esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Signal Analysis
### Multi-Fault Separation of Simulation Signal Analysis
Figure 4: The time domain response of \(s_{1}\), \(s_{2}\) and the single channel compound signal \(x_{1}(t)\): (**a**) the time-domain of \(s_{1}\); (**b**) the time-domain of \(s_{2}\) and (**c**) the analyzed single channel signal \(x_{1}(t)\). Figure 5: The intrinsic mode functions (IMFs) provided by RPSEMD. After this, the single-channel observation signal \(x_{1}(t)\) and its intrinsic mode function \((IMF_{1},IMF_{2},...,IMF_{7})\) are collected to form a new multi-dimensional signal \(x_{\text{lim}f}=(x_{1},IMF_{1},IMF_{2},...,IMF_{7})^{T}\), whose correlation matrix is expressed as \(R_{x}=E(x_{\text{lim}f}(t)x_{\text{lim}f}^{\text{H}}(t))\). Following this, the singular value decomposition (SVD) to the correlation matrix \(R_{x}\) is performed, with the feature vector \(\Lambda=diag\{\lambda_{1},\lambda_{2},...,\lambda_{8}\}\) being obtained. According to the BIC value shown in Figure 7, the number of source signals is determined as two, which is consistent with the physical truth. In addition to the original single-channel signal, it was necessary to select one component from the inherent IMFs obtained by RPSEMD, thus satisfying the basic conditions of BSS. Figure 6: The IMFs provided by EEMD. Figure 7: The Bayesian information criterion (BIC) of the composite original signal. Since the IMF\({}_{2}\) has the maximum correlation with the original signal, it can be chosen as the preferred mode component and is used together with the original single-channel signal to form \(x_{\text{lim}f}=\left(x_{1},IMF_{2}\right)^{T}\). It is employed as a new multi-channel observation signal to achieve the blind source separation by the approach of feature matrix joint diagonalization, based on the four-order accumulation. The results provided by the proposed method are plotted in Figure 8. When comparing Figures 4 and 8, it was found that the time-domain waveform of the two components obtained by the proposed method corresponds well with the original source signals. To verify the effectiveness of the proposed method, EEMD based on feature matrix joint diagonalization is applied to the simulation signal. Since the IMF\({}_{5}\) generated by EEMD has the maximum correlation with the original signal, the new observed multi-channel signal is expressed as \(x_{\text{lim}f}=\left(x_{1},IMF_{5}\right)^{T}\). Following this, the result is plotted in Figure 9, which shows that the compound fault can hardly be identified. Thus, we can make a conclusion that the proposed method not only realizes the decomposition of single-channel vibration signal, but also further achieves the BSS. Figure 8: Result of blind source separation provided by the proposed method: (**a**) The first component obtained by the proposed method and (**b**) The second component obtained by the proposed method. Figure 9: Results of blind source separation provided by the EEMD: (**a**) The first component obtained by EEMD and (**b**) The second component obtained by EEMD.
## 3 Results and Discussion
### Requirements Evaluation
#### 3.3.9 Affordability  
Although 80% of the more than 600 million people with disabilities live in developing countries [2; 3], the development of medical devices usually only considers the needs and infrastructure of people from high-income countries [2]. As an example, prostheses such as Michelangelo, BeBionic and i-Limb, which all cost over USD 50,000, are not feasible for a typical user from a low-income country, not only because of their high cost but also because it is not robust enough for their day-to-day functions and because its installation requires specialized personnel. In a low-income scenario, active prosthetic options are limited to body-powered technologies. The Hosmer 5X hook, which is used widely by people in low-income countries, has an approximate value of USD 500, which is affordable for a public health system in a developing country.  
The manufacturing cost of 1 kg of carbon fiber for large-scale production (>50,000 units) in 2020 is estimated at USD 10/kg. The cost of the material is 55% of the total cost, while the remaining 45% corresponds to labor, tools, equipment, and overhead [35]. Similarly, for low-scale production (<200 units), the manufacturing cost increases to USD 50/kg. Considering these costs and the weight of the Carbon HK VO hook (100 g) presented in this study, it has an estimated manufacturing cost of USD 1-5 for the total amount of carbon fiber parts in the device, corresponding to 80% of the product weight. Although this cost does not include nanoparticles coating, profit, transportation, administrative costs, or taxes, this prosthesis is estimated to have a commercial cost of USD \(\leq\)5500 per unit, an affordable cost for developing countries.
