esa-sceva/satcom-chunk-collection huggingface dataset:
# A Normalization Strategy for Weakly Supervised 3D Hand Pose Estimation
## 3 Method
### Overview  
The hand structure, as depicted in Figure 1a, is composed of a set of 21 joints denoted as \([\mathbf{j}_{0},\dots,\mathbf{j}_{20}]\in\mathbb{R}^{21\times 3}\). The datasets, such as the STB dataset [18], provide annotations for the root joint \(\mathbf{j}_{0}^{\text{P}}\) at the center of the palm, while other datasets, such as the HO-3D [40] and RHD datasets [13], annotate the root joint, \(\mathbf{j}_{0}^{\text{W}}\), at the wrist location.  
Figure 1b depicts the dispersion of distinct individuals from diverse datasets within a tri-dimensional feature space characterized by variables denoted as \(\phi_{\text{ARB1}}\), \(\phi_{\text{ARB2}}\), and \(\phi_{\text{ARB3}}\). Notably, this illustration highlights a clustering tendency among individuals of the same group, whereas considerable variations in features are evident among individuals of different groups. The box plot in Figure 1c illustrates the discrepancies in features between the predicted hand pose generated by a model trained on non-normalized datasets and the ground truth pose. This highlights the inconsistency in biomechanical features resulting from training the model on non-normalized datasets.  
To mitigate disparities in \(\mathbf{j}_{0}\) positions and variations in biomechanical features, a normalization strategy comprising two steps was introduced. First, for a specific detected individual pose, adjustment of the \(\mathbf{j}_{0}\) position of the training data was made based on the reference \(\mathbf{j}_{0}\) location, as illustrated in Figure 1d. Second, normalization of the biomechanical features of the training data was performed by replacing the static biomechanical parameters with those of the target individual, as demonstrated in Figure 1e. Additionally, data augmentation was introduced by randomly rotating the normalized 3D pose, resulting in increased data diversity. By projecting these poses onto a 2D plane, 2D-3D pose pairs were obtained. These pairs represented the authentic mapping rules for the given individual and maintained consistent biomechanical features. Training a lifting model using such data enabled the generation of poses aligned with the features of the target individual.
## 5 Performance Evaluation
### Implementation and Evaluation
#### 5.3.2 Part II-Ensemble Learning algorithms
In general, the ensemble algorithms that use RF, DT and KNN together, they can provide a promising performance, especially in aspects of accuracy and recall (i.e., all above 79.5%). In particular, the results show that the combination of five KNN, DT, RF, CNB and SVM algorithms can provide the best cross validation scores (with 79.93% accuracy/recall, 81.79% precision and 79.10% f1_score) in comparison to other combinations. The second rank is the combination of KNN, DT, RF, CNB and MLP (with 79.92% accuracy/recall, 81.76% precision and 79.14% f1_score). However, because of using MLP, its training phase is time consuming. Regarding the execution time, we can see that the combinations which contain SVM consume more time than other combinations for the training phase. It is because training of SVM is time consuming.
## 2 Models and Assumptions  
For our simulations, the highly accurate multi-GNSS simulator \"Spirent GSS8000\" was used to simulate realistic GPS signals for different antenna patterns [10]. Basically, we defined the trajectory, the antenna patterns, the transmitters' ephemeris, the signal level characteristics of all simulated carrier frequencies at the radio frequency (RF) output, and the Spirent simulator provided simulated signals power levels at the receiver position. In our tests, we defined the GPS constellation of 29 satellites according to the almanac of 1 July 2005. The other assumptions are presented in the following paragraphs of this section.
## 3 Results and Discussion
### Effect of Illumination on Ascorbic Acid Content  
Ascorbic acid (AA) is involved in numerous functions in plants, from antioxidative defense and photosynthesis to growth regulation [44]. Light triggers seed germination and influences the  
Figure 2: Total phenolic content (**a**) and protein content (**b**) in dark-grown chia microgreens and microgreens after exposure to light for 24 and 48 h. Data represent mean values from three experiments with five replicates (n = 15). The error bars show standard deviation (SD). Different letters signify values that are statistically different at \(p\leq 0.05\) according to [PERSON]’s LSD test.  
biosynthesis of ascorbic acid [45]. Higher irradiance levels can increase the content and accumulation of AA in plants [12].  
