esa-sceva/satcom-chunk-collection huggingface dataset:
# Research of Modal Analysis for Impeller of Reactor Coolant Pump
## 2 Modal Analysis of the Impeller
### Modal Simulation  
Due to the speed limitation of the slip ring mentioned in the above section, it is impossible to test a high-speed rotating impeller on our test bench. Therefore, the modal analysis of the rotating impeller in the air (Condition D, the rotational speed is 1800 r/min, which is the rated rotational speed of an RCP [17]) was carried out on a simulation platform. In order to ensure the accuracy of simulation, models under the other three conditions were also simulated. Modal simulation schemes for the impeller under Condition A, B and D are similar to those of researches mentioned in the introduction. Due to space limitation, this section only introduces the simulation of the impeller under Condition D.  
Since the rotating impeller in water is subject to hydraulic load, before performing modal analysis, it is necessary to calculate pressure distribution on the surface of the impeller by flow simulation. The flow field state of the impeller in the tank is similar to that of a blender [18]. Fluid domain around the impeller is artificially divided into three parts: Internal rotating water, external still water and upper air. The flow simulation adopts the VOF (volume-of-fluid) two-phase flow model. The upper surface of the static water is set to free surface. In the water part, k-epsilon Reynolds average stress turbulence model is used for calculation, and the mixed-plane function is used for data transmission at the rotor-stator interface.  
After flow simulation, pressure distribution on the impeller boundaries was extracted to solid boundaries of the modal simulation model. The model contains the impeller solid and external water.  
Figure 1: Schematic diagram of the test bench. (1. Motor, 2. Frame, 3. Bearing, 4. Tank, 5. Slip ring, 6. Impeller, 7. Piezoelectric accelerometer, 8. Charge amplifier and NI Data Acquisition Card).  
Figure 2: Test signal spectrogram.  
Fluid-solid acoustic coupling method is adopted in the modal simulation. Axial position constraint is set on the axial surface of the impeller. The upper surface of external water is set to a free surface. Parameters of the impeller solid are: A density of 7850 kg/m\({}^{3}\), an elastic module of 210 GPa and a Poisson's ratio of 0.3. Parameters of the water are: A density of 998.2 kg/m\({}^{3}\) and a sound speed of 1500 m/s.
## 3 Results
### Application for Reciprocating Compressor Fault Diagnosis
The peak and the RMS value of the envelope frequency spectrum for four PF components are both lower than those of the original one, but the peak index is significantly higher, and this verifies that LMD is an effective approach for extracting fault frequency. In these four PF envelope frequency spectra, the DIMIRLMD has the greatest peak index and peak value, demonstrating that it successfully suppresses the interference frequency in addition to more fully retaining fault frequency. Figure 14:0 Envelope frequency spectrum of first PF1 in CSILMD. Figure 14:1 Envelope frequency spectrum of first PF1 in MPCHLMD. Figure 14:2 Envelope frequency spectrum of first PF1 in DIMIRLMD. Table 6 provides the mean value and 3 times the standard deviation of iterations for the first three PF components to further illustrate the effectiveness of DIMIRLMD with quantitative indicators. Twenty sample signals in an oversized bearing clearance fault state were decomposed using four different LMD methods. As previously mentioned, the more precise the envelope function is, the fewer iterations are needed to complete the decomposition. We can see that DIMIRLMD has the lowest mean value and the smallest standard deviation of iterations for each PF component, which indicates a higher accuracy of the envelope function and a better immunity to noise interference. Similar to the average index of orthogonality (_IO_), the index of energy conservation (IEC) is also a widely used index to evaluate the performance of various self-adaptive decomposition methods [38]. The energy of the original signal and its PF components should be conservative; hence, the IEC is equal to 1 in theory. The mean value and 3 times the standard deviation of _IO_ and IEC for the four LMD methods are also presented in Figure 14:3 We can know that DIMIRLMD has the lowest mean value and the smallest standard deviation of _IO_ for each order PF component, which illustrates DIMIRLMD has the best PF component hierarchical structure. Furthermore, the mean value of IEC is closer to 1 than those of the other three LMD methods. The aforementioned findings also suggest that DIMIRLMD is effective in extracting fault features from signals with nonstationary characteristics and significant noise interference, particularly vibration signals from reciprocating compressors operating with excessive bearing clearance.
