esa-sceva/satcom-chunk-collection huggingface dataset:
# Data Hiding and Authentication Scheme for Medical Images Using Double POB
## 4 Analysis of Simulation Result
### Recovery Evaluation  
In order to verify the performance of the tamper recovery scheme in the proposed scheme, ShareA\" of Brain 01 is used as an example here, and an attack test is carried out on ShareA\" with embedded secret data and authentication bits. Three classic attack methods are adopted, including content cropping, text addition, and content exchange. The attack results and tampering recovery results are shown in Figure 11.  
The first column shows a content cropping attack on ShareA\", from which a block with a size of 200 \(\times\) 200 is cropped. The second column performs a text addition attack on ShareA\". The third column is replaced with \"Pulmonary 01\" in the upper left corner of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Test Images**} & \begin{tabular}{c} **Embedding** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Embedded** \\ **Capacity (bits)** \\ \end{tabular} & \
\begin{tabular}{c} **Single** \\ **POB (ms)** \\ \end{tabular} &
\begin{tabular}{c} **Double** \\ **POB (ms)** \\ \end{tabular} \\ \hline \(512\times 512\) & Brain 01 & Encrypted ShareA’ & 52,428 (10\%) & 9740.6 & 21,453.5 \\ \(512\times 512\) & Brain 01 & Encrypted ShareA’ & 157,286 (30\%) & 11,404.6 & 24,227.8 \\ \(526\times 256\) & Pulmonary 01 & Encrypted ShareA’ & 65,536 (50\%) & 4594.2 & 8070.3 \\ \(526\times 256\) & Pulmonary 01 & Encrypted ShareA’ & 104,857 (80\%) & 4752.9 & 8586.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of time efficiency between single POB and double POB embedding in the same image with different embedding capacities.  
Figure 11: Results of attack test. (**a–c**) ShareA”; (**d–f**) the attacks of content cropping, text addition, and content exchange carried out on ShareA”; (**g–i**) the recovered ShareA.  
ShareA\"; its size is 256 \(\times\) 256, and the last line represents the recovery result of ShareA\" after the attack. In addition, the same attack was tested on ShareB\", and the same results were obtained.  
Four different images were tested for different tampering methods and recovered; Table 4 shows the PSNR and SSIM values of the recovered images.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Easy Incorporation  
Regardless of the accessibility of high-end specifications of the wireless protocols, some hybrid systems probably exist, ranging from small to medium geographical areas. The goal of hybrid systems is to easily incorporate wired and wireless systems without affecting performance and efficiency. This kind of incorporation is accomplished through particular Internet-enabled devices. The problem of consistent incorporation has been researched in a few past investigations [77] which coincide with the introduction of wireless for modern systems. Nevertheless, a concise methodology is required to research this problem, as prior research may not be straightforwardly good with continuous tasks regarding Time-Sensitive Networking (TSN) and 5G-New Radio (5G-NR).  
A teleoperation system was proposed based on the 5G-NR to examine and study the performance of haptics over Internet protocols [78]. The feasibility of the proposed system ensured reliable communication and did not violate the requirements of QoS.
# An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
###### Abstract  
As Intelligent Transport System (ITS) applications are diversified and amount of ITS data increases, high throughput and reliability are required in next-generation V2X communications. In order to meet such increased throughput and reliability requirements, IEEE 802.11 bd, the next-generation V2X communication standard, has commenced standard development. One of the main features of IEEE 802.11 bd is a 20-MHz bandwidth transmission. In this paper, a novel wide-bandwidth channel access scheme in next-generation Wireless Local Area Network (WLAN)-based vehicular communications is proposed. The proposed scheme is designed to provide fairness with other ITS devices and channel efficiency considering adjacent channel interference. By using the proposed scheme, through extensive simulations, it is verified that, while satisfying the fairness requirement with other ITS devices, the channel access delay of wide-bandwidth packet transmission can be optimized.  
