esa-sceva/satcom-chunk-collection huggingface dataset:
# Accurate and Detailed Transversal Road Section Characteristics Extraction Using Laser Scanner
## 2 Algorithm  
In order to locate the vehicle laterally on the road it is necessary firstly to know the transversal characteristics of the road. To do this, the algorithm shown in Figure 1 is proposed. The algorithm begins by obtaining the raw data from the laser scanner. From that set of points, the function of road boundaries detection that offers as output the width of the roadway is implemented. Two methods are considered in parallel. The same set of points serves as input data for the lane lines detection function, where the lane through which the ego-vehicle moves is distinguished from the rest, since it is expected that a greater density of points will be available for fitting. This second function offers lane widths as output. We proceed to evaluating whether the width obtained for each lane is included in a preset range, with which the shoulder can be differentiated from the other lanes, as well as checking if a line has been lost due to the lack of points of it; this implies the generation of a new estimated line between those already calculated. In the cases of both the lanes and the road boundaries calculation, when the functions do not offer satisfactory results, the information of the previous scan is used. Finally, the possibility of having a digital map is contemplated, which can be used to corroborate the number of lanes obtained (and use this feedback for the selection in the lane identification function) and classify them, distinguishing the lanes of the main roadway from the additional lanes as the merging or exit ones.  
In the following subsections, the main functions of lane detection are described in detail.  
Finally, after the road characteristics extraction process, it is simple to determine the lane through which the vehicle moves, its lateral position in, and if it should perform a lane change maneuvers.
# Chiral Metasurfaces: A Review of the Fundamentals and Research Advances
## 3 Types of Chiral Metasurfaces
### Multi-Layered Chiral Metasurface
In addition, multi-layered chiral metasurfaces have been proposed to obtain a dual-band AT of linearly and circularly polarized waves [127; 138; 139]. A bilayer chiral metasurface that can produce dual AT for linearly polarized light and CPL in NIR was suggested by [135] (Figure 3m). The metasurface gives a maximum AT of around 0.65 and 0.6 for the linearly polarized and circularly polarized waves, respectively (Figure 3n). Moreover, [16] suggested a novel chiral folded metasurface consisting of antisymmetric SRRs that have spin-selective transmission and pronounced controllable intrinsic chirality in the NIR (Figure 3o). The design consists of breaking the mirror symmetry by folding the components of the metasurface at a certain angle, as shown in Figure 3o. A CD value of 0.7 was obtained due to the intrinsic chirality of the chiral metasurface (Figure 3p). Sequential deposition and lithography techniques, such as electron-beam lithography, are often used to fabricate multi-layered chiral metasurface [70; 84; 85; 86; 134; 136; 137]. These techniques are generally more complex compared to the fabrication of single-layered chiral metasurfaces due to the need for precise control and alignment of multi-layers. However, they offer greater versatility in tailoring the chiroptical response, and they are essential for achieving advanced functionalities along a broad range of wavelengths. Sharp CD peaks in multi-layered metasurfaces have various potential applications, including thermal-radiative detection, chiral-optic spectroscopy, and NIR telecommunication systems [84; 127; 136; 137; 138; 139].
# On Training Data Selection in Condition Monitoring Applications--Case Azimuth Thrusters
## 3 Results
### Comparison of Tests to Identify Normally Distributed Residuals  
Figure 12 illustrates the percentage of normally distributed residual sets in training subset candidates based on five different hypothesis tests applied. The percentage values give an indication firstly on the adaptability of each hypothesis test to varying datasets and secondly on their usability in system identification. The 'Adaptive training subsets' approach was used in the tests, and therefore, the number of subset candidates varied in different datasets, as shown in Section 3.1. The residuals of all eleven features (see Table 1) were tested in each subset. Therefore, a 100% result requires a varying number of normally distributed residual sets in different datasets. In the case of N = 1000, for example, the number would be 11 \(\times\) 4198 (the number of features \(\times\) the number of training subset candidates).  
