esa-sceva/satcom-chunk-collection huggingface dataset:
# ROS-Based Unmanned Mobile Robot Platform for Agriculture
## 3 Mobile Robot System
### Cam-Lidar Calibration  
To use heterogeneous cameras in one system, we extract extrinsic parameters between each camera and Lidar sensors [30]. This is to use the information of the object predicted by the camera in the lidar sensor. Camera parameters include intrinsic, extrinsic, and distortion coefficients. Before estimating the extrinsic parameters between the camera and Lidar sensor, we need to extract the intrinsic parameter of the camera [31]. The intrinsic parameters are defined as:  
\[\left[\begin{array}{ccc}f_{x}&0&0\\ s&f_{y}&0\\ c_{x}&c_{y}&1\end{array}\right] \tag{2}\]  
where \(c_{x}\) and \(c_{y}\) are the optical centers in pixels, and \(f_{x}\) and \(f_{y}\) are the focal lengths in pixels. \(s\) is a skew coefficient, which if the image axes are not perpendicular, is non-zero. It calculates camera parameters based on feature points extracted from several images of the checkerboard. Figure 7 represents checkerboard images captured from different viewing angles to obtain the camera parameters. We calculate the 3D positions of corresponding feature points from captured checkerboard images. Then we find camera parameters that minimize the projection errors when the 3D points project to the checkerboard images.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Configuration** \\ \hline Chassis & 2 WD mobile robot \\ Motor controller & FIM2360 \\ Battery & 12 V \(\times\)2 \\ Motor & AC Induction Motor (24 V) \(\times\)2 \\ Board & Jetson nano board \(\times\)2 \\ Router & CNR-L500 (LTE) \\ OS & Ubuntu 18.04 \\ Camera & YOITCH webcam \\ Lidar & Velodyne vlp-16 \\ ROS & Melodic \\ Program language & Python3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hardware and software specification of the mobile robot platform.  
To estimate the extrinsic matrix, we solve the perspective-\(n\)-point system. Perspective-n-point [32] is the problem of estimating the pose of a calibrated camera given a set of \(n\) 3D points of the world and the corresponding 2D projection of the image. When there are outliers in the point correspondence set, PnP is error-prone. Therefore, RANSAC can be used to make the final solution for the camera pose more robust against outliers [33]. We employ PnP with a RANSAC algorithm to estimate rotation and translation transforms between the camera and the LiDAR. We pick the corresponding points by selecting the four corner points of the checkerboard in both the camera and the LiDAR frames, as shown in Figure 8. The calibrated extrinsic parameter is as follows:  
\[[\mathrm{R}|\mathrm{t}]=\left[\begin{array}{ccc|c}r_{1,1}&r_{1,2}&r_{1,3}&t_ {1}\\ r_{2,1}&r_{2,2}&r_{2,3}&t_{2}\\ r_{3,1}&r_{3,2}&r_{3,3}&t_{3}\\ \end{array}\right] \tag{3}\]  
where \(R\) is the rotation matrix, and \(t\) is the translation matrix.
## 1 Introduction
### Executive Function Assessment  
EFs are traditionally evaluated with standard paper-and-pencil neuropsychological tests, such as the Modified Wisconsin Card Sorting Test [9] or the Trail Making Test [10], which allow standardized procedures and goals that make them valid and reliable. However, these tests have been shown to be unable to predict the complexity of executive functioning in real-life settings reliably [11; 12; 13; 14; 15; 16] and seem inadequate in terms of sensitivity and specificity [16; 17]. Thus, a more ecological evaluation of EFs is essential to evaluate the specific cognitive profile of individuals [12; 14; 18] and how executive deficits affect daily functioning [14]. In an attempt to refine EF assessment, [PERSON] and colleagues proposed the development of neuropsychological assessments based on models derived from directly observable everyday behaviours. This \"function-led\" approach diverges from the emphasis on abstract cognitive \"constructs\" without regard for their ability to predict the complexity of \"functional\" behaviours found in real-life situations. Hence, there has been a need for new neuropsychological instruments that measure different components of executive functioning within real-life scenarios [18] such as the Multiple Errands Test (MET) [11] and the Executive Function Performance Test. For example, the MET evaluated patients while carrying out different daily tasks (e.g., shopping tasks) in a real supermarket, following specific rules within a specified time frame. The assessment in real-life settings provided a more accurate estimate of the patient's deficits than laboratory conditions [19]. However, it revealed further limitations, such as long times, high economic costs, difficulty of organization, lack of safety, poor controllability of experimental condition, or applicability to individuals with motor deficits [20]. For example, MET is conducted in real time in a real-life setting, limiting the use for patients who are not independent, and requiring high physician costs and time demands. Therefore, the ecological limitations of traditional neuropsychological tests and many difficulties in administering tests in real-life scenarios have led to the increasing use of technological tools and virtual reality (VR) for the assessment of EFs in real life.
