esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Analysis of the Results  
### The Dataset  
In order to verify the effectiveness and the real-time characteristics of the proposed feature extraction method, we have applied it to the Lung Image Database Consortium image collection (LIDC-IDRI) dataset. The LIDC-IDRI database is the largest open lung nodule database in the world, which contains 1080 cases. For each of the images in the samples, four experienced chest radiologists performed a two-stage diagnosis. In the first stage, each radiologist independently diagnosed and marked the location of the patient. In the subsequent second stage, a radiologist independently reviewed the other three radiologists' marks and gave his/her own final diagnosis.  
Such a two-stage process can verify all results as completely as possible without the interference of other radiologists. Therefore, the diagnosis results can be used as a gold standard reference. According to the diagnostic results of corresponding lesions, XML files were marked, and the CT images of 50 cases of sequential juxtapleural nodules were selected. At the same time, some of the image datasets used in the experiment were collected from the positron emission tomography/computed tomography (PET/CT) detection center of a collaborating hospital. In the experiment, the imaging data of 47 patients in the hospital were selected. For each patient there were 299 serial lung CT images, and the sequential images of juxtapleural-drawn pulmonary nodules were selected for the experiment.
# Propagation of Optical Coherence Vortex Lattices in Turbulent Atmosphere
## 3 Numerical Results  
### Spectral Density Distribution  
Figure 1 displays the spectral density distribution of the OCVLs in turbulent atmosphere at various propagation distances with \(\lambda=532.8\) nm, \(M=3,l=2\), \(\omega_{0}=1\) mm, \(\delta_{0}=3.4\) mm, \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\), and \(d~{}=~{}0.3\) mm. From Figure 1a), one finds that the spectral density distribution of the OCVLs at source is a single beam spot with doughnut beam profile. The single beam spot gradually evolves into \(3\times 3\) hollow beam arrays (see Figure 1e) with the increase of the propagation distance \(z\), which is similar to its evolution properties in free-space [44]. However, when we further increase the propagation distance, the effect of turbulence on beam properties accumulates gradually. The hollow beam arrays disappear gradually and finally become a Gaussian beam profile in the far field (see Figure 1h).  
Figure 2 displays the spectral density distribution of the OCVLs in turbulent atmosphere at \(z\) = 5 km for several values of the relative distance with \(\lambda=532.8\) nm, \(M=3,l=2\), \(\omega_{0}=1\) mm, \(\delta_{0}=3.4\) mm, \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\), and \(d=0.3\) mm. (**a**) \(z\) = 0; (**b**) \(z\) = 1 m; (**c**) \(z\) = 2 m; (**d**) \(z\) = 3 m; (**e**) \(z\) = 10 m; (**f**) \(z\) = 500 m; (**g**) \(z\) = 5 km; (**h**) \(z\) = 20 km.  
Figure 2 displays the spectral density distribution of the OCVLs in turbulent atmosphere at \(z\) = 5 km for several values of the relative distance with \(\lambda=532.8\) nm, \(M=3,l=2\), \(\omega_{0}=1\) mm, \(\delta_{0}=3.4\) mm, and \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\). From Figure 2, one sees that the beam distribution of the OCVLs evolves into Gaussian distribution rapidly when the relative distance \(d\) is small (see Figure 2a). On the contrary, the beam array profile persists for longer distances when the relative distance \(d\) increases  
(see Figure 2d). Figure 3 displays the spectral density of the OCVLs in turbulent atmosphere at \(z=5\) km for several values of the array index \(M\) with \(\lambda=532.8\) nm, \(l=2\), \(\delta_{0}=0.45\) mm, \(\omega_{0}=1\) mm, \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\), and \(d=0.3\) mm. From Figure 3, the beam spot size of the OCVLs increases as the array order \(M\) increases, which indicates the OCVLs spread more rapidly as the array order \(M\) increases. Figure 4 displays the spectral density distribution of the OCVLs in turbulent atmosphere at \(z=500\) m for several values of \(l\) with \(\lambda=532.8\) nm, \(M=3\), \(\delta_{0}=0.7\) mm, \(\omega_{0}=1\) mm, \(C_{n}^{2}=10^{-13}\) m\({}^{-2/3}\), and \(d=1\) mm (see Figure 4a). One finds that the beam arrays keep hollow profiles for longer propagation distances as the topological charge \(l\) increases, which means the OCVLs with large topological charges are less affected by turbulence.