# Utilization of Phosphogypsum and Sediment in Subgrade Material for Pavement Construction
## 3 Results
### Physicochemical and Thermal Characterization of Sediment Samples Treated with Phosphogypsum
#### 3.1.2 Content of Organic Matter and Moisture  
The analysis of sediment samples treated with phosphogypsum reveals certain differences in dry matter, moisture content, and organic matter between mixtures F-3 and F-5. Sample F-5 (containing 5% phosphogypsum) has a higher dry matter content (95.8%) compared to F-3 (containing 3% phosphogypsum) at 92.3%, indicating that more phosphogypsum reduces moisture content. Sample F-3 has a higher moisture content (7.7%) than F-5 (4.2%), showing an inverse relationship between dry matter and moisture. The organic matter content, measured by annealing at 550 \({}^{\circ}\)C to determine mass loss on annealing, was consistent between the two samples.  
For sample F-5, it is 15.25%, and for F-3, it is 15.14%, which suggests that phosphogypsum has a minimal effect on organic matter. From these results, it can be concluded that increasing the content of phosphogypsum in the sediment reduces moisture and increases dry matter, while the content of organic matter remains stable. Figure 2 shows the percentage content of dry and organic matter as well as the moisture content in mixtures F3 and F5.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 3 Decision Function
### Description and Efficiency
#### 3.2.1 False Negatives and False Positives  
We show in Figure 8 (in Figure 9, respectively) the false positive and negative rates we obtained in relation to the threshold value for communications between Germany and Oregon (Poland Oregon, respectively). Those tests have been performed on more than 500 samples gathered over several months. As we saw in Section 2.4, the measurements of our hijacking simulation created such a time gap that this decision function is strong enough to achieve absolute detection, Figures 8 and 9 both highlight this by having a very large interval of possible threshold values leading to no false positive nor false negative.  
Note that those graphs may change depending on the capacity and positional setup of the intermediary. Indeed, it is expected that the number of additional routing equipment visited during the relay is highly related to its efficiency. This would be represented by a false negative rate growing closer to 1 for lesser threshold values. Choosing a suited threshold then becomes a matter of appreciation of how efficient an attacker can get.
# Study on Mechanical Properties of Multi-Cavity Steel-Concrete Composite Floor
## 2 Structure and Prefabrication
### Prefabrication  
The fabrication of multi cavity steel-concrete composite floor is mainly divided into three steps: (1) preparation of porous and nonporous steel plates; (2) welding of steel plates to form a multi-cavity steel framework where the porous steel plates create many regular cubic cells and the nonporous steel plates act as shells (as presented in Figures 2 and 3); and (3) concreting. The self-compacting concrete has good rheology properties and can fill the whole multi-cavity steel framework with the help of interconnecting pores on the inset steel plates [19; 20; 21].
## 3 Acoustic Emission Monitoring
### Chapel XVII  
Firstly, we consider the analysis carried out in Chapel XVII of Varallo. The AE monitoring was led on the frescoed masonry on the north wall of the Chapel. The wall showed some damage: a vertical crack of about 3.00 m in length and fresco detachment. Four AE sensors were located around the vertical crack, while two were placed near the fresco detachment (Figure 3). The AE monitoring was conducted in two phases for a total duration of about 14 weeks. The first phase started on 9 May 2011  
Figure 2: The Asinelli and the adjacent Garisenda Towers in the city center of Bologna. The Asinelli tower is the tallest one on the right.  
and finished on 16 June 2011; the second one was from 5 July 2011 to 5 September 2011. The monitoring results related to the chapel's structural integrity are reported in [2]. We interpreted the AE data considering the amplitude and time distribution of AE signals during the cracking phenomena. From this analysis, we found that the vertical crack, monitored on the north wall of the chapel, evolved during the acquisition period, while the process of detachment of the frescos was mainly related to the diffusion of moisture in the mortar substrate [2].  