# Recognition of Manual Welding Positions from Depth Hole Image Remotely Sensed by RGB-D Camera
## 3 Depth Image Analysis and Body Joint Identification
### Three-Dimensional Body Joint Construction  
Joint coordinates extracted from depth hole filled images cannot provide accurate depth information. Welding, as opposed to general body postures, necessitates complex joint movement. The rotation of the arm, neck, and waist joints appears on a vertical or horizontal basis when performing a welding operation, making it only possible to correctly express a welding posture when accurate depth information is provided. As a result, proper depth information must be included.  
The depth image is expressed in grayscale according to the distance from the depth sensor. Joints far from the sensor are displayed as dark, while those close to the sensor are displayed as bright. Therefore, when the welding operation is taken as a depth image, the welder's body and surrounding objects are expressed as images with different color values according to the distance difference.  
In this study, a strategy is used to restore the joint coordinates damaged by the depth hole in the depth image by utilizing the measured depth information of the body part that is not covered. Given that the depth hole has a significant impact on the welder's arm and body due to welding characteristics, if there is a part of the arm or body for which depth information is measured, the depth hole region is sequentially interpolated and reconstructed using the depth information available. This strategy assumes that recognizing the welding position requires only the relative positional relationship of the joints.  
Figure 14 shows the results of comparing the joints extracted using (a) the ground truth without depth hole, (b) the image with the depth hole, and (c) the image with the depth hole removed. The joint coordinate values can be checked in Table 1, where the error is computed by the difference in the two vectors' magnitude. The joint coordinates extracted from (a) and (c) images do not show a large error. In (c), an error occurs near the neck and hand that were hidden by the depth hole. Unfortunately, this is a theoretically unavoidable limit. The correlation with other joints is still well maintained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Joint** & **Ground Truth** & **Restored Image** & **Error** \\ \hline Head & (489, 175, 75) & (468, 174, 73) & 3.83\% \\ Neck & (475, 214, 74) & (464, 205, 47) & 3.37\% \\ Right shoulder & (433, 214, 77) & (434, 212, 75) & 0.06\% \\ Right elbow & (420, 278, 85) & (420, 276, 85) & 0.21\% \\ Right hand & (475, 253, 98) & (434, 263, 91) & 5.75\% \\ Right pelvis & (461, 342, 88) & (448, 327, 88) & 3.29\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of body joint coordinates between ground truth and restored image.  
Figure 14: Comparison of ground truth and three-dimensional body joints: (**a**) ground truth, (**b**) wrong result with depth hole, and (**c**) result by the restored image with depth hole filling and adjusted depth.
# Studies of General Precipitation Features with TRMM PR Data: An Extensive Overview
## 5 Rainfall Observation of PR Data
### Climate Characteristics of Rainfall  
PR had provided long-term, stable, reliable and uninterrupted data for a dozen years, and this made it convenient for studies on climate characteristics of rainfall over tropical regions (Table 10).