# Satellite Network Security Routing Technology Based on Deep Learning and Trust Management
## 4 Experimental Simulation and Analysis
### Analysis of Transmission Delay under Data Pressure  
To test the latency of data transmission under different traffic conditions, this study increases the length of the packets themselves to provide a performance comparison of various algorithms in this scenario. Since the comparative algorithms require the running of a secure trust model, this paper selects a malicious node count of eight for processing here. Ensuring the presence of malicious interference makes the simulation results more convincing.  
From the graph, it can be observed that as the packet size increases, the transmission latency of each algorithm also increases. Moreover, when the packet size reaches 800 B, the ACO algorithm's transmission latency reaches 1560 ms, indicating that it consumes significant time in countering malicious attacks and may not be suitable for satellite network transmission. On the other hand, the other three algorithms, despite experiencing an increase in transmission latency, can maintain their latency stable at around 800 ms due to their security mechanisms in defending against malicious attacks. This ensures reliable data transmission even as data packet sizes increase.
# Automatic Outdoor Patrol Robot Based on Sensor Fusion and Face Recognition Methods
## Appendix D The Use of GNSS  
In robotic outdoor patrols, the latitude and longitude are given by GNSS satellite instant positioning systems. However, when robots use satellite navigation in patrols, they must be able to determine whether the movement is correct or not, in order to successfully perform patrols and reach their destinations. In this study, we tried to use the triangulation positioning method to calculate the robot's forward direction. We used this method to replace the common but more complex method of robots to distinguish specific directions. Here, the robot outdoor patrol was given a new starting point and target point repeatedly. In the initial state of uncertain direction or movement error, the direction in which the robot moved was not the direction to the target point, as shown in Figure A6. We can see from Figure A6 that the robot is going from the starting point (point B) to the target point (point A), but it has reached the moving point (point C). The longitude and latitude of point A was measured and recorded in advance, and it was used as the target point. The GNSS satellite positioning system can calculate and give the latitude and longitude of points B and C instantly. We can have the latitude and longitude of A, B, and C. The rotation angle of the robot from the current forward direction to the target location can be obtained.
## 2 Design and Transfer Function Acquisition of MRB
### Transfer Function of MRB System  
It is difficult to establish an accurate model for MRB systems with strong nonlinearity [27]. In this work, the MRB was controlled by an excitation current, which can be expressed as the first-order pure delay link: \(G_{S}=\frac{K}{(1+T_{1}s)}\)e\({}^{\text{ps}}\). The rheological effect of MRF was generated in milliseconds, where \(p\) is very small, and the eps could be replaced by \(1/(1+T_{2}s)\). Therefore, the transfer function of MRB can be expressed: \(G_{S}=\frac{K}{(1+T_{1}s)\,(1+T_{2}s)}\), where \(K\) is the gain of MRF brake, \(T_{1}\) and \(T_{2}\) are the time constants. The current-braking torque data were obtained by the data acquisition system, and the mathematical model of MRB was identified by using the system identification toolbox in MATLAB. A group of excitation current and output torque data was used as identification data, while another group of excitation current and output torque data was used as inspection data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Type** & **Torque Signal** & **Torque Range** & **Speed Signal** & **Speed Range** & **Accuracy** & **Manufacturer** \\ \hline HCNJ-101 & 5\(\sim\)15 \(\mathrm{KHz}\) & \(\pm\)500 N-m & 60 \(\mathrm{pulse/roll}\) & 0\(\sim\)3000 r/min & \(<\pm\)0.5\% & Beijing haibohua Technology Co., Ltd. \\ \hline \hline \end{tabular}
\end{table}
Table 4: The main parameters of the torque speed sensor.  
Figure 8: Variation of braking torques with slip speeds.  
