esa-sceva/satcom-chunk-collection huggingface dataset:
# Identification of a Human-Structure Interaction Model on an Ultra-Lightweight FRP Footbridge
## 5 Discussion
### Parametric Analysis  
Employing the closed-loop \(GH_{Cl}\) TF (Equation (7)) and the SDOF MSDA system obtained in Section 4.5, a parametric analysis varying the dynamic properties of the human body and the computed GLFs was carried. The influence of every parameter on the numerical dynamic response of the FRP footbridge was investigated by considering 1000 stochastic samples generated from the following normal \(\mathcal{N}(\mu,\sigma)\) and uniform \(\mathcal{U}(a,b)\) distributions, respectively:  
* \(m_{h}\): \(\mathcal{N}(70.8,5)\) kg
* \(f_{h}\): \(\mathcal{U}(1.5,3.0)\) Hz
* \(\zeta_{h}\): \(\mathcal{U}(0.1,0.6)\)
* \(\mathrm{GLF}_{1}\): \(\mathcal{N}(0.107,0.021)\)
* \(\mathrm{GLF}_{2}\): \(\mathcal{N}(0.172,0.034)\)
* \(\mathrm{GLF}_{3}\): \(\mathcal{N}(0.101,0.020)\)
* \(\mathrm{GLF}_{4}\): \(\mathcal{N}(0.032,0.006)\)  
Figure 15 presents the results, in terms of 1s RMS, for the properties associated to the human body. The coloured band shown in the graphs represents the dispersion of the results obtained from the 1000 models generated for each parameters, and the width of this band corresponds to minimum and maximum values. It can be see that the most influential property on the numerical response is the damping ratio of the body, whereas the frequency was the property leading to the least impact.  
The variation of the dynamic response of the FRP footbridge due to the load factors of the human driving force (\(F_{a}\)) is displayed in Figure 16. The coloured band in the graphs corresponds to minimum and maximum values again, although it is negligible for the first three harmonic factors. This means that their variation does not affect significantly the human-structure coupled vibrating system. As expected, changes in the \(\mathrm{GLF}_{4}\) led to appreciable variation of the results. The impact of this parameter affects greatly the system, even more than the properties of the human body.  
[PERSON] et al. (2019) recently discussed that regardless the adopted properties to describe the dynamic model of a pedestrian, HSI models usually lead to a good assessment of the dynamic behaviour of a lightweight footbridge. Therefore, the identified model that considers four harmonics of the human action and accounts for interaction phenomenon could be used to predict the response of other lightweight pedestrian structures affected by higher harmonics of a single human walking.  
Figure 14: Step frequency variation: (**a**) Test subject 1 (\(m_{h}=71.8\) kg). (**b**) Test subject 7 (\(m_{h}=73.4\) kg).  
Figure 16: _Cont._  
Figure 15: Parametric analysis varying the properties of the human body: (**a**) Mass. (**b**) Frequency. (**c**) Damping ratio.
## 6 Summary and Conclusions  
Three receiver structures have been assessed by processing data collected in a recent experiment conducted over a range of 7.4 km. In this high frequency (10-14 kHz) experiment, coherent underwater communications of different symbol rates were achieved, e.g., 1 to 2 kilo-symbol/s. In a large time scale, in terms of period of 202.044 s, the time-variant characteristics of underwater channel are observed by the communication results in terms of output SNR.  
As shaping pulses, it is shown that the difference between LFM and RRC is minimal. The LFM shaping pulse provides a simple method for PPC processing, where the received channel probe signal of LFM is immediately used. Using a RRC shaping pulse, it is flexible to select a channel estimation method for PPC processing, e.g., the MP algorithm. In addition, the block-based approach can be implemented in time-varying channels.  
As evident, PPC-DFE achieves the worst performance in the assessment, and the performance of PPC-McDFE approximates that of McDFE. Time-variant reverberations result in unpredictable spatial coherence, which may impact on the performance of PPC-DFE. Therefore, it is preferable that the adaptive multichannel combining obtains much spatial gain, especially in the scenarios of a small number of receivers. For instance, it is preferable to use PPC-McDFE instead of PPC-DFE in a channel of long time spread.  
In the sparse channel, the MP algorithm has been assessed in two modes. One is the conventional single block approach, and the other is the multi-block approach. The multi-block approach assumes that the channel is constant within each block of a short time interval, and then PPC processing is extended to time-varying channels. Comparing with PPC-DFE, PPC-McDFE is less sensitive to the channel variations. It has been demonstrated that the MP algorithm improves the performance of communications using PPC processing, and thus the MP algorithm is suggested in sparse channels.  
