esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of an Active and Passive Control System of Hand Exoskeleton for Rehabilitation
## 3 Design of Hardware and Software  
### Control System Architecture  
The control system of the hand rehabilitation exoskeleton is composed of the hardware system, the mechanical system, and the human-robot interface rehabilitation software, as shown in Figure 2a. The control system adopts hierarchical control strategy, the upper layer control and the interface are designed on the tablet based on Android, and the lower layer control and the data collection are designed on the system on programmable chip (SOPC) based on field-programmable gate array (FPGA) chip. Nios II soft core is embedded into the SOPC as the core processor. The lower layer control algorithm is integrated on the Nios II, which can accomplish high-intensity data collection, calculation, and real-time control. The lower layer hardware and the upper layer interface software are connected by Bluetooth communication module.  
The basic working principle is as follows: the hand rehabilitation exoskeleton is worn on the patient's hand whose motion status is detected by the force sensors and angle sensors. The force  
Figure 1: A hand exoskeleton system for rehabilitation: (**a**) overview; (**b**) the symmetric pinion and rack mechanism (SPRM) with the parallel mechanism; (**c**) the three joint modules; (**d**) the exoskeleton is attached to the extended middle finger by elastics and the exoskeleton is on the beginning state; (**e**) the middle finger with the exoskeleton flexes to the max range.  
sensors can detect the forces exerted on the exoskeleton tips in real time. The angle sensors send the data of exoskeleton displacement to the Micro-Controller Operating Systems II (MicroC/OS-II, stylized as \(\upmu\)C/OS-II) system, which provides the control of exoskeleton with status parameters. The control system sends the displacement information of exoskeleton joints to the Android system by Bluetooth communication module in order to actuate the motion of the finger model in a virtual environment.
# Real-Time Security Health and Privacy Monitoring for Saudi Highways Using Cutting-Edge Technologies
## 4 Results and Evaluation
### Case Study  
To evaluate the proposed system, a case study was used. Six parameters were used for the evaluation of the existing solution and proposed model. A brief description of these parameters is given in Table 3.  
Equation (1) was used to evaluate the overall performance of the existing and proposed solution:  
\[SP=f(ER,EMH,ELH,PIE,LS,E) \tag{1}\]  
where \(SP\) refers to system performance that is the weighted sum of all the six parameters, \(ER\) refers to early reporting of the accident. \(EMH\) refers to early medical help. \(ELH\) is used for early legal help while \(IE,LS,E\) is used for post-incident evidence, life-saving, and efficiency respectively.  
Each parameter's value will be rated on a three-point scale: completely addressed (yes), moderately handled (partial), and unaddressed (no). These three scales will have weights of 1, 0.5, and 0, respectively.  
Now, we evaluate the overall performance of the existing and proposed solution.
# Physics-Informed Graph Neural Networks for Attack Path Prediction
## 2 Materials and Methods
### Architecture
#### 2.4.3 Node Classification Task  
A distinct architecture is employed for the start/end node classification task. During inference, an encoder [64] compresses the input embedding tensor \(\mathcal{H}\) into a lower-dimensional representation \(\mathcal{Z}\in\mathbb{R}^{|V|\times 2}\), where \(z\leq H\). The encoder consists of \(k=4\) fully connected layers with trainable parameters and ReLU activations, progressively reducing the dimensionality (\(128\to 64\to 36\to 18\to z\)) to compress the embeddings. The latent representation \(\mathcal{Z}\) is then passed to an MLP [64], responsible for classification. The MLP processes \(\mathcal{Z}\) through a fully connected layer followed by a ReLU activation, then a second fully connected layer with normalization. It outputs a probability vector \(\hat{Y}\in\mathbb{R}^{|V|}\). The probabilities are then converted to a prediction vector, where all values are set to 0 except for a single index corresponding to the predicted start or end node, using Youden's J index, as previously discussed. Two separate classifiers are trained: one for predicting start nodes and the other for end nodes. For algorithmic complexity, refer to Appendix E.
# Coffee-Leaf Diseases and Pests Detection Based on YOLO Models
## 3 Materials and Methods
### Diseases and Pests in Coffee Leaves  
Based on [31], the rust disease, caused by the fungus _Hemileia Vastatrix_, manifests itself as yellowish spots that develop into an orange color, covering the underside of the leaves.  
Figure 1: Examples of images contained in the dataset, illustrating different conditions of coffee leaves, including the presence of diseases and pests.  
As the disease progresses, the infected leaves fall prematurely, reducing the plant's ability to carry out photosynthesis and compromising its fruit production.  
Leaf miner, on the other hand, is caused by the larvae of the _Leucoptera Coffeella_ insect, which dig galleries in the leaves, forming whitish, dry trails. Severe infestations result in early defoliation, directly impacting the productivity and quality of the coffee beans.  
