esa-sceva/satcom-chunk-collection huggingface dataset:
# Torque Characteristic Analysis of a Transverse Flux Motor Using a Combined-Type Stator Core
## 4 Three-Dimensional Finite Element Analysis of TFM
### Air-Gap Flux Density  
An arc is drawn under two pairs of poles in the air-gap, and the radial flux density on the arc is shown in Figure 6a: the black line refers to the flux density under no-load condition when the magnetic field is excited by the permanent magnet; the red line refers to the flux density under load condition when the magnetic field is excited by both a permanent magnet and armature current; the blue line refers to the flux density under load condition when the permanent magnet part is replaced by air and the magnetic field is exited only by an armature current; the pink line refers to the difference between the black line and the red line, and it is almost coincident with the blue line. This verifies that the total magnetic field under load condition is comprised of the permanent magnet field and the armature current field.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Data** & **Parameter** & **Data** \\ \hline Rotor out Diameter \(D_{\text{out}}\) & 248 mm & Air-gap Diameter \(D_{\text{a}}\) & 226 mm \\ Stator inner Diameter \(D_{\text{in}}\) & 140 mm & Height of Axial bridge \(h_{\text{b}}\) & 10 mm \\ Air-gap length \(\delta\) & 1 mm & PM \({}^{1}\) thickness \(h_{\text{m}}\) & 2 mm \\ Axial length of single-phase \(l_{\text{a}}\) & 45 mm & Pole-pitch \(\tau_{\text{m}}\) & 11.83 mm \\ Axial length of PM \(l_{\text{m}}\) & 11 mm & rotor teeth length \(l_{\text{rt}}\) & 11.83 mm \\ Armature winding turns \(N\) & 150 & Pole pair number \(p\) & 30 \\ Rated speed \(n\) & 100 r/min & Rated current \(I\) & 12.73 A \\ \hline \end{tabular} \({}^{1}\) permanent magnet.  
\end{table}
Table 1: The main design parameters of stator core transverse flux motor (CS-TFM).  
Figure 5: The three-dimensional finite element model of single-phase CS-TFM: (**a**) mesh result; and (**b**) magnetic field distribution under rated current.  
Figure 6: The radial flux density of air-gap: (**a**) waveform; and (**b**) harmonic components.  
The harmonic analysis is done to calculate the amplitude of each component by Fast Fourier Transform (FFT), and the results of harmonic analysis regarding the current modulated field are shown in Figure 6b: the amplitude of fundamental harmonic is 0.197 T, and the constant component is 0.346 T.
# Improved Anomaly Detection by Using the Attention-Based Isolation Forest
## 4 Attention-Based Isolation Forest
### Loss Function and Attention Weights  
First, we reformulate the decision rule \((s(\mathbf{x},n)>\tau)\) for determining anomalous instances by establishing a similar condition for \(E[h(\mathbf{x})]\). Suppose that \(\gamma\) is a threshold such  
Figure 1: A general scheme of ABIForest, which illustrates how iForest is modified by incorporating an attention mechanism.  
that the condition \(E[h(\mathbf{x})]\leq\gamma\) indicates that instance \(\mathbf{x}\) is detected as an anomaly. Then, it follows from (4) that \(\gamma\) can be expressed through the threshold \(\tau\) as:  
\[\gamma=-c(n)\cdot\log_{2}(\tau). \tag{11}\]  
Hence, we can write the decision rule about the anomaly as follows:  
\[\text{decision}=\begin{cases}\text{anomalous},&\text{if }E[h(\mathbf{x})]- \gamma\leq 0,\\ \text{normal},&\text{otherwise}.\end{cases} \tag{12}\]  
We also introduce the instance label \(y_{s}\), which is \(1\) if the training instance \(\mathbf{x}_{s}\) is anomalous and \(-1\) if it is normal. If labels are not known, then prior values of labels can be determined by using the original iForest.  