Figure 16: Performance comparison between the one block approach and the multi-block approach.
# An Entity-Relation Extraction Method Based on the Mixture-of-Experts Model and Dependency Parsing
## 3 Methodology
### Relation Classification Layer  
The classification of the triplet's category can be achieved by enumerating all possible combinations of (hi, rk, hj) and designing a classifier to assign the corresponding high-confidence labels, where rk is the randomly initialized relation representation, and hi and hj are, respectively, the representations of the head and tail entities. In this layer, different experts are implemented using MLPs with different weights. The architecture of the mixture-of-experts model is shown in Figure 4. The gating network receives the data and selects the most suitable expert model for processing based on the data's features, calculating the corresponding scores for the relation. Let G denote the gating network, with the input data being \(x\); the output of the gating network is the weight distribution across the expert models:  
\[G(x)=[w_{1},w_{2},\dots w_{n}]\]  
where \(w_{i}\) represents the weight of the i-th expert model, and \(\sum w_{i}=1\).  
Let the \(i\)-th expert model be denoted as \(E_{i}\), and the triplet score matrix computed by \(E_{i}\) for the input data \(x\) is:  
\[S_{i}=E_{i}(x)\]  
where \(S_{i}\) is a two-dimensional matrix, and the elements in the matrix represent the scores for the head entity, tail entity, and the possible relations between them. The final triplet score matrix S is the weighted combination of the triplet score matrices calculated by each expert model:  
\[S=\sum_{1}^{n}(w_{i}*S_{i})\]
# Nitrogen Removal Using a Membrane Bioreactor with Rubber Particles as the Fouling Reducer
## 3 Results & Discussion
### Nutrient Removal by a Reactor with Intermittent Aeration  
#### 3.2.1 Organic Substance Removal  
Figure 7 shows the variation in COD concentration and the removal efficiencies of an intermittently aerated MBR with the addition of 0.5% rubber particles. The initial COD concentrations were maintained at an average level of 215 mg COD/L while using aeration/non-aeration periods of 40 min/20 min, 30 min/30 min, 20 min/40 min, or 10 min/50 min in the MBR. The effluent COD concentration was less than 12.5 mg/L  
Figure 5: TMP profile and flux of MBR with intermittent aeration (20 min aeration and 40 min non-aeration).  
Figure 6: Images of membranes after the experimental period. (**A**) Without fouling reducer, (**B**) with 0.5% (V/V) GAC, and (**C**) with 0.5% (V/V) rubber particles.  
(with over 94% COD removal efficiency). The bioreactor performed well in organic carbon removal. These results indicate that changes in aerobic duration have virtually no effect on COD removal.
# Calibration and Validation of a Measurements-Independent Model for Road Traffic Noise Assessment
## 4 Conclusions
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference Timespan** & **Error Metrics** & **Full Dataset** &
\begin{tabular}{c} **Dataset without** \\ **Outliers** \\ \end{tabular} \\ \hline \multirow{4}{*}{1 h} & Mean error (dBA) & \(-0.82\) & \(-1.02\) \\  & Std dev of the error (dBA) & \(2.25\) & \(1.92\) \\  & MAE (dBA) & \(1.90\) & \(1.76\) \\  & MPE & \(-1.17\%\) & \(-1.44\%\) \\  & MAPE & \(2.65\%\) & \(2.46\%\) \\ \hline \multirow{4}{*}{15 min} & Mean error (dBA) & \(-0.72\) & \(-0.88\) \\  & Std dev of the error (dBA) & \(2.28\) & \(1.91\) \\ \cline{1-1}  & MAE (dBA) & \(1.85\) & \(1.69\) \\ \cline{1-1}  & MPE & \(-0.36\%\) & \(-0.76\%\) \\ \cline{1-1}  & MAPE & \(2.12\%\) & \(1.56\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Error metrics for 1 h and 15 min timespan datasets, with and without outliers. the scope of this paper and could be a subject of further research, in which the ranges of applicability for the model can be quantitatively estimated. