esa-sceva/satcom-chunk-collection huggingface dataset:
# An Experimental Study on Alkali Silica Reaction of Concrete Specim Using Steel Slag as Aggregate
## 3 Results and Discussion  
### Fundamental Properties of Steel Slag  
We performed various tests to evaluate the physical properties and chemical compositions of steel slag before using it as aggregate in concrete (mortar). The results and specification criteria are presented in Table 5. As shown, the density and unit weight volume of the steel slag satisfied the required criteria, however, the absorption rate was slightly higher than the value prescribed by KS F 2527 [25]. This is because the steel slag has several voids in its surface and an irregular shape. The voids and shape of the steel slag are determined by the production process of steel. A more advanced steel production technology can be used to obtain steel slag that satisfies the required criterion.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Criteria** & **ASTM C 1260** & **ASTM C 1293** \\ \hline Non-reactive & 0.0\% to 0.1\% & \\ Inconclusive & 0.1\% to 0.2\% & Below 0.04\% \\ Potentially reactive & Above 0.2\% & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Expansion criteria according to ASTM C 1260 and ASTM C 1293.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Contents** & **ASR Expansion of Mortar** & **ASR Expansion of Concrete** \\ \hline W/B ratio & 0.47 & 0.45 \\ Mineral admixture (replacement ratio) & FA (20\%), BFS (30\%) & FA (20\%), BFS (30\%) \\ Particle size distribution (mm) & Below 4.75 & 4.75 to 19 \\ Specimen size (mm) & \(25\times 25\times 285\) & \(76\times 76\times 285\) \\ Moisture condition & Immersion in solution & 100\% humidity \\ Temperature (\({}^{\circ}\)C) & 80 & 38 \\ Period for testing (days) & 1, 3, 7, 10, 14 & Until the expansion limit is reached \\ Alkali condition & 1 N NaOH solution & Alkali content of 1.25\% are added to the mixing water \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test variables.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Criteria** & **ASTM C 1260** & **ASTM C 1293** \\ \hline Non-reactive & 0.0\% to 0.1\% & \\ Inconclusive & 0.1\% to 0.2\% & Below 0.04\% \\ Potentially reactive & Above 0.2\% & 0.04\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Expansion criteria according to ASTM C 1260 and ASTM C 1293.  
As shown in Table 5, the content of CaO and MgO were below the specified value, however, the content of FeO was above it as the existing steel production process cannot smelt iron. This can be resolved by refining the steel production process.
# Well-Separated Pair Decompositions for High-Dimensional Datasets
## 3 Deep Embedded WSPD
### Computing WSPD in High Dimensions  
Given a function \(f_{\theta}\), the algorithm for computing a WSPD of a high-dimensional dataset \(S\subset\mathbb{R}^{d}\) is given by the following steps:  
1. Compute a lower-dimensional representation \(S^{\prime}=f_{\theta}(S)\).
2. Compute a \(\text{WSPD}(S^{\prime},s)\), for any given \(s>0\), as proposed by Theorem 1. Let \((A^{\prime}_{i},B^{\prime}_{i})\), \(i=1,\ldots,k\), denote the dumbbells.
3. Reconstruct \((A_{i},B_{i})\), \(i=1,\ldots,k\), in the original \(\mathbb{R}^{d}\) space. Note that not all of them are well separated, i.e., they are not dumbbells anymore.
4. Refine all not well-separated pairs \((A_{i},B_{i})\) until they become dumbbells.  
We will refer to the above steps as the NN-WSPD algorithm. Note that the reconstruction step in NN-WSPD can be performed efficiently. What requires further explanation is the refine step, which guarantees that the set of computed dumbbells in \(\mathbb{R}^{d}\) is in fact again a well separated pair decomposition of \(S\). Suppose \((A_{i},B_{i})\) for some \(i\) is not well separated and let \(r(B_{i})>r(A_{i})\). We propose the following steps in Algorithm 1 to refine \((A_{i},B_{i})\).  
The complexity of the refine step depends on the recursion depth. We will experimentally demonstrate that the depth is relatively small for all the datasets that I tried in practice, introducing just a moderate number of new dumbbells.
