esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Bridges are the most vulnerable elements in highway networks, which play an important role in economic growth and social development [1]. Due to the increasing heavier truck-loads and exposure to wet environments, bridges are inclined to face rapid performance deterioration. Routine maintenance is necessary to guarantee the bridge network at a high service level and extend the service life of bridges. However, the available budget cannot cover the cost of maintaining all bridges simultaneously in a network. Hence, there is a growing demand for developing an efficient maintenance scheduling strategy for bridge networks.  
In the literature, a large body of studies were carried out focusing on the optimization of bridge maintenance strategy, which is defined as an optimal resource allocation problem by most of the researchers [2; 3]. In summary, there are two categories of the existing approaches dealing with this problem. The first category is the mathematical programming method [4] and the second category is the simulation-based method [5]. These methods can identify which bridges to maintain, but cannot determine the time sequence of maintenance activities for the bridges. Additionally, the interaction between the maintenance activities and the traffic delays in the network has not been completely investigated.  
To this end, this paper aims to concentrate on the optimal maintenance scheduling strategy (OMSS) for bridge networks. Our work makes the following contributions. (1) We propose a nonlinear bi-level model to formulate the OMSS problem, which aims to minimize the total traffic delays during the maintenance period and ensures that the available maintenance budget covers as many bridges as possible. (2) We transform the bi-level model into an equivalent single-level model and employ the simulated annealing algorithm (SAA) as the model solution. (3) We use a highway bridge network to test the effectiveness of the proposed model and algorithm and compare the OMSS generated by our model with two commonly used empirical strategies.  
The remainder of this paper is organized as follows. Section 2 presents a literature review of the existing studies of maintenance optimization for bridge networks. Section 3 formulates the problem and develops the solution algorithm. Section 4 illustrates the proposed model by a case study. Section 5 analyzes the sensitivity of relevant parameters. Conclusions and future work are discussed in Section 6.
## 4 Signal Processing Algorithms
### Zero-Padding  
A zero-padding methodology can be applied to the captured spectrum to increase datapoints in the Fourier domain achieved by joining an array of zero values to the end of the spectrum array. This does not add any frequency components to the signal but when an FFT is performed the number of frequency bins that will be compared with the signal will be increased, thus improving the datapoint density of the Fourier transform signal. This can be seen in Figure 9a,b showing the FFT peak of a resampled spectrum without and with zero-padding before Fourier-transforming, respectively. However, as array size is increased naturally so will the computational time. This increased datapoint density can improve polynomial fitting to the FFT peak as shown in Figure 9c.  
Figure 9d shows the resolution of the interferometer when processing the raw data by detecting the location based on the maximum intensity detected peak, this value is calculated as being 2.17 \(\upmu\)m according to the number of bins assigned by the 2048 CCD pixel array in the spectrometer. This resolution can be improved through increasing the number of bins as shown through zero-padding in Figure 9b, or by fitting a polynomial to the datapoints surrounding the maximum peak location and extracting the vertex. In practice, a mixture of zero-padding and polynomial fitting can improve the system's ability to resolve depth values.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Window Function** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\ \hline \(\Delta\) PSF FWHM (\(\upmu\)m) & 0.81 & 0.7 & 0.45 & 1.23 \\ \(\Delta\) SNR (dB) & 39.09 & 33.92 & 22.13 & 38.54 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of windowing functions applied to LCI signal, showing change in PSF and SNR from unwindowed data.
