esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated Data Acquisition in Construction with Remote Sensing Technologies
## 4 Integration of Various RS Technologies
### RFID Integration with Other Sensory Data
#### 4.2.1 RFID Integration with a Wireless Sensor Network (WSN)
Having said that, some experiments have shown degraded performance of the RFID systems in object localization as active RFID tags and readers starts to get their required electrical power from WSN nodes [26; 29; 48].
# 5G Physical Layer-Based Procedure to Support Time-Sensitive Networking
## 4 End-to-End Synchronization Using TESC  
To achieve end-to-end synchronization, our method is divided into two distinct parts. The first part initiates at the Grand Master (GM) clock, the main reference clock, and concludes at the SC. This phase closely aligns with the proposal outlined in 3 GPP Release 17 and follows a similar procedure. However, the key distinction lies in terminating the gPTP flow at the SC rather than at the UE. The second part of the synchronization process focuses on the interaction between the SC and the UE. From the perspective of the UE, the SC assumes the role of the primary timing source, providing essential timing information utilizing the NR Radio Frame.  
Figure 5 illustrates an end-to-end synchronization process for the synchronization of an industrial IoT UE with a GM clock. The UPF may include a NW-TT for encoding timing signals following a 5G network protocol by 3 GPP Release 16 and Release 17. Moreover, the SC may include the SC-TT to decode timing signals from the UPF and synchronize the UE to the reference time of the GM.  
Figure 4: TSN Slice.  
Figure 5: End-to-end synchronization procedure.
## 3 The Proposed DSEC Method
### The DSEC Method  
To solve the imbalanced data problem of TSSP, the three-step DSEC method is proposed. The descriptions of each step are summarized below.  
Step 1: Divide the training set \(D\) into stable set \(S\) and unstable set \(U\). Next, utilize the data segmentation strategy to split the stable set into \(T\) stable subsets, \(S=\{S_{1},\,S_{2},\,\ldots,\,S_{T}\}\). Then, combine each stable subset with unstable set \(U\) into \(T\) training subsets, \(D=\{D_{1},\,D_{2},\,\ldots,\,D_{T}\}\). If IR is an integer, the stable samples are as many as the unstable samples in each training subset; otherwise, the unstable samples are more than the stable samples in each training subset.  
Step 2: Train \(T\) AdaBoost classifiers with \(T\) training subsets independently.  
Step 3: Ensemble the decision values from \(T\) AdaBoost classifiers by using the summation rule expressed as follows:  
\[F(\mathbf{x}_{i})=\sum_{t=1}^{T}H_{t}(\mathbf{x}_{i})=\sum_{t=1}^{T}\sum_{r=1} ^{R}\alpha_{tr}h_{tr}(\mathbf{x}_{i}). \tag{17}\]  
Then, determine the transient stability status by  
\[y_{i}=\left\{\begin{array}{cc}1&if\,F(\mathbf{x}_{i})\geq 0\\ -1&\text{otherwise}\end{array}\right.. \tag{18}\]  
The schematic diagram of DSEC method is shown in Figure 3:  
Figure 3: Schematic diagram of the DSEC method.
## 2 Materials and Methods
### Statistical Analysis  
The statistical analysis was completed using SPSS software (version 27.0). In this study, categorical and numerical variables were used. Numerical/categorical data are shown as mean \(\pm\) standard deviation and total number of percentages. HWE analysis and genotype and allele frequencies were studied using SNPStat, while logistic regression models and clinical characteristics using \(t\)-tests were studied with SPSS software. ANOVA analysis was studied using Jamovi software Version 2.3 for the thrombophilic SNPs with Kruskal-Wallis tests, and generalized multifactorial dimensionality reduction (GMDR) analysis was implemented to study (i) gene-gene interactions, (ii) dendrogram analysis and (iii) depletion models in the study groups. The \(p\)-value was found to be significant if the value was \(<\)0.05 (\(p<0.05\)).  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline
**Gene** & **rsnumber** & **Region** & **Location** & **Forward Primer** & **Rerves Primer** & **PCR Size** & **T\({}_{\text{m}}\)** & **Enzyme** & **RFLP Analysis** \\ \hline _FVL_ & rs4020 & G1691A & Exen 10 & TCAGCACAGAGACAACAACCAT & GGTATCATCAGAGACAAAATACCT & 241 bp & 58 \({}^{\circ}\)C & HindIII & G2441 bp; x-209/32 bp \\ \hline _PIFI11_ & rs1799963 & G20210A & 3’UTR & TCTAGACAACAACTICCICCICC & ATAGCACCTGGCAGACAACA & 2345 bp & 60 \({}^{\circ}\)C & HindIII & G345 bp; x-322/23 bp \\ \hline _MTHR_ & rs180113 & C677 & Exen 4 & TGAAGAGAGGTGCTGCTG & AGAGCGCTGGGAGG & 198 bp & 68 \({}^{\circ}\)C & Hind & C1-98 bp: T-175/23 bp \\ \hline \end{tabular}  
\end{table}
Table 1: Involvement of thrombophilic SNPs and their details.