## 4 Conclusions  
For the first time, the health-benefiting phytoconstituents in screw-pressed solid residues derived from various _Nigella_ genotypes were evaluated. The findings illuminated the presence of TPC, antioxidant capacity, and TQ alongside the fatty acids within screw-pressed _Nigella_ solid residue. However, a considerable variation was recorded concerning the phytochemical compositions of the solid residues from different seed sources. A substantial transference of TPC and antioxidant capacity from seeds to their solid residue was observed, though the transition rates for TQ, SFAs, MUFAs, and PUFAs were markedly lower. While the transfer rate of each phytoconstituent from the seeds to their corresponding solid residue varied, a similar pattern of phytoconstituent distribution was observed across both seeds and their respective solid residue. In addition, the present study also recorded differential losses of phytoconstituents during the processing of seeds into oil and solid residue. While the losses of TPC, FRAP, CUPRAC, SFAs, and MUFAs were minimal, the loss of TQ and PUFAs was comparably higher. Moreover, the study did not show strong correlations among TPC, antioxidant capacity, and TQ. Additionally, no strong correlations were observed for TPC, antioxidant capacity, and any of the fatty acids.  
Overall, this study emphasizes the value of screw-pressed _Nigella_ solid residue as more than just a byproduct. By demonstrating its rich, health-benefiting phytochemical composition, it encourages a paradigm shift toward utilizing its solid residue in innovative, sustainable, and health-focused applications. Additionally, the differential losses of phytoconstituents provide insights into the challenges of preserving specific bioactive compounds during processing, highlighting the need to optimize methods to minimize nutrient loss.  
Conceptualization, P.R.T.; methodology, P.R.T. and M.N.; software, P.R.T.; validation, P.R.T.; formal analysis, P.R.T.; investigation, P.R.T.; resources, P.R.T. and M.N.; data curation, P.R.T.; writing--original draft preparation, P.R.T.; writing--review and editing, P.R.T., J.B.J., S.B., T.T., K.W., D.B. and M.N.; visualization, P.R.T.; supervision, M.N.; project administration, M.N.; funding acquisition, P.R.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a CRCNA scholarship and the CQ University Australia Elevate Scholarship.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
## 4 Simulations and Analysis
### Performance Analysis of the Optimization Modules  
In order to analyze the performance of the three modules of the proposed method, we measured the performance of the IGLC-based MVDR beamforming method (labeled as IGLC), the Module 1-based MVDR beamforming method (labeled as Module 1), the Module  
Figure 5: Histograms and DOA errors of the target source. (**a**) Histogram and (**b**) DOA error of the target source when the error of the initial DOA was \(5^{\circ}\); (**c**) histogram and (**d**) DOA error of the target source when the error of the initial DOA was \(10^{\circ}\).  
1 plus Module 2-based MVDR beamforming method (labeled as Modules 1 + 2), and the Module 1 plus Module 2 plus Module 3-based MVDR beamforming method (labeled as Modules 1 + 2 + 3). Figure 6 gives the evaluation results of the SINR, PESQ, STOI and SDI versus the iSNR.  
The results of Figure 6 all show that the performance of the ICLC-based MVDR beamforming method is better than the IDL-SIM method, which confirms the effectiveness of the full loading-based ICLC method. Meanwhile, Figure 6 shows that the Module 1-based MVDR beamforming method is better than the ICLC-based MVDR beamforming method, which indicates that the backward correction of the steering vector of the target source based on the ICCL can effectively improve the performance of the CMB. Moreover, based on Module 1, when Module 2 was used for further optimization, the results of the SINR, PESQ, STOI and SDI improved, which confirms the effectiveness of the reconstruction of the covariance matrix based on steered response power. In addition, based on Module 1 and Module 2, when Module 3 was used, the results of the SINR, PESQ, STOI and SDI were further improved, which confirms the effectiveness of the derivation of the DOA of the target source based on the broadband spatial response power. Meanwhile, the gradual improvement results of Figure 6 also indicate the necessity of these three modules in the proposed method.
