esa-sceva/satcom-chunk-collection huggingface dataset:
# Probability-Based Concrete Carbonation Prediction Using On-Site Data
## 2 Durability Prediction Approach for Carbonated Concrete Structures
### Analysis of Carbonation Durability
#### 2.2.2 Predicted Values Using Latin Hypercube Sampling (LHS)  
LHS is useful for analyzing a large number of parameters. In experimental points, it can be used to evenly distribute in the area of model parameters and fill the space while carrying out a small simulation [28]. LHS divides the range of input variables into \(K\) ranges and then selects \(K\) samples evenly, choosing one from each \(K\) range randomly and non-repetitively [29]. If carbonation depth is \(X_{m}\) at the measured time, \(t_{m}(m=1,\,2,\,3,\,\cdots,\,\mathrm{M})\), the mean \(\bar{X_{m}}\) and standard deviation \(\sigma_{m}^{\prime X}\) of the carbonation depth can be initially predicted from the aforementioned one-dimensional concrete carbonation model with statistical data of selected design variables in Equation (10). Here, the section showing equivalent probability was divided by the number of \(k\).  
\[\bar{X_{m}}=\frac{1}{K}\sum_{K}X_{m}^{\prime k},\;\;\;\sigma_{m}^{\prime X}= \sqrt{\frac{1}{K}\sum\left(X_{m}^{\prime k}-\bar{X_{m}^{\prime}}\right)^{2}} \tag{10}\]  
Figure 3: Hydration rates of main constituents of Portland cement [22].  
where \(\vec{X_{m}}\) and \(\sigma_{m}^{\prime X}\) represent functions of time \(t_{m}\).  
After measuring the carbonation depth of \(X_{m}\) at the time \(t_{m}\) in the field inspection, the likelihood function of \(p_{k}\) is calculated using Equation (11):  
\[p_{k}=exp\left[-\sum_{m}\frac{1}{2}\left(\frac{X_{m}-X_{m}^{rk}}{\sigma_{m}^{X }}\right)^{2}\right] \tag{11}\]  
where the standard deviation \(\sigma_{m}^{X}\) of the likelihood function is estimated from existing experimental values or measurement data.  
The mean of model predictive values is calculated using Equation (12) with a Bayesian approach for statistical distribution.  
\[\vec{X_{m}^{\prime}}=\sum_{k}p^{\prime\prime}\left(X_{m}^{k}\right)X_{m}^{rk} =c_{0}\sum_{k}p_{k}X_{m}^{rk} \tag{12}\]  
where \(c_{0}\) is the reciprocal number for the sum of k likelihood functions.  
The standard deviation \(\sigma_{m}^{\prime\prime X}\) of the mean predictive value \(\vec{X_{m}^{\prime\prime}}\) of the improved carbonation depth can be obtained via the following Equation (13).  
\[\sigma_{m}^{\prime\prime X}=\sqrt{c_{0}\sum_{k}p_{k}\left(X_{m}^{rk}-\vec{X_{ m}^{\prime\prime}}\right)^{2}} \tag{13}\]
# Throughput of Buffer with Dependent Service Times
## 6 Numeric Illustrations
### Verification via Simulations  
The theoretical throughput of the buffer was also verified through simulations. Specifically, the exact model defined in Section 3 was implemented in a discrete-event simulator environment, OMNeT++ [3], and parameterized according to (71)-(73). Then, 16 simulations were performed with different combinations of \(C\) and \(\rho_{1}\). In each simulation run, \(10^{7}\) packets passing through the system were simulated, and the experimental throughput was recorded.  
The numbers are listed in Table 1. Each entry consists of both the theoretical and simulated throughput. As seen, for every combination of \(C\) and \(\rho_{1}\), the theoretical throughput aligns well with the experimental value.
