atlas (v2.2) [10]. For each biological network, we then constructed four networks each according to a theoretical model (Barabasi-Alberts, Erdos-Renyi, scale-free, and hierarchical-modular). We estimated the parameters of each theoretical model from the properties of the corresponding biological network. The models used reproduce one or more intrinsic characteristics of the biological networks, such as power-law distribution, hubs, and scale-free degrees, and hierarchical modular struc  
Figure 4: \(Z\)-score distribution by percentage of randomized items. (**a**) Dataset from [PERSON] et al. (**b**) Dataset from [PERSON] et al. The red dots represent the \(Z\)-score interpolation of the corresponding data sets.  
Figure 3: Visual representation of clustered data used to assess the method. (**a**) Dataset from [PERSON] et al. (**b**) Dataset from [PERSON] et al.  
ture [11]. Visual inspection suggested that the classification yielded a highly ordered VP, distinguishing according to the nature of each network (Figure 5). The _PQA_ score for this VP is 0.92 (\(p\)-value = 2.5 \(\times\) 10\({}^{-40}\), Z-score = 13.2) and the proportion of noise was 5.8% (Figure 6). In contrast to the previous examples, here we obtained a highly ordered clustering and a very low proportion of noise, which suggests that although the models recapitulate some of the properties of genetic regulatory networks, each of them is not sufficient to capture their structural properties.
## 3 Results
### Seasonal Dynamics of Climate, Vegetation Indices, and Carbon Fluxes (NEE, GPP)
#### 3.1.3 Seasonal Dynamics of Carbon Fluxes (NEE and GPP)  
At the Brazil site (Figure 3a), the GPPEC rose steadily in November 2015 to higher than 1 g C m\({}^{-2}\) day\({}^{-1}\), reached the top levels in March, and dropped to 1 g C m\({}^{-2}\) day\({}^{-1}\) in October 2016. The GPPEC rose again in November 2016, which indicated the start of a new sugarcane growing cycle (Figure 3a). Following the GPP > 1 g C m\({}^{-2}\) day\({}^{-1}\) criterion, the GPP-based start and end of the growing season (SOS and EOS) at the Brazil site were in November 2015 and October 2016, respectively, which adequately matched the SOS metrics outlined by the vegetation indices (Figure 3a).  
At the USA site (Figure 3b), the GPPEC began to increase in late April, was high in the summer, and decreased below 1 g C m\({}^{-2}\) day\({}^{-1}\) by September in 2018 and November in 2019 and 2020, due to sugarcane harvesting. Following the GPP >= 1 g C m\({}^{-2}\) day\({}^{-1}\) condition, the GPP-based GSL of sugarcane oscillated between April and November (Figure 3b), which fit sufficiently with the growing season metrics based on the vegetation indices (Figure 3d).
# Analysis of Grating Lobe Effects on GEO DSC Distributed Antennas
## 3 Analysis of Grating Lobes in DSC Distributed Antennas
### Two Satellite Antennas  
The most significant difference between DSC distributed antennas and traditional satellite-based distributed antennas lies in the distance between satellites. As shown in Figure 6, assume that in the satellite orbit altitude \(H=3.6\times 10^{4}\) km, two satellite antenna aperture diameters are 1 m, with an operating frequency of \(f=30\) GHz, for an analysis in the ground of a 20 \(\times\) 20 m\({}^{2}\) area, the satellite different distance, and a different phase interference situation. When two antennas in a binary array radiate electromagnetic waves in the same direction at the same time, the intensity of the electric field received at different locations in space will be affected by different phases, so interference will occur. As shown in Figures 7 and 8, we simulated the interference of the far-field antenna pattern after the satellite antennas were grouped in the array at distances of 70 km and 100 km between the two satellites, respectively. Through the analysis of the two satellite antenna simulations, we found that the far-field antenna patterns of the two satellite antenna arrays  
Figure 5: A schematic diagram of the visible region for a 5-element uniform linear array.  
formed obvious interference fringes. At the center of the two antennas' radiation signal coherent superposition, the electric field strength at this point was twice as strong as the single antenna radiation, and the synthesis power was four times the power of the single antenna signal. On the one hand, when changing the antenna carrier phases \(\varphi_{1}\) and \(\varphi_{2}\), the interference fringes will pan, reflecting the impact of the change in antenna phase difference on the power synthesis; therefore, for a certain direction of the field strength size, and where each antenna signal phase alignment is related, adjusting the phase difference between the antennas can change the size of the synthetic power in a certain direction. On the other hand, when changing the relative position of the antennas \(d\) increases, the interference fringes become narrower and the number of fringes increases, reflecting the effect of the change in antenna position on the power synthesis.
# XAI-Based Framework for Protocol Anomaly Classification and Identification to 6G NTNs with Drones
## 6 Online Identification Method  
In the proposed interpretable detection framework, an intrinsically interpretable identification method is designed to capture and output anomalous protocol sequences during the detection process. To achieve this, since all XAI frameworks are extrinsic and cannot be adopted directly, the main principles used to design an intrinsic anomaly identification method for 6G NTN protocols are analyzed as follows.  
The original information of the protocol signals is input instead of the instantaneous envelope and statistical features. The inputs with positive contributions can be inverted to retrieve the original protocol, namely, the anomalous protocol sequences. Additionally, inputs must be formatted to ensure compatibility with the DL models.  
Inputs are processed with the original protocol information efficiently in the DL models. To reduce the computational load associated with the original protocol data, inputs can be segmented into static and dynamic parts on the basis of their variability during 6G NTN operations. The model can be trained to predict the static parts offline, whereas the dynamic parts must be predicted in real time.  
The identification and detection processes are synchronized. An inherently interpretable scheme that detects and interprets anomalous protocols simultaneously, namely, a network that directly identifies anomalous protocol sequences, is designed. Thus, the necessary conditions for real-time prevention can be achieved.  
On the basis of these principles, the original protocol signal information is transformed into images and fed into the model, providing a rich context for tracing the input back to the anomalous data points. The identification method is structured as a two-layer architecture composed of a prediction layer and a detection layer; this method efficiently processes the input protocol data. In the detection layer, traditional in Section 7.1 calculations are replaced with XOR operations, enabling simultaneous identification and detection of anomalous protocol sequences. In the process of actively identifying anomalous data points, an XAI mechanism is employed to provide inherently interpretable baseline detection results, ensuring that the protocol data and original information can be tracked throughout the entire mapping process. This approach concurrently obtains the detection results and the corresponding anomalous protocol data, facilitating effective anomaly identification.  
In the following sections, we leverage the explainable framework to design a self-evolving method for classifying uncovered protocol attacks and develop an active approach for identifying anomalous protocol data, as shown in Figure 8.