esa-sceva/satcom-chunk-collection huggingface dataset:
# Predictive Control for Small Unmanned Ground Vehicles via a Multi-Dimensional Taylor Network
###### Abstract  
Tracking control of Small Unmanned Ground Vehicles (SUGVs) is easily affected by the nonlinearity and time-varying characteristics. An improved predictive control scheme based on the multi-dimensional Taylor network (MTN) is proposed for tracking control of SUGVs. First, a MTN model is used as a predictive model to construct a SUGV model and back propagation (BP) is taken as its learning algorithm. Second, the predictive control law is designed and the traditional objective function is improved to obtain a predictive objective function with a differential term. The optimal control quantity is given in real time through iterative optimization. Meanwhile, the stability of the closed-loop system is proved by the Lyapunov stability theorem. Finally, a tracking control experiment on the SUGV model is used to verify the effectiveness of the proposed scheme. For comparison, traditional MTN and Radial Basis Function (RBF) predictive control schemes are introduced. Moreover, a noise disturbance is considered. Experimental results show that the proposed scheme is effective, which ensures that the vehicle can quickly and accurately track the desired yaw velocity signal with good real-time, robustness, and convergence performance, and is superior to other comparison schemes.  
Keywords:multi-dimensional Taylor network; predictive control; nonlinear system; SUGV; predictive model +
Footnote †: journal:  
_Applied_  
_sciences_
## 3 Positioning Based on DVB-T
### Range Measurement
#### 3.2.4 Re-Acquisition Phase  
Since there is only one sample for each subcarrier, the output of the discriminator will change rapidly once the correlation peak jumps from one delay point to the adjacent one. This is different from the tracking process used in a GPS receiver, where the receiver can track the signal smoothly from one chip to the next one. In order to solve this problem, a re-acquisition function is implemented. If the output of the discriminator is above a given threshold, the receiver will enter the re-acquisition stage. This stage is similar to the acquisition stage, but the correlation is only calculated upon a small set of delay points, which are centered on the delay obtained from the tracking process. Here, we choose three delay points. In fact, the receiver trajectory is continuous, and then, the correlation peak can only jump from one delay point to one of the adjacent two points (depending on the increase or decrease of the range). If all the correlation results are below a given threshold, the tracking is considered as unlocked. The receiver will restart from the first step. Otherwise, the receiver goes back to the tracking process with the new estimated delay obtained in the re-acquisition stage, as shown in Figure 6.
## 3 Results  
### Laboratory Testing  
#### 3.1.1 Effect of Rainfall Intensities on Wind-Induced Soil Losses  
The wind-induced soil losses of samples treated with different biomaterial solutions and wetted with four varying rainfall intensities are shown in Figure 4. Each bar represents the calculated mean from triplicates and indicates the total weight loss (TWL) after four wet-dry cycles. Prior to the first wet-dry cycle, soil treated with chicory vinasses, corn steep liquor, decantation syrup, and palatinose molasses showed only negligible weight losses of 0.09 g (SD = 0.02), 0.12 g (SD = 0.16), 0.04 g (SD = 0.01), and 0.03 g (SD = 0.01), respectively. From Figure 4, it can be seen that wetting of samples increased mass losses at any investigated rainfall intensity. Samples treated with chicory vinasses can be considered the most susceptible to wind erosion, showing the highest mass losses at a rainfall intensity of 0.25 L/m\({}^{2}\) with a TWL of 63.04 g. Samples treated with corn steep liquor, decantation syrup, and palatinose molasses were less prone to wetting and peaked in mass losses at a rainfall intensity of 0.25 L/m\({}^{2}\) with a TWL of 4.38 g, 2.79 g, and 13.52 g, respectively. By comparison, untreated soil has a TWL of 125.28 g (SD = 19.34) after four measuring cycles.  