By selecting the model class and algorithm provided by the system software, the model parameters were calculated and the optimal value was selected to complete the mathematical model identification of the MRB system. The identification steps are as follows: Firstly, the identification data samples were written into the MATLAB command window and the start time and sampling time were set. Meanwhile, to ensure the reliability of the identification data in the real environment, there was no need for pre-processing and interference signal filtering. Secondly, we selected the model structure by clicking Estimate/Process Models, and selected Model Output to view the results. Finally, the best fit was 92.45%, which is a good fitting degree [22]. The transfer function of the MRB system can be expressed as follows:  
\[G(s)\ =\frac{5.327}{0.0667s^{2}+0.5261s+5.327}\]
# Multihop Latency Model for Industrial Wireless Sensor Networks Based on Interfering Nodes
## 4 Simulations
For the validation of the analytical model, it is necessary to know the behaviour of the PDR of the transmission medium. This PDR will not be associated to all the transmissions of a node, but the PDR behaviour has been evaluated only in the Orchestra scheduling dedicated to RPL, which is based on minimal 6 TiSCH [15]. Thus, the PDR behaviour will be associated with the transmissions of RPL messages on a single resource per slotframe. Figure 8 shows the evolution of the PDR based on the number of interfering nodes.
# On the Correlation of Symbols' Vibrational Behavior and Manufacturing Processes
## 4 Results
### Correlation Between Blanks  
The highest correlation values are expected to occur by comparing similar symbols. This is the case for the blank B8 symbols (B8-1, B8-2, B8-3, B8-4, and B8-5) due to their similar mass and shape. The CFDAC was calculated by considering B8-1 to be the reference (pristine) symbol, providing \(\text{CFDAC}_{h(88\cdot 1)_{h}(88\cdot 1)}\), \(\text{CFDAC}_{h(88\cdot 1)_{h}(88\cdot 2)}\), \(\text{CFDAC}_{l_{\text{(B8\cdot 1)_{h}(88\cdot 3)}}}\), \(\text{CFDAC}_{l_{\text{(B8\cdot 1)_{h}(88\cdot 4)}}}\), and \(\text{CFDAC}_{l_{\text{(B8\cdot 1)_{h}(88\cdot 5)}}}\). The CFDACs were used for the calculation of the SCI. Figure 4 shows the absolute values of the real (Re \(\{\}\)) and imaginary (Im\(\{\}\)) parts of the CFDACs, which are confined within the range of [0, 1], and the same holds for the forthcoming plots in the manuscript. Regarding the relationship  
Figure 3: SCI values vs. the numbers of measurement points from 14 to 144 by a step of 14, tested for the stability of the calculations, based on the real (solid lines) and on the imaginary (dashed lines) values of CFDACs.  
between the CFDAC matrix data and the plot, it should be noted that in the plots, the rows increase from bottom to top. Additionally, for all CFDAC plots in the article, the horizontal axis corresponds to the first set of FRFs and the vertical to the second set.  
As expected, in Figure 4, the highest correlation corresponds to \(\text{CFDAC}_{h(8-1),h(8-1)}\), with the values symmetrically distributed along the main diagonal, since cymbal B8-1 is the pristine cymbal and is being correlated with itself. The next four comparisons with the pristine cymbal demonstrate small deviations from the main diagonal in the other CFDACs. These differences are expressed in terms of SCI in Figure 5. It is observed that the correlation between the real parts of the CFDACs is more prominent compared to that between the imaginary parts, leading to lower SCI values for the real parts. For the imaginary parts, the highest SCI value is 0.41, while for the real parts, it is 0.2. Additionally, the results in Figure 5 reveal that the highest correlation occurs between B8-1 and B8-5, indicating that these two cymbals exhibit similar vibrational behavior.  
The behavior of the CFDAC correlation values presents very small deviations, as expected, since the blank B8 group of cymbals is produced in the same factory using the same material and machining processes.
## 4 Current Evidence and Applications
### Personalized Treatment Strategies  
Artificial intelligence can enhance personalized medicine in orthopedics by providing risk stratification tools and optimizing treatment strategies based on individual patient characteristics. In the management of scoliosis, machine learning models have been developed to predict curve progression, aiding in decision-making regarding bracing or surgical intervention [41]. By analyzing factors such as age, sex, initial curve magnitude, and skeletal maturity, these models can identify patients at higher risk of progression, allowing for timely and appropriate interventions. Similarly, AI has been used to predict the risk of postoperative infections in orthopedic surgeries. By incorporating patient comorbidities, laboratory values, surgical factors, and intraoperative data, machine learning models can identify patients at increased risk, prompting the implementation of preventive measures [42]. In pediatric fracture care, predictive models can assist in determining the most appropriate treatment modality. For example, in forearm fractures, machine learning algorithms have been used to predict the likelihood of successful conservative management versus the need for surgical intervention [43]. By considering factors such as fracture displacement, angulation, and patient age, clinicians can make informed decisions that optimize outcomes and minimize unnecessary procedures.
