esa-sceva/satcom-chunk-collection huggingface dataset:
# Prototype of Augmented Reality Technology for Orthodontic Bracket Positioning: An In Vivo Study
## 2 Materials and Methods
### The Reliability of AR-Assisted Bracket Navigation System  
Next, the reliability of the AR system was verified. The resolution of the intraoral scanner was 640 \(\times\) 640 pixels. In the preliminary test, when the operating distance was 12 mm, 33-35 pixels were observed every 10 mm, indicating an interval of 0.285-0.30 mm per pixel. When the effective operating distance increased, the number of pixels per unit also increased, creating little interval as well as high accuracy.
###### Abstract  
Addressing stator-rotor misalignment, usually called eccentricity, is critical in permanent magnet (PM) machines since significantly high radial forces can be developed on the bearings, which can trigger a major fault and compromise the structural integrity of the machine. In this regard, this paper aims to provide insight into the unaddressed identification and analysis of the impact of eccentric tolerances on relevant performance indices of permanent magnet synchronous machines (PMSMs) with modular stator core. Static and dynamic eccentricity are assessed for different slot/pole combinations through the finite element method (FEM), and the results are compared with those of PMSMs with a conventional stator core. The unbalanced magnetic forces (UMF), cogging torque, back-emf, and mean torque variations are described and related to the eccentricity magnitude and classification. The main findings indicate that severe radial forces and significant additional cogging torque harmonics are generated because of eccentricity. Additionally, it is found that the main differences between modular PMSMs and conventional PMSMs rely on the value of slots per pole per phase.  
cogging torque; dynamic eccentricity; finite element analysis; modular stator; permanent magnet; static eccentricity; stator-rotor misalignment; tolerance analysis Article
## 1 Introduction
Laser melting gives rise to an oxidation reaction between iron and oxygen. Slot dimensions are influenced by pressure and temperature due to heat flow. The size of the 0.2 mm laser spot produces a larger slit due to the working pressure and temperature of the thermal energy reaction. Oxygen produces a crack as a result of the combustion reaction. High pressure removes the molten material and createsa slit. The increase in temperature due to laser energy and chemical reaction influences the cut. The cut edges are burned, oxidized and corroded, so oxygen pressure causes the cutting of edges. The temperature also rises due to friction between the gas and the contact surface. The intensity of the chemical reaction in the material is influenced by the laser power and the pressure of the assistant gas. The melting mass is determined by the power and pressure of the cutting gas. The gas jet has more energy than the energy of the melt, so destruction of the surface is achieved. As a result of chemical reactions, oxides appear on the cutting surface, material and in the atmosphere [38]. When heating a metal target with a circular spot of diameter d, the working temperature is given by the relation:  
\[\mathrm{T=}\frac{\mathrm{A\cdot}p}{\mathrm{k\cdot d}}, \tag{1}\]  
where A is the absorbsion coefficient, dimensionless; \(p\) is the laser power (W); k is the thermal conductivity of the material (W/m\(\cdot\)K); d is the spot diameter (mm). The novelty of this research consists in the fact that for the studied material no recommendations regarding the working regime were found in the literature. In order to understand the processes that take place inside the material, the authors considered it necessary to mathematically model aspects related to the cutting width. The obtained results allowed the determination of the optimal variant of the input parameters which leads to obtaining a minimum cutting width. The cost of processing H400 steel by CO\({}_{2}\) laser cutting was also established.
