esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **HR** & **RMSSD** & **BF** & **RCA** \\ \hline Rel1 & \(\mathbf{0.753}\) & \(-0.602\) & \(0.364\) & \(-0.267\) \\ Rel2 & \(\mathbf{0.845}\) & \(\mathbf{-0.796}\) & \(0.561\) & \(-0.311\) \\ Rel3 & \(\mathbf{0.831}\) & \(\mathbf{-0.723}\) & \(0.445\) & \(-0.255\) \\ Rel4 & \(\mathbf{0.752}\) & \(-0.612\) & \(0.449\) & \(-0.225\) \\ Rel5 & \(\mathbf{0.775}\) & \(-0.567\) & \(0.577\) & \(-0.253\) \\ \hline Exe1 & \(\mathbf{0.879}\) & \(\mathbf{-0.771}\) & \(0.349\) & \(-0.420\) \\ Exe2 & \(\mathbf{0.658}\) & \(\mathbf{-0.669}\) & \(0.506\) & \(-0.393\) \\ Exe3 & \(0.393\) & \(-0.574\) & \(0.297\) & \(-0.443\) \\ Exe4 & \(0.108\) & \(-0.519\) & \(-0.019\) & \(-0.372\) \\ Exe5 & \(-0.095\) & \(-0.517\) & \(0.177\) & \(-0.309\) \\ Exe6 & \(-0.065\) & \(-0.645\) & \(0.156\) & \(-0.162\) \\ \hline Rec1 & \(0.487\) & \(\mathbf{-0.799}\) & \(0.472\) & \(-0.445\) \\ Rec2 & \(0.453\) & \(\mathbf{-0.785}\) & \(\mathbf{0.777}\) & \(-0.409\) \\ Rec3 & \(\mathbf{0.625}\) & \(\mathbf{-0.738}\) & \(\mathbf{0.681}\) & \(-0.107\) \\ Rec4 & \(\mathbf{0.735}\) & \(\mathbf{-0.812}\) & \(0.375\) & \(-0.117\) \\ Rec5 & \(\mathbf{0.707}\) & \(\mathbf{-0.712}\) & \(\mathbf{0.646}\) & \(-0.144\) \\ \hline \hline \end{tabular} Statistically significant values are in bold.  
\end{table} 
Table 7: Pearson coefficient of correlation with subjects’ fat mass. Figure 5: Relationship between the parasympathetic cardiac control and the heart rate (**A**) and relationship between the parasympathetic cardiac control and the breathing frequency (**B**) in different phases of the experiment. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dependent Variable** & **Model** & \multicolumn{4}{c}{**Coefficients**} & \multicolumn{4}{c}{**Model Summary**} \\ \hline  & & **B** & **SE** & **Beta** & **Sign.** & \(\mathbf{R^{2}}\) & **Sign.** \\ \hline HRRec1 & Const & \(-\)88.108 & 48.066 & & 0.141 & 0.020 & 0.779 & **0.020** \\  & HRRec2 & \(1.496\) & 0.393 & 0.883 & 0.020 & 0.779 & **0.020** \\ \hline HRRec2 & Const & \(-\)39.098 & 36.869 & & 0.349 & & \\  & HRExe3 & \(1.030\) & 0.306 & 0.860 & 0.028 & 0.740 & **0.028** \\ \hline  & Const & \(39.498\) & 0.169 & & 0.003 & & \\  & HRExe1 & \(1.361\) & 0.005 & \(1.357\) & 0.002 & & \\ HRRec3 & BFExe5 & \(-\)93.153 & 0.304 & \(-\)0.488 & 0.002 & \(1.000\) & **0.001** \\  & HRRel2 & \(-\)0.585 & 0.003 & \(-\)0.517 & 0.003 & & \\  & BMI & \(-\)0.114 & 0.008 & \(-\)0.049 & 0.043 & & \\ \hline  & Const & \(3.118\) & 1.280 & & 0.248 & & \\  & HRExe1 & \(1.039\) & 0.013 & 1.132 & 0.008 & & \\ HRRec4 & BFExe5 & \(-\)67.553 & 0.741 & \(-\)0.386 & 0.007 & \(1.000\) & **0.001** \\  & Age & \(0.545\) & 0.007 & 0.150 & 0.008 & & \\  & Fat mass & \(-\)0.253 & 0.012 & \(-\)0.316 & 0.030 & & \\ \hline HRRec5 & Const & \(20.203\) & 12.613 & & 0.184 & & \\  & HRRel4 & \(0.884\) & 0.194 & 0.915 & 0.010 & 0.838 & **0.010** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Multiple linear regression analysis for the averaged HR over 3 min segments during post-exercise recovery in the supine position.
