esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Performance of Combined Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Range of Errors (\%)** & \multicolumn{2}{c}{**Basic Model**} & **Combined Model** \\ \hline \multirow{2}{*}{\(<\)0.25} & Number & 1595 & 2158 \\  & Proportion (\%) & 50.32\% & 68.08\% \\  & Number & 856 & 691 \\  & Proportion (\%) & 27.00\% & 21.80\% \\  & Number & 408 & 183 \\  & Proportion (\%) & 12.87\% & 5.773\% \\  & Number & 155 & 65 \\  & Proportion (\%) & 4.890\% & 2.050\% \\  & Number & 71 & 30 \\  & Proportion (\%) & 2.240\% & 0.9464\% \\  & Number & 31 & 27 \\  & Proportion (\%) & 0.9779\% & 0.8517\% \\  & Number & 22 & 7 \\  & Proportion (\%) & 0.6940\% & 0.2208\% \\  & Number & 12 & 6 \\  & Proportion (\%) & 0.3785\% & 0.1893\% \\  & Number & 20 & 3 \\  & Proportion (\%) & 0.6309\% & 0.09464\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Statistical table of prediction errors for basic and combined models. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Round Label**} & \multicolumn{2}{c}{**MAPE**} & \multicolumn{2}{c}{**MaxAPE**} & \multicolumn{2}{c}{**R2**} \\ \cline{2-7}  & **Basic Model** & **Combined Model** & **Basic Model** & **Combined Model** & **Basic Model** & **Combined Model** \\ \hline \(1\) & 0.3490\% & 0.2322\% & 2.386\% & 2.798\% & 0.9966 & 0.9983 \\ \(2\) & 0.3527\% & 0.2317\% & 2.813\% & 3.745\% & 0.9966 & 0.9983 \\ \(3\) & 0.3423\% & 0.2321\% & 2.366\% & 2.186\% & 0.9966 & 0.9982 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of cross validation. It can be observed that the combined model consistently demonstrates predictive advantages across various metrics in each of the five testing sets. Referencing the results from the five rounds, the combined model achieves an average MAPE of 0.2336%, a MaxAPE of 2.839%, and an R\({}^{2}\) of 0.9983, all of which outperform the basic model. This indicates that the combined model enhances the predictive accuracy of the basic model, better capturing the complex relationships and trends of the data. The results of multiple cross-validation rounds suggest that the combined model exhibits high stability in its predictions across different datasets.
# Deep Learning Architectures for Diagnosis of Diabetic Retinopathy
## 3 Results
### U-Net  
An interesting finding about the improvement produced by the data augmentation techniques was found when removing the noise and zoom transformations. By using the whole transformations set, the achieved DICE was 0.81 and 0.82 for the validation and test sets, respectively, while reducing these metrics down to 0.80 and 0.79, respectively, after removing the noise and zoom transformations.  
By observing the results in Figure 4, it can be seen that the U-Net is able to correctly segment the regions of the image that have the thickest blood vessels and even some areas where a non-expert human eye would hardly be able to perceive the presence of these vessels. It can also be noted that the major failure area is focused on the smallest blood vessels.
# Contour Mission Flight Planning of UAV for Photogrammetric in Hillside Areas
## 5 Analysis
### Ground Sampling Distance  
The GSD of the image is directly affected by the elevation. Ideally, the GSD of each image should be the same, but the topographic relief will change the height and directly affect the GSD. In this study, the results of calculating the GSD from the images taken by the two methods are shown in Figure 14. The GSD of the contour-type is 4-5.7 cm, and the grid-type is 3.1-5.8 cm, as shown in Figure 14. From Figure 14, we can find that the contour-type is better than the grid-type method. Therefore, this study proposes that the contour-type method based on a DEM can effectively plan the flight height that meets the user's needs and effectively make the difference of GSD smaller.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Case Study 1**} & \multicolumn{3}{c}{**Case Study 2**} & \multicolumn{3}{c}{**Field Test**} \\ \hline  & **Grid** & **Contour** & **Difference** & **Grid** & **Contour** & **Difference** & **Grid** & **Contour** & **Difference** \\ \hline Routes & 5 & 14 & 9 & 6 & 20 & 14 & 8 & 10 & 2 \\ Image number & 35 & 187 & 5 times & 65 & 258 & 4 times & 80 & 166 & 2 times \\ Average shooting & 194 & 199 & 5 & 186 & 200 & 14 & 217 & 244 & 27 \\ distance (m) & 74 & 80 & 6 & 68 & 85 & 17 & 67 & 82 & 15 \\ Overlap (\%) & 56\% \textless{} 30 & 100\% \textless{} & 44 & 35\% \textless{} 30 & 75\% \textless{} 30 & 40 & 65\% \textless{} & 100\% \textless{} & 35 \\ difference within the threshold (\%) & & 30 m & m & m & m & 150 m & 150 m & 150 m & 35 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluating flight route planning methods.  
