esa-sceva/satcom-chunk-collection huggingface dataset:
# Driving Behavior Classification and Sharing System Using CNN-LSTM Approaches and V2X Communication
## 4 Performance Evaluation
### Simulation Result of Driving Behavior Prediction
The proposed 4-layer CNN-2 stacked LSTM takes a classification time of \(\sim\)19.7 ms in the real-time evaluation performed on the TX2 board, and classifies driving behaviors about 1.6 ms faster than the classification time of 4-layer CNN-LSTM, about 21.3 ms. The next high performance networks are 2D-CNN networks and CNN-LSTM networks. 1D-CNN confuses normal and defensive behavior in all scenarios, and LSTM has the worst classification performance of normal behavior. As a result of performance verification based on validation data and test data, we have proven that the proposed 4-layer CNN-2 stacked LSTM classifies driving behavior as high performance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Test: DGIST Campus**} & \multicolumn{4}{c}{**Output Label**} \\ \hline
**Input: 5 s** & **Networks** & **Defensive** & **Normal** & **Aggressive** \\ \hline \multirow{6}{*}{Defensive} & 1D-CNN & 95.11\% & 3.68\% & 0.21\% \\  & 2D-CNN & 91.91\% & 7.71\% & 0.39\% \\  & LSTM & 58.91\% & 2.02\% & 39.07\% \\  & CNN-LSTM & 94.84\% & 5.13\% & 0.04\% \\  & 4 CNN-LSTM & 97.45\% & 2.4\% & 0.14\% \\  & 4 CNN-2 LSTM & 97.74\% & 2.12\% & 0.14\% \\ \hline \multirow{6}{*}{Normal} & 1D-CNN & 5.58\% & 90.83\% & 3.59\% \\  & 2D-CNN & 4.32\% & 94.79\% & 0.89\% \\ \cline{1-1}  & LSTM & 40.05\% & 2.35\% & 57.60\% \\ \cline{1-1}  & CNN-LSTM & 1.95\% & 94.64\% & 3.41\% \\ \cline{1-1}  & 4 CNN-LSTM & 2.53\% & 97.03\% & 0.44\% \\ \cline{1-1}  & 4 CNN-2 LSTM & 0.97\% & 97.83\% & 1.2\% \\ \hline \multirow{6}{*}{Aggressive} & 1D-CNN & 1.11\% & 5.91\% & 92.99\% \\  & 2D-CNN & 1.5\% & 3.56\% & 94.95\% \\ \cline{1-1}  & LSTM & 9.55\% & 2.53\% & 87.92\% \\ \cline{1-1}  & CNN-LSTM & 0.42\% & 2.8\% & 96.78\% \\ \cline{1-1}  & 4 CNN-LSTM & 0.79\% & 4.17\% & 95.04\% \\ \cline{1-1}  & 4 CNN-2 LSTM & 0.0\% & 2.22\% & 97.78\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Confusion matrix of DGIST campus driving behavior classification results.
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Element** & **Atomic Number** & **Absorption Area** & **f1** & **f2** \\ \hline H & 1 & 5.881 \(\times\) 10\({}^{-3}\) & 1.000 & 0.1127 \(\times\) 10\({}^{-5}\) \\ C & 6 & 4.260 & 6.019 & 0.9728 \(\times\) 10\({}^{-2}\) \\ N & 7 & 6.984 & 7.033 & 0.1860 \(\times\) 10\({}^{-1}\) \\ O & 8 & 11.22 & 8.053 & 0.3414 \(\times\) 10\({}^{-1}\) \\ Na & 11 & 28.31 & 11.14 & 0.1237 \\ Mg & 12 & 39.67 & 12.18 & 0.1833 \\ P & 15 & 74.94 & 15.31 & 0.4413 \\ S & 16 & 91.31 & 16.34 & 0.5567 \\ K & 19 & 150.2 & 19.42 & 1.116 \\ Ca & 20 & 174.1 & 20.38 & 1.327 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: X-ray properties of selected elements under 8-keV X-ray. f1 describes the phase shift, f2 describes the absorption. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **H** & **C** & **N** & **O** & **Na** & **Mg** & **P** & **S** & **K** & **Ca** & **Mass-Weighted Average of Atomic Number** \\ \hline Atomic number & 1 & 6 & 7 & 8 & 11 & 12 & 15 & 16 & 19 & 20 & \\ Water & 11.2 & - & - & 88.8 & - & - & - & - & - & - & 7.2 \\ Muscle & 10.2 & 12.3 & 3.5 & 72.9 & 0.08 & 0.02 & 0.2 & 0.5 & 0.3 & 0.007 & 7.1 \\ Fat & 11.2 & 57.3 & 1.1 & 30.3 & - & - & - & 0.006 & - & - & 6.1 \\ Skeleton & 6.4 & 27.8 & 2.7 & 41 & - & 0.2 & 7 & 0.2 & - & 14.7 & 9.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Element proportions of biological tissue calculated by mass percentage. detection in fish. Through the 3D imaging of fat in zebrafish and the calculation of their fat content, we provide a new technical means for fat detection in fish.
