esa-sceva/satcom-chunk-collection huggingface dataset:
# FE-Aided Synchronization Analysis of Line-Start Synchronous Reluctance Motors
## 5 Case Analysis
### Almost Fully Filled Flux Barrier  
The difficulty in designing a LS-SynRM is that multiple flux barriers and rotor conductors have to be arranged in a limited rotor area. Thus, filling the flux barrier with rotor conductors is an attractive solution. A device was designed to prove the concept of the LS-SynRM. The rotor geometry is portrayed in Figure 6. As can be seen, three layers of the flux barriers are filled with aluminum while the conventional rotor slot shape of an IM is employed near the \(d\)-axis of the rotor. The end ring was designed to cover most of the rotor conductors.  
The approximate steady state conditions provided by (17) and (18) are compared with those of time stepping FEA results in Figure 7. As discussed earlier, the approximation neglects the time and space harmonic components. However, the components that were compared gave results quite close to the FEA results.  
Figure 8 shows the steady state torque and slip curve corresponding to (8) with the torque obtained by the time stepping FEA. As can be seen, the results are well matched. For the alternating torque \(T_{1}\) obtained by FEA, only the fundamental component is illustrated, of which the angular velocity is \(2s\omega\).  
\begin{table}
\begin{tabular}{c c} \hline \hline \multicolumn{1}{c}{Stator outer/inner diameter} & 150/95 mm \\ \hline Stack length & 90 mm \\ \hline Number of poles & 4 \\ \hline Number of turns per slot & 48 \\ \hline Number of stator slots & 36 \\ \hline Iron core & 50 PN470 \\ \hline Supply voltage & 380 V \\ \hline Supply frequency & 60 Hz \\ \hline \end{tabular}
\end{table}
Table 2: Key motor data of the 1.5 kW LS-SynRM.  
Figure 6: Rotor geometry of the first prototype.  
Synchronization analysis was carried out with the obtained steady state torque as a function slip. The load is assumed to have a quadratic relationship with the rotor velocity. The results of FEA are compared in Figure 9 with those of the FE-aided analytical approach described in (14). The critical inertia given by the FEA was used to estimate that the synchronization process would fail if the inertia increased 5% more than the given value.  
Figure 8: Steady state torque characteristic. Average and peak alternating torque are portrayed.  
Figure 7: Steady state analysis results on 20% slip operation. (**a**,**b**) respectively compares torque and \(dq\) currents.  
The total cross section area of the rotor conductor for the designed LS-SynRM corresponds to 1.06 times the reference IM. An almost identical amount of rotor conductor was utilized. In such condition, the FEA predicts the critical inertia at the rated load to be 0.023 kg\(\cdot\)m\({}^{2}\).
## 3 Proposed Model
### Phase 1  
Temperature is one factor that impacts the chlorine aggregate ratio in groundwater. The chlorine bulk decay rate in drinking water is one factor that is predominantly influenced by temperature, and the temperature sensor is used to determine this rate. The chlorine sensor detects the amount of chlorine dissolved in the water utilizing PPM (Part Per Million). We have ranged the value from 0 to 10 PPM in the proposed model. Where values 4, 5, 6 come under moderate chlorine content, values 7, 8, 9, 10 come under high chlorine content, and values 1, 2, 3 come under low chlorine content, respectively. The PPM data are transmitted to the sensor hub in real-time, as was just indicated (Raspberry PI). The real-time PPM values from the sensor hub are also sent to the Data Acquisition Centre (DAC) via a communication network.