During the monitoring period, among all regional seismic events we considered 21 earthquakes with Richter magnitudes \(\geq\)1.2, having an epicenter within a radius from 60 to 100 km from Varallo. The strongest earthquake was a 4.3 magnitude event that occurred on 25 July 2011 at 12:31 p.m. in the Giaveno area (epicenter about 80 km from Varallo). Figure 4 displays the AE event rate, which counts simultaneous signal detection by multiple sensors as one event and the sequence of the earthquakes as functions of time, obtained during the monitoring period.  
Figure 3: Chapel XVII. View of the monitored areas. Left side: sensors 5, 6, and the fresco detachment. Right side: sensors 1–4 and the vertical crack.
## 5 Conclusions and Future Works  
This paper addressed the challenges of emergency management in smart campuses by proposing an improved people-counting system architecture, building upon the scenarios outlined in [25]. Our focus was on developing a tool that could effectively support people during emergencies. To achieve this, we designed and integrated new user interfaces (UIs) specifically tailored for emergency management tasks within the existing web application. A usability evaluation was carried out to validate them.  
There are plenty of directions for future works. More tests could be conducted, involving users that have particular responsibilities on the campus during emergencies to gain direct feedback from them. With regard to web applications, data visualization tailored to show the evacuation of people from buildings, showing the flows of people, could be designed and implemented. Moreover, we plan to conduct experimental tests during upcoming emergency evacuation drills on campus to gather real-world data and further validate the system's effectiveness in managing emergency situations.  
Conceptualization, G.D., V.G. and S.M.; Funding acquisition, S.M.; Methodology, V.G.; Software, E.F.; Supervision, S.M.; Writing--original draft, G.D. and E.F.; Writing--review and editing, V.G. and S.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by project ECOSISTER (ECS_00000033) under the MUR National 652 Recovery and Resilience Plan funded by the European Union--NextGenerationEU.  
Not applicable.  
Not new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} UI & User Interface \\ LoRa & Long Range \\ IoT & Internet of Things \\ \end{tabular}  
Figure 9: Average SUS scores obtained during the usability evaluation session.
## 7 Efficacy of Autonomous Healing Techniques  
The efficacy of autonomous healing techniques was assessed using various techniques. It was demonstrated that diverse factors, such as age of the formed cracks, curing regimes, and widths of the cracks, can affect the autonomous healing process in the formed cracks in concrete [84]. Table 3 shows the findings from diverse studies that assessed independent, independent and control variables. It was asserted that an improved autonomous healing can be achieved with both immobilized and free organisms due to the increased precipitation of calcium carbonate stems from an elevated concentration of bacteria in the concrete mix (Table 3). Similarly, the polymer dosage increase can boost the self-healing capacity. Autonomous healing was found to improve due to the presence of magnesium in bulk water; within the natural self-healing process the calcium in bulk water had no positive effect. Various curing regimes of the engineered cementations concrete was found to improve the efficacy of water curing. Additionally, the addition of biological and chemical agents into the concrete mix one at a time was found to enhance the autonomous healing, wherein the improvement due to the former was better than the later. The sole utilization of microcapsules was also found to be less effective than bio-encapsulation. Autonomous healing was impacted less significantly by the moisture and air curing techniques, thereby highlighting the more effective water curing method as observed from the comparison of various curing regimes (Table 3). As a crack became increasingly wider, the efficacy of the autonomous healing was diminished. A crack of 0.81 mm wide was entirely filled, while an almost 10% increase in the CS was observed due to the implementation of nanomaterials [21].  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Techniques of Self-Healing Materials**} \\ \hline No. & Approach & Depth of the crack \\ \hline