Figure 12: Percentage of normally distributed residual sets based on five hypothesis tests. Results highlight the adaptation performance of the tests on different datasets as a part of the system identification procedure. Low percentage indicates poor applicability in practice.  
In general, the results indicate that the number of normally distributed residual sets reduced when the sample size N grew. KS and AD results exhibit the highest percentages of normally distributed sets, which suggests they could be useful tests for practical implementation. Moreover, these methods had the largest change in percentage points when N changed, which indicates they have high adaptability to different datasets. The other tests, namely, Lilliefors', JB and SW tests, resulted in lower percentages, indicating that the identification of suitable training subsets could be challenging if such tests were applied.  
Figure 13 illustrates the operating ranges in the selected training subsets with different hypothesis tests. Relatively similar ranges with wide coverage were selected based on KS and AD. The other tests resulted in the selection of a low number of ranges. The ranges were narrow and mostly located at roughly 500 and 530 rpm speed. The selection of a few narrow ranges is unfavorable for practical monitoring.  
The presented results are somewhat conflicting with the literature. Previously reported simulations reveal that powerful normality tests include methods such as SW or JB, whereas KS is less powerful [36; 37]. By contrast, the results presented here suggest that the KS and AD tests could have high usability in the automated procedures presented, whereas SW and JB appear unfavorable. The selection of an appropriate test requires understanding of the considered application and its data.
# Markov-Embedded Affinity Learning with Connectivity Constraints for Subspace Clustering
## 6 Experiments
### Experimental Setup
The order of the fine-grained neighbor matrix is set to 3. For EDESC, we set the parameters according to [34], with \(\beta\) set within the range \(\{0.001,0.005,0.1,0.5,1,5,10\}\) and \(d\) and \(\eta\) set within the range \(\{1,2,3,4,5,6,7\}\). For the parameters of PSSC, they are set according to [35]. A three-layer convolutional network with a depth set to \(\{20,10,5\}\) and a kernel size set to \(\{5,3,3\}\) is employed. For each method, the experiment considers all combinations of parameters. Brief descriptions of each method are provided in the following. (1) NSLLRR extends LRR on manifolds. We construct the similarity matrix using a binary weighting strategy and retain 5 neighbors on the graph. (2) SSC is a classic method that uses the L-1 norm as a regularization term to achieve sparse representation on the coefficient matrix. (3) SCLA is another type of extension of LRR that approximates the rank with a non-convex LDRA rather than the NN. (4) SSRSC recovers physically meaningful representations. For its parameters, we follow the original paper. (5) S-TLRR and R-TLRR are different forms of TLRR. TLRR is an approach that can exactly recover the clean data of an intrinsic low-rank structure and accurately cluster them as well. S-TLRR and R-TLRR handle slightly and severely corrupted data, respectively. (6) TLRSR can directly perform subspace learning on three-dimensional tensors. (7) ETLRR decomposes the original data tensor into three parts: a low-rank structure tensor, a sparse noise tensor, and clustering. (8) EDESC learns a set of subspace bases from the latent features extracted by a deep auto-encoder, where the bases and network parameters are iteratively refined. (9) PSSC integrates the deep SC and pseudo-supervision to obtain the similarity matrix. By utilizing pseudo-supervision, it enables the model to have better feature extraction and similarity learning capabilities.
# Named Entity Recognition in Online Medical Consultation Using Deep Learning
###### Abstract  
Named entity recognition in online medical consultation aims to address the challenge of identifying various types of medical entities within complex and unstructured social text in the context of online medical consultations. This can provide important data support for constructing more powerful online medical consultation knowledge graphs and improving virtual intelligent health assistants. A dataset of 26 medical entity types for named entity recognition for online medical consultations is first constructed. Then, a novel approach for deep named entity recognition in the medical field based on the fusion context mechanism is proposed. This approach captures enhanced local and global contextual semantic representations of online medical consultation text while simultaneously modeling high- and low-order feature interactions between local and global contexts, thereby effectively improving the sequence labeling performance. The experimental results show that the proposed approach can effectively identify 26 medical entity types with an average F1 score of 85.47%, outperforming the state-of-the-art (SOTA) method. The practical significance of this study lies in improving the efficiency and performance of domain-specific knowledge extraction in online medical consultation, supporting the development of virtual intelligent health assistants based on large language models and enabling real-time intelligent medical decision-making, thereby helping patients and their caregivers access common medical information more promptly.  