###### Abstract  
In this study, to investigate the elimination of micropollutant from aqueous solutions by construction waste, waste red brick (WRB) with and without incorporated of iron species (goethite and hematite) were prepared by a simple method. The prepared materials were systematically characterized and batch experiments were conducted to study the elimination of typical micropollutant cimetidine from the water environment. Results showed that both two iron species could be successfully incorporated onto WRB and the main structure of WRB were maintained. The adsorption process of cimetidine onto bricks was fast due to the microstructure and useful adsorption sites on the surface of bricks. The Langmuir model fitted the experimental data better and the \(q_{max}\) of cimetidine increased about 24.4% and 39.6% for the incorporation of hematite and goethite, respectively. The pH values influenced the adsorption behavior greatly and the favorable pH value was around 6.0. The charge screening effect and competition adsorption may influence the adsorption behavior together. Due to the interaction between cimetidine and bricks, the outer-sphere complexation may be formed in the adsorption process. This study shows new methods for the elimination of the micropollutant from the water environment and offers useful guidelines for the reuse of construction waste.  
cimetidine; goethite; hematite; iron oxide coated; waste red brick Article
## 3 Results and Discussion
### Electrochemical Characterization  
CV curves of the Pt/rGOP electrode, scanned in deaerated 0.5 mol/L H\({}_{2}\)SO\({}_{4}\) solution within a potential range from \(-\)0.2 V to 1.25 V, is shown in Figure 6A. The peak at 0.3 V is associated with the reduction of Pt oxide species. In Figure 6A, the peak of Pt species is invisible, but the analysis of previous data showed that PtNPs were successfully electrodeposited. Additionally, it is obvious that, once PtNPs were electrodeposited on the free-standing graphene paper, the current of Pt/rGOP charge and discharge became much larger than rGOP and Pt foil, indicating Pt/rGOP has the highest surface area. The electrochemical sensing performances of Pt foil and Pt/rGOP have been investigated in 0.1 mol/L PBS (pH = 7.4) containing 1.0 mmol/L H\({}_{2}\)O\({}_{2}\) at a scan rate of 50 mV/s, as depicted in Figure 6B. Compared to the Pt foil, Pt/rGOP has a higher reduction current in the presence of H\({}_{2}\)O\({}_{2}\), reflecting that Pt/rGOP has superior electrochemical activity. EIS is often used to gain important information about the interfacial properties of electrodes, and Figure 6C exhibits the Nyquist plots  
Figure 5: FESEM images of PtNPs on rGOP (**A**–**E**), with different deposition cycles and its (F) EDX analysis.  
of Pt/rGOP and rGOP in the presence of 0.1 mol/L KCl containing 1 mmol/L [Fe(CN)\({}_{6}\)]\({}^{-3/-4}\). It is obvious that the charge-transfer resistance of the Pt/rGOP is much smaller than rGOP. This is mainly attributed to the high loading of PtNPs, with their uniform distribution and construction of an effective conduction pathway to boost the electron transfer on rGOP.
###### Abstract  
Background: Among older adults, there is a high incidence of history of fall (HoF), fear of falling (FoF), and falls on stair descent during gait transitions. Purpose: We aim to evaluate the association between HoF and FoF on spatiotemporal and lower-limb kinematic parameters in older adults during stair descents and gait transitions. Methods: Sixty older adults (>60 years) were evaluated through an optoelectrical motion capture system during stair descents and gait transitions, using the mean value of the task velocity and time; single- and double-support time; peak downward center of mass (CoM) velocity; hip, knee, and ankle positions of ipsi and the contralateral limb; and foot clearance and foot placement, assessed through multivariate analysis of variance. Results: FOF exhibited longer time to complete (\(p\) = 0.009) and double-support (\(p\) = 0.047) and single-support (\(p\) = 0.009) times and a reduced peak downward CoM velocity (\(p\) = 0.043). In the gait transition cycle, HOF exhibited reduced ipsi ankle angles at toe-off (\(p\) = 0.015), and FOF presented reduced ipsi ankle angles at heel-strike (\(p\) = 0.041) and toe-off (\(p\) = 0.026) and reduced contralateral ankle angles at toe-off (\(p\) = 0.022). Conclusion: Older adults with HoF and FoF exhibit biomechanical changes during stair descents and gait transitions, in line with the use of more conservative strategies to avoid falling.  
