esa-sceva/satcom-chunk-collection huggingface dataset:
# Construction and Evaluation of Pepper Core Collection Based on Phenotypic Traits and SSR Markers
## 2 Materials and Methods
### Construction of Core Collection Based on SSR Markers  
The peaks and allele lengths of the SSR-PCR products were analyzed using GeneMarker 2.2.0 software (SoftGenetics, State College, PA, USA). Polymorphic loci were recorded in a binary \"1, 0\" database, with bands coded as \"1\" for presence and \"0\" for absence. Cluster analysis and calculation of genetic diversity parameters were conducted using Popgene software1.32.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Primer Name** & **Sequence (5\({}^{\prime}\) to 3\({}^{\prime}\))** \\ \hline CIDAM-1F & CTTTCAGCGACACACACAG \\ CIDAM-1R & ACAACGTCTGAAATTAGTCCCAA \\ CIDAM-2F & ACATGAATTGTGCCGAGGGAAGT \\ CIDAM-2R & AGGGGTTTTGTGAGCTAGC \\ CIDAM-3F & TGTGACACAAGTGTTGCAG \\ CIDAM-3R & AGACGCCTTTTCCCCTG \\ CIDAM-4F & GGCAGAGAGATAATAATGCCA \\ CIDAM-4R & ACGTGACGATTATTAGAGACT \\ CIDAM-5F & TGGAGGCCAAGTCAACAT \\ CIDAM-5R & GCTATTGTGTGCCGCTC \\ CIDAM-6F & ACACATCATGTCCAACCAAAGT \\ CIDAM-6R & ACTGGATGATGGTGTGTGTGA \\ CIDAM-7F & TCCCTGGTTAACTTTGATTCC \\ CIDAM-7R & TGCCTAAAAGGTGGCGCGT \\ CIDAM-8F & GAGTACAGTCCCCCC \\ CIDAM-8R & TATCTTTGCGGCCCTTCGGTGT \\ \hline \hline \end{tabular} Note: The fluorescence-labeled \(8^{\prime}\) ends of the upstream primers are labeled in red and blue.  
\end{table}
Table 1: SSR-PCR primer pairs used in the study.  
A stepwise clustering approach was employed for sample retention. In each iteration, one individual from the lowest classification level was systematically removed, allowing the remaining individual to proceed to the next round of clustering.  
SSR marker data from 155 germplasm samples were analyzed with compression standards ranging from 10% to 90%, resulting in the identification of nine compressed populations. RAR, RPR, Ii, and phenotype genetic diversity index were calculated using the following formulas:  
\[\text{I}_{\text{i}}=-\sum_{\text{ij}}\text{P}_{\text{ij}}\text{ln}\,\text{P}_ {\text{ij}}\text{RAR}=\frac{\sum_{\text{i}}\text{N}_{\text{i}}}{\sum_{\text{i }}\text{N}_{\text{i}_{\text{o}}}}\times 100\%\]  
where Pij represents the frequency of the j-th allele at the i-th locus, Ni denotes the number of allelic variations in the compressed population, and Nio is the number of allelic variations in the original population.  
A comprehensive comparison of these parameters was conducted to optimize the representation of the original germplasm population while minimizing genetic diversity loss. Consequently, the most suitable molecularly pre-selected core germplasm was selected from the nine compressed populations.
# Experimental and Theoretical Study of One-Way Cyclic Lateral Responses of Piles in Sloping Ground
## Appendix B Power Series Method  
The pile deflection \(\delta_{\text{HH}}\)\({}^{(0)}\) and rotation \(\delta_{\text{MH}}\)\({}^{(0)}\) at the ground surface under unit lateral load (\(P_{0}=1\)) can be expressed as Equations (A9) and (A10), respectively. The rotation \(\delta_{\text{HM}}\)\({}^{(0)}\) and pile deflection \(\delta_{\text{MM}}\)\({}^{(0)}\) at the ground surface under a unit moment (\(M_{0}=1\)) can be expressed as Equations (A11) and (A12), respectively. Hence, the pile deflection and rotation at the ground surface can be obtained by Equations (A13) and (A14), respectively. Furthermore, the bending moment and subgrade reaction profile can be derived by Equations (A15) and (A16).  
