esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Publication of Agricultural Scientific Literature Using Property Graphs
## 5 Discussion
### Progress beyond the State-of-the-Art  
The inclusion of the contents of the articles is a differentiating point between our model and some state-of-the-art solutions commented on in Section 1. The models in Nature SciGraph and The Semantic Lancet project [16] only represent and process the abstracts of the articles. Although abstracts should summarize the whole article, other sections usually have relevant information to classify or extract features from the article. For this reason, our model is in line with OpenBioDiv, which provides the full text content organized by sections. In our case, our model also divides the content of a section in paragraphs, providing a finer granularity. Moreover, having a model containing the whole structure of the articles allows for the development of visualization applications since all the information is available.  
Another differentiating factor in our model is the semantic enrichment. Nature SciGraph and OpenBioDiv only provides keywords for each article. In the case of Nature SciGraph, these keywords are taken from FoR, a standard classification for fields of research, while OpenBioDiv is not using any controlled vocabulary. For its part, _The Semantic Lancet project_ generates an RDF representation of the abstracts, which involves the generation of RDF triples describing the meaning from this textual content. This translation from natural language to RDF is complex and it probably requires human supervision to avoid errors in the process. This could make the resulting RDF graphs difficult to compare because of a lack of homogeneity between them. This problem is not present in the model provided by _Open Research Knowledge Graph_ (ORKG) [19]. In this case, they train a neural network for named entity recognition, which classifies entities as process, method, material and data, thus providing an homogeneous model that enables comparison between articles. Nonetheless, classifying instances directly under this four generic classes derives in a lack of domain knowledge that prevents the semantic understanding of the meaning of each instance. In our case, our semantic enrichment process consists on annotating the articles with entities from domain ontologies. Thus, these ontologies, which are also included in the knowledge graph, provide the semantic context for each annotation.
## 3 Results and Discussion
We have also found the experimental value of EF from the ordinary Raman and SERS spectra of 4-MPBA on the substrate with \"medium-sized\" (length of 1.1 \(\upmu\)m) NWs (see Figure 5). The formula for the experimental EF includes the relation between SERS and Raman intensities as well as the relation between the number of molecules in the case of Raman and SERS. Here we compare data for most intensive spectral peak at \(\sim\)1600 cm\({}^{-1}\), which clearly resolved in all the spectra. As a result, we have found the EF for the \"wet\" substrate as high as 7 \(\times\) 10\({}^{8}\). We attribute the difference between experimental and theoretical EF to the specific distribution (adsorption) of 4-MPBA molecules on the SERS-substrate with developed relief. Figure 8: E-field distribution in the nanometric gap between two Ag-NWs for two types of substrates: (**a**) “wet” and (**b**) “dry” substrates with “long” Ag-NWs; (**c**) “wet” and (**d**) “dry” substrates with “medium-sized” Ag-NWs.
## 5 Conclusions  
In this study, the InVEST model, GeoSOS-FLUS model, and landscape pattern index were combined to construct a comprehensive biodiversity index, which quantitatively assesses the spatiotemporal change characteristics of biodiversity in Guilin from 2000 to 2020. The effectiveness of the conservation measures was examined by comparing the simulation results of different scenarios in the future. The conclusions are as follows:  
1. The overall biodiversity situation in Guilin exhibited a positive trajectory from 2000 to 2020. However, it also displayed a gradual decline, with an annual average biodiversity index decreasing from 0.875 to 0.870. The spatial distribution of biodiversity was differentiated, with the overall distribution characterized by \"higher in the northwest, southwest, and east, and lower in the northeast, southeast, and central parts of the country\".
2. The biodiversity hotspots area was distributed in discontinuous patches in the northwestern, southwestern, and eastern parts of Guilin. The total area showed a slightly increasing trend from 2000 to 2020. Ecological protection measures have designatedbiodiversity hotspots as priority areas for protection, with emphasis on the protection of WTL and WL and the reasonable control of CL and AL.