# Data Collection for Target Localization in Ocean Monitoring Radar-Communication Networks
## 1 Introduction
### Our Contribution  
Against the aforementioned background, we provide a UAV-assisted uplink data collection and target localization network in the ocean surveillance scenario, where the buoys transmit the data to the UAV and localize the target cooperatively. Specifically, data collection between the UAV and buoys is carried out with NOMA and the UAV can be deployed at different locations with its mobility, which achieves higher system throughput under limited communication essentials. The transmit power at the buoys not only affects the data collection but also impacts the target location performance. In order to improve the communication and radar-sensing performance, we formulate a two-objective optimization problem of UAV location and transmit power at the buoys to maximize the system throughput and minimize the Cramer-Rao bound (CRB) of localization simultaneously. We list a CRB-constrained (CRBC) problem to only maximize the system throughput and remark its solution as the baseline to analyze the performance achieved by our design. Simulation results demonstrate the efficiency of our design in balancing the system throughput and target localization performance. The main contributions of this paper are summarized as follows:  
* To the best of the authors' knowledge, this is the first time a UAV-assisted radar-communication network has been investigated where several buoys conduct uplinkNOMA data transmission with the UAV while cooperatively sensing the radar target. In order to improve the data collection performance, we exploit the flexible mobility of the UAV and the efficiency of NOMA. Meanwhile, the buoy transmit power is optimized to meliorate the data collection performance and radar target localization simultaneously.
* In order to maximize the system throughput and minimize the CRB of localization, we formulate a two-objective optimization problem of the UAV location and the transmit power of the buoys. To tackle the NP-hard problem, we proposed a joint communication and radar-sensing many-objective optimization (CRMOP) algorithm that achieves a superior balance of data collection and radar target sense.
* In order to facilitate a better comparison with the CRMOP algorithm proposed in this paper, we consider a baseline and propose a CRBC algorithm based on the traditional optimization method where the CRB is regarded as a constraint to maximize the system throughput. Through comprehensive simulations, we demonstrate that our proposed algorithm not only achieves significantly higher throughput but also ensures reliable target localization accuracy.
## 5 Conclusions  
In summary, GP exert the anti-inflammatory effect in LPS-induced 264.7 microphage cells by suppressing the inflammatory cytokines. Meanwhile, the supplement of GP attenuated IBD symptoms and proinflammatory cytokines secretion along with resuming the tight-junction proteins in DSS-induced colitis mice. Moreover, the GP could regulate the structure of gut microbiota by promoting the relative abundances of _Akkermansia_, _Parsutterella_, and suppressing the relative abundances of _Peptococcus_, _Bifidobacterium_ and _Saccharibacteria_. Despite that both assessments in cells preliminary and the following gavage test in vivo could not specifically illustrate which mechanism GP conducts, the beneficial function was interpreted. Thus, the GP may be regarded as a potential ingredient for ameliorating the inflammatory level in gut health and would be also developed as the multi-targeted functional foods for the treatment of IBD.  
**Supplementary Materials:** Microbial sequencing data have been uploaded to the GSA website [[https://ngdc.cncb.ac.cn/gsa/s/rgqDm4](https://ngdc.cncb.ac.cn/gsa/s/rgqDm4) Pg]([https://ngdc.cncb.ac.cn/gsa/s/rgqDm4](https://ngdc.cncb.ac.cn/gsa/s/rgqDm4) Pg) (accessed on 18 February 2022) with the accession number of CRA006111. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/nu14071479/s1](https://www.mdpi.com/article/10.3390/nu14071479/s1)]([https://www.mdpi.com/article/10.3390/nu14071479/s1](https://www.mdpi.com/article/10.3390/nu14071479/s1)), Figure S1: Effects of the GP in RAW264.7 cell viability; Figure S2: The DAI scores of mice in different groups; Figure S3: The OTU numbers (A), Shannon Rarefaction curves(B) and Venn diagram of OTU(C); Figure S4: Predicted and functions annotation was performed by PICRUSt; Table S1: The composition of peptides in GP; Table S2: Primer sequences used in RT-PCR assay; Table S3: The scoring criteria for histopathological values.  