1 & Microencapsulation & Maximum depth of 32 mm \\ \hline
2 & Bacteria & Maximum depth of 27.2 mm \\ \hline \multicolumn{3}{c}{**Approach and Measured Variables of Self-Healing**} \\ \hline No. & Approach & Widths of the cracks \\ \hline
1 & Natural & Repairing of cracks under 60 μm wide \\ \hline
2 & Auxiliary cementitious materials & Cracks of width less than 200 μm \\ \hline
3 & Polymer and epoxy & Cracks of maximum width of 138 μm \\ \hline
4 & Bacteria & Maximum cracks width of 970 μm \\ \hline \end{tabular}
\end{table}
Table 1: Techniques, approaches and measured variables of self-healing.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**No.**} & **Scale** & **Tests** \\ \hline
1 & Macrostructure & UPV, CS, FS, toughness, stiffness, water absorption \\  & & (permeability), and chloride resistance or permeability. \\ \hline
2 & Microstructure & Water absorption. \\ \hline
3 & Microscopic assessment & Visual observation and FS. \\ \hline
4 & Nanostructure & FS, UPV and nanomechanical measurements. \\ \hline \end{tabular}
\end{table}
Table 2: Scale of structural tests adopted for the assessment of self-repairing efficacy.
## 3 Sizing Method of PV-HESS System
### Model of Life-Cycle Cost
#### 3.1.2 Cost of Battery Degradation  
The degradation cost of the batteries is the OCT due to irregular charging and discharging while operating the system. After conducting several experimental studies on the degradation process of LiFePO\({}_{4}\) cells, [PERSON] et al. proposed a semi-empirical model for estimating the battery capacity degradation, as follows [28]:  
\[Bat_{\text{loss}}=A_{0}e^{-(E_{\text{a}}+B\cdot C_{\text{-}Rate})/RT_{\text{ bat}}}\cdot(A_{\text{h}})^{z} \tag{10}\]  
here, \(Q_{\text{loss}}\) is the battery capacity degradation, \(C_{\text{-}Rate}\) is the absolute value of the battery charge and discharge current rate, \(R\) is the ideal gas constant, \(E_{\text{a}}\) is the activation energy, \(A_{0}\) is the pre-exponential coefficient, \(A_{\text{h}}\) is the ampere-hour (Ah) throughput, \(B\) is the third undetermined coefficient, \(z\) is the second undetermined coefficient, and \(T_{\text{bat}}\) is the temperature of the battery in Kelvin. Note that this model is based on the Arrhenius degradation model, wherein the effects of different charge and discharge rates, temperatures, and charge and discharge powers are considered.  
Equation (10) can be rearranged as follows:  
\[A_{\text{h}}=\left(Bat_{\text{loss}}e^{(E_{\text{a}}+B\cdot C_{\text{-}Rate} )/RT_{\text{bat}}}/A_{0}\right)^{\frac{1}{2}} \tag{11}\]  
Next, finding the derivative of \(A_{\text{h}}\) in Equation (10) yields the following:  
\[\overset{\cdot}{Bat}_{\text{loss}}=zA_{0}e^{-(E_{\text{a}}+B\cdot C_{\text{- }Rate})/RT_{\text{bat}}}\cdot(A_{\text{h}})^{z-1} \tag{12}\]  
Combining Equations (11) and (12) over one time step from \(t\) to \(t+1\) yields the following:  
\[Bat_{\text{loss,$t+1$}}-Bat_{\text{loss,$t$}}=\Delta A_{\text{h}}zA_{0}{}^{ \frac{1}{2}}e^{-(E_{\text{a}}+B\cdot C_{\text{-}Rate})/zRT_{\text{bat}}}\cdot Bat _{\text{loss,$t$}}^{\frac{z-1}{z}} \tag{13}\]  
here, \(Q_{\text{loss,$t+1$}}\) and \(Q_{\text{loss,$t$}}\) denote the degradations of the battery from time \(t\) to time \(t+1\), respectively, and \(\triangle A_{\text{h}}\) is the accumulated A-h throughput of the battery during the same time period, determined as follows:  
\[\Delta A_{h}=\int\limits_{t}^{t+1}|I_{bat}|\text{dt} \tag{14}\]  
[PERSON] et al. calibrated the parameters in Equation (10) with the values listed in Table 3[29].  