# Personalized Privacy Protection Based on Space Grid in Mobile Crowdsensing
## 6 Discussion
### Impact of Minimum Reward Task Allocation Model on System Performance  
Table 7 indicates that the task allocation success rate of the PAG significantly outperforms that of the AG algorithm. Furthermore, after selecting the PAG to establish a spatial index, Table 8 demonstrates that the time priority task allocation mechanism surpasses both the location priority mechanism and the reward mechanism in terms of the average travel distance required to complete tasks. This not only affects the time to complete the task but also increases the cost of the platform. Consequently, for data collection tasks, the PAG spatial index algorithm proposed in this paper, coupled with the reward-first task allocation mechanism, can efficiently realize cost-effective data collection on the platform.  
The results indicate that the personalized adaptive grid (PAG) exhibits superior performance in terms of privacy protection and task allocation success rates compared to other spatial index structures, particularly when the privacy budget is substantial. However, the communication costs associated with the PAG are relatively higher, albeit justifiable  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(\epsilon\) & \multicolumn{2}{c}{**AG**} & \multicolumn{2}{c}{**PAG**} \\ \hline  & **Time First** & **Location First** & **Time First** & **Location First** & **Salary First** \\ \hline
0.1 & 0.3561 & 0.2112 & 0.7456 & 0.2674 & 0.1790 \\ \hline
0.4 & 0.3249 & 0.2064 & 0.5458 & 0.1891 & 0.1945 \\ \hline
0.7 & 0.3130 & 0.2229 & 0.4871 & 0.2434 & 0.1683 \\ \hline
1.0 & 0.3224 & 0.2322 & 0.4206 & 0.1649 & 0 considering the enhanced privacy protection and task completion rates. Moreover, the use of a reward-first task allocation mechanism further optimizes data collection processes on the platform, leading to cost-effective and efficient data collection. The findings underscore the importance of considering privacy protection and task allocation strategies in designing efficient spatial index structures for data collection platforms.
# Low-Cost Underwater Communication System: A Pilot Study
## 4 Results  
During the first phase of the experiment, the prototype of the modem was tested with the use of BPSK signals and a number of different symbol speeds. Additionally, the signal received by the transducer was recorded on a digital oscilloscope.  
The result is presented in Figure 6. The start of the frame can be seen. In addition, the moment of phase change in the BPSK signal is evident. The speed of the transmission was set up to 1000 symbols per second, which gives 1000 bps data link. In Figure 6, a high level noise is visible. Despite this, the modems were able to communicate.  
In the next Figure 7, the IQ signal of the QPSK modulation is shown. In this particular trial, the modulation speed is 2000 symbols per second, which gives a 4000 bps transmission rate. The observed noise slightly influences the IQ signal, but the integral calculated over the symbol period gives a mean value. Thus, the noise is suppressed and finally a right decision about the value of a symbol is possible.  
In theory, the frequency band of the QPSK signal can be found by  
\[B=\frac{2R_{b}}{\log_{2}M}, \tag{6}\]  
where \(R_{b}\) is a symbol rate, and \(M\) is a number of possible phase values utilized by a modulation. In the case of QPSK modulation, \(M=4\). The bandwidth can be taken from  
Figure 6: Received BPSK signal registered at the output of the amplifier. Signal registered with the electronics oscilloscope sampling at 500 kHz.  
Figure 7: IQ signals before integration taken from the microcontroller thanks to the HITL simulations.  
the ultrasound transducer characteristics--see Figure 5\(B\approx 2400\) for the standard 3 [dB] level. Therefore, one can find the theoretical maximum symbol rate according to  
\[R_{b}=\frac{B\log_{2}M}{2}\approx 2400\;\text{[symb/s]}. \tag{7}\]  
In the experiment, setting up a symbol rate higher than 2000 [symb/s] resulted in some transmission errors. It is probably so, because too small power was emitted by the ultrasound transducer. The power of the ultrasound signal can be increased by increasing the voltage, but this requires a modification of the charger circuit.