# Advanced Construction of the Dynamic Matrix in Numerically Efficient Fuzzy MPC Algorithms
## 1 Introduction
### MPC Algorithms Based on Linear Models  
The simplest MPC algorithms are based on linear process models; see, e.g., [1,13]. In such a case, the superposition principle holds; therefore, the vector of predicted output values \(\boldsymbol{y}\) can be decomposed into two parts:  
\[\boldsymbol{y}=\widetilde{\boldsymbol{y}}+\boldsymbol{A}\cdot\Delta\boldsymbol {u}\text{,} \tag{12}\]  
\[\widetilde{\boldsymbol{y}}=\left[\begin{array}{c}\widetilde{\boldsymbol{y}}^ {1}\\ \vdots\\ \widetilde{\boldsymbol{y}}^{n_{y}}\end{array}\right]\text{, }\widetilde{ \boldsymbol{y}}^{j}=\left[\begin{array}{c}\widetilde{y}^{j}_{k+1|k}\\ \vdots\\ \widetilde{y}^{j}_{k+p|k}\end{array}\right]\text{,} \tag{13}\]  
where \(\widetilde{\boldsymbol{y}}\) is called the free response of the control plant describing the influence of the past values of control signals on the process and \(\boldsymbol{A}\cdot\Delta\boldsymbol{u}\) is called the forced response, depending on the future changes of the control signals \(\Delta u\); the matrix \(\mathbf{A}\) is called the dynamic matrix and has the following form:  
\[\mathbf{A}=\left[\begin{array}{cccc}A^{11}&A^{12}&\ldots&A^{1n_{x}}\\ A^{21}&A^{22}&\ldots&A^{2n_{x}}\\ \vdots&\vdots&\ddots&\vdots\\ \mathbf{A}^{n_{y}1}&\mathbf{A}^{n_{y}2}&\ldots&\mathbf{A}^{n_{y}n_{x}}\end{array}\right], \tag{14}\]  
\[\mathbf{A}^{jm}=\left[\begin{array}{cccc}a_{i}^{j,m}&0&\ldots&0&0\\ a_{2}^{j,m}&a_{1}^{j,m}&\ldots&0&0\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ a_{p}^{j,m}&a_{p-1}^{j,m}&\ldots&a_{p-s+2}^{j,m}&a_{p-s+1}^{j,m}\end{array} \right], \tag{15}\]  
where \(a_{i}^{j,m}\) denote the step response coefficients of the control plant, describing the influence of the \(m\)th control on the \(j\)th output; for details, see, e.g., [9].  
After applying the prediction based on a linear model (12), the performance function (1) is transformed into a function that depends quadratically on the decision variables \(\Delta u\):  
\[J_{LMPC}=(\mathbf{\overline{y}}-\mathbf{\overline{y}}-\mathbf{A}\cdot\Delta\mathbf{u})^{T} \cdot\mathbf{\kappa}\cdot(\mathbf{\overline{y}}-\mathbf{\overline{y}}-\mathbf{A}\cdot\Delta \mathbf{u})+\Delta\mathbf{u}^{T}\cdot\mathbf{\Lambda}\cdot\Delta\mathbf{u}\enspace. \tag{16}\]  
The prediction (12) depends linearly on the decision variables; therefore, after using it in the constraints on the output values (4), the optimization problem (1)-(4) becomes an easy-to-solve, standard quadratic optimization problem, with linear constraints. Unfortunately, the control performance offered by an LMPC algorithm, applied to a nonlinear process, may be unsatisfactory. In order to improve it, one can use an MPC algorithm based on a nonlinear model.
# Research on Testing Method of Oil Characteristic Based on Quartz Tuning Fork Sensor
## 3 Oil Ageing Processes and Sensors for Fluid Property Detection
### Oxidation
Entry into Phase 3 is the appropriate time to perform an oil change. During Phase 4, oil had already reached a very high level of degradation. There is a high risk of dramatic engine failure. An oil change has to be performed immediately at this point [38]. The oxidation process depends on engine application characteristics and engine oil composition. Oxidation measurements for three different 15W40 engine oils are presented in Figure 8. The three engine oils were stored at 120 \({}^{\circ}\)C and regularly measured until the end of Phase 2 of oil oxidation. The results show different measurement gradients for the oils, but the general trends described above are confirmed. Brand C oil shows a consequent resistivity increase during Phase 1 and then relatively low increases in viscosity and dielectric during Phase 2. Brand A oil shows a lower resistivity increase during Phase 1 but much higher increases in viscosity and dielectric during Phase 2. These different gradients could be linked to additive compositions. Indeed, the type and concentration of additives present in oil will favor or prevent different reactions yielding oxidation and degradation products [39]. \begin{table}
\begin{tabular}{c c} \hline \hline
**Stage** & **Oxidation A/cm** \\ \hline Phase 1 & 0–3 \\ Phase 2 & 3–16.5 \\ Phase 3 & 16.5–31.5 \\ Phase 4 & 31.5–40 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Four different phases of oil oxidation process. Since oxidation is dependent on oil composition and engine application characteristics, precise and complete oxidation modeling seems difficult. However, one can determine a typical physical change by focusing on Phase 1 and Phase 2. The solution consists of modeling Phase 1 and Phase 2 and detecting the entry into Phase 3, which is linked to a consequent level of degradation. For example, the 15W40 engine oils presented in Figure 9 could be modeled with the following equations at constant temperature:  
\[\eta_{\text{th}} =\eta_{\text{i}}.\Big{(}1+21\times 10^{-3}.\text{Ox.}\Big{)} \tag{11}\] \[\rho_{\text{th}} =\rho_{\text{i}}.\Big{(}1+0.5\times 10^{-3}.\text{Ox.}\Big{)}\] (12) \[\varepsilon_{\text{th}} =\varepsilon_{\text{i}}.\Big{(}1+0.5\times 10^{-3}.\text{Ox.}\Big{)}\] (13) \[\text{Rp}_{\text{th}} =\text{Rp}_{\text{i}}.\Big{(}1-12\times 10^{-3}.\text{Ox.}\Big{)} \tag{14}\]  
Figure 8: Engine oil (15W40 brands A, B and C) oxidation at 100 \({}^{\circ}\)C: (**a**) viscosity; (**b**) density; (**c**) dielectric; and (**d**) Rp. The models are plotted in Figure 9 with the related accuracy specifications given with full blue lines. The red circles represent detection of the entry into Phase 3 when \(\eta\geq 1.05.\mathrm{n_{th}}\) and \(\mathrm{Rp}\geq 1.05\mathrm{Rp_{th}}\).
