esa-sceva/satcom-chunk-collection huggingface dataset:
# Bearing Fault Identification Using Machine Learning and Adaptive Cascade Fault Observer
## 6 Conclusions  
In this paper, an adaptive cascade observer with SVM technique was developed for fault identification of rotating machinery. This algorithm was industrialized using the following steps: (a) time-series signal approximation using the fuzzy orthonormal regressive (FOR) technique, (b) signal estimation using an adaptive cascade observer, and (c) fault identification using the SVM technique. The fuzzy orthonormal regressive technique was developed to model the AE and vibration (non-stationary and nonlinear) bearing signals in normal conditions to develop a high-accuracy observer. Moreover, to obtain accurate classification of the signals in various conditions, an adaptive cascade observer was developed. Therefore, the linear observation technique using a FOR proportional-integral (PI) observer (FOR-PIO) was developed in the initial step. After that, to increase the power of uncertainties rejection in the FOR-PIO, the structure procedure was settled. Next, to improve the performance of FOR-structure PI observer (FOR-SPIO) the fuzzy like observer was selected and the developed cascade observer. Moreover, the adaptive technique was selected to develop the reliability of the cascade observer and the developed FOR-adaptive cascade observer (FOR-ACO). Additionally, the fault was identified by support vector machine (SVM). The effectiveness of the adaptive cascade observer with the SVM fault identifier was validated by a vibration and AE datasets. Regarding the results, the average vibration and AE fault diagnosis using the FOR-ACO with the SVM fault identifier are 97.8% and 97.65%, respectively. In the future, the parallel deep-learning technique will be used to improve the accuracy and flexibility of the cascade observers.  
All of the authors contributed equally to the conception of the idea, the design of experiments, the analysis and interpretation of results, and the writing of the manuscript. Writing--original draft preparation, F.P. and J.-M.K.; writing--review and editing, F.P. and J.-M.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry & Energy(MOTIE) of the Republic of Korea (No. 20192510102510).  
The authors declare no conflict of interest.
# Crouch Gait Analysis and Visualization Based on Gait Forward and Inverse Kinematics
## 2 Materials and Methods
### Gait Forward Kinematics  
The gait kinematics analysis proposed in this work uses a robotics approach considering an eight-DoF simplified version of the conventional gait model [33] presented in Figure 2. First, the correspondences between the anatomical reference planes and three fundamental directions [2] with the 3D Cartesian space of the model (see Figure 2) are (anterior, \(x^{+}\); superior, \(y^{+}\); right, \(z^{+}\)) and (sagittal, \(XY\); frontal, \(YZ\); transverse, \(XZ\)), respectively. The eight-DoF model features both the left and right lower limbs as eight rigid-body segments: the right pelvis (\(l_{1R}\)), right femur (\(l_{2R}\)), right tibia (\(l_{3R}\)), right foot (\(l_{4R}\)), left pelvis (\(l_{1L}\)), left femur (\(l_{2L}\)), left tibia (\(l_{3L}\)), and left foot (\(l_{4L}\)). In this work, the eight movements and their corresponding angles and rotation axis are: pelvic rotation (\(q_{1}\), \(y_{0}\)), lateral pelvic list (\(q_{2}\), \(x_{0}\)), right hip flexoextension (\(q_{3R}\), \(z_{1R}\)), right knee flexoextension (\(q_{4R}\), \(z_{2R}\)), right ankle dorsi/plantar flexion (\(q_{5R}\), \(z_{3R}\)), left hip flexoextension (\(q_{3L}\), \(z_{1L}\)), left knee flexoextension (\(q_{4L}\), \(z_{2L}\)), and left ankle dorsi/plantar flexion (\(q_{5L}\), \(z_{3L}\)). This approach could be extended to model the pelvic tilt movement as well as other joint movements. In this paper, we do not consider the surface constraints on gait kinematics.