## 2 System Overview
### Transmission Protocol
The mapping relationship between the underwater acoustic feedback data and optical communication data is shown in Figure 9. Since the underwater acoustic communication adopts the MFSK modulation method, which limits the data of each frame within the size range of 42 bytes, 42 bytes of data are used as a frame, in which the first byte is used as the frame header of protocol identification, and the remaining 41 bytes are used to represent the flags array of receiver reception. Since the corresponding data \"/x00\" will be recognized by the underwater acoustic communication module as the end symbol, and some eight bits of data corresponding to characters will be encoded as special symbols, this will result in data loss. Hence, it is not possible to directly use the bit \"0\" or \"1\" to represent the reception of the corresponding optical communication data packet position. In the protocol, considering the stability, we use one character to represent the position of five data packets, so 32 different characters are needed to represent all receiving states. Here, a total of 32 characters from \"0\" to \"9\" and \"a\" to \"v\" are mapped to 32 states from \"00000\" to \"11111\", where \"1\" means successfully received, and \"0\" means not received. We use one character to represent the reception of five packets, then 41 bytes of data can represent a total of 205 packets. When the transmitter receives the ARQ data packet, it will retransmit the data packet marked \"0\".
# Parameter Estimation of a Class of Neural Systems with Limit Cycles
## 2 The Spiking Neuron Model  
In this section, we introduce the general spiking neuron models. A conductance-based spiking neuron model is described by  
\[\dot{x}(t)=F(x(t))+Bu(t)+\xi(t), \tag{1}\]  
where \(x(t)\in\mathbb{R}^{n}\) is the state which usually consists of the membrane potential, gating variable, recovery variable and adaptation variable; \(\xi(t)\in\mathbb{R}^{n}\) denotes the disturbance in the neuron system; \(u(t)\in\mathbb{R}\) is the external input injected to the neuron; \(B\in\mathbb{R}^{n}\) is a constant vector, which is typically taken as \(B=[1,\underbrace{0,\cdots,0}_{n-1}]^{\top}\) when the external input acts on the membrane potential.  
The system described by (1) is quite general as it includes many spiking neuron models such as the Hodgkin-Huxley model, the Hindmarsh-Rose (HR) model, the FHN model and so on. In this paper, we shall focus on parameter estimation of the FHN model using different identification algorithms, but the proposed approach is applicable to other neuron models in a similar manner. The FHN model in a dimensionless form [25; 26] can be written in the form of (1)  
\[\begin{bmatrix}\dot{v}(t)\\ \dot{w}(t)\end{bmatrix}=\begin{bmatrix}\mu(v(v+a)(b-v)-w)+u(t)\\ c_{1}v-c_{2}w\end{bmatrix}+\xi(t) \tag{2}\]  
where \(v\) denotes the voltage potential of the neuron membrane and \(w\) denotes the inactivation of the sodium channels; \(\xi\in\mathbb{R}^{2}\) denotes the disturbance in the neuron system and is the white noise with zero mean. The parameters \(a,b,c_{1},c_{2},\mu\) are unknown to be estimated later in detail. In this paper, we consider a constant external input \(u(t)\equiv J\) resulting in periodic spiking dynamics and the external input \(J\) will also be estimated. Note that when the parameters are specified as \(a=0.1\), \(b=1\), \(c_{1}=1,c_{2}=0.5,\mu=100\) and the input current value is taken as \(J=0.5\), the neural system from \((v(0),w(0))=(-0.3,0.6)\) without disturbance exhibits periodic spiking dynamics and converge to a limit cycle (see Figure 1).  
Figure 1: A limit cycle of the FHN model.  
In the real nervous system, the parameters of the spiking neurons are difficult to be measured or determined, the identification of system parameters has always been an important topic in the field of neural computing and system control. Motivated by this, the present work is directed towards developing efficient algorithms to identify the parameters of spiking neuron models.
