esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodologies
### 3D CFD Method Verification  
In this study, the analyses of the CAM results are based on the 3D simulation results. To perform the 3D simulation in this study, the commercial numerical software NUMECA was used to simulate the linear cascade and the TA36. The mesh generation for the calculation was completed by the IGG module of the NUMECA. The Fine module of the NUMECA is widely used to calculate the steady flow in turbomachinery. To validate the applicability of the 3D CFD method, the simulation of a controlled diffusion airfoil (CDA) linear cascade was carried out.  
The experiment results of the linear cascade have been published in the works of [PERSON] [19]. In their papers, the coordinates of the blade geometry have been listed. Based on this, the calculation model was constructed. The H-O-H topology was adopted for the linear cascade to get a better mesh quality. After the grid independence check, the total number of grid points is nearly 7.1 \(\times\) 10\({}^{5}\), shown in Figure 4. Moreover, it is observed that the y+ is less than five after the calculation results are obtained. In this study, the Spalart-Allmaras (S-A) turbulence model was applied. At the inlet, the flow direction is axial, the total temperature is 288.15 K, and the total pressure is 101,325 Pa. At the outlet, the boundary condition is adjusted to make sure that the Mach number is the same as the experiment condition. No-slip condition is applied at the wall surface.  
When the convergent results were acquired, the pressure distribution on the blade surface was compared with the experiment results, shown in Figure 5. From this figure,  
Figure 4: Grids used in the simulation of the controlled diffusion airfoil (CDA) linear cascade. (**a**) 3D grids; (**b**) grids on the S1 surface.  
the distribution is nearly the same except for some points. Therefore, it can be reached that the NUMECA software is suitable to be applied in the further simulation of 3D models.
# An Electric Load Simulator for Engine
## 4 Development and Validation of the System Model
### Model Validation  
The simulation model of the electric load simulator was mainly composed of the drive actuator, the controller, the power drive module, and the load actuator, as shown in Figure 10. The drive actuator and the load actuator were both composed of the electromagnetic linear actuator, so the simulation model of the electromagnetic actuator module was set up according to the differential Equation (9). The power driver module had the transfer function as \(\frac{K_{s}}{T_{\mathit{S}}+1}\), which can be considered as an inertial component. In the function, \(K_{s}\) is the amplification factor of the power driver and \(T_{s}\) is the lag time constant. The PID controller module was setup according to the transfer function Equation (10).  
The drive actuator was used to simulate the displacement curve of the exhaust valve. Since the drive actuator is tightly coupled with the load actuator in actual operations, the load actuator passively followed the motion of the drive actuator. The target load force was set as the gas force obtained from 3D CFD (Computational Fluid Dynamics) simulation. The load actuator was used to simulate gas force while following the movement of the drive actuator under the control of the PID strategy.  
In order to verify the accuracy of the mathematical model, the test platform was built to obtain the displacement and current values of the load actuator. The test bench included the electromagnetic actuator, displacement sensor, DSP controller, and control computer. Input 6 V voltage to the electromagnetic actuator at 10 ms, then the current and displacement signals can be obtained from the DSP controller and the control computer.  
The experimental results of the load actuator were compared with the simulation results of the load actuator, as shown in Figure 11. It can be concluded from the comparison chart that the trends of the simulation results and experimental results were consistent. Considering that the load actuator should follow the motion of the drive actuator, the movement of the load actuator was set to be the same as the displacement curve of experimental results during simulation. The error of current between simulation results and experimental results was kept below 0.8 A, and the dynamic load accuracy was less than 5% FSR, which meets the system requirements. It was verified that the mathematical model can be used in the following research. In some ways, the errors of experimental results were due to the vibration of the test bench.  
Figure 10: Simulation model of the electric load simulator.
# Comparison of the Sensitivity of Various Fibers in Distributed Acoustic Sensing
## 2 Approach
### Setup
This backscattered signal, directed to the photodetector, conveyed information about the acoustic event's frequency, amplitude and location along the fiber. Figure 4: Schematic of the DAS setup interrogator used in the study. To assess the FUT sensitivities across a range of acoustic frequencies, a composite signal was transmitted to the speaker. This signal included frequencies from 100 to 2000 Hz in 100 Hz increments and from 2000 Hz to 7000 Hz in 500 Hz increments. The system could not reproduce frequencies above 7000 Hz without distorting lower frequencies, as the required volume levels caused the entire setup to detect vibrations beyond the FUT segment. Additionally, data acquisition limitations prevented the resolution of frequencies below 119 Hz.
