esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Adjacent Camera-Based Dangerous Driving Trajectory Recognition for Ultra-Long Highways
## 3 Methodology
### Anomaly Trajectory Recognition Based on Trajectory Rules
#### 3.3.4 Blind Zone Trajectory Prediction Based on Bidirectional LSTM  
In long-distance tunnels, there is a certain blind spot between multiple cameras, which makes it impossible for us to directly obtain vehicle trajectory information and poses a safety hazard in road traffic management. Long Short-Term Memory (LSTM) is a specialized type of Recurrent Neural Network (RNN) architecture that effectively addresses the vanishing gradient and exploding gradient problems. LSTM incorporates input gates, forget gates, and output gates on top of the basic RNN structure. The architecture of LSTM is depicted in Figure 6.  
Bidirectional LSTM can simultaneously consider both the forward and backward information of a sequence, thereby enhancing the sequence modeling capability. The network architecture of Bidirectional LSTM is illustrated in Figure 7.  
Compared to traditional LSTM, Bidirectional LSTM adds a reverse LSTM layer in its structure. The input sequence is processed separately through the forward and backward LSTM layers, and then the outputs of the two LSTM layers are concatenated along the time dimension. This allows the model to utilize information both before and after the current time step, enabling a more comprehensive sequence modeling and prediction.  
During the training of the blind spot vehicle trajectory prediction model based on Bidirectional LSTM, preprocessing is conducted as a preliminary step. The feature sequences, composed of time steps, lateral and longitudinal positions of vehicle trajectories, vehicle speed, acceleration, and other information, are transformed into arrays. Each trajectory's feature sequence is partitioned into three segments: the front and rear segments are used as  
Figure 5: Schematic diagram of vehicle movement trajectory.  
inputs, while the middle segment serves as the expected output. Subsequently, the Bidirectional LSTM model is trained. During each forward pass, the hidden parameters of the model are computed, and predictions are generated. The error between the predictions and the ground truth is then calculated, and this error is employed in the backward pass. The model's various parameters are updated using the principle of gradient descent to minimize the objective function and complete the training process.
# Modeling and Control of an Articulated Multibody Aircraft
## 1 Introduction
### Related Work
#### 1.1.3 Control Design for Multibody Aircraft  
One of the many attributes that emerged from the application of internal moving masses in vehicles is the concept of multiple effectors. The availability of multiple effectors with overlapping functions introduces the problem of control allocation in an overactuated system. Feedback control system theory forms the backbone to address the challenges associated with MIMO systems. By comparing the reference signal to the actual response of the system, the controller calculates the control effort required [96; 97]. The response of an aircraft system can be represented by nonlinear equations of motion with 6 DoF, which can further be divided into longitudinal and lateral-directional parts, respectively describing pitching and rolling-yawing motions. Using the dynamics of each of these parts, individual flight controllers can be developed. Analysis of linear systems contain concepts and tools that facilitate control of such systems. Compared to nonlinear system-control theory, linear system-control theory presents a tractable solution and has been used extensively in aircraft flight control [96]. The Proportional Integral Derivative (PID) controller is the most widely used controller in real-world applications, and has been used for flight control as in [98; 99; 100; 101; 102; 103]. However, the gain of the controller needs to be iteratively tuned, which consumes time and effort. Of the more robust control methods, \(H_{\infty}\) control and sliding mode control (SMC) are some popular choices [104; 105; 106; 107; 108]. However, \(H_{\infty}\) control is conceptually difficult and challenging to implement because it requires in-depth mathematical knowledge and model information. On the other hand, SMC is susceptible to chattering due to switching of the high-frequency control. Dynamic inversion is another common control method used for flight control [98; 109]. Although this method is intended for nonlinear systems, estimation of the analytical inversion of nonlinear systems online is very difficult.  
