esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Structure and Principle
### Realization of Checkerboard Laser Grid  
The effect of the grid laser emitter on the ground is shown in Figure 4. The laser emitter illuminates the ground to present a checkerboard grid effect, and the laser line in the grid will change shape when  
Figure 3: Algorithm flow.  
Figure 2: System schematic.  
illuminating obstacles on the ground. Moreover, the cameras can capture these shape changes for further image processing or machine learning to identify obstacles. Due to the high brightness, high directivity, and high monochromaticity of the laser, when the camera captures the laser mesh, the corresponding filter or the corresponding color detection in image processing can improve the capture of the special detection of the mesh and reduce the amount of image processing in the later stage.  
As shown in Figure 4, the laser emitter is obliquely irradiated to the ground; the center line of the laser emitter is at \(\alpha\) degrees to the horizontal plane; the laser emitter is at \(h_{2}\) above the horizontal ground; and the distance between the laser emitter and the checkerboard laser grid is \(l\).  
As shown in Figure 5, in an ideal case, the laser emitter is illuminated on the level ground, presenting a uniformly distributed laser grid with a single grid length of \(x_{0}\), and the width is \(y_{0}\). In reality, the laser grid is deformed by the laser emitter tilted on the ground.  
As shown in Figure 6, the laser network is an 8\(\times\)8 grid; the laser line closest to the laser transmitter is the X-axis, and the central axis of the laser network is the Y-axis. The vertical distance between the flat laser line and the origin is 0, \(y_{1},y_{2},\,\ldots,\,y_{8}\), respectively. Due to the deformation of the vertical laser line, the endpoint is from \(-x_{4},\,-x_{3},\,\ldots,\,0,\,\ldots,\,x_{3},\,x_{4}\) to \(-x_{4}\,^{\prime},\,-x_{3}\,^{\prime},\,\ldots,\,0,\,\ldots,\,x_{3}\,^{\prime},\,x_{4}\,^{\prime}\). Therefore, the X-direction of each grid is changed to:  
\[x_{ci}=\frac{|x_{i}^{\prime}-x_{i}|}{8},(i=1,2,3,4) \tag{1}\]  
Figure 4: Rendering of the laser grid on the ground.  
Figure 5: The ideal laser grid image.  
Furthermore, the coordinates of each grid intersection can be calculated when the obstacle appears in the laser grid. The coordinate range of the obstacle according to the position of the deformation can also be evaluated.  
As shown in Figure 7, the coordinates of the rectangular obstacles in the figure are: \((-x_{12},y_{14})\), \((-x_{19},y_{33})\), \((x_{11},y_{42})\), \((x_{18},y_{25})\).
## 3 Results and Discussion
### Model Monitoring Results and Analysis
#### 3.4.2 Random Forest Model  
The vegetation change dataset is put into the random forest model for training and testing. This process does not need to input _kNDVI_, only two remote sensing image data and label data in the dataset are needed. The results of monitoring vegetation changes using the random forest model are as follows:  
CPA: 0.5860. It shows that the classification and recognition accuracy of the model for vegetation is low.  
IoU: 0.5831. It shows that the model has some errors in the classification boundary of vegetation and non-vegetation (sandy land). The output of the model is shown in Figure 9.  
The vegetation changes in the whole study area are shown in Figure 10.  
Figure 9: Random forest model output diagram.
# Finite Element Method (FEM) Modeling of Laser-Tissue Interaction during Hair Removal
## 3 Results
### Time-Dependent Curve of Cumulative Hair-Follicle Damage--Protocol #2  
When inspecting the results of cumulative heat damage to the hair follicle for FEM Model #2 and FEM Model #3, shown in Figure 10, we notice that the pulse itself and a short cooling-down period after the pulse contributed the most to the final thermal damage to the hair follicle; in our case, this amounted to 50 ms of the pulse duration and about 20 ms after the pulse, i.e., 70 ms in total. We can hypothesize that the heat damage to an array of hair may have been insufficient to reach necrosis of the hair follicle, which is in line with observations in FEM Model #3 under Protocol #1 (Figure 9).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Maximum Follicle Temperature after Pulse [\({}^{\circ}\)C]**} \\ \hline Fluence of 50 J/cm\({}^{2}\) & Fluence of 60 J/cm\({}^{2}\) & Fluence of 80 J/cm\({}^{2}\) \\ Pulse Duration of 25 ms & Pulse Duration of 50 ms & Pulse Duration of 50 ms \\ \hline No Cooling & Cooling & NO COOLING & Cooling & No Cooling & Cooling \\ \hline
77.9 & 77.9 & 72.6 & 72.6 & 85.5 & 85.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Maximum follicle temperature in FEM Model #2 immediately after irradiation for three combinations of fluence and pulse duration, for modes without forced cooling and with forced cooling.  
