esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 5 Optimal Dispatching of Micro-Energy-Grid
### Constraint Equation
#### 5.2.4 Power Exchange Constraints with Distribution Network  
To ensure the stability of power system in distribution network, power exchange with distribution network should satisfy the following constraints:  
\[0\leq P_{buy}(t,s)\leq I_{sg}(t)P_{grid\max} \tag{33}\]  
\[\left[1-I_{sg}(t)\right]P_{grid\min}\leq P_{sell}(t,s)\leq 0 \tag{34}\]  
where \(P_{buy}(t,s)\) is the purchase power of scenario \(s\) at moment \(t\), which is a positive value; \(P_{sell}(t,s)\) is the sale power of scenario \(s\) at moment \(t\), which is a negative value; \(P_{grid\max}\) and \(P_{grid\min}\) are the maximum and minimum value of power exchange with distribution, respectively network; and \(I_{sg}(t)\)is a 0-1 variable and restricts the simultaneous occurrence of purchasing and selling electricity with 1 representing purchasing electricity from the grid and 0 representing selling.
## 2 Materials and Methods
### Model-Driven Data Construction  
#### 2.4.1 Data Source  
In this study, net ecosystem exchange (NEE) and ER fluxes in 2016 and 2019 were obtained using the static chamber/gas chromatography techniques and the detailed operation processes were consistent with [PERSON] et al. 2012 [68]. The measurement frequency is one or two times per week (except for bad weather or fire prevention periods), and the average value was obtained from four repetitions of each measurement. In addition, air temperature and relative humidity, soil temperature, soil water content, wind speed, net radiation, and thawing depth in 2016 and 2019 were measured using the HMP45C temperature and relative humidity probe (Vaisala Inc, Helsinki, Finland), 107-l temperature probe (Campbell Scientific, NE, USA), CS616-l water content reflectometer (Campbell scientific, NE, USA), W200p wind vane (Vector Inc, Kuopio, Finland), CNR-1 net radiometer (Kipp and Zonen, NY, USA), and manual measurement, respectively. The thaw depth was measured manually once or twice a week during the observation period (except for bad weather and fire prevention period). Along the diagonal directions of the peatland, we measured the depth of the active layer by inserting a steel rod into the soil until maximum resistance, and then recorded the distance from the bottom of the probe to the surface layer. We chose 10 sites in each diagonal line and then calculated the averages. Current climate data and prediction data from five global climate models (GCMs) were selected to assess the sensitivity of peatland responses to climate change. In this study, the current climate data were obtained from the China National Meteorological Center (1959-2018, [[http://data.cma.gov.cn](http://data.cma.gov.cn)]([http://data.cma.gov.cn](http://data.cma.gov.cn)), accessed on 10 December 2021). The future meteorological data (2006-2100) using five GCMs (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, MIROC-ESM-CHEM, NorESM1-M) under different future emission scenarios (Table 1) were obtained from CMIP5 ([[https://cera-www.dkrz.de/WDCC/ui/cerasearch/](https://cera-www.dkrz.de/WDCC/ui/cerasearch/)]([https://cera-www.dkrz.de/WDCC/ui/cerasearch/](https://cera-www.dkrz.de/WDCC/ui/cerasearch/)), accessed on 10 December 2021). Previous studies have tested the performance of CMIP5 GCMs in northeastern China and reported that it could effectively represent the warming trend and interannual variation of precipitation in the study area [69; 70]. In this study, the spatial resolutions of the input data and results were \(0.5^{\circ}\times 0.5^{\circ}\).
## 5 Conclusions  
We developed and tested a hybrid model combining hematoma segmentation and HE classification to generate binary absolute HE annotations in a large ICH dataset. This approach uses automated hematoma segmentation on admission and follow-up head CTs, followed by a deep-learning HE class prediction model, to limit expert review of automated segmentation to a subset of patients based on preferences to minimize false-positive or false-negative misclassifications in the final classification. Further improvements in segmentation and classification models can increase the proportion of subjects excluded from expert review of segmentations.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/app15010111/s1](https://www.mdpi.com/article/10.3390/app15010111/s1)]([https://www.mdpi.com/article/10.3390/app15010111/s1](https://www.mdpi.com/article/10.3390/app15010111/s1)): Figure S1: CNN architecture; Figure S2. Example loss and AUC diagram when training/validation; Figure S3: Histogram of HE annotation from model (CNN+SVM) with high sensitivity=0.90; Figure S4: Classification of \(\geq\)3 mL HE using CNN+SVM model; Figure S5: Classification of \(\geq\)6 mL HE using CNN+SVM model; Figure S6: Classification of \(\geq\)9 mL HE using CNN+SVM model; Figure S7: Classification of \(\geq\)12.5 mL HE using CNN+SVM model.  
