esa-sceva/satcom-chunk-collection huggingface dataset:
# SOC Estimation with an Adaptive Unscented Kalman Filter Based on Model Parameter Optimization
## 1 Introduction
For this reason, some people have used the extended Kalman filter (EKF) to estimate the battery SOC. Although some achievements have been made, there is a linearization error in actual use, and the Jacobian matrix is difficult to estimate. In recent years, a novel derivative Kalman filter algorithm, the unscented Kalman filter (UKF), has emerged to realize nonlinear filtering. The UKF directly uses nonlinear unscented transform techniques without the need for Taylor approximations of nonlinear equations. This process allows the mean and variance of the state of the nonlinear system to propagate directly according to the nonlinear mapping, thereby achieving a higher estimation accuracy. In the traditional UKF algorithm [15; 16; 17; 18; 19], the covariance is a constant and cannot satisfy the real-time dynamic characteristics of the noise, which has a certain impact on the accuracy [20; 21]. To eliminate this effect, the traditional UKF algorithm is improved by updating the covariance in real-time, which thus improves the accuracy of the UKF in this paper. This type of algorithm is called the adaptive unscented Kalman filter (AUKF) algorithm. On this basis, a reasonable optimization of the model parameters is studied, and a reasonable optimization method is proposed to further improve the accuracy of SOC estimation. The structure of this paper is arranged as follows. In Section 1, the most commonly used methods for SOC estimation are introduced, and the proposed method of this paper is briefly described. In Section 2, the battery model is established, the model parameters are identified, and the model accuracy is verified. In Section 3, the AUKF based on a second-order resistance capacitance (RC) equivalent circuit model is presented. In Section 4, the basis and method of the reasonable optimization of the model parameters are put forward. In Section 5, the accuracy and initial error convergence of the AUKF before and after model parameter optimization are compared by simulation experiments,and the advantages of the proposed method are demonstrated. Finally, in Section 6, the research results of this paper are summarized, and future research directions are provided.
###### Abstract  
Data on the distribution of the free flow speed (FFS) of cars are used to solve a wide range of tasks in the field of road transport, starting from road design and ending with the development of traffic modeling and simulation programs. The purpose of this study was to obtain the distribution of vehicle speeds on typical sections of the city road network, characterized by the presence of one-way traffic. The data were obtained by field observations using a portable radar. As a result, statistical characteristics and speed distribution laws for four sections of streets in the city of Kharkiv were analyzed. It was shown that the characteristics of FFS distributions differ depending on the functional class of the streets. Average FFS values on main street segments were on average 19 km/h higher. The one-way traffic has less impact on the FFS distribution, especially for arterial streets. The characteristics of FFS distributions differ depending on the type and functional class of streets; they can be described with sufficient accuracy by typical distribution laws, such as Normal, Log-normal, Gamma, and Chi-square. The results of this study can be useful for traffic modeling problems.  
road traffic; speed distribution; free-flow speed; distribution laws of FFS; speed measurement; one-way traffic when choosing speed +
Footnote †: journal:  
traffic modeling, the FFS parameter has wider applications, for example, in the design of road infrastructure and speed limit decisions, and it can also be considered a key factor in road safety [4].  
The main area where the results of the FFS study can be used is traffic modeling, including the construction of computer simulation models using specialized software products, such as PTV VisSim. The need for road traffic modeling may be particularly high in Ukraine in the future, as post-war urban reconstruction will require infrastructure solutions, including reconstruction of the road network.  
Since some simulation software products use a specific driver behavior model, more reliable results will be shown by models built on FFS data collected in that particular region. Such data can take into account the specific national mentality of the driver when driving and include the choice of speed. Therefore, it is relevant to investigate the modes of automobile traffic in real conditions of different cities and regions of the world.  
Conducting research in Ukraine has another obvious advantage, which is the very small use of automatic speed control on the roads. It is obvious that in conditions of speed control the driver will not be free to choose the speed and, therefore, it is impossible to study the influence of road conditions on FFS.  
