esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis Model for Cracking and Flexural Behavior of HPFRCC Beams
### Calculation of Deflection  
Figure 9 shows that while the flexural behavior of a HPFRCC beam follows the apparent moment-deflection curve, two representative behaviors can be observed in the segments; the weakest segment with a localized crack follows the descending part in the moment-curvature relationship, and the others follow the unloading path without localization when the applied moment reaches the maximum moment, which is less than the maximum capacity of those segments. In other words, the crack in the weakest segment continuously opens up to the failure, while the other cracks close after the beam reaches its maximum capacity.  
To calculate the deflection of the beam, therefore, the segments need to be classified into the localized and non-localized elements, as mentioned above, because the localized element exhibits ductile softening behavior while the non-localized elements follow the unloading path after the beam reaches the maximum moment, as illustrated in Figure 9. Deflection induced by non-localized segments can be calculated according to the elastic theory [31] using Equation (5):  
\[\delta_{\text{nonlocal}}=\int\varphi_{\text{nonlocal}}x\text{d}x \tag{5}\]  
For the localized element, the elastic theory can be applied only before the crack localization. After the localization, the calculation of the deflection caused by the segment with a localized crack can be based on a rigid-body rotation theory and a plastic deformation concept, as shown in Figure 10. The deflection induced by the localization can be calculated from the following equation:  
\[\delta_{\text{local}}=\int\frac{l-x}{2}\varphi_{\text{local}}\text{d}x \tag{6}\]  
Consequently, the deflection of the specimen can be calculated through the superposition of the elastic and plastic deformations, since the deflection can be calculated through integrating curvatures along the beam:  
\[\delta_{\text{total}}=\delta_{\text{nonlocal}}+\delta_{\text{local}} \tag{7}\]  
Figure 10: Deformation of beam in rigid-body rotation.  
Figure 9: Deflection of a beam and curvature of segments.
## 3 Analysis of Literature Resources
### AI Techniques to Support APS Systems
#### 3.2.1 Supervised ML Techniques  
Among supervised ML algorithms, there are several Deep Learning (DL) Neural Networks (NNs) that can effectively address multi-objective FJSSPs, ensuring production continuity and stability in dynamic environments [32]. Artificial Neural Networks (ANNs), inspired by the human brain, are well-suited for the optimization of PPC due to their ability to manage continuous action-state spaces through interconnected nodes with adaptive weights [41; 42; 43; 44]. In industrial contexts, ANNs support tasks such as fault detection, quality improvement, forecasting, and shop floor control. They are particularly effective in predicting energy consumption [45] and flow time [46] by enhancing efficiency and due dateaccuracy, as well as integrating sustainability perspectives on production processes. By updating weight parameters rather than each input, as in traditional probabilistic methods which lack learning capabilities [32; 42; 43; 44; 47], they can learn from large datasets without manual heuristics and support both online and offline operation [39; 42; 43].  
Variants of NNs include Recurrent Neural Networks (RNNs), which model sequential data by maintaining information from the previous step [48; 49]. However, they struggle with long-term dependencies due to the vanishing gradient problem, which is mitigated by LSTM networks [48; 49; 50].  
Another type of algorithm belonging to this category is Convolutional Neural Networks (CNNs), which often operate in hybrid models with LSTM or RNNs [48; 49; 50]. Despite their effectiveness, deploying CNNs and RNNs can require high computational power and domain expertise, posing challenges for organizations with limited resources [50].
# Material Point Simulation Method for Concrete Medium Fracture and Fragmentation under Blast Loading
## 3 An Improved HJC Model Considering the Segmentation of the DIF Function
### The Improved Method of the HJC Model
#### 3.2.1 Calculation Expression of the \(DIF_{c}\)  
At the end of the dynamic compression experiments, the dynamic compressive strength under different strain rate conditions was plotted as a scatter plot according to the experimental results, as shown in Figure 6a. The data were then fitted to the experimental results using a variety of function forms in the Origin software with 9.1 version, such as the linear function, the Boxlucas1 function, the Explp1 function, the Parabola function, and the PWL2 function, and the expressions for each function form are shown in Table 1. Where \(A\), \(B\), \(C\), \(D\), and \(E\) are the fitting coefficients. Note that before fitting the data, it is necessary to eliminate some of the large scatter of \"bad data points\". The results of fitting the coefficients for increasing dynamic compressive strength to different functional forms are shown in Figure 6b-f.  