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app13106168/s1](https://www.mdpi.com/article/10.3390/app13106168/s1)]([https://www.mdpi.com/article/10.3390/app13106168/s1](https://www.mdpi.com/article/10.3390/app13106168/s1)). In particular, the calibration data and a graphical representation of the programming lines used for the generation and calibration of the dataset are included in the supplementary materials. Conceptualization, D.R. and C.G. ; methodology, D.R. and C.G. ; software, D.R. ; validation, D.R., A.M. and C.G. ; investigation, D.R., A.M. and C.G. ; resources, C.G. ; data curation, D.R., A.M. and C.G. ; writing--original draft preparation, D.R. ; writing--review and editing, D.R., A.M. and C.G. ; visualization, D.R., A.M. and C.G. ; supervision, C.G. ; funding acquisition, C.G. All authors have read and agreed to the published version of the manuscript. This research was supported by the Sustainable Mobility Center (MOST)--Spoke 7: Connected Networks and Smart Infrastructure (PNRR--Missione 4 Componente 2 Investimento 1.4--project No. CN 0000023). The data used for the calibration, i.e., the database of _Q, P, V\({}_{L}\), V\({}_{M}\), V\({}_{H}\), and d_ used for the computation of the \(L_{oy,h}\) according to Equations (1)-(3), are reported in the Supplementary Materials. The data used for the validation are available upon request at http://lms200 2-2007.ifstat.fr/ [29] (accessed on 15 April 2023). The authors declare no conflict of interest. The outliers removed during the validation step are reported in Tables A1 and A2 for the sake of clarity. List of overestimation outlier data.
## 4 Evaluation
### Hardware-in-the-Loop Simulation
#### 4.2.1 System Setup  
The HIL testbed was developed by combining an autopilot with a heavily developed and commercially available flight simulator, X-Plane. The diagram of the HIL testbed is shown in Figure 8. The flight simulator X-Plane was used to simulate the UAV platform, the sensors, and the external environment in a real system. The switch was used to substitute the real digital radio. Both the autopilot and the ground control station in the HIL testbed were the same as with the actual system.  
The developed HIL testbed is shown in Figure 9. Using the HIL testbed, new approaches and algorithms can be fully tested and evaluated. The HIL testbed has been used in previous work [41; 42; 43; 44; 45]. The approaches that passed the X-Plane-based HIL evaluation can be easily applied in real flight experiments. In this paper, we use the fixed-wing aircraft model PT60 in the X-Plane simulator to validate the effectiveness of the proposed guidance law for the problem of path following and obstacle avoidance. The plane and its main parameters are shown in Figure 10.  
Figure 8: Diagram of the developed HIL testbed.  
Figure 10: The PT-60 plane and the main parameters in the X-plane flight simulator.  
Figure 9: The developed HIL testbed.  
The altitude of the reference path was assumed to be constant. The ADRC-based low-level controller we designed in [46] was used to obtain the desired attitude and velocity. The effectiveness of the low-level controller was validated in simulation and field experiments[41; 42; 43; 44; 45].
# An Improved Circumferential Fourier Fit (CFF) Method for Blade Tip Timing Measurements
###### Abstract  
Rotating blade vibration measurements are very important for any turbomachinery research and development program. The blade tip timing (BTT) technique uses the time of arrival (ToA) of the blade tip passing the casing mounted probes to give the blade vibration. As a non-contact technique, BTT is necessary for rotating blade vibration measurements. The higher accuracy of amplitude and vibration frequency identification has been pursued since the development of BTT. An improved circumferential Fourier fit (ICFF) method is proposed. In this method, the ToA is not only dependent on the rotating speed and monitoring position, but also on blade vibration. Compared with the traditional circumferential Fourier fit (ICFF) method, this improvement is more consistent with reality. A 12-blade assembly simulator and experimental data were used to evaluate the ICFF performance. The simulated results showed that the ICFF performance is comparable to TCFF in terms of EO identification, except the lower PSR or more number probes that have a more negative effect on ICFF. Besides, the accuracy of amplitude identification is higher for ICFF than TCFF on all test conditions. Meanwhile, the higher accuracy of the reconstruction of ICFF was further verified in all measurement resonance analysis.  
blade tip timing; circumferential Fourier fit; synchronous vibration +
Footnote †: journal: Applied Sciences
## 3 Proposed MIDE Algorithm
### Analysis of the Complexity of the Algorithm  
In this subsection, we analyze the computational complexity of the proposed MIDE algorithm, which is dominated by the multiplications operations. Hence, we compute the number of complex-valued multiplications as the measurement of the computational complexity of the algorithm [31].  