**Author Contributions:** Conceptualization and Study Design: A.T.T., K.N.S. and S.P.; Data collection/ curation: G.A.K., D.D., A.M., G.J.F. and A.I.Q.; Investigation: A.T.T., D.D., T.Z., G.A.K., A.D., A.M., A.I.Q., G.J.F., K.N.S., J.N. and S.P.; Methodology, Formal Analysis, Visualizations (Figures): A.T.T., D.D. and S.P.; Data Interpretation: A.T.T., T.Z. and S.P.; Manuscript Writing--original draft: A.T., T.Z. and S.P.; Manuscript Writing--review and editing: A.T., D.D., T.Z., G.A.K., J.Z., A.D., M.-C.C., A.M., A.I.Q., S.B.M., S.M., G.J.F., K.N.S., J.N. and S.P.; Project administration: A.T., K.N.S.  
and S.P.; Resources: K.N.S. and S.P.; Supervision: K.N.S. and S.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Doris Duke Charitable Foundation (2020097) and NIH (K23 NS118056).  
This study was conducted in accordance with the Declaration of Helsinki and approved by the Institutional Review Boards at Yale University, New Haven, CT, USA (200002496), Charite Hospital, Berlin, Germany (PN EA1/035/20), and the participating centers at the ATACH-2 trial (ClinicalTrials.gov identifier: NCT01176565).  
Informed Consent Statement: Informed consent was obtained from all subjects in the ATACH-2 trial. Patient consent in the Yale and Charite datasets was waived due to the retrospective nature of study.  
Data Availability Statement: Data are available upon request and on approval from the respective register holders.  
The authors declare no conflicts of interest.
## 6 Summary and Conclusions  
In this research, a porosity prediction model was established for granular mixtures through the DEM model and BPNN algorithm. A total of 400 mixes were simulated with the discrete element method to provide the database for BPNN training and testing. The prediction results were verified by steel ball experiments. Through this research, the following findings and conclusions were observed:  
1. This study provided an efficient and detailed method to build a porosity prediction model according to DEM model and BPNN algorithm. After model training, testing in MATLAB and verifying in lab experiments, the model's coefficients of determination could achieve 99%.
2. A suitable original database size was compared and recommended: prediction model accuracy could reach about 98% when the original data size was larger than 75. The amount of raw data can be determined according to different accuracy requirements.
3. A shape reduction factor was proposed to achieve the porosity reduction from sphere to real particle shape, which made the porosity prediction results closer to reality and easy to use. In this research, particle shapes were not considered as the primary control parameters, which will be considered in a future study. Evaluation is recommended of particle shapes through combining the findings of this research with experimental testing methods.  
Conceptualization, Y.L.; Data curation, M.L.; Funding acquisition, Y.L.; Methodology, M.L.; Resources, B.M.; Software, P.S.; Supervision, Z.Y.; Validation, P.S.; Writing--original draft, M.L.; Writing--review & editing, Y.L. and P.S. All authors have read and agreed to the published version of the manuscript. This research was funded by National Natural Science Foundation of China, grant number 51978074. The APC was funded by National Natural Science Foundation of China.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Values** \\ \hline The number of samples & 15 \\ The mean value of samples & 0.218 \\ The standard deviation of samples & 0.023 \\ Sampling average error & 0.006 \\ Probability level & 0.95 \\ Free degree & 14 \\ t-value & 2.145 \\ Error range & 0.013 \\ Confidence lower limit & 0.206 \\ Confidence upper limit & 0.231 \\ \hline \end{tabular}
\end{table}
Table 15: The confidence limit calculation of mixture porosity of 15 real aggregate shape models.
