esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Analysis of Room Energy Consumption
#### 3.2.2 Range Analysis of Room Energy Consumption  
Through the range analysis of the energy consumption per square meter, shown in Table 8, and from the comparison of the \(R\)-value (range value) of the four factors, it can be seen that factor A is the most important factor, and factor D has the least influence on room energy consumption. The influence degree of the 4 factors on room energy consumption is as follows: factor A \(>\) factor B \(>\) factor C \(>\) factor D. The relative significance of the factors affecting room energy consumption is consistent with that of indoor air temperature. In this orthogonal experiment, the optimal level of each factor is the level of each factor that corresponds to the lowest energy consumption per square meter. As shown in Table 8, the level corresponding to the minimum \(K\) value of each factor is the optimal level of each factor. When factors A and B are at the fifth level, factors C and D are at the first level; that is, when the length of the room, the width of the room, the width of the opening on the north wall of the room, and the distance from the lower edge of the opening to the floor are 12,300 mm, 8700 mm, 1500 mm and 0 mm, respectively, the energy consumption per square meter reaches its lowest. The optimal combination for room energy consumption, determined by range analysis, is A\({}_{5}\)B\({}_{5}\)C\({}_{1}\)D\({}_{1}\). The test number of the scheme A\({}_{5}\)B\({}_{5}\)C\({}_{1}\)D\({}_{1}\) is set to S27.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Sum of Deviation** & **Degree of** & **Mean Square** & **Variance Ratio** (\(F\)) & **Significance** \\  & **Squares** (\(\mathcal{S}\)S) & **Freedom** (\(\mathcal{df}\)) & (\(\mathcal{MS}\)) & **Variance Ratio** (\(F\)) & **Significance** \\ \hline Factor A & 18.519 & 4 & 4.630 & 429.495 & Highly Significant \\ Factor B & 1.215 & 4 & 0.304 & 28.167 & Highly Significant \\ Factor C & 0.733 & 4 & 0.183 & 17.003 & Highly Significant \\ Factor D & 0.518 & 4 & 0.129 & 12.006 & Highly Significant \\ Error & 0.086 & 8 & 0.011 & - & - \\  & & \(R^{2}\) = 0.996 & & & \\  & & \(F_{0.05}(4,8)=3.84\); \(F_{0.01}(4,8)=7.01\) & & & \\ \hline \hline \end{tabular}
\end{table}
Table 7: ANOVA of room energy consumption.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Item** & **Level** & **Factor A** & **Factor B** & **Factor C** & **Factor D** \\ \hline  & 1 & 38.37 & 33.26 & 30.19 & 30.25 \\  & 2 & 33.92 & 32.00 & 30.87 & 31.19 \\ \(K\) & 3 & 30.52 & 31.29 & 31.58 & 31.45 \\  & 4 & 28.20 & 30.54 & 31.97 & 32.19 \\  & 5 & 26.24 & 30.17 & 32.65 & 32.18 \\  & 1 & 7.67 & 6.65 & 6.04 & 6.05 \\  & 2 & 6.78 & 6.40 & 6.17 & 6.24 \\ \(\overline{K}\) & 3 & 6.10 & 6.26 & 6.32 & 6.29 \\  & 4 & 5.64 & 6.11 & 6.39 & 6.44 \\  & 5 & 5.25 & 6.03 & 6.53 & 6.44 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Range analysis of room energy consumption.
# Free Surface Energy and Hansen Solubility Parameter Vector Field. Interface Thickness
## 3 Data and Discussion
### Surface Free Energy of Solids  
The surface tension of a solid, commonly known as surface free energy, is the result of the interaction of a solid with air or any other gas. Because the cohesion energy of gases is negligible, their solubility parameters will be zero, or, in other words \(\delta_{D,gas}=\delta_{P,gas}=\delta_{H,gas}=0\). Unlike liquids, in which molecules have mobility and can reorient themselves when interacting with a gas to show their least ionised part at the liquid-gas interface, solids cannot do that due to their limited mobility. For this reason, the interaction vector will comprise the following three components: dispersion, polarity, and hydrogen bonding.  