WLAN; next generation V2X; channel access; wide-bandwidth transmission +
Footnote †: journal: Applied  
_Article_  
An Effective Wide-Bandwidth Channel Access in Next-Generation WLAN-Based V2X Communications
## 3 Results  
### C3G Urregulates the Expression of Ucp1 Gene Both In Vitro and In Vivo  
As mentioned above, it has previously been reported that C3G treatment specifically upregulates the thermogenic gene expressions in BAT-cMyc cells [17]. In this study, we treated C\({}_{3}\)H\({}_{10}\)T\({}_{1/2}\) cells with various concentrations of C3G to explore the effect of C3G on the expression of the _Ucp1_ gene. However, this proved to have no effect on BAT-cMyc cell viability or proliferation during brown adipogenesis. In comparison with the PBS medium, C3G at concentrations of 10 \(\upmu\)M, 20 \(\upmu\)M, and 40 \(\upmu\)M was found to significantly upregulate the relative expression level of _Ucp1_ mRNA in the C\({}_{3}\)H\({}_{10}\)T\({}_{1/2}\) cells (\(p<0.05\)) and, at 20 \(\upmu\)M, the C3G could significantly upregulate the expression of Ucp1 (Figure 1A) (\(p<0.01\)). Similarly, through the animal experiments, it was found that C3G treatment could significantly increase the expression of the _Ucp1_ gene of BAT in db/db mice and mice with DIO. The expression of _Ucp1_ in the C3G-treated mice was 3.82 times and 3.06 times higher than those in the two control groups (Figure 1B,C). These results are consistent withour previous research results [4; 17], further demonstrating C3G-induced upregulation of _Ucp1_ mRNA expression, both in vitro and in vivo.  
All figures and tables should be cited in the main text as Figure 1, Table 1, etc.  
Next, we successfully constructed the luciferase reporter gene vector (Figure 1D-G), to examine the role of C3G in the activation of _Ucp1_. The luciferase assay showed that, compared with the PBS-supplemented medium, treatments with various concentrations of C3G and transfection with the \(-2.0\) kb _Ucp1_ promoter-luciferase did not activate the _Ucp1_ promoter (Figure 1H) (\(p<0.01\)). Thus, these results indicate that, while C3G significantly accelerates _Ucp1_ expression, it does not directly activate its promoter.
# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 3 Energy Supply for Wireless Sensor Nodes  
Harvesting energy from ambient sources is challenging since it is limited, unstable, random and variable relative to the application and the environmental condition. To enhance the available energy for wireless sensor networks, several techniques have been proposed to improve the performance of energy autonomous systems from real ambient sources [1]. Nevertheless, both ambient sources and converters have their own limitations. Because the design of the converter is decisive for enough energy yield, the first strategy for improving system performance is the converter design, which should be fitted to the energy source by considering its characteristics (see Figure 2). A further strategy for improvement of the energy yield and realizing a more reliable energy availability is the use of more converters and the development of hybrid converters harvesting energy from the same source in different ways or harvesting energy from multiple sources (see Figure 2). The same applies to energy saving, which could be strategically divided into several storage units allowing a faster availability in restarting phases and a better power density, i.e., the energy per time, especially for the node communication.  
Figure 2: Generalized energy supply strategies of wireless sensor nodes based on energy harvesting, hybrid energy harvesting and wireless energy transfer.  
In several applications, the availability of the measured data should be guaranteed even if ambient energy sources are not sufficient. In these cases, but also in general, supplying wireless sensors by energy transfer is an interesting possibility to couple energy to the system in a dedicated manner. It is also interesting to provide energy in the case of sudden or unexpected changes of environmental conditions as well as in the case of components aging in a way that could not be considered within the system performance assessment in the system design process. All these strategies are detailed in the next sections.
## 3 Data Fusion in the Integration of SINS/DVL/MCP/PS
### Data Fusion with the Subsystems
#### 3.1.3 System and Measurement Equations of SINS/MCP and SINS/PS  
Similarly, the system and measurement equations of SINS/MCP and SINS/PS can be constructed. In Equation (14), only the error in SINS is considered, so the system equations of SINS/MCP and SINS/PS are the same with that of SINS/DVL. The measurement vector of SINS/MCP and \(\mathbf{H}_{\text{SINS/MCP}}\left(t\right)\) can be constructed as:  
\[Z_{\text{SINS/MCP}}=\psi-\psi_{\text{MCP}} \tag{17}\]  
\[\mathbf{H}_{\text{SINS/DVL}}=\left[\begin{array}{cc}0_{1\times 5}&-1&0_{1 \times 9}\end{array}\right], \tag{18}\]  
where \(\psi\) and \(\psi_{\text{MCP}}\) denote the yaw in SINS and MCP, respectively. The variance matrix \(\mathbf{R}\) corresponding to measurement noise \(\mathbf{V}\) in Equation (12) is set according to the random noise in \(\psi_{\text{MCP}}\).  
