esa-sceva/satcom-chunk-collection huggingface dataset:
# Online Efficiency Optimization and Speed Sensorless Control of Single-Phase Induction Motors
## 3 Sensorless Speed Estimation  
By substituting (2)-(5) into (1), STCR can be expressed as a function of supply frequency (\(\omega_{e}\)), slip (s) and the motor parameters. Assuming the motor parameters are fixed, SCTR can be expressed in the following fractional form:  
\[Z_{t}=\frac{I_{m}}{I_{a}}=\frac{N(\omega_{e},s)}{D(\omega_{e},s)} \tag{18}\]  
where \(N\), \(D\) are complex polynomials, which are all functions of \(s\) and \(\omega_{e}\). By taking the absolute value of both sides of (18) and rearranging, the following equation can be driven:  
\[|D(\omega_{e},s)||Z_{t}|-|N(\omega_{e},s)|=0 \tag{19}\]  
At every operating point, \(|Z_{t}|=|I_{m}|/|I_{a}|\) can be calculated from measured rms values of the main and auxiliary winding currents. Since the applied frequency (\(\omega_{e}\)) is known, slip can be calculated by solving (19). For an expeditious and accurate response, a Neural Network (NN) has been employed.  
The NN utilized, which is shown schematically in Figure 2, is a feedforward network with one hidden layer. It consists of two inputs (STCR and frequency \(f\)) in the input layer, 15 neurons in the hidden layer and only one output (estimated slip) at the output layer. To train the NN more accurately, numerous sample data are required. To do so, the STCR will be calculated for 100 values of slip at 16 different frequencies using (1) as shown in Figure 3. Then, entering\(f\) and the calculated STCR as inputs of NN and assigning the corresponding slips as the expected output (targets), the NN will be ready for training.  
The back-propagation Levenberg-Marquardt training method in batch mode is used, which appears to be the fastest method for training moderate-sized feedforward neural networks with the great ability of avoiding the local minima. The learning rate (I\({}_{r}\)) is set to as low as 0.005, since the number of sample training data is relatively large, and an unwisely large learning rate would lead to divergence of the training algorithm. The  
Figure 3: Slip versus STCR with frequency as a parameter.  
Figure 2: Schematic diagram of the feed-forward neural network.  
number of iterations (epochs) is set to 1000. The train goal, which is the trained network total accumulative error to be met by the algorithm, is set to \(10^{-8}\).  
Having attained the slip from the output of the trained neural network, the motor speed can be calculated via following equation:  
\[\omega_{m}=(1-s)\times\frac{2}{P}\times\omega_{e} \tag{20}\]  
The obtained motor speed will further be used as the motor speed feedback and will be applied to the speed control loop. Hence, we are able to have speed regularization without any speed sensors being used. The speed estimation error (\(\omega_{m,error}\)) is defined as follows:  
\[\omega_{m,error}=\frac{\omega_{m,est}-\omega_{m}}{\omega_{m}} \tag{21}\]  
The speed estimation error serves as a performance index of the speed estimator module and will be monitored throughout the simulation.
# Hybrid Machine Learning-Statistical Method for Anomaly Detection in Flight Data
## 3 Local Outlier Factor
### Implementation
#### 3.2.6. A Hybrid Statistical Threshold Analysis
One flight was found to be safe from the parameters related to aviation, but it was categorized as anomalous as it followed a very short approach, which may be a decision taken by Air Traffic Control (ATC). Since it behaved abnormally as compared to another set of normal flights and had a very high LOF score of 1.87, therefore it was considered to be an outlier. Therefore, there was just one false positive in this case. Furthermore, the remaining 58 flights with a LOF score greater than 1 were examined to detect any false negatives, as these flights were labeled normal by the  
Figure 8: Fixing LOF threshold using [PERSON]’s method. hybrid method. Initially, the top 10 flights based on LOF score were picked from these 58 flights. Each flight was examined to find if the flight was anomalous or not. None of these 10 flights were found to be false negative. Since the remaining 48 flights have even lower LOF scores, therefore, they were also assumed to be normal. Figure 9 shows the threshold analysis of three different arrival airports. For each airport, flights from the same tail number were taken, and then after calculating LOF scores, the threshold was fixed, as explained above. For each case, a threshold of LOF score was established as per [PERSON]'s rule. Figure 9 shows that for each airport, the threshold is dynamic and stabilizes after a certain value. Thus, [PERSON]'s rule helped to eliminate these 58 flights with LOF scores greater than 1 and highlighted real anomalous flights. After establishing the threshold for our flight data, the following section shows how additional information may be inferred.