# The Fermentation of Orange and Black Currant Juices by the
## 3 Results and Discussion
### Parameters of the Fermented Juices
In the case of glycerol concentrations, it was observed that replacing the probiotic yeast with the wine strain _S. Dayanus_ resulted in a reduction (_p_ < 0.05) in the amount of glycerol produced from 5.58 \(\pm\) 0.22 g/L to 4.83 \(\pm\) 0.23 g/L. The TPC values for the fermented orange juices ranged from 43.36 \(\pm\) 0.42 mg GAE /100 mL (for fermentation with _S. Dayanus_ yeast) to 49.81 \(\pm\) 0.61 mg GAE/100 mL (for the juice without added glucose and DAP fermented with the probiotic strain). Similar TPC values and a minimal increase in this parameter during fermentation (from about 34 to about 35 mg GAE/100 mL) were described by [PERSON] et al. [31]. For both black current and orange juice fermentation, the ethanol and glycerol concentrations directly corresponded to the glucose and fructose contents of the samples before fermentation. For the BC \"0\" sample without supplementation of glucose and DAP to the currant juice, the final ethanol and glycerol concentrations were, respectively, equal to 19.67 \(\pm\) 1.02 and 2.28 \(\pm\) 0.12 g/L. Similarly, for the sample with the addition of DAP to the currant juice, the final ethanol and glycerol concentrations were equal to, respectively, 16.14 \(\pm\) 1.12 and 1.95 \(\pm\) 0.09 g/L, so it can be concluded that, analogously to the orange juice, no beneficial effect of DAP supplementation was observed on the fermentation carried out by the probiotic yeast _S. cerevisiae_ var. _boulardii_. Glucose supplementation to the currant juice predictably increased (_p_ < 0.05) the final amounts of ethanol and glycerol produced to 66.05 \(\pm\) 2.36 and 4.93 \(\pm\) 0.28 g/L. As with the orange juice, using _S. Dayanus_ yeast instead of the probiotic strain induced an increase in the ethanol concentration of about 8% and a decrease in the glycerol concentration of about 5%. [PERSON] et al. [19] presented, in their report on the fermentation of currant juices, final ethanol concentrations ranging, depending on the yeast strain used, from 34.6 to 44.7 g/L, which are higher than the values we obtained for the currant juice without added glucose but lower than our results obtained for the juices with added glucose. The TPC values for the fermented currant juices ranged from 81.2 \(\pm\) 0.41 mg GAE/100 mL (for fermentation with _S. Dayanus_ yeast) to 84.1 \(\pm\) 0.38 mg GAE/100 mL (for the juice without added glucose and DAP fermented with a probiotic strain). Concerning phenolic compounds, [PERSON] et al. [19] only reported changes within groups of individual compounds without relating these detailed results to the overall TPC value.