## 4 Optimal Design of Stephenson-Chain Double-Toggle Mechanism
### Optimal Design Process
#### 4.3.3 Comparison Between Optimal Designs  
Based on the above results, Table 11 compares the optimal designs.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Design Variables**} & \multicolumn{2}{c}{**Acceleration**} & \multicolumn{2}{c}{**Force Ratio**} & \multicolumn{2}{c}{**Objective Function**} \\ \hline
**No.** & \(\mathbf{r_{2y}}\)**mm** & \(\mathbf{r_{4}}\)**mm** & \(\mathbf{S_{in}}\)**mm** & \(\mathbf{1}\mathbf{A_{max}}\)**1 mm/s\({}^{2}\) & \(\mathbf{(FinI/F_{out})_{max}}\) & \(\mathbf{F_{o}}\) \\ \hline Optimal Watt-chain & \multirow{2}{*}{170} & \multirow{2}{*}{250} & \multirow{2}{*}{276.29} & \multirow{2}{*}{3558.27} & \multirow{2}{*}{2.35} & \multirow{2}{*}{9466.54} \\ mechanism [2] & & & & & & \\ \hline
1 & & & 90 & 153.26 & 2460 & 2.97 & 7890 \\
2 & & 85 & 151.97 & 2407.03 & 2.93 & 7744.06 \\
3 & & 80 & 150.9 & 2366.49 & 2.84 & 7572.98 \\ \hline
4 & & & 90 & 163.75 & 2450.13 & 2.76 & 7660.26 \\
5 & & 85 & 162.5 & 2403.58 & 2.69 & 7497.16 \\
6 & & & 80 & 161.51 & 2355.24 & 2.62 & 7330.48 \\ \hline
7 & & & 90 & 174.04 & 2441.37 & 2.49 & 7372.74 \\
8 & & 85 & 172.85 & 2394.26 & 2.42 & 7208.52 \\
9 & & & 80 & 171.98 & 2353.71 & 2.40 & 7107.42 \\ \hline
10 & & 90 & 184.13 & 2434.46 & 2.39 & 7258.92 \\
11 & & 160 & 85 & 183.03 & 2389.2 & 2.31 & 7088.4 \\
12 & & & 80 & 182.3 & 2350.66 & 2.19 & 6891.32 \\ \hline
13 & & 90 & 194.03 & 2431.05 & 2.26 & 7122.1 \\
14 & & 85 & 193.04 & 2390.02 & 2.21 & 6990.04 \\
15 & & 80 & 192.5 & 2361.21 & 2.17 & 6892.42 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Design results based on Case II (Stephenson –II mechanism).  
Figure 16: The kinematic curves of the optimized design for Case II (Stephenson –II mechanism).
## 4 Theoretical and Experimental Analyses
### Correctness  
1. When \(T=k\), participant \(P_{t}\) updates the original secret share to \(S_{t}^{k}=f_{t}(y)\). \(F(x,y)\) can be expressed as \(F(x,y)=u_{0}(x)+u_{1}(x)y+\ldots+u_{h-1}(x)y^{h-1}\). \(u_{0}(x)\), \(u_{1}(x)\), \(\ldots\), \(u_{h-1}(x)\) are monic polynomials of order \(k-1\). Because \(S_{t}^{k}\) can be expressed as \(S_{t}^{k}=F(t,y)=c_{t,0}+c_{t,1}y+\ldots+c_{t,h-1}y^{h-1}\), \(t=1,2,\ldots\), \(n\), \(c_{1,0}\), \(c_{2,0}\), \(\ldots\), \(c_{n,0}\) can be regarded as the interpolation of \(u_{0}(x)\), and \(c_{t,0}=u_{0}(t)\). Since \(u_{0}(x)\) is a \(k-1\) order polynomial, the threshold of reconstructing \(u_{0}(x)\) with \(c_{1,0}\), \(c_{2,0}\), \(\ldots\), \(c_{n,0}\) is \(k\). Similarly, the thresholds of reconstructing \(u_{1}(x)\), \(u_{2}(x)\), \(\ldots\), \(u_{h-1}(x)\) are all \(k\). In other words, \(k\) secret shares can reconstruct the binary polynomial.
2. When \(T=h\), participant \(P_{t}\) updates the original secret share to \(S_{t}^{h}=g_{t}(x)\). \(F(x,y)\) can be expressed as \(F(x,y)=v_{0}(y)+v_{1}(y)x+\ldots+v_{k-1}(y)x^{k-1}\). \(v_{0}(y)\), \(v_{1}(y)\), \(\ldots\), \(v_{k-1}(y)\) are monic polynomials of order \(k-1\). Because \(S_{t}^{h}\) can be expressed as \(S_{t}^{h}=F(x,t)=c_{t,0}+c_{t,1}x+\ldots+c_{t,k-1}x^{k-1}\), \(t=1,2,\ldots\), \(n\), \(c_{1,0}\), \(c_{2,0}\), \(\ldots\), \(c_{n,0}\) can be regarded as the interpolation of \(v_{0}(y)\), and \(c_{t,0}=v_{0}(t)\). Since \(v_{0}(y)\) is an \(h-1\) order polynomial, the threshold for reconstructing \(v_{0}(y)\) with \(c_{1,0}\), \(c_{2,0}\), \(\ldots\), \(c_{n,0}\) is \(h\). Similarly, the thresholds for reconstructing \(v_{1}(y)\), \(v_{2}(y)\), \(\ldots\), \(v_{k-1}(y)\) are all \(h\). In other words, \(h\) secret shares can reconstruct the binary polynomial.