## 2 Related Work
### Feature Selection Techniques with Classification Algorithms
Additionally, it enhances global search efficacy by replacing stars crossing a black hole's event horizon with nearby new stars, refining the initialization process, and integrating firefly perturbation and mutation operator methods. [PERSON] et al. (2019) introduced a distinct approach by presenting ensemble classifiers utilizing AdaBoost and bagging algorithms combined with correlation-based feature selection. Their methodology employed an ensemble model comprising base classifiers (RepTree, J48, random forest) for conducting simulations, and its effectiveness was assessed using the KDD99 and NSL-KDD datasets. Stacking, an ensemble technique, diverges from the homogeneity of boosting and bagging by integrating heterogeneous machine learning algorithms. Unlike boosting and bagging, which typically rely on homogeneous models, stacking combines diverse base models to enhance predictive accuracy. [PERSON] et al. (2019) introduced a stacking ensemble technique for NIDS, integrating models like K-means, SVM, XGBoost, and BPNN, evaluated using the NSL-KDD dataset. In another study, [PERSON] and [PERSON] (2019) proposed a stacking ensemble strategy based on the gradient boosting and random forest techniques. Their framework, tested on NSL-KDD, demonstrated efficacy in binary and multiclass classification scenarios. [PERSON] and [PERSON] (2019) introduced a stacking ensemble for detecting network intrusions, employing logistic regression and random forest as level 0 learners and SVM as level 1. They implemented a sliding window with k-means clustering to address concept drift and employed ensembles for attack detection in network traffic. Their approach was evaluated for effectiveness using the CICIDS2017 and NSL-KDD datasets. We recently proposed a hybrid feature selection approach to identify the important features and utilized stacking ensemble techniques for classification purposes ([PERSON] et al., 2020). We utilized the mutual information and Boruta algorithms for feature selection. However, this work may still not withstand dealing with the high-dimensional features. Our previous work was evaluated based on two datasets, i.e., UNSW-NB15 and CICIDS2017, for binary and multiclass classification. Unfortunately, it also demonstrated limited detection capabilities for the wide range of attack types in the CICIDS2017 dataset.
## 2 Experimental Section
### Materials Synthesis
#### 2.1.2 Continuous Flow Synthesis of Supported Iron Oxide Nanoparticles on Al- and Zr-SBA-15  
The continuous flow deposition of iron oxide nanoparticles on Al-SBA-15 was performed under a series of conditions using a recently reported, innovative, continuous flow setup depicted in Figure 1[33]. A stainless-steel reactor (1.2 cm\({}^{3}\) volume) was packed with Al-SBA-15 support (typically 0.5 g), set between two plugs of quartz wool to prevent the solid support to move in the reactor upon pumping in the flow of the solution of the metal precursor. Separately, a 0.5 wt% Fe solution in ethanol was prepared using FeCl\({}_{2}\cdot\)4H\({}_{2}\)O (0.89 g in 50 mL ethanol) as the Fe precursor. The solution was filtered off prior to flowing it through the system to avoid the presence of any undissolved metal precursor. The system started with pumping a solution of pure ethanol (0.5 mL\(\cdot\)min\({}^{-1}\), 5 min) through the catalyst bed to wet the catalyst. The temperature of the reactor was then set to 100 \({}^{\circ}\)C and the system was changed to the 0.5 wt% Fe solution which was pumped through the system at different flow rates. The incorporation was conducted under optimized flow rates (0.5 mL\(\cdot\)min\({}^{-1}\)) and residence time conditions (8 min). At the end of the continuous flow process, the system was again switched to ethanol which was pumped through the Fe-incorporated catalyst at 100 \({}^{\circ}\)C in order to remove physisorbed/unreacted Fe species on the catalyst. Materials were then recovered from the reactor, ground with a pestle and a mortar and subsequently oven-dried at 100 \({}^{\circ}\)C. Final materials were calcined at 400 \({}^{\circ}\)C for 4 h under air. For simplicity, Fe/Al-SBA 0.5 mL\(\cdot\)min\({}^{-1}\) materials are denoted as (Fe-AlCF), where this material was prepared with a 0.5 wt% theoretical loading of Fe at a flow rate of 0.5 mL min\({}^{-1}\) during 8 min of reaction incorporation at 100 \({}^{\circ}\)C. Similarly, Fe/Zr-SBA 0.5 mL\(\cdot\)min\({}^{-1}\) (Fe-ZrCF) stands for a material prepared with a 0.5 wt% theoretical loading of Fe at a flow rate of 0.5 mL\(\cdot\)min\({}^{-1}\) during 8 min of reaction incorporation at 100 \({}^{\circ}\)C. Samples were highly reproducible from batch to batch.  
Similarly, a Pd-containing Al-SBA-15 material (PdAlCF) was also synthesized under identical deposition conditions to achieve a loading around 0.5 wt% Pd in the final material.
# Assessment of a Mid-Fidelity Numerical Approach for the Investigation of Tiltrotor Aerodynamics
## 4 Results and Discussion
### Full Vehicle Simulations
#### 4.2.3 Cruise Condition  
In the cruise condition, the proprorotor wake is advected downstream and it shows the coherent helical structure of the vortices that are issued by the blade tips that invest the wing.  