# Distributed Task Allocation for Multiple UAVs Based on Swarm Benefit Optimization
## 5 Discussion
### Algorithm Efficiency  
The evaluation of algorithm efficiency primarily focuses on two dimensions: time complexity and space complexity, with the analysis often employing the Big-O notation for asymptotic representation.  
Firstly, the time complexity of the algorithm is presented. In consideration of the worst-case scenario, the UAV needs to construct a task sequence comprising \(N_{t}\) tasks. When constructing the task sequence, the UAV calculates the benefit associated with each of the \(N_{t}\) tasks. Initially, for the first task, there are \(N_{t}\) possible tasks to choose from. Since the current task sequence is empty, each task can only be inserted into one position, resulting in \(N_{t}\) iterations to obtain the benefits for all \(N_{t}\) tasks. Subsequently, for the second task, there are \(N_{t}-1\) tasks available for selection. Since the current task sequence already contains one task, the position for inserting the second task can be either before or after the existing task, leading to \((N_{t}-1)*2\) iterations to compute the benefits for the \(N_{t}-1\) tasks. This process continues analogously, with the last task having  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & \multicolumn{2}{c}{**Swarm Benefit [50 th / 75 th / 90 th Percentile]**} & \multicolumn{2}{c}{**Average Swarm Benefit**} & \multicolumn{2}{c}{**Average Increement**} \\ \(N_{p}\) & \(N_{u}\) & **CBBA** & **PI** & **BV** & **CBBA** & **PI** & **BV** & **vs. CBBA** & **vs. PI** \\ \hline \multirow{10}{*}{3} & 6 & 2417/241 & 2554 & 2386/2402/2531 & 2445/2448/2578 & 2660 & 2645 & 2685 & 0.96\% & 1.53\% \\  & 8 & 3267/3280/3417 & 3237/326/3405 & 3044/3  
possible insertion points within the task sequence consisting of \(N_{t}-1\) tasks, necessitating \(N_{t}\) iterations. The entire process involves a total number of iterations given by the formula: \(N_{t}*1+(N_{t}-1)*2+(N_{t}-2)*3+\cdots+1*N_{t}=N_{t}*(N_{t}+1)*(N_{t}+2)/6\), resulting in a complexity of \(O(N_{t}^{3})\).  
Secondly, the space complexity of the algorithm is examined. Since the number of variables remains constant, specifically corresponding to the \(N_{t}\) tasks, the space complexity of the algorithm is \(O(1)\).
## 4 Results and Discussion
### MPPT Test  
Lastly, the capability of the POVC to track the maximum power under full-load operation was studied. At the V\({}_{\text{ref}}\) value of 40.66 V, the MPPT results at several different irradiance levels are presented in Table 5. The temperature was fixed at the value of 31 \({}^{\circ}\)C. All the maximum load values were calculated based on the theoretical maximum power at every tested irradiance level. As shown in Table 5, decent MPPT ratios were obtained at all irradiance levels evaluated.  