3. The annual mean value of the biodiversity index of Guilin in the EPS in 2040 showed an upward trend compared with the NDS. It expanded the proportion of the area with high biodiversity value and effectively slowed down the degradation rate of regional biodiversity. The implementation of ecological protection measures can better achieve the goal of the sustainable development of biodiversity in Guilin and ensure regional ecological security.  
In future development, it is crucial to prioritize the protection of vital ecological environments like mountains, water bodies, and wetlands. Human disturbance in these areas should be minimized, and measures such as afforestation and wetland restoration should be implemented.  
Conceptualization, Y.L.; methodology, Y.L. and K.Z.; validation, K.Z. and X.H.; formal analysis, M.L.; resources, Z.C., M.L., H.Y. and X.H.; data curation, Y.L.; writing--original draft preparation, Y.L.; writing--review and editing, Y.L. and K.Z.; visualization, Y.L.; supervision, K.Z., X.H., Z.C., J.C. and M.L.; funding acquisition, J.C., Z.C., H.Y., X.H. and M.L. All authors have read and agreed to the published version of the manuscript.  
This study was supported by grants from the Major Special Projects of the High-Resolution Earth Observation System (84-Y50G25-9001-22/23), the Guangxi Natural Science Foundation (2021 GXNSFBA220001), and the Guangxi Key Laboratory of Spatial Information and Geomatics under grant (19-050-11-22).  
Not applicable.  
Data Availability StatementData are contained within the article.  
The authors sincerely thank the editors and the anonymous reviewers for their constructive feedback.  
The authors declare no conflicts of interest.
## 1 Introduction
#### Healthcare 5.0  
The 2030 United Nations Agenda [5] and all recent global socio-economic developments aim to enhance well-being and increase life expectancy by improving physical and mental health. These goals are achieved through technological advances and new digital initiatives, including Healthcare 5.0, which uses IoT technologies [6], remote monitoring [7], and advanced artificial intelligence applications to offer personalized, high-performance healthcare services [8; 9].  
Healthcare 5.0 is based on three fundamental pillars:  
* Resilience: It is essential that the service continues to operate even in adverse conditions [10], effectively recovering from errors [11], failures, or cyber threats, to ensure the continuity of monitoring (Figure 1).  
Figure 1: Resilience.  
* Reliability: The system must guarantee constant and safe operation, performing expected operations without errors; any malfunctions could have critical consequences on the health of patients [12].
* Personalization: Services must be able to adapt to the specific needs of each patient, optimizing treatment based on factors such as genetics, behavior, environment, and medical care, especially in the presence of multiple conditions.
## 6 Channel Estimation Based Q-Learning Algorithm
Substituting the sum rate from (33) into (34), the discounted sum rate reward, can be expressed as [41]:  
\[R_{t}^{\gamma}=\sum_{l=t}^{\infty}\gamma^{l-t}\sum_{i=1}^{M}\log\Bigl{(}1+SINR_ {i(l+1)}\Bigr{)} \tag{35}\]  
As previously stated, the Q-learning agent is the BS, whose aim is to boost the accumulative transmission sum rate. Therefore, two value functions can be inspected while considering the RL maximization problem [34; 36; 42], the first one is the state value function \(V(s)\)  
\[V(s)=E[R^{\gamma}/(S_{t}=s)] \tag{36}\]  
and the other one is the state-action value function \(Q(s,a)\)  
\[Q(s,a)=E[R^{\gamma}/(S_{t}=s,A_{t}=a)] \tag{37}\]  
where \(E\) denotes the expected value given that the agent follows a certain policy within the applied procedure. Due to unspecified transition probabilities and limited observed states, an optimal policy is difficult to achieve. Therefore, the \(Q\)-learning procedure is developed to approximately achieve the best possible policy. In the developed \(Q\)-learning procedure, the state-action value function \(Q(s,a)\) values are learned via trial and error and are updated according to the following formula [15; 34; 36; 42]:  
\[Q(s,a)\leftarrow(1-\alpha)Q(s,a)+\alpha\biggl{[}R(s,a)+\gamma\underset{a^{ \prime}\in A}{\max}Q(s^{\prime},a^{\prime})\biggr{]} \tag{38}\]  
where \(\alpha\) is the learning rate, \(s^{\prime}\) denotes the new state, and \(a^{\prime}\) is the new action that will be considered by the agent from the action space \(A\) to maximize the new state-action value function \(Q(s^{\prime},a^{\prime})\).
