esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Sugar Analysis by HPLC-Refractive Index Detector (RID)  
Sugars were extracted according to the method of AOAC [28]. 500 mg of honey samples were dissolved in 5 mL of acetonitrile, water (HPLC grade) 50:50 (\(v/v\)), vortexed and filtered using 0.45 \(\upmu\)m syringe filters before injection to HPLC. Fructose, glucose, and sucrose were analyzed with Shimadzu HPLC system equipped with prominence LC-10 AB binary pump, autosampler SIL-20A (Kyoto, Japan), using the mobile phase of 85% aqueous acetonitrile (HPLC grade) at an isocratic flow rate of 1 mL/min. Fructose, glucose, and sucrose were separated with a Shimadzu LC-NH2 column (150 \(\times\) 4.6 mm, 5 \(\upmu\)m) and identified with refractive index RID-10A Shimadzu detector (Kyoto, Japan). A 10 \(\upmu\)L sample was injected to HPLC. Peak retention times of fructose, glucose, and sucrose were compared with those of standards and processed using Shimadzu LabSolutions, Lcsolution workstation V 1.22 (Kyoto, Japan). Quantification was done using the calibration curve of the corresponding external standards. All the samples were analyzed in duplicates.
# ML-Empowered Microservice Workload Prediction by Dual-Regularized Matrix Factorization
## 1 Introduction
Our prediction framework, namely Temporality-Dependency Dual-Regularized Matrix Factorization (TDDRMF), effectively integrates relevant techniques of machine learning with data analysis by combining matrix factorization with temporal regularization and low-rank dependency modeling: matrix factorization decomposes high-dimensional workload data into low-dimensional latent feature matrices capturing short-term fluctuations, long-term trends, and periodic patterns; temporal regularization integrates autoregressive and periodic constraints [22; 23] to enhance multi-scale temporal dynamics modeling; low-rank nuclear norm regularization [24] models key inter-service dependencies by imposing structured constraints on the microservice relationship matrix. To adapt to workload variations in real-time environments, the framework incorporates a dynamic error detection and parameter update mechanism, providing a balanced trade-off between prediction accuracy and computational efficiency. The contributions of this paper are summarized as follows:  
* **Workload dynamics modeling:** We apply matrix factorization with temporal regularization to model the dynamics of microservice workloads for more accurately capturing the principal patterns of workloads. * **Microservice dependency capturing:** We apply nuclear norm regularization to capture the primary dependencies between microservices by constraining the low-rank structure of the microservice feature matrix. * **Workload variation capturing:** We introduce a dynamic error detection and update mechanism that leverages localized window data to capture workload variations in real-time environments and enable the framework adaptive to dynamic and evolving environments. The rest of this paper is organized as follows. Section 2 presents an overview of related work, Section 3 presents the proposed methodology, Section 4 shows the empirical results, and Section 5 concludes the paper and evaluates paths for future work.
# Investigation on Ship Hydroelastic Vibrational Responses in Waves
###### Abstract  
The hydroelastic vibrational responses of a large ship sailing in regular and irregular head waves are investigated numerically and experimentally. A 3D time-domain nonlinear hydroelastic mathematical model is established in which the hydrostatic restoring forces and incident wave forces are calculated on the instantaneous wetted hull surface to consider nonlinear effects of the rigid motion and elastic deformation of the hull in harsh waves. Radiation and diffraction wave forces are computed on the mean wetted surface based on the 3D frequency-domain potential flow theory. The slamming loads are calculated by momentum theory and integrated into the hydrodynamic forces. The 1D Timoshenko beam theory is adopted to model the vibrational structural response and is fully coupled with the presented hydrodynamic theory in time-domain to generate the hydroelastic equation of motion. Moreover, self-propelled segmented model tests were conducted in a laboratory wave tank to experimentally investigate the hydroelastic responses of a target ship in regular and irregular head seas. The numerical and experimental results are systemically compared and analyzed, and the established hydroelastic analysis model turns out to be reliable and effective in the prediction of ship hydroelastic responses in waves.  