Mass losses of each biomaterial-treated soil wetted with higher rainfall intensities (1-4 L/m\({}^{2}\)) were significantly lower than those moistened with only 0.25 L/m\({}^{2}\). With increasing rainfall intensity, the TWL of soil treated with chicory vinasses decreased to 5.35 g (1 L/m\({}^{2}\)), 0.6 g (2 L/m\({}^{2}\)), and 2.12 g (4 L/m\({}^{2}\)), respectively. Corn steep liquor behaved similarly and showed a reduced TWL of 1.24 g (1 L/m\({}^{2}\)), 0.7 g (2 L/m\({}^{2}\)), and 2.38 g (4 L/m\({}^{2}\)). By contrast, all soil samples treated with decantation syrup and palatinose molasses had a TWL of \(<\)1 g. A summary of the laboratory test data is shown in Table 2, Appendix A.
## 5 Experiment
### Accuracy Verification of the Weld seam Feature Point Extraction Algorithm  
The accuracy of weld seam tracking based on real-time visual guidance depends on the sensor's ability to detect feature points on the weld seam contour accurately in each frame. The closer the detected feature points are to the actual weld seam trajectory, the higher the precision of the visual guidance. To evaluate the feasibility of the proposed large depth-of-field line laser vision sensor for real-time visual guidance, an S-shaped V-groove weld workpiece was designed and fabricated, as shown in Figure 13. This workpiece is 60 mm thick and 300 mm long, with a level angle of 45\({}^{\circ}\). The complex S-shaped trajectory offers a more rigorous test of the sensor's tracking performance.  
Figure 12: Experimental setup.  
While tracking the evaluation workpiece using visual guidance, the sensor continuously detects the positions of three feature points on the V-groove weld seam contour in front of the welding torch, enabling real-time guidance of the torch's movement. In this study, the actual weld seam trajectory was manually sampled and fitted to serve as the reference trajectory. The feature points detected by the sensor during tracking were plotted and compared with the actual weld seam trajectory, as shown in Figure 14.  
Data from ten experiments were collected to calculate the average deviation between the sensor-detected weld seam trajectory and the actual trajectory. The results for the three feature points are presented in Figure 15, while the average deviations across the ten experiments are summarized in Table 2. The deviation for feature point 1 is 0.4681 mm, for feature point 2 it is 0.6450 mm, and for feature point 3 it is 0.4894 mm. These results confirm the effectiveness and robustness of the proposed weld seam extraction algorithm.  
Figure 14: Comparison of weld seam detection trajectory and actual trajectory.  
Figure 13: S-shaped trajectory V-groove weld workpiece.  
By calculating the average deviation between the sensor-detected weld seam trajectory and the actual trajectory, the deviation values for feature points 1, 2, and 3 were found to be 0.4673 mm, 0.6436 mm, and 0.4964 mm, respectively. These results demonstrate the effectiveness and robustness of the proposed weld seam extraction algorithm.
# A Novel Cloud Approach for Connected Vehicles
## 3 GeoVCDN Approach
### Geographical Routing ICN Procotol
#### 3.2.2 Packet Naming  
In the classical internet architecture, to retrieve data, it is first necessary to reach a host. The naming of the host is understandable for the network. Oppositely, the ICN data name is totally opaque for the network. Every application is, then, free to use its own naming scheme for the data. It is understood that flat name is theoretically usable in the ICN network. However, there is a consensus on using hierarchical names. The hierarchical name helps scalability, which is very important in IOT. Additionally, it perfectly fits our context cloud architecture, which is also hierarchical. The name of data has only to be unique in a specific context and not in the entire network.
###### Abstract  
Maintaining balance is critical to minimizing astronauts' risk of falling and reducing injury or suit damage. Previous studies involving spacesuits have not examined the effects of the superior shift of the center of gravity (CoG) on astronauts' ability to balance. Here, the purpose of our study was to investigate the effects of CoG shift due to a simulated Extravehicular Mobility Unit (xEMU) on balance. Seventeen participants' standing balance was examined for three test configurations: unsuited, weighted with an Extravehicular Mobility Unit (xEMU) test, and xEMU hard upper body torso (HUT). Using a Tekscan forceplate walkway, the CoG locations were determined. Balance assessments were performed to determine the limits of stability and standing balance performance during wide or tandem stances with eyes open/closed. The center of pressure (CoP) time series was examined in terms of displacement, velocity, and frequency measures. During the eyes-open wide stance, the xEMU vest significantly increased the mediolateral balance parameters, while the HUT significantly increased the total displacement (TOTEX), mean velocity (MVELO), and mean frequency (MFREQ) of the CoP. In the eyes-closed wide stance, the HUT significantly increased these parameters. In the eyes-closed tandem stance, the xEMU vest significantly decreased the parameters. The xEMU vest significantly reduced the TOTEX, MVELO, and MFREQ (improved standing balance), while the HUT decreased standing balance ability, seen with significant increases in said parameters. By quantifying CoG's effect on balance, our results form the basis for future balance and posture studies of xEMU spacesuits.  