Ascorbic acid (AA) content significantly varied between dark-grown and illuminated chia microgreens (Figure 3). It is clearly visible that the length of illumination is associated with increasing the content of AA. Analysis of the results showed that there were statistically significant differences (\(p\leq 0.05\)) between all three chia samples.  
[PERSON] and [PERSON] [11] obtained similar results in mung bean sprouts grown under different light regimes (24 and 12 h light and dark-grown), showing that the content of AA increased due to the longer exposure to illumination.  
When raw seeds and sprouts, such as soybean, mung bean, cowpea, and buckwheat, were compared, studies found that the AA content was lower in raw seeds [7]. The germination process significantly increased AA content in various edible seeds and sprouts. It was suggested that an increase of AA content and its accumulation in sprouts is due to de novo synthesis [11]. Namely, the synthesis of AA depends on electron transport within the photosynthetic apparatus. Since AA also plays an important role in photoproduction, exposure of dark-grown chia microgreens triggered increased AA synthesis in order to prevent photodamage in etiolated plants and diminished the production of reactive oxygen species [46].
# Regression Equations for Estimating Landslide-Triggering Factors Using Soil Characteristics
## 2 Methodology
### Estimation of Landslide-Triggering Factors Using Soil Properties
According to studies on numerical analysis, the distribution of particle size influenced the stress-strain behavior of soil mass changes, which resulted in a decrease in the angles of internal friction and an increase in volumetric strain [55; 56]. If the permeability coefficient and shear strength can be estimated roughly using the soil test results, which were relatively simple and inexpensive, then the soil characteristics in landslide areas can be identified more quickly and easily. The change of dependent variables (permeability coefficient and shear strength) from the change of independent variables (soil properties) can be estimated by analyzing the correlation between permeability coefficient and shear strength constant in slopes where landslides took place.
## 2 Methodology  
### Spatial Division Method  
For South Korea, excluding Ulleungdo and Dokdo, the longitude ranges between 126\({}^{\circ}\) and 130\({}^{\circ}\), and excluding Jeju Island, the latitude ranges between 34\({}^{\circ}\) and 38\({}^{\circ}\). To minimize the differences in the subdivision stages, a base-4 encoding method is used to create cells that are nearly square-shaped, equally dividing both latitude and longitude. This method ensures that cells are divided symmetrically across the defined range. Given the geographical constraints of South Korea and the need for efficient spatial data handling, the number of cell divisions relates directly to the level of detail required. As shown in Table 1, the number of cells in the first level of division is kept small, and the maximum number of times a region can be divided is limited to once. This approach aims to optimize the spatial resolution without overcomplicating the data structure, facilitating more efficient and quicker spatial searches within the bounds of the typical regional shapes of South Korea. This method is particularly advantageous for real-time applications such as traffic management and navigation services, where rapid retrieval and processing of spatial data are critical.  
The method for dividing cells from area 'a' to 'd' is illustrated in Figure 1. A step-by-step breakdown is provided below:  
* First, when dividing the cell into four parts in a single operation, for cell number 2 in Figure 1: Latitude is represented as '1' (bottom part of the division). Longitude is represented as '0' (left part of the division). The binary number '10' is converted to the quaternary number '2'.  
* Second, the given GPS coordinates are divided up to 15 iterations. This iterative division allows for increasingly granular geographic targeting, enabling precise localization.
* Third, if the code of the coordinates is 123123123123123: The initial cell code is '1'. After 5 divisions, the cell code becomes 12312. Coordinates with the same code are calculated as being in the same cell.  
This cell division methodology is designed to efficiently manage and navigate large-scale spatial data, improving the performance and accuracy of location-based services and spatial analyses.