## 3 Results and Discussions
### Low Frequency Mini-Brillouin-Zone Mode
#### 3.2.4 Indications of Stimulated Subharmonic Decay of MBZ-Center Mode
This is an additional indication of the nonlinearity of the processes leading to the decay of \(\sim\)32 GHz mode and the plausibility of the stimulated subharmonic emission of the \(\sim\)15 GHz edge mode. Figure 8: **(a)** Left: FFT amplitude of the acoustic signal in the time interval from 400 ps to 800 ps for the pump power of 67 mW. Right: short time FFT analysis by using a long moving window length. **(b)** Dependences on time of 15 GHz, 32 GHz and 63 GHz mode amplitudes, which are obtained from short time FFT image (see right-hand figure of **(a)**). Figure 7: **(a)** Coherent acoustic phonon and **(b)** spectrum in the time interval from 500 ps to 800 ps at the pump power from 43 mW (bottom) to 67 mW (top) in steps of 4 mW. **(c)** Normalized frequency amplitude to the amplitude at lowest pump power for each frequency. **(d)** Frequency amplitude ratio between 32 GHz center mode and 15 GHz edge mode. All the above-presented demonstrations (Figures 7 and 8) and arguments indicate that the source of the MBZ-edge mode excitation is a near \(k=0\) eigenmode of the DBR, which, in turn, provides evidence for monitoring folded CLAPs and edge modes in a long-period non-doped transparent semiconductor SL. In summary, up to three of the MBZ-center at \(k\) 0 and -edge modes at \(k=\pi/d_{DBR}\) starting from 15 GHz to 110.1 GHz are excited and observed in the optically transparent DBR region of the MQW-SESAM sample. A possible explanation for this unusual behavior is presented which assumes the generation mechanism of the zone-center modes is electrostriction in the DBR. Stimulated subharmonic decay of these zone-center modes then leads to the excitation of the zone edge modes in the DBR, which can be detected because of dynamic zone folding and Raman-Nath-type light scattering following the hypothesis. The nonlinearities revealed in the experimentally-observed acoustic dynamics of the MQW-SESAM structure provide strong a indication of the dominant role of the stimulated subharmonic decay processes in the detection of the zone-edge mode frequencies. We have to bear in mind that the structure we investigated is complex, which potentially leads to the overlap of multiple coherent acoustic phonon sources in the frequency domain and a possible subsequent interplay between them such as enhancement or cancellation. For the time being, it is hard to identify the contributions from different mechanisms of the CLAPs excitation and detection in the individual spectral components of the signal. We discuss some of these peculiarities in Appendix A, where one can not only gain a deeper understanding of potential acoustic mechanisms, but can also find an explanation for important missing modes and additional modes in Figure 4b, as well as the reason why only the edge mode of \(\sim\)15 GHz excited via stimulated harmonic decay is revealed through the nonlinearities in the experiment.
# Learning Airfoil Flow Field Representation via Geometric Attention Neural Field
## 3 Experiment
### Geometry Attention Analysis  
In this section, we will analyze the GeoAttention mechanism and list some visualization results. As discussed in Section 2.4, the concept of GeoAttention posits that the geometric information of various locations should carry different weights for different query locations. Moreover, it distinguishes between scenarios where the query location is within an volume flow field and those at geometric boundaries. This distinction will also be visualized through the normalized attention scores at different locations for these two scenarios.  
The normalized geometric attention weights at query positions within the volume flow field are depicted in the Figure 13(a). We present the variations of GeoAttention around the airfoil for the query points. The red dots in the figure indicate the positions of the query points. We observe that the weights of GeoAttention change as the positions of the query points vary. When the query point moves clockwise from above the airfoil to the tail of the airfoil, we can see that the weights near the underside of the airfoil close to the tail increase gradually. As shown in the second row of the Figure 13(a), when the query point moves counterclockwise from the left side of the airfoil to below the airfoil, the weights at the front of the airfoil decrease, then increase again, while the weights at the front of the underside of the airfoil decrease, then increase again.  
The normalized weight distribution situation for query points located on the airfoil surface, as shown in Figure 13(b). Comparatively, when query points are on the surface of the airfoil, similar to when they are in the volume flow field, the weights vary with the change in query position. However, when query points are on the boundary of the airfoil, the variation in weights is relatively more pronounced. When the point moves counterclockwise at the tail of the airfoil, we observe a significant change in weights, as shown in Figure 13(b), in the first row of the first two images. When the point is on the underside of the tail airfoil, the weight at the tail end of the airfoil's underside is greater. When the query point shifts to the upper surface, there is a significant change in the weight distribution on the underside.  
GeoAttention provides a basis for establishing the relationship between the query coordinates and geometric information weights. Starting from the tip of the tail, we observe a sudden change in attention weights. We speculate that this is due to the fact that airfoils are typically described by two NURBS curves, which usually maintain high-ordercontinuity [41]. However, the sudden change occurs at the tail section where the two NURBS curves connect. At this junction, the points do not maintain the continuity of the surface shape, leading to a sudden change in attention weights.