esa-sceva/satcom-chunk-collection huggingface dataset:
# Ontology-Driven Cultural Heritage Conservation: A Case of _The Anales of Confucius_
## 5 The Methodology of Lunyu Ontology  
Ontology building follows a lifecycle of several stages [33]. Some of them have to be specialized, others have to be newly introduced to take into account the specificities of the domain. The theory of concept underpinning the ontology can also strongly impact the methodology of engineering it. Following the ISO principles on Terminology where \"a term is a verbal designation of a concept\" and \"a concept is a unique combination of (essential) characteristics\" (An \"essential characteristic\" is a characteristic (abstraction of a property) of a concept and is indispensable to understanding that concept (ISO 1087). Essential characteristics correspond to rigid predicates in DL [34] and to rigid properties in the OntoClean method [35]. Unlike essential characteristics, which define the concept, \"descriptive characteristics\" own values which describe the current state of an object, e.g., weight, color, etc.) (ISO 1087, ISO 704), we adopt a \"term-and-characteristic\" guided method derived from previous work [36; 37; 38]. Identifying essential characteristics becomes the first goal to achieve.  
The problem of identifying essential characteristics is a new and central phase of our methodology. For concrete concepts, this phase is aimed at identifying differences between objects (chair with leg versus chair without a leg). For abstract concepts, this phase is aimed at identifying differences between meanings of concepts because the concepts do not have a physical entity corresponding to it, e.g., the meaning of the term \"??(\"titual\") is a hierarchical system that regulates the external behavior. Thus, the essential characteristics were /system/, /hierarchical/, /external/, /behavior/.  
The term-and-characteristic guided methodology includes seven steps, each aiming at different tasks. Step 1: identify the scope of the domain and the objectives; Step 2: identify terms and objects; Step 3: identify essential characteristics; Step 4: define concepts;Step 5: build ontology using one of the available tools; Step 6: integration of other resources; Step 7: evaluation.  
This chapter is dedicated to step 3 (identifying essential characteristics) and step 4 (combining essential characteristics into concepts). The first step has been presented in the previous chapter 3. There is no phase of identification of terms and objects, since the experts know the terms to be defined. The Lunyu ontology engineering in the OWL phase as well as the evaluation phase will be presented in dedicated chapters.
# Direct-Write Spray Coating of a Full-Duplex Antenna for E-Textile Applications
## 3 Results and Discussion
### Dielectric Properties of the Printed Textile-Based Antenna  
Table 2 provides a list of the dielectric properties of various assembly methods with textile layer combinations and the inclusion of spray-coated BT dielectric ink. In the previous study, the textile antennas were made from eight-layer nonwoven fabrics [44], with a dielectric constant and dielectric loss tested by SPDR of 1.75 and 0.008, respectively. The newly designed textile antenna substrate made of six knit fabrics spray coated with BT dielectric ink sandwiched between two nonwoven fabrics (Figure 2) showed a similar thickness (1.97 mm) and dielectric constant (1.78) to those of the previous study. The direct-write spray-coating process of BT dielectric ink successfully increased the dielectric constant of the six-layered knit fabric structure from 1.43 to 1.61. This demonstrates the potential of using BT dielectric ink to manipulate the dielectric properties of porous materials. As previously mentioned, the dielectric loss can influence the radiation efficiency of the antenna, for which a smaller value is preferred [45]. Although the dielectric loss increased from 0.009 to 0.013 by adding the BT dielectric ink into the knit structure, this is still low compared to the previous studies on textile antennas [25].  
Figure 3: Fundamental characteristics of UV-curable ink with BT nanoparticles. (**a**) Chemical reaction mechanism of acrylate and urethane UV-curing process [43]. (**b**) Scanning electron microscopy (SEM) of BT nanoparticles (50,000\(\times\); 1 \(\upmu\)m of scale bar). (**c**) Size distribution of the BT nanoparticles. (**d**) BT nanoparticle size distribution according to intensity of UV-curable ink tested by DLS. (**e**) X-ray diffraction (XRD) result of the BT dielectric ink after UV curing.
# Research on Unsupervised Classification Algorithm Based on SSVEP
## 5 Conclusions  
SSVEP is referred to as the rhythmic EEG signal induced by continuous visual stimulation. There are now several methods of studying SSVEP. The primary target of this study was to achieve the quick classification of unsupervised SSVEP using FBCCA. The unsupervised model required no training and was highly robust. Additionally, the experimental analysis revealed that the FBCCA algorithm outperformed the CCA algorithm in terms of the recognition classification and \(ITR\). The acquisition time should be chosen based on the needs of the application scenario when there are five subbands and four harmonics. The longer the acquisition time, the more appropriate it is for precision-required application scenarios, whereas the shorter the time, the more appropriate it is for high real-time required application scenarios.  
