esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Smart Assembly Workstation  
When choosing the technology for the construction of a manual assembly workstation (Figure 1), the main guide was the low price and availability of the products. We used two Raspberry Pi 3B+, manufactured by Sony UK Technology Centre, Pencoed, UK, (RPi) servers with Raspbian GNU/Linux 9.6, manufactured by Raspberry Pi Foundation, Cambridge, UK for the manual assembly workstation control system. Two RPI controllers were used because we wanted to build a modular manual assembly workstation. The code is written in the Python programming language. The desired current hardware states on manual assembly workstation are written in the Microsoft SQL Express database, and there the hardware states are recorded by the program LASIM product management (LPM) at each step of the assembly. The Raspberry Pi controllers are connected to the base via the network and send a motion signal to the appropriate hardware when the hardware state changes.  
We have illuminated the work surface with additional adjustable lights (Figure 1), (i) due to the possibility of adjusting the intensity and direction of the light beam. The lights are controlled by a Pulse-width modulation (PWM) signal, which enables the control of light intensity. The lights have their own 12 V power supply. The lights are mounted on a two-axis stand with radio control (RC) servomotors. These are connected to Raspberry Pi via the RC servo motor/I2C interface card. The laser pointer (pick-by-light (Figure 1)), (ii), which points to the area in the buffer, is also mounted on the same stand and connected in the same way. The storage of parts contains two separate containers (left and right: LZ, DZ) each with 6 sections (LZ1-6, DZ1-6) driven by a linear servo motor (Figure 1), (iii). They are also connected to the Raspberry Pi controller. We used adjustable table (Figure 1), (iv) to raise the work surface to the appropriate height for each worker. The switches for lifting the table were connected to Raspberry Pi via a relay board. The table had its own power supply by plugging it into a socket. The rotatable table (Figure 1), (v) has a stepper motor and a 24 V power supply. The stepper motor is controlled by the Arduino controller via the stepper motor interface.  
For safety reasons, movement is limited to slow speeds and low power motors are used to avoid injury. RC Servomotors generate typical noise, which could be reduced by using high-quality motors. Replacing these motors with better motors would also increase the accuracy and repeatability of laser and light positioning.  
Figure 1: Smart manual assembly workstation with implemented tools.
## 2 Structural Analysis and Design of the Blade
### The FE Formulation  
The finite element can be applied to solve the modal analysis problem using the following equation to determine the steady-state response of wind turbine blades [27; 28]:  
\[[K]\{u\}=\{F\} \tag{7}\]  
where \(\{F\}\) is load vector, \(\{u\}\) is displacement vectors, and \([K]\) is the stiffness matrix of the system. In Equation (7), it was assumed that the inertia forces and damping forces are equal to zero. Suppose a structure is placed in a suitable position at a zero moment in time, where the structure can be vibrated. This Vibration is known as the natural frequency, and this frequency follows certain patterns known as distortion patterns called mode shapes. The mode shapes and Vibration of the structure depend on mass and stiffness [27]. Assuming that the external force vector and the damping are zero, where the free vibration equation can be written as [28]:  
\[[M]\{\ddot{u}\}+[K]\{u\}=0 \tag{8}\]  
Figure 7: FE model wind turbine mesh.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mesh Segments** & **No. Nodes** & **No. Elements** &
\begin{tabular}{c} **Max. Total** \\ **Deflection [mm]** \\ \end{tabular} & **Difference** \\ \hline
1 & 19,824 & 3694 & 18.392 & - \\ \hline
2 & 26,722 & 7431 & 18.371 & \(-\)0.021 \\ \hline
3 & 34,624 & 11,385 & 18.365 & \(-\)0.006 \\ \hline
4 & 45,857 & 15,124 & 18.361 & \(-\)0.004 \\ \hline
5 & 74,154 & 24,475 & 18.356 & \(-\)0.005 \\ \hline
6 & 108,789 & 35,970 & 18.354 & \(-\)0.002 \\ \hline
7 & 225,602 & 75,053 & 18.358 & 0.004 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The mesh convergence of wind turbine blade.  