## 1 Introduction
### Background and Motivation  
In a smart factory, robot manipulators perform various tasks in their workspace [8, 9, 10]. Most of the tasks performed by robots correspond to moving an object from a starting point to a goal point without collision. Therefore, it is important to create a path for the manipulator to move without colliding with other objects within the workspace. There are usually fixed obstacles and moving obstacles in a workspace. The fixed obstacle does not move while the robot is operating. So most path planning takes these fixed obstacles into account. In the case of only fixed obstacles, there are several path planning algorithms [11, 12]. In industrial sites, a direct teaching method is used to design the path of a robot by an expert directly. The more interesting case is when there is a moving obstacle in the workspace. In this paper, we consider the case where an obstacle moves along a designated path. This is similar to the situation in a real factory. Moreover, as mentioned before, robots need to collaborate with each other. Therefore, manipulators must treat each other as obstacles and avoid collisions when moving together at the same time. In general, since many robots operate simultaneously in a factory, it is common for one robot to consider other robots' entire workspace a fixed obstacle. This makes the robots move very inefficiently and the path between two arbitrary points in the workspace longer.  
In path planning, handling moving obstacles in a systematic way is a challenging problem. In a heuristic approach relying on experts, they have to predict and calculate how obstacles move when planning a path but it is almost impossible. A famous algorithm for path planning is a sampling-based algorithm, called PRM (probabilistic roadmap) [13]. In this algorithm, after sampling from the workspace, the sampled nodes are connected to find a collision-free path connecting the starting and goal points. For such an algorithm, when the number of joints or the robot increases, the amount of computation increases as the dimension of the state increases. Furthermore, if the obstacle moves at irregular speeds, the problem becomes even more difficult and the existing path planning can not solve the problem efficiently. This paper focuses on using both a deep reinforcement learning (SAC: soft actor-critic) algorithm and deep learning (LSTM: long short-term memory) network to solve the optimal path plan without collision using a multi-arm manipulator for the case where there are both fixed and moving obstacles in the workspace at the same time.
## 6 Results
### Experimental Results
#### 6.2.4 Vehicle Communication Performance Test  
After completing the link construction, the performances of CTDHR and TDCR were compared in the test, where the data generation rate of the vehicles gradually increased from 0.4 to 8 Mbps in steps of 0.4. At each data generation rate, tests were performed in 100 different simulation environments and the test results were averaged. The duration of a single test was 1 s. Figure 6 shows the results of the objective equations, total delay, and cellular network bandwidth consumption obtained from the CTDHR and TDCR comparison experiments.  
The overall latency of CTDHR is less due to the smaller number of clusters and shorter VANET link for CTDHR at the same vehicle density. Moreover, the routing policy of CTDHR uses VANET to transmit data packets as much as possible, so the cellular network bandwidth consumption of CTDHR is also smaller than that of TDCR. As shown in Figure 6, when the data generation rate of the vehicle is greater than 4.4 Mbps, the delay of TDCR suddenly increases, indicating that congestion occurs in the network at this time. However, CTDHR does not experience congestion until the data generation rate is greater than 5.2 Mbps. CTDHR reduces the time delay and cellular network bandwidth consumption generated in the system as much as possible, so the result of its objective equation is also smaller than that of TDCR.  
In addition to cellular network bandwidth consumption and delay, we also compared the QoS satisfaction rate and packet loss rate of the two methods. In the experiment, if the real delay from the generation of \(b_{ik}\) to the transmission to the RSU/BS was less than \(l_{ik}\), the QoS requirements were considered as met.  
As shown in Figure 7, CTDHR has better performance in terms of both packet loss rate and QoS satisfaction rate. CTDHR can effectively address the situation in which the remaining capacity of the link is less than the data generation rate of the newly added node when generating the routing path. Therefore, CTDHR avoids the situation where traffic is concentrated on one node, which effectively relieves the pressure on each forwarding node in the topology and is less likely to cause congestion than TDCR. The final performance is that with an increase in the vehicle data generation rate, the packet loss rate of CTDHR is much smaller than that of TDCR. In addition, the CTDHR routing policy is more flexible. According to the QoS of each packet, the appropriate transmission methods are selected, which also makes CTDHR satisfy the QoS of the packet at a higher rate.  
