esa-sceva/satcom-chunk-collection huggingface dataset:
# StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
## 4 Architecture
### Training  
Learning the end-to-end mapping function from cover and hidden image to embedded image and embedded image to decoded image requires the estimation of millions of parameters in the neural network. It is achieved via minimizing the weighted loss of \(L_{1}\)-loss between the cover and the embedded image, \(L_{1}\)-loss between the hidden and the decoded image, and their corresponding variance losses (variance should be computed across images' height, width and channel). \(C,H,E,D\) symbols stand for the cover image (\(C\)), the hidden image (\(H\)), the embedded image (\(E\)) and the decoded image (\(D\)) in correspondence (See Equations (6)-(8)).  
\[E_{i} =F_{\text{CE}}(C_{i},H_{i};\Theta_{\text{CE}}) D_{i} =F_{\text{ED}}(E_{i};\Theta_{\text{ED}}) \tag{6}\] \[L_{\text{CE}} =\frac{1}{n}\sum_{i=1}^{n}|E_{i}-C_{i}| L_{\text{HD}} =\frac{1}{n}\sum_{i=1}^{n}|D_{i}-H_{i}| \tag{7}\]  
\[\text{Loss}=\frac{1}{4}(L_{\text{CE}}+L_{\text{HD}}+\text{Var}(L_{\text{CE}})+ \text{Var}(L_{\text{HD}})) \tag{8}\]  
\(L_{\text{CE}}\) is used to minimize the difference between the embedded image and the cover image, while \(L_{\text{HD}}\) is for the hidden image and the decoded image. Choosing only to decode the hidden image while not both the cover and the hidden images are under the consideration that the embedded image should be a concentration of high-level features apparently similar to the cover image whose dimension is half the shape of those two images, and some trivial information has been lost. It would have pushed the neural network to balance the capacity in embedding the cover and the hidden if both images are extracted at the decoding process.  
Furthermore, adopting variance losses helps to give a hint to the neural network that the loss should be distributed throughout the image, but not putting at some specific position (See Figure 12 for differences between. The embedded image without variance loss shows some obvious noise spikes (blue points) in the background and also some around the dog nose).  
Figure 12: Variance Loss Effect on Embedding Results.
## 6 Conclusions  
Automation of SASW inversion was proposed in this study by adopting four ML algorithms, namely MLP, RF, SVR, and LR. This research can potentially contribute by automating the inversion procedure, which will allow us to evaluate the soil stiffness properties conveniently and rapidly. The SVR algorithm shows the lowest range of the RMSE, which indicates a better model performance, while LR shows the highest RMSE among all of the algorithms. After a depth of 3 m, the distribution of data yields RMSE to increase abruptly. The R\({}^{2}\) for SVR is found to be higher than for all algorithms. The confidence limit for MLP and LR after 3 m is lower than 80% and, thus, is considered not reliable for design purposes. The SVR and RF algorithms demonstrate being a better predictor than all algorithms, as the confidence limit is greater than 80% up to 6 m of depth. RF provides instantaneous output analysis, as no hyperparameter tuning is required. MLP provides better performance after 3 m for a few tests only. Among all algorithms, SVR shows the potential to be used as an alternative in estimating the shear wave velocity profile of soil for a given experimental dispersion curve.  
For future study, it is recommended to train ML algorithms with more training data. Moreover, a log transformation of the datasets can be performed beforehand so as to remove the skewness of the data. In this study, the empirical relationship considered in dataset preparation was one-third of the wavelength to an equivalent depth. Another relationship, such as half of the wavelength, can be considered in the future. Furthermore, the ability of ML algorithms to simplify the inversion of SASW could provide an efficient comparison with inversion in the global search algorithm.  
Conceptualization, S.M.M., N.A.R., K.A.M.N. and M.A.Z.; methodology, S.M.M., N.A.R., K.A.M.N.; software, S.M.M. and M.A.Z.; validation, N.A.R.; formal analysis, S.M.M.; investigation, S.M.M., N.A.R. and K.A.M.N.; resources, S.M.M., N.A.R. and K.A.M.N.; writing--original draft preparation, S.M.M.; writing--review and editing, S.M.M., N.A.R., K.A.M.N., M.A.Z. and S.A.P.R.; visualization, S.M.M., N.A.R.; supervision, N.A.R., K.A.M.N. and M.A.Z.; project  
Figure 20: R2 between the SASW and SVR for (**a**) 0–0.5 m, (**b**) 0.5–1 m, (**c**) 1–1.5 m, (**d**) 1.5–2 m, (**e**) 2–2.5 m, (**f**) 2.5–3 m, (**g**) 3–3.5 m, (**h**) 3.5–4 m, (**i**) 4–4.5 m, (**j**) 4.5–5 m, (**k**) 5–5.5 m, and (**l**) 5.5–6 m of depth.  
administration, S.M.M., N.A.R. and K.A.M.N.; funding acquisition, S.A.P.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Universiti Kebangsaan Malaysia under the grant DIP-2020-003.  