For 40 targets with a frequency difference of 0.2 Hz, the FBCCA algorithm achieved an 88.9% identification rate and a maximum \(ITR\) of 88.64 bits/min, which provides a reference value for subsequent SSVEP research. Compared to the CCA algorithm, the classification accuracy may rise by more than 20% when the data length is 2 s. However, we used offline datasets for research and only simulated online testing, which has its limitations. This research can continue to conduct real-time online testing in different application scenarios to solve this problem in the future.  
Conceptualization, Y.W., R.Y. and X.L.; methodology, Y.W. and W.C.; software, R.Y. and X.L.; validation, J.N.; formal analysis, X.L. and R.Y.; investigation, J.N.; resources, X.L.; data curation, X.L.; writing--original draft preparation, X.L. and R.Y; writing--review and editing, R.Y. and J.N.; visualization, R.Y.; supervision, Y.W. and W.C.; project administration, Y.W., W.C. and R.Y.; funding acquisition, Y.W. and W.C. All authors have read and agreed to the published version of the manuscript. This research was funded by Beijing Natural Science Foundation (4202026).  
Not applicable.  
The data presented in this article (the third \"China Brain-Computer Interface Competition\") can be found online at [[https://www.datafountain.cn/competitions/338/datasets](https://www.datafountain.cn/competitions/338/datasets)]([https://www.datafountain.cn/competitions/338/datasets](https://www.datafountain.cn/competitions/338/datasets)) (accessed on 20 July 2022).  
The authors declare no conflict of interest.
## 4 Results on \(d\)-Dimensional Grids
### An asymptotic estimate  
We have given upper and lower bounds on \(k_{d}(n)\) in terms of the size of the middle layer, thereby proving \(k_{d}(n)\in\Theta(L(\lfloor\frac{(n-1)d}{2}\rfloor,n,d)|)\). But how does this size grow? Unfortunately, we are not aware of a closed formula for \(|L(\lfloor\frac{(n-1)d}{2}\rfloor,n,d)|\) in general. But the following folklore result [14] describes an asymptotic estimate.  
**Lemma 11**.: \(|L(\lfloor\frac{(n-1)d}{2}\rfloor,n,d)|\in\Theta(\frac{n^{d-1}}{\sqrt{d}})\)__  
Proof.: Let us consider \(d\) independent and identically distributed random variables \(X_{1},\ldots,X_{d}\) each of which can take any integer value from \(0\) to \(n-1\) with equal probability. Each \(X_{i}\) has expectation \(\frac{n-1}{2}\) and variance \(\frac{n^{2}-1}{12}\). Therefore, the random variable \(S_{d}^{n}:=X_{1}+\ldots+X_{d}\) has expectation \(\frac{d(n-1)}{2}\) and variance \(\frac{d(n^{2}-1)}{12}\). By the central limit theorem, the distribution of \(\frac{S_{d}^{n}-\frac{d(n-1)}{2}}{\sqrt{\frac{d(n^{2}-1)}{12}}}\) converges towards the standard normal distribution. This means, that the probability of the event \(S_{d}^{n}=\left\lfloor\frac{d(n-1)}{2}\right\rfloor\) asymptotically behaves like \(\frac{1}{\sqrt{2\pi}}\int_{-1/(2\sqrt{d(n^{2}-1)/12})}^{1/(2\sqrt{d(n^{2}-1)/ 12})}\exp(-\frac{t^{2}}{2})\text{d}t\). For increasing \(n\) and/or \(d\), the integration domain shrinks around \(0\), hence the integrand stays in an interval \([1-\varepsilon,1]\) where \(\varepsilon\searrow 0\). The probability lies in the interval \(\left[(1-\varepsilon)\sqrt{\frac{6}{\pi d(n^{2}-1)}},\sqrt{\frac{6}{\pi d(n^{2}-1)}}\right]\). Multiplying this by \(n^{d}\), which is the number of all vertices in the grid, leads to the fact that the number of vertices in the middle layer asymptotically behaves like \(\Theta(\frac{n^{d-1}}{\sqrt{d}})\).  