Keywords:ship hydroelasticity theory; wave loads; slamming loads; segmented model test; irregular waves +
Footnote †: journal: Applied Mechanics and Physics Letters
## 1 Introduction
### Contributions  
* This paper is an updated version of the previous work published in the IEEE CVPR workshop [40]. The previous work revealed that the proposed ROI-aware FES is very effective in improving leaf disease identification performance. In this line of research, it is interesting that the proposed ROI-aware FES can also be applied to leaf disease detection. Therefore, in this paper, we illustrate how to incorporate the proposed ROI-aware FES, that is, a new spot feature extractor, into the conventional YOLO framework. The advanced YOLO model that incorporates the ROI-aware FES and feature fusion is referred to as AE-YOLO in this study. In addition, it is revealed that the proposed AE-YOLO can improve leaf disease detection and surpass state-of-the-art object detection models. The proposed AE-YOLO is also expected to be applicable not only for the detection of apple leaf diseases, but also for the detection of pests and diseases in other crops.
* To the best of our knowledge, this study is the first attempt to introduce a novel deep learning architecture that considers the leaf spot attention mechanism and is applicable for both leaf disease identification and detection. Until now, existing models such as VGG, ResNet, and YOLO have been adopted for leaf disease identification and detection [15; 16; 17; 18; 19; 20; 37; 38]. However, in the proposed architecture, a new ROI-aware FES and feature fusion are introduced to find spot areas and encode spot information. The major contribution is to show a novel deep learning architecture that can incorporate the leaf spot attention mechanism based on the ROI-aware FES into existing classification and detection models.
* Previous studies have targeted leaf images with a single background color and a single leaf [17; 21]. These images are simple, and good results can be obtained by applying only TL. However, in this study, more complicated images are tested. In other words, the background has few branches and leaves. This is a much more challenging problem. The leaf disease dataset adopted in this study will be open to the public for research purposes.  
* This study reveals how to incorporate the conventional approach [5; 9; 39] (including three steps, i.e., clustering, feature extraction, and classifier learning) into a single deep learning architecture. In the proposed networks, the clustering and feature extraction steps are replaced by the proposed ROI-aware FES, and the classifier learning is replaced by the SAC-SubNet or YOLO detection subnetwork. The major difference between the proposed method and the conventional approach is that the proposed method performs three steps simultaneously. In addition, unlike conventional clustering [39], which might fail to extract spot colors from leaf images because of the similarity in their background and spot colors, the proposed method incorporates the clustering algorithm into the deep learning architecture; thus, more accurate feature clustering can be obtained.
# Weighted Group Sparse Regularized Tensor Decomposition for Hyperspectral Image Denoising
## 2 Notations and Tensor Ring
### Notations  
Following the nomenclature in [27], we summarize the notations used in this paper as follows. \(x\) is a scalar, \(\mathbf{x}\) is a vector, \(\mathbf{X}\) is a matrix and \(\mathcal{X}\) is a tensor. \(x_{i_{1},i_{2},\ldots,i_{N}}\) or \(\mathbf{X}(i_{1},i_{2},\ldots,i_{N})\) is the \((i_{1},i_{2},\ldots,i_{N})\)th element of \(\mathcal{X}\). \(x_{i_{2},i_{3}}\) or \(\mathcal{X}(:,i_{2},i_{3})\) is the \((i_{2},i_{3})\)th column of a third-order tensor \(\mathcal{X}\). \(\mathbf{x}_{i_{1},i_{2},i_{3}}\) or \(\mathcal{X}(i_{1},:,i_{3})\) is the \((i_{1},i_{3})\)th row of a third-order tensor \(\mathcal{X}\). \(\mathbf{x}_{i_{1},i_{2},:}\) or \(\mathcal{X}(i_{1},i_{2},:)\) is the \((i_{1},i_{2})\)th tubal of a third-order tensor \(\mathcal{X}\). \(\|\mathcal{X}\|_{F}=\sqrt{\Sigma_{i_{1},i_{2},\ldots,i_{N}}|x_{i_{1},i_{2}, \ldots,i_{N}}|^{2}}\) is the Frobenius norm. \(\|\mathcal{X}\|_{1}=\Sigma_{i_{1},i_{2},\ldots,i_{N}}|x_{i_{1},i_{2},\ldots,i_{ N}}|\) is the \(l_{1}\)-norm. \(\mathbf{X}_{(k)}\) is the mode-k unfolding of \(\mathcal{X}\). \(<\cdot,\cdot>\) is the inner product. \(\bigotimes\) is the Kronecker product. \(\bigcirc\) is componentwise multiplication. \(\times_{k}\) is the mode-k tensor matrix product.  
In order to increase the readability of this article, we summarize the abbreviations of the names in Abbreviations part.