## 4 Research Methods
\begin{table}
\begin{tabular}{r c c c c c c} \hline  & **Class** & **Frequency** & \(\sigma\)**-Sigma** & **SD** & **MSD** & **S/N** \\ \hline  & 0.000000–0.260000 & 371 & \(1\sigma\downarrow\) & & & \\ \cline{2-6}
**0** & 0.260000–0.520000 & 743 & \(1\sigma\downarrow\) & & & \\ \cline{2-6}
**0** & 0.520000–0.780000 & 1147 & \(1\sigma\uparrow\) & & & \\ \cline{2-6}  & 0.780000–1.040000 & 1413 & \(2\sigma\uparrow\) & & & \\ \hline  & 1.040000–1.300000 & 1403 & \(2\sigma\uparrow\) & & & \\ \cline{2-6}
**1** & 1.300000–1.560000 & 1371 & \(2\sigma\uparrow\) & & & \\ \cline{2-6}  & 1.560000–1.820000 & 1402 & \(2\sigma\uparrow\) & & & \\ \cline{2-6}  & 1.820000–2.080000 & 1531 & \(4\sigma\uparrow\) & & & \\ \hline  & 2.080000–2.340000 & 1675 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**2** & 2.340000–2.600000 & 1779 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 2.60000–2.860000 & 1886 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 2.860000–3.120000 & 1968 & \(4\sigma\uparrow\) & & & \\ \hline  & 3.120000–3.380000 & 2042 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**3** & 3.380000–3.640000 & 2025 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 3.640000–3.900000 & 2228 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 3.90000–4.160000 & 2181 & \(4\sigma\uparrow\) & & & \\ \hline  & 4.160000-4.420000 & 2273 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**4** & 4.420000–4.680000 & 2451 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 4.680000–4.940000 & 2597 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 4.94000–5.200000 & 2795 & \(4\sigma\uparrow\) & & & \\ \hline  & 5.20000–5.460000 & 2960 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**5** & 5.460000–5.720000 & 3273 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 5.720000–5.980000 & 3767 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 5.980000–6.240000 & 4574 & \(4\sigma\uparrow\) & & & \\ \hline  & 6.240000–6.500000 & 5510 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**6** & 6.500000–6.760000 & 5290 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 6.760000–7.020000 & 4287 & \(4\sigma\uparrow\) & & & \\ \hline  & 7.020000–7.280000 & 3279 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**7** & 7.280000–7.540000 & 2688 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 7.540000–7.800000 & 2574 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 7.80000–8.059999 & 2458 & \(4\sigma\uparrow\) & & & \\ \hline  & 8.059999–8.320000 & 1963 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}
**8** & 8.320000–8.580000 & 1469 & \(4\sigma\uparrow\) & & & \\ \cline{2-6}  & 8.580000–8.840000 & 1179 & \(1\sigma\uparrow\) & & & \\ \cline{2-6}  & 8.840000–9.09999 & 1023 & \(1\sigma\uparrow\) & & & \\ \hline  & 9.09999–9.360000 & 901 & \(1\sigma\downarrow\) & & & \\ \cline{2-6}  & 9.360000–9.620000 & 854 & \(1\sigma\downarrow\) & & & \\ \cline{2-6}
**9** & 9.62000–9.87999 & 714 & \(1\sigma\downarrow\) & & & \\ \cline{2-6}  & 9.879999–10.139999 & 550 & \(1\sigma\downarrow\) & & & \\ \hline \end{tabular}
\end{table} 
Table 1: The SD, MSD, and S/N of white light components’ residual stress defects area.
## 1 Introduction
The primary focus of this paper is to develop a novel algorithmic approach for an RS that harnesses the potential of SRI and BIM data to suggest energy-efficiency improvements. By capitalizing on the newly introduced SRI framework and integrating it with BIM data, the developed RS algorithm aims to assist building managers and designers in optimizing energy performance through relevant suggestions. To ensure a streamlined development process and the effective utilization of data from SRI and BIM, the paper is structured around two distinct algorithms (one per use case), each leveraging a specific data source. * The first use case concentrates on the thermal envelope of the building and relies on BIM data. This includes essential information, such as the U-value of walls, roofs, and windows, as well as recommendations for insulation materials. By leveraging these data, the RS can provide valuable insights and recommendations for improving the thermal performance of buildings. These recommendations can guide designers and building managers in making informed decisions and implementing energy-efficient design strategies. * The second use case centers around the SRI data source, which provides information about a building's readiness for smart services, including building automation systems, lighting control, heating, cooling, ventilation, and more. By analyzing these data, the RS can generate tailored recommendations for enhancing energy efficiency through the integration and implementation of smart technologies. It is important to note that while the recommendations do not guarantee an immediate increase in efficiency, they represent significant opportunities for improvement by adopting the suggested smart-ready services. The archival value addition of the 'use case' approach used in this study lies in the combination of information that comes from two different sources. Furthermore, the study introduces a new approach to using BIM and SRI data, which is the main novelty of this work. However, it does not delve into the computational aspects of the proposed RS and instead focuses on the algorithmic aspects. The rest of the paper is organized as follows. Section 2 presents a literature review on three main terms related to this study: namely, RS, SRI, and BIM. Section 3 details the methodology used to develop the algorithms for both use cases. Results are discussed in Section 4 of the paper with comparisons of alternative Machine Learning (ML) algorithms and their results. Section 5 discusses the results, implications of this work, challenges, and directions for future work. Finally, Section 6 summarizes the important aspects of this research while also providing the main takeaway message.