Figure 11: V\({}_{\text{B}}\) at varying irradiance levels from (**a**) simulation and (**b**) experiment.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Irradiance** & **Theoretical** & **Maximum** & **V\({}_{\text{MPP (V)}}\)** & **I\({}_{\text{MPP (A)}}\)** & **P\({}_{\text{MPP (W)}}\)** & **MPPT Ratio** & **Converter** \\
**(W/m\({}^{2}\))** & **P\({}_{\text{MPP (W)}}\)** & **Load (f)** & **V\({}_{\text{MPP (V)}}\)** & **I\({}_{\text{MPP (A)}}\)** & **P\({}_{\text{MPP (W)}}\)** & **(\%)** & **Ratio (\%)** \\ \hline
1000 & 128.1 & 12.91 & 24.75 & 5.093 & 125.7 & 98.13 & 97.45 \\ \hline
800 & 108.9 & 15.18 & 25.97 & 4.140 & 107.3 & 98.53 & 97.39 \\ \hline
600 & 86.35 & 19.15 & 27.29 & 3.137 & 85.6 & 99.10 & 97.28 \\ \hline
400 & 60.36 & 27.39 & 28.74 & 2.094 & 60.2 & 99.73 & 96.89 \\ \hline
200 & 31.11 & 53.14 & 29.64 & 1.049 & 31.1 & 99.94 & 95.01 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulated MPPT test results at different irradiance levels.  
Experimental replication of the prior simulation results was challenging to conduct because the maximum load value for any given set of environmental conditions should be determined. Since the irradiance and temperature are continually fluctuating, this is nearly impossible to ascertain. Furthermore, the maximum load is frequently very low, ranging from 10 \(\Omega\) to 55 \(\Omega\), and the load bank is incapable of producing such a required load. Thus, the POVC was operated as a normal P&O MPPT algorithm in the experimental test. This was achieved by setting the V\({}_{\text{ref}}\) to a very high value to force the POVC to track the maximum possible V\({}_{\text{ref}}\) value during any environmental condition. Therefore, output voltage regulation was not considered in these tests. P-V and I-V curves were used to illustrate the obtained MPPT experimental results, as shown in Figure 12. As can be seen, except at the two lowest irradiances, the POVR successfully brought the PV operating point very close to the actual MPP. It is also worth noting that the POVR's tracking zone extended from 0 V to V\({}_{\text{MPP}}\). This fact strongly relates to the theory presented in Section 3.
## 4 Design and Simulation Flight of Control Law for Jumping and Steep Descent of Drones
### Digital Simulation Flight During Drone Takeoff and Landing Phase  
Due to the task requirement of achieving fast climb with less energy during the takeoff and climb process of the drone, precise altitude tracking is not required. Therefore, only the attitude controller designed in Section 3.2 is currently considered to control the takeoff process of the drone. Firstly, a digital simulation is conducted on the takeoff strategy with minimum-energy climb (flight speed of 15 m/s, climb angle of 45\({}^{\circ}\), and climb rate of 10.6 m/s) under ideal conditions. The simulation results are shown in Figure 32:  
Figure 32: Cont.  
According to the simulation results of the takeoff process, it can be seen that the drone can basically complete the takeoff climb task according to the designed takeoff climb strategy.  
During the takeoff phase, the drone takes about 13 s to jump from the ground to a stable climbing state and climbs a total height of about 120 m. At the moment of takeoff and landing, the maximum angle of attack of the drone reaches about 28\({}^{\circ}\), but it quickly decreases to the stall angle of attack range within 1 s. Therefore, it can be considered that there is no high-angle-of-attack state of the drone during takeoff and climb. It takes about 48 s for the drone to climb to a cruising altitude of 500 m, and during the stable climb phase, the drone's climb state is maintained at a 45\({}^{\circ}\) climb angle, 15 m/s airspeed, \(-0.56^{\circ}\) angle of attack, and 10.7 m/s climb rate, meeting the design requirements for the drone's takeoff mode. The controller has good performance.  
A complete simulation model of the glide slope controller is established, and a digital simulation of the entire process of drone descent is conducted. The response of each state variable is shown in Figure 33:  
Figure 33: _Cont._  
Figure 32: Digital simulation of takeoff climb process.  
Figure 33: Cont.  