## 1 Introduction
There is only one study that evaluates the influence of PLE temperature on the recovery of phenolic compounds from burdock roots using biobased solvents such as cyclopentyl methyl ether (CPME), ethyl acetate (EtOAc), and 2-methyltetrahydroofuran (2-MTHF), achieving higher extraction yields using 100% 2-MTHF at 100 \({}^{\circ}\)C. This temperature was ideal for the maximum recovery of bioactive phenolic compounds from burdock roots using biobased solvents, avoiding undesirable reactions that degrade the target compounds [33]. However, to date, there are no comprehensive evaluations of how PLE temperature affects the stability and recovery of phenolic compounds from burdock roots when aqueous-organic solvents are used, which are the most commonly used solvents in the extraction of phenolic compounds from natural matrices. Optimizing the extraction temperature conditions is important because the efficiency and selectivity of phenolic compound recovery can vary significantly depending on the solvent used. In this context, the present study aimed to optimize the ethanolic PLE conditions for the extraction of bioactive phenolics from burdock roots, specifically assessing three extraction temperatures (50 \({}^{\circ}\)C, 100 \({}^{\circ}\)C, and 150 \({}^{\circ}\)C) to identify the optimal balance between yield and the preservation of bioactivity. Additionally, this work explored how the phenolic composition and bioactivity of burdock roots are influenced by the growing altitude and agronomical practices. Roots were collected at different altitudes in Italy from three distinct ecotypes and subjected or not subjected to weeding, as follows: Spontaneous Land (SPLE, not subjected to weeding and collected at an altitude of 150 m), Organic Land (OLE, subjected to weeding and collected at an altitude of 150 m), and Spontaneous Mountain (SPME, not subjected to weeding and collected at an altitude of 800 m). In this sense, the influence of the extraction solvent used can be compared with a previous study of our research group, where PLE with biobased solvents was employed for the recovery of phenolic compounds from the same ecotypes of burdock roots [33]. These samples were compared in terms of the total phenolic content (TPC) using the Folin-Ciocalteu assay, antioxidant capacity via 2,2-diphenyl-1-picrylhydrazyl (DPPPH), and oxygen radical absorbance capacity (ORAC) methods, and anticholinergic activity through AChE and BChE enzyme inhibition. Moreover, a detailed phenolic profile of ethanolic PLE extracts was obtained using high-performance liquid chromatography with diode array detection coupled to ion trap mass spectrometry (HPLC-DAD-IT-MS), providing a comprehensive characterization of the detected bioactive compounds.
# Micro-Slit Cutting in an Aluminum Foil Using an Un-Traveling Tungsten Wire
## 4 Result and Discussion
### Surface Roughness  
For each slit, 5 areas on its sidewall were randomly selected to measure surface roughness. Figure 9 shows the 3D views of some slit side walls machined under different velocities. The average roughness is summarized in Figure 10.  
Figure 10: Average roughness of slit side walls using different initial and secondary velocities.  
Figure 9: 3D views and profiles of the side wall of slits under different initial and secondary velocities: (**a**) 50 and 50 \(\upmu\)m-s\({}^{-1}\); (**b**) 50 and 500 \(\upmu\)m-s\({}^{-1}\); (**c**) 25 and 50 \(\upmu\)m-s\({}^{-1}\); (**d**) 10 and 50 \(\upmu\)m-s\({}^{-1}\).  
It is observed that the average roughness Ra that increases with secondary velocity becomes fast. At the initial velocity of 50 \(\upmu\)m\(\cdot\)s\({}^{-1}\), the average roughness Ra is 0.110 \(\upmu\)m when the secondary velocity is 50 \(\upmu\)m\(\cdot\)s\({}^{-1}\) and it increases to 0.225 \(\upmu\)m when the secondary velocity is 500 \(\upmu\)m\(\cdot\)s\({}^{-1}\). With the increase of the feeding rate, the tungsten micro-wire does not have enough time to cause smooth plastic deformation in aluminum foil, leaving a rough surface.  
It is also observed that the average roughness Ra is higher when the initial velocity reduces to 25 or 10 \(\upmu\)m\(\cdot\)s\({}^{-1}\), where the average rough Ra is 0.194 \(\upmu\)m and 0.206 \(\upmu\)m with the secondary velocity at 50 \(\upmu\)m\(\cdot\)s\({}^{-1}\), as shown in Figure 9. That is because when the secondary velocity is much faster than the initial velocity, the cutting process is not so smooth when changing the feeding rate, which reduces the surface quality of the machined surface.