We can combine Lemmas 8, 10 and 11 to  
**Theorem 2**.: \(k_{d}(n)\in\Theta(|L(\lfloor\frac{(n-1)d}{2}\rfloor,n,d)|)=\Theta(\frac{n^{d-1 }}{\sqrt{d}})\)__
# Design of an Imaging Optical System for Large-Sized Stepped Shaft Diameter Detection
## 6 Conclusions  
A highly advanced and low-distortion dual-telecentric imaging system was designed for the purpose of large-sized stepped shaft diameter detection. The system exhibits excellent imaging quality, with a detection range of 110 mm, a working distance of 60 mm, and an Airy disk radius of 3.204 um. At a spatial cutoff frequency of 71.4 lp/mm, the MTF transfer functions across various fields of view are all above 0.6, while the field curvature remains within 0.1 mm and distortion is controlled within 0.1%.  
When applied to the experimental system for measuring the diameter of a large-sized stepped shaft using standard components, it was found that the introduction of this imaging system significantly reduced measurement errors.  
Furthermore, besides its high-precision detection capabilities for large-sized stepped shafts, this system holds promising applications in fields such as projectors and laser optical paths.  
Figure 9: Measurement results. (**a**) Measurement of the 652 mm standard part; (**b**) measurement of the 750 mm standard part.  
**Author Contributions:** Conceptualization, J.D. and J.L.; methodology, J.L. and Y.Z. ([PERSON]); software, J.L. and H.Z.; validation, J.L., Y.L. and Y.Z. ([PERSON]); formal analysis, Y.Z. ([PERSON]); investigation, J.L.; resources, J.D.; data curation, J.L.; writing--original draft preparation, J.L.; writing--review and editing, J.D.; visualization, J.L.; supervision, Y.Z. ([PERSON]); project administration, J.D.; funding acquisition, J.D. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Zhongshan Institute of Changchun University of Science and Technology introduced innovative research team project; Project Number: CXTD2023006. And the Department of Science and Technology of Jilin Province under Grant 20210201088 GX.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
The authors are grateful to the School of Optoelectronic Engineering, Changchun University of Science and Technology, and Zhongshan Institute of Changchun University of Science and Technology.  
The authors declare no conflicts of interest.
###### Abstract
Normally, LAT is defined as the ability to judge the right and left side of a depicted body part (e.g., hand or neck movement or facial activity; see Figures 1-3) and is important for a functioning body schema and movement planning [20; 21; 22; 23]. It is a well-observed phenomenon in several clinical conditions, like complex regional pain syndrome, chronic facial pain [11], shoulder pain [24],and low back and neck pain [20; 25]. There is only one study exploring laterality recognition in individuals with episodic migraine [26]. Further information regarding this topic is provided in Appendix A.  
Figure 1: Left–right recognition of hands. Reprinted with permission from ref. [27]. Copyright 2012 Noigroup Publications. test subject should recognize in a certain time frame and with the highest accuracy possible. Further information regarding this topic is provided in Appendix B. Corresponding changes in the cortical matrix in response to abnormal LAT and FER are described in the scientific literature. Patients with features of alexithymia show significantly less activity in the emotion-processing centers of the brain [32]. Neuronal networks in the premotor cortex, which are necessary for a physiological body schema and the proper functioning of a body part, are thought to show alterations [27]. Studies have shown that neuroplastic adaptations in different brain areas, e.g., the limbic system, the somatosensory and sensorimotor cortex, the prefrontal cortex, and the premotor cortex, preserve pain and correlate with deficits in LAT and FER [33]. In addition to pharmacological treatment, effective pain management in clinical practice necessitates the integration of appropriate communication strategies; behavioral therapy techniques; and motor, sensory, and cognitive training. This approach requires a comprehensive understanding of the neurophysiological and psychosocial elements of pain. Specifically, in treating patients with chronic headaches, incorporating laterality training and facial emotion recognition can enhance therapy. Laterality training, as part of a graded motor imagery program, has shown promising results in several clinical trials, notably in reducing pain and improving motor function and quality of life [23; 34; 35; 36; 37]. However, before starting sensorimotor or cognitive training, it is necessary to investigate the nature and degree of LAT and FER limitations in the population of interest. This cross-sectional study aims to investigate changes in laterality recognition, facial emotion recognition, alexithymia, stress levels, depression, anxiety, and headache impact (days and severity) in individuals with high-frequency or chronic migraine compared to healthy subjects.
# Design of CubeSat-Based Multi-Regional Positioning Navigation and Timing System in Low Earth Orbit
## 3 Results
### Mission Analysis
#### 3.3.3 Solar Eclipse Analysis  
The solar eclipse analysis performed in STK resulted in an average eclipse duration of 31.5 min, a maximum eclipse duration of 35.6 min, and a maximum of 14 eclipses per day. The S/Cs having an altitude of 550 km have an orbital period of 95.4 min.