The simulation results show that, during the approach process, the drone is in a low-speed high-angle-of-attack state, and the aerodynamic force of the drone is weakened. Longitudinal force balance is achieved by raising the head and increasing the throttle, and the pitch angle slowly increases from around 19\({}^{\circ}\) to around 45\({}^{\circ}\); the sliding angle slowly decreases from around 40\({}^{\circ}\) to around 7\({}^{\circ}\); and the fluctuation range of the angle of attack is around 5\({}^{\circ}\). The descent into the near range takes about 18 s, with a drone speed of about 5.8 m/s when touching the ground, a sinking rate of about 0.8 m/s, and a descent angle of about 7\({}^{\circ}\). It can safely complete the descent flight mission according to the designed descent strategy.
## 4 AI for the Study of Inflammatory Diseases of the Orbit  
Inflammatory diseases of the orbit, such as Non-Specific Orbital Inflammation (NSOI), represent complex, idiopathic, and often relapsing clinical conditions. They make up approximately 6-16% of orbital lesions, with greater prevalence in the female sex, and are characterized by variable clinical manifestations, such as dacyoadenitis, myositis, and inflammation of the lacrimal glands. Despite the well-established use of corticosteroid drugs, the recurrence rate exceeds 50 percent.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \begin{tabular}{c} **Objective of** \\ **the Study** \\ \end{tabular} & **AI Model Used** & **Data Input** & **Results** & **Validation** \\ \hline [PERSON] et al. & \begin{tabular}{c} SFT vs. \\ schwannoma \\ \end{tabular} & LASSO-logistic & \begin{tabular}{c} A total of 106 radiomics \\ features. MRI T2 WI + \\ CET1 WI; 3T, 1 \(\times\) 1 mm \\ resampling \\ \end{tabular} & AUC & \begin{tabular}{c} Three cohorts \\ (train, val, \\ multicenter test) \\ \end{tabular} \\ \hline [PERSON] et al. & \begin{tabular}{c} IgG4-ROD vs. \\ MALT \\ \end{tabular} & SVM + PCA & \begin{tabular}{c} A total of 107 features, \\ LASSO-selected. T1 WI, \\ T2 WI; 3T MRI, 3–4 mm \\ slice, STIR, DWI \\ \end{tabular} & AUC up to 0.821; \begin{tabular}{c} Five-fold CV on \\ train and \\ test setting \\ \end{tabular} \\ \hline [PERSON] et al. & \begin{tabular}{c} LNH vs. \\ pseudotumor \\ \end{tabular} & XGBoost + SHAP & \begin{tabular}{c} MRI scans + clinic. \\ T1/T2 + lab markers \\ \end{tabular} & \begin{tabular}{c} AUC 0.887; high \\ decision \\ transparency \\ \end{tabular} & \begin{tabular}{c} Hold-out val. + \\ SHAP analysis \\ \end{tabular} \\ \hline [PERSON] et al. & \begin{tabular}{c} Metastasis vs. \\ primitive tumor \\ \end{tabular} & ResNet & \
\begin{tabular}{c} CE-MRI dynamic \\ sequences. Axial \\ CE-MRI; \\ multiparametric \\ \end{tabular} & AUC \textgreater{} 0.92;
\begin{tabular}{c} Five-foldCV + \\ internal testing set \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Applications of AI in the differential diagnosis of neoplastic lesions.  
In light of the heterogeneous and multifactorial nature of NSOI, artificial intelligence (AI) is emerging as a key resource for understanding pathogenetic mechanisms, identifying biomarkers, and developing new, more precise diagnostic approaches.