spacesuit; extravehicular activity; EVA; center of pressure; balance +
Footnote †: journal: Applied Sciences  
10 April 2024  
Revised: 29 April 2024  
Accepted: 30 April 2024  
Published: 9 May 2024
## 2 Wireless Multimedia Sensor Networks
### Directional Sensor Coverage
#### 2.4.3 Camera Sensor Collaboration  
Because of the individually limited FoVs, applications such as continuous tracking would require collaboration among multiple camera sensors. Figure 6 shows how [PERSON] et al. [103] classify camera sensor collaboration schemes. Based on whether overlapped FoVs exists or not, the authors distinguish camera senor collaboration schemes into spatial-based collaboration where overlapped FoVs exist, and predictive collaboration in case of the opposite. In spatial-based collaboration, a graph of overlapped FoVs needs to be constructed and camera pairs with overlapped FoVs ought to be jointly calibrated and synchronized. Instead for predictive collaboration, neighborhood camera sensor distributions and target motion information are required to predict into which camera sensor's FoV the target is going to move. Regardless of the collaboration schemes, one common task to establish efficient camera sensor collaboration is to carefully design task handover procedures as described in [104].
# Security Challenges of Location Privacy in VANETs and State-of-the-Art Solutions: A Survey
## 3 Related Work
### Blockchain-Based Scheme  
Presently blockchain technology have gained popularity among researchers because of its widespread use in many fields, including academic, finance, banking and medicine. More specifically, blockchain composed of an unlimited number of blocks which are connected in a sequential manner to form a blockchain. The blockchain is effective due to the features such as decentralization, immutability, transparency, shared security, anonymity, and more efficient for untrusted environments [67].  
Various types of blockchain-enabled security models have been introduced recently. The paper [68] proposed an efficient privacy preserving data communication architecture which is based on a blockchain model to ensure privacy in cluster-based VANETs. They leverage a rainfall optimization algorithm (ROA), the vehicles are grouped into various clusters and a cluster head (CH) is selected for each cluster. The ROA-based clustering along with blockchain-based communication is known as ROAC-B technique. First, the vehicles are grouped and then the transmission is initiated using blockchain. The data is generated at random depending on parameters such as node count, grid size, and communication range. Clusters are made according to the features of nodes such as navigation, speed, position etc. To make an effective clustering a node must reside in a single cluster. Consequently, every cluster has a CH which is responsible for the entire cluster and identifies entering or leaving nodes. As a result it provides load balancing and creates a minimum overhead on the network.  
In VANETs infrastructure, blockchain technology play a vital role in authentication of vehicles because of the fact that any vehicle can access the past event if the information is placed in the public blockchain [69]. Application of existing blockchain is not sufficient to the VANETs environment, as transaction form of event messages is adopted instead of bitcoin transaction for the cryptocurrency feature. To address medium access control (MAC) layer threats, the work proposed in [70] is based on trust-based that make use of blockchain technology to improve security and privacy to thwarts MAC layer attacks. They integrate blockchain with MAC packet structure to achieve secure communication between VANETs components. Every block is composed of vehicle \(i\) (\(V_{i}\)), identity of the vehicle \(i\) (\(ID_{i}\)), message (\(M_{i}\)), timestamp (\(t_{i}\)), hash value and transaction root value. Safety messages are involved here and blockchain act as a medium of trustworthiness for event messages. A public blockchain is considered to store information of all the nodes based on geographical region. According to the blockchain whether public or private, various set of consensus protocol are offered. Hence, security level of blockchain is also depends on the consensus protocols.