## 3 Results and Discussions
### Validating the Hybrid ML Models
In addition, with respect to the computational time for modeling, the training phase of the ANFIS-SFLA model took about 45 min using 64 bit Windows computer with processor Intel(R) Core (TM) i5-3210M CPU @ 2.50 GHz, whereas only a fraction of second was consumed for evaluating new beam configuration. Once constructed, the ANFIS-optimized model can simulate new configurations without generating new beam configuration, as needed in classical FE modeling. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**R**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} & \multicolumn{2}{c}{**Error Mean**} & \multirow{2}{*}{**Error StD**} & \multirow{2}{*}{**Slope**} \\ \hline \multirow{2}{*}{ANFIS-RCSA} & Training & 0.927 & 0.032 & 0.055 & \(-\)0.007 & 0.054 & 0.749 \\  & Testing & 0.920 & 0.032 & 0.054 & \(-\)0.006 & 0.054 & 0.747 \\ \hline \multirow{2}{*}{ANFIS-CA} & Training & 0.951 & 0.022 & 0.045 & \(-\)0.005 & 0.045 & 0.814 \\  & Testing & 0.948 & 0.022 & 0.044 & \(-\)0.005 & 0.044 & 0.815 \\ \hline \multirow{2}{*}{ANFIS-SFLA} & Training & 0.962 & 0.017 & 0.041 & \(-\)0.006 & 0.041 & 0.822 \\  & Testing & 0.960 & 0.017 & 0.040 & \(-\)0.006 & 0.040 & 0.822 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of prediction capability.
# Electromagnetically Induced Transparency in All-Dielectric U-Shaped Silicon Metamaterials
## 4 Conclusions  
We have numerically investigated the EIT-like phenomena in the proposed all-dielectric Si metamaterial. A characteristic EIT-like resonance peak with the high Q-factor and the high transmission is observed due to the coupling and interference between the bright mode supported by the horizontal nanoscale bar and the dark mode supported by the two vertical nanoscale bars. A nanoscale metamaterial refractive index sensor with high sensitivity and FOM has been achieved. The proposed Si metamaterials have a simple shape and can be easily fabricated by electron beam lithography [23; 24; 25; 27]. The excellent optical performance enable the dielectric Si metamaterial with a wide range of potential applications including slow-light devices, sensing and storage of quantum information.  
[PERSON] and [PERSON] conceived and supervised all the simulation; [PERSON] performed the simulation; [PERSON], [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed to the simulation software usage; [PERSON] and [PERSON] wrote the manuscript; [PERSON] and [PERSON] revised the manuscript.  
This research was funded by the National Nature Science Foundation of China (11674099, 61378033 and 11621404), Natural Science Foundation of Shanghai (16 ZR1409400), Shuguang Program (15 SG22), Shanghai International Cooperation Project (16520710600), and the 111 Project (B12024).  
The authors declare no conflict of interest.  
Figure 6: (**a**) Simulated transmission spectra of the metamaterial structure with refractive index ranging from 1 to 1.1. (**b**) The EIT-like peak wavelength of the metamaterial structure as a function of refractive index. The squares are the measured data, and the red line is the linear fit.
# Degree of Conversion and Mechanical Properties of Modern Self-Adhesive Luting Agents
## 1 Introduction  
Self-adhesive luting agents, the latest development in the field of adhesive luting materials, have become popular in contemporary clinical practice. The main reason was that their mechanical properties, solubility, adhesive strength with dentine, and various substrates (alloys, polymers, ceramics) were considered comparable with the multi-step adhesive resin composite luting agents, but attainable within a single application step. These materials are composed of (a) crosslinking aromatic and aliphatic dimethacrylate monomers, (b) acidic methacrylate adhesive monomers (phosphate, carboxylic), (c) ion-leachable glass filler particles or basic compounds, to neutralize bulk residual acidic monomers, (d) silanated reinforcing filler particles, (e) acid-compatible dual-cure catalysts and stabilizers, and (f) pigments [1, 2]. Most products also contain sources of fluoride release (fluorosilicate glass fillers, \(\gamma\)bF\({}_{3}\), NaF, etc.).  
