esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experiments
### Experiment 3--Reconstruction of Liver Tumor
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \begin{tabular}{c} **Surface** \\ **Reconstruction** \\ **Method** \\ \end{tabular} & \begin{tabular}{c} **Mean** \\ **Distance** \\ \end{tabular} & **std** & **rms** & \begin{tabular}{c} **Hausdorff** \\ **Distance** \\ \end{tabular} & \begin{tabular}{c} **Medial** \\ **Distance** \\ \end{tabular} & \
\begin{tabular}{c} **Area** \\ **Difference** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Volume** \\ **Difference** \\ **(\%)** \\ \end{tabular} \\ \hline \multirow{4}{*}{User 2} & BPA & 0.606759 & 0.709767 & 0.932945 & 2.85121 & 0.304864 & \(-\)2.72 & 119.87 \\  & Power Crust & 0.565943 & 0.586972 & 0.814723 & 2.37748 & 0.38321 & 11.60 & \(-\)25.79 \\  & Poisson & 1.04422 & 0.845751 & 1.34295 & 3.33231 & 0.737621 & 44.81 & 13.57 \\  & VSR & 0.199237 & 0.253187 & 0.321874 & 1.31926 & 0.0916052 & 19.80 & -9 \\ \hline \multirow{4}{*}{User 3} & BPA & 0.179038 & 0.311993 & 0.359301 & 1.56732 & 0 & 6.94 & \(-\)94.72 \\  & Power Crust & 0.196016 & 0.31971 & 0.374599 & 1.2938 & 0.0102139 & 16.88 & \(-\)25.98 \\  & Poisson & 0.66439 & 0.61721 & 0.906199 & 3.37821 & 0.492152 & 90.58 & 23.22 \\  & VSR & 0.0703013 & 0.123795 & 0.142199 & 0.718006 & 0.019699 & 23.09 & \(-\)6 \\ \hline \multirow{4}{*}{User 4} & BPA & 0.542069 & 1.06084 & 1.13987 & 5.79188 & 0.153002 & 1.63 & 345.95 \\  & Power Crust & 1.68e + 009 & 3.75e + 009 & 4.10e + 009 & 1e + 010 & 13.5631 & \(-\)100 & \(-\)100 \\ \cline{1-1}  & Poisson & 1.85742 & 1.97787 & 2.71109 & 8.48791 & 0.996965 & 91.69 & 1140.05 \\ \cline{1-1}  & VSR & 0.166469 & 0.265851 & 0.313325 & 1.31816 & 0.0547988 & 18.85 & \(-\)11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Difference between the reconstructed surfaces from the contours drawn by Users 2, 3, and 4 and the reconstructed surface from the contours drawn by User 1 (LIVER TUMOR). Figure 12: Surface reconstruction of a hepatic tumor by four methods. The numbers of contours are the same in different rows, but the contours were drawn by different users: **(a)** User 1; **(b)** User 2; **(c)** User 3; **(d)** User 4. For each row, from left to right: the contours used, the surface reconstructed by BPA, the surface reconstructed by Power Crust, the surface reconstructed by Poisson, and the surface reconstructed by VSR. It should be noted that the Hausdorff distance of the Power Crust method is missing in Figure 13 since its value is much greater than that of the other methods. * In all cases, the surface reconstructed by the VSR method best visually resembles the original surface compared with the other methods. All metrics show that the VSR-reconstructed surfaces show fewer differences from the surface reconstructed from the contours drawn by User 1 compared with the other methods, which indicates that the reproducibility of the VSR method is the best of four methods.
# The BCL Method for DSSC: Basis and Applications +
## 4 A Big Challenge: Understanding the Absorption Band of Ruthenium Dyes
### Results
#### 4.1.4 Analisys of MLCT Band
This function is defined through Equation (12):  
\[f(\beta)=g_{\beta}\cdot\rho_{T}=k_{\beta} \tag{12}\]  
Consequently it is possible to calculate the delivered energy to a specific fragment in the hot excited state through Equation (13)  
\[E_{D}(\beta)=E_{A}f(\beta) \tag{13}\]  
Calculated values for amount of the absorbed energy channelled to the B and C fragments is displayed in Figure 12.  