3. When \(k<T<h\), participant \(P_{t}\) updates the original secret share to \(S_{t}^{T}=\left\{S_{t}^{h},f_{t}(e_{1}),f_{t}(e_{2}),\ldots,f_{t}(e_{h-T})\right\}\). Here \(e_{1}\), \(e_{2}\), \(\ldots\), \(e_{h-T}\) are \(h\)-\(T\) integers randomly chosen and different from \(1\), \(2\), \(\ldots\), \(n\). Since \(f_{t}\big{(}e_{j}\big{)}=F\big{(}t,e_{j}\big{)}=g_{e_{j}}(t)\), \(f_{t}\big{(}e_{j}\big{)}\) can be regarded as an interpolant of \(g_{e_{j}}(t)=F\big{(}x,e_{j}\big{)}\). Since \(g_{e_{j}}(t)\) is a \(k\)\(-1\) order polynomial when \(T>k\), \(g_{e_{j}}(t)\) can be reconstructed from \(T\) updated shadows \(f_{t}\big{(}e_{j}\big{)}\), where \(t=1\), \(2\), \(\ldots\), \(T\). Therefore, \(F(x,e_{1})\), \(F(x,e_{2})\), \(\ldots\), \(F(x,e_{h-T})\) can be reconstructed from \(S_{1}^{T},S_{2}^{T},\ldots,S_{T}^{T}\). \(F\big{(}x,e_{j}\big{)}\) can be expressed as \(F(x,e_{j})=v_{0}(e_{j})+v_{1}(e_{j})x+\ldots+v_{k-1}(e_{j})x^{k-1}\). Therefore, additional \(h\)-\(T\) additional interpolations can be obtained. Since \(T\) interpolations can be obtained from \(S_{1}^{T},S_{2}^{T},\ldots,S_{T}^{T}\) and \(v_{1}(y)\), \(v_{2}(y)\), \(\ldots\), \(v_{k-1}(y)\) are the \(h-1\)st degrees, so \(F(x,y)\) can be reconstructed.
## 5 Conclusions  
It is a crucial and hard task to forecast short-term solar power which has a great impact on the control and management of the electric grid. This paper presented an application of Gaussian process regression coupled with core vector regression on two radiometric stations from South Africa. Computation of the covariance function is a key element in Gaussian process forecasting and a more accurate kernel was adopted by considering one with a minimum enclosed ball. The results showed that this improvement on Gaussian process regression produced results that were better than the benchmark models. The bulk of the discussions highlighted that the Gaussian process regression model coupled with core vector regression provides accurate and robust solar power forecasts. This study could be useful to system operators of power utility companies who have to integrate intermittent renewable energies such as solar power with electricity generated by other energy sources on the national grid.  
Future research can extend the results of the present study by using additive quantile regression models for probabilistic forecasting including other probabilistic forecasting techniques such as Bayesian structural time series models. Furthermore, we think it will also be more interesting to include non-weather variables like calendar effects and denoising the data first using either wavelets or empirical mode decomposition before applying the forecasting models.  
The following are available online at [[https://www.mdpi.com/article/10.3390/a14060177/s1](https://www.mdpi.com/article/10.3390/a14060177/s1)]([https://www.mdpi.com/article/10.3390/a14060177/s1](https://www.mdpi.com/article/10.3390/a14060177/s1)), analytic data used in the study and a detailed analysis of the benchmark models.  
Conceptualization, E.C. and C.S.; methodology, E.C. and C.S.; software, E.C.; validation, E.C., C.S. and A.B.; formal analysis, E.C.; investigation, E.C. and C.S.; data curation, E.C. and C.S.; writing--original draft preparation, E.C.; writing--review and editing, E.C., C.S. and A.B.; visualization, E.C. and C.S.; supervision, C.S. and A.B.; project administration, C.S. and A.B. All authors have read and agreed to the published version of the manuscript.  
Not applicable.  