# Base Pounding Model and Response Analysis of Base-Isolated Structures under Earthquake Excitation
## 4 Conclusions  
A base bounding theoretical model with linear spring-gap element is proposed in this paper. On the basis of this theoretical model, the finite element models of a structure with lead-rubber bearing and friction pendulum isolation bearing are built to analyze their seismic response. Some meaningful conclusions obtained are as follows:  
1. The base bounding theoretical model proposed in this paper can be applied easily and efficiently to analyze base-isolated structures when considering base bounding.
2. There is a most unfavorable clearance width between adjacent structures and the response of base-isolated structures increases in bounding.  
Figure 21: Structural displacement under different earthquakes (model B). (**a**) El Centro earthquake; (**b**) Taft earthquake; and, (**c**) Artificial excitation.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multicolumn{3}{c}{**Maximum Displacement (mm)**} \\ \cline{2-4}  & **Without Pounding** & **With Pounding** & **Amplification** \\ \hline El Centro & 81.80 & 126.98 & 0.55 \\ Taft & 109.34 & 118.73 & 0.09 \\ Artificial & 100.04 & 81.19 & \(-\)0.19 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Maximum structural displacements for model A.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Earthquake**} & \multicolumn{3}{c}{**Maximum Displacement (mm)**} \\ \cline{2-4}  & **Without Pounding** & **With Pounding** & **Amplification** \\ \hline El Centro & 106.82 & 129.20 & 0.21 \\ Taft & 125.73 & 129.58 & 0.03 \\ Artificial & 177.13 & 85.39 & \(-\)0.52 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Maximum structural displacements for model B.  
3. The number of stories in a building should not be uniquely considered to estimate the magnitude of impact force. More considerable factors should be considered, such as the type of structure and the material characteristics
4. Significant amplification of the story hear-force, velocity, and acceleration were observed in the analysis, which can bring many risks to base-isolated structures. Therefore, it is necessary to consider base bounding in the seismic design of base-isolated structure.  
The authors would like to express their gratitude to the support of Natural Science Foundation of China (51778538).  
[PERSON] proposed the simulation method and wrote the paper; [PERSON] performed the calculation and analyzed the data; [PERSON] and [PERSON] helped analyzing the data; [PERSON] conceived the idea, provided valuable discussions and revised the paper.  
The authors declare no conflict of interest
###### Abstract  
The research presented in this paper is based on the need for personalized medical implants, whose serial production is impossible, so the need for production process adjustments is inevitable. Conventional production technologies usually set geometrical limitations and generate a lot of waste material, which leads to great expenses, especially when the material used for production is an expensive Ti alloy. Additive technologies offer the possibility to produce a product almost without waste material and geometrical limitations. Nevertheless, the methods developed for additive production using metal powder are not significantly used in biomedicine because there is insufficient data published regarding the properties of additively produced parts, especially from the fatigue and fracture standpoint. The aim of this research is the experimental determination of fracture mechanics properties of additively produced parts and their comparison with the properties of parts produced by conventional technologies. Drawing is the first production process in the comparison, and the second one is selective laser melting (SLM). The Ti-alloy Ti-6 Al-7 Nb, used for medical implants, was selected for this research. Experimental testing was performed in order to determine \(\Delta K_{\text{th}}\) fracture mechanics parameters and resistance curves according to ASTM E1820. Test specimen dimensioning and the experiments were carried out according to the respective standards. For the drawn test specimen, the value obtained was \(\Delta K_{\text{th}}=3.84\) MPam\({}^{0.5}\), and the fracture toughness was \(K_{\text{c}}=84\) MPam\({}^{0.5}\), while for SLM produced test specimens the values were \(\Delta K_{\text{th}}=4.53\) MPam\({}^{0.5}\), and \(K_{\text{c}}=21.9\) MPam\({}^{0.5}\).  
keywords: fracture toughness; crack growth curve; resistance curve; additive technologies; powder metallurgy; selective laser melting; Ti-6 Al-7 Nb +
Footnote †: journal: applied sciences
# Evaluation of Structural Behavior of Hysteretic Steel Dampers under Cyclic Loading
## 3 Experimental Results
### Strain Distribution  
The strains of the specimens as measured by the strain gauges are shown in Figure 6. SD1 showed a maximum strain of about 0.023 after the vertical member reached its first yield strain at 17.9 kN in the forward direction for first cycle. On the other hand, no yielding was observed in the semicircular connecting member. As shown in Figure 6, energy dissipation of SD1 was concentrated in the vertical member.  