Another relevant disease is phoma, caused by the fungus _Phoma Costarricensis_, which is characterized by dark, circular lesions on leaves, branches, and fruit. These spots can grow and join together, leading to tissue necrosis and causing intense defoliation, especially in conditions of high humidity.  
Cercospora, caused by the fungus _Cercospora Coffeicola_, presents circular brown lesions with light edges and a dark center, giving it an eye-like appearance. In addition to the leaves, the disease also affects the fruit, causing it to fall prematurely, and is especially aggravated in humid environments and in plants with nutritional deficiencies.  
Figure 2 illustrates the different types of diseases analyzed in this study, highlighting the affected regions on the coffee leaves and the corresponding classification.
## 2 Materials and Methods
### Gridded Precipitation Products
#### 2.3.2 CHIRPS  
Since 1981, the CHIRPS product has provided daily precipitation data with a spatial resolution of 0.05\({}^{\circ}\) for a quasi-global coverage of 50\({}^{\circ}\)N-50\({}^{\circ}\)S [12]. The most recent product is Version 2.0, which was released in February 2015. The CHIRPS product and associated data can be found at: [[https://climateserv.servinglobal.net](https://climateserv.servinglobal.net)]([https://climateserv.servinglobal.net](https://climateserv.servinglobal.net)) (accessed on 3 June 2021). The main datasets used for the construction of the CHIRPS product are the monthly precipitation climatology (CHPclim) information based on thermal infrared data archived from CPC and NOAA National Climate Data Center (NCDC), the Version 7 TRMM 3B42 data, the Version 2 atmospheric model rainfall field from the NOAA Climate Forecast System (CFS), and rain gauge stations [46]. First, the cold cloud duration (CCD) data are calibrated with TRMM 3B42 to generate 5-day CCD-based precipitation estimates, which are then converted to fractions of long-term mean precipitation estimates [46; 47]. The fractions are then multiplied by CHPclim data to remove systematic bias, and the resulting product is known as the CHIRP product [28]. Finally, the CHIRP product is combined with data from rain gauge stations using a modified inverse distance weighting algorithm to generate the CHIRPS [46]. All of the preceding processing is carried out on a 5-daily basis. Using a simple redistribution method, the CCD and CFS data are finally used to disaggregate the 5-daily products to daily precipitation estimates [47].
# Sign Language Sentence Recognition Using Hybrid Graph Embedding and Adaptive Convolutional Networks
## 3 Proposed Hybrid Graph Embedding and Adaptive Convolution Networks
### Adaptive Convolutional Network
#### 3.5.2 Multi-Scale Sensor Data Fusion  
The method creates a cohesive picture by merging features from different spatial or temporal scales.  
\[\text{F}_{\text{fused}}=\sum_{\text{i}=1}^{\text{M}}\text{w}_{\text{i}}\cdot \text{F}_{\text{i}} \tag{11}\]  
In Equation (11), \(\text{F}_{\text{fused}}\) represents the fused feature representation capturing information from multiple scales. \(\text{F}_{\text{i}}\) represents the features extracted at scale i. Each scale represents features computed with different kernel sizes or temporal windows, capturing fine-grained and coarse-grained information.\(\text{w}_{\text{i}}\) represents learnable sensor data fusion weights for each scale, determining the contribution of \(\text{F}_{\text{i}}\) to the final fused representation, and M represents the number of scales used. Dimension: \(\text{F}_{\text{fused}}\in\mathbb{R}^{d}\), where \(d\) is the feature dimension.  
Feature extraction, learning weights, and summing are used for local and global patterns. Small-scale features capture local details, while large-scale features capture patterns. Learnable weights favor task-specific informative scales. Sensor data fusion ensures multi-scale awareness and task-specific weighting, improving accuracy and efficiency. Summarization integrates information across scales.  
Sign language sentence recognition using Hybrid Graph Embedding and Adaptive Convolutional Networks is robust using Algorithm 1. Initiates an empty graph and keypoints, extracts keypoints from sensor data frames, builds vertices with spatial and temporal edges, and assigns edge weights using Gaussian similarity. Spatial-Temporal Graph Convolution Networks aggregate graph node characteristics, whereas adaptive convolution generates frame-wise kernels. Finally, graph and convolution module embeddings are concatenated and processed through dense layers and a softmax classifier to predict words, accurately recognizing complicated spatial-temporal sign gestures.