named entity recognition; deep learning; model fusion; fusion context mechanism; online medical consultation Article
## 3 Results
### Harvest Components: Crop Yield, Protein Content, Starch, and Hectoliter Weight  
The crop yield and grain quality parameters showed significant differences (\(p\) < 0.0001) between fertilizing treatments (Table 4). All the fertilized plots had higher yields than the unfertilized control plots. We can observe that the highest performance was obtained in the plots fertilized with conventional strategies P(MAP) + N(Urea) (8618 kg ha\({}^{-1}\)), followed by P(Str + C) + N(Urea), which showed a similar yield compared to the inorganic treatment. The fertilizers applied significantly increased the protein content (+31.6%) and HW (5%) in grain compared to the unfertilized control. P fertilization combined with N treatments, N(Urea) and N(Urea + C), showed a more positive effect on the protein content and HW than N(BlM + C). On the contrary, starch was significantly higher in the unfertilized control, followed by P(BM + C)+N(Urea + C).  
Figure 5: Comparison of measured and predicted fCover using PlanetScope and Micasense.  
Figures 6 and 7 show the analysis of key crop components--such as yield, protein content, starch, and hectoliter weight--which are modeled using PlanetScope and MicaSense data based on specific spectral bands. The timing of fertilization with phosphorus (P) plays a key role in influencing these results, as reflected in the phenological data at the beginning of the season. The PlanetScope model showed moderate correlations between predicted and actual values for the different crop components. For crop yield monitoring, the PlanetScope model showed an R\({}^{2}\) of 0.54, with the best predictions occurring during phases of rapid plant growth after fertilization. The model used spectral bands B04 (green)-B06(red) with a GDD of 569 (Figure 6a). For starch content, the PlanetScope model achieved an R\({}^{2}\) of 0.56 using bands B04 (green)-B08 (NIR) with a GDD of 761 (Figure 6b), effectively capturing starch accumulation, particularly after nitrogen application at filtering. HW, an indicator of grain quality, showed an R\({}^{2}\) = 0.51 with bands B02 (blue)-B07 (red edge) (GDD of 414) (Figure 6d). The consistency in predicting HW suggests that early phosphorus and nitrogen fertilization helped to maintain grain density. Protein content showed a slightly lower correlation (R\({}^{2}\) = 0.49) using bands B02 (blue)-B06 (red edge) and a GDD of 554 (Figure 6c).  
(R\({}^{2}\) = 0.34). These low R\({}^{2}\) values indicate that the MicaSense model struggled to capture meaningful linear relationships between the spectral data and the measured outcomes in this experiment.
# Modeling and Analysis of Contactless Solar Evaporation for Scalable Application
## 3 Result and Discussion
### Heat Loss Suppression for Scalable SCE
#### 3.4.2 Heat Loss Suppression on the Water Side  
In order to reduce heat loss on the water side, a layer of foam insulation was added starting from 5 mm below the water surface. The cross-sectional temperature distribution of the absorber-emitter, straight hole, air layer and 5 mm thick water layer under different foam insulation thicknesses of 0 mm, 10 mm, 35 mm and 45 mm is shown in Figure 12a. When the thickness increases from 0 mm to 10 mm, the overall temperature of the absorber-emitter, vapor diffusion hole and air layer rise significantly, indicating that the foam insulation layer plays an effective role in suppressing heat loss on the water side. However, when the thickness increases from 35 mm to 45 mm, the temperature rise of absorber emitter and air layer is slight. To further explore the relationship between evaporation rate and foam insulation layer thickness, the heat flow distribution under different foam insulation layer thickness was analyzed, as shown in Figure 12b. The results show that the convective heat loss on the absorber surface and other heat losses rise slightly as the foam insulation increases, mainly due to the temperature increase of the absorber-emitter. In addition,  
Figure 11: Energy flows distribution of absorber–emitter and water surface during heat energy transfer from the top to bottom under the condition that the thickness of air layer is 2 mm, the hole diameter is 1.1 mm, and the hole area ratio is 14%.  
