esa-sceva/satcom-chunk-collection huggingface dataset:
# Trajectory Control Approach for Single-Stage Soft-Switching Grid-Tied Inverters
## 1 Introduction
This paper also uses this structure to analyze the control methodology. In typical energy conversion circuit controller designs, the dynamic response is analyzed based on the averaged value of the switching period. This allows the application of frequency-domain analysis methods through linearization. While frequency-domain analysis is highly effective for designing dynamic response and stability, it is applicable only at or near a specific operating point. Therefore, conventional approaches limit the performance of microinverters, as they operate over a wide range of grid voltages and currents and must handle diverse operational conditions. To achieve enhanced performance, an advanced approach is required for more effective management of these complex conditions. The state-trajectory control method employed in this paper directly controls the voltage and current in the resonant circuit within the time domain, allowing immediate response to the conditions within the circuit. This method also achieves steady-state operation with minimal switching actions, offering a more effective approach for resonant circuits. In this study, a state trajectory control using a finite state machine model is used to manage grid-side current and achieve the advanced functions for a single-stage soft-switching grid-tied inverter. Advances in digital signal processing platform-based controllers en  
Figure 1: Single-stage soft-switching module-integrated inverters. able real-time signal processing, significantly enhancing response times and supporting complex, high-speed control algorithms. When these controllers are implemented, the same logic configuration can be replicated with minimal per-unit cost, making them highly cost-effective for large-scale production. This scalability, combined with parallel processing and fast response times, makes them suitable for applications such as microinverters. The state trajectory control method directly regulates the energy state of the resonant filter circuit on the state plane, resulting in low sensitivity to circuit parameter variations and enabling a fast dynamic response. The proposed soft-switching control algorithm applied to a module-integrated microinverter is implemented using a finite state machine model to efficiently execute the digital control logic. The control algorithm was implemented on a Texas Instruments (TI) 28379D digital controller, and the validity of the model was further confirmed through Hardware-In-the-Loop (HIL) testing using PLECXIM RT-box.
# Assessment of Transformer Fault Severity from Online Dissolved Gas Analysis Using Positive CUSUM
## 7 Conclusions  
This research article analyzes fault severity in transformers using dissolved gas analysis of transformer oil, employing the Positive CUSUM method from NEI. It evaluates fault severity in transformers through case studies involving online DGA results from arc furnace transformers at two steel plants. These case studies compare the results with NEI, NEI score, and cumulative NEI. This study found that the Positive CUSUM method provides more evident trends in assessing fault severity from hydrocarbon gas compared to NEI, NEI score, and cumulative NEI. In contrast, using NEI values derived from carbon oxide gas, along with the CO\({}_{2}\)/CO ratio specified in Annex F of IEEE C57.104-2019 [10], yields better results in evaluating the severity trends of faults related to cellulose insulation.  
Because of the widespread acceptance of using DGA to assess the condition of insulation within transformers, making it an important topic within power transformers, there has been increasing installation of online DGA monitors for critical power transformers in production processes at factories. A further research study to further develop the Positive CUSUM method will integrate artificial intelligence (AI) with various optimization techniques to identify optimal parameters [27; 28; 29; 30; 31]. Consequently, in the future, when combining this AI-enhanced Positive CUSUM method with online DGA data sources to monitor the severity trends of internal transformer faults, there will be an increase in case studies for the analysis and further development of real-time diagnostics for online DGA-monitoring systems.  
Writing--original draft, N.C.; Writing--review & editing, S.W. and N.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to express their gratitude to Rajamangala University of Technology Phra Nakhon for its support in providing knowledge and access to several technological databases of which the university is a member. This support has facilitated the authors' convenience in carrying out prior studies. The authors also extend their thanks to Data Entry Group Co., Ltd., for the information regarding the dissolved gas analysis from both steel plants, which enabled the application of the principles in this study to actual data obtained from an online dissolved gas analysis instrument.  
Figure 24: Positive CUSUM with threshold.
## 2 Background Work  
### Data Orchestration  
Data Orchestration in software engineering is linked mainly with service-oriented programming, virtualization, and cloud computing. A definition of orchestration in this context is the coordination and management of various entities to achieve the best possible outcome [18]. Depending on the context, orchestration can be further defined to better describe its purpose in the specific context. In service-oriented programming, the term can be defined as \"a combination of services to create higher-level services and processes\" [19]. In the field of computer-supported collaborative learning (CSCL), it is defined as: \"the process of productively coordinating supportive interventions across multiple learning activities occurring at multiple social levels\" [20]. The amount of data produced on an organization, or even applicational, is increasing daily at high rates. Therefore, the technologies used for storage management systems change frequently to best cover the current needs. One can say that if the data are managed well, there is no need for data orchestration, but this is a challenging task to accomplish, especially for legacy applications, already existing and producing data for years, creating data separation and through multiple management systems. Data orchestration is needed to unify data management and research between all systems. Data orchestration solves deduplication, lead-to-account matching, and data cleansing problems. In general, it minimizes missed data and ensures data integrity. Furthermore, by using data orchestration, privacy laws can be followed quickly, guarantee compliance with them, and provide the necessary information they require (how, when, and where the user data are used).