Of particular interest for traffic modeling is the study of the statistical characteristics of FFS distributions, as well as finding the parameters of theoretical distribution laws. In this study, the impact of one-way traffic was analyzed in analyzing the FFS distributions for different sections of urban streets, as this factor has not received much attention in the scientific literature.
## 3 Results and Discussion
### Emulsion Stability  
The principle of emulsifying performance is to emulsify crude oil into an O/W emulsion and displace crude oil from rock fractures, which is used as an important reference standard for the application of surfactants in oilfields. The adsorption of surfactants at the interface reduces the interfacial free energy, forming a layer with a different composition and a much higher solute concentration than the inner part of the solution. The interfacial layer formed by substance enrichment determines the stability of the emulsion.  
The Table 2 shows that, with the increase in temperature, the water separation time of the emulsion is shortened and the emulsifying property weakened. With the increase in  
Figure 8: Contact angle images of 0.2 mmol/L DCB (**a–d**) and DSB (**A–D**) solutions on paraffin surface at different times: (**a**) 0 min, 75.60\({}^{\circ}\); (**b**) 2 min, 50.33\({}^{\circ}\); (**c**) 10 min, 32.78\({}^{\circ}\); (**d**) 20 min, 28.36\({}^{\circ}\) for DCB and (**A**) 0 min, 95.60\({}^{\circ}\); (**b**) 2 min, 70.33\({}^{\circ}\); (**c**) 10 min, 42.78\({}^{\circ}\); (**D**) 20 min, 35.26\({}^{\circ}\) for DSB.  
Figure 7: Variation of the contact angle with different surfactant concentrations on the paraffin surface at 25 \({}^{\circ}\)C for DCB (**a**) and DSB (**b**).  
temperature, the viscosity of the system decreases and the touching rate of water droplets increases, which leads to a decrease in system stability and the shortening of demulsification time. It can also be observed that, with the increase in surfactant solution concentration, the water separation time of the emulsion showed a trend of first increasing and then decreasing. When the concentration was 0.2 g/L, DCB and DSB took the longest time to separate 10 mL water: 401 s and 428 s, respectively. When the solution concentration was low, the molecules were loosely arranged in the interfacial film and the emulsion formed was unstable. With the increase in concentration, the molecules were closely arranged at the interface and the strength of the interface film was high, which hindered the aggregation of liquid beads and increased the stability of the emulsion. However, as the surfactant concentration continued to increase, the solution tended to be saturated, and the excess surfactant molecules aggregated with each other, resulting in the flocculation of the oil phase dispersed in the system, and the demulsification time became shorter.
## 1 Introduction
However, there is still the problem of slow measurement. Thus, CMOS PDSH circuits improve some problems of PDSH circuits based on OTA chips but also have disadvantages, including inconvenient construction and higher cost, in addition to the above. In summary, CMOS PDSH circuits have drawbacks such as slow measurement speed, large pedestal voltage, and overshoot voltage, as well as a long production cycle. Although the OTA-based PDSH circuit can decrease the pedestal voltage and the overshoot, the voltage step phenomenon results in significant measurement error. Therefore, neither the CMOS-based PDSH nor the OTA-based PDSH circuit can meet ns-scale pulse peak detection. In order to accurately measure the peak value of nanosecond pulse signals, this study undertook a comprehensive theoretical modeling of the PDSH circuit. The accurate modeling aims to identify the primary factors that influence the measurement speed of the PDSH circuit. Meanwhile, in order to correct the voltage step error in PDSH circuit measurements and improve measurement accuracy, this study also proposes an equivalent circuit modeling approach for the voltage step process in the PDSH circuit. This equivalent circuit predicts the error due to the parasitic parameters. Finally, the study corrects the predicted error. With these three steps, i.e., modeling, prediction, and results correction, the proposed method can meet both the time requirements and voltage step requirements of ns-scale pulse peak detection. Figure 1: Basic PDSH circuit ([PERSON] et al., 2020 [15]). The rest of this paper is organized as follows. Section 2 investigates the PDSH circuit based on OTA, and the mathematical model is built to describe the main factors affecting PDSH measurement speed. Meanwhile, Section 2 also presents the equivalent circuit model. The simulations and experiments are carried out to verify the mathematical and equivalent circuit models' correctness in Section 3. Finally, Section 4 concludes this paper.
