esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments  
### Creating the Validation Platform  
Xilinx XCZU3 EG-SBVA487 SoC was used as the core device to verify the design of integrated artificial intelligence lightweight deployment. An ARM quad-core Cortex-A53 application processing unit and ARM dual-core Cortex-R5F real-time processing unit were integrated in the PS side of the device. The PL side of the device integrates up to 7.6 MB of Block RAM (BRAM) and up to 360 digital signal processing modules, known as DSP48E, as  
Figure 6: Implementation of full-connection modules from the perspective of hardware logic.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **No Optimization** & **Pipelined** \\ \hline FC Layers’ Latency \& Interval (clock cycles) & 128,856 & 45,696 \\ \hline \multirow{3}{*}{Utilization Estimates} & BRAM\_18K & 79 & 88 \\  & DSP48E & 9 & 14 \\ \cline{1-1} \cline{2-3}  & FF & 29,061 & 29,259 \\ \cline{1-1} \cline{2-3}  & LUT & 24,651 & 28,196 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of resource usage of full-connection layers before and after optimization.  
well as other logical resources. The hardware platform based on this device was equipped with 2 GB LPDDR4 memory [39].  
Based on the modified LeNet-5 network and IP packaging and reuse technology, the design created an image recognition system within MPSoC [40; 41]. As shown in Figure 7, the hardware accelerator IP core used to realize image recognition function was connected to the PS side of the chip through AXI specification. In the figure, the IP core functions of each module are as follows:  
1. LeNet-5 module. This module implements the image recognition hardware accelerator structure based on the modified LeNet-5 network structure.
2. Zynq Ultrascale + MPSoC module. This module is an abstraction of PS in the MPSoC. It contains the quad-core Cortex-A53 APU and the dual-core Cortex-RSF RPU.
3. AXI Interconnect module. In compliance with the AXI specification, multiple AXI memory-mapped master devices were connected to multiple memory-mapped slave devices through a switch structure, which was used as a bridge to connect S_AXI peripherals.
4. AXI SmartConnect module. Similar to AXI Interconnect, it was used to connect AXI peripherals to PS, in this structure primarily as a bridge to connect M_AXI.
5. Process System Reset module. The processor system reset module was used in this structure to generate reset signals for PS and three other modules.  
After Xilinx Vivado software's synthesis and implementation of the overall structure, the logical resources used in PL are shown in Table 4.  
According to the power analysis tool in Vivado software, the total power consumption of the image recognition body system is only 3.22 W, while the power consumption of LeNet-5, the hardware accelerator module used to realize the forward inference of the image recognition, is only 1.363 W.
# Additive Manufacturing in Underwater Applications
## 3 Materials
### Biomaterials and Others  
However, bioprinting is not typically developed for underwater applications as the environment is very often associated with wet conditions. One of the most important areas for this research is enhanced adhesion in wet conditions for different materials. This type of test was performed i.a. for a photo-crosslinkable bio-ink containing a mixture of gelatin methacryloyl (GelMA), methacrylated hyaluronic acid (HAMA), and mesenchymal stem cells (MSCs) [55]. This material shows sufficient adhesion on diverse substrates, including biological tissues, and also in different aqueous environments [55]. It also offers the possibility of using similar mixtures for underwater bioprinting.  
In addition, hydrogels have been tested for the possibility of underwater adhesion because they are suitable for this environment [56; 57], including in parts for soft robots, such as suckers [58]. An important advantage of their application for biomedical purposes is their good underwater adhesion, but this can be decreased by the material's swelling and the formation of a water molecular layer on the surface of the hydrogel [56]. In this area, some eutectogels have also been tested as a potential material for underwater electrodes [59].  
Other materials of this group are used rarely for 3D printing applications. Non-typical, successful trials were performed for a gradient-index phononic crystal lens based on the hyperbolic secant index profile, applied as an acoustic material for underwater applications, including ultrasound [60].  
In the literature, there is only a small number of studies on the possibilities for the 3D printing of multi-material compositions in one process, as well as a limited number of investigations on the application of composites in AM for underwater applications [61].
## 6 Conclusions  
This primary research focused on the embedded system of sensor nodes for monitoring temperature and humidity using electronic circuits and antennas manufactured with SSAIL technology. This technology benefits sensor design by enabling antenna and RF-harvesting circuit formation on various plastics, even on the device's body. Thus, the devices can be miniaturised. The energy harvester and communication part of a node was designed and tested. The communication module used to support the energy-harvesting antenna circuits was designed to enable the rapid and cost-effective fabrication of circuits on flexible material in three dimensions. The simulation and measured results of the fractal antenna show a perfect match at 2.43 GHz, and it fully covers the lower Wi-Fi frequency band.  