## 2 Materials and Methods
### Statistical Analysis  
Statistical differences in soil properties among the three studied land uses (F, G, A) were assessed by nonparametric Kruskal-Wallis tests followed by [PERSON]'s test with Bonferroni adjustment for multiple comparisons (\(p<0.05\)), implemented using the 'dunn.test', 'tidyr', and 'dplyr' packages in R software (version 4.3.2). Multivariate statistical analysis was performed using R software, with a principal component analysis (PCA) and hierarchical cluster analysis (HCA) implemented on the basis of Euclidian distances and Ward'sminimum variance method (ward.D2). The analysis was executed using the 'factoextra', 'ggplot2', and 'dendextend' packages, with confidence ellipses (95%) computed for PCA group discrimination. Partial least squares discriminant analysis (PLS-DA) and receiver operating characteristic (ROC) curve analysis were performed utilizing the'mixOmix' and'mROC' packages in R software. PLS-DA was applied to assess our ability to discriminate between the three land uses (F, G, A) by deriving latent variables (LVs) that optimize class separation on the basis of the soil volatilome profile. Model performance was further assessed by ROC curve analysis, where AUC values quantified model accuracy and robustness, confirming statistically significant separations. In addition, a VIP (Variable Importance Projection) score was calculated from the PLS-DA model to evaluate the contribution of each volatile organic compound (VOC) to class discrimination. VOCs with VIP scores exceeding 1 were considered significant as this threshold is widely recognized in the literature to indicate the variables that have a greater influence on the model than the average predictor.
# A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning
## 2 Materials and Methods
### Feature Extraction
#### 2.4.5 Statistical Features  
The number of satellites and the distribution of satellite CNR is different in indoor and outdoor scenarios. As shown in Figure 10a, the cumulative probability of indoor scenarios is close to 0.7 when the number of visible satellites is under 8, while the cumulative probability of outdoor scenarios is lower than 0.05 when the number of visible satellites is under 8. Figure 10b shows that the satellite CNR collected by different devices in the same scenario is different, but the distribution density tends to be consistent. In other words, the statistical data of the number of satellites and CNR are effective features to distinguish complex indoor and outdoor scenarios.  
To utilize more descriptive information under sliding windows of different lengths, we not only regard the number of the visible satellite as a feature, but also extract Mean, Variance, Std, Min, Max,  
Figure 10: The statistical feature of GNSS measurements in indoor and outdoor scenarios. (**a**) The cumulative distribution probability of the number of visible satellites using different smartphones in an indoor and outdoor environment; (**b**) The violin plot of visible satellite CNR using four types of smartphone in the same scenario.  
Median, Range, InterQuartile Range, Skewness and Kurtosis of satellite CNR from \(\mathbf{G}\) and \(\mathbf{G_{p}}\) as features. Furthermore, we consider the mean of PDoP, HDoP and VDoP in \(\mathbf{S_{u}}\).
## 3 Data Pre-Processing
### Variational Auto-Encoder Based on Gaussian Mixture Model  
The measurement error corresponding to each operating distance follows a single Gaussian distribution, and the measurement errors corresponding to multiple working distances follow the Gaussian mixture distribution as a whole. We adopt a variational auto-encoder based on a Gaussian mixture model to estimate the statistical characteristics of the measurement error data.
## 3 Results  
### Absorption Coefficient of the Formalin Fixed Normal Liver Tissue  
To investigate whether liver tissue can be identified by THz imaging, we first precisely measured the absorption coefficient of the normal liver tissue in the THz region. According to references, THz waves are mainly absorbed by the water content in the liver tissue. Because water is a polar molecule, the absorption coefficient is high owing to the dielectric relaxation in the THz region. The absorption coefficient of Marinol, which was used in the experiments as water-insoluble mounting medium, is less than 0.59 cm\({}^{-1}\) at a frequency of 0.1 THz. Therefore, when the thickness of a sample is \(\leq\)200 \(\upmu\)m, the absorption due to the mounting medium in the THz region is negligible. The refractive index of Marinol in the THz region is approximately 1.55, which is almost equal to that of the liver tissue (-1.57). The reflection of the THz waves is negligible at the boundaries between the quartz slides and Marinol. Subsequently, the transmission of the sample in the THz region is calculated by normalizing the CSR intensity passing through the Marinol fixed sample sandwiched between quartz slides. Figure 3 shows the transmission images of normal liver tissues with thicknesses of 100 and 200 \(\upmu\)m in a 2 mm\({}^{2}\) area.  