# A New Method of Angle Measurement Error Analysis of Rotary Encoders
## 3 Analysis of Angle Measurement Error
### Description of Ideal Condition  
Figure 3 shows the rotary encoder working in ideal condition. \(O_{1}(O_{2})\) represents the geometric center of the grating disk, \(O\) represents the axis of rotation in the XY plane, and \(O_{\rm O}\) represents the axis average line in the XY plane. The grating disk geometric center and axis of rotation are the same point because of no installation eccentricity. The axis of rotation and axis average line are in the same line because of no error motions of the rotating shaft. The shadow rectangle represents the reading head, the circle in dashed lines represents the radial grating, \(Z_{O1}(Z_{O2})\) represents the index line of the grating disk, \(OM\) is the line between axis of rotation and center of the reading head, and \(OC\) represents  
Figure 1: Error motions of axis of rotation.  
Figure 2: Rotation joint of parallel dual-joint coordinate measuring machine.  
the optical radius. When the grating disk works in ideal condition, the optical radius is a constant \(r\). The index line rotates from position \(Z_{O1}\) to position \(Z_{O2}\) when the rotating shaft rotates \(\theta\).
# Black Ice Classification with Hyperspectral Imaging and Deep Learning
## 3 Data Collection  
Figure 2 displays the camera settings with respect to black ice and the position of the black ice. Figure 2B displays two samples, one is a visible black ice sample inside a black box and the other one is a mostly invisible black ice sample spread on the asphalt. The images were taken from distances of 10 m, 20 m, and 30 m in broad daylight [08:00-10:00 a.m.]. The dataset was labeled by MATLAB as shown in Figure 3. In the figure, the ground truth image shows the black ice sample on the road and the concentrated black ice sample in the box: yellow color represents the concentrated black ice and green color represents black ice spread on the road. The figure illustrates two black ice samples: one is on the asphalt and the other one is inside the box. We aim to detect both samples, as it is hard to detect black ice on asphalt. After data acquisition, further processing was done as shown in Figure 4. For the main part of this paper, a 2D-3D CNN model was used with hyperspectral data. It displays a workflow for the whole classification process. Hyperspectral image data for black ice are not present, so we have collected our own dataset at different distances with a Specim HS-CL-30-V10E (400-1000 nm), and the data acquisition was done with Spectral DAQ-200 The whole process for data acquisition can be seen in the figure. Figure 5 shows a visual explanation of Principal Component Analysis (PCA) on hyperspectral image data and Figure 6 displays the model architecture used for classification.  
Figure 2: (**A**) The camera set up. (**B**) The black ice set up at 10 m distance from the camera.  
Figure 4: Overall workflow of the process for the black ice classification method.  
Figure 5: PCA on input hyperspectral image data.  
Figure 3: (**A**) The ground truth image at 10 m, created by MATLAB R2021a software. (**B**) Hyperspectral image at 10 m distance taken from the mentioned camera.  
Figure 6: The architecture of the 2D–3D Convolutional Neural Network.
# GNSS-Assisted Visual Dynamic Localization Method in Unknown Environments
## 2 GNSS-Assisted Visual Dynamic Localization Method Framework
### Initialization
#### 2.1.1 Positional Solution Using Epipolar Geometry Constraints  
Orb feature point extraction and matching were performed for \(I_{1}\), \(I_{2}\), and \(I_{3}\) to obtain matching homonymous points. Taking the \(k\)-th homonymous point as an example, the normalized coordinates of the pixels in \(I_{1}\), \(I_{2}\), and \(I_{3}\) were noted as \(x_{k}^{C_{1}}\), \(x_{k}^{C_{2}}\), and \(x_{k}^{C_{3}}\).  
The following constraints were established based on the pair of epipolar geometry equations, as (1) shows below:  
\[x_{k}^{C_{2}}\text{T}\text{E}_{C_{1}}^{C_{2}}x_{k}^{C_{1}}=0\text{ and }\text{E}_{C_{2}}^{C_{1}}=\text{K}^{\text{T}}\text{F}_{C_{2}}^{C_{1}}=[t_{C_{2 }}^{C_{1}}]_{\times}\text{E}_{C_{2}}^{C_{1}}, \tag{1}\]  
Figure 1: The overall process framework of the visual dynamic positioning method with GNSS assistance.  