# NESO-Based Path Following Control for
## 2 Preliminaries and Problem Formulation
### Problem Formulation  
In this subsection, the nonlinear dynamics model in the horizontal plane and integer line-of-sight (ILOS) guidance law are described to formulate the motion control problem of path following for underactuated hovercrafts with SLC and uncertainties.
# High-Precision Pointing and Tracking System Design for Near-Space Balloon-Based Optical Observation
## 2 Methods and Analysis
### Secondary Precise Tracking System
#### 2.3.1 Mechanical Design  
Due to the large size of the coronagraph, the secondary stable platform fully utilizes existing mature technologies and engineering experience to optimize design, reduce development costs and technical risks, and shorten development cycles. It adopts modular, standardized, programmable, and interchangeable design technologies to facilitate product maintenance and upgrading. It also adopts derating design, redundancy design, and margin design to ensure product reliability and environmental adaptability. The overall mechanical design of the secondary stable platform is shown in Figure 5. The height of the platform is more than 1400 mm, and the length of the platform is about 1900 mm. The mechanical limit of the azimuth angle is set from \(-172^{\circ}\) to \(+172^{\circ}\), and the pitch angle is set from \(-5^{\circ}\) to \(+85^{\circ}\). The electrical limit of azimuth angle is set from \(-160^{\circ}\) to \(+160^{\circ}\), and the pitch angle is set from \(+5^{\circ}\) to \(+78^{\circ}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Index** \\ \hline Horizon pointing range & Azimuth: \(-160^{\circ}\) to \(+160^{\circ}\) \\  & Pitch: \(+5^{\circ}\) to \(+78^{\circ}\) \\ Search angular velocity & \(\geq\)3\({}^{\circ}\)/s \\ Search angular acceleration & \(\geq\)3\({}^{\circ}\)/s\({}^{2}\) \\ Tracking accuracy & \(\leq\)10\({}^{\prime\prime}\) \\  & Image size: 1600 \(\times\) 1200 \\ Precision tracking camera & Maximum frame rate: 80 fps \\  & Measurement accuracy: 1\({}^{\prime\prime}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Index of the secondary platform.  
Figure 4: Diagram of primary platform stable pointing system.  
The azimuth axis system is composed of an azimuth frame, an azimuth motor, bearingss, and a rotary transformer, as shown in Figure 6. The azimuth motor directly drives the azimuth axis frame to rotate, and the azimuth axis frame is connected to the mechanical frame rigidly. The azimuth rotary transformer is connected to the mechanical base plate and the azimuth axis frame to achieve azimuth angle measurement. The servo control board, motor drive board, and power board are installed on the base frame, with a removable cover on the frame side, making it easy to maintain the circuit boards.  
The pitch frame is used to install the coronagraph, and it mainly consists of a pitch frame, a pitch motor, bearings, a gyroscope, and a rotary transformer, as shown in Figure 7. The pitch motor and rotary transformer are located on a different side of the mechanical frame for weight balancing. The gyroscope is installed on the pitch frame and directly measures the angle change of the coronagraph. To minimize system weight, the gravity center of the coronagraph system should be as close as possible to the pitch axis.