The author would like to appreciate the people involved in the research project and acknowledge Virtual Instrument and System Innovation Sdn Bhd and Universiti Kebangsaan Malaysia for the technical and financial support under the grant DIP-2020-003.  
The authors declare no conflict of interest.
# Intellectual Property Challenges in the Age of 3D Printing: Navigating the Digital Copycat Dilemma
## 2 Copyright and Patent Issues in 3D Printing Hardware
Additionally, the wide accessibility of digital blueprints complicates enforcement since files can be easily shared and modified, heightening the risk of both accidental and intentional infringement [51]. On the other hand, the expansion of 3D printing technology raises significant issues around patent exhaustion and cross-licensing, challenging traditional concepts of intellectual property in manufacturing. Patent exhaustion, the principle that a patent holder's rights are exhausted after an authorized sale, is complicated in 3D printing by the widespread sharing of digital files. Unlike physical items, digital files that represent patented objects can be repeatedly distributed and reused, challenging the legal boundaries of \"first sale\" rights. Since each print from a digital file effectively creates a new instance of the patented object, this scenario pushes the limits of patent exhaustion, potentially allowing unauthorized parties to reproduce patented products without direct oversight from patent holders [48]. Cross-licensing in 3D printing also presents unique challenges, especially for industries that rely on collaborative or interdependent designs. Traditional cross-licensing agreements assume well-defined product boundaries and control over production; however, in 3D printing, the ability to modify and customize digital designs blurs these lines. As digital files circulate, third-party users can alter designs to bypass certain licensing terms or incorporate elements from multiple patents, resulting in derivative works that may unintentionally infringe on multiple patents. Thus, enforcing cross-licensing agreements in 3D printing requires new frameworks that account for the flexibility of digital modifications and the high likelihood of complex overlapping patent rights [27; 52]. Moreover, 3D printing democratizes production in ways that strain both patent exhaustion and cross-licensing norms. This shift towards a \"prosumer\" market, where consumers manufacture customized items for themselves, makes it harder to control licensing across the diverse networks that share and reproduce designs. Traditional patent holders, who previously relied on centralized manufacturers to uphold licensing agreements, must now grapple with a decentralized and often international market of creators. For industries affected by high customization rates, such as medical or automotive sectors, this challenge necessitates new cross-licensing models that can function across digital platforms and safeguard patent rights amidst the highly accessible and shareable nature of 3D files [51; 52].
# City Architectural Color Recognition Based on Deep Learning and Pattern Recognition
## 3 Research Methodology  
Shanghai, a major city in China, is undergoing meticulous planning and regulation of its architectural colors as an essential landscape component, pioneering the integration of smart urban planning systems. Presently, the utilization of these smart systems is centered around defining urban zones, artificial facilities, and traffic detection. However, the recognition of architectural colors is still limited, often requiring color collection through photographs or manual measurements. Even though differentiated planning has been applied per area, there remains a gap in showcasing diverse architectural colors or overall color cohesion in urban planning guidelines. This research primarily investigated Shanghai's buildings, capturing their images and segmenting the structures and landscapes within intricate photos created by blending architectural and natural scenery. Through advanced computer image processing methods, colors were extracted. This approach offers a visual means to assess the harmony between architecture and landscape colors across various settings, laying a practical foundation for color planning. Concurrently, this system enhances the color identification aspect of the smart city planning system. The associated image processing technologies encompass deep learning, convolutional neural networks, and K-means clustering algorithms.