Three interesting remarks can be obtained through the analisys of this figure. First, the absorbed eenergy increases with the number of chromophoric ligands coordinated to the metal center: C2 > C1 and C4 > C3. C4 has the higher capacity to absorb sunlight, the same conclution obtained before through the analisys of extintion molar coeficients. Nevetheless, interestingly, the amount of delivered energy to the dicarboxibipirydine group (B) is independent of the absorbed energy, while increasing the number of chromophoric ligands does not imply an increase in delivered energy to this fragment. The reason for this is that the chromophoric ligand compites with dicarboxibipirydine to concentrate absorbed energy. In this sense, the C3 complex appears to be the more effective.
## 2 Related Work
### Overview of Existing Solutions
Therefore, incorporating these elements into the decision-making process for service placement can optimize the user experience, improve customer satisfaction, and provide a more comprehensive response to the various constraints and needs of the system. This need for context-aware, intelligent placement strategies has also  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref. ** & **Use of Fog** & **Dynamic** & **Mobility** & **Latency** & \ \begin{tabular}{c} **Recommendation** \\ **System** \\ \end{tabular}  & \begin{tabular}{c} **History and** \\ **Preferences** \\ \end{tabular}  & **Simulation** \\ \hline
[17] & ✓ & ✓ & ✗ & ✓ & ✗ & ✗ & ✓ \\
[18] & ✓ & ✓ & ✗ & ✓ & ✗ & ✗ & ✓ \\
[19] & ✓ & ✗ & ✗ & ✓ & ✗ & ✗ & ✓ \\
[20] & ✗ & ✗ & ✗ & ✓ & ✗ & ✗ & ✗ \\  
been recognized in other IoT domains. Recent interdisciplinary surveys, such as the one by [PERSON] and [PERSON] [33], emphasize the importance of hybrid AI models and intelligent decision making in dynamic, data-intensive IoT environments. Although focused on agriculture, their findings are relevant to Fog Computing, where similar challenges such as resource constraints, real-time responsiveness, and user context awareness also arise. Another critical observation is the limited use of simulation tools in some studies, where optimization solvers have been employed instead, such as in the work [20]. However, simulation is a crucial step in research for several reasons. Firstly, it provides essential insights by enabling performance evaluation across various scenarios and metrics, such as latency and response time. Secondly, it provides flexibility to explore various scenarios and approaches. Additionally, simulation is a cost-effective and practical alternative to assess the performance of a system in a virtual environment. Future research efforts should focus on methods that integrate user mobility, historical patterns, and contextual surroundings into the service placement process. These factors can significantly influence the quality and efficiency of service placement on available resources, providing a comprehensive framework to meet the diverse constraints and needs of Fog Computing environments. Additionally, the lack of focus on meeting user-requested deadlines remains a persistent challenge, highlighting the need to improve latency to meet the stringent demands of real-time applications.
# Advanced Physical-Layer Technologies for Beyond 5G Wireless Communication Networks
## 2 Special Issue Overview  
The motivation behind this Special Issue (SI) of MDPI SENSORS was to consolidate all the stakeholders, and the entirety of academia and industry, and solicit the cutting-edge original research to identify and discuss the latest concepts, innovations, standards, potential use cases, open research problems, technical challenges, and promising solution methods in B5G/6G systems from the perspective of the physical layer. Accepted papers in this SI covered a wide range of research in the broader domain of physical-layer technologies to be implemented in the B5G/6G era. Topics of interest for this SI include:  
* Massive MIMO systems, such as advanced massive MIMO, cell-free massive MIMO, and beamspace massive MIMO.
* Adaptive signal processing, and analytical modeling and design.
* Novel modulation and coding techniques.
* Radio resource and interference management.
* Communications over terahertz (i.e., 0.1-10 THz), and mmWave (i.e., 30-300 GHz) bands.