We propose the following loss function:  
\[L(h(\mathbf{x}_{s}),\mathbf{x}_{s},\mathbf{w})=\max(0,y_{s}(E[h(\mathbf{x}_{s })]-\gamma)). \tag{13}\]  
It can be seen from (13) that the loss function is \(0\) if \(E[h(\mathbf{x}_{s})]-\gamma\) and \(y_{s}\) have different signs, i.e., if the decision about an anomalous (normal) instance coincides with the corresponding label. Substituting (8) into (13), we rewrite the optimization problem (10) as:  
\[\mathbf{w}_{opt}=\text{arg}\min_{\mathbf{w}\in\mathcal{W}}\Bigg{[}\sum_{s=1}^ {n}\max\Bigg{(}0,y_{s}\Bigg{(}\sum_{k=1}^{T}\alpha(\mathbf{x},\mathbf{A}_{k}( \mathbf{x}),\mathbf{w})\cdot h_{k}(\mathbf{x})-\gamma\Bigg{)}\Bigg{)}\Bigg{]}. \tag{14}\]  
An important question is that of how to simplify the above problem to get a unique solution and how to define the attention weights \(\alpha(\mathbf{x},\mathbf{A}_{k}(\mathbf{x}),\mathbf{w})\) depending on the trainable parameters \(\mathbf{w}\). This can be done by using [PERSON]'s \(\epsilon\)-contamination model.
# Complete Systematic Error Model of SSR for Sensor Registration in ATC Surveillance Networks
## 6 Bias Estimation with Opportunity Traffic
### Biases Values Initialization  
The initial values of the biases will be estimated using a LSE method using a small number of measurement (for example the measurements received in an antenna scan period). As it is proposed in [6] a pseudomeasurement (\(X_{b}\)) of the bias vector will be constructed and the (64) will be solved with the LSE method.  
\[X_{b}=X_{m,1}-\hat{X}_{m,2} \tag{63}\]  
\[X_{b}=H_{b}\;b+w \tag{64}\]  
where \(X_{m,1}\) is a measure from the radar 1, \(\hat{X}_{m,2}\) is the interpolation from two measures from the radar 2 to the same time of \(X_{m,1}\), \(w\) is the measurement white Gaussian noise projected to stereographic plane and:  
\[H_{b,i}=\left[H_{b1},\;-H_{b2},\;H_{b1,atm}-H_{b2,atm}\right] \tag{65}\]  
\[b=\left[\begin{array}{c}b_{1}\\ b_{2}\\ b_{atm}\end{array}\right] \tag{66}\]  
where \(H_{b,i}\) is the projection matrix of the \(i\)-th measurement. The biases are estimated using the pseudoinverse matrix [5]:  
\[b_{0}=\left[H^{\prime}R^{-1}H\right]^{-1}H^{\prime}R^{-1}X_{b} \tag{67}\]where:  
\[H=\left[\begin{array}{c}H_{b,1}\\ \vdots\\ H_{b,i}\\ \vdots\\ H_{b,n}\end{array}\right]R=diag(R_{1}\ldots R_{n}) \tag{68}\]  
\(R_{i}\) is the noise covariance matrix of each pseudomeasurement (it is the addition of the noise covariance matrix from \(X_{m,1}\) and \(\hat{X}_{m,2}\) of the \(i\)-th measurement) and n is the number of pseudomeasurements used in the initialization. The initial covariance matrix (\(P_{0}\)) used in the next section is calculated with the projection of the noise over the biases [5].  
\[P_{0}=\left(H^{\prime}R^{-1}H\right)^{-1} \tag{69}\]
# A Multi-Objective Modified PSO for Inverse Kinematics of a 5-DOF Robotic Arm
## 3 Multi-Objective Inverse Kinematics Problem Statement  
The inverse kinematics, IK, problem consists of computing a set of joint's motions that enable the end-effector of a robot manipulator to reach a specific target point. However, some industrial operations, such as welding, may need also a specific orientation of the end-effector besides its position. For example, the welding operation demands a functional tilt of the welding tools toward the welded surface. Such a constraint needs to be handled by a multi-objective strategy.  
Classically, a problem may be solved by retrieving the inverse kinematics model by inverting the forward model, which is simple to build. However, it is not as simple as that, because the inverse model may not be existing at some specific singular points where the forward model is not invertible.  
Therefore, the proposed method transforms the IK problem into an optimization consisting of minimizing the end-effector position and orientation based on the forward kinematics model of the systems, which is built using the Denavit-Hartenberg approach. Such an approach allows to avoid classical inverse kinematics solvers challenges including singularities, which may harm the existence of an inverse expression.  
The proposal consists of addressing the constraints and needs with mono-objective functions, one for each, and then applying a metaheuristic solver with a Pareto strategy  
Figure 1: Pareto front of a multi-objective problem with two objective functions.  
to obtain the best trade-off among them. For example, when the need is about position and orientation, such as in welding or painting, two main functions are addressed:  
* A minimization of the end-effector position toward a target point.
* A minimization of the end-effector orientation toward a fixed one.