## 5 Conclusions  
The URANS model was used to obtain the transient aerodynamic load of the train passing a bridge with a crosswind region. Based on the variance characteristics of the train derailment coefficient and wheel load reduction rate, the effect of wind barrier height and the running speed on running safety were also studied. The time-varying changes of the train's running posture and acceleration in five degrees of freedom were also compared and analyzed. The main conclusions can be concluded as below:  
Figure 17: Time-varying values of the indicators along five degrees of freedom of the head car with various running speeds: (a) Dy; (b) Dz; (c) Rx; (d) Ry; (e) Rz.  
Figure 18: Time-varying values of the accelerations along five degrees of freedom of the head car with various running speeds: (a) Ay; (b) Az; (c) A\({}_{\text{Rx}}\); (d) A\({}_{\text{Ry}}\); (e) A\({}_{\text{Rx}}\).  
Wind barriers of different heights provided significant changes to the train's aerodynamic coefficients. The ends of the wind barrier essentially changed the local flow pattern, which is reflected in the obvious fluctuations in the aerodynamic coefficients when the train enters and exits the wind barrier, especially for the leaving. The running speed of the train gives a difference in the aerodynamic coefficient values instead of the developing pattern.
2. The maximum Derailment coefficient continued to decrease with the increase in the height of wind barriers, which hardly affected the wheel load reduction rate. The fluctuation range and maximum value of the Derailment coefficient and the wheel load reduction rate of the first wheel increased with the train speed.
3. For a general high-speed train running on a bridge in the crosswind, the 4 m high wind barrier was proven to effectively protect the running posture of the train, while the 6 m high one consistently exhibited over-protection. The 2 m high wind barrier had no apparent influence on the train's posture.
4. Further work may lie in the optimization of the severe aerodynamic changes and safety threats of the train passing by the wind barrier ends. It may be achieved by adding a longitudinally extended transition wall at the ends.  
Conceptualization, J.Y. and T.C.; methodology, E.D.; software, E.D.; validation, W.Y. and S.C.; formal analysis, S.C.; investigation, S.C.; resources, W.Y.; data curation, J.Y.; writing--original draft preparation, J.Y.; writing--review and editing, B.Z.; visualization, J.Y.; supervision, T.C.; project administration, T.C.; funding acquisition, W.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (Grant No. 51978670).  
The authors acknowledge the computing resources provided by the High-Performance Computing Center of Central South University.  
The authors declare no conflict of interest.
## 4 Discussion
### Future Directions
Liability concerns also need to be addressed, particularly in cases where AI-generated recommendations lead to adverse effects or injuries. Regulatory bodies should work alongside AI developers and medical professionals to establish standardized risk assessment protocols and ethical guidelines, ensuring that AI-enhanced exercise plans are not only effective but also safe, equitable, and unbiased. Furthermore, ongoing monitoring and validation of AI models should be mandated to prevent algorithmic drift and ensure that recommendations remain aligned with the latest scientific evidence. Public transparency initiatives, such as AI explainability features, can further enhance trust by allowing users to understand the rationale behind AI-generated exercise plans, fostering greater adoption and confidence in these technologies. Furthermore, interdisciplinary efforts should focus on designing AI systems that complement, rather than replace, human expertise, and longer-term experimental studies are needed to assess whether AI-driven programs can evolve to match the efficacy of human-designed training over extended periods. In this regard, rigorous validation studies comparing AI-generated recommendations with human expertise and patient outcomes are necessary to substantiate the clinical and practical utility of these tools. In addition to these comparative studies, generative AI itself could be leveraged as a validation tool by assessing proposed exercise plans against the latest scientific criteria and evidence-based guidelines. By analyzing vast repositories of sports science literature, clinical exercise recommendations, and biomechanical data, generative AI can provide real-time evaluations of exercise regimens, highlighting potential discrepancies, missing elements, or biases in the plan. This approach would not only enhance the reliability of AI-generated prescriptions but also support fitness professionals and clinicians in refining exercise interventions to align with the most current best practices. Thus, generative AI has the potential to function as a collaborative tool, supporting sports scientists, physiotherapists, and exercise physiologists by automating routine tasks, synthesizing complex datasets, and enhancing decision-making processes. However, this requires a clear delineation of roles, ensuring that AI systems are deployed as part of an integrated, human-centered approach to exercise prescription.
