esa-sceva/satcom-chunk-collection huggingface dataset:
# Laboratory Evaluation and Finite Element Modeling of SBS and Basalt Fiber Modified Mixtures
## 3 Finite Element Modeling  
The [PERSON]'s Logit model has been used to perform finite element analysis of the 3% SBS with 0.3% BF and 7% SBS mixtures, as shown in Equation (5). The ABAQUS version 2018 is used for modeling [29].  
\[\varepsilon(t)=\bigg{[}\frac{1}{E_{0}(1-\omega)}+\frac{1}{E_{2}}\bigg{(}1-e^{ \frac{E_{2}}{\pi_{2}}}\bigg{)}+\frac{1}{A^{\varepsilon_{1}}}\bigg{]} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Flexural Tensile** & **Failure Strain** & **Curving Stiffness** \\  & **Strength [MPa]** & \(\varepsilon_{u}\)/**10\({}^{-3}\)** & **Modulus [MPa]** \\ \hline
76-22 with 7\% SBS & 5.31 & 3.15 & 1851 \\
76-22 with 3\% SBS & 6.24 & 4.32 & 1685 \\ and 0.3\% BF & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: Flexural properties of different scenarios.  
Figure 4: Flexural properties of different scenarios.  
where \(\epsilon(t)\) is the creep strain of the [PERSON]'s model, \(E_{0}\) is the dynamic modulus, \(E_{2}\) is the internal damper parameter, n\({}_{2}\), \(A\) and \(B\) are the model viscoelastic fitted parameters and \(\omega\) is the damage value. Values are obtained at a temperature of 30 \({}^{\circ}\)C in order to reduce the bias against rutting progression in higher-temperature conditions, as shown in Table 7. This model uses the external damper and external spring by simulating the material decay, thereby increasing the material modeling accuracy in predictions.  
A dual wheel loading setup is used to simulate the traffic loading with a magnitude of the cumulative axle load of 100 kN, with a traffic speed of 90 Km/h and traffic volume of 50,000 axle passes, as shown in Table 8. Poisson's ratio for the asphalt mixture is 0.41 and the elastic modulus is 950,000 kPa.
## 3 Finite Element Modeling (FEM) of Human-Seat Interactions
### _Sat_ Cushion Modeling  
The seat cushion was modified to integrate the pneumatic spring. This process involved excising a rectangular block from the cushion, following its core to match the pneumatic spring's shape, and reinserting it after the spring installation. The spring, comprising a latex tube and fabric tape, was designed to ensure elasticity and durability, with its mechanical properties being adjustable through internal pressure control.  
A 3D half-symmetric model of the seat cushion was created using SolidWorks 2017 and imported into MSC.Marc for FEA. The cushion was meshed with 288,327 tetrahedral elements, ensuring accurate simulation (Figure 5a). The foam block and pneumatic spring assembly were also meshed and modeled, considering the nonlinear and viscoelastic properties of polyurethane. Experimental data on polyurethane deformation were used to calibrate the viscoelastic model through a 9 th order Prony series, providing realistic material behavior under compression (Figure 5b).
## 3 Problem Description and Proposed Solution
### Problem of Finding a Root Cause  
The O&M entity is defined as **subject**: let \((S_{1},S_{2},..S_{n})\) denote \(n\) subjects, and APM monitor all the subjects \(S\) by collecting the performance metrics corresponding to the subject \(S_{i}\). \((M_{i},S_{j})\) means that metric \(M_{i}\) locates on subject \(S_{j}\), and some key performance metrics are also commonly called \(KPI\).  
The observation data of a KPI \(M\) are a kind of time-series sequence data, which can be denoted as \(KPI_{data}=\{m_{t_{1}},m_{t_{2}},...,m_{t_{n}}\}\), where \(m_{t_{i}}\) is the observation value for the KPI at the timestamp \(t_{i}\).  
Let \(Y_{t_{i}}\) be the variable indicating whether an anomaly happened at the moment \(t_{i}\), \(Y_{t_{1}}\) is 0 or 1.  
Let us define an \(n\times t\) matrix \(M\), where \([M]_{i,j}=m_{i,j}\), \((i\in[0,n],j\in[0,t])\) is the metric value of subject \(S_{i}\) at timestamp \(j\), and the total number of the given observable subjects is \(n\). Because there is more than one metric for monitoring, all the metrics observation data could be denoted by \(M_{N}=\{\left[M_{1}\right]_{i,j},\left[M_{2}\right]_{i,j},...,\left[M_{n} \right]_{i,j}\}\).  