history of fall; fear of falling; geriatrics; stair descent; biomechanics +
Footnote †: journal: Applied sciences
## 5 Results and Discussion
### Electric Resistivity  
Figure 20 shows the ERT results of specimens manufactured with different contents of NS, SP, SF and PPF. Increasing SF content resulted in a fall in ERT. Moreover, using 1% NS had a significant effect on improving ERT, and this property was enhanced by using NS at up to 2% while employing NS at more than 1% resulted in decreasing the ERT value. Alternatively, increasing the SP content did not have a significant effect on this property. Conversely, increasing the PPF content considerably improved the electric resistivity. Also, the ERT was significantly improved when PPF and 1% NS were combined.  
Figure 19: Effect of the fibres on the porosity of HSC containing NS and SP (**a**) SF and (**b**) PPF.  
Figure 20: Influence of the fibres on the electrical resistivity of HSC containing NS and SP (**a**) SF and (**b**) PPF.  
Therefore, this property was enhanced by about 57%, 62% and 68% when 0.5% SF and 1% NS were used with 0%, 5% and 10% SP, respectively. Previous investigations showed the same influence of PPF and SF on ERT thus confirming the results of our study. Recently, [PERSON] [57] performed a study on the influence of different fibre types on the ERT. It showed that the main reason for reducing ERT results due to SF incorporation could be associated with the easier transfer of electricity across steel fibres while electricity transfer by polymer fibres is negligible.
## 3 Maintenance Process Modeling Based on SCPN
### Maintenance Task Expression  
In Petri nets, transitions are used to describe events that change the state of the system. In maintenance processes, system states will be changed when a maintenance task has been completed.  
Figure 1: Hierarchical chart of maintenance actions.  
Hence, timed transitions drawn as hollow bars are used to express maintenance tasks in the maintenance process model. As the time duration of maintenance tasks may follow various distributions, the firing rate of the timed transition may not always be a constant value. We will use the firing time set instead of the firing rate set in the SCPN when modeling the maintenance process. The set of firing time is expressed as \(\mathbf{\Pi}=\{\pi_{1},n_{2},\cdots,n_{m}\}\), \(\pi_{i}(i=1,2,\cdots m)\) is the firing time of the \(i\)th transition.  
In addition to timed transitions, there are also immediate transitions drawn as solid bars in our maintenance process. The immediate transitions have no time attributes, and they will be fired as soon as they are enabled. Namely, the firing time of the immediate transition is 0. In this study, the immediate transitions are usually used in competitive structures, which will be discussed in Section 3.4.3. When there are immediate transitions, it means only one of the several maintenance events can be started.
## 3 Methods and Materials
### Model Testing and Performance Evaluation  
K-fold CV is a commonly used technique to evaluate algorithm performance and optimize hyperparameters. The dataset is divided into k subsets, with one subset used as the validation set and the remaining subsets as the training dataset. This process is repeated k times, ensuring that each subset serves as the validation set once.  
In many studies, the value of k set to 10 strikes a balance between comprehensive testing, computational efficiency, and unbiased estimation of model performance. By using K-fold CV, a more robust assessment of the algorithm's general performance can be obtained compared to using a single train-test split or different values of k [11].  
To evaluate the performance of our models, we employed three metrics: accuracy, sensitivity (Sen), and specificity (Spe). In binary classification tasks, sensitivity (also known as the true positive rate) measures the proportion of positive instances correctly identified, specifically the percentage of individuals with ASD who are accurately classified as having the disorder. On the other hand, specificity (also known as the true negative rate) quantifies the proportion of negative instances correctly identified, representing the percentage of TD individuals correctly classified as not having the disorder. Accuracy, the third metric, indicates the overall percentage of correctly classified instances across all classes [5].