\[\delta_{\text{HH}}^{(0)}=\frac{1}{\alpha^{3}EI}\cdot\frac{B_{2}D_{1}-B_{1}D_{2 }}{A_{2}B_{1}-A_{1}B_{2}}\] (A9)  
\[\delta_{\text{MH}}^{(0)}=\frac{1}{\alpha^{2}EI}\cdot\frac{A_{2}D_{1}-A_{1}D_{ 2}}{A_{2}B_{1}-A_{1}B_{2}}\] (A10)  
\[\delta_{\text{HM}}^{(0)}=\delta_{\text{MH}}^{(0)}=\frac{1}{\alpha^{2}EI}\cdot \frac{B_{2}\mathbb{C}_{1}-B_{1}\mathbb{C}_{2}}{A_{2}B_{1}-A_{1}B_{2}}\] (A11)  
\[\delta_{\text{MM}}^{(0)}=\frac{1}{\alpha EI}\cdot\frac{A_{2}C_{1}-A_{1}C_{2 }}{A_{2}B_{1}-A_{1}B_{2}}\] (A12)  
\[y_{0}=P_{0}\delta_{\text{HH}}^{(0)}+M_{0}\delta_{\text{HM}}^{(0)}\] (A13)  
\[\varphi_{0}=-\Big{(}P_{0}\delta_{\text{MH}}^{(0)}+M_{0}\delta_{\text{MM}}^{(0 )}\Big{)}\] (A14)  
\[M_{z}=\alpha^{2}EI\bigg{(}y_{0}A_{3}+\frac{\varphi_{0}}{\alpha}B_{3}+\frac{M_ {0}}{\alpha^{2}EI}C_{3}+\frac{P_{0}}{\alpha^{3}EI}D_{3}\bigg{)}\] (A15)  
\[Q_{z}=\alpha^{3}EI\bigg{(}y_{0}A_{4}+\frac{\varphi_{0}}{\alpha}B_{4}+\frac{M_ {0}}{\alpha^{2}EI}C_{4}+\frac{P_{0}}{\alpha^{3}EI}D_{4}\bigg{)}\] (A16)  
\begin{tabular}{c c c c c} \hline \hline \(z_{a}=\alpha z\) & \(A_{2}\) & \(B_{3}\) & \(C_{3}\) & \(D_{3}\) \\ \hline
0.0 & 0.00000 & 0.00000 & 1.00000 & 0.00000 \\  
\hline \hline \end{tabular}  
**Table A4. Dimensionless coefficient of pile responses (\(A_{4}\), \(B_{4}\), \(C_{4}\), and \(D_{4}\)).**  
\begin{tabular}{c c c c c} \hline \hline \(z_{a}=\alpha z\) & \(A_{4}\) & \(B_{4}\) & \(C_{4}\) & \(D_{4}\) \\ \hline
0.0 & 0.00000 & 0.00000 & 0.00000 & 1.00000 \\
0.1 & \(-0.00500\) & \(-0.00033\) & \(-0.00001\) & 1.00000 \\
0.2 & \(-0.02000\) & \(-0.0
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 2 Research Backgrounds
### EfficientNet  
The EfficientNet used in this study is an innovative architecture that was proposed by researchers to maximize the efficiency of CNN models. It was introduced by [PERSON] in 2019 [22; 24; 25]. CNNs are a form of deep learning technology that specialize in image analysis. They learn patterns directly from data, effectively recognizing specific objects, such as faces, and classifying images. CNNs use the principle of convolution to detect complex patterns and features within images. Due to these characteristics, CNNs are utilized in various fields, including object detection, face recognition, and natural language processing [10; 24; 26; 27]. CNNs have historically evolved by increasing the size of the model to enhance performance, which has resulted in higher computational costs and a larger number of parameters as performance improved [20; 21; 26; 27]. EfficientNet emerged as an alternative, which addressed these issues with the traditional CNNs [7; 22; 23; 24].  
EfficientNet provides an efficient method for improving model performance by simultaneously adjusting the network's depth, width, and resolution for optimization, as shown in Figure 1. This approach successfully maintains model performance while effectively managing model size [7; 22; 23; 24]. This feature has garnered attention in various applications, aiming to develop lightweight models that operate effectively in environments with limited computational resources.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref.** & **Database** & **Classifier** & **Accuracy** \\ \hline \multirow{3}{*}{[15]} & \multirow{3}{*}{Self-correction} & Faster R-CNN & \multirow{3}{*}{Fine dandruff: 97.41\%} \\  & & Inception\_ResNet\_v2\_Atrous & \\  & & model & \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{Self-correction} & Faster R-CNN & \multirow{3}{*}{Dandruff: 97.22\%} \\  & & Inception\_ResNet\_v2\_Atrous & \\  & & model & \\ \hline \multirow{3}{*}{[7]} & AI-Hub Scalp & \multirow{3}{*}{EfficientNet-B0} & Fine dandruff: 62.4\% \\  & Images & & Perifollicular erythema: 77.9\% \\ \hline \multirow{3}{*}{[22]} & AI-Hub Scalp & \multirow{3}{*}{EfficientNet-B0} & Fine dandruff: 77.5\% \\  & Images & & Perifollicular erythema: 84.8\% \\ \hline \multirow{3}{*}{[23]} & AI-Hub Scalp & \multirow{3}{*}{EfficientNet-B6, ResNet-152, and ViT/16} & Fine dandruff: 77.09\% \\  & Images & & Perifollicular erythema: 81.38\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of recent research studies.  