On the basis of the above definition, the root cause finding problem is formulated as follows: Given \(M_{N}\), assuming an anomaly is observed from metric \(M_{i}\) at a certain timestamp \(t_{i}\), that is, \(Y_{t_{i}}=1\), our goal is to find a set of subjects \(S_{rc}\) and their metrics \(M_{rc}\) as the root cause of the anomaly.
## 2 Results  
### Identification and Physicochemical Properties of EhFPS  
The FPS protein domains in the rubber tree were identified using the conservative structure databases InterPro and CDD, and BLASTP comparison was performed in the constructed protein sequence library of _E. Hirta_. Resultantly, eight _FPS_ candidate genes, namely _EhFPS1_, _EhFPS2_, _EhFPS3_, _EhFPS4_, _EhFPS5_, _EhFPS6_, _EhFPS7_, and _EhFPS8_, were found as per position sequence in the _E. Hirta_ genome (Table 1). Analysis of the physicochemical properties of EhFPS proteins showed that the encoded amino acid lengths ranged from 323 to 389 aa, and the molecular weight of the protein was between 37.44 and 45.24 kDa. The smallest member was EhFPS5, consisting of 323 amino acids, with a molecular weight of 37.44 kDa, whereas the largest member was EhFPS3, consisting of 389 amino acids, with a molecular weight of 45.24 kDa. The isoelectric points were between 5.11 and 6.52, with mostly acidic proteins, and the average hydrophobicity index (AHI) was less than 0, ranging from \(-\)0.353 to \(-\)0.255, indicating that EhFPS proteins were all hydrophilic. In the secondary structure of the EhFPS protein, the highest proportion was the \(\alpha\)-helix, and 75% of the proteins had an \(\alpha\)-helix index greater than 60%; EhFPS5 had the highest index of 65.94%, and EhFPS4 had the lowest index of 56.72%. \(\beta\)-turn had the lowest percentage, with EhFPS8 accounting for the highest percentage (3.76%). Subcellular localization analysis showed that except for EhFPS3, EhFPS4, and EhFPS8, which were localized in chloroplasts, the others were localized in the cytoplasm.
# An Analysis of the Wind Parameters in the Western Side of the Black Sea
## 3 Results
### Annually Evaluation of the Wind Data  
A more detailed analysis for every year can be seen in Figure 5. As it can be observed from Figure 5a, the wind speed maximum average value is located in December (EuxRo 3-8.0 m/s), which is closely followed by November with an average of 7.8 m/s. On the opposite, the lowest value is found in July (a mean value of 4.9 m/s). From Figure 5b, a maximum value of the wind speed of 9.7 m/s in January 2016 can be observed, closely followed by 8.7 m/s in February, whereas a minimum of 4.8 m/s is seen in July 2016. For Figure 5c a major value is reported in December, during which an average of 9.6 m/s may be observed, while a minimum value of 4.9 m/s was found in June. In Figure 5d it is presented the 2018 year, the most energetic year, where we can witness a maximum average value of 9.8 m/s in January and a minimum of 3.4 m/s in July. From the values analyzed in 2019 (Figure 5e), it can be noticed that January (9.3 m/s) and February (9.1 m/s) present in general much higher values compared to July and June, where the values are located in the range 4.1-4.5 m/s. The 2020 data (Figure 5f) seems to be in good agreement with the data from above, a maximum average value has been noticed in winter (February--8.9 m/s) and a minimum in summer (June--4.7 m/s). Based on the analysis made for each month in the 6-year interval considered, it resulted that the greatest average values were encountered in January 2018--9.8 m/s (Figure 5d), followed by January 2016--9.7 m/s (Figure 5b).
## 4 Experiments and Results
### Experimental Setup  
The experiments were conducted on the Windows 10 Professional operating system, running on a powerful hardware configuration comprising 64 GB of RAM and an Intel(R) Xeon(R) W-2155 CPU operating at 3.30 GHz. The system was further enhanced with an NVIDIA GeForce RTX 3060 GPU, boasting 12 GB of dedicated memory. To facilitate the experiments, the system was configured with CUDA version 11.7, Tensorflow 2.10.0, and Python 3.10.9.  
Figure 10: Example of images in each classes of DFU.  
