esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Well logging is a significant procedure that assists geophysscists and geologists with making predictions regarding boreholes and efficiently utilizing and optimizing the drilling process. The current study area is positioned in the Punjab Territory of Pakistan, and the geographic coordinates are 30020\({}^{\prime}\)10 N and 70043\({}^{\prime}\)30 E. The objective of the current research work was to interpret the subsurface structure and reservoir characteristics of the Kabirwala area Tola (01) well, which is located in the Punjab platform, Central Indus Basin, utilizing 2D seismic and well log data. Formation evaluation for hydrocarbon potential using the reservoir properties is performed in this study. For the marked zone of interest, the study also focuses on evaluating the average water saturation, average total porosity, average effective porosity, and net pay thickness. The results of the study show a spotted horizon stone with respect to time and depth as follows: Dunghan formation, 0.9 s and 1080.46 m; Cretaceous Samana Suk formation, 0.96 s and 1174.05 m; Datta formation, 1.08 s and 1400 m; and Warcha formation, 1.24 s and 1810 m. Based on the interpretation of well logs, the purpose of petrophysical analysis was to identify hydrocarbon-bearing zones in the study area. Gamma ray, spontaneous potential, resistivity, neutron, and density log data were utilized. The high zone present in the east-west part of the contour maps may be a possible location of hydrocarbon entrapment, which is further confirmed by the presence of the Tola-01 well.  
2D seismic interpretation; contour mapping; petrophysical analysis; porosity +
Footnote †: journal: Applied Sciences  
1
## 3 Results and Discussion
### Test Results of the Application Rate Measuring System  
Figure 10 shows the model ratio coefficient \(k\) of the calibrated relationship model obtained by the application rate calibration system in the three test stages for each of the target application  
Figure 8: Dynamic cumulative application rate of fertilizing and seeding.  
Figure 9: Relationship between cumulative rotation cycles and cumulative application rate.  
rates. Figure 11 shows the absolute relative deviation after each test between the model ratio coefficient \(k\) measured by the application rate calibration system and the original calibration parameters. The relative deviation \(E_{k}\) defined in Equation (4) is used to describe the change of relationship model parameters. The relevant statistical data are listed in Table 2.  
\[E_{k}=\left|\frac{k_{real}}{k_{org}}-1\right|\times 100\%, \tag{4}\]  
where \(k_{real}\) is the model ratio coefficient measured by the application rate calibration system and \(k_{org}\) is the ratio coefficient of the original relationship model.  
Figure 11: Relative deviation changing the trend of the model ratio coefficient.  
Figure 10: Model ratio coefficient as monitored by the application rate measuring system.  
From the test results, it can be observed that the calibration relationship model was relatively stable for a short period. However, the model parameters for the fertilizing varied more obviously than those for seeding, probably due to the fertilizers being more prone to moisture absorption and their working performance being affected easily by environmental changes. The relative deviation of model parameters increased significantly with usage-time increase or environmental changes.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 3 Methodology
### Overview of the Improved Algorithm
#### 3.2.2 Cloud Masking  
In the STM method, the cloud-contaminated pixels usually have a negative effect on the anomaly detection results. It was therefore necessary to remove the cloud-covered pixels from the IRS images. Clouds have strong reflectivity in NIR band and low BTs in LWIR band. The following Equations are the rules for extracting clouds [26]:  
\[R_{5}>0.4 \tag{7}\]  
\[T_{8}<285\mathrm{K} \tag{8}\]  
where \(R_{5}\) is the reflectance in band 5, and \(T_{8}\) is BT in band 8 of the IRS image. The pixels that satisfied both conditions were masked off as clouds.
## 2 Data and Segmentation Methods
### Dataset
#### 2.1.2 Brain Dataset  
Dataset consisted of 22 MRI T1 weighted brain scans from different patients and each scan contained 257 sagittal slices of human brain. The pictures had resolution 400 \(\times\) 400 pixels and were provided in PNG format. MRI data were provided by the Department of Radiology from The University Hospital Brno and all original slices were labelled by two independent human experts resulting into two sets of ground truth masks suitable for semantic segmentation labeling all pixels either as brain tissue (white) or non-brain tissue (black). Patients involved were in the age of 35-55 years old both male and female and were in good physical condition. Data have been anonymized and approved for research and scientific purposes.  