where \([M]\) is the mass matrix, \(\left\{\ddot{u}\right\}\) is the acceleration vector. Equation (8) is the equation of motion of the undamped free vibration system. In order to solve the last equation, the harmonic solution is assumed in the following form:  
\[\left\{u\right\}=\left\{\varnothing\right\}\sin\omega t \tag{9}\]  
where \(\omega\) is the natural angular frequency of mode and \(\left\{\varnothing\right\}\) is the vector of contractual amplitude for mode shapes. After deriving the displacement in Equation (9) twice with respect to time, it can be got the following:  
\[\left\{\ddot{u}\right\}=-\omega^{2}\{\varnothing\}\sin\omega t \tag{10}\]  
Substitute Equations (9) and (10) into Equation (8) to get the following formula:  
\[\left([K]-\omega^{2}[M]\right)\{\varnothing\}=0 \tag{11}\]  
Equation (11) is the form of the eigenvalue problem (structural Vibration). It is a set of homogeneous algebraic equations for the eigenvector components. The eigenvalue problem can be written as follows:  
\[\left([A]-\lambda[I]\right)\,X=0 \tag{12}\]  
where \([A]\) is a symmetric matrix or dynamic matrix, \(\lambda\) is the value of eigenvalue, \([I]\) is an identity matrix, and \(X\) is an eigenvector. By substituting Equation (11) into Equation (12) by entering one of the two matrices \([K]\) or \([M]\), and then using the Cholisky method (square root method), it can be obtained the dynamic response [27].
# Actuation Mechanisms and Applications for Soft Robots: A Comprehensive Review
## 2 Actuation Materials and Mechanisms for Soft Robots
### Fluid  
Fluid can be designed as an actuation mechanism that converts the pressurised energy of fluid into mechanical energy to realise the motion of a soft robot. The basic principle is to change the pressure in the fluid chamber in a soft robot, expand the channel in the robot, and therefore deform the robotic structure controllably. There are three types of fluid-based actuation mechanisms for soft robots, i.e., pneumatic, hydraulic, and pneumatic-hydraulic [89]. Fluid is a commonly used actuator having the advantages of convenient operation and control, large output power, and easy automation.
## 5 Experiment  
### Settings  
We utilize PyCharm Professional 2020.3.2 as the simulation software for our experiments, running on the Ubuntu 18.04.6 operating system. In the multi-agent model, each agent's DQN model consisted of three fully connected hidden layers, with RMSProp serving as the optimizer. The learning rate was set to 0.001, and the Q network of each agent was trained for 3000 iterations. To gradually decrease the exploration rate from 1 to 0.01, we used linear annealing, after which we kept it constant. Three baselines were used in our paper: Single Agent Reinforcement Learning (SARL), Random Access Control (RAC), and mMTC Priority (mMTCP). The SARL algorithm uses only one communication link as an agent and updates the state only through its reward value during the training process, ignoring the state of other links in the environment. The RAC algorithm randomly selects the bandwidth and transmission power for communication links. The mMTCP algorithm prioritizes the transmission of the mMTC service without considering the impact on the URLLC link. The mMTC traffic grows by \(2^{n}\times 1024\), \(n\in\{0,1,\cdots\}\) bytes. To ensure the validity of the URLLC link, we set the transmission power to its maximum value. The parameter settings used in the experiment are summarized in Table 1.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Number of URLLC links & 4 \\ Number of mMTC links & 4 \\ Carrier frequency & 2 GHz \\ Bandwidth & 4 MHz \\ F-AP antenna gain & 8 dBi \\ F-AP receiver noise coefficient & 3 dB \\ AloT devices antenna gain & 1 dBi \\ AloT devices receiver noise coefficient & 5 dB \\ mMTC link power & [50, 100, 150, 200] mW \\ URLLC link power & 200 mW \\ Noise power & \(-114\) dBm \\ mMTC business volume & \(2^{n}\times 1024\) bytes \\ mMTC limitation of service transmission delay & 100 ms \\ URLLC limitation of service transmission delay & 1 ms \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameter Settings.