Optimal control is another control design approach, which has the advantage of being able to provide the best possible solution for a given cost function. Popular optimal control schemes include linear quadratic regulator (LQR), linear quadratic integrator (LQI), and model predictive control (MPC). Such schemes provide the optimal solution, and can also include multiple loop feedback at the same time [97; 102; 110; 111]. Control Allocation (CA) is another strategy that has been developed to handle overactuated systems, which answers the question of how to distribute control action among a redundant set of effectors to achieve a control design. In control allocation, actuator selection is separated from the controller task so that the controller only handles virtual control [112]. Common methods for solving the control allocation problem include optimization-based CA [113; 114; 115], direct CA [116] and daisy chaining CA [117; 118]. A detailed description of these methods can be found in [112; 116; 119].
# A Distributed Storage and Access Approach for Massive Remote Sensing Data in MongoDB
## 6 Conclusions  
Faced with the deficiencies of slow reading and writing speed, difficult horizontal expansion and low query efficiency in the process of managing massive remote sensing data with traditional relational database management systems, this paper proposes a distributed storage and access method for massive remote sensing data using the MongoDB sharding cluster architecture, with structured metadata stored in the form of document and unstructured image data stored with the GridFS mechanism. The result shows that the proposed method can overcome the weak points of traditional methods, scale out the database, and is more suitable to manage massive remote sensing data. For future work, we plan to study the influence of the number of data nodes on the performance of the distributed system.
# Enhanced Privacy-Preserving Architecture for Fundus Disease Diagnosis with Federated Learning
## 3 Methodology
### Data Preprocessing  
For this study, three classes were used: normal (N), glaucoma (G), and cataract (C). The dataset size has 3402 images with a 64:16:20 train-validation-test split. As explained in more detail later in the paper, FL architecture had several clients, and the dataset was evenly (and randomly) split across these clients to ensure consistency.  
Data preprocessing is an ML technique that transforms raw data into a more understandable and desired form [31]. The majority of the time, this means making changes to the dataset to facilitate and support more effective and efficient model training. Since the data are non-IID and vary throughout, the dataset must be preprocessed before any model training. This would allow for improved accuracy, reliability, and overall robustness of the ML models when trained in this study [32].  
Images were first augmented using RGB Contrast Limited Adaptive Histogram Equalization (CLAHE) Transform, which was originally developed for the effective enhancement of low-contrast medical images like those used in this study [33]. RGB CLAHE enhanced the ocular images by improving image contrast and overall luminance, emphasizing the blood vessels and veins for more efficient model training.  
After feature augmentation, all images were cropped using a simple \"bounding box\" image extraction algorithm (crops out an outermost, square-shaped border based on the colored pixels of the fundus images, removing any white space outside the square border). After the edges of the eye were extracted correctly, all images were cropped to remove the blank space to prevent models from analyzing anything besides the ocular image.  
Figure 3 demonstrates the data preprocessing steps.  
Figure 2: Fundus images from the ODIR dataset showing anatomical structures and abnormalities due to various ophthalmological diseases: (**a**): Normal, (**b**): Glaucoma, (**c**): Diabetic Retinopathy, (**d**): AMD, (**e**): Hypertension, (**f**): Cataracts, (**g**): Other abnormalities, (**h**). For this study, only Normal (**a**), Glaucoma (**b**), and Cataracts (**f**) were used for our dataset.  
Afterward, several general transformations were applied to the entire dataset. First, the images were resized to 224 \(\times\) 224 pixels to ensure all data were consistent for the models to train on. Then, the data were augmented using random horizontal and vertical flipping to improve overall model robustness, especially because of this study's use of a heterogeneous dataset. There was a 50% probability of the data being mirrored. Studies have shown that performing data augmentation like this will reduce the chances of model overfitting and improve the model's generalization ability.  
Due to TL being implemented in this study's proposed architecture, the data were normalized using the ImageNet dataset's mean and standard deviation. Normalizing the data to match what the VGG-19 neural network was trained on ensured that the TL used in this architecture remains efficient.