SD2 reached yield strain at the positive loading phase of the first cycle in both the vertical and horizontal members. The vertical member first reached yield strain at 18.3 kN, and then the horizontal member yielded at 24.5 kN. Like SD1, the energy dissipation of SD2 was concentrated on the vertical member. In the case of SD2, the strain of the vertical member increased up to about 0.046, and the horizontal connecting member was deformed up to about 0.0028.  
Similar to SD1, SD3 did not yield in the semicircular connecting member. On the other hand, both the vertical and the lower horizontal members reached yield strain in first cycle. The first yielding was observed at 36.2 kN in the lower horizontal member, and then at 55.5 kN in the vertical member. The vertical member of SD3 showed a maximum strain of about 0.027, showing similar strain characteristics to SD1. It can be seen from Figure 6 that the lower horizontal member, which is unique  
Figure 5: Deformation of steel dampers at target displacement: (**a**) SD1; (**b**) SD2; (**c**) SD3.  
Figure 6: Strain distributions of tested specimens: (**a**) SD1; (**b**) SD2; (**c**) SD3.  
to SD3, exhibited a maximum strain of about 0.066, and both the vertical and the lower horizontal members greatly contributed to energy dissipation. It is noted that the maximum strain range of the damper can be controlled by planning the geometric dimensions of the energy dissipation members of the damper.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 5 Fine-Tuning of Transport Properties  
Epitaxial graphene on SiC and 3C-SiC suffers from strong intrinsic n-type doping. The substrate-induced n-type doping in EG/SiC and EG/3C-SiC translates into the shift in the \(\mathbf{E_{\mathrm{F}}}\), away from the \(\mathbf{E_{\mathrm{D}}}\), such that the ambipolar properties of graphene cannot be exploited [79]. The substrate interaction causes larger charge density and lower mobility with strong temperature dependence when compared to freestanding graphene. Some of these issues can be compensated by tuning of the transport properties using different methods discussed in the following section.
## 4 Anomaly Detection Method Based on DeepESN
### Anomaly Detector  
The methodology introduced here aims to provide a way to detect anomalies in an unsupervised manner. The main architecture proposed is composed of two main parts: the deep learning model and the anomaly detector. The deep learning model is a RNN with a focus on comparing LSTM and ESN models and capable of performing a next-step prediction task taking \(\mathbf{x}(t-1)\) as input and providing the prediction \(\mathbf{\hat{x}}(t)\). The anomaly detector takes as input the prediction of the deep learning model \(\mathbf{\hat{x}}(t)\), the related real input \(\mathbf{x}(t)\), and the standard deviation of errors of the training set \(\sigma\) and indicates class 0 for normal behavior or class 1 when it detects an anomaly. Going deeply into the anomaly detector (architecture shown in Figure 5), it performs the squared error between \(\mathbf{x}(t)\) and \(\mathbf{\hat{x}}(t)\) and adds it to a queue of fixed size \(Q\). To detect the anomaly, the standard deviation (\(\mathbf{q}\)) of the queue \(Q\) is computed resulting in a vector of 1 and 0 according to the condition \(\mathbf{q}>e\sigma\). A vector is provided for the detection of anomalies for each feature.
## 5 Results
### Summary of Results  
* SwinT vs. AlexNet: both models classified most classes effectively, but the SwinT slightly outperformed AlexNet for 'Cooling or Lavaflow'.
* Challenges with similar classes: Both models found it difficult to distinguish the semantically close classes. For instance, 'Degassing' was often confused with 'No Activity', highlighting the need for additional data or finer-grained features.
* Advantages of the SwinT: The SwinT's Transformer-based architecture better resolved the inter-class confusion. This was evident from the confusion matrices, where the SwinT demonstrated superior performance when distinguishing between challenging classes, such as 'Cooling or Lavaflow' and 'Degassing'. Specifically, the SwinT correctly classified 73 out of 75 instances of 'Cooling or Lavaflow' with minimal misclassifications, compared with AlexNet's 72 out of 75. Additionally, the SwinT  
Figure 5: Confusion matrix for SwinT. Rows represent true classes; columns represent predicted classes.  
showed fewer misclassifications between 'Degassing' and 'No Activity' (eight misclassifications) compared with AlexNet (nine misclassifications). The global attention mechanism of SwinT allowed it to capture long-range dependencies and contextual information more effectively, leading to an improved classification accuracy regarding these complex scenarios.