## 2 The Assumptions and Definitions
### Definitions
See Figure 1.  
At every time index \(k\), the pursuer heads towards the heading point \(\mathbf{h}_{k}\). Consider a circle centered at \(\mathbf{r}_{k}^{p}\), whose radius is \(v_{k}^{p}T\). Due to (6), \(\bar{L}_{k+1}\) intersects this circle at two points. Between these two points, the heading point \(\mathbf{h}_{k}\) is the point which is the closest to \(\mathbf{r}_{k+1}^{e}\). This way, the pursuer maneuvers to decrease the distance between the pursuer and the evader, while not rotating the LOS. Let us draw both \(x\)-axis and \(y\)-axis satisfying the following conditions:  
* Both \(x\)-axis and \(y\)-axis are normal to each other, and they intersect at \(\mathbf{c}_{k}\). \(\mathbf{c}_{k}\) is set as the origin. * the coordinate of \(\mathbf{r}_{k+1}^{p}=\mathbf{h}_{k}\) is \((\delta_{k},0)\). * The coordinate of \(\mathbf{r}_{k}^{p}\) is \((0,d_{k}^{c})\). First, we handle the case where \(\chi_{k}^{e}>\pi/2\). This case, the evader maneuvers to decrease the distance between the pursuer and the evader. Utilizing the geometry in Figure 1, the coordinate of \(\mathbf{r}_{k+1}^{e}\) is  
\[co_{1}(\mathbf{r}_{k+1}^{e})=(d_{k}-\alpha_{k},0). \tag{9}\]  
Here, \(\alpha_{k}=\sqrt{(v_{k}^{e}T)^{2}-(d_{k}^{c})^{2}}\) is positive. Next, we handle the case where \(\chi_{k}^{e}\leq\pi/2\). This case, the evader maneuvers to increase the distance between the pursuer and the evader. Utilizing the geometry in Figure 2, the coordinate of \(\mathbf{r}_{k+1}^{e}\) is  
\[\mathbf{r}_{k+1}^{e}=\mathbf{r}_{k}^{e}+T\mathbf{v}_{k}^{e}. \tag{2}\]0
## 2 Literature Review
### Research Gap  
Scholars have conducted some research on PBSC carbon emission reduction, providing a sufficient research basis, but there are still the following shortcomings:  
1. Current research demonstrates that participants in each link of the PBSC, from design to dismantling, can reduce carbon emissions. Still, there is less research on how the emission reduction strategies of these participants interact with each other. Compared with studies on secondary and tertiary supply chains, the model constructed in this paper covers the entire PBSC, rather than only partial links or conceptualized upstream and downstream enterprises. This paper's four-way evolutionary game model reflects participants' emission reduction strategy choices in the whole PBSC, which is more comprehensive.
2. Besides the impact of PBSC participants and external institutions on carbon emission reduction, emission reduction information and PBSC synergy also have a significant effect. Most current studies consider the impact of policy and social factors, such as subsidies, taxes, carbon trading, etc., on carbon emission reduction in PBSC, ignoring the effect of emission reduction information. In addition, current research exists on how synergies and free-rider effects in the supply chain play a role in carbon emission reduction, but there are fewer studies for PBSCs.
3. Most studies recognize the impact of stochastic disturbances on carbon emission reduction in PBSCs. Still, few scholars have included them in a systematic analysis to explore the interaction of players' emission reduction strategies. Most of the existing studies construct evolutionary game models in an ideal state, which lacks authenticity. In addition, there are still fewer methods to study PBSC carbon emission reduction using stochastic evolutionary game models.  