when the foam insulation layer thickness increases, the bottom heat loss can be divided into three stages: The first stage is the rapid decline stage, when the thickness of foam insulation layer increases from 0 mm to 10 mm, the bottom heat loss decreases from 16.1% to 7.1%. The second stage is the gentle decline stage, when the thickness of the foam insulation layer increases from 10 mm to 35 mm, the conduction heat loss decreases from 7.1% to 2.4%. The third stage is the slow decline stage, when the thickness of foam insulation layer increases from 35 mm to 45 mm, the bottom heat loss only decreases from 2.4% to 1.7%. The variations of the latent heat proportion and evaporation rate are opposite to that of bottom heat loss. The evaporation rates are 0.595 kg.m\({}^{-2}\).h\({}^{-1}\), 0.645 kg.m\({}^{-2}\).h\({}^{-1}\), and 0.652 kg.m\({}^{-2}\).h\({}^{-1}\) at the thicknesses of the insulation layer are 10 mm, 35 mm and 45 mm, respectively. It can be deduced from the above analysis that the insulation layer thickness has an optimal value (35 mm) to take into account the economy and insulation performance.
## Materials and Methods
### Eddy Covariance Measurements  
A cluster of six EC systems has been established in tallgrass prairie pastures as a part of the GRL-FLUXNET, a network of 17 integrated flux measurement systems established at the USDA-ARS, GRL [8; 23]. The EC systems are composed of an open path infrared gas analyzer (LI-7500 RS, LI-COR Inc., Lincoln, NE, USA) and a 3-D sonic anemometer (CSAT3, Campbell Scientific Inc., Logan, UT, USA). The EC systems collect CO\({}_{2}\), H\({}_{2}\)O, and surface energy fluxes within the soil-plant-atmosphere complex. Additional meteorological variables include air temperature (T\({}_{\rm a}\)), relative humidity (RH), vapor pressure deficit (VPD), soil water content (SWC), soil temperature, soil heat flux, net radiation (R\({}_{\rm n}\)), and photosynthetic photon flux density (PPFD). In addition, P13 contains an Oklahoma Mesonet station as a part of more than 110 surface observing stations across Oklahoma for statewide monitoring of the mesoscale environment [24]. Precipitation data used in this study were obtained from the Oklahoma Mesonet website ([[http://mesonet.org/](http://mesonet.org/)]([http://mesonet.org/](http://mesonet.org/))).  
We computed 30-min values of eddy fluxes using the EddyPro software version 6.2.0 (LI-COR Inc.). Flux data flagged as poor quality (i.e., quality flags 2) and statistical outliers (\(>\)3.5 times ofstandard deviation based on 2-weeks running window) were discarded [23]. Sensible (H) and latent (LE) heat fluxes beyond the ranges of \(-\)200 to 500 W m\({}^{-2}\) for H and \(-\)200 to 800 W m\({}^{-2}\) for LE were excluded [25; 26]. The REddyProc package from the Max Planck Institute for Biogeochemistry, Germany ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebRPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebRPackage)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebRPackage](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProcWebRPackage))) was used to fill gaps in data and to partition (nighttime-based) net ecosystem CO\({}_{2}\) exchange (NEE) into gross primary production (GPP) and ecosystem respiration (ER) [27; 28].
