esa-sceva/satcom-chunk-collection huggingface dataset:
# Resolving Gas Bubbles Ascending in Liquid Metal from Low-SNR Neutron Radiography Images
## 5 Experimental Validation
### Error Analysis
One can see that higher body motion velocity indeed corresponds to greater errors, which decline over time as the body decelerates. Note that, between Figure 32a,b, it is evident that the intrinsic error signal due to the global filter quickly obscures the error contribution due to motion blur. However, it also seems that the global filter is affected by the motion blur more than the local filter--as seen in (b), the \(S_{\Delta}\) rather quickly relaxes to a quasi-stationary value at about \(N\sim 250\), while in (a) the error decay persists until \(N\sim 500\). Similar dynamics can be observed in (c) and (d) as well. Figure 31: **(a)** Mean and (b) maximum \(\delta S\) values for the cases with a moving reference body. The results for the global and local filtering are represented with matte and glossy bars, respectively. Figure 32: An illustration of the effect of body motion on the shape detection errors: examples from two image sequences. Neutron transmission along (**a,b**) the shorter and (**c,d**) longer axis of the reference body. (**a,c**) show the \(S_{\Delta}\) dynamics over consecutive frames for the global filter and (**b,d**) show the results for the local filter. (**a,b**) show the first 1000 frames for visual clarity. Note that 3000 images were analyzed in total for both shown cases. The error time series show here are obtained from raw data by removing the outliers above and below the Gaussian TV-filtered (regularization parameter 1) \(q=0.9\) QSEs (3-rd order splines, \(\left\lvert 90\%\cdot N\right\rvert\) spline knots) and applying the Gaussian TV filter (regularization parameter 2) to the remaining data points. The velocity curves are as in Figure 27a. We assess the reference void detection failure rates in the image sequences used for the code validation--the results are summarized in Table 2. There are no detection failures for static body imaging and for a moving body with neutron flux transmission along the shorter axis, and very small percentages are found for the longer axis case. The cases with synthetic artefacts have significantly higher failure rates for the global filter. However, all cases exhibit values < 4%, which indicates acceptable performance. Note that the peak errors are within 14% for tests without synthetic artefacts, and within 20% for instances with artefacts added to images.
# Dynamic Stability of an Electric Monowheel System Using LQG-Based Adaptive Control
###### Abstract  
This paper presents the simulation and calculation-based aspect of constructing a dynamically stable, self-balancing electric monowheel from first principles. It further goes on to formulate a reference model-based adaptive control structure in order to maintain balance as well as the desired output. First, a mathematical model of the nonlinear system analyzes the vehicle dynamics, followed by an appropriate linearization technique. Suitable parameters for real-time vehicle design are calculated based on specific constraints followed by a proper motor selection. Various control methods are tested and implemented on the state-space model of this system. Initially, classical pole placement control is carried out in MATLAB to observe the responses. The LQR control method is also implemented in MATLAB and Simulink, demonstrating the dynamic stability and self-balancing system property. Subsequently, the system considers an extensive range of rider masses and external disturbances by introducing white noise. The parameter estimation of rider position has been implemented using Kalman Filter estimation, followed by developing an LQG controller for the system, in order to mitigate the disturbances caused by factors such as wind. A comparison between LQR and LQG controllers has been conducted. Finally, a reference model-assisted adaptive control structure has been established for the system to account for sudden parameter changes such as rider mass. A reference model stabilizer has been established for the same purpose, and all results have been obtained by running simulations on MATLAB Simulink.  
monowheel system; inverted pendulum cart system; LQR control; LQG control; Kalman filter estimation; reference model-assisted control; linearization; self-balancing vehicle +
Footnote †: journal: Applied Sciences
## 6 Evaluation and Performance
### Experimental Results and Comparison with Other Schemes  
The performance of the scheme was compared with other popular solutions, such as lattice-based blind signatures or partially blind signatures. The comparison highlights the advantages of the proposed scheme, especially in the context of resource-constrained applications.  
The results describe how an efficient implementation of the proposed scheme works and show that it has a response time suitable for real-time applications, as shown in Table 3. In addition, memory consumption is minimized by simple arithmetic operations.  