## 5 Safe and Accurate Aerial Manipulation
### Control Methods for Aerial Manipulation
#### 5.2.2 On Robust UAV Stabilization  
In order to deal with both internal and external disturbances of aerial manipulators, appropriate control techniques must be implemented. First, consider the generalized multirotor UAV dynamics (4) in state-space representation, as follows:  
\[\frac{d}{dt}\begin{bmatrix}\mathbf{x}\\ \mathbf{\dot{x}}\end{bmatrix}=\begin{bmatrix}\dot{\mathbf{x}}\\ -\mathbf{M}^{-1}b+\mathbf{M}^{-1}\mathbf{R}u+\mathcal{D}(t)\end{bmatrix} \tag{8}\]  
where \(\mathbf{x}=\begin{bmatrix}\mathbf{p}^{T}&\phi&\theta&\psi\end{bmatrix}\), \(\mathbf{M}=\begin{bmatrix}m\mathbf{I}^{3\times 3}&\mathbf{0}^{3\times 3}\\ \mathbf{0}^{3\times 3}&\mathbf{J}\end{bmatrix}\), \(\mathbf{R}=\begin{bmatrix}{}^{W}\mathbf{R_{B}}&\mathbf{0}^{3\times 3}\\ \mathbf{0}^{3\times 3}&\mathbf{I}^{3\times 3}\end{bmatrix}\), \(b\in\mathbb{R}^{6}:b=\begin{bmatrix}m\mathbf{g}\mathbf{e}_{3}\\ \omega\times\mathbf{J}\omega\end{bmatrix}\) and \(\mathcal{D}(t)\in\mathbb{R}^{6}\) is the external disturbances vector function, usually unknown but bounded and Lipschitz continuous, or at least with a removable discontinuity. In this context, there are some approaches that use higher order dynamics and the isolation of nonlinearities and disturbances [41]. To illustrate how robust control works on a UAV system, consider it as a 6-dimensional motion unit mass such as the following [42]:  
\[\frac{d}{dt}\begin{bmatrix}\mathbf{x}\\ \mathbf{\dot{x}}\end{bmatrix}=\begin{bmatrix}\dot{\mathbf{x}}\\ u+\mathcal{F}(\mathbf{x},\dot{\mathbf{x}},t)\end{bmatrix}, \tag{9}\]where \(0<|\mathcal{F}(\mathbf{x},\dot{\mathbf{x}},t)|\leq\mathcal{F}_{max}\) is a bounded disturbance term, including both the UAV dynamics and external disturbances. The target is to lead the UAV (9) to a displaced equilibrium \(\mathbf{x}^{d}\). Below are presented some techniques to achieve such a target.
# A Review of the Potential Climate Change Impacts and Adaptation Options for European Viticulture
## 4 Potential Climate Change Impacts  
### Ongoing Climate Change  
The above-mentioned close relationships between grapevine development and weather/climate make viticulture particularly vulnerable to climate change and susceptible to its detrimental impacts. Through the combination of (i) generally increasing air temperatures and (ii) shift of the ripening period toward earlier (usually warmer parts of the season), climate change has a two-fold impact from the recent-past warming, especially clear during the ripening period [80].  
Historical temperature trends for the main viticultural regions worldwide show that the growing season mean temperatures increased by 1.3 \({}^{\circ}\)C between 1950-1999 and 1.7 \({}^{\circ}\)C from 1950 to 2004 [81]. Associated with increasing temperatures over the last few decades, alterations in the grapevine growth and physiological development have been documented [56; 57; 82; 83; 84]. Changing climates are thereby influencing grapevine yield, as well as berry and wine quality [81; 85]. In this regard, it was found that higher temperatures during the growing season promoted a decrease in the grape berry total acidity content [12; 86], upward trends in sugar content or probable alcohol [87], and a decoupling between technological and phenolic maturity [83]. Earlier phenological timings and growing season shortening were observed in many viticultural regions [88; 89; 90; 91; 92]. These earlier phenological timings may lead to ripening during excessively warm conditions [93], with increased alcohol content, decreased acidity, and modifications in wine sensory profiles [94; 95]. The 2019 ProWein Business Report (available at [[https://www.prowein.de/](https://www.prowein.de/)]([https://www.prowein.de/](https://www.prowein.de/))) presented the results of an industry survey, with the participation of more than 1700 enterprises, stating that the sensory profiles of wines have changed over the past decades. As such, climate change is also potentially threatening the wine typicity of traditional winemaking regions [80].