## 2 Materials and Methods
### Search Databases and Inclusion Criteria  
A literature search was performed on publications available in three databases: ProQuest Research Library, Web of Science, and PubMed (Medline). For inclusion in the current study, an article had to conduct exposure assessments of healthcare workers to hazardous drugs via biological exposure monitoring using urine samples, be written in English, and be published between the years 2005-2020. A decision was made to exclude articles published before 2005 as they would most likely be outdated since interventions were introduced in many facilities because of the 2004 NIOSH Alert [16], as well as the fact that there have been vast improvements in the laboratory analysis of biological samples, i.e., instrument sensitivity/detection limits have improved since 2005. Abstracts, editorials, case studies, review articles, study protocols, non-urine specimens, method development articles, and non-occupational and animal studies were excluded from this review.
# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 3 Proposed Model
### Algorithmic Hybridization Technique  
The hybridization of JADE and SCA employs a cascading approach. Initially, the entire population undergoes optimization using the JADE process, as shown in Figure 1. JADE's adaptive mechanism allows it to explore the search space efficiently, identifying regions of interest based on the fitness evaluations of the population. Once a substantial foundation of potential solutions is established, the algorithm, as shown in Algorithm 1, transitions to the SCA phase.  
```
1:Initialize population \(P\) randomly within bounds
2:Evaluate the fitness of each individual in \(P\)
3:\(t\gets 1\)\(\triangleright\) Initialize iteration counter
4:while\(t\leq\) Max_iter do
5:if\(t\leq\) Max_iter/2 then\(\triangleright\) JADE phase
6:for each individual \(i\) in \(P\)do
7: Generate mutant \(V(i,:)\) using Equation (2)
8: Perform crossover to form \(U(i,:)\) using Equation (3)
9: Evaluate fitness of \(U(i,:)\)
10:if fitness(\(U(i,:)\)) \(<\) fitness(\(P(i,:)\)) then
11: Replace \(P(i,:)\) with \(U(i,:)\)
12:endif
13: Adapt parameters \(uCR\) and \(uF\) based on success
14:endfor
15:else
16:for each individual \(i\) in \(P\)do
17: Update position of \(i\) using Equation (4)
18: Evaluate fitness of the new position
19:if new fitness is better then
20: Update individual's position and fitness
21:endif
22:endfor
23:endif
24:\(t\gets t+1\)
25:endwhile
26:return Best solution found
```  
**Algorithm 1** Hybrid JADESCA algorithm.  
In this second phase, the SCA method refines the solutions obtained from JADE. By applying sine and cosine functions, SCA adjusts the positions of the population members towards the best-found solutions. This method not only ensures that the solutions are thoroughly exploited but also allows for occasional expansive moves away from the current region to avoid local optima traps. The sequential application of JADE and SCA ensures that each solution benefits from both global search capabilities and local exploitation, leading to a more robust convergence towards the global optimum.  
The cascading hybridization strategy effectively combines the adaptive strengths of JADE with the directional exploitation capabilities of SCA, making it well-suited for solving complex optimization problems that require both exploration and exploitation.
# Type Hierarchy Enhanced Event Detection without Triggers
## 5 Conclusions and Future Work  
This paper proposes a novel event detection framework with enahnced type hierarchy called THEED. We construct a type hierarchy concept module using Probase to enhance the semantic representation of event types, and we assign hierarchy concepts with confidence. In addition, dividing the input into word-level and context-level representations helps the model extract different features. Experimental results demonstrate that our approach performs better on event detection without triggers. Notably, our proposed method achieves competitive performances compared with state-of-the-art trigger-based models. In the future, we will explore better representations with the help of external knowledge graphs to improve the accuracy and generalizability of event detection without triggers. We also plan to complete the event extraction task without triggers.  
Conceptualization, Y.Y., Z.L., J.G. and F.G.; methodology, Y.Y. and J.G.; software, Y.Y.; validation, Y.Y., J.G. and F.G.; formal analysis, Y.Y.; investigation, J.G. and F.G.; writing--original draft preparation, Y.Y.; writing--review and editing, J.G. and F.G. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Natural Science Foundation of China grant number U1836118, Key Laboratory of Rich Media Digital Publishing, Content Organization and Knowledge Service grant number ZD2022-10/05, and National key research and development program grant number 2020 AAA0108500.  