The first expert labelled the data very precisely and accurately and these labels are used as a reference data for training and evaluation. The second expert labelled the data as it is done regularly on everyday basis in medical praxis. These data are used for comparing the algorithm accuracy to accuracy of a human expert segmentation. An example of reference ground truth mask and mask labelled by our system can be seen in Figure 2.
# Neural Network DPD for Agramizing SM-VCSEL-SSMF-Based Radio over Fiber Link Performance
## 3 Comparison with Volterra Method
### Estimation Algorithm  
The estimation starts with collecting the coefficients, e.g., \(c_{kq}\), \(d_{kqr}\) and \(e_{kqr}\), into a R \(\times\) 1 vector \(\mathbf{v}\). \(R\) represents the total number of coefficients. \(\mathbf{v}\) is associated with a signal whose time is sampled over the same period. Coefficients \(c_{21}\) are associated with signal \(x(n-1)|x(n-1)|^{2}\). \(\mathbf{Z}\) represents the collection of all such vectors into a \(N\times\ R\) matrix. Upon convergence, the output of the predistorter training block becomes \(z_{p}(n)=x(n)\), and hence, \(z(n)~{}=~{}u(n)\). For a total number of samples equal to \(N\), the output can be written as follows:  
\[z_{p}=Zv \tag{8}\]  
Figure 5: Schematic of indirect learning architecture for the Memory Polynomial (MP)/ Generalized Memory Polynomial (GMP)-based DPD methodology.  
where \(\mathbf{z}=\left[z(1),...\ z(N)\right]^{T}\) and \(z_{p}=\left[z_{p}(1),...\ z_{p}(N)\right]^{T}\), while \(\mathbf{v}\) is a \(R\)\(\times\)\(1\) vector that contains the set of coefficients \(c_{kq}\), \(d_{kqr}\) and \(e_{kqr}\). The LS solution is the solution for the following equation expressed as:  
\[\left[Z^{H}Z\right]\hat{\sigma}=Z^{H}x \tag{9}\]  
The LS solution that minimizes the cost function is expressed as  
\[C=\sum_{n=1}^{N}\left|z_{p}(n)-x(n)\right|^{2} \tag{10}\]
## 1 Introduction
### Applicable Cycles  
The three cycles pertaining to this study are extensively described in [3]. Figures 1-3 show the cycle schematics for the SCR, ICR, and IC, respectively. All cycles include compressor(s) and a turbine as part of the turbomachinery, the precooler, and the reactor. However, the recuperator, which is used to provide heat exchange from the turbine outlet hot gas to the High Pressure (HP) coolant, only features in the SCR and ICR. The IC has a Low Pressure (LP) region, which is maintained by the Low Pressure Compressor (LPC) prior to the coolant entering an intercooler. The intercooler reduces the coolant temperature to the same as the cycle inlet temperature (\(T_{1}\)) before it enters the High Pressure Compressor (HPC), whereby temperature increase is achieved at the exit of the HPC. The ICR also employs an intercooler and a second compressor albeit at a modest Overall Pressure Ratio (OPR), which is split between both compressors. The modest pressure ratio is due to the heat exchange provided by the recuperator. This is not the case with the SCR as it only utilises a single compressor and no intercooler but has the benefit of raising the reactor Core Inlet Temperature (CIT) due to the recuperator, meaning the cycle only requires a low Pressure Ratio (PR). The other notable difference are the cycle efficiencies. The ICR and IC increase the Specific Work (SW) and Useful Work (UW) by reducing the work required by the compressors. This means that the ICR has an efficiency which is \(\sim\)3% greater than the SCR and 6.6% greater than the IC when optimised turbine cooling methods are considered [3]. However, the ICR has a disadvantage in that the increased capacity, due to the number of components, adds complexity to the plant configuration. On the other hand, the IC offers a simpler component configuration than the ICR but it requires reactor Core Outlet Temperatures \(\geq\)1000 \({}^{\circ}\)C to significantly improve the efficiency, which will make it competitive. This was investigated as part of this research work and is documented in [3]. The benefits of using helium as opposed to air are documented in [4; 5; 6] and provide a good theoretical platform for off-design operation, control, and transient operation of a helium nuclear gas turbine plant.  
Figure 1: Typical Simple Cycle Recuperated (SCR) [7]. Single shaft spool connection to the turbomachinery which includes a precooler, reactor, and recuperator. The schematic shows 6 stations.