# A New Intra-Cluster Scheduling Scheme for Real-Time Flows in Wireless Sensor Networks
## 3 Performance Evaluation
### Impact of Clusters  
In this section, we analyze the performance of the proposed and previous scheme with respect to a number of clusters. We fix the number of flows to 6 while the numbers of clusters are ranges from 4 to 8. As shown in Figure 6a-e, the proposed scheme shows high acceptance compare to the previous scheme in terms of a number of clusters. Because for clusters that have only outgoing or incoming flows, in that case, the intra-cluster schedule cannot use intra-cluster slots of each other.  
Whereas in the proposed scheme for clusters that have either incoming or outgoing flows, then intra-cluster schedule can still utilize the intra-cluster slots if one another at that time.  
Figure 5: Simulation results w.r.t flows.
# XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
## 3 XBeats System Architecture
### Hardware Specifications
#### 3.1.3 Data Logging  
The ECG patch integrates a high-performance and reliable MMC for continuous data logging. The data logging process is encapsulated in an independent task running simultaneously with the data acquisition task. A data retention service routine is also integrated within the system for more extended periods, as recommended by cardiologists. Consequently, the device monitors all incoming ECG signals for critical heart conditions detection. After this grace period, newly received data is not recorded until previously saved data is downloaded. This is another system design decision with an alternative option that could be overwriting older data with recent ECG signals. Cardiologists recommend our design choice [36]. The system will alert patients and healthcare providers to reconnect the smart ECG patch or offload the data logger to record ECG signals.
# An Adaptable Train-to-Ground Communication Architecture Based on the 5G Technological Enabler SDN
## 3 Proposed ACS Architecture
### Access Network  
The access network also constitutes a dedicated network under the management of the railway operator. The control plane is the same as in the core network, except for the forwarding, which is based on L2 matching flow rules (e.g., media access control (MAC) origin-MAC destination). The data plane is divided into two subnets: WiFi and LTE. These subnets provide wireless connectivity to the OBE, which is in charge of offering T2G connectivity services to the applications running in the train.
## 1 Introduction
For analysis and improved understanding of the UHF MIMO RFID system concept, we built a set of two devices, i.e., a tag equipped with two antennas and a reader with one transmit and one receiver antenna. Therefore, in this study, we analyze the bit error rate performance of a dual-antenna UHF passive RFID tag with polarization-time coded backscatter diversity proposed in [14] and later the corresponding simulations and measurements. Our main contributions in this study are to bring real measurement results of the so-called UHF MIMO RFID system not yet revealed in previous studies. The experimental results were compared with theoretical and simulated ones presented by several previous studies. We also demonstrate mathematically that our system model is a special case of that in [15]. This paper is organized as follows: in Section 2, we describe the system model and later the mathematical analysis; in Section 3, we describe the experimental verification of the proposed UHF MIMO RFID system; and in Section 4, we provide concludes for this study.
## 2 Distribution Network Reliability Evaluation Method  
### Component Modeling  
(1) Information and physical component outage model  
In the reliability evaluation of the distribution network considering the sense-control terminal, it is necessary to model the information element and the physical element, respectively. Among them, the information components are various inductive control terminal devices, and the physical components include feeders, switches, and distribution transformers. For the sense-control terminal components and physical components, the two-state Markov model can be used for the fault outage model of both [14]. The two states include the normal working state and the fault outage state. The failure rate of the component is \(\lambda\) and the repair rate is \(\mu\); the two-state model of the component is established as follows: Figure 1 shows the state probability and transition probability of the components in the two-state model.  
The probability of fault repair state is:  
\[P_{u}=\frac{\lambda}{\lambda+\mu} \tag{1}\]  
The probability of normal operating state is:  
\[P_{d}=\frac{\mu}{\lambda+\mu} \tag{2}\]  
(2) Load curve model  
In order to consider the impact of load time series characteristics on the reliability of distribution systems, this paper introduces the time series value of load, which is more in line with the actual power outage situation in reliability analysis, making the simulation results closer to the real value. The typical load model is used to calculate real-time load \(L_{t}\) at fault time t by using the annual-week, weekly-day, and daily-hour load curves.  
\[L_{t}=L_{p}\times P_{w}\times P_{d}\times P_{h}(t) \tag{3}\]  
In the formula, \(L_{p}\) is the annual peak load, \(P_{w}\) is the ratio of the weekly peak load to the annual peak load corresponding to the week at the fault time, \(P_{d}\) is the ratio of the daily peak load to the weekly peak load corresponding to the day at the fault time, and \(P_{h}(t)\) is the ratio of the hourly load to the daily peak load corresponding to the fault time.