# Linear Vibration Analysis of Shells Using a Seven-Parameter Spectral/_hp_ Finite Element Model
## 6 Numerical Results
### Numeric Validation
#### 6.2.1 Plates  
In this section, comparisons for plates with constant, linear, and parabolic variations in thickness are presented. Tables 6-8 present the results for the boundary conditions listed on Cases I, II, and III, applied to squares plates with constant thickness. The values shown in parentheses represent the relative error in percentage. In general, a good agreement is observed with the results previously reported and those obtained by means of solid elements, with relative errors smaller than 5.8%. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dimensionless Frequency Parameter**} & \multirow{2}{*}{**Present**} & \multicolumn{6}{c}{**Formulation**} \\ \cline{3-8}  & & **[PERSON] \&** & **[PERSON] [44]** & **[PERSON] [45]** & **[PERSON] [46]** & **[PERSON] [46]** & **3D** \\  & & **[PERSON] [43]** & **(FOPT)** & **(3D)** & **(FOPT)** & **(HOPT)** & **(HOPT)** & **172,800** \\ \hline \multirow{2}{*}{\(\lambda_{1}\)} & 1.8758 & 1.9311 & 1.9311 & 1.9342 & 1.9311 & 1.9317 & 1.8424 \\  & & (2.86) & (2.86) & (3.02) & (2.86) & (2.89) & (1.81) \\ \hline \multirow{2}{*}{\(\lambda_{2}\)} & 4.5866 & 4.6077 & 4.605 & 4.6222 & 4.6050 & 4.6088 & 4.4577 \\  & & (0.46) & (0.40) & (0.77) & (0.40) & (0.48) & (2.89) \\ \hline \multirow{2}{*}{\(\lambda_{3}\)} & 4.5866 & 4.6077 & 4.605 & 4.6222 & 4.6050 & 4.6088 & 4.4577 \\  & & (0.46) & (0.40) & (0.77) & (0.40) & (0.48) & (2.89) \\ \hline \multirow{2}{*}{\(\lambda_{4}\)} & 7.0193 & 7.0724 & 7.0642 & 7.1030 & 7.0642 & 7.0732 & 6.7263 \\  & & (0.75) & (0.64) & (1.18) & (0.64) & (0.76) & (4.36) \\ \hline \multirow{2}{*}{\(\lambda_{5}\)} & 8.1452 & - & - & - & - & - & 8.1454 \\  & & (0.02) & & & & & (0.002) \\ \hline \multirow{2}{*}{\(\lambda_{6}\)} & 8.6764 & - & - & - & - & - & 8.3994 \\  & & & & & & & (3.30) \\ \hline \multirow{2}{*}{\(\lambda_{7}\)} & 8.6764 & - & - & - & - & - & 8.4148 \\  & & & & & & & (3.11) \\ \hline \multirow{2}{*}{\(\lambda_{8}\)} & 8.7731 & 8.6153 & 8.6055 & 8.6617 & 8.6054 & 8.6188 & 8.6739 \\  & & (1.83) & (1.95) & (1.29) & (1.95) & (1.79) & (1.14) \\ \hline \multirow{2}{*}{\(\lambda_{9}\)} & 8.7864 & 8.6153 & 8.6055 & 8.6617 & 8.6054 & 8.6188 & 8.6739 \\  & & (1.99) & (2.10) & (1.44) & (2.10) & (1.94) & (1.30) \\ \hline \multirow{2}{*}{\(\lambda_{10}\)} & 9.2255 & - & - & - & - & - & 9.2259 \\  & & & & & & & (0.004) \\ \hline \multirow{2}{*}{\(\lambda_{11}\)} & 10.4887 & 10.8101 & 10.7932 & 10.8790 & 10.7932 & 10.8145 & 10.3310 \\  & & (2.97) & (2.82) & (3.59) & (2.82) & (3.01) & (1.53) \\ \hline \multirow{2}{*}{\(\lambda_{12}\)} & 10.9272 & 10.8101 & 10.7932 & 10.8790 & 10.7932 & 10.8145 & 10.3310 \\  & & (1.08) & (1.24) & (0.44) & (1.24) & (1.04) & (5.77) \\ \hline \multirow{2}{*}{\(\lambda_{13}\)} & 10.9272 & - & - & - & - & - & 10.4821 \\  & & & & & & (4.25) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results for a square plate of ratio \(b/h=10\) and SSSS boundary conditions.
## 3 Results and Discussion
### Catalytic Activity
#### 3.8.2 Reusability  
The catalyst reusability was then examined for PdAS(10)-MA over consecutive reaction cycles and stability testing was carried out in the post-reaction supernatant by AAS. The reusability test consisted of carrying out 5 consecutive cycles under the same reaction conditions, recovering the catalyst by filtration after each batch reaction, and evaluating the catalyst performance, morphology, and textural properties.  
Figure 9a shows the I-Ph conversion at the time of maximum conversion for each reaction cycle. A slight increase in time to reach the maximum conversion level was observed, indicating a small but progressive deactivation over five consecutive cycles. Moreover, hot filtration tests were carried out to evaluate the presence of soluble active species in the case of PdAS(10)-MA for the first and fifth cycles. The conversion profile shown in Figure 9b indicates that for the fresh PdAS(10)-MA catalyst, when the reaction was stopped after 5 min and the catalyst filtered, the I-Ph conversion was maintained at 37% for almost 5 h. In addition, for the fifth cycle, stopping the reaction and filtering after 240 min (Figure 9c) followed by maintaining the reaction conditions for 15 h allowed the contribution of the solubilized species to be detected.  