## 7 Conclusions  
In this paper, a signal structure based on the aperiodic long code spread spectrum is designed to improve the concealment and security of communication based on a transparent forwarding point-to-point satellite communication system. It can achieve reliable signal acquisition without special physical layer synchronization overhead, which can effectively shorten signal transmission time and improve the concealment of communication. The receiver is designed based on a matched filter with high-power clock multiplexing to reduce hardware complexity and hardware resource consumption. In addition, the decision threshold is estimated based on the auxiliary channel in real time to realize the adaptive synchronous decision in the noisy environment and ensure the acquisition success probability. The simulation results show that under typical short burst conditions, the transmission time of a burst signal can be shortened by up to 50% compared with the matching filter acquisition method with synchronization cost, and the acquisition success probability can reach more than 99.99% when Eb/N0 = 3 dB. In this paper, xc7k325 tfg676-2 hardware is used to simulate on the Vivado platform, and the hardware resource consumption of the capture matching filter can be reduced by more than 40%.  
In future work, we will study the acquisition technology of the burst spread spectrum satellite communication with high dynamic and large frequency offset.  
Conceptualization, S.P. and L.Y.; methodology, S.P. and L.Y.; software, L.Y. and S.P.; validation, L.Y.; formal analysis, S.P. and Y.W.; investigation, L.Y. and Y.T.; resources, S.P. and Y.T.; data curation, Y.T. and Y.W.; writing--original draft preparation, L.Y.; writing--review and editing, S.P., L.Y. and Y.W.; visualization, L.Y.; supervision, S.P. and Y.W.; project administration, Y.T.; funding acquisition, S.P. and Y.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by an Adaptive Satellite Communication Technology Project, under grant number 2024-173 ZD-040.  
The original contributions presented in the study are included in the article, and further inquiries can be directed to the corresponding authors.  
Authors [PERSON], [PERSON] and [PERSON] were employed by the company The 54 th Research Institute of China Electronics Technology Group Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resource** & **Utilization** & **Available** & **Utilization\%** \\ \hline LUT & 81,982 & 203,800 & 40.23 \\ FF & 125,913 & 407,600 & 30.89 \\ BRAM & 269 & 445 & 60.45 \\ \hline \hline \end{tabular}
\end{table}
Table 9: 8\(\times\) clock multiplexing.
## 2 Materials and Methods
### Lens Compensation  
Often lenses that are mass produced are not adjusted individually to maximize performance because of the time and cost lens adjusting requires. However, if the number of lenses to be made is not too large then it is possible to perform compensation to remove linear astigmatism aberration.  
One way to achieve compensation is to roll a lens on its mechanical seat so that one lens surface remains aligned, and the other surface becomes tilted. If \(d\) is the lateral displacement of the lens due to rolling, then the tilt \(\alpha\) of the surface can be estimated by \(\alpha=d/R\), where R is the radius of curvature of the surface resting on the seat. Thus for a radius curvature of 50 mm and a lens displacement of 0.025 mm the surface tilt becomes about 1.5 arc-minutes.  
The sensitivity tables for the above triplet and retrofocus objective lenses show that the last surface of these lenses is much more sensitive to linear astigmatism than to uniform coma. Then by allowing enough diameter clearance for the last lens element of these objectives, it would be possible to compensate for linear astigmatism aberration without significantly introducing uniform coma aberration.  
If an objective lens has already been manufactured and suffers from linear astigmatism aberration, then it is possible to add, after the objective, a thin optical wedge to correct for the aberration. The uniform coma and linear astigmatism contributed by a thin optical wedge of apex angle \(\alpha\) is given by [19].  
\[W_{UC}=-\frac{1}{2}\frac{1-n^{2}}{n}u^{2}\alpha y \tag{5}\]  
\[W_{LA}=-\frac{1-n^{2}}{n}u\overline{u}\alpha y \tag{6}\]  
where \(n\) is the index of refraction of the wedge, \(u\) and \(\overline{u}\) are the first order marginal and chief rays slopes in image space respectively, and \(y\) is the marginal ray height at the wedge. The ratio of linear astigmatism to uniform coma is given by \(2\overline{u}/u\). For the triplet lens above the ratio is \(2\overline{u}/u=8.4\), and for the retrofocus lens is also \(2\overline{u}/u=8.4\). The distance between the objective lens and the thin wedge can be used to fine tune the correction because linear astigmatism depends on the marginal ray height at the wedge. However, the wedge apex angle should be minimized to avoid a significant residual of uniform chromatic aberration.  
