esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Methodology  
To facilitate the analysis, the following assumptions are taken  
1. The cutter is regarded as a rigid body, regardless of the effect of detail configurations, i.e. round borders or chamfers, of the cutting edges.
2. The influences of temperature, confining pressure, and drilling fluid are neglected.  
Figure 3: Structure parameters of 3-RDE non-planar cutter: (a) Top view; (b) Main view; (c) Section view.  
Figure 2: Illustration of (**a**) Conventional planar-cutter and (**b**) Three-ridged diamond element (3-RDE) non-planar cutter.  
accuracy, robustness and hourglass control was employed to discrete the rock model, and the rock's area nearby the cutter was finely meshed. The model was totally divided into 313,734 elements (331,185 nodes). And the international system of unit (m-kg-s) was applied in the numerical model, while for convenience, results were displayed according to the engineering conventions. The mechanical parameters of rock material are listed in Table 1. Non-reflecting boundary and fixed constraint were applied to the rock surfaces except the top one. The linear cutting speed of cutter is 0.3 m/s, and the diameter of the cutter is 16 mm. The contact type between the cutter and rock was eroding surface to surface. Considering the friction between the cutting surface and rock, the friction coefficient of contact surfaces was set to 0.4 [18]. In the cutting process, the reaction forces on the cutter can be divided into axial force \(F_{n}\), tangential force \(F_{h}\) and the radial force \(F_{r}\). The tangential force is opposite to the direction of cutting speed; the axial force is along the positive direction of the \(z\)-axis, and the radial force is determined by the right-hand rule.\(\alpha\) is the back-rake angle, and \(h\) is the cutting depth. The cutting edge of the 3-RDE cutter is always perpendicular to the upper surface of the rock in the absence of a special declaration.
# Research on Wear Detection of End Milling Cutter Edge Based on Image Stitching
## 2 Collection and Splicing of Side Edges of End Mills
### Tool Acquisition Cylindrical Model  
The collection model of the side edge of the end mill is shown in Figure 3. First, the end milling cutter is simplified as a cylindrical model. There are four helixes on the cylindrical model to simulate the side edges of the end mill. The red area in the model is the camera recognition area, and the camera recognition sequence is from bottom to top.  
The acquisition method first establishes four image data containers to store images of different side edges. Periodically, four frames of end mill side edge images in the horizontal direction of the acquisition model are collected. Each frame of the image contains the information of each side edge. The collected images of each side edge are stored in corresponding containers, respectively, and this process is called the first layer collection. After the first layer is collected, while rotating the end mill clockwise, the spindle is moved down a certain distance so that the camera can collect the following wear area information of the first layer side edge image. At this time, the spindle stops moving downward, and the camera collects four frames of side edge images again and stores the corresponding container. This process is the second layer of collection. After that, the spindle moves down the same distance again, and the above process is repeated until the camera captures every side edge of the entire end mill. The image area acquired by each layer in the acquisition process is shown in Figure 4.  
Figure 3: Schematic diagram of the process of collecting pictures of end mills.  
Figure 2: Tool wear detection system.  
The spindle speed and the diameter of the end mill will affect the quality of image stitching. The increase in the spindle speed and the diameter of the end mill will decrease the quality of image stitching. However, the quality improvement of the stitched image is slight for the spindle speed of less than 10 r/min and the end mill diameter of less than 15 mm.