# Real-Time Exercise Mode Identification with an Inertial Measurement Unit for Smart Dumbbells
## 3 Methodology  
### Estimation of Sensor Fusion  
The raw data from the output of the IMU contain many errors; therefore, we used the Kalman filter to reduce the noise. It includes two steps to process the data, i.e., prediction and update [22; 23; 24]. The equations of the algorithm are summarized as follows:  
Prediction:  
Predicted state estimate \[\hat{x}_{k}^{-}=F\hat{x}_{k-1}^{+}+\text{B}u_{k-1}\] Predicted error covariance \[P_{k}^{-}=FP_{k-1}^{+}F^{T}+Q\] Update:  
Updated state estimate \[\hat{x}_{k}^{+}=\hat{x}_{k}^{-}+K_{k}\vec{y}\] Updated error covariance \[P_{k}^{+}=(1-K_{k}H)P_{k}^{-}\]  
where \(F\) is the transition matrix of state, B is control-input matrix, \(u_{k-1}\) is the control vector, \(P\) is called state error covariance, \(K_{k}\) is the Kalman gain, \(\vec{y}\) is the measurement residual. These equations are used to create the filter after denoising in order to obtain the output angle. During model testing, the accuracy of the Kalman filter was used as an encoder to verify the results. We obtained a root mean square (RMS) value of 3.5998. The estimated angle using a Kalman filter is shown in Figure 5.
## 2 Methods
### Databases for T-DRL Trainings  
To derive the optimal hand poses from the transformer network and incorporate them into the proposed DRL, this work utilizes the databases of a 6-DoF object affordance and 21-DoF hand pose.  
The information of the 6-DoF object affordance is derived from GraspNet [34] which computes the point clouds of an object and produces 6-DoF affordance information. The probability of possible grasping success rates is determined according to the object shape and hand pose with a parallel gripper, as shown in Figure 1. GraspNet [34] is trained with 206 objects of five categories (boxes, cylinders, mugs, bottles, and bowls) and trained based on a variational autoencoder, which estimates a predefined number of grasping poses in the Cartesian coordinates. The 6-DoF object affordance is denoted as \(\phi_{i}^{M}\) in this study, where \(i=1,\ldots,M\), with \(M\) denotes the number of grasps and each \(\phi_{i}\in\mathbb{R}^{6}\) denotes the position and orientation of an anthropomorphic robot hand attempting to grasp the object.  
The information of the 21-DoF hand pose, corresponding to the joint angles of a five-fingered anthropomorphic hand, is derived from the ContactPose database [35]. This database consists of grasping demonstrations made by human hands, holding various household objects. The database also includes the point clouds of objects with the corresponding 3D coordinates of the 21 finger joints used in a human hand pose. The grasping data was collected with two intentional grasps of 'use' (i.e., using the object after grasping) and 'handoff' (i.e., handing over the object after grasping) for fifty different objects. We selected objects representing six geometric shapes (spherical, rod, cube, oval, cylindrical, and curved) from the available 24 objects, including: apple, banana, camera, hammer, bottle, and light bulb. Each object has a total of 25 grasping demonstrations that were reduced to 10 corresponding to the right hand, with their functional intent being 'use'. Henceforth, the extracted 21-DoF joints that coordinate the hand poses are denoted as \((\varphi_{i}^{N})\) where \(i=1,\ldots,N\) denotes the number of demonstrations and each \(\varphi_{i}\in\mathbb{R}^{21}\) denotes the joint angles in the hand pose reference system.  
As input to the transformer network, we need a data tuple containing five 21-DoF hand poses and five 6-DoF object affordances. Two of the ten 21-DoF hand pose data for each object were classified for testing and eight for training. One tuple was formed by matching with the 6-DoF generated at the exact same location as the center position of the 21-DoF. By randomly selecting five of the eight tuples, a list of approximately 16,500 unique tuples is generated from the combination of 6-DoF object affordance \(\phi_{i}^{M}\) and 21-DoF hand poses \(\varphi_{i}^{N}\) for the six objects.
# Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model
## 4 Experiment Analysis  
In this experiment, the experimental environment used is PyCharm 2023.2.1, Python 3.8, Tensorflow 2.12.0, CUDA 11.4, and GPU 3090. Four real air quality datasets were employed to evaluate the model's performance. The following questions were validated and answered: (1) whether the APSO-CNN-Bi-LSTM model can achieve more minor prediction errors than the previous model; (2) whether the APSO-CNN-Bi-LSTM model had better convergence; (3) whether the adaptive inertia weight PSO algorithm and the CNN in the APSO-CNN-Bi-LSTM model played a role in the prediction of this model.
## 5 Case Study Applications
In terms of the variation in time requirement for obtaining the solution at different application sizes (number of islands in the cluster), the time increase results from two fac  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**MODEL** & & & & & & & \\ \hline \multicolumn{2}{c}{**Size**} & & & & & & \\ \multicolumn{2}{c}{**(Islands/Routes/ Potential Hub-Ports)**} & & & & & & \\ \hline \hline \multirow{6}{*}{Small (15/2/3)} & \multirow{2}{*}{**Run**} & \multirow{2}{*}{**Performance Measure**} & \multirow{2}{*}{**Fitness Function**} & \multirow{2}{*}{**Traveling Distance (nm)**} & \multirow{2}{*}{**Passenger (hh:mm)**} & \multirow{2}{*}{**Number of Trials Until Best**} & \multicolumn{1}{c}{**Time until Best Trial (h:mm:ss)**} \\ \cline{5-8}  & & & & & & \\ \cline{5-8}  & \multirow{2}{*}{**Run**} & \multirow{2}{*}{**Measure**} & \multirow{2}{*}{**Function**} & \multirow{2}{*}{**(nm)**} & \multirow{2}{*}{**(h:mm)**} & \multirow{2}{*}{**(h:mm)**} & \multirow{2}{*}{**Best**} & \multicolumn{1}{c}{**Time until Best Trial (h:mm:ss)**} \\ \cline{5-8}  & & & & & & & \\ \hline \hline \multirow{6}{*}{ \begin{tabular}{c} Small (15/2/3) \\ \end{tabular}  } & \multirow{2}{*}{1} & D & 378 & 378 & 6141:58 & 5191 & 0:01:26 \\ \cline{3-8}  & & PH & 4080:00 & 441 & 4080:00 & 20,371 & 0:05:47 \\ \cline{3-8}  & & D–PH  
tors: the larger problem size and the larger solution space. Since the main operation in the optimization process is ordering ports to be served by the vessels, the time requirement appears to follow a slight exponential growth with the problem size. The results of Table 3 indicate average run times in the order of 4, 20, and 60 min for the cases of 15, 50, and 100 islands, respectively. The current work has been developed upon certain assumptions that may be relaxed in a later stage. In particular, it is assumed that passengers initiate their trips exclusively from the mainland (which is effectively the case during the summer touristic period). In a more general case, inclusion of internal passenger flows among cluster islands as well as the consideration of vessel roundtrips with different passenger demand levels on return can be considered. Both extensions can be inserted rather straightforwardly to the model along with vessel capacity considerations (capacitated problem). Another research direction is to introduce a fourth level of decision-making, allowing the algorithm to inherently optimize the number and the type of the required vessels. Finally, the unavoidable uncertainties in the input parameter values (e.g., demand fluctuation, delayed arrivals, etc.) may be considered as part of a stochastic analysis. As a final comment, although the proposed algorithm is designed and adapted to Short Sea Shipment transportation particularities, it can find broader application in problems where the objective is to efficiently regulate people or product flows (e.g., in the supply chain and, more specifically, in the distribution of goods from the production lines to the customers through either direct shipping or intermediate cross-dock terminals).