## 3 Gaussian Processes for Mobile Networks
### GP Mean Function
#### 3.1.1 Simulation-Based Mean  
Knowledge about cell tower locations and antenna characteristics allows for the use of a detailed ray-tracing simulation as part of the mean function. The simulation is performed using the MATLAB 2023a Antenna Toolbox and a detailed 3D-OpenStreetMap of buildings in the mapping area. Cell tower locations and antenna configurations are included based on the preliminary setup of the 5G network at the Skandinavienkai of the Port of Lubeck, Germany. The point-cloud-based elevation information that was already used for the planning of the measurement flights described in Section 2 was translated into a 3D-OpenStreetMap using ArcGIS Pro by Esri (Redlands, CA, USA). The process and the resulting simulation for one example cell are visualized in Figure 4.  
The ray-tracing simulation is computed for one individual cell on a dense spatial grid, then interpolated linearly using nearest neighbors for use as a continuous mean function:  
\[m_{\text{SIM}}(x)=m_{0}+P_{\text{SIM}}(x), \tag{4}\]  
where \(m_{0}\) is an offset parameter used to tune the magnitude of the simulation to the measurements. It is learned as a hyperparameter based on training data.
## 8 Challenges and Recommendation for Further Research  
One of the major challenges with IoT device is its operation in different environmental conditions. Since, the IoT device is exposed to the environment; it should perform well under high/low temperatures, exposure to electromagnetic interference (EMI), etc., In addition, device-supported communication protocols and efficient and energy harvesting techniques should be used. The device should be reliable and secure.  
Since the communication layer uses internet for transferring data, the internet connection must be secure as attackers can manipulate the sensor data. The data collected from IoT devices should be accessible only to the license holders, as it contains customers' private information. IoT devices possess limitation in storage and computation power.  
Suitable protocols must be setup by such as \"trust mechanism\" for detecting attacks and stealing of power from the grid.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Future Developments** & **Role Expected—Data Science, AI and Statistical Tools** \\ \hline \multirow{3}{*}{Self-learning mode} & For enhancement of grid intelligence, the self-learning algorithms update the system \\  & configuration after the occurrence of each event. These algorithms must be supplemented \\  & with availability of data. \\ \hline Automation of grid & AI approaches can provide better handling and management of distributed resources. \\ \hline \multirow{2}{*}{Self-healing mode} & Real-time detection of data from sensors and controllers for isolating the faulty circuit from \\  & the healthy circuit. Online condition monitoring of the grid. \\ \hline \multirow{2}{*}{Cyber security} & Adaptive security strategies exploring machine learning, knowledge detection and \\  & information theory methods. \\ \hline Power quality enhancement & AI approaches to reduce power losses in the distribution side. \\ \hline \hline \end{tabular}
\end{table}
Table 7: Future developments in smart grids—leveraging data science and statistical tools.
## 3 Proposed System Architecture
### Fuzzy Logic Controller (FLC)
Prominent Mamdani defuzzification methods, such as the smallest of maxima (SoM), largest of maxima (LoM), and mean of maxima (MoM), stem from variations in the max criterion. These methods determine the smallest, largest, or mean output value on the basis of inputs whose membership values reach their maximum. Mamdani systems are commonly used in expert system applications because they benefit from their intuitive and easily understandable rule bases, which are often described on the basis of insights from human experts. In contrast, the Sugeno FIS uses a weighted average approach instead of the complex iterative process used in Mamdani systems. Unlike the Mamdani FIS, the Sugeno FIS does not include an output MF. Instead, it uses singleton output MFs that are either constant or linear functions of input values. This makes the Sugeno FIS less computationally complex than the Mamdani FIS. However,  
Figure 3: Triangular and trapezoidal MFs, with red circles indicating key transition points a, b, c, and d, to highlight significant changes in the functions for clear visualization. the interpretability and expressive capabilities of the Mamdani FIS are compromised in the Sugeno FIS because of its non-fuzzy rule consequents. As a result, the output is represented as a constant value rather than a fuzzy set during rule evaluation [28; 29; 30]. Ultimately, the choice between the Mamdani and Sugeno FIS depends on carefully weighing the advantages and limitations. This prompts the system designer to select an option that best suits the specific system requirements. * During the Defuzzification stage, the decision of the inference block is converted into numerical values. This block serves as a bridge between the controller and the actual system, providing accurate values for further processing [31].