* Emerging networks such as wirelessly powered, aerial-access (e.g., unmanned aerial vehicles, satellites, etc.), vehicular ad hoc, and cooperative relaying networks.
* Advanced solutions, such as NOMA, RISs, joint sensing, and communication.
* Implementation and challenges of physical-layer security.
* Licensed or unlicensed spectrum interoperability and dynamic spectrum allocation.
* Requirements and solutions for the 5G NR/6G physical layer.
* Energy-efficient operations (e.g., energy harvesting, wireless power transfer).
* Internet of Everything.
* Mobile edge/fog computing.
* AI techniques and ML/deep-learning algorithms for physical-layer design and optimization, including MIMO signal detection, real-time channel estimation, multiuser detection, and channel modeling and propagation.  
The theme of the SI is reflected in the accepted papers; 25 papers were submitted, out of which only the top 15 research articles were accepted and published.
Given an initial guess \(\mathbf{x}_{0}\) for an iterative scheme, it follows from Equation (1) a residual vector \(\mathbf{r}_{0}=\mathbf{b}-\mathbf{A}\mathbf{x}_{0}\). Knowing \(\mathbf{r}_{0}\), one attempts to search a descent vector \(\mathbf{u}\), which can correct the solution to \(\mathbf{x}=\mathbf{x}_{0}+\mathbf{u}\) to abide the rule of \(\|\mathbf{r}\|<\|\mathbf{r}_{0}\|\). The residual and descent vectors are two very fundamental concepts in the formulation of the iterative schemes. We will point out that the purpose of extrapolation techniques [32; 33; 34; 35] is designed a feasible manner to search a better descent vector basically, such that the residuals can be reduced quickly. In the paper we will unify the splitting iteration methods, and simplify the extrapolation techniques from the formulation in terms of descent and residual vectors. The new ideas in this formulation to solve the linear system are developed by improving the existent splitting iteration methods, of which the maximization technique and the spectral analysis of iteration matrix are concerned with. Absolute convergence of the new splitting iterative scheme (NSIS) is proven.
# Dynamic Response of Reinforced Recycled Aggregate Concrete Pavement under Impact Loading
## 3 Experimental Results and Discussion
### Strain Due to Impact Load  
The material strain was detected by five strain gauges applied to the concrete and by three strain gauges applied directly to the reinforcement steel. Figure 13a shows the strain evolution of the 7 MR specimen, used as the reference specimen in comparison with all other specimens. Figure 13b shows a zoomed detail of the graphs under first impact. After the impact, a compressive strain of the concrete on the top can be seen, while a tensile strain of the concrete on the bottom can be observed. The C1 strain gauge was placed in central impact region and was disrupted about 2.0 ms after the first contact of the drop-weight. The failure of the C2 strain gauge could be determined from the horizontal plateau of strain-time history in Figure 13a. The C4 strain on the bottom was very small, indicating that, for the specimen with four free edges, the strain in the corner area of the RCP specimen could be ignored during the impact process, something which could be confirmed by the sketched cracks profiles in Figure 9. The compression strain of C5 on the top lasted for about 30 ms. At 14 ms, the maximum compressive strain of C5 reached about \(-2248\)\(\upmu\). Corresponding to the compression strain of the concrete mentioned above, the peak value of the tensile strain of S1 was measured 10,292 \(\upmu\). Regarding the strain of the reinforcement steel, the tensile strain decreased with the increase in distance from the impact area.  
Figure 12: Deflected shapes of the (**a**) 6 MR specimen, (**b**) 7 MR specimen, (**c**) 8 MR specimen, (**d**) 7 MN specimen, and (e) 7M1R specimen.  