Figure 10: Time variation of cumulative heat damage to the hair follicle during laser irradiation with a beam of radius 6.0 mm, fluence of 80 J/cm\({}^{2}\), and pulse duration of 50 ms without forced cooling. Results are shown for FEM Model #2 and FEM Model #3.
# Research on Wellbore Trajectory Prediction Based on a Pi-GRU Model
## 3 Experiments and Analysis  
In this section, the optimal hyperparameter configuration obtained from the orthogonal experiment was applied to the Pi-GRU model. The test results were compared and analyzed with those of other data-driven models. Moreover, to evaluate the performance of the model in more detail, we conducted tests on the data of another mine located in the same mining area.
# Application of Machine Learning for Bulbous Bow Optimization Design and Ship Resistance Prediction
## 2 Materials and Methods
### CFD Simulation  
In recent years, the computational fluid dynamics (CFD) method has been widely used to simulate the navigation state of hulls under complex environmental conditions. Therefore, this paper realizes the hydrodynamic virtual experiments of the surface hull model based on the CFD core solver, predicts the drag performance and motion attitude under the design speed, and obtains the experimental results of the ship resistance.  
In the numerical simulation of the free surface bypassing problem of a surface vessel, the free surface is treated by the level-set method [20]. The hull flow field is computed in the region where the distance function \(\varphi\leq 0\), which avoids the transition problem of the airflow field. In the Level-Set method, the position of the free surface is determined by the value of the numerical function \(\varphi\). The distance function \(\phi\) satisfies the following equation:  
\[\frac{\partial\phi}{\partial t}+u_{j}\frac{\partial\phi}{\partial x_{i}}=0 \tag{7}\]  
where \(\phi\) denotes the distance of any point in the flow field from the free surface.  
The flow was simulated using the RANS equation [21], which separates the instantaneous quantities in the turbulent flow into mean and pulsating values. An averaging operation was conducted on the N-S equation to obtain the Reynolds stress term. Additional conditions and relations needed to be sought to obtain the turbulence model to avoid the unsolvability of the RANS equations during the solution process. The SST \(k\)-\(\omega\) turbulence model equations are defined as follows:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho ku_{ i})=\frac{\partial}{\partial x_{j}}(\Gamma_{k}\frac{\partial k}{\partial x_{j}})+G_ {k}-Y_{k} \tag{8}\]  
Special dissipation rate \(\omega\) equations:  
\[\frac{\partial}{\partial t}(\rho\omega)+\frac{\partial}{\partial x_{i}}(\rho \omega u_{i})=\frac{\partial}{\partial x_{j}}(\Gamma_{\omega}\frac{\partial \omega}{\partial x_{j}})+G_{\omega}-Y_{\omega} \tag{9}\]  
where \(\Gamma_{k},\Gamma_{\omega}\) are the diffusion coefficients for \(k\) and \(\omega\); \(G_{k}\), \(G_{\omega}\) are the turbulence generation terms; and \(Y_{k}\), \(Y_{\omega}\) are the turbulence dissipation terms.  
In the solution process, the finite difference method [22] is used. The partial derivatives in the partial differential equations are replaced by the difference quotients of the function values at adjacent discrete points, forming a system of algebraic equations. Each node corresponds to an algebraic equation, with the unknowns being the function values at that node and its adjacent nodes. The SIMPLE algorithm [23] is used to handle the pressure-velocity coupling problem, and the pressure field is computed on the grid to solve the momentum equation.  
Figure 4: Process of the deformation of a bulbous bow. (**a**) extract control points; (**b**) move control points; (**c**) generate bulbous bow surface.
# Nonlinear Distortion Cancellation using Predistorter in MIMO-GFDM Systems
## 3 Results and Discussion
### Constellation Diagram  
The next effect analyzed was the constellation diagram after the GFDM was given a MIMO nonlinear distortion, as shown in Figure 12. Phase distortion occurs when an amplifier does not delay all frequency components by the same amount. The output phase distortion has a logarithmic shape instead of linearly increasing, as in the Saleh model. This means that the output phase shift is almost constant at high input amplitude values [9]. Phase distortion causes the constellation of signals on the receiving side to be no longer at the original point. The constellation diagram of the signal on the receiving side was no longer at the original point, called the \"warping\" effect, and there was also a spread of signal constellations in small groups, called effects \"clustering\". This will affect the decision point area on the receiver side. As a result, the received signal was difficult to detect. If detected, the received signal had an error, resulting in a decrease in performance. The constellation diagram for MIMO-GFDM exposed to nonlinear distortion experienced an enlargement of the decision point area. If the IBO gets smaller, the nonlinear distortion gets bigger and the decision point in the constellation also enlarges.  