It can be found in Algorithm 1 that the computational complexity is composed of three parts including (1) preprocess, (2) the \(\mathbf{x}\)-update procedure, and (3) the \(\mathbf{x}_{d}\)-update procedure.  
1. Preprocess: The first part comes from the related computation before the iterative process. The main factors affecting the computational complexity of the preprocess are the computation of \(\mathbf{D}\) and the multiplication of the \(N_{t}\times N_{t}\) diagonal matrix and the \(N_{t}\times N_{r}\) matrix. Let \(\mathbf{h}_{i}\) be the \(i^{\text{th}}\) column of the complex-valued channel matrix \(\mathbf{H}\). Then, the diagonal calculation can be presented as: \[\mathbf{D}_{ij}\!\!=\!\left\{\begin{array}{cc}\frac{1}{\mathbf{h}_{i}^{ \text{th}}\mathbf{h}_{j}},&i=j\\ 0,&i\
eq j\end{array}\right.\] (23) Therefore, the complexity of the preprocessing is counted as \(N_{t}^{2}+N_{t}N_{t}\).
2. \(\mathbf{x}\)-update procedure: The second part comes from the x-update procedure, which involves the computation of two multiplications of the \(N_{t}\times N_{t}\) matrix and the \(N_{t}\times 1\) vector. Thus, the complexity is counted as \(2N_{r}N_{t}\).
3. \(\mathbf{x}_{d}\)-update procedure: The third part originates from updating the value of \(\mathbf{x}_{d}\). As can be seen in the expression of \(\mathbf{x}_{d}^{k}\), the computation of this part includes the update of the Euclidean distance and two scalar multiplications with \(N_{t}\times 1\) vectors. Then, the complexity in this part is counted as \(3N_{t}\).  
Therefore, the overall required number of complex multiplications by the MIDE algorithm is \(N_{t}N_{r}+N_{t}^{2}+K\left(2N_{r}N_{t}+3N_{t}\right)\). For comparison, the similar calculations of the complexity of the typical detection algorithms (e.g., LMMSE with full matrix inversion, AltMin, ADMM) are given, as well as the one of the proposed MIDE in Table 1.  
Note that all these algorithms utilize the approximation approaches to solve the ML problem. It is obvious that the proposed MIDE algorithm and AltMin algorithm have lower computational complexity at each iteration among the compared iterative approaches. The numerical analysis will be provided for further analysis of the computational complexity, which depends on the number of iterations \(K\) and is presented in Section 4.
## 4 Presumptions and Network Structuring
### State of the Art
#### 4.3.3 Data Aggregation Using the Variance Method (KAB)  
Calculation of variance for the data set measurements is a way to find out the redundant data. The variance (\(P\)) can be identified between data measurements in a group of sets by applying the ANOVA model method. This method is used to detect data pair sets with similar values and can be eliminated.  
For a threshold value \(\mathcal{E}\), the variance is calculated according to the Bartlett test and by applying the chi-square table.  
When \(P>\mathcal{E}\), the data pair is considered to be redundant and can be eliminated.  
If \(P\leq\mathcal{E}\), the data pair is valid and can be transmitted from the cluster head to the sink node.  
The k-means algorithm is applied to the ANOVA model based on the calculated mean value of the data sets. Then, \(n\) number of parent nodes is divided into \(\sqrt{\frac{n}{2}}\) number of children nodes. At the end of each time period, one group of sets is selected from each data set along with their IDs for transmission.
## 2 Materials and Methods
### ASNase Immobilization over MWCNT  
The ASNase immobilization by physical adsorption onto different MWCNTs was investigated according to the procedure described elsewhere [27] by combining 2 mg of each material with 200 uL of several ASNase solution concentrations, ranging from 0.05 to 0.7 mg/mL (ASNase activity ranging from 4 to 9.5 U/mL) in a suitable buffer. The pH value was varied using different buffers: citrate-phosphate buffer for pH 4.0, phosphate buffer for pH 6.0 and 8.0 and carbonate-bicarbonate buffer for pH 9.0. Immobilization was achieved by swirling the mixtures in a multifunctional tube rotator (Grant Instruments Lda., model PTR-35) for a set amount of time and then the samples were centrifuged at 112\(\times\)\(g\) using a MicroStar 12 VWR centrifuge for 10 min to separate the MWCNTs from the supernatant. The contact time was improved at the selected best pH by immobilizing  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Base Material**} & **Diameter Range (nm)** & **Length (\(\mu\)m)** & **Purity (\%)** & **Ash (wt \%)** & **Specific Surface Area Range (m\({}^{2}\)/g)** \\ \hline
**MWCNT-10** & \textless{}10 & \textgreater{}5 & \textgreater{}97 & \textless{}3 & 250–500 \\
**MWCNT-1020** & 10–20 & \textgreater{}5 & \textgreater{}97 & \textless{}3 & 100–160 \\
**MWCNT-2040** & 20–40 & \textgreater{}5 & \textgreater{}97 & \textless{}3 & 80–140 \\
**MWCNT-60100** & 60–100 & \textgreater{}5 & \textgreater{}97 & \textless{}3 & 40–70 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Multi-walled carbon nanotubes (MWCNTs) physical properties according to manufacturer data.  