## Literature Review
### Studies on CBAP
The experimental results showed that a better decision could be achieved when taking these factors into consideration. In [PERSON] et al. [34], the authors proposed a two-stage decision model for dealing with the CBAP. The arrival times and handling times of ships in the model are treated as uncertainties. In the first stage, this model first initiates a baseline schedule that is further adjusted by using a reactive recovery strategy in the second stage. The objective of that approach was to minimize penalty costs incurred by deviating from the initial schedule. For the CBAP in a large-scale environment, the authors proposed a metaheuristic approach based on the simulated annealing approach. Our literature review shows there is still a lack of implementation of SFLA-based approaches for dealing with the BAP, especially the DCBAP. The CBAP is not completely equivalent to the cutting-stock problem (CSP), because not all rectangles (placing items)--which correspond to calling ships in the CBAP--are already available in the CSP, as a result of the incoming ships in the CBAP. In addition, the constraints in the CBAP can be graphically portrayed. For example, some rectangles in a berth plan cannot be placed before corresponding predetermined vertical lines that represent the estimated arrival times (ETAs) of calling ships. This restriction is simply based on the fact that ships cannot be berthed before they have arrived at the port. Thus, the static version of the CBAP is identical to the fixed-orientation CSP but the dynamic BAP is not [1]. In many CBAP studies, such as those by [PERSON] [4], [PERSON] et al. [28], and [PERSON] et al. [29], the authors only considered arrived ships and assumed that a ship's handling time was independent of its berthing location. Although incoming ships were taken into consideration in some studies, such as those by [PERSON] and [PERSON] [30; 31] and [PERSON] and [PERSON] [5], and fixed handling times had been assumed for those ships, Ref. [31] can only be regarded as a static version of CBAP, as ships were able to be served before their ETAs, while penalty costs were then imposed in the objective function for the use of such services. This relaxation downgrades the dynamic version of BAP to a static one [5]. Consequently, its constraint is also equivalent to that of the CSP with fixed orientation. In this present research, the CBAP to be solved is quite different from the conventional CSP, as the handling time of a ship is dependent on the ship's berthing location, and incoming ships are to be taken into consideration. In addition, heuristics have been widely used to deal with the CBAP. However, high-level heuristics, i.e., metaheuristics, have seldom been used to deal with the CBAP. In particular, there is still a lack of using SFLA to solve the BAP. Such applications are still at the emerging stage.
# Blockchain-Based Federated Learning: A Survey and New Perspectives
## 4 Blockchain-Based Federated Learning: State-of-the-Art
### Model Classification for Blockchain-Based Federated Learning
#### 4.1.2 Consensus Algorithm  
The process of selecting a billing node from a set of miner nodes through a variety of lead mechanisms [78], including voting-based, evidence-based, coalition-based, randomness-based, or other hybrid algorithms, is a key part of most blockchain consensus algorithms. The ongoing process of selecting an account node from a set of miner nodes is a key part of most blockchain consensus algorithms. One of the advantages of the blockchain consensus algorithm is that any completed request can be seen by subsequent requests but cannot be changed, so the blockchain has higher security.  
There are many benefits to using consensus algorithms to facilitate federated learning training. Blockchain-based FL uses blockchain consensus mechanisms to select nodes toparticipate in training, which can further improve the efficiency and robustness of FL. For example, a model migration method based on blockchain can achieve efficient model training [21]. Additionally, in BFL, we also need to consider other attributes of combined nodes and combine them with certain weights to select appropriate accounting nodes [32].  
In an evidence-based mechanism, ledger nodes must demonstrate that their specific ability in each round of consensus is superior to other nodes. Inspired by this, some scholars also designed similar algorithms for BFL to ensure its robustness. For instance, honest nodes can earn equity awards more frequently, but the miner who makes the most contribution in each round of communication can create a legal block [79]. Moreover, the local weighting method of nodes [51] can be used to improve the accuracy of fast and stable learning of model convergence and achieve the robustness of BFL.  
According to the characteristics of the coalition-based mechanism in which agent nodes alternately or elect to obtain the accounting authority of each block, researchers proposed to design a committee consensus mechanism to improve the training efficiency and optimize the training process [80].  
By using blockchain, BFL can reduce the risk of a single point of failure through a decentralized data ledger without relying on any central server, and all network entities can transparently track any update events and user behavior.  
DeepChain [49] provides blockchain-based value-driven incentives to force participants to behave correctly. At the same time, DeepChain guarantees the data privacy of each participant and provides auditability for the entire training process.
# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 2 AI Techniques for Healthcare
### Deep Learning: Convolutional Neural Networks and U-Net Architectures
Although it is relatively simple to represent an image as a vector, the reverse process is significantly more complex. The idea of a U-Net architecture is to make the functionality mapping learned during the representation of the input image into a vector, and use it to  
Figure 4: Example of a convolutional neural network for image classification. reconstruct the portion of interest of the output image. In this way, the structure of the input image is preserved and distortion introduced by reconstruction is reduced. The U-Net structure includes the typical elements of deep learning architectures, such as convolutional and pooling layers. Figure 5 shows the structure of a sample U-Net, with some parameter values that should be regarded as an example. The basic structure of the U-Net includes a contracting path, a bottom section, and an expanding path. The contracting path consists of some convolutional layers followed by max pooling layers. Each convolutional layer typically makes use of a 3 \(\times\) 3 kernel, followed by a ReLU activation function. Max pooling layers are used to reduce the spatial dimensions of the feature maps. The bottom of the U-shape typically consists of two 3 \(\times\) 3 convolutions followed by a ReLU activation function. The expanding path consists of up-sampling (or transposed convolution) layers that increase the spatial dimensions of the feature maps. Each up-sampling step is followed by a concatenation with the corresponding feature map from the contracting path, referred to as skip connections, providing the decoder with high-resolution features from the encoder. Following the concatenation, two 3 \(\times\) 3 convolutions and a ReLU activation function refine the up-sampled feature maps. The number of feature channels halves with each up-sampling step. The described basic architecture is the basis of many research proposals. In [36], a deep learning-based segmentation model is proposed. It performs binary segmentation through a P-Net [37] architecture, which draws inspiration from the well-known VGG-16 [38] CNN model. The proposal displays good performance, although it requires a long training phase. Finally, it is worth mentioning the research activities that make use of 2D segmentation to obtain a three-dimensional representation of organs. In particular, 3D U-Net [39] is an architecture which extends the original U-Net structure [35], with appreciable results. The reader interested in developing DL applications could make use of different libraries. Among these, we mention TensorFlow [40], PyTorch [41], and CUDA Toolkit [42].
## 2 Methodology
### Ensemble Kalman Filter  
The ensemble Kalman filter (EnKF) is a Bayesian approach which aims to approximate the posterior distribution by a set of random samples. In the EnKF, the distributions are considered to be Gaussian, and the Monte Carlo approach is applied to approximate the error statistics, as well as to compute an approximate Kalman gain matrix for updating the model and state variables.  
Consider a general stochastic dynamic model with the transition equations of the system state expressed as:  
\[x^{f}_{t+1,i}=f(x^{a}_{t;i},u_{t,i},\theta^{f}_{t+1,i})+\omega_{t,i},\ i\ =\ 1,\ 2,\ \ldots,\ ne \tag{6}\]  
where \(x_{t}\) is the sate vectors at time \(t\); \(\theta\) is the system parameters vector which are assumed to be known and time invariant; the superscript \(f\) indicates the \"forecast\" sates; the superscript \(a\) indicates the \"analysis\" states; \(ne\) represents the number of ensembles; \(u_{t}\) is the input vector (deterministic forcing data); \(f\) represents the model structure; \(\omega_{t}\) is the model error term, which follows a Gaussian distribution with zero mean and covariance matrix \(\sum\limits_{t}^{m}\). For the evolution of the parameters, it is assumed that the parameters follow a random walk, presented as:  
\[\theta^{f}_{t,i}=\theta^{a}_{t-1,i}+\tau_{t,i},\ \tau_{t,i}\sim N(0,\sum \limits_{t-1}^{\theta}) \tag{7}\]  
Prior to the update of the model states and parameters, an observation equation is applied to transfer the states into the observation space, which can be characterized as:  
\[y^{f}_{t+1,i}=h(x^{f}_{t+1,i^{\prime}},\theta^{f}_{t+1,i})+v_{t+1,i},\ v_{t+1,i} \sim N(0,\sum\limits_{t+1}^{y}) \tag{8}\]  
where \(y_{t+1}\) is the observation vector at time \(t+1\); \(h\) is the measurement function relating the state variables to the measured variables; \(v_{k+1,i}\) reflects the measurement error, which is also assumed to be Gaussian with zero mean and covariance matrix \(\sum\limits_{t+1}^{y}\). The model and observation errors are assumed to be uncorrelated, i.e., \(E[\omega_{t}\rho_{t+1}^{T}]=0\). After the prediction is obtained, the posterior states and parameters are estimated with the Kalman update equations as follows [27]:  
\[x_{t+1,i}^{a}=x_{t+1,i}^{f}+K_{xy}[y_{t+1}+\varepsilon_{t+1,i}-y_{t+1,i}^{f}] \tag{9}\]  
\[\theta_{t+1,i}^{a}=\theta_{t+1,i}^{f}+K_{\theta y}[y_{t+1}+\varepsilon_{t+1,i} -y_{t+1,i}^{f}] \tag{10}\]  
where \(y_{t}\) is the observed values; \(\varepsilon_{t,i}\) represents the observation errors; \(K_{xy}\) and \(K_{\theta y}\) are the Kalman gains for states and parameters, respectively [28]:  
\[K_{xy}=C_{xy}(C_{yy}+R_{t})^{-1} \tag{11}\]  
\[K_{\theta y}=C_{\theta y}(C_{yy}+R_{t})^{-1} \tag{12}\]  
Here \(C_{xy}\) is the cross covariance of the forecasted states \(x_{t+1,i}^{f}\) and the forecasted output \(y_{t+1,i}^{f}\); \(C_{\theta y}\) is the cross covariance of the parameter ensembles \(\theta_{t+1,i}^{f}\) with the predicted observation \(y_{t+1,i}^{f}\); \(C_{yy}\) is the variance of the predicted observation; \(R_{t}\) is the observation error variance at time \(t\).