This study introduces a four-level stochastic evolutionary game model of PBSC, which is innovative in its completeness, comprehensiveness, and realism. Unlike two-level and three-level models, this four-level model encompasses a broader range of PBSC segments and scopes. It considers the impact of both internal and external factors on carbon emission reduction, offering a more comprehensive analysis. The incorporation of Gaussian white noise to simulate disturbances endows the model with practicality by reflecting real-world uncertainties in enterprise decision-making, thus transcending the idealized assumptions in traditional game models.
## 2 The Proposed Method
### Overall Framework
The training procedure of the proposed method is as follows: First, the input normal images are divided into non-overlapping \(N\times N\) image patches and fed into the teacher network for training. The teacher network extracts image features using an encoder, fuses multi-scale feature information through the MSAC, and then reconstructs the image via skip connections and the teacher generator. Subsequently, the learned feature representations from the teacher network are transferred to the student network using knowledge distillation. Then, the student network receives the non-overlapping image patches, reconstructs images using enhanced feature maps in the student generator, and inputs the reconstructed images into the enhanced anomaly discriminator for adversarial training. The training aims to minimize the generator's loss while maximizing the enhanced anomaly discriminator's loss.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
## 2 Materials and Methods
### Feature Extraction Module  
The designed feature extraction model utilizes the encoder component of the Transformer architecture, integrating a newly designed multi-head, multi-scale dilated convolution layer to achieve the extraction of both local and global fault information from time-series data. This module includes a multi-head self-attention layer, multi-head dilated convolution layer, feedforward layer, residual connection, layer normalization, and residual connection. The following is the detailed design and principle of the module.
# A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings
## 3 Feature Extraction
### Features for Bearings
They are the ball pass frequency of outer race (BPFO), ball pass frequency of inner race (BPFI) and ball spin frequency (BSF), which are defined as follows [24]:  
\[f_{BPFO}=w\frac{N_{B}}{D}\bigg{(}1-\frac{B_{D}}{D}\cos(\alpha)\bigg{)}, \tag{12}\]  
\[f_{BPFI}=w\frac{N_{B}}{D}\bigg{(}1+\frac{B_{D}}{D}\cos(\alpha)\bigg{)}, \tag{13}\]  
\[f_{BSF}=w\frac{D}{B_{D}}\bigg{(}1-\bigg{(}\frac{B_{D}}{D}\cos(\alpha)\bigg{)}^ {2}\bigg{)}, \tag{14}\]  
where \(D\), \(B_{D}\), \(N_{B}\) and \(\alpha\) are the bearing diameter, ball diameter, number of the balls, and the contact angle, and \(w\) is the rotating frequency of the shaft, respectively. By examining the amplitudes at these frequencies--BPFO, BPFI, and BSF, the fault can be identified in the frequency domain as shown in Figure 6. For example, Figure 8 represents the frequency spectrum after performing fast Fourier transform (FFT) for the envelope signal of CWRU dataset where the outer race was artificially damaged. The peak is apparent at the \(f_{BPFO}\), proving the presence of fault at the outer race. The corresponding Matlab function to extract the fault frequency features is _[feature, feature_name]_ = _Bear_feat(x,fs,bf,cutoff)_ where _bf_ is the vector of bearing fault frequencies given by Equations (12)-(14), and _cutoff_ is to account for the inaccuracy of these frequencies in the real bearing. Figure 7: Bearing geometry. Figure 9 shows the extracted time domain and bearing-specific features altogether for the CWRU bearing dataset: 10 normal, 10 outer race fault, and 10 inner race fault, with each feature normalized by their mean and SD, respectively. While the gear example handled two classes: only the normal and fault, this is a three-class classification: normal, outer and inner, which is more complex for purposes of distinction. Nevertheless, the visual inspection of the results indicates that the RMS, STD, and SF are good classifiers whereas the others are not.
# Recent Advances in Nanoparticle-Mediated Treatment of Inflammatory Bowel Diseases
## 13 Lysine-Proline-Valine-Loaded Nanoparticles  
The anti-inflammatory tripeptide Lys-Pro-Val (KPV) was loaded into nanoparticles and administered in a mouse model of DSS-induced colitis to assess the anti-inflammatory effects [31]. The authors evaluated the impact of KPV at low concentrations on the colonic lumen and its ability to reduce inflammation. They demonstrated their biocompatibility and rapid drug release in the intestine at pH values of 6.2, which determined a decrease in intestinal inflammation in vitro. In order to achieve therapeutic efficacy, KPV-loaded NPs required a 12,000-times lower drug dose than KPV alone. They concluded that nanoparticles were able to protect the drug throughout the stomach.  