Figure 2: Relationship of the three-frame-image position transformation.  
where \(\mathbf{E}_{C_{2}}^{C_{1}}\) represents the essential matrix from \(I_{1}\) to \(I_{2}\), \(\mathbf{F}_{C_{2}}^{C_{1}}\) represents the fundamental matrix from \(I_{1}\) to \(I_{2}\), \(\mathbf{R}_{C_{2}}^{C_{1}}\) represents the rotation matrix from the movement of \(C_{1}\) to \(C_{2}\), and \(t_{C_{2}}^{C_{1}}\) represents the translation vector from the movement of \(C_{1}\) to \(C_{2}\).  
The SVD decomposition was performed on the essential matrix \(\mathbf{E}_{C_{2}}^{C_{1}}\) to obtain the four combinations corresponding to \(\mathbf{R}_{C_{2}}^{C_{1}}\), \(t_{C_{2}}^{C_{1}}\). Usually, any point can be substituted to determine the depth of a point, and when the constraint that the feature point needs to be in front of the camera is utilized, the only correct result of the pose to solve \(\mathbf{R}_{C_{2}}^{C_{1}}\), \(t_{C_{2}}^{C_{1}}\) is selected.
## 5 IEEE 802.15.4 Technology
### The IEEE 802.15.4 Topology Formation Procedure  
The IEEE 802.15.4 defined a mechanism to support a PAN coordinator in channel selection when starting a new PAN, and a procedure, called _association procedure_, which allows other devices to join the PAN. A PAN coordinator wishing to establish a new PAN needs to find a channel free from the interference that would render the channel unsuitable (e.g., in a multi-sink network, a channel may be already occupied by other PANs). The channel selection is performed by the PAN coordinator through the Energy Detection (ED) scan which returns the measure of the peak energy in each channel. It must be noticed that the standard only provides the ED mechanism, and it does not specify the channel-selection logic. The operations accomplished by a device to discover an existing PAN and to join it can be summarised as follows: (i) search for available PANs; (ii) select the PAN to join; (iii) start the association procedure with the PAN coordinator or with another FFD device, which has already joined the PAN. The discovery of available PANs is performed by scanning beacon frames broadcasted by the coordinators. Two different types of scan that can be used in the association phase are proposed:  
1. passive scan: in beacon-enabled networks, the associated devices periodically transmit beacon frames, hence the information on the available PAN can be derived by eavesdropping the wireless channels;
2. active scan: in non-beacon-enabled networks, the beacon frames are not periodically transmitted but shall be explicitly requested by the device by means of beacon request command frame.  
After the scan of the channels, a list of available PANs is used by the device to choose the network to try to connect with. In the standard, no specific procedure to select a PAN is provided and so, this selection among potential parents is open for different implementations. Hence, the device sends an association request frame to the coordinator device by means of which the selected network was discovered. The association phase ends with a successful association response command frame to the requesting device. This procedure basically results in a set of MAC association relationships between devices, named in the following parent-child relationship.
## 2 Structure of the FOG-Based MWD  
The MWD prototype utilizes a FOG-based IMU, as shown in Figure 1. MWD is composed of three-axis FOGs sharing one laser light source, three quartz flexible accelerometers with one A/D converter circuit, a navigation computer, and a mechanical bracket to support the above units [24]. All the components are orthogonally mounted along the lengthwise axis of the MWD so that the diameter can be minimized to satisfy the demands of a borehole environment. The FOGs and accelerometers are arranged in three mutually orthogonal directions, as shown in Figure 2. The three-axis FOGs measure the angular velocity of the carrier, while three-axis accelerometers provide the 3D acceleration measurements of the body. The navigation computer is mainly utilized to collect all sensor data, pre-process the data, and run the navigation algorithm.  