Figure 5: Concrete specimen.  
Figure 6: Fitting results of the compression dynamic increasing factor: (**a**) the scatter plot of the experiment result, (**b**) Linear curve fit result, (**c**) Boxlucas1 curve fit result, (**d**) Exp1p1 curve fit result, (**e**) Parabola curve fit result, (**f**) PWL2 curve fit result.  
It can be seen that the Boxlucas1 function has the highest goodness of fit compared to the other functional forms when the goodness of fit of the different functional forms is analyzed and compared. Therefore, to describe the compression dynamic increasing factor with greater accuracy, the Boxlucas1 function is used. Meanwhile, it is not difficult to see that the dynamic compressive strength of concrete is significantly enhanced under blast loading when analyzing the dynamic compressive strength scatter plots and various types of data fitting plots. Here, we define this property as the \"strain rate segmentation characteristic\" of the dynamic compressive strength of concrete.  
Based on the consideration of the \"strain rate segmentation characteristic\", the expression of the compression dynamic increasing factor is proposed as follows:  
\[DIF_{c}=\begin{cases}DIF&\dot{\epsilon}\leq 10^{2}s^{-1}\\ -0.07892\times\left\{1-e^{[1.28491\times\log{(\dot{\epsilon}^{*})}]}\right\}& \dot{\epsilon}>10^{2}s^{-1}\end{cases} \tag{7}\]  
In the equation, \(DIF=1+C\ln{\dot{\epsilon}^{*}}\) is the formula for calculating the dynamic increasing factor in the HJC model, \(\dot{\epsilon}\) is the strain rate, and \(\dot{\epsilon}^{*}=\dot{\epsilon}/\dot{\epsilon}_{0}\) is the dimensionless equivalent strain rate (\(\dot{\epsilon}_{0}=1s^{-1}\) is reference strain rate).
## 3 Results and Discussion
### Elastic Recovery Test Results  
Table 13 gives elastic recovery test results and S/N values calculated according to the \"larger is better\" performance statistic. The elastic recovery percentage (\(\mathrm{R_{E}}\)) value was calculated using the formula given below. An \(\mathrm{R_{E}}\) value of 0% indicates that the material does not show elastic behavior, while 100% means that the material can ultimately return to its original shape [73]. The d value in the formulation indicates the distance between the ends of the cut sample, and L denotes the sample's elongation.  
\[\mathrm{R_{E}}=\frac{\mathrm{d}}{\mathrm{L}}100 \tag{6}\]  
As given in Figure 10, the optimum levels that maximized the elastic recovery were A3, B4, C4, and D2. Elastic recovery testing is mainly used to evaluate asphalt binders' ability  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Experiment No** & **M1** & **M2** & **Mass Loss** & **S/N** \\  & **(gr)** & **(gr)** & **(\%)** & **S/N** \\ \hline
1 & 147.88 & 147.71 & \(-\)0.4944 & \(-\)33.88 \\
2 & 147.92 & 147.75 & \(-\)0.4976 &  
to recover after elastic deformation [74]. The Cecabase RT BIO10 additive was the most critical parameter affecting the elastic recovery. The analysis revealed that the addition of Cecabase RT BIO10 led to an increase in elastic recovery up to 0.40%, followed by a slight decrease. This is attributed to the additive enhancing the microstructure of the asphalt mixture, leading to a more uniform and resilient mixture. The elastic recovery slightly increased as the mixing temperature and mixing time increased due to the bitumen's lower aging [8]. The mixing speed at 2000 rpm increased the elastic recovery, providing effective mixing without causing excessive shear stress, and decreased slightly at higher speeds.