## 3 Feedforward Control Considering Non-Uniform Creep Difference and Hysteresis Nonlinearity
### Module Function Introduction
#### 3.2.3 Clockwise Operator and Clockwise Operator Model
Now, consider the input-output curve segment of the clockwise operator model described by Equation (15), where the input values lie between two consecutive thresholds \(u=u_{\max}-r_{i}\) and \(u=u_{\max}-r_{i+1}\). This segment of the curve is a superposition of unilateral clockwise operators. However, all clockwise operators with thresholds smaller than \(r_{i}\) have not yet closed because the input is not large enough. This implies that none of the clockwise operators with thresholds smaller than \(r_{i}\) contribute to the curve segment between \(u=u_{\max}-r_{i}\) and \(u=u_{\max}-r_{i+1}\). On the other hand, all clockwise operators with thresholds lower than \(r_{i+1}\) will be active on this segment. As shown in Figure 6, once the input increases to less than the maximum input minus the threshold of the clockwise operators constituting the clockwise operator model, their output will increase at the same rate as the input and can be described by a line segment with unit slope. However, as indicated by Equation (15), the coefficient \(\omega_{i}\) modifies the slope of this line segment. This holds true for all clockwise operators constituting the clockwise operator model, meaning that the segment between two consecutive thresholds is a superposition of line segments and must itself be a line segment. The slope \(k_{i}\) between the inputs \(u=u_{\max}-r_{i}\) and \(u=u_{\max}-r_{i+1}\) can be described solely by the coefficients as shown in Equation (16). Using this equation, the problem of estimating the coefficients is reduced to finding the slope of the line segment that best fits the system between consecutive threshold inputs. \[k_{i}=\sum_{j=1}^{i}\omega_{j} \tag{18}\]
# A Combined Approach of Field Data and Earth Observation for Coastal Risk Assessment
## 2 Methodology
### Marine Weather Conditions  
Wind and wave conditions during the SAR acquisition greatly influence the backscattering on the sea surface. In the presence of waves, the values of the backscattering coefficient (\(\sigma_{0}\)) are much higher than under calm conditions. In particular, X-band SAR data seems to be more sensitive to the waves compared to L-band SAR [61].  
Strong wave motion can lead to erroneous definition of the coastline, which would be landward by several pixels compared to the real position [39]. Therefore, sea conditions are an essential element  
Figure 1: Diagram flow of the COSMO-Beach coastal monitoring system.  
to avoid selecting \"noisy\" images, whose processing could produce unreliable results and large errors in shoreline extraction.  
The exploitation of wind and wave data allows a preliminary selection of the SAR images archived in the catalogue, and to discard _a priori_ all the images acquired during storms or immediately following them. Post storm images, indeed, could be useful for the quantification of damage, but they could lead to a wrong assessment of coastal dynamics. Since wave buoys are often too sparsely distributed along the coastal zone and very few coastal areas are provided with local wind and wave measures, an analysis has been carried out aimed at extracting information on sea conditions directly from SAR data amplitudes, to be exploited in the case of unavailability of in situ measurements. Wind and wave fields can be directly inferred from SAR amplitude through a number of complex algorithms [62; 63; 64]. Actually, we are not interested in the estimation of the wave field, but rather in selecting only the SAR images acquired under calm weather conditions. Therefore, a simplified procedure has been proposed and implemented in the COSMO-Beach processing chain that consists in a first-order statistical analysis, based on the computation of mean and standard deviation of backscattered amplitude data in open sea areas located offshore the target regions.