Figure 14: GSD statistics for two types of route planning.  
In the contour route planning method, the elevation difference is considered to obtain a more consistent resolution for each captured image. From the elevation difference of each image in Figures 11-14, it can be found that the elevation difference of the contour-type is better than that of the grid-type, with a maximum gradation of 125-150 m. When the two aerial images were matched, the accuracy of contour-type matching was 1.06 pixels, and the accuracy of grid-type matching was 1.68 pixels. The minor elevation difference of the contour-type kept the resolution of the images relatively the same, which improved the accuracy of image matching.
## 4 Discussion
### Impact of Target Speed on Circular Tracking Movement  
Previous studies on motor control characteristics for circular tracking movements used Cartesian or polar coordinate parameters [13; 14; 17]. Three polar coordinate parameters are widely used: \(\Delta R\), \(\Delta\theta\), and \(\Delta\omega\)[13; 14]. Based on these parameters, our previous study analyzed the motor control and impact of target speed and visual feedback about the target during 2D tracking movements [14].  
In our previous study, Cartesian coordinate errors allowed us to confirm the circular tracking movement spatial accuracy (or spatial error) in terms of each axis in 3D space. However, \(\Delta\theta\) and \(\Delta\omega\) in polar coordinates of this study enable us to analyze motor control characteristics in 3D space in terms of the position and velocity control accuracy, respectively. Our results showed that a higher target speed increases position and velocity errors as well as the circular movement spatial error.  
In this study, we confirmed these results for 3D space irrespective of the rotation axis (Figures 3-5). In our previous study, we mapped 3D joint movements onto a 2D plane. In contrast, for this study, we directly tracked motor control characteristics in 3D space. We found that for the circular tracking movement motor control at slow target speed in 3D space, \(\Delta\theta\) and \(\Delta\omega\) (which indicate the temporal characteristics) were more sensitive than \(\Delta R\) (Figures 3-5). In other words, we confirmed that in 2D and 3D spaces, tracking errors (i.e., the three parameters) increased as the target speed increased. The observed movement delay is because the brain's neurotransmission, sensory processing, has a 200 ms delay in reaching the human visuomotor control system [18; 19]. Furthermore, it is more difficult to acquire target motion visual information [20] when the target speed increases.  
We also compared the frontal plane (_ROT_(0)) movements, similar to the 2D experiments, with those on the sagittal plane (_ROT_(90)), which are typical 3D movements. We quantitatively showed that circular tracking performance, position control accuracy, and velocity control accuracy in a 3D space is more difficult than in a 2D space (Figures 3A, 4A and 5A).  
In future work, we will investigate the impact of the target visual feedback in terms of the control strategy characteristics in 3D circular tracking movements [14; 21; 22; 23; 24; 25; 26].