## 3 Data  
In this paper, we use the published measured data from Covenant University, Ota, Ogun State, Nigeria [9; 10]. The reason for choosing this dataset in our study is that a smart campus environment approximately resembles urban microenvironments comprising spatially distributed buildings alongside roads, trees, parking lots, etc. This can characterize complex electromagnetic propagation phenomena, which might not be easily captured only based on empirical PL models. Measurement campaigns in [9; 10] are conducted by means of smooth drive tests with an average speed of 40 km/h along three survey routes (A, B, and C) within the university campus as a mobile receiver moves away from three collocated directional 1800 MHz base station transmitters.  
The measurement routes (A, B, and C) can be distinguished based on a Digital Terrain Map (DTM) of the study area presented in [9]. Based on a comparison between the original reference with the Google Satellite View of Covenant University's main campus location, the measurement routes (A, B, and C) as well as the position of the three collocated transmitters (marked by Tx) are shown in Figure 4. Thereby, the approximated transmitter's location is indicated by Tx. Route A, route B, and route C are marked by green, blue, and orange, respectively.  
The PL data and the terrain information about the smart campus environment include longitude, latitude, elevation, altitude, clutter height, and distance of separation between the corresponding transmitter and the receiver points [9]. There are a total of 3617 measurements along with the three different routes. Further details regarding experimental design, test materials, as well as exploratory data analysis in terms of distributions of variables and correlation statistical analysis, are available in the original published data set study [9].  
Figure 4: Google satellite view of the study terrain describing drive tests moving away from the three collocated transmitters (marked by Tx) along the three survey routes (A, B, and C) within the university campus marked by green, blue, and orange, respectively.
# A Predictive Model to Correlate Amino Acids and Aromatic Compounds in Calabrian Honeys
## 3 Results and Discussion
The obtained results showed strong positive linear correlations between aldehydes with isoleucine (\(r=0.931\)), valine (\(r=0.922\)), leucine (\(r=0.932\)) and the better correlation with phenylalanine (\(r=-0.972\)); furans with isoleucine (\(r=0.982\)), leucine (\(r=0.982\)), and the highest correlation with phenylalanine (\(r=0.983\)); hydrocarbons with serine (\(r=0.793\) the lower correlation considered), glutamic acid (\(r=0.941\)) and aspartic acid (\(r=0.888\)); ketones with serine (\(r=0.905\)), alanine (\(r=0.929\)), and alcohols with tyrosine (\(r=0.927\)); Also, the results showed a strong negatively linear correlation between ketones and glutamine (\(r=-0.964\)), histidine (\(r=-0.979\)), lysine (\(r=-0.958\)) and asparagine (\(r=-0.964\)); esters and arginine (\(r=-0.980\)). Instead, proline, tryptophan, and threonine showed a poor correlation with all the classes of aroma compounds (Table 2). Finally, Table 2 showed the \(p\) value always being lower than 0.0001 and the correlation curve for aroma compounds vs. amino acids. The summarized data of 25 honey samples for the amino acids content are showed in Table 3.  