**Author Contributions:** Writing--original draft preparation, L.X.; conceptualization, W.Z.; methodology, L.F.; software, S.C.; writing--review and editing, W.Z.; visualization, Y.Y. and L.W.; supervision, W.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was supported by the Youth Project of the National Natural Science Foundation of China (32001720) and China Postdoctoral Science Foundation (2020M681632).  
**Institutional Review Board Statement:** The study was conducted in accordance with the National Guidelines for Experimental Animal Welfare and Ethical Committee of Experimental Animal Center of Nanjing Agricultural University (protocol code NJAU.No20210508058 and approved at 15 June 2021).  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect of Nano Hydrolcite on the Aging Resistance of a High Binder Content Stone Mastic Asphalt
## 3 Results and Discussion
### Mechanical Performance  
#### 3.2.1 Indirect Tensile Strength and Water Sensitivity  
The asphalt mixtures presented adequate performance in indirect tensile strength (ITS) and indirect tensile strength ratio (ITSR). The results of ITS and ITSR of the control and modified mixtures, under unaged and aged conditions, are presented in Figure 2. The error bars represent the respective standard deviation. ITSdry and ITSwet are the ITS of specimens under dry conditions (with no water sensitivity conditioning) and water sensitivity conditioned, respectively.  
However, in general, the differences are within standard deviation, for all aging and conditioning conditions the modified asphalt mixture presented higher ITS. Compared with the control material, the modified asphalt mixture presented 3% and 12% higher ITSdry and 9% and 11% higher ITSwet, for unaged and aged conditions, respectively.  
As expected, the aging process caused an increase in ITS, that can be explained by the increase of the asphalt mixtures' brittleness. After aging, the control asphalt mixture presented 3% and 4% increase in ITSdry and ITSwet, respectively. In the case of the modified asphalt mixture, after aging the ITSdry and ITSwet increased by 12% and 6%, respectively. The effect of LTOA aging in the ITS of these SMA 8 seems less intensive if compared with conventional asphalt concrete (AC). For instance, in the study of [PERSON] et al. [51], that used the same test setup, the ITS of a conventional AC 14 mixture (with 4.5% of bitumen 35/50) evolved around 48%, from 1.29 MPa (unaged) to 1.91 MPa (LTOA aged). Other study [52], that tested (ITS tests at 20 \({}^{\circ}\)C) a Superpave mixture type SP-III with maximum aggregate size of 19 mm and 4.4% of bitumen PG 76-22, identified an increase of 28% in ITS after LTOA aging (unaged: 1.25 MPa; aged: 1.60 MPa). The high ITS values, as well as, the smaller effect of LTOA aging in these SMA 8 can be explained by the higher binder dosage and binder type. Regarding water sensitivity, the aging process had no clear effect in ITSR.
## 5 Conclusions  
This study presents an integrated design and optimization approach for radial inflow turbines and it is applied to the redesign of T-100 turbine in the Sundstrand Power Systems T-100 Multipurpose Small Power Unit. In this paper, as Part II, a flexible 3-D multidisciplinary optimization system is developed. Both nozzle aerodynamic optimization and rotor multidisciplinary optimization are performed using the present approach for T-100 and T-100 re turbines, respectively. Higher total-to-static efficiency and lighter rotor weight are pursued in the whole process. From the above study, the mainly following conclusions can be drawn:  
1. Preliminary design plays a critical role in the design process of radial inflow turbines. Therefore, it is necessary to carry out an automated preliminary design and optimization for making a good foundation. In the present work, the total-to-static efficiency increases by 2.2%, and the rotor weight decreases by 0.49 kg (37.3%), comparing with that only conducting 3-D optimization on the T-100 baseline design. The centrifugal stress is constrained at the allowable level.
2. Large trailing edge height results in an improved efficiency, but does deteriorate the stress condition. Backward sweep trailing edge lowers the stress level, which compensates for the negative effect of trailing edge enlargement. Large blade turning at the middle and tip sections improve the efficiency, but moves the high-stress zone toward blade mid-span on the suction surface, which adds extra bending forces. In general, the stress level can be controlled through blade thickness and blade angle adjustment.
3. In the design process of radial inflow turbines, some key parameters like the blade number, rotor inlet diameter, and blade height can be easily optimized in the preliminary design stage using some correlations, while some local geometry parameters cannot be adjusted here and only be optimized subsequently in the 3-D design stage. Therefore, an integrated design optimization approach can be established by combining the automated preliminary design module and the 3-D multidisciplinary optimization module.  