## 2 Experimental Approach and Optimization  
### Setup  
The identical experimental setup was used as the one described by [PERSON] et al. [10] and consisted of a reference rail of 3.3 m length tactiely measured by METAS (Swiss Federal Institute of Metrology) on two parallel longitudinal lines. The setup is shown in Figure 1. For the optical measurement, four Micro-Epsilon optoNCDT 2300-10 LL laser triangulation sensors are mounted on a sensor plate. The sensor plate is connected to the slider via pneumatic actuators. The pneumatic actuators allow a defined vertical movement to be applied to the sensor plate. Guides restrict major lateral deflection during longitudinal movements of the slider. The arrangement is automatically moved along the rail by a linear drive with a velocity of 0.031 m s\({}^{-1}\). The sampling distance of the measurements is adjusted to the METAS reference measurement via resampling. This results in a data point distance of 48 \(\upmu\)m. The four sensors record analog data in a range from \(-10\) V to 10 V, with a sampling rate \(f_{5}\) of 30 kHz. Analog signals from the sensors are recorded with an NI 9222 module within a cRIO-9045 from National Instruments using LabView. In addition, an encoder signal of a Leine Linde RSI 593 is recorded using an NI 9402 module to determine the position along the rail. The measurements are not triggered via the encoder.  
The lateral position of the setup (track setting) was fixed while the longitudinal profile was measured. The regular sinusoidal lateral movement of the train, known as hunting oscillation, cannot be approximated with this setup. Each measurement is repeated three times. The experimentally obtained data and the data from the reference measurement were processed identically, as described by [PERSON] et al. [10]. Levels in decibels were calculated as described in EN 15610 [9]:  
\[L=10\ \log_{10}\left(\frac{r^{2}}{r_{0}^{2}}\right) \tag{1}\]  
\(L\) is the level in decibels, \(r\) is the value of roughness in micrometers and \(r_{0}\) is the reference value of roughness with a value of 1 \(\upmu\)m. The peripheral areas (representing the ends of the rail) of the signal were removed to avoid interference with the chord method. A pre-filter is applied for the wavelength range from 3 mm to 500 mm. For the acoustic roughness, the averaged relative error in percent and the averaged absolute error are calculated for the spectrum from 3 mm to 400 mm. The absolute error was calculated as the difference between the reference and the respective measurement per one-third octave band. The averaged absolute error thus corresponds to the averaging of the deviation over all one-third octave bands of a spectrum.  
Figure 1: Test stand including sensor plate and pneumatic actuators.
## 3 Methodology
### Pareto Analysis  
Pareto analysis is based on the 80/20 rule that is used to identify 20% of the critical faults contributing to nearly 80% of the failures [40]. A Pareto analysis' result is a combination of a bar and a line graph where the component's failure frequency is represented in descending order and the cumulative percentage contribution is shown by a line. Pareto analysis of an HVAC system was used in this study to identify the system performance and reliability issues and identify the root causes of the system failures. The resource allocation can be performed effectively for these critical components rather than distributing efforts equally among all components. Pareto analysis can also be used for reliability allocation, which is often performed during the design stage. The effectiveness of the reliability improvement strategies can be monitored by regularly conducting a Pareto analysis of an HVAC system. This is also an effective tool in identifying if any new factors are emerging as significant contributors to system failure.