## 4 Applications of AML towards Internet of Things (IoT) Robustness
The authors adopted crafting methods of adversarial samples based on the Jacobian-based saliency map technique against several classifiers. For the malware attack scenario, two defense mechanisms were proposed. They include specific selection criteria of adversarial samples for retraining the classifiers. The first mechanism employed the distance from the malware cluster centered on adversarial sample selection while the other used a probability measure derived from kernel-based learning (KBL) [100]. The two sample selection methods increased the detection accuracy by 6% compared to the random selection method. In [78], the researchers explored over-the-air spectrum poisoning attacks against an IoT communication system. They pointed out the energy efficiency and detection complexity of attacks. The considered attack targets the spectrum sensing period and modifies the transmitter's input data. The adopted defense strategy confirmed the robustness of the transmitter by intentionally making wrong decisions in specific time slots and thereby misleading the adversary. The time slots are selected based on the low classification scores which means these slots are not close to the decision boundary. In [101], the authors investigated malware detection in black-box attack scenarios through the employment of a GAN-based algorithm namely (MalGAN). The proposed algorithm utilized two detectors to generate adversarial examples attacks and thereby bypassing the target detection models in non-IoT environments. For the experiment, six classifiers were used including random forest (RF), logistic regression (LR), decision trees (DT), support vector machines (SVM), multi-layer perceptron (MLP), and a voting-based ensemble of these classifiers (VOTE). The detection rate was drastically reduced to approximately zero showing the MalGAN method's superiority. The attacker is able to control adversarial examples by changing their probability distribution. For defending the attacks, adversarial training is used where they showed insufficient solutions. The authors in [102] investigated several classifiers' resilience against multiple classifiers such as Support Vector Machines (SVM), Random Forest (RF), Convolutional Neural Networks (CNN), and Auxiliary-Classifier Generative Adversarial Networks (AC-GAN). They are evaluated in darknet traffic and application detection context to control such illegal activities. The CIC-Darknet2020 dataset is used in the performance evaluation where RF performed the best. They used AC-GAN as a sample generator for poisoning attacks to craft adversarial examples based on probability analysis. The reduction of performance in some selected classes reached zero which reflected a successful attack target. Adversarial training is employed as a defense mechanism to enhance models' detection rates. A brief of the surveyed papers is presented in Table 2.
# DAG Hierarchical Schedulability Analysis for Avionics Hypervisor in Multicore Processors
## 5 Hierarchical Scheduling in Hypervisor  
In order to make full use of the advantages of global scheduling and federal scheduling, we adopt the above new method of scheduling real-time DAG tasks. We use a hierarchical scheduling method to divide the whole scheduling problem into two parts:  
1. Scheduling DAG tasks on virtual processors.
2. Scheduling virtual processors on physical processors.  
More specifically, each DAG task is assigned several dedicated virtual processors (and executed exclusively on these processors at runtime). By correctly describing the resources provided by the virtual processor, we can analyze each DAG task independently as in federated scheduling. On the other hand, virtual processors are scheduled on physical processors at runtime, which effectively enables processors to be shared among different DAG tasks. Therefore, our hierarchical scheduling method inherits the advantages of federal scheduling and global scheduling, thus obtaining better schedulability.
## 4 Conclusions  
We investigated the interface layer using the CPW in X-band radar systems to minimize the leakage electric fields and improve the transmission characteristics. To prevent performance degradation in the radar systems, the interface layer with the CPW had three transition parts. Transition part \(A\) was between the SMP connector and the interface layer. Transition part \(B\) was the CPW in which the thickness gradually increased. Transition part \(C\) was for connecting the interface layer and the antenna. In addition, we proposed the interface layer using the CPW in the back-to-back configuration, which could observe the performance deteriorations caused by the interface layer in the radar systems. The reflection and transmission coefficients layer in the back-to-back configuration were measured to be \(-22.4\) dB and \(-0.82\) dB, respectively. To examine the performance when the interface layer using CPW was mounted on the actual radar systems, X-band \(2\times 2\) E-shape antennas with broadband characteristics were connected to the proposed interface layer. The bore-sight gain of the \(2\times 2\) array antenna was \(10.5\) dBi at \(9.5\) GHz. In addition, the HPBWs under the same conditions were \(47.8^{\circ}\) and \(39.1^{\circ}\) in the \(zx\)- and \(zy\)-planes, which are in good agreement with the simulated results. The results demonstrated that the proposed interface layer using the CPW was suitable for X-band marine radar systems.  