In this paper the steel strain of different RCP specimens was compared by means of the strain of the S1, as shown in Figure 14. For 6 MR, 7 MR and 8 MR specimens, the strain values of the S1 strain showed a similar behavior, a rapidly increasing tensile strain followed by a very sharp drop. It can be observed that, with the increase in thickness, the peak S1 strain decreased, and the duration of the tensile strain shortened. For 7 MN, the strain-time history showed a slower and smoother strain evolution than did the other specimens. In terms of peak strain values, the difference between the 7 MR and the 7 MN was 41.5%, while the difference between the 8 MR and the 7 MN was fairly small. It can be seen from Figure 14 that the steel strain of the 7M1R specimen was a compressive strain in the first 2.5 ms after the impact. This effect is indicative of the local material behaviors due to the impact, which is also described in References [32]. After a short duration of 4.0 ms, the S1 strain changes from compressive strain into tensile strain, indicating the transition from local deformation to global flexural behaviors. The more longitudinal reinforcement, the greater the decrease of tensile strain and the smoother the curve shape.
## 2 Radiated Coupling and Active Noise Cancellation
The rest is correctly estimated for the original signal using only a few samples from the beginning. The internal signals can be canceled accurately using this technique by analyzing the mixed signal of the IEMI and active signals. The active cancellation algorithm using MPM can be summarized as follows:  
Figure 3: Signal estimation using the Matrix Pencil Method (MPM). Figure 2: Power inside the cavity amplified due to frequency differences. Step 1. Measure the amplitude, frequency, phase of the IEMI, and apply small active power. Step 2. Measure the sensor power signal \(S_{obv}\) of IEMI at the position to cancel. Step 3. Use MPM to calculate the magnitude, phase, and frequency difference of the mixed-signal. Step 4. Apply the value calculated in step 3 to the internal active power source  
Step 5. Determine whether the internal electromagnetic field is reduced below the standard value. If the standard value is not satisfied, then repeat from Step 2. Figure 4 shows the result of analyzing the signal using the MPM and canceling the internal field using the result. This method has the advantage of canceling the internal IEMI by analyzing the signal through the MPM, but there is the time taken to analyze the MPM, that is, the \"blind time.\" During this time, the friendly equipment is unprotected from external attacks, and this attack can even be amplified. Therefore, reducing this time is very important for electronic protection. To reduce this time, the time to analyze the signal must be reduced, and a faster algorithm than the conventional MPM is required.
## 6 Experimental Results and Discussion
### Waveform Test on DIFRT  
The DIFRT method requires the camera to capture the in-pipe image. Then this image is converted to a grayscale image as the test original image. After compression, quantization, data frame synthesis, and modulation, these test images could be transmitted in the form of binary rectangular waves. As illustrated in Figure 10, part of the image data frame transmitted in the water pipe was recorded. The image transmission rate in this experiment was set 50 kbps. Moreover, the PWM method was used for modulation, the digital symbols '1' and '0' were defined based on the pulse width in one period. When a symbol '0' was sent, it took a total of 20 us, including 16 us low level and 4 high level. For symbol '1', it required 12 us low level and 8 us high level in the same period of 20 us. The green waves indicated the original image data sent by the transmitter, while the purple waves in Figure 10a-c represent the signals received by the first, second, and final VLRC node, respectively. From observing Figure 10a, we could observe the purple wave was able to maintain a good synchronization and small distortion from the green wave. That means the light signal could keep relatively high transmission quality in the 1 transmission channel although the effective voltage of the signal decreased slightly from 1 V to 980 mV. Compared with the original image signal, the purple waves in Figure 10b had some deformation and distortion due to the rising time delay of the second VLRC node and channel noise. The purple wave in Figure 10c demonstrated that a little waveform distortion from the receiver (second VLRC node) is the source of bit error rate which would have a certain influence on the transmission quality. Moreover, the effective voltage of the signal decreased to 320 mV.  
Figure 9: (**a**) PSNR performance and (**b**) SSIM performance for AISRT test.