# Robust Flow Field Signal Estimation Method for Flow Sensing by Underwater Robotics
###### Abstract  
The flow field is difficult to evaluate, and underwater robotics can only partly adapt to the submarine environment. However, fish can sense the complex underwater environment by their lateral line system. In order to reveal the fish flow sensing mechanism, a robust nonlinear signal estimation method based on the Volterra series model with the Kautz kernel function is provided, which is named KKF-VSM. The flow field signal around a square target is used as the original signal. The sinusoidal noise and the signal around a triangular obstacle are considered undesired signals, and the predicting performance of KKF-VSM is analyzed after introducing them locally in the original signals. Compared to the radial basis function neural network model (RBF-NNM), the advantages of KKF-VSM are not only its robustness but also its higher sensitivity to weak signals and its predicting accuracy. It is confirmed that even for strong nonlinear signals, such as pressure responses in the flow field, KKF-VSM is more efficient than the commonly used RBF-NNM. It can provide a reference for the application of the artificial lateral line system on underwater robotics, improving its adaptability in complex environments based on flow field information.  
underwater robotic; machine learning; signal estimation method; flow sensing; Volterra series model; underwater targets recognition 2021117799. [[https://doi.org/10.3390/app11167759](https://doi.org/10.3390/app11167759)]([https://doi.org/10.3390/app11167759](https://doi.org/10.3390/app11167759))
# Vehicle Target Tracking Algorithm Based on Improved Strong Tracking Unscented Kalman Filter
## 3 Multidimensional Adaptive Factor-Based Strong Tracking UKF Algorithm
### Introduction of Multidimensional Adaptive Decay Factors
\begin{array}{cc}N_{k+1}^{(ii)}/R_{k+1}^{(ii)}&,C_{k+1} ^{(ii)}>P_{ZZ,k+1}^{(ii)}\\ 1&,C_{k+1}^{(ii)}\leq P_{ZZ,k+1}^{(ii)}\end{array} \right.\quad i=1,\ldots,m \tag{9}\]\[N_{k+1}=C_{k+1}-H_{k+1}\Big{(}P^{\prime}_{k+1|k}+D_{k}Q_{k}\Big{)}H_{k+1}^{T} \tag{10}\]  
Equations (9) and (10) represent the comparison of \(C_{k}^{(ii)}\) with \(P^{(ii)}_{ZZ,k+1}\) (for \(i=1,\ldots,m\)). When \(C_{k}^{(ii)}\) satisfies the first condition of Equation (9) and its value is greater than 1, the second condition is satisfied, and the state covariance update equation is  
\[P_{k+1|k}=P^{\prime}_{k+1|k}+D_{k}Q_{k} \tag{11}\]  
The residual covariance matrix can be updated as  
\[P_{ZZ,k+1}=H_{k+1}P_{k+1|k}H_{k+1}^{T}+J_{k+1}R_{k+1} \tag{12}\]  
Thus, Equation (1) can be written as  
\[H_{k+1}\Big{(}P^{\prime}_{k+1|k}+D_{k}Q_{k}\Big{)}H_{k+1}^{T}+J_{k+1}R_{k+1}= C_{k+1} \tag{13}\]  
One side of the update occurs through \(D_{k}\), adjusting the system model mismatch and the estimation of noise. The other side adjusts \(J_{k}\), responding to observation errors and improving the noise accuracy.
# Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas
## 2 Materials and Methods
### Convolutional Neural Network (CNN) Architectures
#### 2.4.2 2D CNN  
We implemented two networks, one whose input is the Mel spectrogram, and another whose input are the MFCCs. The configuration of the proposed CNNs is similar to the one used in the 1D CNN, containing 3 layers that compute convolutions and max pooling.
## 4 Conclusions  
The experimental results of this study showed the effect of different factors on the AR properties. The main conclusions drawn based on the test results are as follows:  
Figure 8: Creep S and m of asphalt rubber.  
Figure 9: Rutting resistance factors of virgin asphalt and asphalt rubber at different temperatures.  
(1) The CR dosage was a crucial element towards the improvement of the AR characteristics. The CR content of 20-25% (external blending) was optimal for the physical properties of SK90# matrix asphalt, while 22-26% was better for SK70# matrix asphalt. The addition of crumb rubber could significantly improve the high temperature performance of the asphalt, and its cracking and rutting resistance. The variation of asphalt performance within the dosage range was basically consistent in the same interval.  