This flow behaviour is clearly reproduced by the DUST simulations, as shown by the iso-surface of the Q-criterion that is illustrated in Figure 24 for \(\psi=270^{\circ}\). Moreover, the wing control surfaces are not deflected and the nacelle axis is aligned with the freestream velocity, thus only the shedding from the nacelle is still visible and it significantly decreased with respect to the analysed conversion mode flight conditions.  
The aerodynamic interaction between proprorotor and wing creates an effect on the blade performance similar to what was described for the simpler proprorotor/wing configuration in Section 4.1.1. Indeed, the comparison of the contours of the blade non-dimensional normal force and pitching moment computed over the last rotor revolution shows a doublet loading, particularly for \(M^{2}c_{n}\) (see Figure 25), which was related to the passage of the blade in front of the wing. This feature and the general behaviour of the load polar distributions computed by DUST and CFD are in quite good agreement, with some limited discrepancies being observed for the pitching moment only that, similarly to what was observed in Section 4.1.1, could be related to a combined effect of a slight disagreement of both normal and chord-wise force components. Nevertheless, the outcomes of this comparison confirm the capabilities of the mid-fidelity approach that was implemented in DUST to reproduce the flow features related to proprotor/wing interactions and also capture the behaviour of the rotor aerodynamic performance for full-vehicle cruise conditions.  
Figure 24: Instantaneous flow field computed by DUST in cruise flight condition at \(\psi=270^{\circ}\), \(\theta_{N}=0^{\circ}\), \(V_{\infty}=160\) kts, \(\Omega=517\) RPM; iso-surface of Q-Criterion.
# A Service-Constrained Positioning Strategy for an Autonomous Fleet of Airborne Base Stations
## 6 Evaluation and Results
### Non-Static Scenarios
#### 6.4.1 One UAV Decays  
The first non-static scenario consists in the loss of one UAV, which means that all other UAVs have to cover the temporary non-served users with the highest possible fairness. To evaluate this situation, in this experiment, after 2500 iterations, UAV I (green) is removed.  
As can be seen in Figure 6, after 2500 iterations without the decayed UAV, which is equivalent to 12.5 min (\(T_{it}=0.3\) s), the convergence state is reached again. The bitrate per user has been reduced (7.47 kbps), as the same number of users have to be shared among one less UAV, the fairness is maintained very high (0.9999) and all constraints are still fulfilled on average.
## 2 Materials and Methods  
Doped ZnO was synthesized using a solution-based method. The precursors used were ZnO(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O (99.5% of purity, from PanReac Application, Castellar del Valles, Spain), Al(NO\({}_{3}\))\({}_{3}\) (98% of purity, from Sigma Aldrich), Ga(NO\({}_{3}\))\({}_{3}\) (99.9% of purity, from Sigma Aldrich, Wilson, USA), and In(NO\({}_{3}\))\({}_{3}\) (99.9% of purity). The 3% metal-doped ZnO solutions (Zn\({}_{0.97}\)M\({}_{0.03}\)O) were prepared dissolving ZnO(CH\({}_{3}\)COO)\({}_{2}\cdot\)2H\({}_{2}\)O (0.1834 mol) and the dopant precursor Al(NO\({}_{3}\))\({}_{3}\), Ga(NO\({}_{3}\))\({}_{3}\), or In(NO\({}_{3}\))\({}_{3}\) (5.7 \(\times\) 10\({}^{-3}\) mol) in a mixture of ethanol (25 mL) and methanolamine (0.190 mol). Doped and undoped ZnO thin films were deposited by spray pyrolysis system onto soda-time glass (1.5 \(\times\) 1.5 cm\({}^{2}\)). Before the deposition, substrates were cleaned with a 1:2 HCl:HNO\({}_{3}\) solution, ultrasonicated in distilled water, and then in ethanol for 5 min each. The distance between the substrate and the spray head nozzle was kept as 20 cm, the flow rate was 1 mL/min and the substrate temperature was raised at 425 \({}^{\circ}\)C on a hot plate (surface temperature was measured with a laser thermometer ST80 ProPlus Enchanced), 20 mL of solution was sprayed for each sample. The deposited doped films were then individually annealed under N\({}_{2}\)/H\({}_{2}\) (5% H\({}_{2}\)) atmosphere in a tubular furnace. The applied thermal cycle corresponds to a heating velocity rate of 20 \({}^{\circ}\)C/min up to a maximum temperature of 400 \({}^{\circ}\)C, where it had remained for 30 min followed by a free cooling.  