Conceptualization, J.C., D.J. and H.C.; methodology, J.C., D.J., C.-H.L. and H.C.; software, J.C. and D.J.; validation, D.J., C.-H.L. and H.C.; formal analysis, J.C., D.J. and H.C.; investigation, J.C. and D.J.; resources, J.C., D.J., C.-H.L. and H.C.; writing--original draft preparation, J.C. and D.J.; writing--review and editing, J.C., D.J. and H.C.; visualization, J.C. and D.J.; supervision, H.C.; project administration, H.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
This research has been supported by the Challenging Future Defense Technology Research and Development Program (9127786) of Agency for Defense Development in 2019.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Sample Preparation  
The AZO-RGO NCTF was prepared using electrospray deposition on a SiO\({}_{2}\) (300 nm)/Si substrate. The substrate surface was cleaned by sonicating in acetone, ethanol and de-ionized water, and modified by plasma treatment at 200 W for 5 min under an oxygen atmosphere to increase the hydrophilicity. The precursor solution for the deposition of AZO-RGO NCTF was prepared by mixing zinc acetate dihydrate (Zn(CH\({}_{3}\)CO)\({}_{2}\cdot\)2H\({}_{2}\)O), aluminum chloride hexahydrate (AlCl\({}_{3}\cdot\)6H\({}_{2}\)O) as a dopant material, and graphene oxide (GO, 5 wt%) in ethylene glycol (C\({}_{2}\)H\({}_{4}\)(OH)\({}_{2}\)) solvent. The precursor solution was sprayed through the nozzle (inner diameter: 0.1 mm, outer diameter: 0.23 mm) onto the heated substrate. The applied voltage, precursor solution feed rate, deposition time and hot plate temperature were maintained at 10 kV, 2 uL min\({}^{-1}\), 30 min and 150 \({}^{\circ}\)C, respectively. As-deposited film was subjected to rapid thermal annealing (RTA) at 600 \({}^{\circ}\)C for 30 min under an inert atmosphere. This process allowed the formation of single-phase ZnO and a reduction of graphene oxide (GO). Furthermore, AZO TF was also fabricated using the precursor solution without GO under the same deposition conditions in order to investigate the effect of graphene on the charge transport behavior.
# An Advanced Soil Classification Method Employing the Random Forest Technique in Machine Learning
## 1 Introduction
The application of support vector machines for estimating soil properties and classifying soil types based on known chemical and physical properties in sampled profiles is introduced by [PERSON] et al. [29]. The review of the existing literature suggests that ML models hold promise for achieving accurate soil classification tasks. This study utilizes the RF method, renowned for its high accuracy and robustness, to classify soil types. The parameters for this classification are derived from the USCS, a widely adopted method for categorizing soils based on their properties and behavior. These parameters include grain size distribution, Atterberg limits, coefficient of uniformity (Cu), and coefficient of curvature (Cc), which are defined within specific ranges to create a synthetic database for training the RF model. The importance of input factors in soil classification was evaluated using out-of-bag samples in the RF model. Through rigorous validation techniques, including cross-validation, the performance of the RF model was thoroughly assessed, demonstrating its capability to accurately classify soils. Finally, the RF model developed in this study is utilized to predict the characteristics of 47 soil samples. These predictions are then compared with the actual soil properties to confirm the reliability of the model. Notably, even in scenarios where one input factor is missing, the RF model demonstrates the ability to accurately identify soil properties, a capability that traditional methods lack, thus emphasizing the strengths of the RF model investigated in this study.
# An Improved Circumferential Fourier Fit (CFF) Method for Blade Tip Timing Measurements
## 2 The Improved Circumferential Fourier Fit (ICFF) Method  
### A Brief Introduction to TCFF  
The theory of TCFF is consistent with the CFF method. It uses multiple sensors (usually four) distributed at different positions in the circumferential direction of the casing to monitor blade vibration. On the condition that the EO is known, TCFF is equivalent to the CFF method, and the blade vibration parameters can be directly calculated according to the CFF method. In the case that the EO is unknown, the blade amplitude, vibration phase, direct current offset, and fitting residual error are calculated using the CFF method with each possible EO, which is selected within a certain range. After the traversal is completed, according to the principle of least squares, the EO corresponding to the minimum fitting residual error is the true EO, and the blade vibration parameters calculated using this EO are the true vibration parameters. More details about TCFF are provided by the literature [31]. The TCFF method introduces the idea of EO traversal to overcome the shortcomings of CFF, which must rely on known EO to identify blade vibration. Although TCFF did not make any changes to the CFF theoretical method, except to introduce EO traversal, to a certain extent, TCFF has wider engineering applicability than the CFF method. However, neither TCFF nor the CFF method takes into account the influence of blade vibration on the ToA. The ICFF method proposed in this paper further considers the effect of blade vibration on the ToA to achieve a higher accuracy of blade amplitude identification. Considering that TCFF can perform EO traversal, which overcomes the disadvantage that CFF must rely on known EO, this paper uses TCFF as a comparison object to illustrate the performance improvement of ICFF.