The data used in this study are from Southern African Universities Radiometric Network (SAURAN), website ([[https://sauran.ac.za](https://sauran.ac.za)]([https://sauran.ac.za](https://sauran.ac.za)), accessed on 17 June 2020).  
The authors acknowledge Southern African Universities Radiometric Network (SAURAN) for providing the data.
# Agnostic Envelope Linearization of Dynamically Supplied Power Amplifiers for Mobile Terminals
###### Abstract  
This paper presents an envelope linearization technique to compensate for the nonlinear distortion of envelope tracking (ET) power amplifiers (PAs) for 5G new radio (NR) mobile terminals. The proposed envelope optimization (EOPT) method is agnostic of the nonlinear distortion generated in the envelope supply path and can compensate for the nonlinear distortion at the ET PA output without the need to monitor the output at the envelope tracking modulator (ETM). The linearization system in the envelope path is based on the envelope generalized memory polynomial (EGMP) behavioral model. Since the ETM output is not available, an iterative nonlinear least squares solution inspired in the deep deterministic policy gradient (DDPG) algorithm is proposed to extract the coefficients of the EGMP model. The EOPT method is validated on a system-on-chip (SoC) ET PA board designed for mobile terminal applications. Experimental results show the suitability of the proposed method to guarantee the linearity requirements (i.e., adjacent channel power ratio below \(-36\) dB) with \(16.8\%\) of power efficiency when operating the ET PA with 5G new radio test signals of 60 MHz bandwidth operating at 2.55 GHz (band 7). The linearization performance of the proposed EOPT method is comparable to the envelope leakage cancellation (ELC) approach (but saving the need for an analog to digital converter to monitor the ETM output), and can outperform a conventional I-Q digital predistorter based on the generalized memory polynomial (GMP) behavioral model.  
digital predistortion; envelope tracking; power amplifier; RF leakage 22, 3773. [[https://doi.org/10.3390/s22103773](https://doi.org/10.3390/s22103773)]([https://doi.org/10.3390/s22103773](https://doi.org/10.3390/s22103773))
###### Abstract  
This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems.  
multi-criteria decision analysis; decision support systems; PROMETHEE method; military aircraft +
Footnote †: journal: Applied Informatics  
**Copyright**: **C** 2021 by the authors. Licence MDP, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Integration of a Three-Dimensional-Printed Titanium Implant in Human Tissues: Case Study
## 2 Materials and Methods
### Implant Design and Fabrication  
Preoperative computed tomography (CT) and MR images were obtained to design implants for the radius and ulna. Virtual bone cutting was performed with an adequate bone margin; the implant shape was derived from the mirror images of contralateral normal bones [10]. To enhance the immediate fixation strength and promote osteogenesis, the bone junction was designed to contain a half-cylindrical plate with multiple screw holes and a 1-cm skirt to surround the end of the host bone at the base of the plate (Figure 3A,B). A mesh with an edge length of 2.5 mm (dode-thin structure; Magics RP 22, Materialise, Belgium) was used for the surface at the end of the host bone (Figure 3C). The implant body and half-cylindrical plates had a robust structure with a rough surface (average roughness of 48 nm). The implant was fabricated using an electron melting beam 3D printer system (ARCAM A1, Arcam AB, Molndal, Sweden) and Ti6 Al4V metal powder. The length and weight of the final implant were 15 cm and 67 g, respectively.  
Figure 3: Bone implants of the radius and the ulna. Photographs showing (**A**) 3D-printed implants of both forearm bones with the host bone models, (**B**) a polar view of the ulnar implant, and (**C**) the mesh-structured junctional area of the implants. The asterisk (*) indicates the ulnar implant.  
Figure 2: Revisional surgery. Photographs showing (**A**) wide excision of the ulnar implant, (**B**) the soft tissue bed after resection of the recurred tumor, and (**C**) the retrieved ulnar implant with the recurred tumor.
# Structural Particularities, Prediction, and Synthesis Methods in High-Entropy Alloys
## 2 Structural Particularities of HEAs, Prediction and Estimation Concepts  
High-Entropy Alloys (HEAs) are distinguished by their distinctive structural attributes, such as complex crystal structures, notable lattice distortions, and sluggish atomic diffusion. These particularities result in exceptional mechanical and thermal properties. Predicting and estimating the behaviors of HEAs involves advanced modeling techniques such as thermodynamic simulations, machine learning, and experimental validation to accurately optimize their performance.