The curing capacity of the self-adhesive luting agents has been assessed in several studies, leading to the conclusion that it is lower than the conventional resin luting agents [3] with the self-curing mode, providing a significantly lower degree of conversion than dual-curing [3, 4, 5, 6], regardless of the differences in monomer composition. The delayed onset and slower rate of conversion were attributed to the presence and neutralizationrate of the acidic monomers [2; 7]. It has been shown that the extent of acid-neutralization varies between materials and curing modes [6; 7; 8; 9], with the dual-curing mode demonstrating prolonged neutralization time [6]. The mechanical properties have been tested by a variety of methods, such as uniaxial [8; 9; 10; 11] and biaxial testing for flexural strength and modulus [5], compressive strength, diametral tensile strength, hardness and sliding wear testing [7; 11; 12; 13; 14] and instrumented indentation testing (IIT), the latter determining an array of properties (i.e., hardness, modulus, elastic work, recovery, creep, etc.) by a single indentation [6; 15]. In most of these studies, the self-adhesive luting agents were found to be slightly or significantly inferior to composite resin luting agents when tested under the same curing modes. Moreover, the improved conversion documented in dual-cured materials had a positive impact on the mechanical properties of the same materials over the self-curing mode.  
Recently, new materials have been introduced to improve the performance of the self-curing component, providing enhanced buffering capacity to the demineralization by Ca-release and expanding the clinical applications to luting of etchable glass ceramics through the incorporation of a silane.  
The aim of the study was to comparatively evaluate the composition, curing capacity, and the IIT-derived mechanical properties of the new materials vs. a typical self-adhesive luting agent and an adhesive monomer-free luting agent. The null hypothesis was that there are no statistically significant differences among the materials tested and the controls.
## 2 Methods  
The methodology applied in this work is outlined in Figure 1. Initially, the available ECG recordings were processed to eliminate noise and artifacts, and then we extracted the HRV series through their RR intervals. From there, the phase space reconstruction translated the HRV into a higher-dimensional representation, while the interpolation standardized the signal at 4 Hz to ensure time-frequency consistency across all samples. These processes were followed by the construction of a distance matrix and wavelet transforms, respectively, transforming the HRV data into 2D image-like format. These transformed outputs were then used to train and validate various ML and DL models whose operations are depicted in Figure 2. All these steps are detailed in the next subsections.
## 3 Applications
### Traffic Noise Emission Model  
In noise studies, several different types of weighting network are performed on sound spectra to calculate the equivalent sound level for different objectives. Although there is criticism that A-weighting is not well correlated with the human perception of loudness [33], A-weighting is the standard weighting for outdoor community noise measurements and is commonly used for noise measurements within architectural spaces and vehicles [34]. Thus, in the present study, the equivalent traffic noise levels and the corresponding requirements are collected in the form of \"A\" weighted equivalent continuous sound levels (\(L_{A\alpha}\)).  
In addition, traffic noise from a stream of vehicles varies over time in strength depending on many different factors such as the number of vehicles that passed by, speeds of the vehicles, weather conditions in the tests, and so on. The time averaged noise level is employed to convert the fluctuating values of a certain time interval into a simple mean value. An important noise level indicator adopted by many standards to estimate the impact of long-term noise to humans, the continuous 24-h time-averaged noise level (\(L_{A\alpha\text{ 24h}}\)) is adopted as the long-term interval noise indicator. This is used for comparing the acoustic performances of new and old pavements in the present research. T  
Figure 3: Traffic noise measurement instrument (**a**) and installation (**b**).  
flow conditions are different at all times, but over a period of 1-h, it may not fluctuate significantly. As a result, a 1-h time-averaged noise level (\(L_{Aeq\text{ 1h}}\)) is another long-term noise indicator adopted in the research, as well as 24-h time averaged noise levels. In this study, the notion of \"long-term\" merely refers to the noise statistics (i.e., \(L_{Aeq\text{ 24h}}\), \(L_{Aeq\text{ 1h}}\)) and does not indicate other factors (e.g., traffic flow).  
Many studies have reported to address traffic noise prediction [35; 36; 37]. Some studies indicated that traffic noise intensity and traffic flow volume obey a logarithmic relation [38; 39; 40]. Consequently, an empirical traffic noise prediction model will be employed as follows:  
\[L_{Aeq}=A\ln Q+B \tag{20}\]  
where \(Q\) is the volumes of the traffic flow, while \(A\) and \(B\) are parameters relying on the pavement and test conditions and can be determined by experiments.
## 4 Improved Student's t-Based Unscented Filter
### Improved Student's \(t\)-Based Unscented Filter  
Assuming \(\hat{\mathbf{x}}_{k-1|k-1}^{\mathbf{a}}\) and \(\mathbf{P}_{k-1|k-1}^{\mathbf{a}}\) are known, based on theorems 1-2, the calculation flow is as follows.  