Conceptualization, Q.D.; Methodology, Q.D. and S.S.; Software, L.F. and H.L; Writing-Original Draft Preparation, S.S.; Writing-Review & Editing, Q.D. and Z.F.  
Figure 24: Stress of T-100 and T-100 re optimized design. (**a**) T-100 optimized, (**b**) T-100 re optimized.  
**Funding:** This study is partially supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20130201130005).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## Appendix A
### Transcriptome Assembly, Annotation, and Differential Expression Analysis of the Genes  
Clean data were obtained by removing reads containing adapters, reads containing poly-N, and low-quality reads from raw data. Simultaneously, Q20, Q30, GC-content, and sequence duplication levels of the clean data were calculated. All the downstream analyses were based on clean data with high quality. Bowtie2 was used to compare clean reads to the reference gene sequence, and then RNA-seq using expectation maximization (RSEM) was used to calculate the expression level of genes and transcripts [39]. The quantification of the gene expression levels was represented by fragments per kilobase of transcript per million fragments mapped reads (FPKM).  
All assembled non-redundant and filtered single genes were annotated using BLAST software ([[http://ftp.ncbi.nlm.nih.gov/blast](http://ftp.ncbi.nlm.nih.gov/blast)]([http://ftp.ncbi.nlm.nih.gov/blast](http://ftp.ncbi.nlm.nih.gov/blast)) (accessed on 12 August 2021)) against the contents of the Nr, Nt, Pfam, SwissProt protein, KOG, GO, and KEGG databases. The phyper function in R software (V4.1.3) was utilized for enrichment analysis, calculating \(p\)-values, and subsequently correcting the false discovery rate (FDR) of \(p\)-values. Genes with \(|\log_{2}\)FC\(|>1\), \(p<0.05\) and FDR \(<0.05\) were classified as DEGs.
# Numerical Evaluation of Ultrasonic Detection of Solid Occlusions in CSP Molten Salt Piping
###### Abstract  
This work presents a numerical evaluation of the applicability of non-invasive ultrasonic techniques for detecting solid inclusions in concentrated solar power (CSP) systems using molten salts. Under stagnant conditions, molten salts, such as the NaNO\({}_{3}\)-KNO\({}_{3}\) mixture, solidify when the temperature drops below approximately 240 \({}^{\circ}\)C at ambient pressure. The study was conducted using finite element method (FEM) simulations in the time domain to model ultrasonic wave propagation and interaction with the solidified inclusions. Numerical analyses revealed that the energy of ultrasonic signals transmitted from the source and captured by the receiver can be used to quantify the energy reflected and scattered by solid inclusions and to statistically determine their spatial distribution within the piping. The study highlights the potential of detecting solid occlusions through statistical analysis of ultrasonic signals. Such an approach would enable decision making for trigger heating strategies to re-melt the solidified inclusions by raising the system's temperature. This approach contrasts with traditional temperature-based control, which fails to quantify the degree of solidification and may result in frequent, energy-intensive heating whenever the temperature drops below the threshold. The proposed ultrasonic-based detection method thus offers a more energy-efficient and precise solution for managing solidification in CSP molten salt piping.  
ultrasonic testing; CSP; molten salt; solid inclusion Article
# Advances and Challenges of Corrosion and Topology Detection of Grounding Grid
## 4 Challenges and Opportunities
#### 2.2.2 Strong metal interference  
The substation has a large number of metal facilities, and the connection of several important facilities to the grounding network brings great difficulties to the detection of the grounding grid. For example, a large number of underground metal pipes, cables, leads and cement roads with steel structures will have a great impact on the distribution of surface magnetic fields. This makes it difficult to distinguish the grounding grid information.
# Fast Normalization for Bilinear Pooling via Eigenvalue Regularization
## 4 Experiments
### Experiments on ImageNet1K  
We evaluate our method on large-scale image classification using the ImageNet1k dataset, which includes 1.28 M training images, 50 K validation images and 100 K testing images from 1000 classes. For a fair comparison with Dropcov, we apply Global Covariance Pooling (GCP) instead of bilinear pooling. GCP centers each feature vector \(\mathbf{x}_{i}\in\mathbf{X}\) to a zero mean: \(\mathbf{x}_{i}-\frac{1}{N}\left(\sum_{i=1}^{N}\mathbf{x}_{i}\right)\) before bilinear pooling. We implemented our approach into ResNet-18 and ResNet-50, and trained the networks from scratch. The counterparts include Plain GCP [13] (i.e., GCP without normalization), iSQRT-Cov [20], Layer Normalization (LN) [41] and DropCov [10].  