# Adaptive Trajectory Tracking Control for
## 4 Stability Analysis
### Tracking a Circle  
In the case of USV initial conditions, the control parameters and external disturbances do not make any changes, a circle trajectory tracking simulation experiment with a radius of 20 m is carried out to further verify the correctness of the proposed scheme. The reference trajectory is described as \(x_{d}~{}=~{}20\cos(0.05t)\), \(y_{d}~{}=~{}20\sin(0.05t)\). The tracking results are shown in Figures 7-10.  
Figure 5: Comparison results of \(\tau_{u}\).  
Figure 6: Comparison results of \(\tau_{r}\).  
Although no adjustments have been made to the control parameters, it can be seen from Figure 7 that the proposed scheme and ([PERSON], et al., 2012) still have satisfactory tracking results, which show that they have good generality. Figure 8 plots the tracking errors of the proposed scheme and ([PERSON], et al., 2012). It can be observed that the proposed scheme can keep \(x_{e}\) and \(y_{e}\) stable near the equilibrium point and they have very small fluctuations. However, the performance of ([PERSON], et al., 2012) is not so satisfactory. Figures 9 and 10 depict the comparison results of control inputs. Notice from the simulation results that similar to the straight line tracking, the ([PERSON], et al., 2012) control inputs \(\tau_{u}\) and \(\tau_{r}\) generate large chattering phenomena, which are not allowed in actual engineering.  
Figure 8: Comparison results of tracking errors.  
Figure 7: Comparison results of circle trajectory tracking.  
Thus far, the numerical simulations of straight line trajectory tracking and circle trajectory tracking have achieved good results, indicating the superiority and robustness of adaptive trajectory tracking control strategy proposed in this paper.
## 3 Results and Discussion
### Characterization of AlBA/TA Hydrogel Thread
The thread strength at pH 7.4, known as the average physiological pH range in the human body, was 4.7 \(\pm\) 1.1 MPa; at pH 8.0, the mean pH in the colon, the thread strength was 5.1 \(\pm\) 0.8 MPa; at pH 2.0, the average pH in the stomach, the same quantity was 13.3 \(\pm\) 2.1 MPa (Figure 3d). These results indicate that the AlBA/TA hydrogel thread could maintain sealing performance as a thread with higher tensile strength over a broad pH range. TA is a natural polyphenolic compound derived from plants. The pyrogallol and catechol groups present in the molecular structure interact with AlBA to form a sticky hydrogel. Moreover, TA has excellent antioxidant, antibacterial, and hemostatic properties [24; 25]. In addition, we observed that TA-bound AlBA/TA hydrogel threads continuously released tannic acids in the body. It was established that the release followed a linear tendency from the 4 th day to the 25 th day after a burst release recorded during the first 3 days (total TA/its metabolites of 1.0 nmol per hour) (Figure 3e). The result might be due to the dissociation and degradation of TA caused by hydrolysis of its ester groups. Interestingly, although the TA was degraded over time, the thread-like shape of the swollen hydrogel fiber was maintained for 25 days and its mechanical strength became slightly lower than that of the initial hydrogels (data not shown). According to previous reports, boronic acid can also have an intermolecular interaction with cis-diol in alginate backbone in pH 7.4 [19; 21]. Thus, the partial complexation between boronic acid and alginate can help to maintain the fibrous shape even though the TA was continuously degraded from the hydrogels. Additionally, the hydrogel thread swollen in the physiological buffer can be stretched up to 118.4% of its original length (Figure 3f) and 3D mesh structures could be easily designed using these hydrogel fibers (Figure 3g).
# Solutions to the Problem of Freight Transport Flows in Urban Logistics
## 2 Materials and Methods
### Theoretical Aspects of the Flows of Freight Road Traffic in the Urban Logistics System
These parameters affect the structure of vehicles in the realization of goods flows, the intensity of traffic congestion by commercial vehicles, route planning, duration of loading-unloading operations, operational costs of carriers and service providers, etc. [25]. Thus, summarizing the opinions of different researchers, it can be stated that the distribution of freight transport flows in cities is mainly influenced by the geographical location of the city, the location of industrial and commercial places, and the locations of distribution centers in the territory and periphery. Furthermore, the mobility of freight transport in a city is influenced by road infrastructure, traffic regulations and restrictions, and parking capacity. The solution to the problem of improving the distribution of freight transport flows in urban logistics could be the optimization of these transport flows in the first and last mile stages, eliminating heavy transport, and using self-service terminals in rural logistics centers and ecological transport for the collection or distribution of small loads.