# 3 GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes
## 3 QoS Analysis
### Link Reliability QoS Parameter
The main observations from Figures 7 and 8 are that at low SNR values (1-5 dB) the BER values are so low that the addition of additional wireless interference due to traffic congestion does not affect either the reliability in the receiver (the BER values) or the throughput values. However, in the medium SNR values (between 5 and 7 dB), the influence of traffic density is most evident in BER and throughput performance. Specifically, in 6 dB SNR value an increase in the BER values at about \(10^{-2}\) dB is presented. In terms of throughput, also, an exponential decrease is presented in throughput values (from 11.5 Mbps to 7.3 Mbps) for the corresponding 85 to 105 traffic density values and a SNR value of 7 dB. These SNR values can be considered as threshold values, in which the desirable data rate exceeds the maximum channel capacity between transmitter and receiver. Of course, solutions for such problems have been proposed in the literature, in which the transmitter adapts accordingly the transmitting power in order to incorporate the wireless interference in the area [32]. However, these solutions are beyond the scope of this paper. Figure 6: BER vs. SNR for GEMV channel, 6 different vehicle density values, 12 Mbps data rate, 60 km/h vehicle speed, different decoding algorithms, 3 iterations, frame length of 512 bits. Figure 8: Throughput vs. Vehicle density for GEMV channel, 3 different SNR values, 12 Mbps data rate, 60 km/h mobile terminal speed, SOVA algorithm, 3 iterations, frame length of 512 bits. Figure 7: BER vs. Vehicle density for GEMV channel, 6 different SNR values, 12 Mbps data rate, 60 km/h mobile terminal speed, SOVA algorithm, 3 iterations, frame length of 512 bits. Finally, for large SNR values (8-10 dB), the desirable data rate can be supported by the channel capacity corresponding to these SNR values. In conclusion, the obtained results show that the required system QoS affects the number of the nodes that are located within the communication range of the pair transmitter\(-\)receiver and also the exact location of these additional nodes. Decreasing the number of interfering nodes that are located within the communication range of the transmitter\(-\)receiver pair, packet loss is also decreased due to channel conditions.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 3 Inform@Risk Monitoring System
### Inform@Risk LoRa Base Module
For all other values, usually mean values can be chosen. The calculation is done by the microprocessor to save power and on-air time during LoRa communication, which uses the most power. With an average sleep interval of 15 min, the measured lifetime should be at least two years for the basic infrastructure node with six AA batteries (daily power consumption at 3.3 V is ca. 5-6 mAh). After the measurement, calculations and LoRa-uplink, the device receives an optional downlink from the gateway. This can be one of several predefined commands such as setting the looptime (time taken for measurements and time the device is asleep), changing measurement duration or activating/deactivating individual sensors on the device. After receiving commands, the device makes the changes and goes into a sleep mode, in which current consumption is minimized. More information on the firmware can be found in the documentation provided on the Github page (Section 6).
## 3 Attitude Controller Design
### Attitude Error Model  
Without loss of generality, the reference attitude is \(\eta_{2d}=\eta_{2d}(t)\), therefore, the tracking error is  
\[\tilde{\eta}_{2}=\eta_{2}-\eta_{2d},\tilde{\
u}_{2}=\
u_{2}-J_{2}^{-1}(\eta_{2 })\dot{\eta}_{2d} \tag{33}\]  
We define the energy function, which is the sum of the potential and kinetic energy, as  
\[H(\tilde{\eta}_{2},\tilde{\
u}_{2})=\frac{1}{2}\tilde{\eta}_{2}^{\top}K_{1} \tilde{\eta}_{2}+\frac{1}{2}\tilde{\
u}_{2}^{\top}I^{2}\tilde{\
u}_{2} \tag{34}\]  
where \(K_{1}=K_{1}^{\top}>0\) (positive definite matrix), \(I=I_{0}+I_{A}=[I_{1}^{\top},I_{2}^{\top},I_{3}^{\top}]^{\top}\).  