# Nonlinear Vibrations of Low Pressure Turbine Bladed Disks: Tests and Simulations
## 4 Numerical Analysis  
The numerical simulations were carried out in Policontact software [28]. This simulation environment performs linear and nonlinear forced response analysis in the presence of friction contacts. The experimental post-processed data serve as an input for Policontact analysis since several parameters requested by the software are adjusted in order to match the experimental response.  
Two different linear conditions can be calculated first:  
* _Free response_: a linear forced response in the absence of UPDs, i.e., with no active friction contacts on the blade platforms.
* _Full-stick response_: a linear forced response in the presence of UPDs that are always in contact with the blade platform in a stick condition. Therefore, this configuration imposes higher stiffness and thus a higher critical frequency on the overall system.  
The following input parameters are required by Policontact software to characterize the contact and determine the nonlinear forced response: the nodal diameter value, modal damping value, force amplitude, friction coefficient, number of nodes in contact (contact locations), and contact stiffnesses. The determination of each of them will be discussed individually in separate subsections.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Configuration** & **Average Value** & **Standard Deviation** \\ \hline Maximum response & & & \\ amplitude & Tuned (no UPDs) & 142.5 μm & 33.1 μm \\ Maximum response & Tuned + aluminum & 128.5 μm & 30.4 μm \\ amplitude & damper & & \\ Maximum response & Tuned + steel damper & 105.1 μm & 27.6 μm \\ Amplitude & Tuned (no UPDs) & 509.8 Hz & 4.6 Hz \\ Critical frequency & Tuned + aluminum & & \\  & damper & 506.2 Hz & 7.5 Hz \\ Critical frequency & Tuned + steel damper & 512.5 Hz & 6.6 Hz \\ \hline \end{tabular}
\end{table}
Table 1: The average value and standard deviation of the maximum response amplitude as well the critical frequency for different configurations.
# Publishing Anonymized Set-Valued Data via Disassociation towards Analysis
## 5 Experiments
### Neighbor Datasets and Knowledge Discovery
#### 5.2.1 Mining Frequent Itemsets  
In order to go deeper in the analysis of the neighbor datasets, we turn our attention to the most frequent itemsets. The discovery of frequent itemsets help decision-makers develop new strategies by gaining insights into which items are frequently found together. For this investigation, we use the support metric, defined as the proportion of transactions in the dataset \(D\) which contains an itemset \(X\).  
\[support(X)=\frac{|X|}{|D|}. \tag{10}\]  
Table 5 presents the itemsets with support greater or equal to 0.008. We are able to extract 17 itemsets, representing the most frequent itemsets. To compare the accuracy of knowledge extraction between the original and re-associated dataset, we compared their ranks in terms of frequency. Despite the drop of the count of those itemsets, we can notice that they reappear in the re-associated dataset in the same rank range with mild changes in the ranking, yet they are still the 17 most frequent itemsets in the re-associated dataset. We further tested the three most frequent itemsets, shown in Table 6. We could not detect their presence with the same minimum support, so we dropped it from 0.008 to 0.005, which means they are less frequent in the dataset. However, we found that even for less frequent itemsets we can retrieve them in almost the same rank range. This shows that a neighbor dataset is a faithful representation of the original dataset, by preserving the most frequent itemsets in the same rank range even with the drop in the support.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Frequent 2-Itemset Acronyms** & **Count in \(\mathcal{T}\)** & **Count in \(\mathcal{T}^{\prime}\)** & **Rank in \(\mathcal{T}\)** & **Rank in \(\mathcal{T}^{\prime}\)** \\ \hline Itemset1 & 1204 & 1047 & 1 & 1 \\ Itemset2 & 916 & 765 & 2 & 2 \\ Itemset3 & 877 & 707 & 3 & 3 \\ Itemset4 & 771 & 643 & 4 & 4 \\ Itemset5 & 738 & 583 & 5 & 6 \\ Itemset6 & 722 & 603 & 6 & 5 \\ Itemset7 & 631 & 494 & 7 & 9 \\ Itemset8 & 621 & 517 & 8 & 7 \\ Itemset9 & 615 & 408 & 9 & 15 \\ Itemset10 & 615 & 517 & 10 & 8 \\ Itemset11 & 590 & 482 & 11 & 10 \\ Itemset12 & 576 & 453 & 12 & 12 \\ Itemset13 & 571 & 427 & 13 & 13 \\ Itemset14 & 552 & 458 & 14 & 11 \\ Itemset15 & 509 & 408 & 15 & 14 \\ Itemset16 & 506 & 375 & 16 & 17 \\ Itemset17 & 496 & 385 & 17 & 16 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Frequent two-itemsets (minimum support = 0.008).