One of the key features of EfficientNet is optimizing computational resources to enhance the efficiency of the network. In scalp image analysis where large amounts of data and high resolutions have been required, EfficientNet has proved to be extremely helpful. Moreover, despite its smaller model size, EfficientNet provides high accuracy, making it suitable for scalp diagnoses using mobile devices or in environments with limited resources [7; 22; 23; 24].
# Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks
## 3 Fast Connectivity Construction Framework
### Framework Operations  
In this subsection, operations of our fast connectivity construction framework are presented. As shown in Figure 2, there are two phases for this framework that mostly covers the context-aware service loop. In the training phase, a mobile device in a D2 Dnetwork acquires context and trains the context on the light-weight multi-layer neural network (LMK). In the connecting phase, the mobile device asks for connectivity path prediction of LMK to calculate the adequate networking path in the current time and location. Furthermore, both phases in the proposed framework are carried out within each mobile device in a D2D network.  
There are two types of context in the training phase as DNN training data sets: connectivity context and signal state context. The connectivity context is about MAC layer link information and network layer routing path data over the MAC layer links. The signal state context is currently measured by radio signal strength (RSS) values, and they are prepared as a table. All the data are exploited to train LMK. In other words, when a mobile device joins a D2D network such as a producing sector in the smart factory or a social group over social networking domains, it starts acquiring such context during a round and training the context into LMK. After that minimum condition for using LMK, the device continuously maintains the LMK in update-to-date status.  
In the connecting phase, each mobile device in a D2D network prepares two contexts in terms of signal state context for real-time RSS values and connectivity context in the MAC layer. The mobile device queries connecting path prediction for updating the routing table in the network layer to the pre-trained LMK. Then, the mobile device performs the control plane for networking and updates the routing table in the network layer. Finally, each mobile device can be ready to send data packets to other mobile devices in the D2D network.
## 4 Results and Discussion
### Discussion
#### 4.2.1 Laboratory Test Samples of Lengthsui Auxiliary CSGRD
* A 90-Day Test Age: The correlation is nearly non-existent at \(-0.003\) with a \(p\)-value of 0.991, indicating no meaningful relationship between mortar margin and compressive strength. * A 180-Day Test Age: The relationship turns positive with a Pearson coefficient of 0.204 and a \(p\)-value of 0.540. This indicates a weak positive correlation, though it is also not statistically significant. 2. Remarks on Mortar Margin and Compressive Strength Development The mortar data in Table 22 reveal that the relationship between mortar margin and compressive strength is inconsistent and weak. None of the correlations are statistically significant. 3. Comparison between the correlation and  
significant, suggesting that the mortar margin is not a reliable predictor of compressive strength in this dataset. 3. Overall Remarks on both Paste and Mortar Margins and Compressive Strength Development From Table 22, both the paste and mortar margins in the mix proportioning of the materials showed weak correlations with compressive strength across the various test ages. As curing time increases, the relationship for paste margin tends to strengthen slightly, but neither paste nor mortar margins demonstrated statistically significant relationships with compressive strength. This suggests that while these margins may influence CSGR properties, their impact on compressive strength development was likely overshadowed by other variables beyond paste and mortar margins that could have affected hydration product formation, leading to weak bonds between constituents based on the compression test data analysis [4], such as the aggregate type, type of cement, and fly as well as their properties, water-cement ratio, and curing conditions (refer to SL678-2014, the technical guidelines for cemented granular materials for the requirement for aggregates, sand ratio, apparent density, binders, and water/binder ratio) [14]. Also, the outcome related well to the USACE experiment findings on the HCRCC mixes test, which explained the effect of too much Stone Powder (SP) on workability and compressive strength. The fine aggregate in this experiment was found to have a high fine content, which increased the demand for water during mixing, adversely affecting workability and strength development negatively over time [16; 17].