## 2 Antenna Modelling
### Efficiency  
Efficiency in antenna design determines how well an antenna converts electrical energy into electromagnetic waves. It measures the antenna's signal reception efficiency. High-efficiency antennas convert a large portion of electrical energy into electromagnetic waves, improving transmission and reception. Efficiency is measured as a percentage by dividing radiated (or received) power by input power. A higher efficiency means more input power is radiated or captured, while a lower efficiency means more energy is lost as heat or other methods. High-efficiency antennas have many benefits: Increased Signal Quality: Efficient antennas boost signal power, improving signal quality, coverage, and communication reliability. High-efficiency antennas reduce signal loss and transmit signals farther due to less power lost to heat or inefficiency. Energy Efficiency: Efficient antennas reduce signal transmission and reception power in wireless communication devices, extending battery life. However, antenna efficiency is sometimes difficult to design and manufacture: High-efficiency antennas may need intricate radiating elements and feeding mechanisms to reduce losses. Precision Manufacturing: To ensure efficiency, manufacturing must meet strict tolerances and employ high-quality materials, which increases production costs. Antenna efficiency varies with frequency, making it difficult to maintain high efficiency across a large range of working frequencies.
## 4 Experiment and Result  
### Experimental Datasets  
In this study, we utilize the publicly available Chinese medical text datasets MCSC-Set [22] and CMeEE [12] to assess our named entity recognition model. The MCSCSet (Medical Chinese Sentence Classification Set) is a specialized dataset for medical text classification research, derived from a diverse range of sources, including medical books, theses, online medical Q&A platforms, and electronic medical records. These diverse sources ensure the richness and representativeness of the dataset, providing researchers with a high-quality annotated resource that effectively supports medical information extraction. Statistics for the MCSCSet dataset are presented in Table 1.  
CMeEE (Chinese Medical Named Entity Recognition Dataset) is a dataset specifically designed for Chinese medical named entity recognition (CMNER) and was released by CHIP2020 (China Health Information Processing Conference). It includes text extracted from medical books, theses, electronic medical records, and online medical Q&A platforms. The texts are labeled with various medical entity categories, including diseases, symptoms, drugs, medical tests, treatments, and body parts. Training and evaluating models using this dataset can enhance the accuracy and efficiency of medical text processing. Statistics for the CMeEE dataset are presented in Table 2.  
For data preprocessing, each sentence in the dataset is decomposed into individual characters and annotated based on whether the character is part of an entity. Annotation categories are represented as follows: \"B\" (beginning of entity), \"I\" (inside of entity), \"E\" (end of entity), and \"O\" (outside of entity). Each character is assigned a corresponding label, and the processed data are saved in JSON format for ease of subsequent use.
## 2 Experiments and Methodology
### Lomb-Scargle Periodogram Analysis  
The continuous time series implied the spectral characteristics of stations. LSP is effective in extracting weak periodic signals in time domain sequences, again to some extent eliminating spurious signals due to non-uniformity in the time domain [23] and is applicable to unequally spaced data. LSP can be subjected to Fourier analysis driven and derived from the principles of Bayesian probability theory, which has been shown to be closely related to box-based phase folding techniques in some cases [24].  