Figure 11: Comparison of PAPR GFDM+predistorter with IBO variations.  
Figure 12: GFDM constellation without predistorter.  
From the results of the investigation in Figure 13, after the MIMO-GFDM was given a predistorter the constellation returned to its original point in order not to interfere with the detection process on the receiving side. The signal is again not in a distorted phase and amplitude. From the constellation picture, there was a division of the decision region so that the detection process follows the changes. This could be perceived as an error vector getting better after being given a predistorter. The error vector magnitude (EVM) for GFDM without predistorter was -4.97 dB, and after the GFDM system the predistorter decreased to -9.0 dB.
## 4 Results
### Experimental Parameter Setting  
In this experiment, the HRNet model used for feature extraction is HRNetV2-W48 [51]. As for the variables \(\lambda_{1}\), \(\lambda_{2}\), \(\lambda_{3}\), \(\alpha\), and \(\beta\) in the loss function of BPDNet, we set their values based on the reference HiSup [49]: \(\lambda_{1}=1.0\), \(\lambda_{2}=0.1\), \(\lambda_{3}=1.0\), \(\alpha=8.0\), \(\beta=0.25\). Furthermore, for the variables \(\theta\), \(\eta\), and \(T\) in the loss function, we conducted experiments to determine their optimal values for achieving the best results with BPDNet. Based on the experimental results, we found that setting \(\theta=1.0\), \(\eta=1.0\), and \(T=1.0\) yielded the best performance for BPDNet.  
All experiments in this paper were conducted on an NVIDIA RTX3090 GPU. The batch size for the models was set to 2, and the training process consisted of 60 epochs. Random flipping augmentation was applied during the training of all methods. The AdamW optimizer was used for training, with an initial learning rate of \(6\times 10^{-5}\), \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and a weight decay coefficient of 0.01. The learning rate decay strategy employed the poly strategy.
# Optimization of Optical Trapping and Laser Interferometry in Biological Cells
## 4 Experimental Results and Discussions
### LI Sensitivity Depending on Optical Properties
The silica particle, though it has been widely used for LI experiments, is therefore not suitable as a probe for LI measurements in living cells. Although LI sensitivities are expected to take negative values when \(n_{\mathrm{P}}\) is larger than \(n\), we did not observe the corresponding phase shift by \(\pi\) in the sinusoidal response of the QPD signal. When the refractive index of silica particles (\(n_{\mathrm{P}}=1.43\)) is close to that of surrounding cytoplasm, the laser deflection by the probe is extremely small. Under this condition, various artifacts originating from, e.g., laser diffraction at the cell surface and by the other organelles, would not be negligible. On the other hand, LI in cells using melamine particles provide reasonable agreement to theoretical predictions for all the experimental conditions explored in this study. This is the reason why the melamine particle is chosen for LI experiments in living cells [15]. Nevertheless, the relative standard deviation of the LI sensitivity is larger in living cells compared to that in glycerol, clarifying the necessity of in-situ calibration with feedback-tracking technique in cells. Figure 5: _Cont._
###### Abstract  
This paper deals with the application of non-destructive geophysical techniques of investigation in the urban environment of the city of Nicosia (Cyprus). The main aim of the research was, in the frame of the Eleftheria Square redesign project, to image subsurface properties in order to reduce the impact of hazards on the old buildings (therefore preserving the cultural heritage of the place), and on the new infrastructure under construction. Since 2008, electrical resistivity tomography (ERT), ground penetrating radar (GPR) and induced electromagnetic method (EMI) were employed during the different phases of the project to provide an understanding of geological stratigraphy, the detection of buried objects (archaeological structures and underground utilities) and the solution of unexpected events (such as water infiltration in the course of works). The geophysical results proved the efficiency of the adopted methods, adding scientific value to the knowledge of the studied area. The new gathered information helped the public administration technicians to plan direct and targeted interventions and to modify the original design of the project according to the discovery of archaeological findings.  
applied geophysics; urban geophysics; Eleftheria Square; ERT; probability-based ERT inversion (PERTI) method; GPR; EMI +
Footnote †: journal: Applied Sciences
## 3 Finite Element Analysis and Field Monitoring
### Boundary Conditions and Basic Assumption  
According to the theory of geomechanics [40], when the grid size is six times that of the excavated cavity, the influence of the soil in the construction excavation is relatively small. Therefore, the three-dimensional finite element model set up in this paper is the positive direction of the X axis horizontally to the right, the positive direction of the Y axis in the direction of the tunnel excavation, and the positive direction of the Z axis vertically from the surface upwards, with the dimensions of X = 200 m, Y = 10 m, and Z = 70 m, respectively, as shown in Figure 4.  