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 2 Data and Methods
### \(N_{e}\) Retrievals
#### 2.3.3 Lower F-Region  
The bottom-up algorithm can be extended to retrieve the \(N_{e}\) in the lower \(F\)-region if the RO \(hTEC\) profile reaches a higher altitude. However, most of the high-rate RO measurements do not go above \(h_{t}\)\(\sim\) 120 km in the normal operation. On the other hand, MetOp-A has acquired some high-rate RO data with a top up to \(\sim\)290 km, allowing to test the new algorithm for the lower \(F\)-region retrieval. Figure 6 shows the extended \(N_{e}\) retrievals from the ascending (nighttime) and descending (daytime) orbits in October 2021, revealing the \(E\)- to \(F\)-region transition across the meridional plane at two local times (8-11 h and 20-23 h). Because the \(E\)- and \(F\)-region dynamos are electrically connected, this global coverage in the lower ionosphere with a high spatiotemporal sampling is critically needed for studying and understanding their coupling processes.  
In addition to MetOp-A, the Spire constellation also produces ROs with a top routinely up to \(\sim\)175 km, higher than most of the GNSS-RO operations. The Spire's high-rate data occasionally go up to an even higher altitude for large scintillation cases. As shown in Figure 6, these high profiles provide only sparse sampling at altitudes above 175 km, not enough to produce a reliable climatology. Below that altitude, the Spire sampling seems to be uniform. Different from MetOp-A, the Spire constellation covers all local times. In other words, the Spire zonal mean is truly daytime and nighttime from all local times, whereas the MetOp-A zonal means are from two Sun-synchronous local times (Appendix B). Nevertheless, the Spire daytime and nighttime climatologies below 175 km exhibit the distributions similar to those from MetOp-A.  
Figure 5: COSMIC-2 \(N_{e}\) retrievals in the tropics (5\({}^{\circ}\)S-5\({}^{\circ}\)N) as a function of local time from 1 January 2021. The \(N_{e}\) value in the linearization state vector \(\mathbf{x}_{0}\) is the red line, and the FIRI models for three tabulated F10.7 (75, 130, 200 sfu) levels are the blue curves.
# An Improved Method for Evaluating Image Sharpness Based on Edge Information
## 2 Principle and Design of the Sharpness Evaluation Method  
### Image Edge  
The edge information of an image is crucial for vision and it is also one of the important features of an image. Figure 1 simulates the situation when the ideal step edge is blurred using a black and white image with drastically changing grayscale values. Additionally, it can be seen that, when the image is blurred, the edges of the image will spread and the grayscale curve slows down accordingly. Obviously, there is a positive correlation between the degree of edge diffusion and the degree of image blurring.  
It should be noted that the edges in a clear image are not always step edges, and there are also impulse edges and roof edges depending on the variation of grayscale values, as shown in Figure 2. However, the clear image is gradually smoothed after blurring, which leads to the disappearance of impulse edges and roof edges; this is obviously different from the relationship of step edges with the degree of image blurring. Therefore, which approach is used to extract the step or approximate step edges is directly related to the accuracy of the sharpness evaluation method. Section 2.4 of this paper gives a detailed solution to this problem, which will not be discussed here for now.
###### Abstract  
Complete and accurate spatial magnetic field description is the premise of effectively assessing the power supply capability of a high-speed railway (HSR). Its evaluation indicators are the current distributions and the integrated impedance of traction network. This paper proposes a spatial magnetic-field description method for the auto-transformer (AT) power supply system. Due to the limitations of previous approaches, all the real loop circuits of the AT system are considered for structuring a loop circuit matrix. At first, different description processes are divided, respectively, into those for the right side and the left side of the load. Then, considering that two types of return conductors exist in an AT system, a certain current ratio deduced in existing studies is introduced. As the introduced current ratio of the left side of the load is approximate, an iterative scheme is adopted. By constantly adjusting current ratio to satisfy a constraint condition of circuit voltages, accurate traction network impedance is obtained. Last, in order to verify the effectiveness of the proposed description method, two real-life experiments in a Chinese HSR line are performed, which indicate that the proposed method can not only directly reflect complete and accurate current distribution, but also deduce the exact traction network integrated impedance.  