\[\overset{\rightarrow}{S}_{SG}=\delta_{DS}\hat{i}+\delta_{PS}\hat{j}+\delta_{ HS}\hat{k} \tag{39}\]  
Therefore:  
\[\sigma_{SG}=\tau_{SG}\left|\overset{\rightarrow}{S}_{SG}\right|^{2} \tag{40}\]  
And:  
\[\sigma_{SG}=\tau_{SG}\left[4\delta_{DS}^{2}+\delta_{PS}^{2}+\delta_{HS}^{2}\right] \tag{41}\]  
Table 15 in Appendix A shows the surface free energy data of 21 solid materials with different degrees of polarity. Additionally, the interaction vector values and calculated free energy data are displayed.  
Figure 6 graphically shows the surface tensions (free surface energies) of the solids included in Table 15 in Appendix A and their linear regression. The correlation is satisfactory as \(R^{2}=0.9828\), despite the difficulty of accurately determining the experimental values of the free surface energies of solids. The constant, \(\tau_{SG}=0.0260\) nm, is very similar to the values of liquid surface tensions (\(\tau_{LG}\approx 0.0243\) nm \(\approx 0.0257\) nm).
## 4 Discussion and Conclusions  
Fault diagnosis of rolling bearing acoustic emission signals is of great importance in the maintenance of industrial equipment as it can effectively prevent equipment failures and downtime, ensuring the smooth operation of the production process. However, extracting fault characteristics of rolling bearings from complex acoustic signals is a challenging task. This paper proposes a comprehensive method called CRAMNet to address this challenge. CRAMNet combines GAF, ResNet1D, ResNet2D, and the multi-head self-attention mechanism to achieve efficient fault diagnosis of rolling bearing acoustic radiation signals. The conclusions drawn are as follows:  
1. Experimental results show that CRAMNet achieves nearly 100% accuracy throughout the entire training process. CRAMNet's fast convergence and high stability during training further demonstrate its superiority in diagnosing rolling bearing faults.
2. The experimental results demonstrate that CRAMNet excels in terms of precision and recall. Compared with several traditional models (including CNN, LSTM, DenseNet, and CNN-Transformers), CRAMNet outperforms in all evaluation metrics, achieving an accuracy of up to 100%. Specifically, the combination of CRAMNet's residual network and multi-head self-attention mechanism significantly enhances its fault diagnosis capability for rolling bearings, proving the effectiveness and advancement of this method.
3. The research findings not only provide an effective tool for the fault diagnosis of rolling bearing acoustic radiation signals but also offer new ideas and methods for the condition monitoring and fault diagnosis of other industrial equipment. Future research can further optimize the model structure and integrate more advanced algorithms to improve fault diagnosis accuracy and efficiency, exploring its application potential in other types of equipment.  
Conceptualization, X.X. and Y.L.; methodology, X.X.; software, X.X. and Y.L.; validation, X.X. and Y.L.; formal analysis, X.X. and Y.L.; investigation, X.X. and Y.L.; resources, X.X. and Y.L.; data curation, X.D.; writing--original draft preparation, X.X.; writing--review and editing, X.X. and X.D.; visualization, X.D.; supervision, X.D.; project administration, X.X. and Y.L.; funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (No. 52275119).  
Not applicable.  
The detailed data supporting the results of this study are available from the corresponding authors upon request.  
Thanks to [PERSON] of Shenyang Jianzhu University for providing the experimental bench.
## 2 State of the Art of the Federal Elementary Flow List
### Source Data
#### 2.3.6 Available Mappings  
Mapping files are the essential tools of harmonization across platforms. Mapping files connect one source to another, usually at a flow level. Flow mappings are provided within FEDEFL software package for several commonly used existing LCI or LCIA sources to ensure compatibility with the FEDEFL and each other. In many cases, mapping files are generated by matching flowables and contexts separately. This reduces the potential of inconsistent matching across flowables when tracked in multiple contexts and inconsistent contexts. Upon generating a mapping file, flow Universal Unique Identifiers (UUID) are checked to ensure that the target flowable and context exists in the FEDEFL. The methods section describes the simplistic mapping method used to determine interoperability for the analysis.  