The selection of hyperparameters in this study was influenced by available computational resources. The batch size was set to 8, and the input images were resized to dimensions of 224 by 224 pixels with RGB channels. The model was trained for 40 epochs. A fixed learning rate of \(10^{-6}\) was employed.  
As explained in the previous section, the DFU-Helper framework uses a Siamese Neural Network, which is composed of two identical sub-networks. The following section explains the backbone used for the sub-networks.
## 3 Conclusions  
This comprehensive multi-approach study has successfully undertaken the synthesis, characterization, and separation of a de novo peptide sequence capable of spontaneous self-assembly into unique intricately branched dendritic structures under physiological conditions. Initially, the peptide was synthesized through solid-phase peptide synthesis (SPPS) employing the Fmoc/tBu strategy and was effectively characterized using RP-HPLC and MALDI-Tof MS. FTIR analysis revealed that the FEYNF-NH\({}_{2}\) peptide self-assembly is primarily driven by the formation of \(\beta\)-sheet structures. The fluorescence analysis showed the presence of a quenching phenomenon, observed within a brief timeframe for two distinct concentrations of the FEYNF-NH\({}_{2}\) peptide. In addition, the microscopic examination through POM and TEM imaging provided valuable insights into the elaborate framework, revealing its potential for forming gel-like structures. For future research, one may consider exploring alternative methods to gain additional insights into the intricate dynamics of peptide behavior under various conditions.  
The application of in silico studies employing molecular docking simulation (using the AutoDock-VINA algorithm included in YASARA-Structure) has unveiled critical insights into the peptide-peptide interactions and the conformational behavior of the protonated dimer (FEYNF@FEYNF(+2)). These computational methods have not only illuminated the self-assembling pathway but have also shed light on the intriguing gelation process. The prevalence in FEYNF to a \(\beta\)-sheet structure is an essential disclosure, indicating the structural evolution of the peptide and its capacity to form organized, sheet-like arrangements.  
Moreover, the revelation of noncovalent interactions between the FEYNF peptide's protonated dimer (as a ligand) and the widely acknowledged Drew-Dickerson dodecamer d(CGCGAATTGCG)2 holds paramount importance. This DNA sequence, often employed as a key model in structural and molecular biology research, underscores the practical applicability of the FEYNF peptide. This discovery not only validates the versatility of the FEYNF peptide but also marks a significant advancement, connecting peptide self-assembly with DNA recognition. This linkage opens promising avenues for the development of innovative biomaterials and targeted therapeutics.
# EAAnet: Efficient Attention and Aggregation Network for Crowd Person Detection
## 1 Introduction
### Attention Mechanism  
The attention mechanism mimics the attention mechanism in the human visual system, enhancing neural networks' focus on the different dimensions of input data to better process complex inputs. And common attention mechanisms include channel attention which is exemplified by SENet [32] (Squeeze-and-Excitation Networks) and ECA (Efficient Channel Attention) [33], and spatial attention exemplified by GENet (Gather-Excite Network) [34].  
On one hand, the SENet employed GAP (Global Average Pooling) to compute global features for each channel, achieving global awareness across different channels. In contrast, the ECA attention mechanism used a 1D convolutional layer to capture inter-channel features and generate attention weight. On the other hand, GENet gathered information from different regions within the feature maps and utilized this information to reconstruct new feature maps. However, these attention mechanisms fail to simultaneously capture both channel and spatial information, leading to an insufficient extraction of crucial information, especially the fine-grained information for small and crowd objects and unsuitable for complex scenarios.  
Meanwhile, the CBAM [19] mechanism combined both channel attention and spatial attention, which helped with capturing information from both the channel and spatial dimensions. This enhances the model's ability to extract important information from feature maps, addressing challenges posed by crowd occlusion, blurred backgrounds, and low-resolution images in datasets.
###### Abstract  
Technology can facilitate the provision of healthcare to older adults. Wearable devices are thus increasingly prevalent amidst perpetual component miniaturization and cost reduction. This study aimed to determine whether existing application (app) interfaces are suitable for older adults by comparing the perceived usability and emotional reactions of younger users and older users to the health information display formats of wearable interfaces. Based on the outcomes of a literature review and expert recommendations, four health display interfaces--text, diagram, image, and animation--were developed and revised. Thirty respondents in Miaoli, Taiwan, were invited to participate in a questionnaire and interviews. The collected data were analyzed and discussed to develop design recommendations. The findings of this study were as follows: (1) the diagram interface had the lowest performance; (2) the respondents preferred the animation interface, which produced strong affective valence, thereby suggesting that animation generated positive emotions, yielding a result consistent with expert views and existing design principles; and (3) older users were more accepting of the text interface than the younger users, who exhibited negative emotions toward the text interface, highlighting a significant generation gap.  