The crystal structure of ZnO doped with Al, Ga, and In, and undoped ZnO thin films (called ZnO:Al, ZnO:Ga, ZnO:In, and i-ZnO, respectively) were studied using an X-ray diffractometer (D4 Endeavor, Burker-ASX) equipped with a Cu K\(\alpha\) radiation source. Data were collected by step-scanning from 10\({}^{\circ}\) to 80\({}^{\circ}\) with a step size of 0.05\({}^{\circ}\) 20 and 3 s counting time per step. The surface morphology was studied using a Scanning Electron Microscopy (SEM) model JEOL 7001F. The layer thickness was determined by the microscopy too, from cross-section micrographs. Optical properties and bandgap energy of the films were conducted by UV-vis-NIR spectroscopy in the wavelength range 200-1000 nm (step size 1 nm) using a Cary 500 Scan Varian spectrophotometer. The transmittance spectra were obtained applying BaSO\({}_{4}\) integrating sphere as white reference material. Electrical measurements were carried out by the four-probe method using Ossila T2001A3 to obtain the sheet resistance and resistivity data.
# A State Recognition Approach for Complex Equipment Based on a Fuzzy Probabilistic Neural Network
## 5 Industrial Application  
In this section, a state recognition system based on the proposed approach was developed and applied in the field of mining automation production as shown in Figure 7. The information database was acquired from the 71506 coal mining face in the NO. 13 Mine of Yangquan Coal Industry Group Corporation.  
In order to illustrate the application effect of the proposed system, running parameters of the shearer were collected from the programmable logic controller (PLC) and transported by wireless mesh switches and fiber channel switches. The three-dimensional virtual reality system and the monitoring platform were established to show real-time working state of the shearer. An industrial test lasting for an hour was conducted, and the state of shearer was updated every 10 s. The current and temperature data of the coal mining shearer were collected in each time interval. Then, a comprehensive running state was obtained and recorded automatically. The running log of the shearer was shown in Table 2, where _Alarm_ was recognized 21 times by the proposed system, and one of them was a false _Alarm_. Moreover, an _Alarm_ event was missing. Thus, the accuracy of the industrial was 90.91%. The average  
Figure 7: State recognition system for the shearer.  
computing time of the recognition process was 0.8632 s, which could basically meet the practical production.  
The shearer operator adjusted the shearer according to the real-time state recognition system, and the running parameters trended towards stable, where the cutting current was shown in Figure 8. It could be calculated from the figure that the average value of the left and right currents were 27.9578A and 27.2498, respectively, and the maximal values were about 10.95% and 4.77% larger than the average. In addition, the standard deviations of the two curves were 0.9601 and 0.6794. As the geological condition of the working area was complicated and changeable, the slight fluctuation was caused by the changing hardness and the variable thickness of the coal seam.
## 5 Evaluations
### Informal Evaluation on Visualization System
#### 5.2.1 Use Cases  
The email-Eu dataset was chosen for case studies, aiming to analyze and explore the dataset to validate the utility of the system. First, there is the variation in network scale. Based on the temporal views (Figure 13), the network scale exhibits clear periodicity, with a cycle of 7 days aligning with the patterns of weekdays, weekends, and a three-day holiday in the second week. Additionally, there are subtle differences in the network scale during weekdays, with Monday and Friday having slightly larger networks than the other workdays. This aligns with people's work habits, as emails are typically sent before and after weekends. Furthermore, the temporal views reveal significant structural changes as the network evolves, with a substantial number of edges being added and removed between adjacent networks.  
Secondly, there are similar structures within the email network. While observing the evolution of the network, the network contains a significant number of \"star-shaped structures\", as shown in Figure 14. In the context of real data, nodes in the network represent email senders or recipients. The reason for the frequent occurrence of these \"star-shaped structures\" is attributed to mass email distribution. Senders employ the group email feature to send emails to multiple recipients simultaneously, but there are no email records between the recipients, leading to this start network structure.  
Lastly, for adjacent network structures comparison: As indicated by the temporal views, the structural changes between adjacent networks are quite substantial, making it challenging to directly compare their structural differences. The Figure 15 presents snapshots of the network at the 5 th and 6 th time steps. The upper part directly shows the layout results, three structures seem to be retained after the network update (marked with blue circles), concerning relative positions and structural shapes. However, when we use 3-step animation to highlight different node changes (as shown in the bottom of Figure 15), emphasizing deleted, newly added, and retained nodes and structures. It becomes apparent that, despite the top right and the bottom structures (marked with blue circles in the top part) having similar shapes and relative positions as the former time step, they are newly added structures. Only top left structure is retained.  