There are currently several available mapping files. Existing mapping files were used to support the analysis for this paper and guide the selection of the sources for the analysis. Currently, mapping between the FEDEFL and the GREET model, USLCI database version 2019Q4, and TRACI 2.1 are available. The open-source software openLCA has been working with the Federal LCA Commons to integrate flow list management changes; therefore, a mapping to the reference list from openLCA is also available [29]. Since openLCA is a repository for various datasets, original data sources connected with the openLCA reference list were also not a part of the mapping process. In addition to Federal LCA Commons sources, mappings to LCIA methods ReCiPe and ImpactWorld+, and LCA sources IDEA and ecoinvent, are available. All these mappings are one-way mappings to the FEDEFL and are available online on the FEDEFL github repository. One-way mappings are mappings that have been completed as a part of the Federal LCA Commons and have not in theinstances of ReCiPe, ImpactWorld+, IDEA or ecoinvent been reviewed or approved by the original source.
# Optimized Score Level Fusion for Multi-Instance Finger Vein Recognition
## 4 Proposed Method
### Feature Extraction  
In the proposed method, a histogram of oriented gradients (HOG) is adopted for finger vein feature extraction. HOG is a popular feature descriptor proposed for object recognition [2]. Based on optimization findings, the finger vein image can be optionally normalized with power-law compression to achieve illumination invariance, which is determined by the hyperparameter _transform_sqrt_. The finger vein image is then divided into small connected regions called cells with the size of _pixels_\(\times\)_pixels_. In each cell, the gradient is computed, which is then used to create a histogram where the histogram bins, with the size of _orientations_, are based on the orientation of the gradient, and the histogram value is based on the magnitude of the gradient. Next, the cells are grouped into large spatial blocks and each block is then normalized. The final histogram is computed from normalized gradient orientation of all cells within the block. This normalization results in better invariance to changes in illumination and edge contrast. There are four choices of block normalization, determined by the hyperparameter _norm_, used in HOG for optimization purposes:  
* L2: \(f(v)=v/\sqrt{||v||_{2}^{2}+\epsilon^{2}}\)
* L2-Hys: L2, followed by limiting the maximum value of v to 0.2, and renormalizing.
* L1-sqrt: \(f(v)=\sqrt{v/(||v||_{1}+\epsilon)}\)
* L1: \(f(v)=v/(||v||_{1}+\epsilon)\)  
where \(v\) is the histogram vector of a block and \(\epsilon\) is a very small constant to prevent division by zero. After that, all the histograms are appended to form the final HOG feature set. In summary, the hyperparameters \(\lambda\) of HOG are:  
\[\lambda_{HOG}=[\text{orientations, pixels, cells, norm, transform\_sqrt}] \tag{1}\]  
Figure 3: Preprocessing.
# Early Results on GNSS Receiver Antenna Calibration System Development +
###### Abstract  
Precise global navigation satellite system (GNSS) positioning is based on carrier phase observations, where the understanding of the receiver antenna's phase center corrections (PCCs) is critical. With the main goal of determining the PCC models of GNSS receiver antennas, only a few antenna calibration systems are in operation or under development worldwide. In this paper, a new automated GNSS receiver antenna calibration system, recently developed at the Laboratory for Measurements and Measuring Technique (LMMT) of the Faculty of Geodesy of the University of Zagreb in Croatia, is briefly presented. The developed system is an absolute field calibration system based on the utilization of a Mitsubishi MELFA RV-4 FML-Q 6-axis industrial robot. The antenna's PCC modeling is based on triple-difference carrier phase observations and spherical harmonic (SH) expansion. Our early calibration results for the global positioning system (GPS) L1 frequency show sub-millimeter agreements with the IGS approved Geo++ GmbH values.  
GNSS; antenna calibration; industrial robot; phase center correction (PCC) 202320235898. [[https://doi.org/10.3390/ecsa-10-16227](https://doi.org/10.3390/ecsa-10-16227)]([https://doi.org/10.3390/ecsa-10-16227](https://doi.org/10.3390/ecsa-10-16227))
## 4 Design of the On-Off Resonance-Tuning Module
### MRE Property Analysis  
First, the variational properties of the Young modulus, stiffness, and damping of the MREs were analyzed to design the appropriate MREs for application in a BWT. The MREs for the BWT were designed to function for shear stiffness with a thin rectangular shape. Thus, the dimension of the MREs for the property analysis was also a thin rectangle with a length of 14 mm, a height of 10 mm, and a width of 5 mm. Two specimens of MREs in parallel structures were used in this property analysis.  