## 3 Results and Discussion
### Me\({}^{201}\)Hg and \({}^{199}\)Hg(II) Concentrations  
Bioaccumulation in glass eels under Me\({}^{201}\)Hg and \({}^{199}\)Hg(II) exposure (100 ng.L-1) with time is presented in Figure 2. After seven days of Hg species exposure followed by eight days in commercial water without contaminant, glass eels reached exogenous Me\({}^{201}\)Hg concentrations at 10 times higher than \({}^{199}\)Hg(II) ones with 139.6 \(\pm\) 16.2 and 14.8 \(\pm\) 2.3 ng/g dw, respectively. These results confirm a preferential uptake of MeHg, as previously documented for invertebrates [20,21] and fish [9,10]. Indeed, MeHg is well-known to readily go across biological membranes and to exhibit a higher uptake efficiency than Hg(II) [22]. In addition, MeHg and more precisely the MeHg- thiols compounds complexes have a high level of similarity with endogenous substrates for the active transporters that regulate the cellular uptake and efflux of molecules into the cells, and thus promote its higher accumulation in tissues [8]. Finally, very low concentrations of \({}^{201}\)Hg(II) (deriving from Me\({}^{201}\)Hg) and Me\({}^{199}\)Hg (deriving from \({}^{199}\)Hg(II)) were measured in glass eels, demonstrating the low transformation potentials for Me\({}^{201}\)Hg to demethylate and \({}^{199}\)Hg(II) to methylate.  
Figure 1: Qualitative distribution of endogeneous \({}^{200}\)Hg in a 50 μm thick section of a glass eel specimen (scale represents \({}^{200}\)Hg signal intensity in counts per second).  
Figure 2: Exogenous mercury species concentrations derived from the spiked Me\({}^{201}\)Hg (**A**) and \({}^{199}\)Hg(II) (**B**) with time.
# A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine
## 4 Experimental Results and Comparative Analyses
### Total Migration Time  
Figure 3 shows the total migration time for both the SPEC VIRT_SC 2013 and Linux Kernel Compile. The \(\alpha\) is set to 0.3, 0.5, 0.7 and 1.0. We also compare with the original hybrid-copy algorithm: the last column in the Figure 3 represents the results of the original hybrid-copy algorithm. We found that as the \(\alpha\) increased, the total migration time decreased in Novel Hybrid-Copy algorithm because additional iterative copies are conducted with the increase of \(\alpha\).  
When the \(\alpha\) is set as 1.0, the copy operation before the post-copy phase executed only once. Unlike the original hybrid-copy algorithm, it does not transfer all memory pages in the iterative copy phase. Only half of the memory pages selected by Markov Model are transferred in advance. So, less memory pages would be transferred in total. Less migration time should be spent in the Novel Hybrid-Copy algorithm when \(\alpha\) is set to 1.0. The results in Figure 3b are broadly in line with our forecasts. However, Figure 3a shows a theoretically inconsistent result because the migration time was not only associated with the number of transferred memory pages. It also related to the real-time network conditions, VM states, and the number of the page faults occurred during the post-copy phase. In fact, because all memory pages are transferred at most twice in the original hybrid-copy algorithm, there is little reduction in total migration time.
## 3 State-Of-The-Art
### Primary Research in Literature
#### 3.2.3 Commercial Applications  
Vehicular communications, in addition to increasing road safety in the various ways described above, also provide a new approach for multimedia broadcast services and applications (MBMS). It is noteworthy that the development of new technologies, such as the vehicular communications, will greatly help the multimedia content to migrate to mobile platforms [109]. An example of this new contribution is the ability of communications to support streaming video while vehicles circulate on public roads [110]. This is possible thanks to the combination of real-time and non-real-time data; the first one is used to send inter-vehicle messages for safety purposes while the latter is used to transmit multimedia content (e.g., video or audio) [111].
# A Deep Learning Approach with Feature Derivation and Selection for Overdue Repayment Forecasting
## 4 Experiments
### Interpretability
#### 4.7.2 Analysis of Derived Features  
The derived features can be fed into machine learning models with interpretability to further explore the latent logic of overdue repayment behavior. We choose XGBoost as the classifier model and set the features and corresponding targets as inputs. After convergence, we analyze the importance of features and explore the most meaningful subindexes.  