health information; interface formats; older adults; wearable devices; usability; emotional reaction +
Footnote †: journal: Applied Sciences
# Defect R-CNN: A Novel High-Precision Method for CT Image Defect Detection
## 2 Methods  
### Mask R-CNN  
Mask R-CNN is a deep learning model extended from Faster R-CNN. It performs object detection and generates accurate segmentation masks for each detected object. Its architecture comprises four components: the feature extraction network, the region proposal network, the classification and regression module, and the segmentation mask module, as illustrated in Figure 1.  
Mask R-CNN typically employs ResNet and the Feature Pyramid Network (FPN) as the backbone to extract multi-scale feature maps. This design preserves rich semantic information and enables the model to detect targets at various scales, facilitating the extraction of detailed features. The Region Proposal Network (RPN), as the first step in the two-stage detection process, generates a set of candidate regions from the feature map. The RPN scans the feature map through a sliding window, generating anchors and predicting whether each anchor contains a target. By refining these anchors for classification and regression, the RPN provides high-quality candidate regions for subsequent classification and segmentation stages. To further improve detection accuracy, Mask R-CNN replaces the regions of interest (ROI) Pooling used in Faster R-CNN with ROI Align, which eliminates coordinate errors from floating-point rounding. ROI Align retains finer-grained features through bilinear interpolation, enhancing both detection and segmentation accuracy. After generating candidate regions, Mask R-CNN performs multi-task learning for each region, including category classification, bounding box regression, and instance segmentation. The classification module predicts the target category, while the bounding box regression module refines the target's position. The segmentation module then generates a binary  
Figure 1: Mask R-CNN architecture.  
mask for each target, using a convolutional network to predict ROI pixel by pixel, yielding pixel-level segmentation results.
## 4 Results
### Circumference  
Several simulations were carried out, with and without aerodynamic effects. In the case of aerodynamic effects, a trajectory with a radius greater than the critical one was also studied. The initial speed was intentionally small, so as to better analyse the performance of the developed algorithm.  
The first simulation was run without aerodynamics effects, and \(\mathbf{R}=\mathbf{100}\) m. Figure 5 shows speed and accelerations as functions of the arc length. As expected, the vehicle accelerated to reach the maximum speed as soon as possible (Figure 5 (left)). Meanwhile, Figure 5 (right) shows that the lateral acceleration increased at the same time the longitudinal acceleration decreased, because of the effect of the friction ellipse (see Equation (5)). When the maximum speed was reached (around \(s\approx\mathbf{150}\) m), Equation (5) shows that the maximum achievable longitudinal acceleration was zero, therefore the vehicle carried on at that speed.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Quantity** & **Symbol (unit)** & **Value** \\ \hline Mass & m (kg) & 620 \\ Drag factor & \(k_{x}\) (Ns\({}^{2}\)/m\({}^{2}\)) & 0.72 \\ Downforce factor & \(k_{z}\) (Ns\({}^{2}\)/m\({}^{2}\)) & 2.15 \\ Power & P (kW) & 550 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main vehicle parameters.  
Figure 6 shows the same quantities for a simulation with aerodynamics. The downforce effect clearly increased the maximum achievable speed with respect to the previously analysed case. By looking at the longitudinal acceleration, it increased only until around \(\mathbf{s}\approx\mathbf{80}\) m, then it was still positive, but decreased as a result of the vehicle power limit.  
Using the vehicle parameters from Table 1 in Equation (10), the critical radius was 144 m. Figure 7 shows the results for \(\mathbf{R}=\mathbf{200}\) m, which is above the critical radius. The maximum speed still increased with the vehicle speed \(\mathbf{v}\), but was never reached. With \(\mathbf{R}\) being larger than the critical radius, the maximum velocity will always grow more than the vehicle velocity. However, this time, the maximum velocity was saturated because of the power limit, which intervened rather early (Figure 7 (right)). This implies that, when the negotiated trajectory was approached, there was still a potentially large availability of grip. Theoretically, without a power limit, the speed would keep increasing.  
Figure 5: Circumference, no aerodynamics, R = 100 m: (left) speed and (right) accelerations with respect to \(s\).  