Figure 3: Transmissions of the normal tissue with widths of (**a**) 100 \(\upmu\)m and (**b**) 200 \(\upmu\)m in terahertz imaging. Each graph illustrates the transmission data for 7 \(\times\) 7 points in 2 mm\({}^{2}\) using the three-dimensional and two-dimensional hue maps.  
The average transmissions are 58.3 \(\pm\) 1.7% for a thickness of 100 \(\upmu\)m and 35.2 \(\pm\) 1.7% for a thickness of 200 \(\upmu\)m. The measured dispersion of the transmission is relatively small, and our THz imaging system can identify a difference of approximately 1% with respect to the transmission. The absorption coefficient \(\alpha\) is 52.9 \(\pm\) 1.0 cm\({}^{-1}\), which was calculated from the measured transmission data using the least squares method. Therefore, we found that the appropriate thickness of the samples for the THz imaging experiments is 200 \(\upmu\)m.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 3 Experimental Results and Analysis  
### Experimental Platform and Hyperparameter Settings  
In this study, the target detection algorithm was implemented on a computer hardware platform consisting of an Intel(R) Core(TM) i9-12900K CPU and two NVIDIA GeForce GTX 3090 ti GPUs. The software setup includes CUDA 11.3, the Windows 10 operating system, the PyTorch 1.11.0 deep learning framework, and the programming language Python. During the training process, the SGD optimizer was employed for parameter updates. The training was conducted with a batch size of 32, an initial learning rate of 0.01, a decay coefficient of 0.0005, and a momentum factor parameter of 0.937. The total number of training iterations was set to 150, and the input resolution of the model was 640 \(\times\) 640 pixels.
## 3 Results and Analysis on a Meta-Fiber Platform
### Large-Scale Spot Array Generation on a Fiber Array Platform  
By cascading the designed metasurface collimating splitter to a fiber array, a larger matrix of spot arrays can be generated in the far field, as shown in Figure 7a. The lens can be used to transform the lateral shift in the focal plane to the angular deflection in the far field, functioning as a Fourier transformer. The incident light is collimated and deflected by the lens component at a smaller angle, while it is deflected by the Dammann grating component at a larger angle. The Fourier function of the lens can be expressed as:  
\[l_{x}=\frac{f}{n_{sub}}\cdot u_{x},\ l_{y}=\frac{f}{n_{sub}}\cdot u_{y} \tag{7}\]  
where \(l\), \(f\), \(n_{sub}\) and \(u\) denote the lateral distance to the optical axis on the focal plane, the focal length, the refractive index of the substrate and the direction cosine. The refractive index of the substrate is the same as in the design above, while the focal length is increased to 2000 \(\upmu\)m to make sure that the generated spots are within the light cone. A 3 \(\times\) 3 collimating splitter cascading to a 5 \(\times\) 5 fiber array, which is able to generate up to a 15 \(\times\) 15 spot array, is demonstrated and simulated using the angular spectrum propagation theory, as shown in Figure 7b. In the simulation, a 5 \(\times\) 5 single-mode fiber array is placed at an adjacent distance of 127 \(\upmu\)m, which is the typical value in a commercial fiber array. The metasurface is designed with a size of 900 \(\upmu\)m \(\times\) 900 \(\upmu\)m to cover the effective area of the incidence. The Gaussian beam from each of the 5 \(\times\) 5 fiber arrays is collimated and diffracted into a 3 \(\times\) 3 spot array by the metasurface, resulting in the final 15 \(\times\) 15 spot array in the far field. The deflected angle can be estimated by adding the deflection angle of the  
Figure 6: Influence of the fabrication error on the metasurface. **(a)** Sidewall angle variation and **(e)** diameter variation. Influences of the sidewall angle variation on **(b)** the divergence angle, **(c)** the uniformity and **(d)** the total diffraction efficiency, respectively. Influences of the diameter variation on **(f)** the divergence angle, **(g)** the uniformity and **(h)** the total diffraction efficiency, respectively.  
Dammann grating according to Equation (3) and the deflection angle of the collimating lens according to Equation (7). The compact design has great potential for generating large-scale spot arrays and it can be transplanted to a VCSEL platform for higher integration, meeting the requirements of small size, weight and power (SWaP) of smart devices.