To begin with, we select some of the static background features as the input for the XGBoost model. After convergence, the importance weights of the features are illustrated in Figure 7. In the figure, _product_id_0_3 stands for the four types of loan products. _sub_industry_name_0\(6\) stands for the seven types of sub-industry and _industry_id_0_2 stands for the three types of industry: consumer finance, Internet finance and their integration. The meaning of all these features has been included in the description file of our dataset. From Figure 7, we can conclude that the type of product and industry has a significant impact on the overdue repayment behavior. Surprisingly, most people assume that the amount of owed money may greatly influence the overdue repayment, but as illustrated by our results, the owed money (start_money in the chart) has a similarly low impact to birthday, region and gender.  
Furthermore, we analyze the importance weights of all the 42 features obtained by our proposed feature derivation and selection method. The results are illustrated in Figure 8. The longitudinal axis denotes the order number of features, and the meaning of features has been provided in our dataset. By summarizing the key words of the features with five largest importance weights, we conclude that the last behavior has more of an impact on whether a future repayment will be overdue or not  
Figure 6: Visualization of differential attention weights.  
Figure 7: The importance of static background features calculated by XGBoost.  
than the others, which agrees with the result after analyzing the attention weights. Additionally, whether the type of product is a very short-term cash loan matters a great deal, corresponding to the above conclusions from analyzing the background features.
## 4 Experiments and Results
### PCA as Fragmentation-Wave Enhancement Tool  
The aim of the following set of experiments is to determine the behavior of the _PCA transformation to enhance the presence of fragmentation in common twelve-lead ECG_. For this purpose we applied PCA over the beat templates, as according to medical knowledge, the fragmentation is the result of a missconductions over the myocardium, thus it must be replicable for the beats within each lead. Being said that, we could argue on how PCA will manage the fragmented wave, and if it will consistently consolidate the presence of this singular additive element over a certain component. In other words, and with a much wider view, we need to determine how the presence of fragmentation in the ECG could alter the order in the PCA-components output, and how these new components will be consolidated in the PCA output. To do so, we propose the use of a subrogated database to compare the PCA output between non-affected and affected equivalent cases. Our database for this purposed consisted of the control beat templates and for their synthetically fragmented copies, as described earlier. We also compared the PCA output of the control and subrogated through the _Pearson correlation coefficient_ (PCC) to identify effective fragmented waves contribution transference among different principal components. As a result, we built the confusion matrix presented in Table 1. As it can be observed in this table, components from 6-th to 11-th exhibited a ratio of paired components lower than 0.90, showing a relevant leakage among components. We can conclude that the use of this technique does not make possible the isolation of the fragmentation contribution into a few components. To obtain better knowledge of how PCA methodology is affected by fragmentation, and how fragmentation is distributed over the output components, we defined the dispersion coefficient \(\epsilon_{disp}\) given by  
\[\epsilon_{disp}=1-\frac{\sum_{j,i=0;i\
eq j}^{N}C(i,j)}{\sum_{j,i=0}^{N}C(i, j)} \tag{4}\]  
where \(N\) is the number of principal components, and \(C\) denotes the confusion matrix coefficients. This coefficient reflects the aggregated leakage effect of confusion matrix, and for the case of PCA applied over twelve-leads, the obtained value was 0.14. As it can be seen in Figure 2, which illustrates the swapping effect among in-and-out components, the existence of a low-power fragmentation waves (red lines in the third column) induces a some position swapping in the PCA output, and this effect can be observed in the fourth column, which shows the correlation between the fragmented and the non-fragmented components. In order to further improve the dispersion coefficient, we proposed the use of linearly independent leads in the PCA process, to elaborate whether the inclusion of not duplicated information could make a difference in this topic. According to medical knowledge, leads \(I\), \(II\), \(III\), \(aVF\), \(aVR\), and \(aVL\) are related according to the following equations,
# Real-Time Remote Sensing of the _Lobesia botrana_ Moth Using a Wireless Acoustic Detection Sensor
## 1 Introduction
Under this perspective, the aim of this article is to present a real-time remote sensing of _Lobesia botrana_ using a wireless acoustic detection sensor with a matched filter where the novelty relies on an operational in-field acoustic detection of the _Lobesia botrana_ through a matched filter obtained with previous acoustic measurements and neural networks for the flying acoustic signal of the moth.