## 5 Experimental Results
### Analysis of Time Interval Correlation by Attention Mechanism  
In comparison to other time intervals, the network focuses more on learning the entry flow of nearby time intervals within the same day. Taking the prediction of entry flow at Zhonglou Station as an example, Figure 4 illustrates the attention weight distribution obtained from an attention layer with a time interval of 48 steps. The horizontal axis represents the time steps, with each tick indicating one time interval, and the color intensity indicates the corresponding attention weight. For instance, consider Figure 4a, where the prediction time is 8:45-9:00. Time steps 2, 46, 47, and 48, corresponding to the time intervals 9:00-9:15, 20:00-20:15, 20:15-20:30, and 20:30-20:45, respectively, have relatively higher attention weights. These time intervals represent specific periods on the same day, as well as on previous days and weeks. Furthermore, Zhonglou Station is located near popular tourist attractions, and the entry flow during these time intervals remains high throughout the day, as the predicted time interval also falls within the peak period.
## 3 The FAURAS Framework  
Figure 1 depicts the block diagram of the proposed FAURAS framework. In this framework, requests from multiple clients to the video server are routed through a proxy which is located in between (e.g., at network edges). At the proxy, as soon as a client joins and starts its streaming session, a fair bandwidth is calculated and assigned separately to each client presenting in the network. This corresponds with the function of the **Bandwidth Allocation** module. The **Request Overwrite** module, on the other hand, is responsible for rewriting the segment's request into one with the bitrate matching the fair share. The overwriting decision of this module is based on a QoE-aware strategy that takes into account the QoE-related metrics, that is, rebuffering event and bitrate degradation amplitude. In addition, when the Request Overwrite module modifies a bitrate request, a notification message is signaled to the corresponding client via HTTP headers so that the client will not discard the upcoming pushed segments. The whole procedure of FAURAS is kept transparent so that it does not require significant modifications for both the server and client side. In the following subsections, the function of each module is presented and discussed in details. Some notations and definitions used in this paper are summarized in Table 1.  
Figure 1: Block diagram of the FAURAS framework.
## 2 Background Information
### Numerical Simulation of Hydrodynamic Noise
#### 2.2.1 Direct Numerical Simulation (DNS)  
Direct numerical simulation (DNS) is a highly accurate method for flow noise prediction by solving the full Navier-Stokes equations. In the absence of a turbulence model, DNS enables the detailed capture of the fine structures of turbulence and the sound field. Due to its high degree of accuracy, DNS is particularly well-suited for analyzing the generation mechanism of turbulent noise and the transient behavior of turbulence. [PERSON] [60] presented a detailed comparison of the effects of DNS and Reynolds-averaged Navier-Stokes (RANS) models on the evolution of particle distributions over time and proposed an improved vortex interaction model. A subsequent comparison of the model's outcomes with those obtained from DNS revealed that an improved vortex interaction model can predict particle distributions with greater accuracy. This indirect confirmation of the reliability of DNS calculations is a significant finding.  
Its core Equation (7) is as follows:  
\[\frac{\partial_{u}}{\partial t}+u\
abla u\ =\ -\frac{1}{\rho}\
abla p+v\
abla^{2}u+f \tag{7}\]where  
\(u\): velocity field (\(u=(u,v,w)\)),  
\(p\): pressure,  
\(\rho\): density,  
\(v\): dynamic viscosity,  
\(f\): volume force.  
The primary disadvantage associated with DNS is its substantial computational expense, a limitation that curtails its applicability in large-scale and high-Reynolds-number flow fields. Consequently, it is predominantly employed in studies encompassing small-scale, simplified geometric models or local flow fields. In practical applications, computer resources are often inadequate to perform a full DNS calculation [61; 62]. Furthermore, under high-Reynolds-number conditions, the resource demand for DNS increases exponentially, which further restricts its application range.