Keywords:high-speed railway (HSR); spatial magnetic-field description method; auto-transformer (AT) power supply; real loop circuits; traction network integrated impedance; current distribution +
Footnote †: journal: Applied Physics Letters
## 4 Results and Analysis
### Temporal Evolution of Ecological Footprint in Xuzhou Central Area
#### 4.1.2 Calculation of the Ecological Footprint and Ecological Pressure Index  
The per capita ecological footprint, ecological capacity and ecological deficit in Xuzhou central area from 2005 to 2014 were measured as shown in Table 3.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Year** & **Ecological Footprint** & **Ecological Capacity** & **Ecological Deficit** \\ \hline
2005 & 1.0617 & 0.1004 & \(-\)0.9613 \\
2006 & 0.9880 & 0.1153 & \(-\)0.8728 \\
2007 & 1.0387 & 0.0903 & \(-\)0.9484 \\
2008 & 0.9432 & 0.1208 & \(-\)0.8225 \\
2009 & 1.0809 & 0.1084 & \(-\)0.9725 \\
2010 & 1.1164 & 0.1239 & \(-\)0.9925 \\
2011 & 1.1723 & 0.1132 & \(-\)1.0591 \\
2012 & 1.1766 & 0.1202 & \(-\)1.0564 \\
2013 & 1.2441 & 0.1157 & \(-\)1.1284 \\
2014 & 1.1718 & 0.0865 & \(-\)1.0854 \\ \hline \end{tabular}
\end{table}
Table 3: Temporal change of the per capita ecological footprint profit and deficit in Xuzhou central area from 2005 to 2014 (hm\({}^{2}\)/person).  
Figure 2: (a) Equivalence factors and (b) yield factors of Xuzhou central area from 2005 to 2014.  
As shown in Table 3, from the general view, the per capita ecological footprint showed a rising trend of 0.1101 \(\mathrm{hm^{2}/person}\). There were fluctuations during study period which may be caused by the deduction of consumption and equivalence factors. The ecological footprint reached its highest point of 1.2441 \(\mathrm{hm^{2}/person}\) in 2013, and the equivalence factors of forestland, grassland, water area, fossil energy land and built-up land both increased. The per capita ecological capacity decreased from 0.1004 \(\mathrm{hm^{2}/person}\) to 0.0865 \(\mathrm{hm^{2}/person}\). The per capita ecological profit and deficit presented as the ecological deficit from 2005 to 2014, which increased by 0.1241 \(\mathrm{hm^{2}/person}\). The fluctuation trend of ecological deficit is basically the same as ecological footprint.  
The ecological pressure index represents the pressure tolerance of the regional ecological environment. The ecological pressure index of Xuzhou central area was calculated and the results are shown in Figure 3.  
As shown in Figure 3, the change of the ecological pressure index from 2005 to 2014 presented with a rising trend, although there were declines in 2006, 2008, 2010, and 2012. It is worth noting that the ecological footprint and ecological capacity both decreased in 2014, however the ecological pressure still increased. The reason for this situation was that the decreasing rate of the ecological footprint was smaller and that of the ecological capacity was bigger, which led to the overall increase of the ecological pressure. According to the grade of the ecological pressure index, the ecological situation of Xuzhou central area was extremely unsafe from 2005 to 2014.
# Fall Detection Based on Recurrent Neural Networks and Accelerometer Data from Smartphones
## 1 Introduction  
### Problem  
The rapid aging of the global population has created a growing need for technologies that increase safety and improve the quality of life for older adults. Advances in artificial intelligence, combined with the widespread availability of devices such as accelerometers, have made it increasingly feasible to monitor human activity with high precision, enabling the prevention or mitigation of injuries that may pose serious risks to health or life. As the number of elderly individuals continues to rise [1], so does the demand for systems capable of preventing or minimizing the consequences of events that threaten well-being. Falls represent one of the most frequent and dangerous causes of injury in this population, often leading to fractures, long-term disability, or even death [2]. Furthermore, a fall may signal the onset of critical medical conditions such as strokes or cardiac events, which require immediate intervention. As a result, the development of accurate, reliable, and responsive fall detection systems has become an increasingly important area of research.  
In this article, we address the problem of fall detection using accelerometer data. This area has attracted increasing interest in recent years, leading to the development of numerous methods and datasets related to human activity recognition. However, it is important to highlight that many of these studies focus predominantly on general activity classification, with fall detection receiving comparatively less attention. Consequently, the resulting models often suffer from limited accuracy or insufficient prediction speed for real-time applications. In response to these limitations, we propose a solution that achieves high accuracy and fast inference, making it suitable for practical deployment. In addition, we observed that authors often do not provide source codes, which hinders reproducibility and comparative evaluation. To promote transparency, we use the publicly available UniMiB-SHAR dataset [3] in our experiments, which contains accelerometer data collected from smartphones during various activities, including falls and activities of daily living (ADLs). We also share all the data and code used in our experiments publicly, enabling other researchers to reproduce our results and build upon our work.