## 1 Introduction
Although this method achieved shorter running times compared to other methods, it only utilized a single-objective genetic algorithm that treats running time as the objective function, without considering whether the path itself is the shortest. This paper presents an improved method for time-optimal trajectory planning of the manipulator. Firstly, the quintic non-uniform rational B-spline (NURBS) curve is used to fit the manipulator trajectory passing through the path points. Then, to address the shortcomings of the traditional fast non-dominated sorting genetic algorithm II (NSGA-II), such as slow convergence speed and easy entrapment in local optimal values, the traditional NSGA-II is improved by using the reinforcement-learning algorithm to optimize the crossover and mutation probabilities and by introducing the variable neighborhood search (VNS) algorithm to enhance the capabilities of local search. The improved algorithm and other multi-objective optimization algorithms are tested on the test function to verify the reliability of the improved algorithm. Finally, the proposed approach utilizes an improved version of the NSGA-II to plan the time-optimal trajectory of the manipulator, taking into account both the path and time optimization simultaneously. The variables are designed as path points and their running time. The first objective function is designed as the total joint increment of the six joints of the manipulator along the whole trajectory, the second objective function is the total time of the current path, and a penalty function is introduced to limit the manipulator's angular velocity, angular acceleration, and angular jerk. By utilizing the iterative improvement of NSGA-II, the proposed method generates the optimized trajectory for the manipulator, incorporating both path planning and time optimization simultaneously. This study conducts simulation verification on MATLAB using PUMA560 and demonstrates that the proposed method yields positive effects on the trajectory optimization of the manipulator compared with other trajectory planning methods. The remaining sections of this paper are as follows. Section 2 introduces the commonly used PUMA560 manipulator and established its model. Section 3 discusses the NURBS interpolation and establishes the interpolation matrix. Section 4 presents the improvedNSGA-II and compares it with other algorithms. Section 5 performs trajectory planning for the manipulator and compares it with other methods. The final section presents the conclusion of the paper.
## 5 Experimental Results and Analysis
The IGD performance is more favorable on eleven test functions, particularly on DTL5, MaF2, MaF3, MaF4, MaF7, and SMOP3, where the box plots of IGD are more squashed than the other algorithms, and on DTLZ1, DTLZ2, DTLZ3, and NSGAIII, where the box plots are virtually identical, indicating that the algorithm and Pareto frontier are a better fit. Figure 2: Comparison of Pareto Approximation Frontiers for Five Comparison Algorithms on Three-objective SMOP3. \begin{table}
\begin{tabular}{l c c c c c c} \hline
**Problem** & **M** & **D** & **CMOPSO** & **SPERA** & **SMPSO** & **MoDSA** \\ \hline SMOP3 & 3 & 20 & \(1.3217\times 10^{-6}.991\times 10^{-3}\) - & \(1.1988\times 10^{-6}.997\times 10^{-3}\) + & \(1.2044\times 10^{-6}.03\times 10^{-3}\) + & \(2.3338\times 10^{-6}.12\times 10^{-3}\) - & \(1.2188\times 10^{-6}.10\times 10^{-3}\) - \\ SMOP3 & 5 & 20 & \(2.8987\times 10^{-6}.105\times 10^{-3}\) - & \(1.4187\times 10^{-6}.03\times 10^{-3}\) - & \(1.4241\times 10^{-6}.03\times 10^{-3}\) - & \(2.6767\times 10^{-6}.04\times 10^{-3}\) - & \(1.3525\times 10^{-6}.071\times 10^{-3}\) + \\ SMOP3 & 8 & 20 & \(3.3209\times 10^{-6}.148\times 10^{-3}\) - & \(2.3996\times 10^{-6}.08\times 10^{-3}\) + & \(2.9496\times 10^{-6}.154\times 10^{-3}\) - & \(2.9915\times 10^{-6}.153\times 10^{-3}\) - & \(2.2820\times 10^{-6}.03\times 10^{-3}\) + \\ SMOP3 & 13 & 20 & \(2.7198\times 10^{-6}.206\times 10^{-3}\) - & \(1.5233\times 10^{-6}.256\times 10^{-3}\) - & \(2.7267\times 10^{-6}.248\times 10^{-3}\) - & \(2.3267\times 10^{-6}.03\times 10^{-3}\) - & \(1.2949\times 10^{-6}.03\times 10^{-3}\) + \\ \hline \end{tabular}
\end{table} 
Table 5: ICD values for five algorithms with different target dimensions on SMOP3 test function. Figure 4 depicts box plots of the population HV metrics acquired by the five methods following 30 individual tests on the 12 specified test functions. From Figure 4, it can be seen that that the HV values of MaOISSA on the 12 test functions are generally high and the box plots are flat, particularly on DTLZ2, DTLZ5, MaF2, MaF7, and SMOP3, where the HV values of the algorithms have more significant improvement compared to other algorithms, and on DTLZ1, MaF3, and MaF4, where the HV values of the algorithms are essentially the same, indicating that the algorithms have better robustness compared to MaOISSA. Therefore, MaOISSA is able to solve most of the high-dimensional multi-objective optimization problems, even though it is unable to achieve optimum solutions for all test functions. Figure 4: Five algorithms for HV indicator box plots on 12 test functions. Figure 3: Five algorithms for IGD indicator box plots on 12 test functions.