[PERSON]'s modulus (\(E\)) of the MREs can be derived by using the cantilever formula for MREs of a length \(L\) and the area moment of inertia \(I\) as  
\[E=\frac{L^{3}}{3I}\frac{k_{mre}}{2}=\frac{L^{3}}{3I}k_{mre,each}, \tag{12}\]  
\[I=\frac{HW^{3}}{12} \tag{13}\]  
where \(k_{mre}\) is the total stiffness of two MREs in parallel, and \(k_{mre,each}\) is the stiffness for each MRE. The area moment of inertia (\(I\)) for this MRE is defined using the dimension of the cross-sectional area (\(H\times W\)) while taking the direction of the force into account, as shown in Figure 3. An MRE can be considered a cantilever beam under shear force \(F_{L}\), and to analyze shear stiffness, a mass of 180 g was attached between the two MRE specimens, and a dynamic load force (\(F_{L}\)) was applied in the x-direction, as shown in Figure 3.  
The change in the properties of MREs due to a magnetic field is summarized in Table 1. As explained in Section 3.3, the target total stiffness of the MREs was about 570 N/m to tune the BWT at the resonant frequency of \(f_{BWT}=2.8\) Hz. Therefore, using the results in Table 1 and Equation (12), the MREs for the BWT prototype were designed with dimensions of approximately \(L=23\) mm, \(H=16\) mm, and \(W=11\) mm.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **B = OFF (0 mT)** & **B = ON (170 mT)** & **Increment (\%)** \\ \hline Stiffness, \(k_{mre}\) (N/m) & 146.1 & 368.8 & 152\% \\ Young’s modulus, \(E\) (kPa) & 641.4 & 1619.3 & 152 \\ Damping coefficient, \(c_{mre}\) (Ns/m) & 2.2 & 2.8 & 27\% \\ \hline \end{tabular}
\end{table}
Table 1: The changes in stiffness, Young’s modulus, and damping under a magnetic field for the MRE samples. Two MREs in parallel structures were implemented in the analysis.
## 3 Experimental Setup
### Test Conditions
#### 3.6.4 Ambient Temperature  
Ambient temperature is a parameter that influences the results. The ambient temperature at the start of each test ranged from 19.00 (\({}^{\circ}\)C) to 20.70 (\({}^{\circ}\)C).
## 6 Results and Analysis
### Responses of Submarine Pressure Hull to Uindex
#### 6.2.2 The Displacement Response at Different Locations  
Figure 17 illustrates the deformation response in the translation direction (\(U_{2}\)) at nodes \(A\) and \(B\) owing to 20 kg TNT at various offset distances (5.5 m, 15.5 m and 20.5 m). It is observed that the vertical displacement (\(U_{2}\)) at nodes \(A\) and \(B\) move in opposite directions throughout the transient response and follow the same variation. In addition, the figure shows a rigid body translation of the MICEPH. Figure 17a shows the high-frequency response measured at nodes \(A\) and \(B\), at an offset distance of 5.5 m. It is revealed that at a small offset distance, the structural wavelengths are longer than the acoustic wavelengths. Additionally, the surrounding fluid acts on the structure as a simple damping mechanism and the energy transported away from the structure through the acoustic radiation. Furthermore, Figure 17b illustrates the intermediate-frequency response at nodes \(A\) and \(B\), at an offset distance of 15.5 m. At an intermediate offset distance, the structural wavelengths are nearly similar to the acoustic wavelengths. In addition, the surrounding fluid added mass and radiation damping on the structure of the hull. Moreover, Figure 17c demonstrates the low-frequency response at nodes A and B, at offset distance of 20.5 m. It is revealed that the structural wavelengths are nearly shorter than the acoustic wavelengths and the surrounding fluid added effective mass to the structure of the hull on the wetted interface. Figure 18 presents the translation (\(U_{2}\)) in the vertical direction of two points located at the top (node \(C\)) and bottom (node \(D\)) of the amid-ship section at different offset distances (5.5 m, 15.5 m and 20.5 m) due to 20 kg TNT. It is observed that the upper point (node \(C\)) is moved in a direction opposite to the lower point (node \(D\)). The figure also illustrates that the displacement at node \(C\) is higher than its counterpart at node \(D\). In addition, the pressure hull shows an elastic deformations and rigid body motions with a significant heating and pitching response. These results match those achieved by [60]. Similarly, Figure 19 illustrates the axial displacement response (\(U_{2}\)) in the vertical direction of points located at the center of each end of the MICEPH (\(E\) and \(F\)). It is observed that the two nodes move in opposite direction with almost similar frequencies forming the accordion motion. Additionally, the shape of the displacement response (\(U_{2}\)) is significantly affected by the offset distance. While the offset distance increases, the frequency decreases. The aforementioned results agree well with that acquired by [40; 43].  