# Research on High-Performance Fourier Transform Algorithms Based on the NPU
## 2 Related Works  
### The Applications and Acceleration Research of the Fourier Transform  
#### 2.1.1 The Applications of Fourier Transform  
The Fourier Transform is a widely adopted spectral analysis method in the preprocessing stage of intelligent computing applications. These applications span various domains, including image recognition, fatigue detection, material design, and more. In detail, [PERSON] et al. [16] proposed a feature extraction approach that involves preprocessing images in various graphic formats using FFT and spectral analysis methods. [PERSON] et al. [17] proposed a fatigue detection system that utilizes FFT for feature extraction and noise elimination. [PERSON] et al. [18,19] calculated the thermal conductivity based on the microstructure geometry of the provided material using an FFT-based method. Subsequently, they developed a machine learning method to design materials according to desired performance.
# Ionic Liquid-Modified Porous Organic Polymers as Efficient Metallocene Catalyst Supports
###### Abstract  
Porous organic polymers (POPs) are widely used in various areas such as adsorption, separation and catalysis. In the present work, ionic liquid-modified porous organic polymers (IL-POPs) synthesized by dispersion polymerization were applied to immobilize metallocene catalysts for olefin polymerization. The prepared IL-POPs were characterized by Fourier transform infrared spectrometer (FT-IR), nitrogen sorption porosimetry, X-ray photoelectron spectroscopy (XPS), thermal gravimetric analysis (TGA), inductively coupled plasma atomic emission spectrometer (ICP) and scanning electron microscope (SEM) analysis. The IL-POPs obtained pores with surface specific area (SSA) ranging from 16.9 m\({}^{2}\)/g to 561.8 m\({}^{2}\)/g, and total pore volume (TPV) ranging from 0.08 cm\({}^{3}\)/g to 0.71 cm\({}^{3}\)/g. The supported catalysts Zr/MAO@IL-POPs exhibit great activity (3700 kg PE/mol-Zr-bar-h) in ethylene polymerization, and the GPC-IR results show that the polyethylene has narrow molecular weight distribution (2.2 to 2.8). The DSC results show that the melting point of prepared polyethylene was as high as 138 \({}^{\circ}\)C, and the TREF analysis results indicate that they have similar chemical composition distribution with elution temperature at 100.5-100.7 \({}^{\circ}\)C.  
ionic liquid; porous organic polymer (POP); metallocene catalyst; ethylene polymerization; heterogeneous polymerization +
Footnote †: journal:  
Academic Editor: [PERSON]
# Reliability Analysis of C\({}^{4}\)ISR Systems Based on Goal-Oriented Methodology
## 6 Conclusions  
This paper applied the GO method to detailed analyze the reliability of a C\({}^{4}\)ISR system. In the analysis, the impact of common cause failures and shared signals have been considered, which are common phenomena of hard-and-software integrated systems like the C\({}^{4}\)ISR system, and which also makes classical reliability analysis techniques for instance FTA, BN, etc. are not applicable to analysis the reliability such a system. Due to this, this paper constructs a GO method to analysis the reliability of a C\({}^{4}\)ISR system. Overall, the reliability of the C\({}^{4}\)ISR system is computed to be some 0.85 and the reliability as well as the failure probability of units of the C\({}^{4}\)ISR system are reached. Moreover, critical failures of hardware failures like communication module failures and motherboard module failures as well as software failures like network module application software failures and decompression module software failures are ascertained by the GO model as well. The results achieved are in line with the experience accumulated among the historical operations of the C\({}^{4}\)ISR system. This paper contributes to the reliability analysis of all hard-and-software integrated systems. However, in the future more practical factors should be considered in the GO model constructions, including the degradation of mechanical elements, human factors, and environmental factors, which are unneglectable for reliability analysis of the C4 ISR system and will extend the capability of the GO methodology.  
Conceptualization, Y.L. and H.-Z.H.; data curation, Y.L. and T.Z.; methodology, Y.L.; formal analysis, Y.L. and H.-Z.H.; formal analysis, Y.L. and H.-Z.H.; writing--original draft preparation, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No data were used to support this research.  
The first author thanks the helpful discussion with doctor [PERSON] in Centre for Marine Technology and Ocean Engineering (CENTEC), Instituto Superior Tecnico, Universidade de Lisboa, Lisbon, Portugal.  
The authors declare no conflict of interest.