To further validate the practical performance of the proposed TBS scheme, we present a comparative analysis with existing blind signature schemes, using either experimental results from our testbed (ARM Cortex-M4) or performance metrics reported in the literature.  
As shown in Table 4, the proposed TBS scheme provides one of the lowest signature sizes and fastest signing times, especially in resource-constrained environments. The flexibility of the parameter \(T\) does not introduce significant overhead, making the scheme well-suited for real-time and embedded applications. Data for other schemes were extracted from published sources and normalized to comparable cryptographic strength where possible. All implementations assume classical security parameters.
# Temperature Control Unit--Modeling and Implementation of a Particle Filter on a Microcontroller
## 4 Research Objects--The Mathematical Models
### Real Object--Estimation without Using a Physical Model  
In this model, the state-space equations are based not only on the dynamics of the system model under consideration but on the speed of temperature changes in the previous step. This approach allows for effective state estimation without knowing the exact dynamics of the process.  
The discrete-time state variable \(x_{1}^{(k)}\) is the estimated temperature and accounts for the rate of change in the output of the previous step, as shown below:  
\[x_{1}^{(k+1)}=x_{1}^{(k)}+\Delta y^{(k)}=x_{1}^{(k)}+\left(x_{1}^{(k)}-x_{1}^{ (k-1)}\right)=2x_{1}^{(k)}-x_{2}^{(k)}. \tag{11}\]  
The variable \(x_{2}^{(k)}\) is used to store the history, representing the state variable from the previous step. Therefore, noise with zero error variance is added to it to obtain \(x_{2}^{(k+1)}-x_{1}^{(k)}\)The matrix expression of the state-space equations, taking into account the process noises and based on (11), is given by  
\[\left\{\begin{array}{l}\begin{bmatrix}x_{1}^{(k+1)}\\ x_{2}^{(k+1)}\end{bmatrix}=\begin{bmatrix}2&-1\\ 1&0\end{bmatrix}\begin{bmatrix}x_{1}^{(k)}\\ x_{2}^{(k)}\end{bmatrix}+\begin{bmatrix}v_{1}^{(k)}\\ v_{2}^{(k)}\end{bmatrix}\\ y^{(k)}=\begin{bmatrix}1&0\end{bmatrix}\mathbf{x}^{(k)}+n^{(k)}\end{array} \right. \tag{12}\]  
It should be noted that the proposed system is autonomous. The change of state is caused only by the presence of system noise, and the parameters to be adjusted are the variances of the noises. The best estimation (based on the graphs) was observed for the parameters \(\sigma_{v1}^{2}=10^{-6}\), \(\sigma_{v2}^{2}=0\), and \(\sigma_{n}^{2}=0.0225\).  
This approach can be associated with dynamic linearization, which is used, among others, in the ADRC control algorithm [38]. The result of this linearization is a model in the form of a double integrator. Therefore, the discrete-time characteristic polynomial for (12) is \(M(z)=(z-1)^{2}\).
## 2 Air Pressure Compensation Based on IE-FF Coupling Model
### IE-FF Coupling Modeling
The air pressure fluctuation outside the car has a direct influence on the air pressure inside the car. Therefore, the flow field inside and outside the car has a certain degree of coupling. To obtain a more accurate law of air pressure change in the car, it is necessary to perform a coupling calculation on the IE-FF of the car, and to establish an IE-FF coupling variation model. On the basis of obtaining the air pressure fluctuations outside the car, the air pressure fluctuations inside the car can be captured by the  
The air in the elevator car is selected as the control body, and the air in the control body is the ideal gas. Consider the elevator car as a cavity with a certain volume, and the air in the cavity can be compressed. The mass conservation law is:  
\[\frac{dm}{dt}=q_{mi}-q_{mo}\pm q_{ml}, \tag{14}\]  
where \(q_{ml}\) is the mass flow rate exchanged from the outside into the inside through the ventilation system; \(q_{mo}\) is the mass flow rate exchanged from the inside into the outside through the ventilation system; \(q_{ml}\) is the mass flow rate of air leakage through the gap. Ignoring the viscosity of the air in the car and assuming that the air in the car is an ideal gas, Equation (14) can be rewritten as:  
\[\frac{MV_{i}}{RT_{i}}.\frac{dp_{i}}{dt}=q_{mi}-q_{mo}\pm q_{ml}, \tag{15}\]  