## 2 Materials and Methods
### Volatile Compound Analysis  
Headspace solid-phase microextraction (HS-SPME) was employed to extract volatile compounds from the oil samples [31; 43] with slight modifications. Initially, 5 g of the oil sample was precisely weighed into a 5 mL headspace vial, together with 30 mg of 2-methyl-3-heptanone (20 mg/mL) serving as an internal standard. The vial was subsequently sealed and equilibrated for 10 min at 60 \({}^{\circ}\)C. Following this, volatile aroma compounds were extracted using SPME with a 50/30 \(\upmu\)m divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fiber (Supelco, Inc., Bellefonte, PA, USA) for 30 min at 60 \({}^{\circ}\)C.  
To analyze the volatile compounds in the oil samples, a GC-MS system (7890B-5977B, Agilent, Santa Clara, CA, USA) equipped with a Stabilwax column (60 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m, Restek Corp. Bellefonte, PA, USA) was employed. The fiber was desorbed into the GC port for 25 min at 250 \({}^{\circ}\)C before analysis using GC-MS in spitless injection mode. High-purity helium served as the carrier gas at a flow rate of 1.2 mL/min. The oven conditions were programmed as follows: the oven temperature increased from 40 \({}^{\circ}\)C to 240 \({}^{\circ}\)C at a rate of 5 \({}^{\circ}\)C/min and was held for 15 min. The transfer line and ion source temperatures were set at 245 \({}^{\circ}\)C and 250 \({}^{\circ}\)C, respectively. A full-scan mode of 33-350 \(m/z\) was performed to acquire the data, and the data were characterized using a combination of the relative retention index (RI value) and mass spectrum matching according to the NIST 2017 mass spectral library.
# Design of an Autonomous Cooperative Drone Swarm for Inspections of Safety Critical Infrastructure
## 4 Drone Design
### Sensors for Cable Detection  
Overhead cable detection onboard the drone is needed for two primary tasks: autonomous recharging from powerlines, and inspection of power transmission infrastructure. During these tasks, the drone must at all times know the positions of nearby powerlines to enable safe and efficient navigation between them. Choosing the right sensors for the task is crucial to ensure that the drone remains lightweight. Therefore, we have compared suitable sensors to determine their relative performances and impacts several parameters [33]. Based on this study, a mmWave radar sensor is chosen as the primary sensor to perform overhead cable detection. This type of sensor emits radio signals in the lower mmWave frequency range and determines the relative location of objects in its field of view by analyzing the echo of the emitted signal. The metallic nature of powerlines makes them very reflective of radio waves, which means that a radar sensor can achieve a high signal-to-noise ratio in a powerline detection task. The specific sensor used, an IWR6843 AOPEVM from Texas Instruments, weighs just 12 g and can detect and localize overhead powerlines at ranges exceeding 10 m in a wide field of view while consuming less than 4.5 W. Another advantage of this sensor is the sparsity of data it produces, which will require less computation from the resource-limited onboard computer. The data produced by the mmWave radar sensor are in a point cloud format where a powerline in the field of view is typically represented by less than three points.  
In addition to the mmWave radar sensor, the cable detection sensor package is supplemented with a USB camera. The sparsity of the mmWave sensor data makes it insufficient to determine the direction of the localized powerline cables. However, this information can be retrieved from images relatively simply using standard algorithms such as Hough Lines. Specifically, an AR0144 USB camera is used, which features a 720p RGB sensor with a global shutter to reduce vibration-induced artifacts in the captured images. Figure 4b shows the two sensors of the powerline perception system.