Due to the particularity of the working environment in oil wells, the FOGs-based MWD working manner and characteristics are different from other application fields such as aerospace, aviation, and land navigation. Comprehensive analysis shows that the detection and location of oil wells have the following characteristics:  
1. Long working hours: the FOGs-based MWD system keeps working underground for more than 200 h. Putting the MWD system down into the well and pulling it up to the ground costs too much time and money [25].
2. Strong vibration: FOGs in MWD system hold up to strong shocks due to the obstruction of the underground stone.
3. High rotation speed. FOGs in MWD systems work with a high-speed rotation up to 300 rounds per second.
4. High Temperature. The temperature of the working environment increases with the increase in the drilling depth at a rate of 30 \({}^{\circ}\)C/km.
5. Data loss. The data transmission method of the MWD system is mud pulse. When drilling the oil well, the mud pulse transmits the data collected downhole to the monitoring equipment on the ground. Its transfer rate is low. Its bit error rate is high and the reliability is very low.
6. Eclectic battery-powered. A battery provides power to support the MWD system working underground.  
When drilling the oil well, FOGs' signal in MWD system is non-stationary, since the FOGs can be easily influenced by the increasing temperature, humidity, radiation, and sudden power failure. Occasionally, the data received aboveground appears to be discontinuous. Even a slight variation in the gyroscope stability can generate significant measurement error. Accordingly, it is crucial to identify the dynamic instability of the FOGs in MWD.  
Figure 1: 3D graphic model of the MWD.  
Figure 2: Sensors of IMU.
## 1 Introduction  
The dual-wavelength lasers in the range of 3\(\sim\)5 \(\upmu\)m have versatile applications such as military confrontation and laser radar [1, 2]. A multiple optical parametric oscillator (MOPO) is an effective way to obtain a multi-wavelength tunable laser in the same spectral region [3, 4, 5, 6, 7]. However, due to the multiple frequencies and complex energy conversion path in the process of synchronous oscillation, which produces the gain competition of the multiple parametric outputs easily, this can result in power distribution imbalance, beam quality deterioration, spectrum broadening, and so on [8, 9, 10, 11, 12, 13, 14]. In 2015, [PERSON] et al. demonstrated a straight cavity MOPO using an aperiodically poled MgO:LN(MgO:APLN). The 1.57 \(\upmu\)m and 3.84 \(\upmu\)m cross period resonant outputs were realized through a single polarized crystal with the output power of 2.4 [EMAIL_ADDRESS] \(\upmu\)m and 1.31 [EMAIL_ADDRESS] \(\upmu\)m [15, 16]. In 2020, the college of Advanced Interdisciplinary Studies from National University of Defense Technology used a traditional Raman fiber oscillator as a dual-wavelength pump source, the output power of 2 [EMAIL_ADDRESS] \(\upmu\)m and 4.98 [EMAIL_ADDRESS] \(\upmu\)m were achieved [17, 18].  
In this study, an external-cavity pumped high repetition rate MOPO based on a single MgO:APLN crystal is reported. The gain competition in the process of MOPO under different repetition rates and transmittance is experimentally demonstrated. Moreover, the output power of dual-wavelength mid-infrared is maximized by selecting the repetition rate and transmittance @1.57 \(\upmu\)m. When the pump power of the 1.064 \(\upmu\)m laser was 34.5-W, the repetition rate was 63 kHz, the maximum output powers of 2.79 [EMAIL_ADDRESS] \(\upmu\)m and 4.92 [EMAIL_ADDRESS] \(\upmu\)m were obtained with the transmittance T = 60%@1.57 \(\upmu\)m, corresponding to optical-optical conversion efficiencies of 8.1% and 14.3% and quantum efficiencies of 32.24% and 27.71%, respectively.