during different periods (30, 60, 120 min). At the same time, control was made with free ASNase under the studied conditions. For each assay, duplicate runs were made.
## 1 Introduction
Two important extensions of the stabilization problem are also proposed. In the first one, an integral action is added to reject some constant perturbations. The cases of a constant perturbation affecting the input voltage or a constant variation of the output load can be rejected using this approach for perturbations of a reasonable amplitude. The second one considers the rejection of measurable perturbations. The cases of perturbations affecting input voltage or output load can be considered as measurable if a voltage or a current sensor is added to the converter. Then, the stabilization control law can be modified to take into account the information concerning the perturbations. The end of the section is devoted to numerical experiments which illustrate the potential of the proposed control laws for a boost converter. The paper ends with a short conclusion giving some lines of future research. **Notations:** The notations are quite standard. Along the paper, matrices of appropriate dimensions are denoted by capital letters. \(\mathbb{R}\) denotes the set of real numbers, \(\mathbb{R}\geq 0\) (\(\mathbb{R}\geq 0\)) denotes the set of positive real numbers (strictly positive real numbers). For a symmetric matrix \(P\), \(P<0\) (\(P\leq 0\)) means that \(P\) is negative definite (negative semidefinite). \(P>0\) means that symmetric matrix \(P\) is positive definite (i.e., \(-P<0\)). For matrices \(A\) and \(B\), \(A<B\) means that \(A-B<0\). For a matrix \(A\) or a vector \(y\), \(A^{T}\) and \(y^{T}\) denote their transposes. The Euclidian norm of a vector \(y\) is denoted by \(|y|\). The matrix denoted by \(diag(A_{1},A_{2},\ldots,A_{N})\) is a block diagonal matrix whose diagonal blocks of appropriate dimensions are \(A_{1}\), \(A_{2}\), \(\ldots\), \(A_{N}\). \(I\) denotes the identity matrix of appropriate dimensions
## 3 Results & Discussions
### Slope Stability Analysis of the Unsaturated Slope  
The slope stability analysis under partially saturated conditions is based on the factor of safety approach combining the hydrological and soil-mechanical analyses. Classical methods for rainfall-triggered shallow slope stability analysis can also be extended to unsaturated conditions by considering the suction stress in this study [6].  
The transient effect of infiltration on the factor of safety is shown in Figure 8. The safety factor of the slope ranged mainly from 1.01 to 1.25 between May 2014 and August 2017. Figure 8 shows the safety factor of the corresponding monitoring slope considering the suction stress in the unsaturated condition. The safety factor of the slope featured continuous fluctuations due to the variations of the suction stress in response to the rainfall-infiltration and evaporation in the unsaturated topsoil. Correspondingly, the ground vibration detector has recorded responses during the rainy periods. Particularly Figure 9 shows that ground vibrations caused 20, 17 and 33 responses to be recorded (70 responses in total) from 23 August to 25 August 2016 as a result of precipitation of 27.8 mm, 34.2 mm, and 27.4 mm on those days (89.4 mm in total). The modeled factor of safety dropped to the lowest point of 1.01 during these days. The evidence of ground vibration responses must have been due to some small-scale slope instability. When the modeled factors of safety came close to unity, it indicated the quasi-stable state of the slopes.  
Figure 8: Modeled factors of safety based on fluctuation of suction stress for different position in the slope during 21 May 2014 to 22 August 2017.  
Figure 9: Ground vibration records and modeled factor of safety changes in the Yindongzi landslide site from 1 May to 30 August 2016.