## 1 Introduction  
With the implementation of a new technology, cost as well as risks are considered along with the benefits of change in the imaging technique. CT (Computed tomography) was developed in the 1900s, but it was expensive and exposed the subject to a lot of radiation, and thereby it was applied only in specific conditions such as severe anomalies. With CBCT (Cone-Beam Computed Tomography) came the advantages of low-cost and low radiation exposure, and it represents the best in 3-dimensional imaging for dental application. CBCT has a vast range of uses and applications; however, its use in pediatric dentistry is still questionable [1]. The most commonly used radiographic examination for pediatric patients is dental radiographs [2]. Dental radiology is of great help in the oral examination of infants and children along with adolescents and differentially abled children. They help in diagnosing oral disease and maxillofacial defects. The main reason for doing radiographic examination in children is detecting caries, injuries of the dental and maxillofacial region, developmental tooth anomalies and other pathological conditions of the oral region. However, the risks associated with it should never be neglected. Guidelines are present to avoid unnecessary radiation exposure. DRI (Digital radiological imaging) reduces the radiation dosage because less exposure time is required to obtain the image [3]. CBCT has also been used to analyze resorption related to an unerupted tooth and determine bony pathologies [4]. CBCT gives a very high diagnostic quality. It uses volumetric tomography and gives a 3-dimensional image. It gives a higher radiation exposure to patients than the traditional techniques, but the exposure is less than CT medical tomography. The advantage of CBCT is that it gives more information and more correct evaluation [5]. CBCTtypically has a radiation exposure of greater than that used for conventional radiographs. This is of concern in children. This has increased efforts on dose adjustment and justifying the use of CBCT in the pediatric field [6,7]. CBCT needs only a single scan for capturing the data, thus the time required is less (less than 30 sec) when compared to the conventional CT [8]. There has been a rapid increase in the number of children getting dental treatment in the past decade. Up until a few years back, IOPAR (periapical radiographs) and OPG (panoramic radiographs) were generally used for the diagnosis of dental problems. These techniques created a 2D representation that was difficult to use for diagnoses as well as treatment planning. Thus, 3D modalities were introduced which have been used popularly in dentistry, but many skeptics of its application in children remain [9]. This study was done with the assumption that dental practitioners as well as students still have a lot of doubts on the use of CBCT for children, as very few pediatric cases are being referred for CBCT.
# The Detection of Fake News in Arabic Tweets Using Deep Learning
## 3 Methodology
### Proposed Model Architecture
#### 3.4.1 CNN Architecture  
CNN learns local patterns from the given training data using convolution calculation. It has been known that the CNN architecture significantly impacts the overall performance of the model [37]. In this work, the layered architecture of the CNN-based model has three parallel convolutional layers with different kernel sizes. Each convolutional layer is followed by a max pooling layer to reduce the dimensionality of the feature maps without affecting the network's efficiency. Then, the concatenation layer takes the output features from the pooling layers and combines them in a single matrix. Finally, the flattened layer is needed to reshape the concatenated features into a one-dimensional vector.
## 6 Limitations and Future Work  
This study has several limitations that need to be considered. Firstly, the experiment used wireless Bluetooth in-ear earphones as the stimuli without extending the research to other products, thus limiting the general applicability of the findings. Secondly, the experiment participants were mainly students from the same school, aged 18 to 27. The limited age range and homogeneity in the background do not adequately represent the esthetic preferences of other audiences in different regions. Thirdly, eye-tracking technology was mainly used to measure and analyze the data. Although this method makes the experimental results present a certain degree of objectivity, an individual's product appreciation is a multifaceted physiological and psychological process influenced by factors including emotions, experiences, cultural background, and socioeconomic status, which may be overlooked in a quantitative analysis. Fourthly, the absence of a control group with neutral earphone images limits this study's ability to isolate the impact of stylization on user attention. Furthermore, the structural designs generated by AI tend to be rough, making it challenging to ensure the rationality of local design details [47].  
To address the limitations of this study, expanding and diversifying the sample will improve the general applicability of the findings. Future studies should compass a broader range of products beyond Bluetooth earphones and invite participants from varied ages, regions, and cultural backgrounds. Regarding the research methodology, in addition to eye-tracking technology, interviews, questionnaires, and other methods should be combined to delve into participants' preferences and experiences.