# Effect of Hydrogen Ion Presence in Adsorbent and Solution to Enhance Phosphate Adsorption
## 3 Results and Discussion
### Kinetic  
#### 3.5.1 Removal Efficiency  
The phosphate removal efficiency for 96 h is drawn at different initial solution pH in Figure 6. The tendency of removal efficiency (%) is strongly associated with the isotherm equations (\(Q_{m}\)) except for the GAC-w at pH 4 and the GACA-w at pH 9. At the acid condition, GAC-wo obtains \(\sim\)70% removal efficiency, this being the highest value in the current experimental condition, while others finally reach \(\sim\)60%. For the GAC-w, the removal behavior is almost the same as that of the GAC-wo until 6 h, and it decreases by 10% to \(\sim\)60% after 6 h, corresponding to desorption. At a pH of 7, 60% removal is obtained, except for GAC-wo, indicating that the pH effect is slight. At the base condition, regardless of the pH adjustment, the GAC shows only 40% removal efficiency, this being the lowest. Both GACAs obtain \(\sim\)60% removal efficiency but this is finally decreased to 40% after 6 h for the GACA-w; this is also likely for the GAC-w at pH 4.  
Another drawing was added to Figure 6 with the right y-axis calculating the ratio of removal efficiency at equilibrium (R\({}_{\text{e}}\)) to removal efficiency at a certain time (R\({}_{\text{t}}\)). The red solid line can indicate the achievement of 90% adsorption per total adsorption. A period of 9 h is sufficient to reach 90% removal for the GACA regardless of the initial pH and the pH adjustment, while the GAC requires 20 and 48 h at pH 7 and 9, respectively. The removal efficiency shapes with rapid and slow slopes at the beginning and later has been accounted for by [PERSON] et al. (2007) [35], who proposed the two-site irreversible sorption model at the exterior site by instantaneous occurrence and at the interior site by intra  
Figure 6: Phosphate removal efficiency (%) (left y-axis) and removal efficiency ratio (R\({}_{\text{t}}\)/R\({}_{\text{e}}\)) (right y-axis) as a function of time (h) at initial pH of 4 (a), 7 (b), and 9 (c).
###### Abstract
To overcome these challenges, ensemble learning techniques have been introduced to enhance diagnostic performance. Adaptive boosting (AdaBoost), a widely used ensemble learning algorithm, iteratively trains multiple weak classifiers while assigning higher weights to misclassified samples, thereby constructing a robust classifier [17]. This approach effectively mitigates the impact of noise and outliers while improving classification performance under imbalanced sample distributions. For example, [PERSON] et al. [18] applied a reinforced AdaBoost model to wind turbine-blade ice fault detection, while [PERSON] et al. [19] combined AdaBoost with extreme learning machines to enhance wind power prediction accuracy. In this study, AdaBoost is incorporated into the CNN-LSTM hybrid model to leverage the advantages of multiple weak classifiers, further improving fault feature recognition and enhancing system robustness. This integration not only reduces misclassification rates but also ensures high diagnostic precision under variable operating conditions. This study aims to develop a deep learning-based fault diagnosis model by integrating 1D-CNN and LSTM networks while incorporating AdaBoost for ensemble optimization. The 1D-CNN extracts local temporal features and fuses multi-sensor data, BiLSTM captures bidirectional temporal dependencies and global information, and AdaBoost enhances the overall model performance. This approach addresses the limitations of conventional fault diagnosis methods, significantly improving the accuracy, real-time responsiveness,and robustness of wind turbine-blade fault detection. The findings not only provide theoretical and technical support for wind turbine fault diagnosis but also offer valuable insights for the design of intelligent monitoring and maintenance systems in practical engineering applications. The remainder of this paper is structured as follows: Section 2 details the design of the integrated model, Section 3 describes the wind turbine-blade fault dataset and diagnostic model training, Section 4 presents experimental validation, and Section 5 concludes with general findings and future research directions.