1. Initialization  
\[\hat{\mathbf{x}}_{0|0}^{a}=\left[\begin{array}{c}\hat{\mathbf{x}}_{00}\\ 0\end{array}\right]\mathbf{P}_{0|0}^{a}=\left[\begin{array}{cc}\mathbf{P}_{k|k}&0\\ 0&\mathbf{\Xi}_{0}\end{array}\right]. \tag{66}\]  
2. Sigma points construction  
According to Equation (64), we construct sigma points \(\zeta_{i,k-1|k-1}=\left[\begin{array}{cc}\left(\zeta_{i,k-1|k-1}^{x}\right) ^{\mathrm{T}}&\left(\zeta_{i,k-1|k-1}^{\eta}\right)^{\mathrm{T}}\end{array} \right]^{\mathrm{T}}\). Then, compute the propagated sigma points  
\[\begin{array}{c}\mathbf{X}_{i,k|k-1}^{x}=A_{k-1}\zeta_{i,k-1|k-1}^{x}\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\, \[\hat{z}_{k;k-1}^{*}=\sum_{i=0}^{2n}w_{i}\theta_{i,k;k-1} \tag{73}\]  
\[\mathbf{P}_{k;k-1}^{zz}=\frac{v_{3}-2}{v_{3}}\Bigg{(}\!\!\sum_{t=0}^{2n}w_{i}\theta_ {i,k;k-1}\mathbf{\theta}_{i,k;k-1}^{\mathsf{T}}+\mathbf{F}_{k}\mathbf{S}_{k}+(\mathbf{F}_{k}\mathbf{ S}_{k})^{\mathsf{T}}\!-\!\hat{z}_{k;k-1}^{*}\!\Big{(}\hat{z}_{k;k-1}^{*}\!\Big{)}^{ \mathsf{T}}+\Xi_{k}\!\!\Big{)} \tag{74}\]  
\[\mathbf{P}_{k;k-1}^{xz}=\frac{v_{3}-2}{v_{3}}\Bigg{(}\!\!\sum_{i=0}^{2n}w_{i}\zeta_ {i,k;k-1}\mathbf{\theta}_{i,k;k-1}^{\mathsf{T}}-\hat{x}_{k;k-1}\hat{z}_{k;k-1}^{*}+ \mathbf{S}_{k}\!\!\Bigg{)} \tag{75}\]  
\[\mathbf{P}_{k-1;k-1}^{xz}=\frac{v_{4}-2}{v_{4}}\Bigg{(}\!\!\sum_{i=0}^{2L}w^{ \prime}_{i}\mathbf{X}_{i,k;k-1}^{x}\!\Big{(}\!\mathbf{X}_{i,k;k-1}^{x}\!+\!\mathcal{L }_{i,k-1|k-1}^{\eta}\!\Big{)}^{\mathsf{T}}\!-\!\hat{x}_{k;k-1}\!\Big{(}\hat{z}_ {k-1|k-1}^{*}\!\Big{)}^{\mathsf{T}}\!\Bigg{)}. \tag{76}\]  
At time \(k-1\), by substituting Equations (71)-(76) into Equations (30)-(36) and (48)-(52), we can easily obtain \(\hat{x}_{k;k}^{a}\) and \(\mathbf{P}_{k;k}^{a}\) in Equations (46) and (47).  
**Remark 3**.: _The difference between ISTUF and RSTUF in Reference [8] can be summarized in Table 1._
# Forecasting Multivariate Time Series
### Forecasting
#### 2.5.2 Multivariate Forecasting  
In the proposed framework, multivariate time series forecasting utilizes a range of statistical and machine learning methods. While statistical approaches for multivariate time series are limited, the framework incorporates key techniques like vector autoregression (VAR), vector moving average (VMA), and vector autoregression moving average (VARMA).  
Unlike univariate techniques such as AR and ARIMA, vector-based methods like VAR, VMA, and VARMA are bidirectional, allowing variables to influence each other. However, these techniques require stationarity, often confirmed using the ADF test. VAR establishes relationships between variables through a system of equations, with lag selection guided by criteria like Akaike information criterion (AIC). VMA operates with a moving average matrix and a k-dimensional vector, while VARMA integrates both autoregressive and moving average orders. For machine learning-based forecasting, the framework employs techniques like MLP, regression tree, random forest, RNN, GRU, and LSTM, each detailed in the preceding section.