## 2 Why the Electroencephalogram (EEG)?
### qEEG and Cerebral Haemodynamics and Metabolism  
Studies have suggested that different qEEG characteristics are related to cerebral hemodynamics and metabolism [181; 182; 183; 184; 185; 186; 187; 188; 189; 190]. Cerebral cortex metabolism disturbance is associated with and may be responsible for cortical neural synchronization anomalies that may manifest as abnormal qEEG oscillations [191]. Additionally, changes in the characteristics of qEEG oscillations (amplitude, power, frequency) are proportional to cerebrovascular damage (CVD) [119]. The qEEG has been shown to be a reliable marker of the decline in neuronal integrity associated with a decline in blood flow [192; 193; 194; 195; 196; 197; 198]. Additionally, studies show a sensitivity greater than 80%, false-positive rates below 5-10%, and correlations of 70% between qEEG and blood flow in ischemic and non-ischemic regions, thus suggesting that the qEEG can reliably detect focal features that can be quite abnormal even if the computer tomography (CT) or MRI scans are still normal (dysfunction without infarction) [199]. Similarly, in patients with subarachnoid hemorrhage, only qEEG could differentiate patients with and without cerebral infarction and not doppler/color-coded duplex sonography [200]. Further, recent meta-analyses have shown that qEEG has prognostic potential in predicting patient independence and stroke severity beyond that afforded by standard clinical assessments [201] (see also [202; 203]). Indeed, qEEG changes precede that of multimodal monitoring or confirmation of infarction on CT [204].  
Cerebral hemodynamics and metabolism are regulated by a complex interaction between different homeostatic mechanisms where neurotransmitters play a significant role.
# Vision-Guided Autonomous Robot Navigation in Realistic 3D Dynamic Scenarios +
## 6 Obstacle Avoidance
### Discussion and Limitations  
In the context of our approach, it is assumed that all obstacles are situated at ground level. Consequently, the navigation mesh must be reconstructed to eliminate walkable areas blocked by these obstacles. While our mobile robot possesses a height of 0.98 m, allowing it to navigate through tunnels of similar dimensions, the situation changes when dealing with taller robots. If a robot's height surpasses the maximum clearance of a tunnel, a collision becomes inevitable. It is worth noting that the upper structure of the  
Figure 16: Path replanning for obstacle avoidance. In (**a**), the robot’s path (yellow) successfully avoids a static obstacle (a chair). However, the appearance of a human pedestrian creates multiple obstacles, resulting in an unfeasible path. This prompts the algorithm to clear the previous obstacle configurations, as shown in (**b**), and regenerate the NavMesh. Finally, (**c**) shows the replanned path, taking into account both the robot’s pose (green point) and the presence of static and dynamic obstacles.  