Figure 17: Displacement-time history responses (\(U_{2}\)) at nodes \(A\) and \(B\) on the MICEPH.  
Figure 19: Displacement-time history responses (_U2_) at nodes \(E\), \(F\) on the MICEPH.  
Figure 18: Displacement-time history responses (_U2_) at nodes C and \(D\) on the MICEPH.
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Experimental Results
#### 5.2.1 Imperceptibility Analysis  
Watermark's imperceptibility is evaluated by calculating PSNR and SSIM between original and watermarked images. Watermarked and original images should be very similar. Higher PSNR values indicate higher imperceptibility and less distortion. SSIM values should be close to 1 to indicate that there are no substantial distortions in the watermarked image in comparison to the original image.  
Table 1 show that the PSNR values exceed 37 dB and all SSIM values are very close to the exemplary value 1. Figure 16 shows an example of the original image, corresponding generated watermark, watermark signed by a one-time ticket generated by the SUC, and the resulting clone -resistant WM image. To complete the signing process, AES-128 has been used as mentioned above. As one can see in this figure, there is no significant perceptual difference between original and Clone-Resistant Watermarked versions of the image.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Image** & **SSIM** & **PSNR** \\ \hline Chest & 0.9861 & 38.15 \\ Tspine & 0.9895 & 37.77 \\ Hands & 0.9997 & 49.52 \\ Skull & 0.9995 & 52.89 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structural similarity index (SSIM) and peak signal to noise ratio (PSNR) average values between watermarked and original images without attacks.  
Figure 16: An example of the original image, corresponding generated and signed watermarks, and the resulting watermarked image.  
Figure 14: DICOM images used in experiments.  
Figure 15: Hands image and generated watermark used in experiments.  
We can see from Table 2 that the average value of SSIM between the original image and the attacked watermarked image is equal to 0.9823, and the average value of PSNR between the original image and the corresponding attacked watermarked image is equal to 53.45 dB which shows that the proposed watermarking approach ensures a good level of imperceptibility.
# Evaluating Short-Wave Effects in Railway Track Using the Rail Surface Signal
## 3 Results and Discussion
### Insulated Rail Joints  
Insulated rail joints are still an indispensable part of the infrastructure for many networks. Dividing track into blocks for the train control system, the joints isolate sections of track from each other. However, insulated rail joints form points of inhomogeneity often leading to a poor-quality condition. This is true for the insulating joints as well as for components in the area of the joint. In the rail surface signal, insulated joints appear through three signal deflections being symmetrical in relation to each other. The first and the last deflection represent the welding joints where the factory-made insulated joint is welded into the track. The middle deflection corresponds to the insulated spot. An over-linear deterioration could be observed at the insulating spots examined so far. Figure 4 shows the typical signal characteristics and the temporal quality development of the insulated spot and the adjoining welding joints.  
The quality index for the time series analysis is the minimum value of the deflections in place of the respective asset. For the area shown, there is neither a maintenance operation documented nor evident in the data. It is noticeable that the quality level of the insulated  
Figure 4: The time development of the rail surface signal in response to an insulated rail joint.  
rail joint is already significantly worse directly after installation compared to the welding joints, and additionally deteriorates disproportionately over the years. We suspect that this is a manifestation of the self-reinforcing effect already described.