In Table 2, Top-1 classification accuracy on the validation dataset, inference speed and training time are reported. Our method and plain GCP do not apply the normalization but ours clearly outperforms plain GCP in terms of accuracy with equal inference speed. Meanwhile, iSQRT-COV applies square root normalization with Newton's iteration and achieves competitive accuracy. But its running speed is unsatisfactory due to the normalization which has a computational complexity of \(O(D^{3})\). Layer Normalization is an element-wise normalization method, which normalizes each sample with its mean (\(\mu\)) and variance (\(\gamma\)) and subsequently applies a linear transformation to each element. Its improvement in accuracy is marginal and consumes more inference time than ours. DropCov and RegCov achieve the best balance between inference speed and accuracy. However, for the training time, at the first stage of our method, we share the same network with iSQRT-COV. The square root normalization effectively normalizes the bilinear representation and leads to fast convergence [20]. As for DropCov, it repeatedly samples a random subset of features for each iteration and a proportion of data could not be seen until several epochs. This behavior makes the training hard and spends more epochs for convergence [27]. Therefore, although the square root normalization proceeds in our method, its training time is acceptable and is even smaller than DropCov for ResNet-50.
# A Data-Driven Approach for Generating Synthetic Load Profiles with GANs
## 3 Results
### Experimental Datasets
#### 3.1.5 Data Preprocessing  
To ensure consistency across datasets and support model training, several preprocessing steps are applied, as follows:  
* Normalization--all datasets are min-max normalized independently to scale hourly consumption values into the [0, 1] range. This normalization is performed per dataset to avoid leakage of information across domains and to ensure that the generator outputs remain within valid ranges for each specific case;
* Missing Value Handling--for the Household dataset, which contained intermittent missing values due to sensor outages or gaps in measurement, linear interpolation was applied within each 24 h profile to fill missing hourly entries (when gaps were \(\leq\)2 h). Days with more than two missing hours were excluded from the dataset;
* Profile Validation--only days with complete 24 h load profiles (after preprocessing) are retained. This ensures uniform input dimensionality for the GAN training.  
These preprocessing steps are performed for the comparability and integrity of the datasets, while also enabling fair benchmarking across GAN architectures. All preprocessing is implemented in Python (v 3.10) using the NumPy and Pandas libraries.
# Shielding Grounding Optimization Method for Spaceborne Multi-Cable
## 4 Results and Analysis  
### Analysis of Logical Expressions  
In accordance with the method described in Section 2 to establish the CST simulation model of the scanning mirror drive control system cable (Figure 7), and to obtain the logical expression as shown in Equation (2), where the first subscript of \(R\) is the initials of the cable name, the second subscript indicates the near end (\(N\)) or far end (\(F\)) of the shield. \(R\) means grounded; \(R^{\prime}\) means ungrounded. The logical expression was analyzed as follows:  
\[\left\{\begin{array}{l}Y_{1}=R^{\prime}_{AN}\cdot R^{\prime}_{AF}\cdot R^{ \prime}_{MN}\cdot R^{\prime}_{MF}\\ Y_{2}=R^{\prime}_{AN}\cdot R^{\prime}_{AF}\cdot R_{MN}+R^{\prime}_{AN}\cdot R^ {\prime}_{AF}\cdot R_{MF}\\ Y_{3}=R_{AN}+R_{AF}\end{array}\right.\ R_{MF} \tag{2}\]  
(1) \(Y_{1}\) means that the shielding effect is best when the two ends of the angle measurement cable shield are ungrounded and the two ends of the motor driver cable shield are ungrounded. The expression does not appear in the parameters of the field excitation cable, indicating that the field excitation cable shield is not relevant, i.e., no matter how the field excitation cable shield is grounded, it will not affect the result. (2) \(Y_{2}\) means that the shielding effect is moderate when the two ends of the angle measurement cable shield are not grounded and at least one end of the motor driver cable shield is grounded, which is also independent of the field excitation cable. (3) \(Y_{3}\) means that the shielding effect is worst when at least one end of the angle measurement cable shield is grounded, and it is unrelated to the grounding state of both the motor driver and field excitation cable shields.