The measurement vector of SINS/PS and \(\mathbf{H}_{\text{SINS/PS}}\left(t\right)\) can be constructed as:  
\[Z_{\text{SINS/PS}}=h-h_{\text{PS}} \tag{19}\]  
\[\mathbf{H}_{\text{SINS/DVL}}=\left[\begin{array}{cc}0_{1\times 8}&-1&0_{1 \times 6}\end{array}\right], \tag{20}\]  
where \(h\) and \(h_{\text{PS}}\) are the heights in SINS and PS, respectively. The variance matrix \(\mathbf{R}\) corresponding to measurement noise \(\mathbf{V}\) in Equation (12) is set according to the random noise in \(h_{\text{PS}}\).
## 2 Materials and Methods
### Processing and Simulation
#### 2.3.2. Ladybug Tools and Validation  
We calibrated the buildings' geometry of the built environment through the Rhinoceros 3D modeling tool [38] for the four gentrification nodes. Trees and light structure elements such as shades and louvers were excluded in the modeling process since our focus was oriented toward the spatial arrangement of the buildings and its impact on human outdoor comfort. The focus was on direct radiation and other metrological variables such as air temperature, wind, and relative humidity at specific receptor points.  
Ladybug tools are a collective free, open-source computer application that supports environmental analysis and design [39]. These tools [39] were utilized since it connects and integrates different simulation engines to assess human thermal comfort and the built environment within a specified period. The simulation period is relatively limited compared to other tools, such as ENVI-met [40]. It is clear from previously undertaken research that the Ladybug tool has an acceptable accuracy; [PERSON] et al. highlight the capacity of the tools to simulate the radiant temperature on the street level [41], [PERSON] and her colleagues computed the human thermal comfort index UTCI through ladybug [42]. Moreover, the work conducted by [PERSON] et al. proved that the workflow of ladybug tools is sufficient to generate human thermal comfort levels and other outdoor microclimate parameters in a short period with minor errors [43].  
We attempted to validate the initial computational results through hourly field measurements for air temperature and relative humidity on the 28 th of June. A portable TroTec Bc [40] measuring device was used to record the parameters mentioned above at 1.5m above ground level since this is the approximate human breathing level [44]. The device's accuracy for measuring air temperature is \(\pm 1\)\({}^{\circ}\)C, and the relative humidity is \(\pm 2\%\)[40] (see the results in Figure 11). Pearson's coefficient of determination (R\({}^{2}\)) [45] was calculated to statistically validate the predicted outputs and the onsite measurement. The coefficients' desirable values are the ones close to 1. A strong correlation was obtained for the air temperature and the relative humidity, 0.92 and 0.97, respectively.  
Figure 10. The generated weather file analysis using Autodesk Ecotect tool [33] indicating the peak summer temperature ranges on July the 21 st market in red border (author).
## 3 Results and Discussion  
### Selection of Promising Individuals  
The selection of the best individuals has been performed considering the specific application of the prototypal WPTS. The prototype and, in particular, its coils, have been designed to charge the battery of a minicar with a maximum power of 600 W. As a matter of fact, for safety reason, the coils and the power converters of the WPTS have been oversized and have been tested for up to 1 kW of output power. It is likely that a WPTS with the same architecture could be easily modified to sustain a power of 3.3 kW, which is the nominal power of the domestic grid in Italy. The control algorithm of the prototype regulates the output power to the required value but, in order to minimize the control effort, it is reasonable to implement CNs that inherently limit the oscillations of the output power due to M variation. Finally, the power transfer efficiency when the coils are aligned must be reasonably high.  
From these considerations, among the individuals obtained at the end of the optimization process, only those that satisfy the following conditions have been considered for the subsequent selection:  
1. Output power in aligned condition between 0.5 kW and 3.3 kW;
2. Ratio of maximum output power to the power in aligned condition lower than 4.5;
3. Power transfer efficiency in aligned condition higher than 0.8.  
It is worth noting that the previous conditions could not be met by any solution found by solving the third optimization problems without the constraint of having a CNr made only of capacitors.  