where \(T_{i}\), \(p_{i}\) and \(V_{i}\) are the temperature, pressure and volume of the air inside the car, respectively; \(M\) is the gas molar mass; and \(R\) is the molar gas constant. Equation (15) reveals the relationship between the air pressure and air flow in the car, which is the basic equation for solving the law of air pressure change in the car. However, the air flow exchanged cannot be solved by accurate expressions. Therefore, in the actual coupling solution, the calculation of the internal and external flow field systems of the car are done by fluid simulation calculation software, ANSYS/Fluent 2020 [31]. In order to simplify the calculation, the data transfer method adopts one-way transfer. That is, the external flow field is solved first, and the initial boundary conditions are transferred to the internal flow field. When the elevator is running in the hoistway, the air pressure in the car will continue to change over time, which is a typical transient analysis problem. The calculation process of transient coupling mainly includes the following steps:  
1. Initialization of boundary conditions: initialize the boundary conditions of the IE-FF according to the initial data of the system;
2. Iterative calculation: According to the initial value, the IE-FF are solved separately, and data exchange is performed; and
3. End calculation: After the iteration termination condition is met, stop the calculation and obtain the calculation result. The coupled iterative calculation process is shown in Figure 2, where \(|\Delta Q|\) represents the residual of each iteration. Figure 1: Coupling characteristic of the IE-FF of the car (rotated 90\({}^{\circ}\)).
# A Feasibility Study for a Nonlinear Guided Wave Mixing Technique
## 4 Results  
### Experimental Result of Guided Wave Mixing Signal Generation  
A mixing signal appears at S1 mode, and the forward direction in the theoretical analysis, but not in the experiment. This result could be interpreted based on the phase velocity. The wedge angle can be determined from the phase velocity. For S1 and A1 modes, the phase velocities of the mixing wave components at 5.77 MHz are (4.7282 and 3.3465) mm/us, respectively. The two primary wave phase velocities are (5.8072 and 4.3877) mm/us, respectively. The S1 mode mixing signal phase velocity is more akin to the primary wave phase velocities than that of the A1 mode. This means that the wedge angle is very similar. In other words, the sensor and wedge receive both the mixing signal and primary waves in the mixing generation experiment with S1 mode. Its situation interrupts frequency analysis. In contrast, the sensor and wedge receive only the mixing signal relatively in the mixing generation experiment with A1 mode, compared to that with S1 mode. Therefore, A1 mode is better for detecting a mixing wave. In mixing signal generation with A1 mode and the forward direction, only a mixing component is detected.
# Prediction of Air Quality Combining Wavelet Transform, DCCA Correlation Analysis and LSTM Model
## 3 Results
### DCCA Coefficients on Each Scale and Their Relationship
#### 3.2.1 AQI Correlation with Temperature  
As depicted by the yellow line in Figure 3, the inter-relationship number curve between AQI and temperature was located at [\(-0.2\), \(0.2\)] near the window size n at [\(1000\), \(2200\)], indicating that, at this time, there was a positive correlation between the AQI and the temperature near the window size \(n<1000\), and a negative correlation between the AQI and the temperature near \(n>2200\), and the inter-relation number was at \(n=12,500\) near the minimum value. Overall, there appeared to be a negative link between the AQI and the temperature as the window length increased, i.e., the higher the temperature, the better the air quality.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Features** & **SNR** & **RMSE** \\ \hline Temperature & 30.087368 & 0.802346 \\ Humidity & 29.952903 & 1.256670 \\ Pressure & 62.939602 & 0.720441 \\ Wind & 10.224503 & 0.452248 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quality indicator of time series following noise reduction by means of wavelet transform.  
Figure 3: The inter-relationship curves of AQI with temperature, humidity, air pressure, and wind speed are color-coded. Blue represents the coefficient between AQI and wind speed, red represents the coefficient between AQI and air pressure, green represents the coefficient between AQI and humidity, and yellow represents the coefficient between AQI and temperature.
# An Energy Efficient MAC Protocol for Wireless Passive Sensor Networks
## 3 IS-MAC Protocol
### Parameters Used in the Improved Algorithm  
1. Current contention window \(CW\): The size of contention window is not fixed, and it can be dynamically changed within a specific range. This parameter is used to record the value of current contention window.