# A Joint Detection and Decoding Scheme for PC-SCMA System Based on Pruning Iteration
## 3 Proposed Algorithm for Joint Detection and Decoding  
In the traditional S-IIDD algorithm, each RN transmits messages sequentially to all connected UNs during every iterative, as shown in Figure 2. However, in the actual updating process, the convergence rates of different branches in the factor graph are different, in addition, not all messages have the same effect on convergence. Furthermore, the complexity of the MPA algorithm is mainly influenced by the updating of RNs. Therefore, the converged branches are pruned according to the confidence stability of them at the end of each iteration, and the system factor graph is dynamically shrunken to effectively reduce the computational complexity. Secondly, a CRC early stopping criterion is added at the end of each iteration, which significantly avoids the convergence error caused by the decoding deviation.  
Figure 1: System model of polar coded sparse code multiple access PC-SCMA.
# Integrating Satellite and Ground Measurements for Predicting Locations of Extreme Urban Heat
## 2 Materials and Methods
### Modeling
#### 2.4.3 Random Forest Regression  
To build a predictive model we selected Random Forest (RF) regression. RF is a nonparametric machine learning technique shown to produce highly-predictive classification and regression models in the field of remote sensing [12; 31; 34; 35; 36]. Although RF provides internal validation and randomization [37], we still performed cross validation due to its ability to detect over-fitting of RF models and present a more conservative estimate of predictive power [36; 38]. To perform this cross validation, we randomly split each of our tables of temperatures and values (e.g., 'Richmond-Morning') into 'training' and 'testing' tables consisting of 70% of the data and 30% of the data, respectively. Each training table was used to build an RF model to predict the observed temperature values based on their measured LULC attributes. We use this training model to predict the remaining 'test' table--including the adjusted R\({}^{2}\) and Root Mean Square Error, which are both measures of predictive capacity--to compare the observed versus predicted values. Once an appropriate model was acquired, it was combined with the 150 focal buffer rasters to predict a continuous surface of temperatures. This process works by assessing the focal buffer values for each pixel in the study area, feeding it through the RF model, and assigning the predicted temperature values to their corresponding geographically-located pixel.
# Sensorless Control for DC-DC Boost Converter via Generalized Parameter Estimation-Based Observer
## 5 Experimental Result  
The controllers are implemented in the TMS320F28379D digital signal processor (DSP) using the low-cost C2000 Delfino LaunchPad, which are produced by Texas Instruments in Dallas, TX, USA. Simulink is linked to Code Composer Studio to embed and implement the controller. Simulink generates C code and a real-time executable that is downloaded to the LaunchPad. Serial communication is used between Simulink and the LaunchPad to monitor and obtain the experimental results of the average control signal, \(d\), and the output voltage signal, \(v\).  
The experimental platform is implemented using a logic level IRLZ44N N-channel MOSFET from infineon in Neubiberg, Germany and an STPS10L60 Schottky diode from STMicroelectronics in Dallas, TX, USA. The 6N137 optocoupler, which is from Fairchild in Silicon Valley, CA, USA, is used as the MOSFET driver. Two TLV237 operational amplifiers and one ISO224 isolated operational amplifier, which are produced by Texas Instruments in Dallas, TX, USA. are implemented to condition the output voltage vo in order to be measured by the DSP. The schematic diagram of the experimental platform is shown in Figure 9.  
The parameters are chosen as \(k_{p}=0.025,k_{i}=0.07,\gamma=10,\)000, \(\lambda=1000,\mu=1\times 10^{-6}\). The experimental result is shown in Figure 10. It is observed that the designed controller will drive the output voltage to the desired value. Moreover, the estimate of the inductor current satisfies the practical requirement. This shows a nice control performance.  
Figure 8: The phase portrait of closed-loop system. The black point is the equilibrium. The red lines are the state trajectories.
# Comprehensive Investigations on Fluid Flow and Cavitation Characteristics in Rotating Disk System
## 2 Numerical Analysis
### Cavitation Inception Mechanism
#### 2.1.4 Methods for Predicting Cavitation  
Early studies on cavitation focused on experimental observations and theoretical studies. Theoretical research methods include bubble dynamics forecasting methods,fuzzy forecasting methods, and pressure field forecasting methods [37]. The pressure field forecasting method is based on pressure changes in fluid. According to the previous analysis, cavitation occurs first at the lowest pressure position in the flow field. Therefore, the flow field pressure coefficient \(C_{p}(x)\) is calculated using experimental measurements or hydrodynamic methods and the minimum pressure coefficient \(C_{pmin}\) is found as follows:  
\[C_{p}(x)=\frac{p_{x}-p_{\infty}}{\frac{1}{2}\rho u_{\infty}^{2}}\leq\frac{p_{v}- p_{\infty}}{\frac{1}{2}\rho u_{\infty}^{2}}=C_{P_{min}} \tag{5}\]  
The relationship between the cavitation number and the minimum pressure coefficient is: \(\sigma_{i}=-C_{pmin}\). Additionally, this method is relatively small in calculation, so the pressure field forecasting method is used to predict the location of cavitation inception.