# STA-GCN: Spatial-Temporal Self-Attention Graph Convolutional Networks for Traffic-Flow Prediction
## 4 Results and Discussion
### Experimental Results  
We compared our model with seven baseline models regarding the performances of different prediction horizons in Table 2, and the AST-GCN showed the best performance on both datasets. Specifically, the traditional time-series prediction methods (ARIMA, SVR) performed well on short horizons (15 min), but struggled with longer horizons (30 min, 60 min), indicating that these methods have difficulty in handling complex nonlinear traffic-flow data. Among the deep-learning models, FC-LSTM, which considers spatial dependence, and the models containing GCN modules outperformed the traditional deep-learning models (FNN and GRU). Although FC-LSTM can capture spatial features, it does not utilize the topological structure of the road network and has a significant amount of data redundancy during training. Therefore, FC-LSTM is inferior to STGCN, ASTGCN, and STA-GCN. The prediction performances of ASTGCN and STA-GCN were better than those of STGCN, demonstrating the effectiveness of adding spatial-temporal attention mechanisms to capture dynamic spatial-temporal features. Furthermore, our STA-GCN outperformed ASTGCN, especially on 60-minute horizons, with the prediction errors were reduced by 4.63% and 3.12% on the PeMSD04 and PeMSD08 datasets, respectively. These results demonstrate the superiority of the hierarchical embedding of spatial-temporal attention and the superiority of spatial-temporal self-attention mechanisms. The training loss function of the model was considered to be converged when the validation set did not decrease further after ten epochs of training. Specifically, the number of training epochs and the final RMSE metric of the three types of model were compared when they reached convergence, as shown in Figure 6. It was found that the STA-GCN model had a faster convergence rate and lower prediction error. To further demonstrate that the predictive performance of STA-GCN is superior to that of STGCN and ASTGCN, we randomly selected one traffic node's weekday and holiday prediction results from the PeMSD08 dataset for visualization. The visualization results for the weekdays are shown in Figure 7. During the time periods when the road-traffic volume suddenly increases (4:00-5:00) or decreases (17:30-18:30), STA-GCN can fit the trends in traffic-volume changes better than STGCN and ASTGCN. Similarly, the holiday prediction results are shown in Figure 8. During the time periods when the road-traffic
## 2 Materials and Methods
### Selection of Driving Behavior Indicators
#### 2.4.1 CF Characteristics  
The assessment of a driver's CF behavior is effectively conducted using speed and headway metrics, which together offer a detailed evaluation of longitudinal driving comfort and CF stability.  
Speed metrics, specifically average velocity (V mean), speed standard deviation (V std), the reduction of velocity (RV), and maximum deceleration (DCC max), are key to assessing driving safety and braking responsiveness [39]. V mean and V std (km/h) are calculated using speed data within a 600 m section preceding the diverging point. RV (km/h) represents the largest absolute difference between peaks and troughs on the speed curve, while DCC max (m/s2) signifies the journey's highest deceleration.  
It should be noted that the V85 speed metric, typically used to measure the maximum speed not exceeded by 85% of vehicles at specific roadway cross-sections, was not employed in this study. Instead, this analysis specifically investigated the trends in vehicle speed distribution at various locations within connection sections. For a more detailed analysis of driver behavior, speed profiles were extracted and the position corresponding to the maximum deceleration was identified for micro-speed characteristic analysis.  
Parallel to speed metrics, headway metrics elucidate the spatiotemporal dynamics of CF behavior [40]. Drivers aim to maintain an optimal distance headway (DH) and a suitable time headway (TH), reflecting the physical and temporal distances between consecutive vehicles. This study analyzes the average DH and TH throughout the journey, providing insights into longitudinal interactions and the impact of various factors on CF behavior.
## 2 Methods and Materials
### Gaussian Process Regression Models
#### 2.1.1 Bayesian Inference  
The function \(f(X)\) of the GPR is characterised by the covariance or kernel function, which is controlled by parameters \(\Theta\) which is a vector of hyper-parameters ([PERSON] et al. [1]). Bayesian inference computes the posterior of the hyper-parameters. We compute the posterior as \(p(\Theta\mid y)=\frac{p(y|\Theta)p(\Theta)}{p(y)}\), where \(p(y\mid\Theta)\) is the likelihood and \(p(\Theta)\) is the prior and \(p(y)\) is a normalising constant, \(p(y)=\int p(y\mid\Theta)p(\Theta)d\Theta\). We used the informative approach to make the inference because it uses priors from the kernel simulations that we developed.
# Design of a Digital Array Signal Processing System with Full Array Element
## 2 System Architectural Design  
Existing array radar signal processing systems are usually composed of discrete ADCs/DACs and multi-stage microwave frequency conversion components. When the radar system has more transceiver channels, it often requires a larger size, weight, and power consumption, which are often difficult to satisfy in resource-constrained application scenarios such as spaceborne, airborne, and missile-borne. At the same time, when the radar signal is broadband, it puts a high demand on the sampling rate and bandwidth of the system.  
This section describes an RFSoC-based broadband digital array signal processing system architecture design, focusing on hardware architecture and software architecture. The system operates over a wide band coverage, which is very suitable for broadband digital array signal processing, particularly for L, S, and C band system applications. In addition, the system has great technical advantages and application prospects in resource-constrained (size, performance, and power consumption) application scenarios.