Given the above, \(G_{\text{loss}}(t)\) can be represented as follows:  
\[G_{\text{loss}}(t)=price_{\text{bat}}\cdot C_{\text{bat}}\cdot\left(2.637*10^{-3}*|I _{\text{bat}}(t)|\cdot e^{-\frac{15162-1516C_{\text{bat}}(t)}{2041.32}}\cdot Bat_{ \text{loss}}(t-1)^{-0.2136}\right) \tag{15}\]
# PpyNT/NR/NBR Composites with Excellent Microwave Absorbing Performance in X-Band
## 2 Materials and Methods
### Characterization  
The morphology of PPyNT was investigated by Field Emission Scanning Electron Microscope (FESEM, JSM-7500F, JEOL, Tokyo, Japan) and Transmission Electron Microscope (TEM, JEM-2100, JEOL, Tokyo, Japan). Structural information of PPyNT was obtained with X-ray Diffractometer (XRD, D/Max 2500V/PC, Rigaku Corporation, Tokyo, Japan) and Fourier-transform infrared (FTIR, VERTEX70, Bruker, Brenen, Germany) spectrometer. The conductivity of PPyNT was measured by a four-probe conductivity meter (ST2253, Jingge Electronic, Suzhou, China). To analyze the phase morphology and filler dispersion of PPyNT/NR/NBR composites, the ultra-thin sections of composites were cut by ultra-microtome (LEICAEM FC7, Leica Microsystems, Wetzlar, Germany) at \(-\)100 \({}^{\circ}\)C and then characterized by TEM. The electromagnetic parameters of rectangular PPyNT/NR/NBR composites with dimensions of 22.86 mm \(\times\) 10.80 mm \(\times\) 2 mm were measured by waveguide method at room temperature on ZNB-20 vector network analyzer (Rhodes & Schwarz, Munich, Germany) in X-Band frequency range (8.2-12.4 GHz).
## 2 Results and Discussion
### Antimicrobial Effect of Distillation Fractions of Area Nut
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **SHDE Extract** & **Hydrolate** & \begin{tabular}{c} **Distillation** \\ **Resolute** \\ \end{tabular} & \begin{tabular}{c} **AMP** \\ **10 \(\mu\)g** \\ \end{tabular} & \begin{tabular}{c} **CIP** \\ **5 \(\mu\)g** \\ \end{tabular} & \begin{tabular}{c} **DA** \\ **2 \(\mu\)g** \\ \end{tabular} & \
\begin{tabular}{c} **TE** \\ **30 \(\mu\)g** \\ \end{tabular} &
\begin{tabular}{c} **FCA** \\ **25 \(\mu\)g** \\ \end{tabular} \\ \hline Bacillus subtilis & \(12.3\pm 1.5\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(21.5\pm 1.5\) & \(35.0\pm 0\) & \(25.5\pm 0.5\) & \(22.5\pm 0.5\) & n.t. \\ Centrocarbons & \(16.0\pm 0.7\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(12.5\pm 0.5\) & \(19.0\pm 0.0\) & \(8.0\pm 0.0\) & \(23.0\pm 1.0\) & n.t. \\ Cechrich oil & \(10.3\pm 1.1\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(6.0\pm 0.0\) & \(21.5\pm 1.5\) & \(6.0\pm 0.0\) & \(13.5\pm 7.5\) & n.t. \\ PCMomonas aeruginosa CCM 3955 & \(11.3\pm 0.8\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(6.0\pm 0.0\) & \(36.0\pm 1.0\) & \(6.0\pm 0.0\) & \(14.0\pm 1.0\) & n.t. \\ Staphylococcus aureus CCM 4223 & \(10.8\pm 0.8\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(28.5\pm 1.5\) & \(29.0\pm 1.0\) & \(30.0\pm 1.0\) & \(16.5\pm 0.5\) & n.t. \\ Streptococcus anglactic COM 