Adding a wedge to correct for linear astigmatism would require measuring the magnitude and orientation of the linear astigmatism and then designing a thin optical wedge. Figure 4 shows a lens arrangement including an optical wedge to correct the linear astigmatism of a perturbed retrofocus lens. The wedge being very weak does not introduce any significant spherical aberration (about 0.1 waves) or uniform chromatic aberration; instead the added wedge will tend to cancel the uniform chromatic aberration from the perturbed lens. A different route to desensitize a retrofocus lens [20] relies on aspherizing some of the lens surfaces.  
Figure 4: Perturbed retrofocus lens and thin optical wedge.
# Optimal Fractional PID Controller for Buck Converter Using Cohort Intelligent Algorithm
## 4 Results and Discussion
### Response of the CI-Optimized System to DC Gain Variations and Dynamic Changes  
The response of the circuit to variation of the DC gain upto 45% is shown in Figure 8. The FOPID controller responds well and shows robustness to a range of gain variations. The system settles in approximately 0.25 ms for each case, with a maximum overshoot of 11.9% for a gain increase upto 45%. The rise time is also small, around 0.4 ms for each case. At the same time, the PID controller has a maximum overshoot of 56.7% for gain increase upto 45%. The system settles around 0.45 ms with more oscillations (Figure 9). The FOPID controller has a smoother response compared to PID.  
Figure 8: Response of CI optimized FOPID to variation of gain.  
Figure 7: CI-optimized system responses for different cost functions.  
The response of the FOPID circuit with CI was analyzed for different set-point conditions and compared with that of a PID controller optimized using the CI algorithm. The PID controlled circuit had much higher overshoot than the FOPID based circuit. A change in the reference value from 15 V to 16 V at 0.03 s is shown in Figure 10a. The response of the circuit to a set-point change from 15 V to 12 V at 0.03 s is shown in Figure 10b. The FOPID controller gave a much smoother transition in both cases compared to the PID controller.  
The dynamic response of the CI-ISE optimized FOPID system was studied for step changes in the load, and compared with the PID controller under the same conditions. The system was subjected to a step load change from 3 \(\Omega\) to 20 \(\Omega\) at 0.006 s and from 20 \(\Omega\) to 10 \(\Omega\) at 0.01 s. The response is shown in Figure 11. The FOPID system had a slightly higher overshoot for the step change at 0.0006 s, but settled to the steady state value in lesser time of 0.4 ms compared to 1.2 ms for the PID controller. At the step load change at 0.1 s, the FOPID controller settled in 0.2 ms, whereas the PID controller took 0.76 ms to settle. The inductor current step variations are more for the PID controller and takes much more time to settle in both the cases.  
Figure 9: Response of CI optimized PID controller to variation of gain.
# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 3 Methodology
### Propagation Models for Simulation
#### 3.15.1. Friis Propagation Model  
Friis is among the earliest propagation models introduced for wireless networks [50], in which radio signals propagate in an omnidirectional manner from the source. The Friis propagation loss model is applied in this scenario. The following is the actual mathematical expression:  
\[\frac{P_{r}}{P_{t}}=\frac{A_{r}A_{t}}{d^{2}\lambda^{2}} \tag{5}\]  
For the situation of an isotropic antenna with no heat loss, we use the following mathematical statement:  
\[A_{isotr}=\frac{\lambda^{2}}{4\pi} \tag{6}\]  
The final equation is thus  
\[\frac{P_{r}}{P_{t}}=\frac{\lambda^{2}}{(4\pi d)^{2}} \tag{7}\]  
The radio waves travel through redundant space, and the power received is calculated using the power broadcast, the gain of the antenna, and the distance between the sender and receiver [50]. This system is based on the transmitter's broadcast range. The message is collected if the recipient is within the transmitter's reach; alternatively, it is dropped.  
\[P_{r}=\frac{P_{t}G_{t}G_{T}\lambda^{2}}{(4\pi d)^{2}L} \tag{8}\]  
The transmission power (W) is \(P_{t}\), the reception power (W) is \(P_{r}\), and the wavelength is (m). \(G_{t}\) and \(G_{r}\) are the transmission and reception gains, respectively. The network loss is \(L\) and the transmission distance and recipient are \(d\). The received radio signal intensity is determined by the broadcast signal intensity, the reception and broadcasting antenna  
Figure 9: Computational framework.  
gain, and the separation between the transmitters to the receiver, according to the equation mentioned above.