KPV-NPs demonstrated their versatility as a drug-delivery system, site-targeted delivery and ability to reduce inflammation while overcoming physiological barriers.
## 3 Results
### Toenail Sample Donation
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & **No SMFc 1** & **Any Type** & \begin{tabular}{c} **Mann–** \\ **Whitney 2** \\ \end{tabular} & \begin{tabular}{c} **White Fish** \\ **River** \\ \end{tabular} & \begin{tabular}{c} **White** \\ **Fish Sea** \\ \end{tabular} & \begin{tabular}{c} **Blue** \\ **Fish** \\ \end{tabular} & \begin{tabular}{c} **Canned** \\ **Tuna** \\ \end{tabular} & \
\begin{tabular}{c} **Canned** \\ **Sardines** \\ \end{tabular} &
\begin{tabular}{c} **Median** \\ **Test 3** \\ \end{tabular} \\ \hline  & n = 73 & n = 24 & \(p\) & n = 4 & n = 4 & n = 7 & n = 7 & n = 1 & \(p\) \\ \hline Al & 8.0 & 17.6 & **0.003** & 24.5 & 16.3 & 20.2 & 12.9 & 13.1 & 0.354 \\ As & 0.109 & 0.090 & 0.467 & 0.066 & 2.448 & 0.148 & 0.054 & 1.169 & 0.488 \\ Ba & 1.250 & 0.984 & 0.316 & 0.964 & 1.880 & 0.735 & 1.110 & 1.444 & 0.436 \\ Be & 0.0001 & 0.0005 & **0.009** & 0.0047 & 0.0003 & 0.0005 & 0.0007 & 0.0003 & **0.013** \\ Cd & 0.031 & 0.053 & 0.417 & 0.020 & 0.050 & 0.058 & 0.021 & 0.587 & 0.622 \\ Co & 0.017 & 0.016 & 0.874 & 0.027 & 0.024 & 0.015 & 0.016 & 0.014 & 0.266 \\ Cr & 1.190 & 0.749 & 0.208 & 0.832 & 1.015 & 0.813 & 1.188 & 0.341 & 0.409 \\ Cu & 5.200 & 3.277 & 0.320 & 4.007 & 6.732 & 2.329 & 4.212 & 12.178 & 0.270 \\ Fe & 25.9 & 32.9 & 0.096 & 48.0 & 34.2 & 32.1 & 38.1 & 26.1 & 0.790 \\ Hg & 0.220 & 0.189 & 0.198 & 0.187 & 0.315 & 0.215 & 0.123 & 0.175 & 0.169 \\ Li & 0.028 & 0.021 & 0.879 & 0.039 & 0.028 & 0.018 & 0.036 & 0.018 & 0.425 \\ Mn & 0.392 & 0.384 & 0.907 & 0.641 & 0.501 & 0.211 & 0.518 & 0.209 & 0.469 \\ Mo & 0.045 & 0.058 & 0.273 & 0.060 & 0.088 & 0.069 & 0.048 & 0.056 & 0.583 \\ Pb & 2.190 & 2.798 & 0.472 & 3.285 & 1.556 & 3.430 & 5.555 & 1.449 & 0.473 \\ Ni & 0.575 & 0.857 & 0.110 & 1.242 & 0.853 & 0.902 & 0.527 & 6.602 & 0.583 \\ Se & 0.811 & 0.784 & 0.947 & 0.926 & 1.082 & 0.747 & 0.788 & 1.141 & 0.322 \\ Sr & 1.620 & 0.616 & **0.017** & 0.717 & 2.211 & 0.509 & 0.569 & 0.452 & 0.234 \\ Tl & 0.003 & 0.002 & 0.163 & 0.003 & 0.008 & 0.002 & 0.003 & 0.018 & 0.562 \\ U & 0.004 & 0.005 & 0.744 & 0.010 & 0.005 & 0.003 & 0.005 & 0.001 & 0.222 \\ V & 0.038 & 0.032 & 0.457 & 0.038 & 0.027 & 0.033 & 0.045 & 0.023 & 0.790 \\ W & 0.005 & 0.010 & 0.326 & 0.016 & 0.005 & 0.026 & 0.006 & 0.027 & 0.620 \\ Zn & 96.3 & 102.5 & 0.598 & 92.5 & 156.4 & 102.0 & 87.6 & 125.5 & 0.256 \\ \hline \hline \end{tabular}  
* No fish consumption in the 8 h prior to donating the toenails sample. 2 No fish consumption vs. any type of fish consumption. 3 No fish consumption vs. type of fish consumption. 3 No fish consumption vs. type of fish consumption.  