In the citrus samples, the amino acids found in abundance were phenylalanine, proline, isoleucine, leucine, asparagine, and valine (ranged from 684 to 1866 mg/kg FW); for the chestnut and eucalyptus samples, proline was the most abundant amino acid (2062 and 1563 mg/kg FW, respectively). In the Acacia samples, proline and asparagine were most abundant. The sulla honeys showed a similar profile, with proline and asparagine as the most abundant amino acids (922 and 912 mg/kg, respectively). The obtained data showed that citrus honeys had the highest content of total amino acids, followed by sulla, acacia, and eucalyptus. The chestnut honey showed a lower level. The most abundant volatile molecules revealed for the citrus samples were benzeneacetaldehyde (38.6%); for the chestnut, eucalyptus, and sulla samples, nonal was the most abundant aromatic compound (11.3, 34.8, and 58.9%, respectively). Finally, in the acacia samples, linalool oxide was the most abundant. The obtained data were graphically represented by a heat map (Figure 2). Figure 2 shows the normalized quantity of the 17 amino acids and the six chemical classes of volatiles quantified in the analyzed honey samples. The relative
## 2 Related Works
### Small Object Detection  
There have been numerous approaches to optimize the YOLO model for better performance in small object detection tasks. [PERSON] et al. [28] proposed a small object detection framework modifying YOLOv3. The extracted feature maps from the backbone are fused with the feature maps of other residual layers to form a new feature prediction layer, and the anchors are refined using a clustering algorithm. It achieved high accuracy in object  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **YOLOv3** & **YOLOv4** & **YOLOv5** \\ \hline Neural Network Type & Full convolution & Full convolution & Full convolution \\ Backbone & Darknet-53 & Darknet-53 & CSPDarknet-53 \\ Neck & FPN & PANet & PANet \\ Head & YOLO Layer & YOLO Layer & YOLO Layer \\ Loss Function & Binary Cross-entropy & Binary Cross-entropy & Binary Cross-entropy and Logit Loss Function \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison between different YOLO architectures [26].  
prediction from satelite images. [PERSON] et al. [29] also proposed a YOLOv3-based network for detecting tiny objects in driving scenes by adding auxiliary feature extraction blocks with multiple residual connections. The added network can extract finer and more detailed features compared to the original YOLOv3 backbone, allowing the model to detect small targets more accurately. [PERSON] et al. [30] modified the backbone of the YOLOv4 network by adding an attention module composed of an efficient channel attention module (ECAM) and a convolutional block attention module (CBAM), along with regenerating anchors based on a clustering algorithm. However, the model is focused on a single task of detecting small targets in transmission line faults rather than a generalized object detection task. [PERSON] et al. [31] proposed TPH-YOLOv5, where they added an additional prediction head based on Transformer to detect objects in different scales. This method showed high mAP performance in aerial-captured scenes. The listed models achieved high accuracy in each target field, but calculation complexity and total model parameters were vastly increased in the exchange of small object detection performance. Additionally, a large-scale dataset for the desired task is required to train each model.
## 3 Antenna Design and Analysis
### Electric Field Effects in DR  
If we analyze the effects of electric field in the DR, it clearly demonstrates the reason of mutual coupling reduction by using the cylindrical air-gap and the metal strips. The electric fields originating from both the ports interact with each other and are a cause of mutual coupling in the DR. This interaction between the E-fields is reduced by using a cylindrical air-gap and a pair of metal strips. At the field intersection point, a cylindrical air-gap is introduced which changes the corresponding phases of the E-field at the boundaries of the air-gap. When the field enters from a higher to lower primitively material cylindrical air-gap, it deviates away from the normal direction [11], thus reducing the interaction between the E-field from the two ports. This effect can be seen at the boundaries where field through each port is tangentially oriented resulting in a considerable decrease in mutual coupling. In order to further reduce the coupling effects, two metallic strips are introduced at the corner of the DR shown in Figure 1a. It is well known that the tangential components of electric field vanish when it strikes on a metallic surface, only the transverse component can exist. This results in further deviation of the E-filed originating from each port from each other resulting in a further reduction in mutual coupling. Air-gaps [12] and metallic strips are being used to increase bandwidth and impedance matching, respectively. However, in this design this combination has been used as a coupling reduction technique. The magnitude E-field plot is shown in Figure 7.