# Pockels Effect at the Interface between Water and Ti Electrode
## 3 Experimental Results
### Electroreflectance Spectra  
#### 3.2.1 Electroreflectance Spectra by Only AC Electric-Field Modulation  
The electroreflectance spectra of the Ti electrode, which has been used for more than 30 h, in 0.1 M NaCl solution are shown in Figure 3. Since the \(\Delta R/R\) spectra showed excellent reproducibility, the thickness of the surface oxide film is considered stable. No degradation of the Ti electrode was observed even after repeated experiments. The obtained signal is due to the Pockels effect, because it was synchronized with \(f\), and it increased almost in proportion to the applied AC voltage, as shown in Figure 4. As expected from the results of the Pockels effect in bulk water (Appendix A), a relatively large signal is obtained. The magnitude of the signal is comparable to that of \(\Delta T/T\) observed at the water-ITO interface (comparison of maximum values). By contrast, the electroreflectance experiment with new Ti electrodes stored in air showed that \(\Delta R/R\) was an order of magnitude smaller than the results in Figure 3, and showed poor reproducibility due to changes in the thickness of the oxide film during the experiment.  
Figure 2: Reflection spectra measured in air (unpolarized light, 5\({}^{\circ}\) incidence) of three Ti electrodes: a new Ti electrode stored in air, a Ti electrode immersed in 0.1 M NaCl electrolyte solution for about 3 h in an electroreflectance experiment, and a Ti electrode that has been used in electroreflectance experiments for more than 30 h.
## 1 Introduction  
Fetal heart rate (FHR) and uterine contractions were continuously traced with a cardiotocogram (CTG), and the recorded chart was visually classified into various patterns; then, abnormal FHR patterns were individually assessed in each case, where the diagnosis was subjective and enlarged inter-observer difference, and therefore various problems developed in the CTG diagnosis. In addition, visual FHR pattern diagnosis was unable to apply computerized diagnosis because of no logical criteria, e.g., no lag time of late deceleration was reported by [PERSON] [1]. Later, [PERSON] _et al._[2] estimated it to be 20 or more seconds. Therefore, FHR diagnosis was computerized to quantitatively analyze FHR changes in an experts' knowledge system, artificial neural network analysis, and FHR frequency spectrum analysis [3; 4; 5; 6; 7]. Meanwhile, our recent studies on fetal movements with actocardiogram (ACG) [8] solved several CTG problems, e.g., a physiologic sinusoidal FHR was unable to differentiate a fatal pathologic sinusoidal condition with CTG, while they were separated when a physiologic sinusoidal condition was developed by fetal periodic respiratory or mouthing movements in the ACG [9]. Artificial birth was recommended before the loss of variability, because FHR variability is the response of the fetal brain to minor fetal movements, and its loss will mean fetal brain damage preceding cerebral palsy [10; 11; 12].  
[PERSON] _et al._ reported computerized FHR diagnosis using a minicomputer in 1980, on the FHR analysis and FHR score calculation with quantified FHR data, challenging the objective FHR diagnosis [3; 4], and furthermore reported artificial neural network computer diagnosis of FHR in 1998, calculating probabilities to be normal, suspicious and pathologic to the outcome of the fetus [5]. Pathologic ominous sinusoidal FHR was diagnosed using an actocardiogram [9] and also by the computer using frequency spectral analysis [6]. The loss of FHR variability was detected with the experts' knowledge system [3; 4] and the frequency spectral computer system [7]. [PERSON] _et al._ analyzed FHR using the computer in fetal heart rate fractal analysis in 2004 [13], [PERSON] _et al._ studied FHR changes and its variability in pregnancy and labor [14], [PERSON] _et al._ studied FHR in labor in 2011 [15], [PERSON] _et al._ analyzed FHR using the neural network approach in 2007 [16], and [PERSON] _et al._ studied FHR using signal processing and wearable technology in 2014 [17].  
FHR monitoring was confused by subjective FHR pattern diagnosis in the past and solved by quantitative studies as follows.