* Issues with Strombolian class: misclassifications of the 'Strombolian' class suggest a need for improved feature extraction or enhanced data augmentation to capture its unique characteristics.
# Asphal Road Pavements to Address Climate Change Challenges--An Overview
## 4 Solutions for Mitigating Climate Change Challenges
### Pavement Solutions
#### 4.2.2 Cool Pavements  
Black asphalt pavements absorb and store more heat than other surfaces [76]. Therefore, materials that constitute them present very low reflectivity or albedo. It is worrying mainly in urban areas since it contributes to UHI. UHI is a complex phenomenon where multiple influencing factors (such as albedo, pavement type, pavement thickness, and thermal properties of constituent materials) interact [77]. To counterbalance UHI, some pavement solutions have been developed. They involve using cool pavements, i.e., pavements that contribute to UHI mitigation (Figure 4). It includes cool/reflective/permeable materials and phase change materials (PCMs).  
Cool pavement surfaces present a high albedo (proportion of the incident radiation that is reflected by a horizontal surface) and high thermal emissivity, thus absorbing less heat. New asphalt pavement has a typical albedo value of 0.05 [78] and it increases with ageing up to 0.15. With a 0.6-increase in pavement albedo, daytime pavement surface temperature could be reduced by 20 \({}^{\circ}\)C [79]. Non-black asphalt pavements are also a solution [80].  
Water retention pavements, such as porous asphalt pavement, also mitigate UHI [77; 79; 81]. That UHI mitigation is not due to a higher albedo, but to the release of the absorbed solar heat in the form of latent heat (due to the evaporation of liquid water) [78]. Their behaviour depends on their air voids content; the cooling effect increases as the air voids content increases [82]. Researchers have enhanced their evaporation characteristics by developing a structure with capillary columns in aggregate. The resulting pavement was 9.4 \({}^{\circ}\)C-cooler than a conventional permeable pavement [83].  
Other cool pavement technologies are based on high-inertia pavements that add phase change materials (PCMs) to the asphalt mixture [84; 85; 86]. PCMs can change their phase states, store heat, and control temperature. They absorb heat during the day more slowly, and release the absorbed heat more slowly during the night. According to the composition, PCMs can be inorganic, organic, or composite [84]. Even though inorganic ones have some advantages over organic ones, they are inadequate for asphalt pavements. For asphalt pavements, organic PCMs should be the best choice [84]. One of the most known is polyethene glycol [86]. Road pavement solar collectors are also a solution to absorb heat from the pavement surface [87].  
[PERSON], et al. [88] presents a comprehensive review on this pavement solution with maintenance solutions. They recommend to use reflective pavements in the maintenance of existing ones and permeable pavements in the construction of new ones. They also outline the main advantages and disadvantages of each solution. Permeable pavements are costly.
## 3 Experiments and Configuration Detail
### Experimental Environment for LCNN Training  
The cross-entropy loss function (\(L_{CE}\)) was used to measure the discrepancy between the predicted and actual labels, as defined in Equation (1). In this equation, \(y\) represents the actual label value, and \(\hat{y}\) represents the predicted value.  
\[L_{CE}=-[y\log(\hat{y})+(1-y)\log(1-\hat{y})] \tag{1}\]  
The Adam optimizer trained the networks [39] with an initial learning rate of 0.001, and the model with the lowest loss value was selected for testing. The He strategy initialized the weights of the convolutional layers [40], and the number of epochs was set to 200 for all models. The batch size was 32, and the images were sent to the networks with original resolution except Efficienetb1 and Efficienetb2.  
The ShuffleNetv2-1.0-SE was compared with MobielNetV1 [20], MobiNetV2 [21], MobiNetV3 [22] and Efficienet [25]. The alpha width factor was set to 1 for MobiNetv1, MobiNetv2 and MobiNetv3. We used 240 \(\times\) 240 and 260 \(\times\) 260 resolution inputs for Efficienetb1 and Efficienetb2, respectively. The experiments were run on a computer with an Intel I5-9600 KF CPU, 16 GB of memory and an NVIDIA GeForce GTX3060 (12 GB) GPU with CUDA support. We used Python 3.7 and TensorFlow 2.4 as the programming environment and tools.