## 3 Proposed Beam Learning with MARL
### Proposed Algorithms
#### 3.4.2 Proposed Reward Function  
The proposed reward processing is detailed in Algorithm 1. The reward function is designed to satisfy two goals, namely maximizing the average beamforming gain, in turn maximizing the sum rate of the system and reducing the inter-beam interference. In the proposed approach, the end-to-end system is implemented, where the ARQ signal is sent by the UE to the BS based on whether a frame was received correctly or not, and the received ARQ is also used as an input for reward modeling, addressing concerns that RSRP alone may provide a misleading indication of beamforming gain maximization in a multi-beam system with interference.
## 3 Preliminaries
### Working Procedure of the ASC-RL  
The dynamic state change process for optimizing network performance is divided into two phases: the training phase and the deployment phase. In the training phase, the system is trained using sensor data \((\mu_{n})\), which is a real-time collection of data, while three other parameters are used in this phase. These are internal parameters, including the state of the sensors \((S_{n})\) and historical parameters such as energy level, remaining energy, and role in detection.  
The environmental parameters are similar to the sensor values. During the deployment phase, the trained agent \((A_{nt})\) is deployed and uses a reward function \(F(s,n)\) to generate various actions \((A_{cn})\). The sensors are set to different states based on \(A_{cn}\). These \(S_{n}\) willbe sent back to \(F(s,n)\) to be \(A_{nt}\) reconfigured in the form of a circle. Figure 3 illustrates the main phases of training and deployment, including the parameters and transitions between them.
## 2 Materials and Methods
### Data Collection  
To investigate recent crustal deformation in the southeastern Korean Peninsula, GNSS surveys were conducted at 23 geodetic control points that form part of the national crustal deformation monitoring network. The most recent campaign was carried out in June 2020, and the collected GNSS data were processed using the precise geodetic analysis software GAMIT/GLOBK (version 10.70) to compute horizontal coordinates.  
These 2020 results were compared with those from two previous GNSS surveys conducted at the same sites: the second campaign on 17 September 2018 and the first campaign on 26 February 2019. All three datasets were processed using consistent software and analysis strategies to ensure compatibility in the temporal comparisons. From these results, horizontal coordinate differences (\(\Delta\)x, \(\Delta\)y, \(\Delta\)t) were calculated to quantify short-term crustal displacement at each site.  
Figure 2: Triangular network constructed from 33 GNSS stations, including 23 geodetic control points (blue squares) and 10 CORS (black circles). A total of 49 non-overlapping triangles were formed and numbered for localized strain rate analysis. The red lines represent major active faults: the western line corresponds to the Yangsan Fault, and the eastern line to the Ulsan Fault, in the southeastern Korean Peninsula.  
To further assess crustal strain across active fault systems, two triangulated GNSS networks were established for comparative analysis. In the first case, 32 triangular elements were constructed by connecting 6 satellite control stations and 17 integrated geodetic control points from the 2020 and 2018 datasets. In the second, a denser network of 49 triangles was created using 10 satellite stations and 23 control points. For both configurations, all baselines were converted to geodesic lines prior to strain computation, and baseline strains were calculated for east-west-oriented segments that intersect major fault zones. These two network resolutions enabled a multi-scale assessment of deformation patterns and spatially variable strain accumulation across the southeastern Korean Peninsula.  
To highlight zones of significant deformation, geodetic lines exhibiting strain rates exceeding 2 ppm/year were extracted and visualized (Figure 3). The maps compare two consecutive observation periods (2018-2020 and 2019-2020), allowing for the identification of spatial and temporal variations in strain concentration. These high-strain-rate baselines were identified and flagged for subsequent spatial analysis, focusing on the central segment of the Yangsan Fault where concentrated strain values were observed.  
During the 2018-early 2020 period, baseline strain rates exceeding 2 ppm/year were observed along the following lines: U0745-U0752 in Triangle 6, CGUG-U0752 in Triangle 16, DUCG-UUS01 in Triangle 19, and UUS01-HDBG in Triangle 22. In contrast, during the 2019-early 2020 period, only the U0745-U0752 baseline in Triangle 6 exhibited strain rates exceeding 2 ppm/year.