###### Abstract  
Microalgae are attracting considerable interest worldwide. In the present study, two native microalgae strains isolated from Tunisian cost were identified as _Chlamydomonas_ sp. and _Naxicula_ sp. We characterized their pigment and protein contents, as well as their carbohydrate and lipid productivity. The predominant fatty acids were found to be \(\alpha\)-linenic acid (C18:3n-3) and palmitoleic acid (C16:1n-7) for _Chlamydomonas_ sp. and _Naxicula_ sp. strains respectively. Microalgae methanol extracts showed important in vitro antibacterial activity against all tested gram negative bacteria. Antioxidant activities of methanol extracts were investigated by determining radical scavenging activity according to DPPH (2,2-diphenyl-1-picrylhydrazyl) and ABTS (2,2\({}^{\prime}\)-azin-bis(3-ethylbenzothazoline-6-sulfonic acid)) methods. MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) assay showed that the newly isolated microalgae were not toxic and have potential application in the fish feeds domain as a supplement in fish meal. Also, the biosorption of cadmium from aqueous solutions by microalgae living cells was evaluated. Large removal capacities were obtained with values ranging from 89.8% to 99.38%. These results are very promising as a starting point for a potential application of these new isolates for in situ bioremediation of heavy metals contaminating aqueous systems.  
antibacterial activity; antioxidant activity; cadmium bioremoval capacity; _Chlamydomonas_ sp.; _Naxicula_ sp.; unsaturated fatty acids abundance
# Research on an ID-PCA Early Fault Detection Method for Rolling Bearings
## 2 Basic Theory  
### Time-Domain Characteristic Information of Vibration Signal  
Vibration signal is one of the common methods to study the early fault detection of rolling bearing [28]. When the rolling bearing fails, it will reflect certain characteristics of the vibration signal. The research shows that some fault characteristic information of the vibration signal can be obtained through the time-domain index of the vibration signal. The time-domain characteristic information of fault can be divided into dimensioned and dimensionless. The dimensioned time-domain indexes and expression methods used in this paper are shown in Table 1.  
The dimensionless time-domain indicators and expressions used in this paper are shown in Table 2.
## 6 Magnetic Resonance Imaging and Artificial Intelligence
### MRI-Arplicated AI for the Estimation of the Vulnerability of the Plaque
#### 6.2.1 MRI in Intracranial Plaque  
In a retrospective study performed in 2018 [157], patients with intracranial stenosis who underwent intracranial MRI using a 3-T whole-body MRI scanner (Skyra; Siemens Healthcare, Erlangen, Germany) between January 2014 and December 2016 were selected with the aim of correlating the basilar plaque characteristics and symptomatic evolution. A dedicated software (3D-Slicer) analyzed 94 radiomic features--including intensity (maximal intensity, mean intensity, and standard deviation of intensity), shape-based features (area, length, etc.), and textures--obtained from the MRI images. Among these, only seven radiomic features in T1 images and three in contrast-enhanced (CE)-T1 images were independently associated with acute/sub-acute symptoms. In contrast, none of the features in T2 images was statistically significant in the final random forest analysis. The combination of radiomic features from T1 and CE-T1 images provides a system with a sensitivity, specificity, and accuracy of 97.0, 79.0, and 83.2%, respectively, resulting in improved accuracy of the stroke risk assessment. Unfortunately, the study was conditioned by some limitations, such as the small size sample, the use of 2D imaging data, and manual segmentation for the plaque boundary.  
Afterward, [PERSON] et al. [158] proposed a histogram texture analysis for distinguishing vulnerable and stable plaques in middle cerebral and basilar arteries, resulting in a higher coefficient of variation (CV) on T1 WIs in vulnerable plaques than the stable ones. The variation of CV, only in the T1 WI sequence, may be linked to the higher variation signal intensity on this sequence in the case of intraplaque hemorrhage, a lipid-rich necrotic core, calcification, and fibrous tissue components in plaques.
## 4 Slp-macgt Model Design
### Secure Data Transmission Based on Fountain Codes  
In the previous section, we realized the multi-path transmission of data packets through the selection of relay nodes mainly to resist eavesdropping attacks and backtracking attacks. The primary objective of this section is to resist an active attack during a passive attack. For a powerful attacker who can record eavesdropping packets and mine them for analysis, encrypted packets are insufficient to protect source location and monitor target location privacy. Therefore, the algorithm of this paper combines network coding technology and proposes a secure data transmission method based on fountain codes to resist active attacks. Luby transform codes (LT codes) are used to encode the data packets sent by the source, which is an error-correcting coding method designed to achieve high error tolerance [32]. Unlike traditional error-correcting codes, fountain codes are characterized by the ability to generate an unlimited number of coded symbols, which makes them superior in unreliable underwater acoustic communication.