# Gaussian Parameters Correlate with the Spread of COVID-19 Pandemic: The Italian Case
## 3 Results and Discussion
### Statistical Analysis--Correlations  
A statistical analysis was carried out with the aim to find specific correlations between raw data corresponding to daily new counts for infected, deceased and recovered people and daily variations of hospitalized and admitted to intensive care (ICUs). Figure 3 reports the correlation matrix of these variables.  
[PERSON]'s rank correlation coefficient was used to ascertain the strength and direction of the relationship between two variables (off-diagonal plots in Figure 3). This statistical analysis was again performed using MATLAB and red values correspond to a statistically significant level with \(p<0.05\). It emerges that the daily number of deceased and recovered people shows a strong positive correlation with that of the infected people of 0.80 and 0.73, respectively. This trivially confirms that both deceased and recovered people increase with infected. Moreover, we observe a corresponding strong positive correlation of 0.75 between the number of daily deceased and recovered people and, an even stronger positive correlation of 0.85 (easily understandable) between daily variations of hospitalized and ICUs. Unfortunately, the exact mechanisms underlying this association are uncertain since several possible reasons may determine these trends. However, in order to at least find a correlation between the time of the events considered crucial and the change in the growth rate of infected, deceased and recovered people, we analyze the centroids values with respect to each other. The tridimensional graph, plotted in Figure 4, shows how peak centroids for all three growth rates are linearly dependent with an average offset of 10 days between infected and deceased peaks and 4 days between recovered and infected peaks. These values correspond to the above mentioned average lag between the growth rate of recovered and deceased people with respect to that of infected people. Unsurprisingly, the obtained angular coefficients of the straight lines vary in a range of values very close to 1. This confirms our previous observation of a correspondence between infected growth rate peaks and deceased or recovered growth rate peaks shifted forward in time.  
Figure 4: Tridimensional representation of the correlation between the timing of the events corresponding to the centroids of the Gaussian mixture used to fit infected, deceased and recovered people. Dashed lines correspond to linear fits between the corresponding variables.  
Figure 3: Scatter plot matrix between daily data for infected, deceased and recovered people (**top left**), and for daily variations of hospitalized and admitted to intensive care (ICUs) (**bottom right**). The results of Spearman test are shown in the off-diagonal plots whereas the diagonal panels report the single variables histograms.
## 1 Introduction
### Novelty and Significance of the Research  
The present study aimed to examine the feasibility of utilizing active microwave thermography as a means of investigating reinforced concrete structures that were strengthened with carbon-fiber-reinforced polymer (CFRP). Conventional heating methods, such as halogen or flash lamps, or contact techniques utilizing hot pots, were suggested in earlier publications on this issue [41; 42; 43]. In our previous publication [44], where we suggested using the active microwave thermography approach, only a preliminary experimental study was conducted. In the work that is being presented here, we proposed a thorough study of the issue that took into account both numerical modeling and experimental investigation. First, a numerical model was developed to analyze the phenomenon being investigated. The objective of the model was to replicate the laboratory configuration and to examine scenarios that were not executed in the experiment, with the aim of assessing the applicability of the approach in a wider context. Consequently, we constructed models that depicted the sample's heating in both modulated and continuous modes. Furthermore, a defect-free sample and a centrally located defective sample were simulated, as per the experimental conditions. Additionally, a scenario where the defect was not centrally located was considered. It was shown that the modulated approach gave better results in terms of the time needed to obtain a detectable thermal signature of the defect. Moreover, the numerical results showed that the microwave heating was not uniform, and the higher temperature was obtained at the sample's center, which may affect the detectability of the defects. Analysis of the additional scenario--namely, a sample with the defect located outside the center--showed that it was possible to detect the defects in any location within the heated region of the sample. Subsequently, the findings obtained from numerical research were subjected to experimental validation. The conducted experimental tests--which were performed on two distinct samples, namely, one with a defect and one without, in the modulated regime--exhibited a significant level of concurrence with the numerical outcomes. It was shown that the constructed model possesses the capability to be employed for subsequent, complex investigations of the analyzed phenomenon.