# Internet Video Delivery Improved by Super-Resolution with GAN
## 8 Conclusions  
In a cloud-based video streaming framework, we super-resolved low-resolution videos using the VSRGAN+ neural network model to reduce the costs associated with the data traffic between the original server and the surrogate servers. We upscaled videos using the VSRGAN+ model, a deep neural network with a perceptual-driven loss function and tailored layers fitting the limited computing resources of the surrogate servers. We showed that the proposed framework promoted a decrease in data traffic of up to 98.42%, when comparing the SR-based content placement approach and a lossless compression-based one.  
To assess the quality of the super-resolved videos, we considered these three metrics: LPIPS, VMAF, and PSNR. We showed that super-revolved videos obtained by a 2x-factor training VSRGAN+ model preserved the perceptual quality, i.e., the super-resolved videos were indistinguishable from the original videos.  
The future direction of this research looks at edge-computing paradigms to explore computing resources on edge servers and end-user devices to support real-time SR. This could improve the quality of video sessions under severe restrictions on throughput on both fronthaul and backhaul networks.  
Conceptualization, J.d.M.L. and C.M.; methodology, J.d.M.L. and C.M.; validation, J.d.M.L. and C.M.; formal analysis, J.d.M.L.; investigation, J.d.M.L.; resources, J.d.M.L. and C.M.; data curation, J.d.M.L. and C.M.; writing--original draft preparation, J.d.M.L. and C.M.; writing--review and editing, J.d.M.L., C.M. and M.S.; visualization, J.d.M.L., C.M. and M.S.; supervision, J.d.M.L. and C.M.; project administration, J.d.M.L. and C.M.; funding acquisition, J.d.M.L. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the PROINT/AM Scholarship Program, FAPEAM (Notice No. 003/2018); and the training program for college professors of the State University of Amazonas.  
The data presented in this study and the implementation source code are available upon request from the corresponding authors.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} API & application programming interface \\ BN & batch normalization \\ CDF & cumulative distribution function \\ CDN & content delivery network \\ CISRDCNN & super-resolution of compressed images using deep convolutional neural networks \\ CNN & convolutional neural network \\ dB & decibeis \\ DNN & deep neural network \\ ESPCN & efficient sub-pixel convolutional neural networks \\ ESRGAN & enhanced super-resolution generative adversarial networks \\ FHD & full high definition \\ FPS & frames per second \\ GAN & generative adversarial network \\ \end{tabular}
# Quantifying Sub-Elite Youth Football Weekly Training Load and Recovery Variation
## 2 Materials and Methods
### Experimental Approach  
The training data corresponded to a total of 18 training sessions and 324 observation cases. The eligibility criteria for individual data sets considered a competitive 1-game week schedule and complete full training sessions. The microcycle included 3 training sessions per week (\(\sim\)90 min). The match data were not included in the analysis. The training days were classified as \"match day minus format\" (MD): MD-3 (Tuesday), MD-2 (Wednesday) and MD-1 (Friday).  
The sampled players were characterized based on one out of five playing positions: central defenders (CD), fullbacks (FB), central midfielders (CM), wide midfielders (WM) and forwards (FW). The number of observations per position role was: CD (\(n\) = 79); FB (\(n\) = 65); CM (\(n\) = 70); WM (\(n\) = 62); and FW (\(n\) = 48). The goalkeeper participated in the training session but was excluded in the present training load analysis. The training sessions had, on average, 18 players. All age groups were trained on an outdoor pitch with official dimensions (FIFA standard; 100 \(\times\) 70 m). The training sessions were performed on synthetic turf pitches, from 10:00 a.m. to 08:00 p.m. and with similar environment conditions (14-20 \({}^{\circ}\)C; relative humidity 52-66%).  
The sampled training sessions were categorized according to specific focus, following the discussion with the coach staff. All sampled training sessions started with a standard warm-up with low-intensity running, dynamic stretching for main locomotive lower limb muscles, technical actions and ball possession. The weekly training overview presented as potentially variable between categories, as different training modes with emphasis on the game-based situations and sport-specific skills for football-specific exercises [32; 34].