## 4 Uniaxial Loading of Critically Damaged Coal Samples
### Fractal Characteristics
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Group**}} & \multicolumn{5}{c}{**Sieving Diameter/mm**} & \multicolumn{5}{c}{**Total**} & \multicolumn{1}{c}{**Fractal**} \\ \cline{2-11}
**Number** & **0.5** & **1** & **2** & **4** & **8** & **12.5** & **20** & **40** & **Weight/g** & **Dimension**\(D\) \\ \hline Control group & 2.55 & 2.07 & 4.06 & 10.22 & 15.83 & 16.8 & 27.82 & 156.59 & 235.94 & 2.08 \\
5 MPa-60\% & 1.36 & 2.25 & 3.46 & 9.01 & 14.86 & 33.94 & 26.02 & 141.18 & 234.08 & 1.97 \\
10 MPa-60\% & 0.79 & 2.44 & 1.91 & 6.58 & 16.64 & 14.7 & 34.23 & 162 & 243.1 & 1.85 \\
15 MPa-60\% & 0.73 & 0.68 & 2.19 & 7.38 & 9.04 & 12.24 & 31.36 & 171.49 & 235.71 & 1.77 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Average mass distribution of each coal sample in different screening zones. sprouting cracks are interconnected or even penetrated, forming macroscopic cracks on the surface of the specimen. Under subsequent uniaxial compression, the presence of macroscopic cracks leads to the existence of a large weak surface on the specimen, and the damage to the sample follows the large weak surface, resulting in increasingly larger fragments of the sample and a reduction in the fractal dimension.
## 5 Experiments and Evaluation
### Model Performance Evaluation  
In order to explore the proposed model performances under different data sparsity, we randomly divide the train set and test set to control the level of data sparsity. For example, the data sparsity is 70%, which means that only 30% of the history data is used for training our model and making predictions.
# SACT: A New Model of Covert Communication Based on SDN
## 4 System Model
### Watermark Extraction Module  
When the data stream arrives at the receiver, SACT extracts and decodes the watermark information through the watermark extraction and decoding module to confirm the communication relationship. After receiving the data stream, the switch passes the information to the SDN controller on the secure channel through the flow table and cache processing mechanism in SDN. Then, the sequence of arrival delay is converted to watermark information by anti jamming mechanism and Viterbi decoding.  
The workflow of watermark decoding module is as follows:  
1. If the switch receives the TCP/UDP protocol packet, it uploads the key information of the packet to the controller for further processing through _Packet-In_ message. If the switch receives other protocol packets, it forwards them directly according to the flow table.
2. After receiving the message, the SDN controller records the arrival time of the next series of homologous packets and calculates the delay sequence of interval arrival \(M^{\prime}\).
3. After antijamming processing, the delay sequence \(M^{\prime}\) is converted into binary sequence \(E^{\prime}\) according to encoding mapping scheme. Then it is converted into binary sequence \(B^{\prime}\) according to Viterbi decoding.
4. Calculating the similarity between the sequence code \(B^{\prime}\) and the original embedded watermark code \(B\).  
If the calculated result is within the confidence threshold, the watermark has been embedded in the detected packet stream. That is, there is an association relationship between the two parties of the monitored communication, otherwise there is no communication relationship.  
Figure 7: Network Time Protocol (NTP) Synchronization.
## 3 Materials and Methods
### Implementation Details
#### 3.2.4 Experimental Setting  
The proposed models were trained with the Huber loss function, which combines the characteristics of MSE and MAE and is less susceptible to outliers in the data than the MSE loss function [63]. It behaves quadratically for small residuals and linearly for large residuals [64]. The parameters of the network were learned to minimize the average of the Huber loss function over the entire training dataset.  
The network weights and biases were initialized with the Glorot-Xavier uniform method and zeros, respectively. [PERSON] and [PERSON] [65] proposed the Glorot-Xavier uniform method to adopt a properly scaled uniform distribution for initialization.  
The successful applications of neural networks require regularization [66]. Introduced by [23], the dropout regularization technique randomly drops a fraction of the units with a specified probability, along with connections during training, while all units are presented during testing. We applied the dropout values of 0.2 and 0.5 to reduce overfitting and have observed that higher dropout value result in a decline in performance. Therefore, we settled on the relatively low dropout value of 0.2 as studied in [67].  
The batch size and maximum number of epochs were set to 32 and 50, respectively, and an early stopping patience of 10 was applied [68]. That is, once the validation loss no longer decreased for the patience period, the training was stopped, and the weights of the model with the lowest validation loss were restored using ModelCheckpoint callback in the Keras library [59].  
The optimization algorithms used for training were the Adam and root mean square propagation (RMSProp) [69], which are adaptive learning rate methods. The RMSProp is usually a viable choice for RNNs [59]. We compared the performance of the proposed models using two different optimizers.  
We applied learning rates of 0.001 and 0.0005 and found that a learning rate of 0.0005 resulted in a better performance. Therefore, the learning rate was set to 0.0005.  
Figure 2: Sliding window technique.  
The ReLU activation function proposed in [22] was used for the dense layers, and the data shuffling technique was not used during training.