## 2 Method
### Optimization Objectives
#### 2.2.1 Energy Consumption  
In this study, the total energy consumption is mainly composed of annual heating and cooling energy consumption. The mechanical ventilation rate was standardized at one air change per hour across all simulation cases, with supply air temperatures set to 20 \({}^{\circ}\)C for heating during winter and 26 \({}^{\circ}\)C for cooling during summer. The hourly simulations were conducted throughout the year, with a frequency of six times per hour. This calculation excludes fluctuations in the energy consumption of electronic equipment and domestic hot water, which do not vary significantly during the building's operation time. The air-conditioning system is regarded as the optimal unit for heating and cooling.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Design Variables** & & **Range and Interval** & **Price** & **CEF** \\  & & & **[\$/m\$]** & **[\$/**m\$]** \\ \hline Insulation type & EPS & 0.041 W \(\mathrm{/}\)m\({}^{2}\cdot\)K & 49.6 & 5020 \\  & XPS & 0.033 W \(\mathrm{/}\)m\({}^{2}\cdot\)K & 93.7 & 5020 \\  & Rock wool & 0.045 W \(\mathrm{/}\)m\({}^{2}\cdot\)K & 74.4 & 1980 \\ Insulation thickness & South wall & 100\(\sim\)300; 20 [mm] & - & - \\  & North wall & 100\(\sim\)300; 20 [mm] & - & - \\  & East/West wall & 100\(\sim\)300; 20 [mm] & - & - \\  & Roof & 200\(\sim\)300; 20 [mm] & - & - \\ Shading & South overhang & 0\(\sim\)1500; 100 [mm] & 27.5 & 28,500 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Design variables of building retrofit strategies.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**No.** & **Glazing Type** & **U-Value** & **SHGC** & **Cost** & **CEF** \\  & & **[W/m\({}^{2}\cdot\)K]** & & **[\$/m\$]** & **[\$/**m\$]** \\ \hline W1 & Double-glazed glass & 2.59 & 0.75 & 106.8 & 121 \\ W2 & Double-glazed high-transmission heat-reflective glass & 2.58 & 0.61 & 122.7 & 121 \\ W3 & Double-glazed low-transmission heat-reflective glass & 2.44 & 0.35 & 122.7 & 121 \\ W4 & Double-glazed glass (Low-E) & 1.79 & 0.43 & 126.8 & 121 \\ W5 & Double-glazed glass (argon) & 1.44 & 0.45 & 165.4 & 121 \\ W6 & Triple-glazed low-transmission glass (Low-E) & 1.35 & 0.27 & 261.9 & 194 \\ W7 & Triple-glazed high-transmission glass (Low-E) & 1.23 & 0.42 & 317.0 & 194 \\ W8 & Triple-glazed high-transmission glass (Low-E, argon) & 1.01 & 0.27 & 358.3 & 194 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Glazing types for retrofit.  
with coefficients of performance (COP) of 0.92 and 3, respectively. The energy consumption calculation formula is as follows:  
\[EC=\frac{Q_{H}}{COP_{H}}+\frac{Q_{C}}{COP_{C}}+E_{L} \tag{1}\]  
where EC is the energy consumption; Q\({}_{C}\) denotes the cooling energy demand; Q\({}_{H}\) represents the heating energy demand; E\({}_{L}\) denotes the lighting energy consumption; COP\({}_{C}\) is the efficiency of the cooling system; and COP\({}_{H}\) is the efficiency of the heating system.
## 4 Discussion of Test Results
### SCB Test Results
#### 4.1.3 Fatigue Life Regression Equation Establishment  
The fatigue life of ECC was determined to be the number of loading cycles at the end of stage II. During fatigue tests, four parallel tests were prepared and tested to obtain the fatigue lives of ECC under different stress ratios, as shown in Figure 9.  
Figure 8: Percentages of fatigue cycles in stages to the total fatigue life under different stress ratios.  
Figure 9: ECC fatigue lives under different stress ratios.  
The fatigue life of ECC decreased significantly with the increase of stress ratio. Therefore, if ECC is applied to OSBD pavement, overloading will dramatically reduce the fatigue life of ECC pavement. According to the results shown in Figure 9, it can be found that there is a power function correlation between fatigue life and stress ratio. Thus, the fatigue life of ECC \(N_{f}\) is transferred logarithmically to be \(\lg N_{f}\). On this basis, the relationship between \(\lg N_{f}\) and stress ratio \(t_{m}\) can be established and plotted, as shown in Figure 10.  