## 2 Channel width Quantization Method Based on Deep Learning
### Methodology  
We propose a preference method that combines CNN with channel width modelling. The basic idea is shown in Figure 1. The conditional data C of the actual workspace include k data points, and the channel models A and B are the models to be selected. To select the model that most closely matches C from A and B, we first sample n times from model A and model B, and randomly select k points each time. The set of sampling points is denoted as Ai and Bi, where I \(\in\{1,\ldots,n\}\). Then, we add tags to the sampling point sets Ai and Bi, the discrete point set from model A labeled \"A\", and by the same token, the discrete points from model B labeled \"B\". Both sampling point sets Ai and Bi are taken as training sets, and the deep convolutional neural network Inception-Resnet-v2 is used for migration training. Finally, using the trained convolutional network model, the river model A or B that most closely matches the conditional data C is identified.  
Based on the basic idea of the method, the calculation flow is as follows:  
1. Input the conditional data C of the actual work area, which is essentially a set containing K points.
2. Input M candidate models; Wm represents the m candidate model, where \(m=1,\ldots,\) M.
3. Define the number of times to sample from the candidate model as \(n\).
4. Define the training dataset PS for convolutional deep learning.
5. Select k points randomly from Wm to get the point set Pm(i).
6. Add the label \(\{m\}\) to Pm(i) as the identity of the \(i\)th candidate model Wm.
7. Add P\(m(i)\) to PS.
8. Increase \(i\) by 1 each time; if \(i\leq n\), go to steps (5), (6), (7); otherwise, go to step (9).  
Figure 1: Channel width model optimization process based on convolutional neural network approach.  
(9) Increase \(m\) by 1 each time; if \(m\leq\) M then go to step (8); otherwise, go to step (10). (10) Training the PS using CNN based on migration learning, to obtain the trained model CNNPS. (11) Using CNNPS to test C, identify the model that best matches C from M candidate models.
## 5 Conclusions and Future Work  
In this paper we have proposed a new efficient data collection algorithm using ferry nodes to collect data from WSN nodes based on a ferry's path selection. Using this algorithm we achieved efficiency measured by two goals which are minimizing the overall total roundtrip travel time of the ferry and minimizing the overall energy consumed in the whole network. Through simulation we proved the efficiency of our algorithm compared to using traditional multi-hopping method to collect data and using fixed predetermined paths. We showed that nonlinear trajectory achieves a better optimization in term of network lifetime, overall energy consumed and the roundtrip travel time of the ferry. The results of the simulation also showed that using a larger number of checkpoints increases the network lifetime, however, it increases the roundtrip travel time of the ferry.  
In our future work, we are considering using multiple ferries to collect data which can be also an improvement over the current proposed algorithm and can decrease the time delay in case of emergency or non-delay tolerant applications. We are also considering changing the algorithm to have a speed controlled fly over ferry instead of stopping at each checkpoints, where the ferry can decrease its speed while flying over checkpoints to collect the data.