### Selection Method of Analyzed Articles Based on PRISMA Methodologies
The random forest algorithm is appreciated for its accuracy and flexibility, to the point that it is widespread in many fields, such as image classification, medical diagnosis, and fraud detection. XGBoost: This is a boosting algorithm commonly used for classification and regression problems. In particular, XGBoost is an advanced form of gradient boosting that improves efficiency and predictive performance through optimizations, such as missing data handling and regularization [44]. Support vector machine: This is a supervised learning method used primarily for classification problems, but it can also be adapted for regression. SVM can classify unlabeled data by using a hyperplane to identify data clustering. The output of SVM is not, however, nonlinearly separable. When using SVM in data analysis, choosing the right kernel and parameters is essential for resolving such issues [44; 49]. Another subcategory of AI is deep learning (DL), which uses deep neural networks to handle complex types of data [49]. Unlike traditional ML techniques, DL uses complex neural network architectures, often with many layers, to extract features and representations from data [33]. The main differences between DP and ML methods are based on three different levels: feature learning, model building, and model training. In traditional ML, in-depth knowledge of the observed phenomenon is necessary because the features are extracted manually, while DL features are learned from the model. Traditional methods use the extracted features for model construction to build a data-based model. In contrast, in the case of DP, a highly hierarchical end-to-end model structure is generated with a non-linear combination of multiple layers. Finally, regarding model training, the traditional ML methods train the parameters step by step; instead, DL models train all the parameters involved simultaneously [33]. Among the DL algorithms, the most used are as follows:  
* Convolutional neural networks (CNNs) are widely used as tools for image classification thanks to the shape extraction process [50];
* Recurrent neural networks (RNNs) are generally used for speech recognition because they use sequential or time-series data. In particular, they are mainly used when it is necessary to make predictions about future outcomes, and they have a significant capacity for learning nonlinear features and processing time-sequence data [51]. Table 1 reports the main algorithms used in the various areas of smart manufacturing, highlighting the main applications, characteristics, advantages, and limitations to facilitate the choice of the most appropriate one depending on the application. For each AI algorithm, the main use in the field of safety, PdM, and process control is also reported.
# Adaptive Frame Sampling and Feature Alignment for Multi-Frame Infrared Small Target Detection
## 4 Experiments
### Implementation Details  
The method in this article was implemented using the PyTorch 1.10 deep learning framework. The computer platform was equipped with an Intel i7-6700 CPU and a NVIDIA GeForce GTX1080 Ti GPU. The model was trained under the Ubuntu 20.04.2 LTS operating system environment. The settings during training included total training epochs of 100, using the Adam optimizer, where the batch size was 8. A dynamic learning rate strategy was sampled during training, with an initial learning rate of 0.0005. The learning rate was gradually reduced according to the cosine law within 66 epochs, and the final learning rate was 0.00025.
# Optimal Sizing of Battery Energy Storage System for Implicit Flexibility in Multi-Energy Microgrids
## 3 Results
### Impact of Power-Based Tariff Design on Peak Power Withdrawal Reduction
However, even under the _ToU level 1_ tariff, the system's optimal scheduling remains essentially the same as that under the _ToU level 0_ tariff. Only when the off-peak tariff is further reduced to one-quarter of its current value (EUR 1.07/kW), as in the _ToU level 2_ scheme, does the system become incentivized to lower its peak demand during system peak hours. To assess to what extent the effects of the ToU power-based tariff might be constrained by the size of the BESS, we performed the same analysis for the MEMG equipped with the largest BESS configuration (4 MW/16 MWh). The results are shown in Figure 15 in the Appendix B. In this case, the peak grid withdrawal recorded during previous days of July under the base case of flat power-based tariff--used as a reference for all ToU scenarios--was 87 kW. It can be clearly seen that with this larger BESS, even under the _ToU level 0_ scheme, the system has sufficient storage capacity available to maintain the peak power withdrawn from the grid during system peak hours (10:00-23:00) at the same level of 87 kW, avoiding any increase in the power-based cost related to peak hours for the day analyzed. This results in a slight increase in the peak withdrawal during off-peak hours, which rises to 397 kW.