**Institutional Review Board Statement:** Not applicable.  
Figure 7: Heatmap for final scores to verify the effect of the type hierarchy concept modular components, and the input sentence is “the army’s 3 rd infantry division 1 st marine division, leading the assault against Baghdad”.
# Antidiabetic Potential of Monoterpenes: A Case of Small Molecules Punching above Their Weight
## 2 Chemistry  
Like all other terpenids, the monoterenes are constructed from repeating units of basic 5-carbon building blocks called isoprenes [12; 13]. Their biosynthesis start from the simplest primary metabolite acetyl-CoA that goes through a series of biosynthetic pathways involving the mevalonic acid. Accordingly, terpenids are often called products of the mevalonic acid pathway of secondary metabolites [14; 15; 16]. The conversion of the primary metabolites into the secondary, terpenids, natural products involve the synthesis of the key 5-carbon reactive intermediates, isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP). The condensation of these two isoprene units gives rise to a 10-carbon skeleton geranyl diphosphate (GPP): the immediate precursor of all monoterenes (Figure 1). Sequential addition of further isoprene units to GPP leads to sesquiterpenenes (15 carbon) and diterpenes (20 carbon) or their dimers, triterpenes (30 carbon) and tetraterpenes (40 carbon) respectively. The two 5-carbon terpenoid skeletons (IPP and DMAPP) could themselves give rise to a handful of 5 carbon derivatives that exist in alcohol, acid or hydrocarbon forms but they mostly incorporate into other secondary metabolites as ether or ester derivatives. Hence, the smallest but most structurally diverse group of terpenids are represented by the monoterenes.  
Despite their small atomic mass (10 carbon), the monoterenes have remarkable structural diversity owing to the diphosphate leaving group of the GPP. The resulting unstable cations (Figure 1) undergoes a series of reactions including addition reaction typically leading to incorporation of a hydroxyl group, double bond rearrangements and unsaturation that all eventually lead to stable structures of the monoterenoid class (Figure 2). In fact, the hallmark of diversity in monoterenoids that arose from just one precursor GPP (or its isomer nervyl diphosphate) through a serious of enzyme catalyzed reactions including cyclization, hydroxylation, dehydrogenation, oxidation and/or reduction, isomerization, and conjugation have been the subject of over a century-old research for natural product chemists.  
For most monoterenoids, their small molecular weight coupled with their nonpolar nature means that they are easily extracted by either non-polar solvents (like hexane), supercritical CO\({}_{2}\) or steam distillation. There are however monoterenes mostly existing in glycosylated form and hence are very polar. The iridoids (Figure 2) are such group of compounds with _cis_-fused cyclopentano pyran ring system in their structure [16]. The monoterenes can also be incorporated into other structures such as polyphenol (e.g., phenolic acids and flavonoids). In this review, biological activities that solely attributes to the monoterenes are discussed.  
Figure 1: An overview of biosynthesis pathway of monoterenes; isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP); glycogen phosphorylase (GP); OPP: diphosphate leaving group.
## 4 Results and Discussion
### Results of the Sensitivity Matrix  
A sensitivity matrix is used to determine values close to zero or parameters that have a negative effect, as shown in Figure 19.  
This optimization study aimed to simultaneously optimize three performance parameters that are critical in the design of the battery box side profile. These are the peak force (minimized), mass (minimized) and absorbed energy (maximized). The optimization process was performed over three design variables (p1, p2 and p3). The main objective of this study was to obtain optimal design solutions that balance these conflicting objectives.