Figure 6: (**a–c**) The comparisons of CTDHR and TDCR for objective function results, total delay, and cellular network bandwidth consumption, respectively.
# Application of Multimodal AI to Aid Scene Perception for the Visually Impaired
## 1 Introduction
### Obstacle Detection and the Role of the Ground Plane
By analyzing recorded images, AI models can detect inconsistencies such as blurriness, unexpected noise patterns, or discrepancies in depth calculations, which may indicate camera malfunctions or adverse environmental conditions. Ensuring accurate and adaptive ground plane estimation is critical to enhancing navigation reliability. AI-driven approaches can aid in detecting environmental changes, such as wet or reflective surfaces, that could alter obstacle perception. Moreover, multimodal AI models can help improve terrain classification and lightning conditions, reducing the likelihood of false positive detections in complex environments.
# Privacy-Preserving Feature Selection with Fully Homomorphic Encryption
## 1 Introduction
### Motivation
One of the most significant advantages of FHE-based MPC is thought to be that FHE realizes outsourced computation in a simple and straightforward manner: parties encrypt their private data with their public keys and send the encrypted data to a single trusted party with sufficient computational power to perform the required computation; although the computational results of the trusted party may be incorrect, if some malicious parties send incorrect data, honest parties are at least convinced that their private data have not been stolen as far as the cryptosystem used is secure. In contrast, when a party shares his/her secret with other parties to perform MPC, even if it uses a secure secret sharing scheme, collusion of a sufficient number of compromised parties may reveal the party's secret. In general, it is difficult to prove the security of MPC protocols for the situation where we cannot deny the existence of active malicious parties, and hence, the security is very often proven assuming that all the parties are at worst semi-honest. In reality, however, even this relaxed assumption is unable to hold. Thus, the property that a party can protect its private data only relying on its own efforts should be counted as an important advantage of FHE-based MPC. On the other hand, the current implementations of FHE are thought to be significantly inefficient, and consequently, their ranges of application are actually limited. This is currently true, but may not be true in the future: the Goldwasser-Mmicali (GM) cryptosystem [23] is considered as the first scheme with provable security. Unfortunately, because the GM cryptosystem encrypts data in a bitwise manner, it has turned out not to have sufficient efficiency in time and memory to be used in the real world. In 2001, however, RSA-OAEP was finally proven to have both provable security and realistic efficiency [24; 25], and is widely used through SSL/TLS. Thus, studying FHE-based MPC does not merely have theoretical meaning, but also will yield significant contributions in terms of application to the real world in the future. In this paper, we propose an MPC protocol which relies on FHE-based outsourced computation as well as mutual cooperation among parties. The target of our protocol is to perform the computation of CWC, a feature selection algorithm known to be accurate and efficient, preserving the privacy of the participating parties. If we fully perform CWC by FHE-based outsourced computation, we have to pay unnecessarily large costs in time in the phase of sorting the features of CWC. Therefore, in our proposed scheme, we add ingenuity so that two parties cooperate with each other to sort the features efficiently. Converting CWC into its privacy-preserving version based on different primitives of MPC--for example, based on secret sharing techniques--is not only interesting but also useful both in theory and in practice. We will pursue this direction as well in our future work.
# Multi-Task Learning for Small Brain Tumor Segmentation from MRI
## 4 Experimental Results
### Data Preprocessing  
The preprocessing is very important for the computer vision. In this paper, we used several preprocessing methods including cropping, normalization, and augmentation. The details of data processing technique are described as follows:  
Crop images. All the brain MR images are randomly sampled with the size of \(128\times 128\times 128\) to fit with the CPU memory. This technique still keeps most of the image content within the crop area and does not affect the datasets, but it will reduce the image size and the computational complexity.  
Data normalization. In medical images, MRI scans are usually obtained from different scanners and different acquisition protocols. Therefore, MRI intensity value normalization is crucial for compensating heterogeneity between images. All input images are normalized to have a zero mean and unit variance. We also take the threshold of 5-95% intensity of each voxel in the MR image.  