Figure 6: Circumference, aerodynamics, R = 100 m: (left) speed and (right) accelerations with respect to \(s\).
# Virtual Gymnasium: Personalized Weight Perception Interface in Lifting Virtual Objects
## 7 Discussion  
### Post Experiment about User Preferecens  
We conducted a post-experiment of participants and performed user interviews with the 15 participants after the first experiment. We asked the participants about their preference for the pseudo-haptic interfaces. Figure 19A shows the results of the preference questions. According to the pseudo-haptic interface result, FCGGUI was ranked first (80%) and \"force arrow\" was ranked second. With the FCGGUI, the participants could control lifting forces with personalized movement trajectories. Thus, it was easier to perceive virtual weights during the lifting process. Participants who chose the \"force arrow\" interface mentioned that the \"force arrow\" showed an intuitive indicator of lifting velocity. The participants had problems perceiving the virtual weights with the \"changing size\" interface.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Participants** & **FCGGUI** & **Real Lifting** \\ \hline P1 & 0.1171 & 0.2308 \\ \hline P2 & 0.1558 & 0.2406 \\ \hline P3 & 0.1326 & 0.2188 \\ \hline P4 & 0.1862 & 0.221 \\ \hline P5 & 0.2078 & 0.2636 \\ \hline P6 & 0.1418 & 0.1987 \\ \hline P7 & 0.2155 & 0.2414 \\ \hline P8 & 0.2036 & 0.2238 \\ \hline P9 & 0.1592 & 0.2104 \\ \hline P10 & 0.1264 & 0.189 \\ \hline Average & 0.1646 & 0.22381 \\ \hline Standard Deviation & 0.036225 & 0.021803 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of the average measured electromyogram.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**ANOVA Result (pa = 0.05)** & **Mean and SD Results, Stored as in Figure 15** \\ \hline F = 19.61114, & FCGGUI (M = 0.1646, SD = 0.001232) \\ pa = 0.000324 & Real Lifting (M = 0.22381, SD = 0.000475) \\ MSw = 0.000894 & \\ \hline \hline \end{tabular}
\end{table}
Table 12: Results of the ANOVA test of the exert force in Experiment 3.  
Some participants mentioned that a large-size virtual dumbbell felt lighter than other sizes of dumbbells.  
We also conducted user interviews with 15 other participants after the second experiment. We asked the participants about their preference for the three two-handed lifting curls. As shown in Figure 19B, the best-preferred exercise was the \"dumbbell curl\" process because the participants could control their left and right hands, respectively. Some participants said they best perceived virtual weights and felt exercise effects regarding the \"shoulder curl\" process. Regarding the \"barbell curl\" process, participants were confused about perceiving the same virtual weights on both hands.
# Risk Assessment of Soil Heavy Metals in the Jiahe River Basin of Yantai City, China
## 1 Introduction  
With the rapid development of industrialization and urbanization, the issue of soil heavy metals has become increasingly prominent, prompting intensified research and advancements in this field [1, 2, 3]. Heavy metal pollution not only degrades the quality of soil, crops, the atmosphere, and water bodies but also accumulates in the food chain, posing a threat to human health [4, 5, 6]. Since the 1970s, global environmental legislationand policies have facilitated systematic investigations into soil heavy metal pollution. The heavy metals of common concern are Cr, Hg, As, Pb, Cd, Cu, Ni, Zn, Co, V, Mn, etc. [7; 8; 9].  
Methods for assessing heavy metal pollution are generally categorized into two types. The first type involves evaluations based on established standards, such as the national standard GB15618-2018 for agricultural soils in China [10; 11; 12]. The second type employs pollution indices, including the contamination factor (C\({}_{\text{f}}\)), ecological risk factor (Er), enrichment factor (EF), and index of geo-accumulation (I\({}_{\text{geo}}\)) [13; 14; 15].  
In recent years, a series of soil environmental quality assessments have been conducted by the China Geological Survey in Shandong Province, China. These efforts aim to enhance the role of geological work in the integrated management of natural resources. This study was based on agricultural soils sampled from the Jiahe River Basin of Yantai City in Shandong Province, China, during a comprehensive geological survey. In this paper, risk assessments of heavy metals were conducted using both national standards and pollution indices. Then, the results of these two evaluation methods were compared to identify an appropriate pollution assessment method for heavy metals that lack national standards. Finally, a comprehensive risk evaluation on 11 heavy metals was performed.