(2) The addition of stabilizers could significantly improve the high temperature stability of asphalt rubber, but only slightly affect its low temperature performance. In addition, the stabilizers used in this study could all reduce the segregation softening point, which was benefit to the storage of asphalt rubber.  
(3) The production modes could significantly affect the physical properties of AR due to different fusion states. The rubber particles are further shredded during the shearing process and would easily disperse and melt in the asphalt, which had much lower viscosity than that of the hot melt method. The AR produced by the shearing method was the optimal choice for site construction because of the more balanced physical performance.  
(4) The production method, preparation process, crumb rubber content and stabilizer will all affect the distribution of rubber powder and properties of asphalt rubber. Though the heat fusion method can improve the elasticity and high temperature property of asphalt rubber, shearing method can give better melt reaction with asphalt, which is more favorable for large-scale engineering applications.  
Conceptualization, H.L.; methodology, H.L.; validation, H.L. and Q.M.; formal analysis, W.L.; writing--original draft preparation, W.L.; writing--review and editing, A.A.T. and G.Z.; supervision and project administration, Q.M. and D.Z. All authors have read and agreed to the published version of the manuscript. This research was funded by the Science and Technology Project of Henan Department of Transportation (2020)-2-3), Science and Technology Project of Shanxi Department of Transportation (No.17-32T, No.17-05K, No.19-10K, No.19-28K), and Rising Tech Star Project of Shanxi Department of Science and Technology (No.2019 KJX-035). The authors declare no conflict of interest.
## 3 Characterizing Public Transportation System
### Urban Data Analysis Process
#### 3.2.1 Data Collection and Processing  
The data analysis process used national [18] and city [19] open data, and public transportation system data (GPS bus location and bus ticket sales payed with smart cards during 2015, over 150 GB of raw data).  
The bus location dataset holds the position of each bus sampled every 10-30 s, including the following information: bus line identifier, trip identifier to set appart different trips of the same bus line, GPS coordinates, instant speed, and time stamp corresponding to the GPS measure. Ticket sales data contain smart card transaction records, including: trip identifier (which allows linking to the bus location dataset), GPS coordinates of the smart card validation, bus stop identifier, time stamp of the smart card validation, unique smart card identifier (hashed for privacy purposes), number of passengers traveling with the same smart card, and leg number (for trips involving transfers).  
The data collection process was straightforward in the case of open datasets. The main efforts on this phase were related to data provided by Intendencia de Montevideo. Several meetings with authorities were celebrated, until an agreement was signed granting access and use to the data for research purposes.  
Regarding the processing phase, the studied data was structured in Python pandas dataframes. Among the many transformations performed to the datasets, the most significant one was related to the Coordinate Reference System (CRS). In order to be able to combine different datasets, all geospatial data was transformed to WGS 84 (EPSG:4326), which is the standard CRS used by GPS.  
To present clear visualizations, the reported results are from tickets sold during May 2015. Pre-hoc analysis of the full dataset suggest that this month is representative of the trends in the complete dataset. The source code for the analysis is configurable to process any subset of the full dataset.