Figure 4: Finite element model diagram. (**a**) Stratum structure method; (**b**) loading–structure method.  
The model boundary conditions are as follows: the top surface was free, the bottom surface was normally constrained and the outer boundary surface of the surrounding rock was normal to the vertical rock face.  
Basic assumptions:  
* Precipitation has been treated during construction, so the influence of ground water on the model is not considered.
* The station is a shallow tunnel excavation where only the weight load is considered. The soil constitutive model adopts the modified Mohr-Coulomb constitutive model and the concrete adopts the elastic constitutive model.
* The calculation model considers the formation material to be isotropic and homogeneous.
* The three-dimensional soil layer adopts the solid element, the lining adopts plate element and the pile adopts beam element. The two-dimensional soil layer adopts plane strain element, the lining adopts the beam element, and the pile adopts the beam element.
## 4 Practical Application
### Calculation of the ZSCC Compensator
Substituting the values of \(\underline{I_{\text{aW}}}\), \(\underline{I_{\text{bW}}}\) and \(\underline{I_{\text{cW}}}\) in Equation (24), the values of reactances in the ZSCC compensator were obtained:  
\[X_{\text{aW}}=\frac{231.00e^{\text{j0.00}}}{9.61e^{\text{j00.00}}}=-24.04\text { }\Omega,\]  
\[X_{\text{bW}}=\frac{240.00e^{-\text{j110.00}}}{32.889e^{-\text{j20.00}}}=-7.29 7\text{ }\Omega,\]  
\[X_{\text{cW}}=\frac{195.00e^{\text{j90.00}}}{38.497e^{\text{j0.00}}}=5.065\text { }\Omega,\]  
From their sign, \(X_{\text{aW}}\) and \(X_{\text{bW}}\) are capacitors and \(X_{\text{cW}}\) is a coil. Adding the line currents that consume the load and the ZSCC compensator, the new line currents supplied by the network in the bus were obtained after including the ZSCC compensator. Their values are given by:  
\[\underline{I_{a}}+\underline{I_{\text{aW}}}=9.609e^{\text{j00.00}}\text{ A }\underline{I_{b}}+\underline{I_{\text{bW}}}=65.841e^{-\text{j134.316}}\text{ A }\underline{I_{c}}+\underline{I_{\text{cW}}}=59.346e^{\text{j99.189}}\text{ A}\]  
Table 4 shows these currents expressed in symmetric components. It is observed that the zero-sequence current is null, therefore, the current in the neutral wire is also null. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Phases** & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{z+}}}\) **(A)**} & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{z-}}}\) **(A)**} & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{z0}}}\) **(A)**} \\ \hline \(\boldsymbol{z}\) & **Mod.** & **Ang.** & **Mod.** & **Ang.** & **Mod.** & **Ang.** \\ \hline a & 32.706 & \(-41.688\) & 39.75 & 127.912 & 0.00 & \(-26.565\) \\ b & 32.706 & \(-161.688\) & 39.75 & \(-112.088\) & 0.00 & \(-26.565\) \\ c & 32.706 & 78.312 & 39.75 & 7.912 & 0.00 & \(-26.565\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Positive-, negative-, and zero-sequence line currents in the bus after ZSCC collocation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Phases** & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{zW+}}}\) **(A)**} & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{zW-}}}\) **(A)**} & \multicolumn{2}{c}{\(\boldsymbol{I_{\text{zWW}}}\) **(A)**} \\ \hline \(\boldsymbol{z}\) & **Mod.** & **Ang.** & **Mod.** & **Ang.** & **Mod.** & **Ang.** \\ \hline a & 8.806 & 160.868 & 16.502 & 153.863 & 23.141 & \(-1.353\) \\ b & 8.806 & 40.868 & 16.502 & \(-86.137\) & 23.141 & \(-1.353\) \\ c & 8.806 & \(-79.132\) & 16.502 & 33.863 & 23.141 & \(-1.353\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Positive-, negative-, and zero-sequence currents that consume the ZSCC compensator. Table 5 compares the values of the active and reactive powers, before and after using the ZSCC compensator. As expected, the active power \(P\) remains constant and \(P_{0}\) and \(Q_{0}\) are null. Considering Equation (2), [PERSON]'s apparent power in the bus was determined by:  
\[S_{T}=3\sqrt{(212.891^{2}+42.094^{2}+50.67^{2})}\ (32.706^{2}+39.75^{2}+0.00^{2}) =34414.22\ \text{VA}\]  
If we compare this value of \(S_{T}\) with the initial value, it is observed that it is slightly lower.