The values of the self- and mutual-inductances reported in Table 2 have been interpolated with a spline to evaluate their value with a position resolution of 1 mm. These latter ones, together with the reactances of the individuals that satisfy the above-mentioned conditions, have been used to compute the profiles of the transferred power as a function of the coils' relative position and some other related quantities used to evaluate the most promising CNs. In more detail, these quantities are:  
* The profile of the transferred power, \(\mathrm{P_{L}(x)}\), as a function of the receiving coil position, x;
* The transferred power in the aligned position, \(\mathrm{P_{L}(0)}\);
* The profile of the per unit (p.u.) transferred power, \(\mathrm{P_{L,p\text{-u}}(x)}\), defined as the ratio of \(\mathrm{P_{L}(x)}\) to \(\mathrm{P_{L}(0)}\);
* The maximum p.u. transferred power, \(\mathrm{P_{L,p\text{-u},max}}\);
* The maximum position, \(\mathrm{xp_{max}}\), where \(\mathrm{P_{L,p\text{-u}}(x)}\geq 1\);
* The power transfer efficiency in the aligned position;
* The maximum of the power per unit \(\mathrm{P_{L,p\text{-u},}}\), \(\mathrm{P_{L,p\text{-u},max}}\).
# Electromagnetic Imaging of Uniaxial Objects by Two-Step Neural Network
## 4 Numerical Results
### Experimental Data Verification  
We selected the training data to mirror case A and case B above, with the sole distinction being the relative permittivity of (1.3-3.3). Within these training data, 8 transmitter antennas were employed, yielding a total dataset of 15,120 (1890 \(\times\) 8) scattered fields. We trained the two-step model with noiseless synthetic data and subsequently tested the network, implementing the measured experimental data sourced from the Fresnel Institute [32]. This dataset was used to validate the proposed two-step neural network for the TM and TE cases. The experimental setup included 8 transmitter antennas and 241 receiver antennas, positioned 1.67 m apart from the source and measurement object. A horn antenna was used to measure the scattered field, resulting in receivers not being evenly distributed around the transmitter. We specifically selected the FoamDieelExt dataset for both the TM and TE cases. This dataset comprises large (SAITEC SBF 300) and small (Berlon) cylinders with diameters of 80 mm and 31 mm, respectively, and dielectric coefficients of \(\varepsilon_{r}=1.45\pm 0.15\) and \(\varepsilon_{r}=3\pm 0.3\) (ref. Figure 11). Here, \"\(\pm\)\" denotes the uncertainty range surrounding the experimental values, and \(D_{e}\) represents the diameter. In our simulation, the scatterer was placed within a 320 \(\times\) 320 mm domain. A 3 GHz wave frequency was incident in both the TM and TE scenarios. The experimental data were normalized using the simulated scattered field received from the opposite side of the incident angle.  
Figure 10: Reconstruction results of dielectric distribution between 7.5 and 8 by TE case with 5% noise. (**a**) Ground truth; (**b**) reconstructed image.  
Figure 9: Reconstruction results of dielectric distribution between 7.5 and 8 by TM case with 5% noise. (**a**) Ground truth; (**b**) reconstructed image.  
The reconstruction outcomes through the experimental data implementing the two-step neural networks for the TM and TE cases are illustrated in Figure 12. Table 4 displays the corresponding RMSE and SSIM values of satisfactory levels for 2 cases. Based on these results, we assert that our proposed method has been validated by the experimental data. Additionally, the two-step neural network proves to be more adept at handling highly nonlinear problems in either case.
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
## 5 Results
### Non-Line-of-Sight Environment  
The results of the throughput measurement in the NLOS environment are shown in Figure 10. For the chip-to-AP link and the Augmented MIMO link, three trials of 100 s each were performed in alternating order, as in the LOS scenario measurement.  
The measured throughput of the chip-to-AP link in the line-of-sight (LOS) environment was 27.5 Mb/s, 24.7 Mb/s, and 23.9 Mb/s on average for each trial. Augmented MIMO achieved average throughputs of 33.3 Mb/s, 34.2 Mb/s, and 43.4 Mb/s. Thus, Augmented MIMO increased the throughput by a factor of about 1.5 on average in the NLOS environment.