# Stable Evaluation of 3D Zernike Moments for Surface Meshes
## 6 Numerical Results
### Computational Complexity for Geometric Moments and 3D Zernike Moments
To understand the differences with the theoretical complexity of \(O(N^{5})\), we need to remember Figure 4. The actual strength of the quadrature needed to compute Zernike moments with a very small error (\(TOL<10^{-8}\)) is much smaller than the exact strength, especially for triangles with small areas. The spheres considered in our tests have more than 20,000 facets, all scaled so that the sphere is with radius 0.75. We found that for nearly all those triangles, the actual maximum strength was constant at the value 7, corresponding to only 13 points in the quadrature. This leads to a significant speedup of the algorithm. We note that this is the complexity expected in practical use of the algorithms, as shapes represented with triangular meshes are usually characterized by a large number of triangles, and those triangles have small areas when the shape is scaled to fit in the unit ball. The algorithm maintains the independence of the contribution from each facet to the moments of the whole shape. This allows for an easy parallelization, which we implemented using POSIX threads. We tested the parallel version of Shape2 Zernike on a Linux server, with Xeon Platinum 8168 CPU at 2.7 GHz with 96 cores and 396 GB of memory. The apparent speedups observed are reported in Figure 14. The calculations were performed on the gargoyle (see above), represented by a mesh with 100,000 triangles, for four maximum moment orders. The speedup factors were derived as an average over five independent runs, to minimize spurious fluctuations. For maximum orders up to \(N=100\), the speedup remains fairly linear over the whole range of cores requested by the program. For a maxi  
Figure 13: CPU times required to compute the Zernike moments of a sphere as a function of the maximum order \(N\) of those Zernike moments. We computed the Zernike moments for three discrete representations of the sphere, each given by a triangular mesh. The corresponding meshes include 20,480, 81,920 (black line), and 327,680 (red line) triangles, respectively. Linear fits to these curves give slopes of 4.2 for the exact algorithm and 2.4 for the finite algorithm, corresponding to apparent complexities of \(O(N^{4.2})\) and \(O(N^{2.4})\) for those two algorithms. mum order of \(N=150\), a saturation appears, and the largest speedup factor is 8 for 16 cores used by the program. For a maximum order \(N=300\), the same saturation occurs, and the maximum speedup factor is only 8, independent of the number of threads. We believe that the saturation effect observed is related to cache thrashing issues on each core, as the total storage required for large \(N\) values becomes important (a maximum order of 150 represents a total of 2,306,676 moments to be computed).
## 4 Study of the TE Noise Reduction with Serrated Treatment Using Linear Microphone Array  
### Serrated TE Configurations  
The TE noise reduction with serrated treatments for NACA65(12)-10 blade was experimentally investigated using the same linear array. In order to systematically parameterize the noise reduction effect and regularity of the TE serration, a total of 12 different serrated blades, as shown in Figure 10(a), were designed and tested in this experiment. Figure 10(b) shows the definitions of the sawtooth amplitude (_2h_) and wavelength (\(\lambda\)). Table 1 gives the design parameters of the serrated TE blades. In this table, \(c\) means the chord of the blade.
## 2 Related Work
### The DCDM Architecture Structure  
The DCDM integration architecture is divided into three components: the first one is the _Message_ with the use of the MAP data structure, the second one is the _Composite_ pattern that is part of the message and allows for encapsulating the data in a universal composite, that is, in a unique and standardized structure that does not change over time; on the other hand the third component is the _Strategy_ pattern with which the best strategy to take for the integration is defined through the use of a Context as appropriate. The components aim to improve the low coupling.  
Figure 1 shows the components of the DCDM architecture. From left to right, the first part indicates the Client, which can be any platform, software unit, system, etc., that uses the architecture. The second component, called _AgnosticMessage_, represents the wrapper or main message where the instructions are sent to the receiver so that it knows how the message will be handled. The information represented as MAP and the Action Type action to be executed in the Entity Name entities also travels there. The third component, _Composite_, represents the structure where the payload called _Payload_ is configured and stored to be integrated. Finally, the _Strategy_ component is where the strategies or algorithms are to be implemented according to the message instructions live.
# A Coupling System for Prediction of Physiological Parameters in an Immersed Condition
## 1 Introduction
By controlling the thermal manikin with the established human thermal model and developing a system coupling the thermal manikin with the thermal model, one can combine the benefits of active regulation of the thermal model with the benefits of accurate heat transfer measurement of the thermal manikin while taking into account the limitations of both human thermal models and the thermal manikin [8; 20]. The coupling system's thermal manikin is outfitted with a thermal regulation system so that it can perceive environmental changes and adapt to them like a human would, while on the other hand, the thermal manikin's real-time testing increases the model's predictive accuracy [21]. The major aim of this study is to develop a coupling system model that can be used to predict human physiological parameters and assess how well life preservers defend against heat in an immersed condition. Two life preservers with intrinsic thermal resistances between 0.09 and 0.41 \({}^{\circ}\)C\(\cdot\)m\({}^{2}\)/W were chosen for the study, and practical tests were carried out in a climate chamber under two different environmental conditions, with the water immersion tests taking place in a water tank that was built into the climate chamber. To determine the coupling system's accuracy in predicting human physiological parameters while wearing a life preserver, the measured heat production, core temperature, and skin temperature data were statistically compared with the predicted results. Afterward, some recommendations for the coupling system's improvement were made.