2. Minimum of contention window \(CW_{\text{min}}\): The lower limit of the range of the current contention window is fixed from the beginning.
3. Maximum of contention window \(CW_{\text{max}}\): The upper limit of the range of the current contention window, \(CW_{\text{max}}>CW_{\text{min}}>0\), is fixed from the beginning.
4. The initial value of contention window \(CW_{\text{init}}\): \(CW_{\text{init}}=(CW_{\text{min}}+CW_{\text{max}})/2\), values of \(CW_{\text{max}}\) and \(CW_{\text{min}}\) will be set in accordance with the network environment.
5. Counter for successful competition \(SC\): This value is used to record the number of consecutive successful transmission and its initial value is 0. The counter plus one as long as the node sends out data successfully. On the contrary, the counter is reset the moment of transmission fails. The counter has an upper limit determined by network load.  
Threshold of counter for successful competition \(SC_{\text{lim}}\): This is the maximum of \(SC\). The current window will change on the basis of the network load when \(SC\) reaches its limit. It is fixed from the beginning.
7. Counter for failed competition \(FC\): The purpose of this counter is to protect nodes that fail to compete and to prevent nodes from unsuccessfully accessing channel for a long time because of its increasing contention window. This value is used to record the number of consecutive failed transmission and its initial value is 0. The value of this counter increases by one while collision happens. Meanwhile, the counter is reset after successfully sending out data.
8. Threshold of counter for failed competition \(FC_{\text{lim}}\): This is the upper limit of \(FC\). The current window will change in line with network load when \(FC\) reaches its limit. It is fixed from the beginning. The values of those parameters used in our simulation are shown in Table 1.
## 5 Simulation and Experimental Results
### Simulation Results: Performance Evaluation of Data Fusion Schemes  
We first evaluate the delays for the proposed data fusion schemes in Section 3. The simulations are performed on Intel(R) Core(TM) Xeon 2.4 GHz using MATLAB R2014a. We calculate average latency of performing Algorithms 1 and 2, and trajectory data fusion in (26) and (27) to synthesize randomly generated 10 data points for 1000 iterations. Specifically, to satisfy conditions in (1) and (2), the confidence for existence, \(m(\mathrm{E})\), is modeled by normal distribution \(\mathcal{N}(0.5,0.03)\), and the rest are divided into equally for \"nonexistence\" and \"uncertainty\", i.e., \(m(\mathrm{N})=m(\mathrm{U})=\frac{1-m(\mathrm{E})}{2}\). We consider 16 classes, and the corresponding confidences are also generated according to \(\mathcal{N}(0.5,0.03)\). As a representative, velocity is mainly considered for trajectory type data, which follows \(\mathcal{N}(70,0.01)\)[74]. The results are presented in Table 1.  
Figure 8: Safety area and collision determination.  
Obviously, the data fusion for object type data takes larger calculation time compared to that of trajectory type. Nevertheless, the latency is only about 1 ms for entire data fusion processes, which demonstrates that the proposed data fusion scheme can operate in real-time.  
Next, we compute the performance of the proposed scheme in terms of the false negative rate (FNR) defined by  
\[\text{FNR}=\frac{\text{Number of false negatives}}{\text{Number of false negatives + Number of true positives}}\]  
where \"_true positive_\" and \"_false negative_\" are existence and nonexistence decisions conditional on the existence of object, respectively. We assume that there is an object and 10 vehicles. Each vehicle has one sensor which is in either normal or defective state. Depending on the state of the sensor, vehicles allocate the confidence in existence or nonexistence according to normal distribution with \(\mathcal{N}(0.7,0.09)\). The rest are divided into other elements uniformly to satisfy the condition in (2), e.g., a vehicle \(i\) with normal sensor can have \(M^{i}_{k}=\{0.8,0.1,0.1\}\).  
In Figure 9, we plot the average FNR values for 10,000 iterations by changing the number of normal sensors. We compare the proposed scheme with classic DS, the weighted DS based on Jousselme distance [25], and the modified DS scheme presented in [26]. As shown in Figure 9, in all cases, the proposed schemes outperform the other data fusion methods in terms of reducing FNR. Specifically, for five normal sensors case, our method can reduce FNR about 18% compared to all the baselines. In addition, the proposed scheme achieves FNR reductions over classic DS (64.8%), Ref. [25] (50.9%), and [26] (65.5%) for seven normal sensors case. This result shows that we can improve the driver's safety, even if there are high conflicts among confidences. In the following, we experiment how the proposed collision warning scheme can prevent from predicted accidents in real-world.