## 3 Optimization, Considering the Cogging Torque and Torque Ripple
### The Optimization of the Rotor Iron Core by Adopting a Rotor Pole Arc Offset  
#### 3.1.1 Air-Gap Flux Density Distributions  
First of all, the electromagnetic torque, torque fluctuation, electromagnetic vibration, and noise is directly related to the air-gap flux density distributions. Reducing the harmonic content in air-gap flux density can effectively weaken cogging torque and torque ripple. Because the excitation magnetic field of the PMSM is sinusoidal when the air-gap magnetic field waveform is close to the sinusoidal waveform, the cogging torque and torque ripple of the PMSM can be effectively reduced, and the control accuracy of the PMSM can be improved. By streamlining the core of the rotor surface, the shape of the air-gap magnetic field can be optimized. The rotor surface arc offset was employed, as illustrated in Figure 2, where \(R\) is the offset distance; i.e., the distance of the rotor pole arc center from the rotor center. The conditions from 0 to 50 mm were analyzed. As a result, the air-gap length became uneven along the rotor surface. The degree of said unevenness grew with the increase of \(R\). Figure 3 shows the 2-D FEA calculated air-gap flux density distributions and the corresponding harmonic contents. After the eccentric design, the air-gap magnetic field was more sinusoidal. With the increase of the offset distance of the rotor outer surface, the amplitude of the air-gap magnetic density fundamental current increased gradually, while the other harmonic components decreased. The distortion rate of the air-gap magnetic field decreased after the eccentricity of the rotor outer circle increased. Under the traditional conditions, without rotor surface offset, the amplitude of the air-gap magnetic density fundamental wave is 0.97 T, which can be increased by 13.4% when the offset distance is 40 mm.  
Figure 3: Comparisons of influence based on finite element analysis (FEA) results for rotors parameterized with various arc offsets.  
Figure 2: Illustration of the rotor pole arc offset.
## 3 Results and Discussion
### The Catalytic Performances of Catalysts  
The catalytic performance of the as-synthesized samples was firstly evaluated using a widely used azo dye, Acid Red 73 (AR 73). It can be seen from Figure 4a that the removal efficiency AR 73 (200 mg/L) catalyzed by FeNi-Pd@CN, Fe-Pd@CN, Ni-Pd@CN, and Pd@CN were 92.0%, 75.85%, 75.17%, and 41.06%, respectively. Clearly, the addition of Fe and Ni boosted the activity towards AR73 removal. Similar results were also found in the test of another common antibiotic, tetracycline hydrochloride (TC): 93.2% of TC (10 mg/L) was removed by FeNi-Pd@CN, which was higher than other metal-doped samples (Figure 4b). Fe and Ni could be the active sites for Fenton reactions, but as discussed in the previous part, their addition also lowers the graphitization degree of g-C\({}_{3}\)N\({}_{4}\), which means constantly adding metal does not always increase activity. Three excessive metal-doping samples (the addition of Fe: Ni were 2:1, 1:2, 2:2, respectively) were also prepared, and Figure S3 shows their TC removal efficiencies significantly reduced compared with FeNi-Pd@CN (the addition of Fe:Ni was 1:1).  
The stability and recyclability of the FeNi-Pd@CN catalyst were evaluated by successive tests of AR 73 (Figure 5) degradation under the same reaction conditions. Only 6% of AR 73 removal efficiency decline was observed after four cycles, indicating good stability of the catalyst. XRD results in Figure S4 also show an almost unchanged spectrum after use. However, Due to the acidic environment caused by formic acid, metal leaching after the first cycle was  
Figure 2: (a) XRD patterns of as-prepared catalysts and (b) FT-IR spectra of as-prepared catalysts, the green band reflects the typical stretching mode of tris-s-triazine in g-C\({}_{3}\)N\({}_{4}\).  
up to Fe 6.55 mg/L, Ni 4.93 mg/L and Pd 19.59 \(\upmu\)g/L measured by ICP-MS. It is speculated that either the unstable chemical bonds in the fresh catalyst samples are broken during the reaction or some metals are not fully anchored on the surface of g-C3N4. In the second cycle, the values decreased to acceptable values, with Fe 1.339 mg/L, Ni 711.068 \(\upmu\)g/L and Pd 6.767 \(\upmu\)g/L, while there is almost no change in degradation activity. In practical applications, metal leaching problems during the first cycle could be solved in the later wastewater treatment procedures, or the catalyst could be pretreated by pickling before use.  
Figure 3: (**a**) XPS survey of samples. (**b**) C1s spectra; (**c**) N 1s spectra; (**d**) Fe 2p spectra; (**e**) Ni 2p spectra; and (**f**) Pd 3d spectra of FeNi-Pd@CN.