## 3 Results and Discussion
### Mechanical Properties of BSMs-PDADMAC Films
Within the interval 0 < W < W\({}_{\mathrm{in}}\), increasing PDADMAC content has no noticeable influence on the elastic modulus and ultimate strain of BSMs-PDADMAC films. With the further increase in W, marked growth in E\({}_{0}\) takes place whereas \(\varepsilon_{f}\) falls down. For \(\sigma_{f}\) = \(f\)(W) plot, the behavior similar to that for E\({}_{0}\) = \(f\)(W) was observed (data are not shown). The W\({}_{\mathrm{in}}\) value (0.04-0.06) is in a good agreement with the value of W\({}_{\mathrm{sat}}\) = 0.036, at which the saturation of the BSMs-PDADMAC complex takes place. Thus, a transition from the negative charge in the BSMs system to the positive charge in the saturated BSMs-PDADMAC complex has a negligible effect on the mechanical properties of the films. In both cases, the properties are determined by the BSM component, whose weight content remains practically constant and close to the maximum (0.96-1). A sharp growth in the rigidity of BSMs-PDADMAC films (increasing elastic modulus and strength and decreasing ultimate strain) is controlled by accumulation of unbound polycation in the formulation when PDADMAC content exceeds the W\({}_{\mathrm{sat}}\) value.
## 4 Results and Discussions
### Experimental Results and Comparison
#### 4.3.2 Results Comparison on the Double Diode Model
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Min** & **Max** & **Mean** & **Std. dev.** \\ \hline GGHS & \(9.8635\times 10^{-4}\) & NA & NA & NA \\ GOTLBO & \(9.83177\times 10^{-4}\) & \(1.78774\times 10^{-3}\) & \(1.24360\times 10^{-3}\) & \(2.09115\times 10^{-4}\) \\ CARO & \(9.8260\times 10^{-4}\) & NA & NA & NA \\ IJAYA & \(9.8293\times 10^{-4}\) & \(1.4055\times 10^{-3}\) & \(1.0269\times 10^{-3}\) & \(9.8625\times 10^{-5}\) \\ PS & \(1.5180\times 10^{-2}\) & NA & NA & NA \\ SA & \(1.9000\times 10^{-2}\) & NA & NA & NA \\ ANS & \(1.0042\times 10^{-3}\) & \(1.4456\times 10^{-3}\) & \(1.1337\times 10^{-3}\) & \(9.9500\times 10^{-5}\) \\ BLPSO & \(1.5704\times 10^{-3}\) & \(2.5312\times 10^{-3}\) & \(2.0554\times 10^{-3}\) & \(2.0186\times 10^{-4}\) \\ CSO & \(1.7013\times 10^{-3}\) & \(2.7735\times 10^{-3}\) & \(2.2421\times 10^{-3}\) & \(2.2059\times 10^{-4}\) \\ CTLA & \(1.3216\times 10^{-3}\) & \(3.1002\times 10^{-3}\) & \(2.0145\times 10^{-3}\) & \(4.0895\times 10^{-4}\) \\ LWOA & \(1.3120\times 10^{-3}\) & \(1.3387\times 10^{-2}\) & \(3.5838\times 10^{-3}\) & \(2.6270\times 10^{-3}\) \\ SOS & \(9.8518\times 10^{-4}\) & \(1.3498\times 10^{-3}\) & \(1.0627\times 10^{-3}\) & \(9.6141\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: RMSE results for the double diode model. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Item**} & \multirow{2}{*}{\(\boldsymbol{V_{\text{L}}}\) **(V)**} & \multirow{2}{*}{\(\boldsymbol{I_{\text{L}}}\) **Measured (A)**} & \multicolumn{6}{c}{\(\boldsymbol{I_{\text{L}}}\) **Calculated (A)**} \\ \cline{3-10}  & & & **ANS** & **BLPSO** & **CSO** & **CTLA** & **LWOA** & **SOS** \\ \hline  
\hline  & \multicolumn{2}{c}{SIAE} & \multicolumn{2}{c}{0.0189} & \multicolumn{2}{c}{0.0283} & \multicolumn{2}{c}{1.6176} & \multicolumn{2}{c}{0.0789} & \multicolumn{2}{c}{0.0247} & \multicolumn{2}{c}{0.0182} \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Fitting results for the double diode model. Figure 5: Extraction results by SOS for the double diode model. (**a**) Current; (**b**) power.