###### Abstract  
Due to a rapid increase in accessible Earth observation data coupled with high computing and storage capabilities, multiple efforts over the past few years have aimed to map land use/land cover using image time series with promising outcomes. Here, we evaluate the comparative performance of alternative land cover classifications generated by using only (1) phenological metrics derived from either of two land surface phenology models, or (2) a suite of spectral band percentiles and normalized ratios (spectral variables), or (3) a combination of phenological metrics and spectral variables. First, several annual time series of remotely sensed data were assembled: Accumulated growing degree-days (AGDD) from the MODerate resolution Imaging Spectroradiometer (MODIS) 8-day land surface temperature products, 2-band Enhanced Vegetation Index (EVIZ), and the spectral variables from the Harmonized Landsat Sentinel-2, as well as from the U.S. Landsat Analysis Ready Data surface reflectance products. Then, at each pixel, EVI2 time series were fitted using two different land surface phenology models: The Convex Quadratic model (CxQ), in which EVIZ = \(f\)(AGDD) and the Hybrid Piecewise Logistic Model (HPLM), in which EVIZ = \(f\)(day of year). Phenometrics and spectral variables were submitted separately and together to Random Forest Classifiers (RFC) to depict land use/land cover in Roberts County, South Dakota. HPLM RFC models showed slightly better accuracy than CxQ RFC models (about 1% relative higher in overall accuracy). Compared to phenometrically-based RFC models, spectrally-based RFC models yielded more accurate land cover maps, especially for non-crop cover types. However, the RFC models built from spectral variables could not accurately classify the wheat class, which contained mostly spring wheat with some fields in durum or winter varieties. The most accurate RFC models were obtained when using both phenometrics and spectral variables as inputs. The combined-variable RFC models overcame weaknesses of both phenometrically-based classification (low accuracy for non-vegetated covers) and spectrally-based classification (low accuracy for wheat). The analysis of important variables indicated that land cover classification for this study area was strongly driven by variables related to the initial green-up phase of seasonal growth and maximum fitted EVI2. For a deeper evaluation of RFC performance, RFC classifications were also executed with several alternative sampling scenarios, including different spatiotemporal filters to improve accuracy of sample pools and different sample sizes. Results indicated that a sample pool with less filtering yielded the most accurate predicted land cover map and a stratified random sample dataset covering approximately 0.25% or more of the study area were required to achieve an accurate land cover map. In case of data scarcity, a smaller dataset might be acceptable, but should not smaller than 0.05% of the study area.  
p  
; land use/land cover classification; Landsat; Sentinel; ARD; HLS
## 3 Measurement Results  
The experimental test bench setup for the fabricated inductive-based digital isolator system is shown in Figure 10a. One power supply is used for supplying the printed circuit board (PCB) domain A_1/domain A_2 and domain B (Figure 10b), while the other power supply is employed to generate reference voltages for the Rx circuit. The continuous input digital signal is supplied to the system's Tx input using a 0.4/0.68 Gbps pulse/pattern generator which is then recovered at the Rx output analyzed on an Agilent 13 GHz In-finiium digital signal analyzer through the 12 GHz active probes adopted for their noise immunity performance. The input and output signals are simultaneously displayed on the oscilloscope for the propagation delay measurement. The 1:1 isolation transformer is used to assign a floating ground to the Tx side's equipment. The bottom layer of our custom PCB prototype is divided into three isolated domains, each one assigned to separate supply rail terminals. Pairs of surface-mounted ceramic capacitors (0.1 \(\upmu\)m and 10 \(\upmu\)m) are placed close to package pins to filter out power supply noise. The zoomed view of the two-layer PCB with 80 mm \(\times\) 70 mm dimensions is also shown in Figure 10b. The HALFCUBE temperature chamber is used to measure the system functionality over a wide range of temperature variations (Figure 10c).  