Analyses by AAS, XRD, and TEM were then carried out for the used PdAS(10)-MA catalyst after the first, third, and fifth cycles, as shown in Figure 10. Leaching of the palladium species in the post-reaction liquid supernatant resulted in a maximum concentration of 4.6 wt.% Pd with respect to the total amount of Pd in the PdAS(10)-MA catalyst. However, XRD measurements for PdAS(10)-MA after the third and fifth cycles show the presence of typical diffraction peaks at 40.0, 46.4, and 68.2, which correspond to the (111), (200), and (220) planes of the Pd face-centered cubic (fcc) crystalline structure (JCPDS 65-2867), respectively. This result is in line with the TEM observations, where the Pd NPs showed an increase in their size distribution after continuous use. According to these results, the increase in the time required to reach the maximum conversion level for the catalyst could be attributed to the increase in the Pd NPs size.  
Figure 10: Physicochemical characterization of PdAS(10)-MA used catalyst. **(a)** TEM micrographs for PdAS(10)-MA catalyst after third reaction cycle, **(b)** TEM micrographs for PdAS(10)-MA catalyst after fifth reaction cycle, and **(c)** XRD diffraction pattern for the fresh catalyst, after third and fifth reaction cycle (\(\bullet\) Metallic Pd (JCPDS 65-2867)).  
Figure 9: Recycle and hot filtration study for PdAS(10)-MA catalyst in the production of MCIN at 120 \({}^{\circ}\)C using DMF as solvent. **(a)** Recycles, **(b)** hot filtration test for the first cycle, and **(c)** hot filtration test for the fifth cycle.
## Magnetic Nanoparticles Aggregation in Magnetic Gel Studied by Electron Magnetic Resonance (EMR)  
**[PERSON] 1,*, [PERSON] 1, [PERSON] 2, [PERSON] 2 and [PERSON] 3**  
Footnote  
+++ ==WARNING: Truncated because of repetitions==
11: footnotemark:  
++  
In particular, they are currently directed towards investigation of properties of magnetofilled composites [1]. In this area, it is important to follow the distribution of the magnetic filler since it can affect the mechanical and magnetic properties of the material [2].  
In an external magnetic field of a high strength (higher than 1 kG) magnetic nanoparticles obtain significant polarization at room temperature and are self-arranged into elongated structures--linear aggregates [3], like chains or more complex multi-chain constructions such as needles made of several laterally joined chains [4].  
In this work, we study the influence of gelation on the aggregation of magnetite nanoparticles in external magnetic fields and determine the dimensional parameters of the aggregates. Magnetic particle aggregation can be revealed by measuring the demagnetizing fields produced by elongated structures formed in magnetic fields [5]. This parameter allows the estimation of the magnetic anisotropy of the samples (gels, films). However, it has a poor sensitivity with fields with a strength higher than 1,000 G. Thus in [5] the parameter of magnetic anisotropy obtained by the torque angle technique changes slightly (15%) for the magnetic gels formed in the field range 1,000-10,000 G. The torque angle technique gives quantitative information on the magnetic anisotropy of samples but it does not allow the quantitative estimation of the aggregates aspect ratio. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) can also be applied to research particle ordering [5; 6; 7; 8]. However, this method requires special preparation of samples and does not give unambiguous results for magnetic gels [5]. To solve the problem we use the paramagnetic sensor technique for magnetic gels--this is investigated for the first time. The technique, developed in [9], is based on the analysis of EPR spectra of paramagnetic particles (stable nitroxide radicals) introduced into a magnetically diluted system. It was tested previously on magnetic liquids [9; 10]. The results obtained for the magnetic liquids using the paramagnetic sensor technique [10] were in agreement with TEM data for similar samples [6; 7; 8]. According to TEM [6; 7; 8] the aggregates' length can reach tens of microns while their width remains hundreds of nanometers, which corresponds to an aspect ratio (\(N_{\rm el}\)) of 50-100.