## 2 Medium Baseline RTK Algorithm
### Determination Process Noise of DD Ionospheric and Tropospheric Delay  
The model strength is limited because Equation (9) considers DD ionosphere delay and troposphere delay as unknown parameters. To improve the model strength, we estimate DD ionosphere delay and troposphere delay as random walk parameters in Kalman filter [15].  
The DD zenith troposphere delay is correlated with baseline distance and station height, and its random walk can be described as follows [17]:  
\[Q_{\
abla\Delta t}=\Big{(}log\Big{(}1+D\cdot 10^{-5}\Big{)}\cdot 0.02+H\cdot 10^{-5 }\Big{)}/\sqrt{3600\cdot dt} \tag{10}\]  
where \(D\) is the baseline distance; \(H\) is the height difference, measured in meters; and \(dt\) is the epoch time difference, measured in seconds.  
Since the estimated ionosphere delay is the slant, it is important to consider not only the baseline length and latitude dependence, but also its correlation with elevation angle. Therefore, the random walk noise of DD ionosphere delay can be given as follows [17]:  
\[Q_{\
abla\Delta t}=\Big{(}D\cdot 5\cdot 10^{-6}\cdot exp((90-lat)/50-1)\Big{)}/ sin(EI)\sqrt{3600\cdot dt} \tag{11}\]  
where \(lat\) refers to the average latitude of the base station and the rover station and \(EI\) is the elevation angle of the satellite.  
In addition, we set the priori DD ionosphere delay and troposphere delay to zero, and give them a large initial standard deviation, i.e., 1.5 m and 0.2 m, respectively, thus avoiding introducing bias [16]. If these initial standard deviation values are too small, the model strength can be enhanced, which may, however, result in a biased float solution when the ionosphere delay is active.
# Hourglass 3D CNN for Stereo Disparity Estimation for Mobile Robots
## 3 Methodology
### Attention-Aware Feature Extraction  
The authors of [12] proposed a feature extractor with an attention-aware module to obtain multistage feature maps from stereo images. Figure 2 depicts the architecture of the feature extractor, which consists of an efficient backbone, an attention module, and three feature aggregation modules.  
With the attention mechanism, the feature extractor can adaptively collect input from multiple scales into encoded features. With an efficient backbone, the blueprint separable convolution (BSConv) [22] is utilized to justify DSConv [23] by analyzing intrakernel correlations of vanilla CNNs. For the purpose of multiscale representation, BSConv [22] is employed to progressively decrease the dimensions of feature maps and generate a sequence of feature maps with varying scales. Feature extraction produces output feature maps at three levels (1/4, 1/8, and 1/16). AAFS [12] includes three aggregation modules for three levels of features. Then, an attention module is employed to recalibrate channel importance and boost feature discriminability. The cost volumes for disparity estimation are formed from the final updated feature maps for each level.
###### Abstract  
This study aimed to evaluate the impact of Twin Block appliance therapy on skeletal, denoalveolar, and facial soft tissue characteristics. The study included 18 participants with Class II skeletal malocclusion who were treated with the Twin Block appliance. Lateral cephalograms and 3D face scans were analyzed before and after therapy for each participant. Dependent \(t\)-test results showed a significant increase in the protrusion of the lower incisors (\(p<0.001\)), proclamation of the lower incisors (\(p=0.021\)), SNB (\(p=0.005\)), Ls:E (\(p=0.040\)), mandibular length (\(p<0.001\)), and soft tissue mandible length (\(p<0.001\)) and a significant decrease in the ANB (\(p=0.003\)), Wits (\(p=0.001\)), ANPG (\(p=0.001\)), overbite (\(p=0.001\)), and the retrusion of upper incisors (\(p=0.002\)). Twin Block therapy caused changes in skeletal and soft tissue characteristics. The increase in the SNB angle and mandibular length, accompanied by the decrease in the ANB and Wits values reduced the skeletal discrepancy. The reduction in the ANPG indicated an improvement in the skeletal profile. Additionally, the increase in the soft tissue mandible length and distance between the upper lip and E-line contributed to improved esthetic soft tissue profile characteristics.  
imaging; three-dimensional; malocclusion; angle class II; orthodontic appliances; functional; orthodontics Article