According to the results shown in Figure 10, there is a good linear relationship between \(\lg N_{f}\) and \(t_{m}\). The fatigue equation of ECC material based on stress ratio can be obtained as follows.  
\[\log N_{f}=-5.0863t_{m}+6.7957 \tag{7}\]  
where, \(t_{m}\in[0.5,0.8]\)
## Nomenclature  
\begin{tabular}{l l}
**Acronym** & **Meaning** \\ AFP & Actual failure probability \\ BEST & Bayesian Equivalent Sample Toolkit \\ CDF & Cumulative Distribution Function \\ COV & Coefficient Of Variation \\ DA2 & Design approach 2 \\ DIN & Deutsche Institut fur Normung \\ EC & Eurocode \\ MCMC & Monte Carlo Markov Chain \\ ORW & Optimal retaining wall \\ PDF & Probability density function \\ RBD & Reliability-based design \\ TFP & Target failure probability \\
**Symbol** & **Meaning** \\ \(c^{\prime}{}_{k}\) & Cohesion of soil \\ \(d_{0,d}\) & Pivot (rotation) point depth \\ \(d_{0,nnm}\) & Nominal pivot (rotation) point depth \\ \(d_{d}\) & Embedment depth \\ \(d_{nom}\) & Nominal embedment depth \\ \(G^{\prime}{}_{d,stb}\) & Submerged weight of the soil column \\ \(h_{1}\) & Average pressure head \\ \(H_{Ed}\) & Sum of horizontal forces \\ \(H_{Ed,w,i}\) & Horizontal forces due to groundwater presence \\ \(H_{Ed,z,i}\) & Horizontal forces due to earth pressure and surcharge load \\ \(H_{nom}\) & Nominal excavation depth \\ \(H_{Rd}\) & Sum of resistant horizontal forces \\ \(H_{Rd,w,i}\) & Horizontal resistances due to groundwater presence \\ \(H_{Rd,z,i}\) & Horizontal resistances due to earth pressure \\ \(h_{s}\) & Total head pressure \\ \(i_{a}\) & Hydraulic gradient (active side) \\ \(i_{p}\) & Hydraulic gradient (passive side) \\ \(k\) & Reduction coefficient \\ \(K_{ach}\) & Coefficient for active earth pressure due to cohesion of soil (active side) \\ \(K_{agb}\) & Coefficient for active earth pressure due to surcharge load (active side) \\ \end{tabular}
# Reversible Data Hiding Scheme in Homomorphic Encrypted Image Based on EC-EG
## 5 Conclusions  
The scheme in this paper uses the method of constructing MCC after EC-EG encryption to perform reversible data hiding in encrypted image. The scheme realizes the separation of additional data extraction and cover image restoration, and improves the embedding capacity under the premise of completely recovering the cover image. The experimental results show that the PSNR value of the scheme is improved in the image due to the self-embedded data by PEE algorithm. The main contributions of this paper can be summarized as follows:  
1. A novel method of reversible data hiding in homomorphic encrypted image based on EC-EG is proposed.
2. By constructing MCC, the embedded capacity is improved.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Image**} & \multicolumn{2}{c}{**Information Entropy**} \\ \cline{2-3}  & **Cover Image** & **Encrypted Image** \\ \hline Lena & 7.445 075 & 7.999 386 \\ \hline F-16 & 6.705 888 & 7.999 290 \\ \hline Man & 7.192 588 & 7.999 236 \\ \hline Hill & 7.094 869 & 7.999 246 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Information entropy of cover image and encrypted image.  
By performing histogram analysis of encrypted image, correlation analysis of adjacent pixels and information entropy analysis, we proved the statistical security of the encryption scheme in this paper.
* However, the method of vacating room before encryption increases the risk of plaintext leakage. Therefore, it should be improved in the future to be a reversible data hiding scheme in homomorphic encrypted image that does not require preprocessing of plaintext.  
Conceptualization, N.Z. and M.Z.; methodology, N.Z.; software, H.W.; validation, M.L.; formal analysis, Y.K.; investigation, N.Z. and M.L.; resources, M.Z.; data curation, N.Z. and H.W.; writing--original draft preparation, N.Z.; writing--review and editing, M.Z. and X.A.W.; visualization, H.W.; supervision, M.Z. and X.A.W.; project administration, M.Z. and X.A.W.; funding acquisition, M.Z. and X.A.W.  
This research was funded by National Natural Science Foundation of China under grant number 61872384, 61772550, U1636114, and 61572521, National Cryptography Development Fund of China under grant number MMJJ20170112, Natural Science Basic Research Plan in Shaanxi Province of China under grant number 2018 JM6028, and National Key Research and Development Program of China under grant number 2017 YFB0802000.  
The authors gratefully acknowledge the helpful comments and suggestions of the reviewers.  
The authors declare no conflict of interest.