Figure 13: Temporal views with periodicity.
# Smartphone-Based Photogrammetry for the 3D Modeling of a Geomorphological Structure
## 3 D Model Generation: Methodology Assessment in a Controlled Environment
### Data Processing
Naturally, all the processing steps of this tool for image elaboration are well known, thanks to the dissemination of the information provided by the developers and the scientific community. Finally, the same datasets processed by the previously mentioned software were elaborated with the SCANN3D application, in order to evaluate its performances. Assuming that the conditions of particular environments require a certain speed in the acquisition of data, we wanted to assess what was the minimum number of images necessary to ensure a complete description of the object. To this end, three different datasets have been created with the frames extracted from the video, containing respectively 50, 25 and 12 images, in each of which an overlap between the images greater than 70% is guaranteed. Naturally, in order to obtain comparable results in AMP and SCANN3D, the High and Ultra settings, respectively, were employed, while in VSFM it is not possible to select such a setting. Data elaborations and analyses were conducted using a computer Hp EliteBook, mounting a processor Intel(r) Core(tm) i7-4600U CPU 2.1 GHz-2.7 GHz, with Ram of 16 GB and an architecture of 64 bit; to which all of the processing times refer. Table 5 shows some information regarding the dense point clouds computation, which demonstrates how results can vary from one software to another. From an initial analysis of these data, it is clear that the AMP software is able to generate products that are much denser than for other software, although it requires a greater computational time. It is also possible to observe that, while AMP was able to reconstruct the photogrammetric model with both of the three datasets, on the other hand twelve images were not sufficient for the VSFM software to extract 3D information from the object. Furthermore, with the SCANN3D application, it was not possible to reconstruct the photogrammetric model with the dataset consisting of 50 images, probably because the processor implemented on the smartphone did not have a sufficient performance to face a similar computational cost.
# Intensity Evolution of Cosine-Gaussian-Correlated Schell-Model Pulse Scattered by a Medium
###### Abstract  
According to first-order Born approximation, the scattering of a partially coherent pulse with cosine-Gaussian correlation by a medium was studied. On the basis of analytic expression, the changes in intensity evolution of the scattered pulse are discussed. The influences of pulse and medium characteristics on the intensity of the scattered pulse were investigated. The intensities of a Gaussian Schell-model (GSM) pulse and a cosine-Gaussian-correlated Schell-model (CGSM) pulse, both scattered by the same medium, are compared, and their similarities and differences are examined in detail. The effective angular width of the scattered pulse could be modulated by the parameters of the pulse and medium. The obtained results could find potential applications in pulsed beam scattering.  
coherence optics; scattering; quasihomogeneous media; CCSM pulse; intensity 1
## 5 Application of eNO\({}_{3}\)R to Brackish Groundwater: A Case Study
### Influence of Applied Voltage and Current Density  
The applied voltage or potential had a relatively strong effect on nitrate reduction. [PERSON] et al. [216] demonstrated that the selectivity of nitrate reduction in products is strongly dependent on the potential applied. In addition, [PERSON] et al. [217] showed that at a potential of \(-0.9\) V, nitrite is the only substance generated at the cathode, which is then oxidized at the anode, thus decreasing the process efficiency [217]. Also, [PERSON] et al. [209] found that the application of increasingly negative cathodic potentials resulted in an improvement in nitrate removal efficiency. Specifically, a more negative potential facilitated the rapid removal of nitrate due to the thermodynamically favorable reduction in nitrite to N\({}_{2}\) occurring at more negative potentials. On the other hand, [PERSON] et al. [218] showed a decrease in selectivity for N\({}_{2}\) due to the reduction in Cu\({}_{2}\)O to metallic Cu caused by the application of a high cathodic potential. It is worth noting that the application of very negative potentials may lead to the formation of undesirable by-products such as ammonia or hydrogen gas. Hence, the cathodic potential should be optimized to achieve both high nitrate removal efficiency and high selectivity for nitrogen gas.  
In addition to the cathodic potential, the current density also plays a significant role in the performance of nitrate removal. In this regard, [PERSON] et al. [200] demonstrated that nitrate removal efficiency increased progressively with increasing current density. It also caused an acceleration of electrode corrosion and produced reducing agents that enhanced nitrate reduction. [PERSON] et al. [219] found that as the current density increased, more energy was utilized to produce hydrogen bubbles. This, in turn, resulted in a reduced selectivity of gaseous nitrogen and an increased selectivity of ammonia, ultimately leading to a lower overall rate of total nitrogen removal.