The sensor network with randomly distributed nodes was shown using a MATLAB simulation based on parameters such as area length, area width, expected sensor reading, and data packet size. Different modes of operation of the Wi-Fi module were analysed and it was concluded that the deep sleep mode uses the least amount of energy. The simulation results of the improved LEACH protocol and the standard LEACH protocol show that the I-LEACH reduces energy consumption by approximately 43% and increases throughput by 40% in a network of 100 nodes. The total power consumption of the sensor network of ten nodes was estimated to be 65.31 mW per day using the specified operation regime. Monitoring of the sensor nodes was accomplished independently through the high-level hosting PHP application and the MySQL database. The sensor data retrieved by the web client was displayed in real-time charts on the web server. Therefore, this model is promising for environmental monitoring air parameters in cities and industrial zones.  
Future research will involve communication between multiple nodes from a network of sensors in diverse locations, parameter evaluations, accounting for radio channels, and real-time data collecting via web clients. In addition, with the advancements in wireless networking and sensor technology, there is an attractive potential for data aggregation, simulation, and testing of lifetime-aware routing and geographical coverage.  
Conceptualization, S.D. and G.R.; Formal analysis, P.R.; Investigation, S.D. and K.R.; Methodology, S.D. and P.R.; Resources, K.R.; Supervision, G.R.; Validation, R.T., G.S. and R.S.; Visualization, S.D., K.R., R.T., P.R., G.S., R.S. and G.R.; Writing--original draft, S.D.; Writingreview & editing, S.D., P.R. and G.R. All authors have read and agreed to the published version of the manuscript.
* [1]**Funding:** This project received funding from the European Regional Development Fund (project No. 01.2.2-LMT-K-718-03-0038) under a grant agreement with the Research Council of Lithuania (LMTLT).
* [2]**Institutional Review Board Statement:** Not applicable.
* [3]**Informed Consent Statement:** Not applicable.
* [4]**Data Availability Statement:** Not applicable.
* [5]**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Results: Seismic Capacity of a WFP Structure Upgraded with Hysteretic Dampers
### Ductility Level for Seismic Performance Levels (SPL) Near Collapse (NC)  
The ductility level attained in the analyses for SPL NC is studied here through the apparent plastic deformation of the \(i\) story along the X and Y directions, calculated separately for the WFP system (flexible system), \(\mu\chi_{x,i}\) and \(\mu\chi_{y,i}\), and for the dampers (the stiff part), \(s\mu\chi_{x,i}\) and \(s\mu\chi_{y,i}\), respectively. For the flexible part (WFP system), \(\mu\chi_{x,i}\) is defined as \(s_{f}\mu_{x,i}\ =\ \left(\delta_{maxX,i}-\delta_{y}\chi_{x,i}\right)/\delta_{y} \chi_{x,i}\), where \(\delta_{maxX,i}\) and \(\delta_{y}\chi_{x,i}\) are the maximum interstory drift--measured at the center of stiffness of the story--and the yield interstory drift of the main structure for the \(i\) story, respectively, along the X direction, and \(\mu\
u_{x,i}\) it is as \(\mu\gamma_{x,i}\ =\ \left(\delta_{maxY,i}-\delta_{y}\chi_{y,i}\right)/\delta_{y} \chi_{y,i}\), where \(\delta_{maxY,i}\) and \(\delta_{y}\chi_{y,i}\) are the counterpart variables defined above but along the Y direction. For the stiff part (dampers), the same definition is used for \(s\mu\chi_{x,i}\) and \(s\mu\gamma_{x,i}\), changing the subindex '\(f\)' to '\(s\)', and using \(s\delta_{y}\chi_{x,i}\) and \(s\delta_{y}\chi_{y,i}\) for the yield interstory drift of the dampers along X and Y directions instead of \(\delta_{y}\chi_{x,i}\) and \(\delta_{y}\chi_{y,i}\).  