# A Review of Oil Spill Remote Sensing
## 2 Detection and Mapping of Oil on a Water Surface
### Satellites Operating in the Optical Region  
The application of visual satellite images for oil spills has been undertaken over the past few decades. Historically, there have only been a handful of satellites, all having low pass rates of as little as once every 26 days, so success was only on cloudless days when there was an overpass [3]. The primary problems were the timing of satellite overpasses and the absence of clouds [3]. The probability of the overpass and the clear skies occurring at exactly the same time gave a low chance of oil detection. This fact is illustrated during the Exxon Valdez spill, which covered large areas of sea for more than a month. There was a single cloudless day that corresponded with a satellite overpass, and that was on 7 April 1989 [3]. The other historical issue was the availability of algorithms to visualize the oil rather than the background. For the Exxon Valdez spill, experts required two months to produce a good image of the oil slick, although the slick coordinates were exactly known. At present, we now have many visual satellites and readily available algorithms to process the data.  
QuickBird, WorldView I and II, new visual imaging satellites, now provide frequent coverage of the planet. Multi-spectral satellites such as MODIS and MERIS now provide various wavelength data for earth observation. Oil detection in the visible wavelength region depends on weather conditions, oil types, and view angles [25]. The barriers to visual imagery are cloud cover and sun glint. Sun glint is oftentimes severe and sometimes confounds an entire scene; however, a number of experts have made advances to remove sun glitter [26]. During the Deepwater Horizon spill, several visual images were employed [4]. Examples of this are shown in Figures 1 and 2. Some researchers have employed MODIS data to characterize oil spills [26; 27]. A multi-spectral image was derived from MODIS and corrected by a supervised classification system to enhance and characterize oil on water.  
Satellite IR data was employed to delineate Kuwait land oil pollution [28]. Hydrocarbon-contaminated areas exhibited a thermal difference as great as 10 \({}^{\circ}\)C, compared to the nearby uncontaminated land. Ground-truth data were an essential part of the analysis process. Groups have used IR data from the AVHRR satellite to map oil spills [29; 30]. The techniques typically employ the IR contrast difference between un-oiled and oiled water. Another group used MODIS SST (sea surface temperature) data to detect oil spills [3].
## 2 Problem Definition
### Problem Statement and Purpose of Assessment  
The ageing of components or change in wind resources due to long-term service could lead to output power fluctuations of the wind turbine. As shown in Figure 2, an obvious deviation exists between the actual measurement data and the baseline data in the MWPTR. Therefore, on-line performance assessment of a wind turbine can be realized by quantifying the deviations of power curves and analyzing the change trends to provide a reference for operations and maintenance decision-making.  
Figure 1: Power curve of a pitch-regulated wind turbine.  
This paper proposes a power performance assessment method by mining 10-min SCADA data; this method could prove to be a useful tool for operation and maintenance staff when assessing the overall performance of a wind turbine and to making maintenance plans. The performance assessment of wind turbines based on power curve monitoring requires a set of complete dataset in the MWPTR and RPOR, which is used to determine the change trend of the wind turbine. The wind speed and output power have different mapping relations in the MWPTR and RPOR; therefore, the assessment method must consider these distinctions in different wind speed regions. In particular, the control objective of MWPTR is to capture the maximum wind power capture, while RPOR is oriented to achieve as much output power as possible while maintaining a stable output power. Based on the analysis derived from Figure 2, the proposed assessment method must possess the following features:  
1. The evaluation results must accurately indicate the variations between the actual power curve and the baseline power curve.
2. The method must be robust and unaffected by the presence of outliers.
3. The method must be able to be implemented simply, and the assessment results must be easy to understand for engineers to understand.