\end{table} 
Table 4: Comparison of the median metal levels (ppb) in toenail samples according to fish consumption in the 8 h prior to sample donation. Statistically significant results are highlighted in bold.
## 3 Aircraft Elastic Deformation Estimation and Inertial Network Algorithm Design
### The Flight Parameters Are Solved by FADS  
From the pressure distribution measured by the pressure sensor array, FADS derives the flight parameters indirectly (angle of attack, angle of sideslip, dynamic pressure, static pressure, Mach number). When the pressure measurement value is obtained by the pressure hole array, the algorithm for solving the flight parameters of the FADS system can be roughly divided into two categories. One is based on the semi-empirical formula model, which is mainly suitable for establishing the relationship between the surface pressure distribution and the atmospheric data according to the physical principle. The other is a data-based solution method, which directly uses interpolation, fitting and other methods to calculate the atmospheric data according to the pressure value measured by the pressure hole array [34; 35; 36; 37; 38; 39].  
In this paper, the polynomial fitting method is used to solve the flight parameters (attack), and only the quadratic term and the following terms are considered. This is because if the degree of the polynomial is too high, the output will be very sensitive to the input error. At the same time, the high degree polynomial takes into account the cross product of all pressure values, and the polynomial is too complex. The pressure values measured by six pressure measuring holes are taken as input values, and the corresponding angles of attack are taken as output values, respectively. The angle of attack can be expressed as  
\[\begin{array}{l}\alpha=a_{0}+a_{1}p_{1}+a_{2}p_{2}+a_{3}p_{3}+a_{4}p_{4}+a_{ 5}p_{5}+a_{6}p_{6}\\ +a_{7}p_{1}^{2}+a_{8}p_{2}^{2}+a_{9}p_{3}^{2}+a_{10}p_{4}^{2}+a_{11}p_{5}^{2}+ a_{12}p_{6}^{2}+\\ a_{13}p_{1}p_{2}+a_{14}p_{1}p_{3}+a_{15}p_{1}p_{4}+a_{16}p_{1}p_{5}+a_{17}p_{1}p_{6 }\\ +a_{18}p_{2}p_{3}+a_{19}p_{2}p_{4}+a_{20}p_{2}p_{5}+a_{21}p_{2}p_{6}+a_{22}p_{3 }p_{4}\\ +a_{23}p_{3}p_{5}+a_{24}p_{3}p_{6}+a_{25}p_{4}p_{5}+a_{26}p_{4}p_{6}+a_{27}p_{5 }p_{6}\end{array} \tag{40}\]  
The process of calculating the undetermined coefficient \(a_{0}-a_{27}\) according to the input and output data is to find the value that minimizes the residual vector 2 norm of the calculated value and the sample value. The optimization objective function is as follows:  
\[J_{\alpha}=\min\left\|a_{c}-\alpha\right\|_{2}^{2} \tag{41}\]where the subscript \(c\) represents the calculated value. In this way, it is actually an objective function optimization problem. The commonly used methods are gradient method, Newton method and so on. In this study, the Levenberg-Marquardt method is used. The LM method is used to approach the minimum value of the two norms by function approximation. The specific steps are shown in Figure 4.