The partial derivative of \(H\) are  
\[\frac{\partial H^{\top}}{\partial\dot{\eta}_{2}}=K_{1}\tilde{\eta}_{2} \tag{35}\]  
\[\frac{\partial H^{\top}}{\partial(I\tilde{\
u}_{2})}=I\tilde{\
u}_{2} \tag{36}\]  
Then, the derivatives of the reference attitude (33) are  
\[\dot{\tilde{\eta}}_{2} =\dot{\eta}_{2}-\dot{\eta}_{2d} \tag{37}\] \[=J_{2}(\eta_{2})\
u_{2}-J_{2}(\eta_{2})(\
u_{2}-\tilde{\
u_{2}})\] \[=J_{2}(\eta_{2})\tilde{\
u_{2}}\] \[=J_{2}(\eta_{2})I^{-1}I\tilde{\
u_{2}}\] \[=J_{2}(\eta_{2})I^{-1}\frac{\partial H^{\top}}{\partial\tilde{ \
u_{2}}}\] \[\dot{\tilde{\
u}}_{2} =\dot{\
u}_{2}-\dot{J}_{2}(\eta_{2})\dot{\eta}_{2d}-J_{2}(\eta_{2 })\dot{\eta}_{2d} \tag{38}\]The product of \(I\) and \(\hat{\
u}_{2}\) is  
\[\begin{split} I\hat{\
u}_{2}=& I\hat{\
u}_{2}-I\hat{ \
u}_{2}(\eta_{2})\hat{\eta}_{2d}-I\hat{J}_{2}(\eta_{2})\hat{\eta}_{2d}\\ =& S(\
u_{2})I\
u_{2}-D\
u_{2}+\tau_{m}-I\hat{J}_{2} (\eta_{2})\hat{\eta}_{2d}-I\hat{J}_{2}(\eta_{2})\hat{\eta}_{2d}\\ =& S(\
u_{2})I\hat{\
u}_{2}+S(\
u_{2})IJ^{-1}(\eta_{ 2})\hat{\eta}_{2d}-D\
u_{2}+\tau_{m}-I\hat{J}_{2}(\eta_{2})\hat{\eta}_{2d}-I \hat{J}_{2}(\eta_{2})\hat{\eta}_{2d}\\ &+I^{-1}J_{2}^{\top}(\eta_{2})K_{1}\hat{\eta}_{2}-I^{-1}J_{2}^{ \top}(\eta_{2})K_{1}\hat{\eta}_{2}\\ =& S(\
u_{2})\frac{\partial H^{\top}}{\partial\hat{ \
u}_{2}}-I^{-1}J_{2}^{\top}(\eta_{2})\frac{\partial H^{\top}}{\partial\hat{ \eta}_{2}}+\tau_{m}-\mathcal{M}+\kappa\end{split} \tag{39}\]  
where  
\[\mathcal{M}=D\
u_{2} \tag{40}\] \[\kappa=S(\
u_{2})IJ_{2}^{-1}(\eta_{2})\hat{\eta}_{2d}-I\hat{J}_{2} (\eta_{2})\hat{\eta}_{2d}-I\hat{J}_{2}(\eta_{2})\hat{\eta}_{2d}+I^{-1}J_{2}^{ \top}(\eta_{2})K_{1}\hat{\eta}_{2} \tag{41}\]  
in which \(\hat{J}_{2}(\eta_{2})\) can be obtained as follows  
\[J_{2}(\eta_{2})=-S(\
u_{2})J_{2}(\eta_{2}) \tag{42}\]  
Then, the attitude error system is  
\[\begin{bmatrix}\hat{\eta}_{2}\\ I\hat{\
u}_{2}\end{bmatrix}=\begin{bmatrix}0_{3\times 3}&J_{2}(\eta_{2})I^{-1}\\ -I^{-1}J_{2}^{\top}\eta_{2}&S(\
u_{2})\end{bmatrix}\begin{bmatrix}\frac{ \partial H^{\top}}{\partial\hat{\eta}_{2}}\\ \frac{\partial H^{\top}}{\partial\hat{\eta}(I\hat{\rho}_{2})}\end{bmatrix}+ \begin{bmatrix}0\\ \tau_{m}-\mathcal{M}+\kappa\end{bmatrix} \tag{43}\]  
The system states are \(\hat{\
u}_{2}\) and \(I\hat{\
u}_{2}\).  
The \(\eta_{2}\) state will follow \(\eta_{2d}\) if the system (43) converges to the zeros.