4. A stable trend of power performance changes must be acquired, as this trend can be used for prediction analysis.
## 3 Results and Discussion
### Effect of Amidoximation Time on Adsorption Capacity  
The effect of amidoximation time on the adsorption capacity of Pd(II) was studied. For that, the PAN-oxime fibers were prepared at different reaction times, from 0 to 30 min at 5 min intervals. Here, the PAN-oxime fiber reacted for 30 min was excluded because it was agglomerated and fiber no longer formed. After the adsorption experiments, the amount of adsorbed Pd(II) ions per unit mass of adsorbent (\(q_{\mathrm{t}}\), mg/g) was calculated by a mass balance equation [20].  
\[q_{\mathrm{t}}=\left(V_{0}C_{0}-V_{\mathrm{f}}C_{\mathrm{f}}\right)/M \tag{1}\]  
where \(V_{0}\) and \(V_{\mathrm{f}}\) are the initial and final solution volumes (L), respectively, \(C_{0}\) and \(C_{\mathrm{f}}\) are the initial and final solution concentrations of metal ions (mg/L), respectively, and \(M\) is the mass of adsorbent (g).  
As shown in Figure 4, it was checked that the PAN-oxime fiber could be used for the recovery of precious metals, and the Pd(II) uptake increased as the reaction time increased. This means that the longer reaction time, the more binding sites that were generated. When the PAN-oxime fiber reacted for 25 min was used, the highest (412.6 mg/g) Pd(II) uptake was observed, while raw PAN fiber (reaction time = 0) has no metal adsorption, but shows a negative value due to water evaporation in this sample. However, for further studies (i.e., isotherm and kinetics), the PAN-oxime fiber reacted for 15 min was chosen because the fiber produced from 20 min reaction was too weak and brittle to be used.
# 3D Face Profilometry Based on Galvanometer Scanner with Infrared Fringe Projection in High Speed
## 3 Experiments  
Figure 4 shows the implementation of the FPP of the 3D shape measurement system using the new MEMS infrared projector. The experimental system consists of one CMOS cameras with infrared band imaging and 1600 \(\times\) 1200 pixels (model UI-3250 CP-M-GL, produced by the company of IDS Imaging Development Systems GmbH in Obersulm, Germany). The camera is used with a 12 mm focal length lens (model FL-CC1214-2M, Ricoh Corporation of Japan). The projector of MEMS is a scanning micro-mirror with a scanning angle of 60\({}^{\circ}\), a projection frequency of 500 fps and a laser central wavelength of 830 nm. The projector for contrast is a DLP projector (model PDC03) with a projection speed of 30 fps and 1280 \(\times\) 800 pixels, (Fuzhou Giant Vinda Photoelectric Technology Co., Ltd., Fuzhou, China). The synchronous trigger signal timing is sent out by the projector and then sent to the cameras as a precisely synchronized external trigger signals to capture pictures simultaneously. A series of experiments is performed to estimate the performance using the measurement system by different phase unwrapping methods.  
Figure 3: The illustration of equivalent phase map \(\phi_{eq}\) calculation as the difference between \(\phi_{l}\) and \(\phi_{h}\) in the multi-wavelength temporal phase unwrapping approach. \(\phi_{eq}\) is also regarded as the reference phase with the phase value ranges from 0 to 2\(\pi\).  
As shown in Figure 5a, the galvanometer scanner is a new device with infrared laser projection. Its size--12 mm width and 22 mm length--is very small compared to a traditional DLP projector. The micro-galvanometer system is a cantilevers structure, which can change the scanning angle of the reflected micro-mirror attached to the spring tips. The scanning angle can be controlled by electromagnetic, electrostatic or piezoelectric methods with a scanning speed up to 32 kHz. As shown in Figure 5b, the laser spot is converted into a line with a cylindrical lens. When the scanning angle of the micro-mirror changes, the laser current changes according to the mode of a sinusoidal wave, then a complete two-dimensional fringe pattern can be projected, as shown in Figure 5c. The light source of the MEMS can use different wavelength lasers according to the needs, for example, infrared laser projection for face measurement.