# Network Defense Strategy Selection with Reinforcement Learning and Pareto Optimization
## 3 Game Model on Pareto Optima and Q-learning
### Goal Definition  
This is a zero-sum game, thus, the goals for the attacker and defender are the inverse of each other, meaning that if the defender aims to maximize a score, the attacker aims to minimize it. The game score at a given time \(t\), where the game is in state \(s_{t}\in S\), is defined as:  
\[\overline{f}_{t}=[f_{t}^{(1)},f_{t}^{(2)},f_{t}^{(3)}\;] \tag{18}\]  
-\(f^{(1)}=\) score for the network availability;  
-\(f^{(2)}=\) score for the node security;  
-\(f^{(3)}=\) score for the defense strategy cost effectiveness  
Figure 5: Game state transition and reward calculation.  
The score for network availability is defined as the sum of the value of communicating services.  
\[f^{(1)}=\sum_{i=1}^{|edges|}\sum_{j=1}^{|services|}\Big{[}b_{x_{i},j}\
eq 0\ \bigwedge\ b_{y_{i},j}\
eq 0 \Big{]}\cdot\big{(}\beta_{x_{i},j}+\beta_{y_{i},j}\big{)}\ |\ (x_{i},x_{i})\in edges \tag{19}\]  
Keeping the nodes secure means keeping them virus-free and making sure the attacker did not steal the data. Let \(n_{i}\) be the state of node in the network, defined in Equation (13), such that \(n_{i}=\ <b_{i},v_{i},w_{i}>\).  
\[f^{(2)}=\sum_{i=1}^{|nodes|}v_{i}\cdot[v_{i}\
eq 0]+\omega_{i}\cdot[w_{i}\
eq 0] \tag{20}\]  
An effective defense strategy means that the defender expended as few resource points as possible while defending the network. The score for the defense strategy cost effectiveness is the difference in resource points between the defender and attacker.  
\[f^{(3)}=p_{d}-\ p_{a} \tag{21}\]  
The Reward function maps a combination of the specific state \(s_{t}\) and a player's action to a reward. This mapping of a state-action pair to a reward is the \(r_{t}\) used in the Q-learning algorithm. We want to incentivize the player's for keeping the network in their desired state as long as possible and for the efficient use of their resource points. This means we can use the cumulative state scores of \(f^{(1)}\) and \(f^{(2)}\) and the final score of \(f^{(3)}\). The reward function for a single transition is then:  
\[R(s_{t},\ a,d)=\ r_{a,d}=\overline{f}_{t+1}-[0,\ 0,\ f_{t}^{(3)}] \tag{22}\]  
An example of a reward calculation is visible in Figure 5.
## 3 Results and Discussion
### Thin Layer Chromatography (TLC) Analysis  
GABA production in the co-fermented milk with various MSG concentrations was monitored by TLC (Figure 3). The conversion of MSG into GABA was greatly dependent upon the MSG concentration, which is expected because MSG is a GABA precursor. During the first fermentation, MSG was consumed by _B. subtilis_ HA, resulting in the production of mucilage. During the second fermentation by _L. plantarum_ EJ2014, the co-fermented milk without MSG showed low production of GABA. However, a higher content of GABA was obtained when MSG was added to the milk. A concentration of 1.5% MSG was optimal for the fortification of GABA in the co-fermented milk as higher concentrations resulted in MSG residues in the co-fermented product, which would be undesirable.
###### Abstract  
We propose a modulation-based adaptive power allocation (MBAPA) technique for nonlinear distortion mitigation in intermediate frequency over fiber (IFoF) systems. The technique allocates the spectral power of each IF band according to the required signal-to-noise power ratio (SNR) of the modulation format. To demonstrate the performance of the technique, transmission experiments were performed in 10 km and 20 km with 24-IF bands using OFDM signals. The feasibility of the proposed MBAPA technique was experimentally verified by reducing inter-modulation distortion (IMD) power and enhancing channel linearity.  
intermodulation distortion; multi-IFoF transmission; mobile fronthaul network; power allocation +
Footnote †: journal: _Photonics_