## 3 Modeling of UAHW Target Recognition Algorithm Based on SMOTE-Stacking
### Feature Extraction  
After signal detection, feature extraction is performed. The extraction of effective features is a crucial step when building a model, and the quality of features plays a decisive role in the prediction process. The linear array scale acoustic decoy has the characteristics of highlight and azimuth extension, and it responds to the detection signal regularly through multiple underwater acoustic transducers. When the response signals of multiple underwater acoustic transducers are superimposed, the azimuth strike characteristics and multi-highlight characteristics of submarine targets can be simulated with high fidelity. The typical characteristics of the scale acoustic decoy response signal are its stable intensity distribution and spatial distribution, and the background between the highlights is \"clean\" [4]. The linear array scale acoustic decoy can not only simulate the reflection characteristics of the target to the wideband incident signal and the radiated noise characteristics of the real target but also simulate the scale characteristics of the real extended target physical structure and the spatial distribution characteristics of the echo signal. Compared with the stable highlight of linear array scale acoustic decoys, submarine echoes also have the characteristics of multi-highlight [31]. Due to the irregular shape of submarines, the intensity distribution and spatial distribution of echo bright spots are not the same at different angles, which is a kind of surface reflection. Its highlight background is not clean because of surface reflection. Based on the physical mechanism of the multi-point reflection and submarine surface reflection of linear array scale acoustic decoys, we extracted target-based energy features from the wideband signal correlation detection output, including five features: single highlight energy distribution features, multi-highlight energy distribution features, multi-highlight background energy distribution features, multi-highlight global spatial distribution features, and multi-highlight local spatial distribution features. This feature set can be used to identify submarine and linear array scale acoustic decoys.
## 4 Optimization of Stator Characteristic Parameters
### Finite Element Modeling
#### 4.2.1 Influence of the Vibration Mode on the Motor  
The working mode of a traveling wave motor is represented by B\({}_{0n}\), where \(n\) is the number of traveling wave wavelengths as well as the modal order. To better generate a traveling wave, the sum of the bending waves of the piezoelectric ceramic is designed to be the same as the expected mode. Therefore, an odd-order working mode is generally selected for the stator. Furthermore, the piezoelectric ceramic should be adjusted accordingly when the working mode changes, as shown in Figure 5. The influence of \(n\) on the stator vibration parameters can be simulated by finite element analysis, as shown in Figure 6. There are clearly many design choices for the motor working mode. The smaller the value of \(n\), the lower the working mode order, the lower the stator modal frequency, the larger the amplitude, the smaller the tangential speed, and the larger the value of \(\beta\). However, if the working mode order is too low, the modal frequency may be lower than the lower limit of the ultrasonic frequency (20 kHz). Therefore, the B\({}_{09}\) working mode was adopted for lower speed and higher torque.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Property** & **QSn6.5 Phosphor Bronze** & \multicolumn{4}{c}{**PZT-8 Piezoelectric Ceramic**} \\ \hline Mass density (kg/m\({}^{3}\)) & 8800 & & & 7500 & \\  & & & & & \\  & & & & & \\ Young’s modulus (GPa) & 110 & & \(\left[\begin{array}{cccccc}120.6&53.5&51.5&0&0&0\\ 53.5&120.6&51.5&0&0&0\\ 51.5&51.5&104.5&0&0&0\\ 0&0&0&31.3&0&0\\ 0&0&0&0&31.3&0\\ 0&0&0&0&0&34.6\\ \end{array}\right]\) \\ Poisson’s ratio & 0.3 & & & &... & \\  & & & & & \\ Piezoelectric constants (C/m\({}^{2}\)) &... & & & \(\left[\begin{array}{cccccc}0&0&-4.1\\ 0&0&-4.1\\ 0&0&14\\ 0&0&0\\ 0&10.3&0\\ 10.3&0&0\\ \end{array}\right]\) \\ Permittivity constants (F/m) &... & & & \(\left[\begin{array}{cccccc}6.04&0&0\\ 0&6.04&0\\ 0&0&6.04\\ \end{array}\right]\times 10^{-9}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the stator materials.  
Figure 4: (**a**) B\({}_{011}\) working mode of 70 HA and (**b**) simulation curves of the stator vibration parameters of 70 HA.