## 4 Discussion
### Contributions
In addition, to verify the effectiveness of the dual-layer model, this study compared and analyzed it with traditional energy scheduling strategies such as 100% grid connection, 50% self-use and 50% grid connection, and spontaneous self-use surplus electricity grid connection, taking a newly built office building as a case study (see Table 10). The results show that the dual-layer model outperforms traditional strategies in economic efficiency, low carbon emissions, and power supply reliability by optimizing the energy flow between photovoltaic power generation, energy storage systems, grid interaction, and building loads. This highlights the model's effectiveness in enhancing overall energy system performance. PEDF systems are ideal for buildings with abundant solar energy and space for photovoltaic installations, as well as those aiming to reduce energy consumption and carbon emissions. From the perspective of the interaction between the building and the power grid, PEDF systems are suitable for buildings with high electrical loads, strong fluctuations, and high load flexibility. Additionally, PEDF systems are particularly effective in office and commercial buildings with a high reliance on DC equipment and strict electrical reliability needs, including both new constructions and renovated existing buildings. In summary, the configuration optimization and evaluation framework proposed is applicable across various scenarios for implementing PEDF systems effectively.
## 3 Dual-Band Nested Antenna Array Design
### Nested Antenna Arrays for 3.5 and 28 GHz  
A compact single-layer 4 \(\times\) 4 CP circular patch antenna array using a corporate feed network was proposed for the n257-band at the center frequency f = 28 GHz previously and its functionality demonstrated by measurements after embedding into the rear spoiler of a passenger car [12]. This patch array was adopted to our proposed nested design and printed on an Isola Astra\({}^{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text  
Figure 5: (**a**) Exploded view of the proposed dual-band nested antenna array; (**b**) Isolation \(1S_{21}1^{2}\) for: n78-band; (**c**) n257- band; (**d**) optical photograph of the manufactured version: Top view; (**e**): Bottom view; (**f**) version embedded into the rear spoiler of a passenger vehicle with magnified view of the array PCB.
# Research on Rollover Stability and an Anti-Rollover Warning System for the Vibroseis Truck
## 2 Rollover Stability Analysis for Vibroseis Truck
### Analysis of the Rollover Stability Mechanism of the Vibroseis Truck
#### 2.2.1 Slope Straight-Driving Stability  
The basic condition for a vehicle's stability is that the gravity of the vehicle and the supporting force of the ground reach a balance. When the vibroseis truck drives straight on a certain slope with a slope angle \(\varphi\), this balance will be broken if the centroid center \(G\) crosses the flip axis. The flip axis is the connection line between the load points of the front wheel and the rear wheel on the rollover side of the vibroseis truck. The critical rollover state is shown in Figure 2, where the wheel track is \(B\), the centroid height is \(H\), the mass of the vibroseis is \(M\), and the acceleration of gravity is \(g\). The critical roll angle \(\theta\) can be calculated.  
According to the principle of moment balance, the equilibrium equation is  
\[Mg\cdot\sin\varphi\cdot H-Mg\cdot\cos\varphi\cdot\frac{B}{2}=0 \tag{1}\]  
Figure 1: Vibroseis truck: (a) lateral view, (b) top view.  
Figure 2: Critical rollover state of the vibroseis truck driving straight on a slope.  
\[\tan\varphi=\frac{B}{2H} \tag{2}\]  
Therefore, we can obtain the critical rollover angle at the rollover critical state:  
\[\theta=\varphi=\arctan\frac{B}{2H} \tag{3}\]  
As the curve in Figure 3 shows, the critical rollover angle \(\theta\) is the inherent characteristic of a vibroseis truck, and its value increases with the increase of the wheel track \(B\) and the centroid height \(H\). A larger wheel track and a smaller centroid height are conducive to improving the stability of the vibroseis truck. For the vibroseis truck, the wheel track \(B\) is 3.65 m and centroid height \(H\) is 1.83 m; therefore, the critical rollover angle \(\theta\) is 44.9\({}^{\circ}\). If the critical value is exceeded, the truck will lose its balance, and a rollover accident occurs on the slope, as Equation (3) shows. Therefore, driving on a slope with a slope angle of less than 44.9\({}^{\circ}\) is crucial to prevent vibroseis trucks from undergoing rollover accidents on a slope.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 4 MROR System and Network Model
### Channel Pool Update  
Every node keeps a pool of channels Cchannels, which is continuously updated based on the \(\varpi_{i}\) of each channel and the proximity of the mobile node with respect to the relative value of \(\ell_{i}^{*}\). As represented in Equation (22), both metrics are then formulated into a channel selection cost function, \(\chi_{i}^{ch}\); where \(k_{\varpi}\) and \(k_{\ell^{*}}\) are normalizing constants. Thus, the channel having the highest value of \(R_{i}^{ch}\) will be on top of the pool. Figure 2 illustrates the full operation of the channel update algorithm.  
\[\chi_{i}^{ch}=k_{\varpi}\cdot\varpi_{i}+k_{\ell^{*}}\cdot\ell_{i}^{*} \tag{22}\]