Figure 15: The replanned path bypassing the additional static obstacle. In the original scenario (**a**), the known corridor is free of obstacles, allowing the robot, shown as the green point, to move directly toward the destination (red point). However, when a previously unknown static obstacle appears in the environment, the robot must update the map to include this new information and adjust its path accordingly. In (**b**), the algorithm generates a new, collision-free path to avoid the obstacle.  
tunnel is not originally situated on navigation meshes or closely connected to the walkable regions. Thus, for very tall robots or autonomous vehicles traversing tunnel-like structures, collision avoidance might be compromised. This limitation underscores the need for a comprehensive consideration of vertical obstacles in real-world scenarios.  
Another limitation emanates from the design of our mobile robot. Situations may arise where the robot struggles to localize its position through visual SLAM due to a paucity of features for tracking. In such cases, our current mechanism necessitates the robot to backtrack to a prior state with observable environmental features to regain positional awareness. However, as no secondary camera is installed to capture the rearward view, backward motion could potentially lead to collisions in the presence of unforeseen dynamic obstacles. Thus, our future work should explore sensor fusion strategies, as the incorporation of additional sensors can augment obstacle detection capabilities, especially in the rearward direction.  
This discussion on obstacle avoidance elucidates both the framework's capabilities and its associated limitations, presenting a well-rounded perspective for readers and highlighting avenues for future research and refinement.
## 3 Discussion
### Sterilization
[36] showed that the application of irradiation-based sterilization method caused a decrease in pore size in PLA. Similarly, [PERSON] et al. [37] reported the reduction of pore size in PLA due to radiation sterilization. Both studies indicated, furthermore, an increase in surface roughness as a result of substantial scission of polymeric chains due to radical formation; this phenomenon might explain some changes in the distribution of pores in our tested composites. Although the E-beam is considered as safe to health products, some changes in pore distribution and compressive strength were observed. More importantly, this sterilization procedure caused the substantial reduction of \(M_{\rm w}\) in PLA (as clearly confirmed by GPC data), which might have further consequences in degradation of composites and mechanical properties in the extended period of incubation by in vivo design. The scission mechanism is predominant, thus PLA macromolecules present in composites undergo partial degradation. Therefore, it is recommended to select the adjusted condition to maintain the physicochemical and structural integrity of composite or take into an account changes occurred during the sterilization prior to implantation.
# YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective
## 1 Introduction
Due to the limitations of a single-camera viewpoint, the camera can only provide a limited field of view, leading to situations where targets are occluded, or there are variations in the viewpoint, thereby increasing the complexity of object detection. Secondly, due to the lack of accurate depth information in images, relying solely on 2D images for object detection makes it difficult to obtain the true size and shape of objects, affecting precise localization and pose estimation. Additionally, environmental factors such as lighting changes and weather conditions can also affect the accuracy of single-camera 3D object detection. Furthermore, real-time performance is also highly demanded for traffic object detection algorithms. For example, [PERSON] et al. [16] achieved accelerated deployment of the YOLOv7-tiny algorithm on FPGAs using high-level synthesis (HLS) tools. This method significantly reduced the usage of digital signal processing (DSP) units and flip-flops, achieving remarkable real-time application latency. To address the challenges of single-camera 3D object detection, this paper thoroughly investigates and explores intelligent transportation systems and object detection. We propose a novel algorithm based on single-camera vision to address the issue of poor accuracy and precision in object detection. The algorithm utilizes information such as the 2D bounding box of the traffic object, projected corner keypoints, and offset vectors relative to the center of the 2D bounding box to assist in detecting 3D object bounding boxes. Additionally, we introduce a 4-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to enhance the saliency of features for objects at different scales and improve detection accuracy. Through experimental evaluations on roadside monitoring datasets [14], our method achieves significant performance results in single-camera 3D traffic object detection. In conclusion, single-camera 3D traffic object detection from a roadside monitoring viewpoint has significant research and application value. By overcoming the limitations and challenges of a single-camera viewpoint, our algorithm can achieve more accurate and reliable traffic object detection, providing a more reliable guarantee for urban traffic safety and efficiency. In the future, we will further optimize algorithm performance and explore integration with other traffic monitoring systems to achieve more intelligent and efficient traffic management and control.