# A Combinational Buffer Management Scheme in Mobile Opportunistic Network
## 4 Simulation
### Simulation Results and Discussion
#### 4.2.2 Analysis of Message Migration  
It can clearly be seen from Figure 5 that the amount of message migration increases as the time increases in CBM policy. In the experiment, we calculate the number of the migration messages \(n_{r(q)}\), that is, we migrate the message \(q\) with the smallest \(U_{q}\) in the node when the message migration condition is satisfied. The possibility of a message \(q\) being migrated or deleted is large in the node if the \(U_{q}\) is small. It is important to note that this policy applies to situations where the size of the buffer is limited. When the buffer is large enough, the node has enough space to store the message and the message does not migrate, that is, the amount of migration is zero.
## 3 Results
### ERD/S Spectrogram Analysis  
Figure 7 shows the results of the ERD/S spectrogram in the C3 area of the brain. For all stimulus types (warmth (W), vibration (V), pressure (P), W + V, V+ P, and W+ V + P), all subjects perceived all tactile parameters presented in all trials; there was a decrease in brain signal power (dB) in the \(\alpha\) band (8-12 Hz) and \(\beta\) band (12-31 Hz) about 0.5 s before and after stimulation, i.e., ERD, followed by an increase in signal strength, i.e., ERS. This is a common phenomenon in the human perception of tactile sensations [17]. ERD was followed by ERS within 0.5 s after stimulus in the \(\beta\) band (12-31 Hz) for the single stimuli.  
ERS occurred after 0.5 s in all cases except P. In the case of P, the duration of ERD was 0.2 s longer. Combined stimuli also showed ERS after 0.5 s, but the duration of the ERD  
Figure 6: Responses of multimodal actuator. (**a**) Temperature waveform. (**b**) Rising time of temperature. (**c**) Vibration frequencies from accelerometer at 200 Hz. (**d**) Vibration profile with frequency between 25 Hz and 250 Hz. (**e**) Pressure waveform. (**f**) Pressure intensities according to level.  
Figure 7: Results of ERD/S spectrum: (**a**) warmth, (**b**) vibration, (**c**) pressure, (**d**) warmth + vibration, (**e**) vibration + pressure, (**f**) warmth + vibration + pressure.  
was 0.4 s longer for W + V + P. For the \(\alpha\)-band (8-12 Hz), the ERD occurred after 0.5 s in all conditions. The ERD was more pronounced in experiments with V stimuli. The peak value of the ERD increased when different types of stimuli were presented together and was largest when all three stimuli were displayed simultaneously. For maximum and minimum peak analysis, the ERD and ERS of warmth (W) had peak values of \(-\)3.27 dB and 2.45 dB, respectively. The ERD and ERS of vibration (V) had peak values of \(-\)2.31 dB and 1.83 dB, respectively. Pressure (P) had the smallest peak values of ERD and ERS at \(-\)2.09 dB and 1.64 dB, respectively.
## 2 Problem Description and System Models
### Constrained Markov Decision Process  
A CMDP is a mathematical framework used to model decision-making problems where an agent aims to maximize a cumulative reward while satisfying certain constraints. It extends the standard Markov Decision Process (MDP) by incorporating constraints that must be satisfied throughout the decision-making process. In the context of reactive power optimization, CMDP is suitable for addressing the voltage security control problem, which involves optimizing control actions, while ensuring safe operation of the distribution network.  
The reactive power and voltage security control problem is formulated as a CMDP, where the device scheduling center is regarded as an agent. We define the CMDP as a tuple \(<\)\(S\), \(A\), \(P\), \(R\), \(C\)\(>\). As illustrated in Figure 2, the agent interacts with the distribution network environment within the established CMDP framework. The distribution network, acting as the environment, receives actions from the agent and updates its state based on system dynamics and constraint conditions. The new state is then fed back to the agent, and the environment generates both reward and cost signals based on the agent's action and the resulting state.