## 4 Result and Discussion
### Assessment of Accuracy and Stability of the Models
On the other hand, a small standard deviation signifies that the model is stable. The statistical parameters for the index of agreement are presented in Table 5. Again, for the results of the HS-ANN models, the rank is indicated by a superscript, and the largest (or smallest) two values are shaded. Even though the maximum values of \(I_{a}\) are also given, they are not discussed further because their variation is not so large to explain the difference of predictive ability or stability depending on the models. It is shown that the HS-ANN model gives the most excellent predictive ability and stability with HMCR = 0.7 and PAR = 0.5 or HMCR = 0.9 and PAR = 0.1. This result corresponds to [PERSON] [33] who suggested that the optimal ranges of HMCR = 0.7-0.95 and PAR = 0.1-0.5. Comparing the statistical parameters between the best HS-ANN model and the ANN model, the HS-ANN model with proper values of HMCR and PAR can give much better and stable prediction than the conventional ANN model. In particular, the small value of standard deviation of the HS-ANN model indicates that the model is excellent in finding the global minimum of the error function.
## 4 Numerical Simulation and Analysis of Result
### Results of Different Particle Sizes  
First, the numerical simulations using different particle initial spacings \(\Delta x\) were carried out. The simulation results were compared to evaluate the influence of the particle size. The seismic acceleration data of the 2008 Wenchuan earthquake (east-west component) as shown in Figure 10 were used as excitation data in the three test cases. Three particle initial spacings, i.e., \(\Delta x=0.05\) m, \(\frac{0.05}{\sqrt{2}}\) m, \(0.025\) m, were chosen for comparison. The fluid particle numbers were 36,910, 73,098, and 147,700, respectively. The boundary particle numbers were 18,159, 25,665, and 36,287, respectively. A constant time marching step \(\Delta\)t = \(2\times 10^{-5}\)s was used for all three cases. The simulation was executed at t = 10 s using 500,000-time steps because here we only focus on the influence of particle spacing.  
The pressure fields of the three cases were compared at different time steps. The results show that the pressure fields for different particle sizes are quite similar. The quantitative comparison of pressure evolution at boundary point B1 to B18 shows that both the amplitude and the phase of pressure oscillation are almost the same for \(\Delta x=0.05/\sqrt{2}\) m and \(\Delta x=0.025\) m, while the results of \(\Delta x=0.05\) m are obviously different  
Figure 8: Schematic diagram of arch ring section.  
Figure 9: The first\(-\)to fourth\(-\)order vibration shape of the arch bridge. (**a**–**d**): first order to fourth order).  
from those of the two other cases. Figure 11 shows the comparison of pressure evolution only for points B3, B5, and B8, which are at different water depth positions.  
It shows that the same results can be obtained using the medium particle size (\(\Delta x=0.0354\) m) compared with the finest particle size (\(\Delta x=0.025\) m). Therefore, in the following simulations, the medium particle size was adopted to reduce the simulation time.  
Figure 11: Comparison of the influence of different particle sizes on the boundary pressure evolution at three boundary points.  
Figure 10: Time series of the seismic acceleration of the 2008 Wenchuan earthquake. (East-west component).
## 4 Performance Evaluation  
We conduct simulation experiments to validate our analytical framework. The network coverage area is assumed to be a circle with radius ranging from 5,000 m to 15,000 m, and the sink is fixed at the center. The central frequency \(f\), the frequency bandwidth \(B(f)\) and the target signal-to-noise ratio \(SNR\) in Equation (4) are set to 20 kHz, 2 kHz and 20 dB, respectively. The minimum transmitter power \(\mathcal{P}_{min}\) and \(\mathcal{P}_{r}\) in Equation (6) are set to 8 W [19] and 1 W, respectively. We choose the packet size \(L_{d}\) to be 1,024-bit, and the transmission rate \(R\) to be 2 kbps. The conversion factor \(\varphi\) in Equation (5) is set to 0.25. The node density \(\rho\) is measured in the number of sensors per square meter (\(m^{2}\)). All the results obtained are the average over 500 randomly selected topologies.