Data augmentation. In deep learning, data augmentation techniques are very commonly used techniques for increasing the amount of sample without adding new data. They help deep neural networks enhance generalization. First, they are capable of generating more data from a limited set of data. This is useful for data sets where for the collection of new data, labeling is very hard and time-consuming as for medical imaging. The second is that they help avoid overfitting. For deep learning networks, one problem that can be encountered is that the network can remember large amounts of data, resulting in overfitting. Instead of just learning the key concepts of data, the model memorizes all the inputs, which causes decreasing model efficiency. In this paper, we used some data augmentation methods such as rotation and flipping and scaling with the range of 0-10 degrees, 0-0.5, and 0.9-1.1, respectively.  
Figure 3: Brain tumor mask based on intratumoral structures and tumor subregions.
# Crouch Gait Recognition in the Anatomical Space Using Synthetic Gait Data
## 2 Gait Recognition Framework  
The proposed framework based on two approaches to recognize crouch gait is shown in Figure 1. The first approach considers the availability of the gait data. In this study, we use synthetic joint angles based on polynomial fitting. The second approach contemplates the calculation of the joint angles from the Cartesian coordinates, using the inverse kinematics method proposed in a previous work [37]. This procedure is used to partially validate the correspondence of the synthesized data with those in the real case, a detailed description is provided throughout this section.  
There are two categories to represent and extract gait recognition features defined as Model-free and Model-based [38]. In this study, we use an 8 DoF model-based approach. The model of the lower limbs during the gait cycle that we use is a reduced version of the conventional gait model [39]. Figure 2 shows the proposed 8-DoF multiplanar and multijoint system.  
Figure 1: Framework for crouch gait recognition in anatomical space using synthetic gait data.  
Figure 2: 8 DoF gait kinematics model. (**Right**) (skeletal model), (**left**) (open kinematic chain).  
The model considers the lower limbs as eight rigid body segments as follows: right pelvis (\(l_{1R}\)), left pelvis (\(l_{1L}\)), right femur (\(l_{2R}\)), left femur (\(l_{2L}\)), right tibia (\(l_{3R}\)), left tibia (\(l_{3L}\)), right foot (\(l_{4R}\)), and left foot (\(l_{4L}\)). Meanwhile, the corresponding joint functions, rotation axis, and reference frame for the eight motions are pelvic rotation (\(q_{1}\), \(\Sigma_{0}\), \(y_{0}\)), pelvic list (\(q_{2}\), \(\Sigma_{0}\), \(x_{0}\)), right hip flexo-extension (\(q_{3R}\), \(\Sigma_{1R}\), \(z_{1R}\)), left hip flexo-extension (\(q_{3L}\), \(\Sigma_{1L}\), \(z_{1L}\)), right knee flexo-extension (\(q_{4R}\), \(\Sigma_{2R}\), \(z_{2R}\)), left knee flexo-extension (\(q_{4L}\), \(\Sigma_{2L}\), \(z_{2L}\)), right ankle dorsi-plantar flexion (\(q_{5R}\), \(\Sigma_{3R}\), \(z_{3R}\)), and left ankle dorsi-plantar flexion (\(q_{5L}\), \(\Sigma_{3L}\), \(z_{3L}\)).  
Gait recognition is an approach to assess and compare different gait performances of different users based on the gait pattern [28]. For recognition of crouch gait, we consider four levels based on crouch severity: crouch-1 (C1), crouch-2 (C2), crouch-3 (C3), crouch-4 (C4), and normal gait (N). The eight joint angles taken as references for the five gaits are obtained from the dataset of the well-known and well-studied model 2392-OpenSim [40; 41]. However, for gait recognition, one joint angle signal for each joint is not sufficient. To overcome this limitation, we implement a synthetic joint angle generator. The resulting synthetic gait angle dataset is stored and subsequently used as input in the recognition system. The workflow of the crouch gait recognition system consists of different modules [42], as shown in Figure 3.