## 4 Methodology
### Analysis  
Finally, the encrypted results are analyzed. The accuracy and execution time of the models were evaluated to determine the effectiveness of FHE in maintaining data privacy without significantly compromising performance. The algorithms for models without and with FHE are shown in Algorithms 1 and 2.  
```
1:Initialize the Model.
2:Record start time using time.time().
3:Fit the model on the training data \(X_{\text{train}},y_{\text{train}}\).
4:Predict on the test data \(X_{\text{test}}\).
5:Calculate the total duration for training and prediction.
6:Calculate accuracy using the predicted and actual test labels.
7:Print the total duration and model accuracy.
8:Generate a classification report for the predictions.
```  
**Algorithm 1** Model (Plaintext)  
```
1:Initialize the Model with FHE enabled (n_bits=8)
2:Fit the model on the training data \(X_{\text{train}},y_{\text{train}}\)
3:Compile the model for FHE execution.
4:Select a subset of the test data \(X_{\text{test\_the}},y_{\text{test\_the}}\) for FHE execution
5:Record start time using time.time()
6:Simulate FHE prediction on the selected test data
7:Execute FHE prediction on the selected test data
8:Calculate the total duration for FHE training and prediction
9:Generate a classification report for the FHE predictions
10:Calculate accuracy using the predicted and actual FHE test labels
11:Print the total duration and model accuracy with FHE
```  
**Algorithm 2** Model with Fully FHE
## 2 Materials and Methods
### Data Assembling and Labeling  
The data-driven fault diagnosis model based on UAV flight data is still a solid choice, considering BIT records with low fault detection rate and abundant false alarms can not diagnose faults properly, at present. The combination sets of concurrent single faults from BIT records might be unknown complicated states for the ground maintenance team in the flight test, so there is a need to determine the specific fault mechanism and effects after the fault analysis program. In most cases, flight data of a new aircraft are labeled manually, which is obviously inefficient. In addition, it is tough to precisely locate the specific time stamp of a fault feature shown by manual labeling, that would affect the performance of the trained diagnosis model. It is necessary to label the flight data efficiently and precisely before improving the diagnosis model.  
A data assembling and labeling method based on flight data and BIT records is proposed here, shown in Figure 2, which could automatically label the flight data, and assist the ground maintenance team to reduce the unnecessary waste of time and human resources.  
The implementation steps are as follows:  
* Export raw UAV flight data with \(n\) variables from different devices, and organize the data as \(m\) instances by the standard time stamp series \(t=[t_{1},t_{2},\cdots,t_{m}]\). On the time stamp \(t_{i}\), the \(n\) -dimensional feature vector representing UAV operation status is expressed as \(x_{i}=[x_{i,1},x_{i,2},\cdots,x_{i,n}]\);
* Export raw UAV BIT records, taking the start time \(t_{\text{start}}\) and end time \(t_{\text{end}}\) of a recorded single fault \(s\) with alarm-level sign \(k\) corresponding to standard time stamp \(t\). On the time stamp \(t_{i}\), \(p_{i}\) single faults are recorded in BIT, expressing the BIT information as \(s_{i}=[s_{i,1}^{k_{j}},s_{i,2}^{k_{j}},\cdots,s_{i,p_{i}}^{k_{j}}]\), where \(s_{i,p_{i}}^{k_{j}}\) represents the \(p_{i}\)-th single fault recorded with alarm-level sign \(k_{i}\). The combination of concurrent single faults \(s_{i}\) is named as the complicated state of UAV at time \(t_{i}\). When exporting, the \(s_{i,p_{i}}^{k_{i}}\) will be expressed as a definite description of fault (or over-limit state) by the BIT system;
* Grab the information in \(s_{i}\), and construct a long string \(l_{i}\) containing all the mentioned definite descriptions. With reference to FMECA and the ground maintenance experience, establish a compound fault dictionary \(cfg_{j}\)= \([s_{1}^{k_{j}},s_{2}^{k_{j}},\cdots,s_{p_{i}}^{k_{j}}]\). Map the long string \(l_{i}\) into the compound fault mode \(cfg_{j}\) by matching search, when \(s_{i}=cfg_{j}\);
* Convert the long string \(l_{i}\) back to the name of the corresponding compound fault mode \(cfg_{j}\), represented as \(y_{i}\) on time stamp \(t_{i}\);
* Label the organized flight data with \(y=[y_{1},y_{2},\cdots,y_{m}]\) according to the standard time stamp series \(t\).