# Bounded-Error-Pruned Sensor Data Compression for Energy-Efficient IoT of Environmental Intelligence
## 1 Introduction
For example, when the error tolerance is assigned as 1%, BESDC can reduce transmission data by at least 55%. For a temperature sensor, 1% error tolerance of two-digit temperature value is around 0.5 degree Celsius in bounded error. Therefore, it is acceptable for applications to make a weather report with 1 degree Celsius bounded error in required QoS2/QoD. The remainder of this paper is organized as follows. In Section 2, we survey the literature for IoT data compression including bounded error and unbounded error methods. In Section 3, we present BESDC and introduce the applied data, temporal and spatial correlation compression schemes. We use two-tier BEA for different devices running BESDC for sensor data. In Section 4, experiments and performance evaluation results of BESDC are shown. Finally, conclusions and future work are given in Section 5.
## 3 Approach  
Landsat images were processed using in-house MATLAB code. All images were read with a 1-km buffer around a central point of the landfill. The buffer generated an image with 67 \(\times\) 67 pixels, which covers approximately 4 km\({}^{2}\) for all bands, including the \(LST\) thermal images. The advantage of this approach is that the landfill's surrounding area can be analyzed if needed [30]. For convenience, the resulting image (67 \(\times\) 67) was masked with an overall landfill shape file, as illustrated in Figure 2. To align our analysis with prior observations of the Bridgeton landfill, we also divided the landfill area into three smaller regions: a lower region (the South Quarry where most of the landfill activities occur), a middle region (the Neck area), and an upper region (the North Quarry and radioactive waste areas), each of which is well defined by its own shape file, as shown in Figure 2.  
Figure 2: (**a**) Bridgton Landfill, divided by regions, and the surrounding area. (**b**) Latitude and longitude of the landfill and overlay of regions on Google Earth.  
This approach has several advantages: (**a**) the effect of any extreme reading due to water or buildings or any other land cover can be isolated to ensure that only readings within the study area are considered; (**b**) each area/subarea can be analyzed separately, as described in the following; (**c**) hotspots that move within each area/subarea can be easily traced over time spans of many years; and (**d**) the status of each landfill subarea can be tracked independently.
# Real-Time Hybrid Navigation System-Based Path Planning and Obstacle Avoidance for Mobile Robots
## 2 Review of Hybrid Navigation Systems  
The main objective of this study was to provide a system in order to manipulate autonomous robots that move from initial pose \(\mathbf{P}_{\text{R}}(0)\), \(\psi_{\text{R}}(0)\) to the desired location and heading \(\mathbf{P}_{\text{D}}\), \(\psi_{\text{D}}\) with a given map. In order to archive this, the robot must have a suitable navigation scheme. According to [14], the robot's navigation scheme can be determined through four main tasks:  
* Collect the data about the robot's workspace in the form of a map;
* Plan a collision-free path from the original position of the robot to the desired position;
* Generate trajectory for the robot that meets the velocity and acceleration limits of the robot;
* Avoid unforeseen obstacles--be they static or dynamic--and keep tracking initial trajectory.  
This study assumes that the robots can localize themselves and obstacles can be detected by the robot on the known map. This work only focuses on three main tasks: path planning, trajectory generation and obstacle-avoidance with the processed sensor data. We propose a hybrid navigation system as in Figure 1. The advantages of this kind of system is clearly demonstrated in the study [14] and have good reality performance in [15]; its constituents can be summarized as follows:Deliberative layer uses prior-data in the form of map to trace a path from the position of the robot to the desired position. This type of path is called a global path. This layer is responsible for the path-planning task;
* Reactive layer uses information extracted from sensors to evaluate and decide to create a path to help the robot respond to unforeseen obstacles. This type of path is called the local path. This layer undertakes the obstacle-avoidance task;
* Executive layer decides which path to follow, while controlling the robot to follow the selected path.  
In the Sections 4-7, we propose methods to solve each task of the robot individually to complete the proposed navigation system seen in Figure 1. In Section 3, we briefly introduce the robot kinematic model. In Section 4, we introduce the A-heuristic algorithm for path-planning and propose our modification based on this algorithm. In Section 5, we apply the PCBC for smoothing the planned path. In Section 6, we introduce the problem in discrete control and explain our re-path algorithm for the trajectory tracking task. In Section 7, we focus on constructing the reactive layer of our navigation system that helps the robot to perceive and choose the appropriate path for avoiding moving obstacles.