# A Systematic Review of Ship Wake Detection Methods in Satellite Imagery
## 3 Results
### Introduction to DL-Based Methods for Wake Detection
In conclusion, it is deemed necessary for the usability of this review to provide a quick introduction to several concepts that are useful to better understand DL architectures and, more specifically, DL-based object detectors. The following subsections contain such an overview.
# The Role of Epigenetics in Periodontal and Systemic Diseases and Smoking: A Systematic Review
## 4 Discussion
### Patients with Systemic Diseases
#### 4.1.1 Smokers and Non-Smokers  
Since smoking is considered a major risk factor for periodontitis [58] and also influences epigenetic changes [59], DNA methylation patterns were evaluated in smokers with periodontitis. Toll-like receptors (TLRs) were evaluated, given their important role in the inflammatory process in periodontitis [60]. Gingival tissue samples from smokers and non-smokers affected by periodontitis as well as healthy patients were found to have major unmethylation of the _TLR4_ gene promoter, whereas the results for the _TLR2_ gene promoter were inconclusive given the mosaic of methylated and unmethylated DNA in the majority of samples [41]. There was also no statistically significant correlation between the DNA methylation status and mRNA transcription levels of both genes between the groups [41]. In another study, individuals with periodontitis displayed a higher percentage of hypomethyllation of the _CXCL8_ gene, compared to healthy controls, independent of smoking habit [42]. This is in concordance with previously published papers that reported an increased expression of _CXCL8_, indicating its role in tissue destruction in periodontal diseases [61; 62].  
In another investigation assessing the methylation status of the extracellular matrix (ECM) organization-related genes, findings indicated that smoking might change the transcription and methylation status of ECM organization-related genes, which aggravated the periodontal disease [40]. These findings suggested that smoking-related changes in DNA methylation and successive changes in the expression of the ECM component-related genes may result in increased susceptibility to periodontitis in smokers by influencing ECM organization, which in turn may affect disease characteristics.  
In a more recent investigation, the DNA methylation pattern of the _SOCS1_ promoter, a potent inhibitor of cytokine signaling [63], was evaluated in epithelial cells from the saliva of smoking and non-smoking patients with periodontitis [64]. Results showed that cells from the saliva of periodontitis patients who smoked were 7.08 times more likely to show a methylated _SOCS1_ promoter in comparison to periodontitis patients who are non-smokers, suggesting that _SOCS1_ may be a consequence of tobacco exposure and not periodontitis. This finding that methylation of _SOCS1_ promoter is solely due to tobacco is consistent with previous studies on _SOCS1_ and periodontitis in which SOCS1 hypomethyllation was reported in chronic and aggressive periodontitis [63; 65].
## 3 Services for Smart Cities: The Human-Centric Designation
These implementations are based on the conceptual design of the Internet-of-Things (IoT), where an ecosystem of interconnected devices and entities has been considered the epicenter of each service and application. This ecosystem involves both the interconnected citizen-users and the smart devices that comprise the pool of physical and virtual entities to which each service refers. This ecosystem is spread throughout a physical urban surface where the service signal is meddled with obstacles and other channel impairments. The reliability of each service is related to the robustness of the network architecture, since the quality of service of the infrastructure correlates significantly with the quality of experience for each end user. These entities should also support broadband connectivity through their physical interfaces, whether wireless or wired, for both downlink and uplink streams of transmission. This pool of services and applications includes smart public transport, smart parking applications, waste bin monitoring systems, and quality-of-life services. As far as the network architecture is concerned, 5G is designated to be the key technological infrastructure in this endeavor.
###### Abstract  
Variational Mode Decomposition (VMD) is widely used for inspection purposes. The initial parameters are usually set manually, which is a limitation of this technique. In this paper, a method to automatically select these parameters through a combination of Singular Value Decomposition (SVD) and Improved-VMD (VMD) is proposed. VMD is applied multiple times with a varying K-value parameter. The original signal and its sub-signals arising from VMD decomposition are all subjected to SVD. An index representing the relevance between sub-signals and the original signal is obtained by comparing eigenvalues, which are calculated by SVD. The result shows the effectiveness of VMD with different initial K-value parameters. SVD is then further applied to the VMD result for the selected K-value parameter to obtain Shannon entropy, which can be used in the detection and classification of corrosion on the underside of the rail. Comparing with current energy-based methods, the Shannon entropy obtained by IVMD-SVD has the advantage of reducing environmental interference to obtain more uniform energy results. The proposed method can improve the effectiveness of VMD for the impact response signal. The classification of underside corrosion of rails can be realised according to the results obtained from the proposed method.  
impact response signal; VMD; SVD; rail; corrosion inspection; machine learning Article