## 3 Results and Discussion
### Failure Modes and Flexural Moments
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Beam NO.**} & **Prestressing** & **Cracking/Reopening** & \multicolumn{3}{c}{**Yielding**} & \multicolumn{3}{c}{**Ultimate**} & \\ \cline{2-9}  & **f\({}_{\text{pg}}\)** & **M\({}_{\text{cr}}\)** & **y\({}_{\text{cr}}\)** & **M\({}_{\text{y}}\)** & **y\({}_{\text{y}}\)** & **M\({}_{\text{a}}\)** & **y\({}_{\text{u}}\)** & **y\({}_{\text{u}}\)** \\  & **(MPa)** & **(k\(\cdot\)N\(\cdot\)m)** & **(mm)** & **(k\(\cdot\)N\(\cdot\)m)** & **(mm)** & **(k\(\cdot\)N\(\cdot\)m)** & **(mm)** & **(mm)** \\ \hline SCB & - & 21.3 & 1.3 & 56.1 & 9.1 & 65.6 & 63.2 & Flexural damage \\ S-SL-0-0 & 7 & 29.1 & 1.2 & 100.7 & 11.3 & 177.6 & 57.2 & Concrete crushing \\ S-C-30-0 & 524 & 47.1 & 1.5 & 117.8 & 10.7 & 172.8 & 35.1 & CFRP tendon rupture \\ S-C-30-0.2 & 513 & 39.6 & 1.6 & 107.9 & 11 & 168 & 42.6 & CFRP tendon rupture \\ S-C-0-0 & 9 & 23.2 & 1.2 & 75.2 & 10.8 & 160.6 & 54.5 & Concrete crushing \\ S-C-50-0.2 & 907 & 47.1 & 1.2 & 140.2 & 12.1 & 166.8 & 26.4 & CFRP tendon rupture \\ CCB & - & 29.7 & 2.5 & 71.6 & 13.1 & 75.1 & 71.3 & Flexural damage \\ C-C-30-0 & 531 & 41.1 & 1.72 & 100.8 & 10.9 & 159.6 & 34.6 & CFRP tendon rupture \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Test results for the specimens. S-C-0-0 showed increases in their ultimate flexural bearing capacity of 170.7% and 144.8%, respectively. This finding indicates that the increase in flexural carrying capacity obtained by the application of straight-line reinforcement is more significant than that achieved through curved reinforcement.
# Beach Deployment of a Low-Cost GNSS Buoy for Determining Sea-Level and Wave Characteristics
## 2 Materials and Method
### Buoy Testing at Rossall  
Rossall, near Fleetwood in NW England, (Figure 2) was chosen for the beach deployment. This location is macro-tidal and has dissipative beach characteristics [22; 23] because it consists of fine sand, has a gentle profile gradient (e.g., 1:50), and has a medium intertidal zone (\(\sim\)300 m). It is likely that spilling waves would be present, which typically dissipate their energy gradually over a wide surf zone. The location of the GNSS buoy was adjacent to an existing coastal monitoring system (consisting of a deployed X-band radar), and would provide a realistic 'typical' setting for an assessment of its capability.  
Figure 1: (**a**) Top part of mooring together with the GNSS buoy, (**b**) ground anchor with swivel, (**c**) complete mooring installed and laid out on the beach at low water.  
The buoy was attached to the ground anchor (Figure 1b) and laid flat on the beach (deployed position: WGS: \(53^{\circ}54^{\prime}48.6^{\prime\prime}\) N, \(03^{\circ}03^{\prime}14.3^{\prime\prime}\) W). It was approximately 250 m from the foot of the concrete sea defences, and between two rock armour groynes that are perpendicular to the sea defences and around 100 m in length. The returned sea level and wave data indicate that a semi-permanent deployment of this GNSS buoy would provide the necessary data for analysis of X-band radar data, which would in turn improve the accuracy of the derived spatial and temporal beach morphology [1].  
Figure 2: Location of Rossall experiment (Lancashire, UK); GNSS buoy, Ordnance Survey GNSS station, Cleveleys wave buoy. Base map source: EDINA Marine Digimap Service ([[http://edina.ac.uk/digimap](http://edina.ac.uk/digimap)]([http://edina.ac.uk/digimap](http://edina.ac.uk/digimap))) (accessed on 1 June 2021).