For the time series of length M, the discrete Fourier transform of an arbitrarily sampled data set is defined as [25]:  
\[F(\omega)=\sum\limits_{k=1}^{M}J(t_{k})e^{-i\omega t_{k}} \tag{3}\]  
Then, the normalized power spectrum of the Lomb-Scargle method is calculated by Equation (4):  
\[P_{j}(\omega)=\frac{1}{2\sigma^{2}}\left\{\frac{\left[\sum\
olimits_{1}^{k} J_{k}\!\cos\omega(t_{k}-\tau)\right]^{2}}{\sum\
olimits_{1}^{k}J_{k}\!\cos^{2} \omega(t_{k}-\tau)}+\frac{\left[\sum\
olimits_{1}^{k}J_{k}\!\sin\omega(t_{k}- \tau)\right]^{2}}  
For the residual coordinate time series \(V\), let the covariance array \(\Sigma\) of \(V\) be:  
\[\Sigma=\frac{1}{m-1}V^{T}V \tag{6}\]  
where the covariance array \(\Sigma\) is a full rank symmetric matrix for which the eigenvalue decomposition is performed as follows:  
\[\Sigma=U\Lambda U^{T} \tag{7}\]  
where \(U^{T}\) is a row full rank matrix composed of eigenvectors of \(\Sigma\) and \(\Lambda\) is a principal diagonal matrix composed of \(k\) non-zero eigenvalues of matrix \(\Sigma\). The eigenvalues of the covariance array \(\Sigma\) correspond to the eigenvectors one by one as \(\left(\lambda_{1},U_{1}\right),\left(\lambda_{2},U_{2}\right),\cdots,\left( \lambda_{n},U_{n}\right)\). At this point, the time series \(V_{i,j}\) can be represented by a set of orthogonal bases \(U_{k}\) as:  
\[V_{i,j}=\sum_{k=1}^{n}a_{k}(i)U_{k}(j) \tag{8}\]  
where \(U_{k}(j)\) is the \(j\)th element of the \(k\)th feature vector and \(a_{k}\) is the \(k\)th principal component of \(V_{i,j}\), expressed in the following form:  
\[a_{k}(i)=\sum_{j=1}^{n}V_{i,j}U_{k}(j) \tag{9}\]  
The eigenvalues are arranged in descending order, and the first few eigenvalues representing a larger contribution to the time series \(V_{i,j}\) are called the main mode components, which are expressed as:  
\[\varepsilon_{i,j}=\sum_{j=1}^{p}a_{k}(i)U_{k}(j) \tag{10}\]  
where \(p\) is the number of selected principal components, and, in general, the sum of selected eigenvalues accounts for more than 80% of the proportion of all eigenvalues.
## Appendix A Methods of ET Partitioning
### The Conditional Eddy Covariance (CEC) Method  
Compared with the MREA and FVS methods, the CEC method considers the similarity assumptions based on both stomatal and nonstomatal processes and avoids the errors associated with the estimation of GPP and WUE, effectively improving the separation ratio of water-carbon flux components [17]. Similar to the MREA method, although the information of interest and valid data from the jet stream are selected, there are two distinct bases of separation: (1) the information provided by the jet stream is re-examined, and the second quadrant turbulence information related to the stomatal processes is emphasized; (2) the first quadrant data processing is not considered to yield the same direct flux results as the MREA method for the nonstomatal components; in other words, the first quadrant data processing is considered to yield the same direct flux results as the MREA method. In other words, the idea that the fluxes derived from the first quadrant are only valid for describing the proportion of the weight of nonstomatal processes in the fluxes is also applicable to the second quadrant data, so that ecosystem transpiration and evaporation can be directly distinguished using ecosystem evapotranspiration under the assumption that the stomatal/nonstomatal flux ratio with a representative flux is equal to the ecosystem T/E ((A4), (A5) and (A6a)).  
\[f_{E}=\frac{1}{N}{\sum{I_{E}w^{\prime}q^{\prime}}\ \ \&\ f_{T}=\frac{1}{N}{ \sum{I_{T}w^{\prime}q^{\prime}}}} \tag{4}\]  
where \(I_{E}\) and \(I_{T}\) represent the evaporation and transpiration flux indicator functions, respectively.  
\[I_{E}=\left\{\begin{array}{l}1\ if\ c^{\prime}>0,q^{\prime}>0,w^{\prime}>0 \\ 0\ otherwise\end{array}\right.\ \ \&\ I_{T}=\left\{\begin{array}{l}1\ if\ c^{\prime}<0,q^{ \prime}>0,w^{\prime}>0\\ 0\ otherwise\end{array}\right. \tag{5}\]  
By assuming that the ratio of ecosystem transpiration to evaporation is equal to the effective stomatal/nonstomatal flux weight ratio in quadrants one and two (A6a), rather than directly as ecosystem transpiration and evaporation, the actual ET flux results for each component can be calculated separately using the half-hourly scale evapotranspiration calculated from all quadrant data (A6b). The same data processing procedure was easily applied to the separation of the Fc components (A6c).  
\[\begin{array}{l}r_{ET}=\frac{f_{E}}{f_{T}}=\frac{E}{T}\ \ \&\ r_{Fc}=\frac{f_{B}}{f_{P}}=\frac{R}{P}\ \ \ (\text{a})\\ E_{CEC}=\frac{ET}{1+\frac{1}{r_{ET}}}\ \ \&\ T_{CEC}=\frac{ET}{1+r_{ET}}\ \ \ (\text{b})\\ R_{CEC}=\frac{F_{C}^{2}}{1+\frac{1}{r_{C}}}\ \ \&\ P_{CEC}=\frac{F_{C}}{1+r_{C}}\ \ \ (\text{c})\\ \end{array}\] (A6)