# Weight-Based PA-GPSR Protocol Improvement Method in VANET
## 4 Proposed Protocol
### System Model and Assumptions  
1. Location and radius: each vehicle was equipped with a Global Positioning System (GPS) and OBU, which have fixed accessible communication ranged [36]. OBU enabled the vehicle to send a beacon within its communication range to perform inter-vehicle communication. In W-GPSR, similar to most geographic routing protocols, it was assumed that there is a centralized location service. Each forwarding node could obtain destination location information by querying the central management organization to meet routing requirements.
2. Communication and sensors: each vehicle was equipped with a wireless network interface that complies with 802.11p or dedicated short-range communication standards for inter-vehicle communication [34]. In addition, each vehicle was also equipped with an onboard diagnostic interface, which was designed to obtain data from multiple mechanical and electronic sensors in the vehicle. There was no infrastructure around the road, and nodes can only exchange data through V2V.
3. Neighbors and paths: the initial location of the vehicle was determined based on a random selection of uniform distribution. When vehicles were within communication range of each other, an edge connection was established between their neighbors. Communication could be established based on direct contact with each other or through neighbors. Due to the dynamic nature of VANET, communication had to be established iteratively using dynamic routing protocols.
4. Nodes share information: these data included information about modes' state vectors, such as physical location, destination, and direction.
5. Model area: the model area was constrained by the plane frame. The network model consisted of roads and intersections that simulated a typical urban environment. The system scenario diagram is shown in Figure 4.
6. The location information provided by the positioning system was accurate, i.e., the location error was not considered.
# Software-Defined NB-IoT Uplink Framework--The Design, Implementation and Use Cases
## 5 Uplink Implementation
### OFDM Generation
#### 5.8.3 Cyclic Prefix Attachment  
The last function of the OFDM module is the CP attachment. The main concept of this block represents the process of replicating the end of each symbol at the beginning of the same symbol. The exact number of CP samples may be obtained based on the time dependencies presented in Table 3 and 1.92 MHz sampling frequency. Thus, the CP length for the 3.75 kHz subcarrier spacing equals 16 samples, and for 15 kHz: 10 samples (every first symbol in a slot) or 9 samples (the other six symbols in a slot). Moreover, the additional frequency shift should be applied to provide the phase continuity of the samples.
###### Abstract  
Carotenoids have attracted significant interest due to their potential use in human health and nutrition, and their global demand increases by 3.7% annually. Currently, synthetic carotenoids dominate the market, but possess challenges such as low antioxidant ability, issues with health benefits, and safety concerns. Microalgae are potential producers of natural carotenoids with extraordinary antioxidant properties, although the yield is often low in their natural cycle. The present investigation aimed to analyze the effect of multiple abiotic factors on enhancing algal carotenoids synthesis and other metabolites without affecting growth. The potential microalgae _Monoraplidium_ sp. were grown under altered nutrient and light conditions employing RSM-CCD. The optimized conditions, such as Nitrogen (5 g\(\cdot\)L\({}^{-1}\)), Phosphorus +Potassium (250 mg\(\cdot\)L\({}^{-1}\)), Sulphur (70 mg\(\cdot\)L\({}^{-1}\)), and light (137.5 um\(\cdot\)m\(\cdot\)2\(\cdot\)S\({}^{-1}\)), resulted in increased biomass (1357.36 mg\(\cdot\)L\({}^{-1}\)), lipid accumulation (40.28% of dry biomass), and carotenoids (16.26 mg\(\cdot\)mL\({}^{-1}\)) as compared to the control conditions. The total carotenoids fraction consisted of astaxanthin (14.8%), violaxanthin (3.61%), lutein, (45.12%), 9-cis-\(\beta\) caroten (7.62%), and \(\beta\)-carotene-5.6-epoxide (24.21%). Among them, violaxanthin (1.32-fold), astaxanthin (1.19-fold), 9-cis- \(\beta\) caroten (1.07-fold), and \(\beta\)-carotene-5.6-epoxide (1.08-fold) content increased while lutein (1.32-fold) content decreased significantly. The improvement in algal carotenoids under novel culture conditions provides a significant advantage to pharmaceutical and nutraceuticaltical industries.  
microalgae; _Monoraplidium_ sp.; abiotic factors; biomass; carotenoids; lipids