The SPL NC was achieved in all cases at the first story (\(i=1\)), for which the analyses stopped when \(IDI_{1}\) exceeded \(IDI_{max}\) = 2.6% in any direction. In contrast, for the second story, the low values achieved for \(\delta_{maxX,2}\) and \(\delta_{maxY,2}\) led to values about zero for both \(\mu\chi_{x,2}\) and \(\mu\
u_{x,2}\). Taking into account that \(\rho\delta_{y,X,1}=\rho\delta_{y,Y,1}\) = 14 mm, \(s\delta_{y}\chi_{x,1}\) = \(s\delta_{y}\chi_{y,1}\) = 5.6 mm and \(IDI_{max}\) = \(IDI_{max,NC}\) = 2.6%, the maximum apparent plastic deformation for the WFP system and for hysteretic dampers would be \(\mu_{max,1}\) = 1.6 and \(s\mu_{max,1}\) = 5.5, respectively. This means that for the structure investigated in this study, the ductility level in dampers is about triple the one considered for the WFP system. Figure 12 offers the mean of the maximum apparent plastic deformation achieved in the WFP system (Figure 12a) and in hysteretic dampers (Figure 12b) under the different sets of ground motion records for each direction. It is seen that under Sets X, 1, and 2, SPL NC is achieved under the X direction for which the input energy of the X component of the seismic action is the highest, because the mean of \(\mu\chi_{x,1}\) and \(s\mu\chi_{x,1}\) correspond to \(\mu\mu_{max,1}\) and \(s\mu_{max,1}\), respectively. The opposite occurs under Sets 4, 5, and 7. Nevertheless, under Set 3 there is no specific direction for which SPL NC is attained.
###### Abstract  
With the increase in the demand for bitumen, it has become essential for pavement engineers to ensure that construction of sustainable pavements occurs. For a complete analysis of the pavement, both its structural and functional performances are considered. In this study, a novel material (i.e., Graphene Nano-Platelets (GNPs)) has been used to enhance both of the types of pavements' performances. Two percentages of GNPs (i.e., 2% and 4% by the weight of the binder) were used for the modification of asphalt binder in order to achieve the desired Performance Grade. GNPs were homogeneously dispersed in the asphalt binder, which was validated by Scanning Electron Microscope (SEM) images and a Hot Storage Stability Test. To analyze the structural performance of the GNPs-doped asphalt, its rheology, resistance to permanent deformation, resistance to moisture damage, and bitumen-aggregate adhesive bond strength were studied. For the analysis of the functional performance, the skid resistance and polishing effect were studied using a British Pendulum Skid Resistance Tester. The results showed that GNPs improved not only the rutting resistance of the pavement but also its durability. The high surface area of GNPs increases the pavement's bonding strength and makes the asphalt binder stiffer. GNPs also provide nano-texture to the pavement, which enhances its skid resistance. Thus, we can recommend GNPs as an all-around modifier that could improve not only the structural performance but also the functional performance of asphalt pavements.  
Graphene nano-platelets (GNPs); asphalt; Scanning Electron Microscope (SEM); structural performance; functional performance +
Footnote †: journal: Applied Physics Letters B
## 5 Hardware Detection Circuit Debugging and Result Analysis
### Low-Pass Filter Module Testing  
After passing through the multiplier channel, the signal is split into an AC frequency signal and a DC signal. As previously discussed in Section 3, a low-pass filter is needed to filter out the AC signals and keep the DC signals valid for the next vector operation. In this section, the output of the multiplier module is simulated in the low-pass filter module to complete the low-pass filter circuit. Figure 13a shows the test circuit for the low-pass filter. The DC source voltage amplitude is 10 mV, and the analog multiplier amplifier output an effective DC signal. The AC source voltage amplitude is 10 mV, with a frequency of 2 MHz, and the analog multiplier amplifier output an effective AC signal. The noise source is 0-2 MHz white noise. These three signals form the input signal for the low-pass filter, from which the DC signal is obtained. The oscilloscope's left channel (Channel-A) detects the input signal waveform, while the right channel (Channel-B) detects the output signal waveform. Multimeter 6 measures the magnitude of the output DC signal, and multimeter 7 measures the amplitude of the output AC signal.  
Figure 13b shows the results displayed by oscilloscope 5, where the blue waveform is the input signal and the red waveform is the output signal after low-pass filtering. From the figure, it can be seen that after the signal passes through the low-pass filter, the high-frequency noise signals have been effectively filtered out; the amplitude of the DC signal of the output voltage is measured by a multimeter as 9.892 mV, and the low-frequency signals have been effectively retained. This low-pass filter design meets the design requirements of a correlator.