The implemented inductive-based digital isolator system is fabricated in a 5 V 0.35 \(\upmu\)m CMOS process. The total area for each die is 2 mm\({}^{2}\), and the two dies are assembled and bounded inside an 84-pins PGA package (Figure 11). Die A, which houses the Tx and the on-chip transformer, is powered from the domain A supply, while die B (which contains the Rx circuit) is driven from the domain B supply rails. The Tx and isolation transformers occupy a silicon area of 142 \(\upmu\)m \(\times\) 126 \(\upmu\)m and 224 \(\upmu\)m \(\times\) 224 \(\upmu\)m, respectively, while the Rx block consumes an active area of 7.3 \(\times\) 104 \(\upmu\)m\({}^{2}\). The coil width of the transformer is 6 \(\upmu\)m, and the spacing is 2 \(\upmu\)m. Two bonding arrangements were implemented to test the system functionality according to two customized coil designs for the isolation transformer: Figure 11a metal-1 to metal-3, and Figure 11b metal-1 to metal-2.
## 3 Methodology
### System Development
#### 3.3.3 Multi-Examiner System Design
For example, when generating conceptual understanding questions, the system controls information quantity and problem context complexity to ensure optimal cognitive load levels. The Revisor constructs a systematic quality control mechanism based on educational measurement theory. It ensures question quality through multidimensional evaluation criteria. The function \(CheckValidity(q,O,KG)\) not only verifies technical correctness but also, more importantly, evaluates the consistency between questions and educational objectives. When quality issues are detected, the system generates specific modification suggestions through the function \(GenerateFeedback(q,O)\) and triggers optimization processes. This closed-loop quality control mechanism ensures that the system can continuously produce high-quality assessment questions. At the agent collaboration level, the system employs an event-driven mechanism grounded in educational assessment theory, forming a complete question generation and evaluation chain. This design is inspired by Cognitive Development and Adaptive Assessment theories, ensuring continuity and adaptability throughout the process. The Identificador assesses cognitive features, triggering the Explorador to construct knowledge networks, which the Fabricator uses to dynamically adjust question strategies. Finally, the Revisor provides the final evaluation of the questions, ensuring that the assessment is not only technically accurate but also aligned with educational goals. This integrated approach to question development and validation promotes the creation of effective assessment tools that meet diverse learning needs. To enhance scalability, the Multi-Examiner system employs a microservice modular design, enabling each agent to function independently through standardized APIs. The system's innovation spans three theoretical levels: (1) systematic application of educational theories, (2) precise cognitive mapping in question design, and (3) formative assessment implementation. This framework integrates educational integrity with AI-driven automation, advancing adaptability in question generation. The design prioritizes educational purpose alongside technical innovation, ensuring generated questions serve meaningful educational needs. The modular architecture allows for continuous adaptation, supporting the integration of new theories and technologies to maintain relevance in educational technology.
# Harnessing FPGA Technology for Energy-Efficient Wearable Medical Devices
## 2 Background
### Continuous Health Monitoring  
Continuous monitoring of a patient's health-related parameters over time helps physicians to identify trends in health deterioration and make informed decisions. The most commonly monitored vital signs include pulse rate or heart rate, respiration rate, blood pressure, blood oxygen level (SpO2), blood glucose level, and body temperature. These vital parameters are tracked by capturing their associated physiological signals and processing them through signal processing algorithms. The relevant physiological signals are electrocardiogram (ECG), photoplethysmogram (PPG), electroencephalogram (EEG), electromyogram (EMG), electrooculogram (EOG), galvanic skin response (GSR), ballistocardiograph (BCG), and electrogastrogram (EGG). These are time series signals which are generally sparse in nature or can be transformed into a sparse domain [16; 17; 18]. The sampling rates associated with these signals are also usually relaxed [19]. Regarding usability, continuously acquiring these signals in hospitals using fixed devices is costly. Therefore, it is necessary to use wearable medical devices which patients can comfortably wear outside of hospitals.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
## 3 System Model
### Physical Network Modeling
A value of 0 for \(m_{i,j}(t)\) indicates the link is disconnected at time \(t\), while a value of 1 indicates that nodes \(n_{i}\) and \(n_{j}\) are connected and can route user traffic through this link.