6187 & \(28.3\pm 1.5\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(17.5\pm 0.5\) & \(20.5\pm 0.5\) & \(22.5\pm 0.5\) & \(14.5\pm 0.5\) & n.t. \\ Streptococcus antis & \(40.0\pm 3.0\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(21.5\pm 1.5\) & \(21.5\pm 2.5\) & \(22.5\pm 0.5\) & \(11.0\pm 1.0\) & n.t. \\ Streptococcus payments NPK01 & \(27.5\pm 1.5\) & \(6.0\pm 0\) & \(6.0\pm 0\) & \(31.5\pm 1.5\) & \(28.5\pm 1.5\) & \(34.0\pm 0.0\) & \(37.0\pm 0.0\) & n.t. \\ Candida albicans & \(15.0\pm 1.7\) & \(6.0\pm 0\) & \(6.0\pm 0\) & n.t. & n.t. & n.t. & n.t. & \(11.5\pm 0.5\) \\ CCM 8215 & \(15.0\pm 1.7\) & \(6.0\pm 0\) & \(6.0\pm 0\) & n.t. & n.t. & n.t. & n.t. & \(11.5\pm 0.5\) \\ \hline AMP—ampicillin, CIP—ciprofloxacin, DA—clindamycin, TE—tetracycline, FCA—fluoconazole, n.t.—not tested. & & & & & & \\ \end{tabular}
\end{table} 
Table 4: Antimicrobial activity of SHDE extract, hydrolate, and distillation residue of area nut sample and antimicrobials as positive control—mean inhibition zones in mm (including disc 6 mm in diameter) \(\pm\) standard deviation, \(n=4\). antimicrobial potential of hydrolate and distillation residue can be explained mainly by the low proportion of individual components and their concentrations in the hydrolate sample compared to the SHDE extract. Thermal degradation of some compounds can also be considered for the distillation residue. Aqueous and methanolic extracts were tested against _Staphylococcus aureus_ and _Escherichia coli_ in an earlier study [39]. However, the aqueous extract did not inhibit any of the tested bacteria, which is in line with our results.
## 2 Materials and Methods
### Methodology
#### 2.5.4 Recurrent Neural Network  
RNN architecture is made up of input, hidden, and output layers that can process dynamic information. However, data must be in a serialized form and periodically connect to hidden nodes (Figure 3). In contrast to other neural networks, RNN follows the order of information. All the input and output values are independent in the traditional neural networks. However, in the RNN model, within the hidden layer inter-association among units occurred with different time intervals. Thus, through this process, information can pass away from one to another layer within the network [41].  
The information shared across the RNN network precisely incorporates the concept of \"loop feedback\". Similar to having a loopback in the Elman or Jordan network, this simple RNN model was implemented with a loopback. In this study, the RNN model consists of \(x_{t}\) as input, \(h_{t}\) as hidden, and \(y_{t}\) as the output vector at time \(t\). Therefore, the expressions can be presented as follows:  
\[h_{t}=\sigma(W_{h}x_{t}+U_{h}h_{t-1}+b_{h}) \tag{6}\]  
\[y_{t}=\sigma\big{(}W_{y}h_{t}+b_{y}\big{)} \tag{7}\]  
where parameter matrices are W and U, \(\sigma(\cdot)\) is represented as loss function of the training sample sequence, and the bias vector b.