## 3 Methodology to Develop and Execute a Redispatch Model in Real-Time Operation
### Theoretical Methodology Defining the Conceptual Structure of a Redispatch  
This theoretical methodology is designed to support the system operator's decision in the optimal execution of a redispatch. However, it is important to emphasize that redispatches must be properly executed according to the system requirements, since their constant random execution without theoretical justification causes the system operator an operational stress that results in a sequence of unforced errors in the dispatches of the plants. Preliminarily, this methodology has a technical structure composed of four symptoms (a, b, c, d) of operating anomalies that must be detected by the system operator before executing a redispatch. Consequently, there are as follows: (a) deviations in generation (Gx) and demand (Dx); (b) fault occurrences in the generation and transmission system (Tx); (c) power limitation in generation plants due to technical causes; (d) uncertainty of solar and wind renewable generation. After detecting at least one of the four technical anomalies that provide preliminary evidence of the need for a redispatch, a more specific analysis is performed, which consists of detecting seven critical system symptoms that determine the final decision to perform a real-time redispatch, as shown in Figure 3.  
The first critical symptom of the system corresponds to the information received in real time by the dispatch and control center (CDC) outside the time limit established in the unit commitment modeling, which immediately causes a deviation in real time. In the second critical symptom, generation limitations of more than 8 h are evidenced. The third  
Figure 3: Theoretical methodology and concepts for a redispatch.  
critical symptom corresponds to the uncertainty of the availability of generation plants that allow a more economical operation. In the fourth critical symptom, there are deviations in generation higher than 5% of the total demand for a period longer than 8 h. The fifth critical symptom is generation and transmission disconnections for a period of more than 8 h. The sixth critical symptom is the availability of fossil fuels that allow a more economical dispatch. Finally, the seventh critical symptom corresponds to generation plants that are generating in a test state towards the system, considering whether this condition prevents or reduces the presence of unsupplied energy.
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 6 A Map of Ground Subsidence Vulnerability  
A map of ground subsidence vulnerability in the target area was created using the SLM model where spatial dependence was applied. To create a map of vulnerability, a program called GeoDa 1.20, which can perform spatial data and modeling, was used. A Queen-based spatial weight matrix was used and the target area was divided into a 100 m \(\times\) 100 m grid. Figure 2a shows the map of ground subsidence vulnerability predicted by the SLM model, in which the vulnerable spots are indicated with red while the relatively safer spots are indicated with blue. Figure 2b shows a map of points where ground subsidence actually occurred in the target area. The figures indicate that the vulnerability of ground subsidence was highly well predicted in the central and eastern parts, where ground subsidence occurred frequently. On the other hand, in the western part where there was no significant ground subsidence, the SLM model did not predict vulnerability well. This is a problem caused by the model's inaccuracy. In the case of the western part, it is estimated that the number of occurrences of ground subsidence caused by factors other than damage to underground utilities was included. Therefore, it is expected that a high-accuracy ground subsidence vulnerability map will be achieved by securing information on various underground spaces, such as ground information, the groundwater level, and subway, and reflecting it in the model in the future.
## 1 Introduction
Furthermore, the paper simulates the proposed algorithm in hostile environments by integrating high energy nodes into the network, with the primary aim of keeping the entire monitored area alive for the majority of the lifetime of the network rather than monitoring only the surrounding region after certain rounds of communication, as shown in Figure 3, and the results are promising. The rest of the paper is organized as follows. Section 2 presents a summary of the related work done in this domain, whereas the proposed system model is explained in Section 3, which includes the network model and energy model for the proposed system. Section 4 discusses the proposed algorithm for cluster head selection and fault tolerance. Investigational outcomes are discussed in detail in Section 5, which also contains the comparative study of our algorithm. Finally, the summary and conclusions are presented in Section 6.
## 4 Experimental Results
### Simulation Results  
In this section, some simulation experiments are shown under a 5G communication base station. The traffic scenarios of the simulation experiments are shown in Figure 10, where \(h\) denotes the height of the base station, \(v\) denotes the forward velocity of the vehicle, \(v_{t}\) denotes the radial velocity of the vehicle, and \(r_{t}\) denotes the radial range of the vehicle. In the simulation experiment, it assumes that carrier frequency \(f_{c}=26\) GHz, bandwidth \(B=400\) MHz, and subcarrier interval \(\Delta f=120\) kHz.  
Figure 10: Traffic scenarios of the simulation experiments.