# Imperceptible-Visible Watermarking to Information Security Tasks in Color Imaging
## 4 Experimental Results
### Performance Metrics for Image Quality Degradation, Imperceptibility, and Robustness
Generally, the SSIM and NCD values nearest to \(1\) and \(0\), respectively, represent a strong similarity between the original image and its corresponding watermarked image. The NC and the BER are two metrics used in this paper to evaluate the similarity between the original \(W\) and the recover watermark patter \(W^{\prime}\) as well as the robustness against the common geometric and imagen processing attacks listed in Tables 2 and 3, respectively. The NC and the BER are denoted by Equations (21) and (22). \[NC(W,W^{\prime})=\frac{\sum\limits_{i=1}^{M\times N}[w_{i}\times w^{\prime} _{i}]}{\sqrt{\sum\limits_{i=1}^{M\times N}\left(w_{i}\right)^{2}}\times\sqrt{ \sum\limits_{i=1}^{M\times N}\left(w^{\prime}_{i}\right)^{2}}}, \tag{21}\] \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Specification**} \\ \cline{3-5} \multicolumn{1}{c}{**Attack**} & \multicolumn{4}{c}{**Image Watermarked by**} \\ \cline{3-5} \multicolumn{1}{c}{} & \multicolumn{1}{c}{**FI**} & **QR** & **UNAM** \\ \hline \multirow{3}{*}{Filter} & Average & \(7\times 7\) & \(3\times 3\) & \(5\times 5\) \\  & Median & \(5\times 5\) & \(3\times 3\) & \(9\times 9\) \\  & Gaussian & \(9\times 9\) & \(3\times 3\) & \(9\times 9\) \\ \multirow{3}{*}{Noise contamination} & Impulsive & density = 0.5 & density = 0.3 & density = 0.5 \\  & & & \(\mu=0\), \(\sigma^{2}=0.0004\) & \(\mu=0\), \(\sigma^{2}=0.00005\) \\ \cline{2-5}  & & Radius = 4, & Radius = 0.5, & Radius = 5, \\ \cline{2-5}  & & Amount = 1 & Amount = 2 & Amount = 2 \\ \cline{2-5}  & & Radio = 4 & Radio = 1.7 & Radio = 4 \\ \cline{2-5}  & & [0, 0.33] & [0, 0.33] & [0, 0.33] \\ \cline{2-5}  & JPEG & QF:90 & QF:90 & QF:75 \\ \cline{2-5}  & JPEG2000 & CR:10 & CR:10 & CR:15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Common image processing attacks used for the experiment. \[BER(W,\ W^{\prime})=\frac{\sum\limits_{i=1}^{M\times N}(w_{i}\otimes w^{\prime}{}_{ i})}{M\times N}, \tag{22}\]  
where \(\otimes\), \(w_{i}\), and \(w^{\prime}_{i}\) are the XOR function, \(i\)-_th_ pixel of the original \(W\), and the recover watermark \(W^{\prime}=\{w^{\prime}{}_{i}\,|\,w^{\prime}{}_{i}=0,\,1,\,\text{and}\,\,i=1, \,2,\,3,\,\ldots,\,M\times N\}\), with size \(M\times N\), respectively. According to Equations (21) and (22), the values of NC and BER nearest to 1 and 0, respectively, represent a strong similarity and robustness against common geometric and imagen processing attacks. Additionally, to evaluate the performance of the proposed algorithm with respect to the geometric and the image processing attacks listed in Tables 2 and 3, a fair performance comparison with the most sophisticated and recent IVW algorithms [23; 24] is provided.
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products
### Biodegradable Pollutants
#### 2.2.7 Heavy Metals  
Metals must either be changed into a stable form or removed because, unlike organic contaminants, they cannot be biodegraded [6]. The remediation of heavy metals is accomplished via biotransformation are recorded to be the processes by which microbes operate, such as;  
* Biosorption is the sorption of metal to the surface of a cell by physicochemical means.
* Bioleaching involves the mobilization of heavy metal by the elimination of organic acids or methylation reactions.
* Biomineralization (heavy metal immobilization by the formation of insoluble sulfides or polymer complexes),
* Intraceal reactions which is the process of forming insoluble sulfides or polymer complexes to immobilize heavy metals.
* Figure 2 shows the microbial mechanisms that affect metal bioremediation.  
Figure 2: Microbial mechanisms used in metal bioremediation systems adapted and modified from [PERSON] et al. [6].
# RNN-Based Sequence to Sequence Decoder for Run-Length Limited Codes in Visible Light Communication
## 3 Learning to RLL Decode  
### Problem Formulation  
We will consider the decoding of RLL codes as a DL problem. The inputs to the NN are the received signals \(\mathbf{y}=\{y_{1},y_{2},\ldots,y_{N}\}\), where \(y_{i}\) is the \(i\)-th symbol of the received signal and \(N\) is the codeword length. The outputs are a group of soft estimates \(\tilde{\mathbf{x}}=\{\tilde{x}_{1},\tilde{x}_{2},\ldots,\tilde{x}_{K}\}\), corresponding to the transmitted data \(\mathbf{x}\), where \(K\) is the length of source  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Source Word** & **Codeword** & **Source Word** & **Codeword** \\ \hline
0000 & 001110 & 1000 & 011001 \\
0001 & 001101 & 1001 & 011010 \\
0010 & 010011 & 1010 & 011100 \\
0011 & 010110 & 1011 & 110001 \\
0100 & 010101 & 1100 & 110010 \\
0101 & 100011 & 1110 & 101001 \\
0110 & 100110 & 1111 & 101100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The 4B6B codebook in VLC.  
Figure 1: The communication system model of UAVs with VLC.  
word. Thereafter, we can obtain the final decoding results \(\widehat{\mathbf{x}}=\{\hat{x}_{1},\hat{x}_{2},\ldots,\hat{x}_{K}\}\) based on the soft estimates \(\widetilde{\mathbf{x}}\) by the corresponding hard-decision rule:  
\[\left\{\begin{array}{l}\hat{x}_{i}=1,\text{ if }\hat{x}_{i}>0.5\\ \hat{x}_{i}=0,\text{ if }\hat{x}_{i}\leq 0.5\end{array}\right.. \tag{2}\]  
We note that an input-output mapping of the NN is a chain function given by  
\[\widetilde{\mathbf{x}}=\mathbf{W}(\boldsymbol{y};\boldsymbol{\theta})= \mathbf{W}^{(L-1)}\Big{(}\mathbf{W}^{(L-2)}\Big{(}\ldots\Big{(}\mathbf{W}^{(0) }(\boldsymbol{y})\Big{)}\Big{)}\Big{)}, \tag{3}\]  
where \(L\) is the number of the NN layers and \(\boldsymbol{\theta}\) represents the network parameters. The NN will be trained to search the optimal \(\boldsymbol{\theta}^{*}\) by minimizing a properly defined the loss function \(\mathcal{L}\), given by  
\[\boldsymbol{\theta}^{*}=\arg\min_{\boldsymbol{\theta}}\mathcal{L}(\boldsymbol {x},\widetilde{\mathbf{x}}), \tag{4}\]  
where \(\mathcal{L}(\boldsymbol{x},\tilde{\mathbf{x}})\) calculates the loss between \(\tilde{\mathbf{x}}\) and \(\boldsymbol{x}\). Our goal is to train the NN to find an optimal mapping function \(\mathbf{W}^{*}:\boldsymbol{y}\to\boldsymbol{x}\) which satisfies the MAP criterion:  
\[\mathbf{W}^{*}(\boldsymbol{y};\boldsymbol{\theta}^{*})=\operatorname*{arg\, max}_{\boldsymbol{x}}P(\boldsymbol{x}\mid\boldsymbol{y}). \tag{5}\]  
Note that the training process is to be carried out offline. After the training and validation, the NN with the optimal \(\boldsymbol{\theta}^{*}\) and \(\mathbf{W}^{*}\) can then be embedded into UAVs to decode RLL codes.
## 3 Results and Discussion
### Mechanical Properties  
The relative density of all tested samples is shown in Figure 6a. It can be seen that deep cryogenic treatment had little effect on the relative density of matrix samples. For impregnated samples, the relative density increased after deep cryogenic treatment due to the decrease of crack width between diamond and matrix.  
As displayed in Figure 6b,c the hardness and bending strength of matrix samples increased slightly after deep cryogenic treatment. The results can be primarily related to the development of stresses. The development of stresses was due to the difference in thermal expansion coefficients between different phases. The development of stresses can increase the dislocation density, which leads to the improvement of the hardness and bending strength.  
The bending strength of all samples is illustrated in Figure 6c. Sample SD1 showed the highest value and the values of SD2 and SD3 were similar to SD0. The interface structure in Figure 5 observed between diamond grids and matrix of all impregnated samples is concordant with the bending strength test results. The average crack width decreased from 2.8 to 2.1 \(\upmu\)m after deep cryogenic treatment for 1 h. This structure feature means that the diamond grids were held by the matrix more firmly, which benefits the stress transfer, resulting in a high bending strength.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Composition** & **Thermal Conductivity (W/m\(\cdot\)K)** & **Thermal Expansion Coefficient (K\({}^{-1}\))** \\ \hline Diamond & 2000 & \(\approx\)1 \(\times\) 10\({}^{-6}\) \\ Matrix material & 125 & \(\geq\)13 \(\times\) 10\({}^{-6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The values of thermal conductivity and coefficient of thermal expansion.  
Figure 6: Mechanical properties of samples. (**a**) The relative density of all samples; (**b**) The HRC values of matrix samples; (**c**) The bending strength of all samples.