esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussions
### Native MPs in the Sewage Sludge
Regarding chemical composition, 14 different polymers were detected in the sludge samples, of which the most abundant were the following: rayon (RA) 39%, polyethylene (PE) 19%, polyester (PEST) 9%, cellulose (CE) 9%, polyethyleneenimine (PEI) 8%, polyacrylamide (PAM) 3%, polyethylene (PET) 3%, acrylic fiber (orlon) (AF) 3%, and polyamide (PA) 2%. The following polymers each accounted for around 1%: epoxy resin (ER), ethylene-vinyl acetate (EVA), polyacrylic acid (PAA), polyether ester urethane (PEEU), and polyether urethane (PEUR) (Figures S2 and S3). According to previous studies, up to 30 distinct polymers have been identified in WWTPs, with their presence primarily influenced by the origin of the wastewater. The sources of MPs in sewage sludge include synthetic textiles, pieces of broken plastic, paints, and agricultural waste. It has been reported that the presence of MPs made of PE, PET, PEST PA, and RA, identified in the present study, is typically associated with wastewater of domestic origin and the release of these polymers from garments during household washing [1; 19; 36]. The MP removal efficiency is directly influenced by MP size and polymer density. MPs that are less dense than wastewater, such as semi-crystalline PE and PP, progress through the WWTP's stages and are typically discovered in the final effluent [19]. In the secondary sludges samples analysed in this study, PP was not found, despite being a common polymer. Conversely, during the various WWTP treatments, MPs that are denser than wastewater become less abundant in the water and more readily settle into secondary sewage sludge [1; 49; 50]. This is the case for RA, PEST, CE, PEI, PAM, PET, AF, and PA, which were the most frequently found MPs in the sludge samples analysed here. Additionally, MPs are mainly found in sludge as fibres and fragments, and, since fibres are often longer, the percentage of fibres in the sludge can impact the size distribution of the MPs [50]. Furthermore, the growth of microbial communities and the adsorption of substances, such as fats and oils, on the MPs can change their apparent density, therefore affecting their tendency to settle or float [51]. This may be the case for the PE-MPs found in the sludges.
## 3 Results
### Microbiome Analysis
#### 3.3.2 Taxonomy Assignment  
The taxonomy of the skin microbiome from the control and treated samples are displayed (Figure 2A,B).  
Estimation of the Phylum  
The relative frequency (RF) of the phylum in control (Cpre and Cpost) (Figure 3A) and treatment (Type and Tpost) (Figure 4A) samples were computed and displayed. The phylum such as Proteobacteria (Cpre: RF = 56.40%; Cpost: RF = 58.63%), Firmicutes (Cpre: RF = 11.22%; Cpost: RF = 14.85%), Actinobacteria (Cpre: RF = 27.59%; Cpost: RF = 18.74%), Cyanobacteria (Cpre: RF = 0.46%; Cpost: RF = 2.00%), Planctomycetota (Cpre: RF = 1.89%; Cpost: RF = 2.50%), Bacteriota (Cpre: RF = 0.42%; Cpost: RF = 3.27%), and Verrucomicrobiota (Cpre: RF = 2.02%; Cpost: RF = 0.02%) was dominantly detected in the control group (Figure 3A).  
Likely, the phylum, Proteobacteria (Type: RF = 63.74%; Tpost: RF = 51.71), Actinobacteria (Type: RF = 25.18%; Tpost: RF = 9.74%), Cyanobacteria (Type: RF = 0.92%; Tpost: RF = 0.32%), Firmicutes (Type: RF = 6.60%; Tpost: RF = 7.65%), Acidobacteriota (Type: RF = 0.61%; Tpost: RF = 2.88%), Bacteriota (Type: RF = 1.25%; Tpost: RF = 3.73%), Planctomycetota (Type: RF = 0.21%; Tpost: RF = 6.54%), Verrucomicrobiota (Type: RF = 1.17%; Tpost: RF = 2.33%), Desulfobacteria (Type: RF = 0%; Tpost: RF = 2.98%), Myxococcotta (Type: RF = 0.09%; Tpost: RF = 0.48%), Chloroflexi (Type: RF = 0.01%; Tpost: RF = 7.25%), Armatimondota (Type: RF = 0%; Tpost: RF = 0.76%), Gemmatimonadota (Type: RF = 0%; Tpost: RF = 0.55%), Spirochaetota (Type: RF = 0%; Tpost: RF = 1.57%), Patescibacteria (Type: RF = 0.03%; Tpost: RF = 0.88%), and Nitrospirota (Type: RF = 0%; Tpost: RF = 0.36%) was mainly detected in the treatment samples (Figure 4A).  
Figure 2: Taxonomical assignment for the control and treatment samples. The comparison of phylum in the control (**A**) treatment (**B**) samples.  
The significant inter and intra-group differences in phylum were estimated in the control and treatment samples using the non-parametric Wilcoxon matched-paired signed-ranks test. The results indicated no significant differences in phylum within and between the groups (Tables S3 and S4). However, based on the RF, the phylum, such as Proteobacteria, Firmicutes, Cyanobacteria, Planctomycetota, and Bacteroidota, were increased in the Cpost samples. Also, Actinobacteria and Verrucomicrobiota were decreased in the Cpost samples (Figure 3A and Table S2). Similarly, the phylum, such as Firmicutes, Acidobacteriota, Bacteroidota, Planctomycetota, Verrucomicrobiota, Myxococcota, Chloroflexi,  
Figure 3: The graph shows the detected phylum (**A**), genus (**B**), and species (**C**) in the control group.  
Patescibacteria, was increased in the Tpost samples compared to the Tpre samples. Additionally, the Proteobacteria, Actinobacteria, and Cyanobacteria were decreased in the Tpost samples compared to the Tpre samples. Likewise, Desulfobacterota, Armatimanodota, Gemmatimonadota, Spirochaetota, and Nitrospirota were detected only in the Tpost samples (Figure 4A and Table S3).
## 6 Implementation  
The ARCOMEM system has been implemented as described in the previous sections. To achieve storage scalability and benefit from distributed processing, we used the HBase and Hadoop combination. Both the online and offline analyses require running analysis modules in map-reduce jobs over the Web data stored in HBase and to write outputs in either HBase along the Web data or as triples in the  
Figure 9: Traditional processing chain of a Web crawler compared to the ARCOMEM approach.  
ARCOMEM knowledge base (see Section 4.2). We created the ARCOMEM framework to ease these tasks. Since Hadoop and HBase are written in Java and as it turned out to be an appropriate language for the analysis modules, we wrote the ARCOMEM framework in Java, too. The framework features a flexible configuration system that allows one to describe the dependencies between processes. Any analysis can be split into as many map-reduce and local processes as needed, and the framework will run them in an order compatible with the dependency graph. The dependencies between different analysis modules can be expressed in the same way.  
When dealing with Web data, robustness is a prime concern, since a wide variety of content will be encountered. We tried to isolate the code dealing with Web content as much as we could: we run certain processes in a separate thread and limit the time and memory available to them. When a resource exceeds these limits, we mark it as problematic in HBase and skip it on subsequent runs. As a last resort, when a whole task fails repeatedly, it is skipped (we make it return immediately with a success status), and the rest of the job is allowed to proceed. We initially studied the use of a trigger on Put operations to run the online analysis as we imported Web content into HBase. Since it requires running analysis code directly in the region servers of Hadoop, we preferred to use a map-reduce job after import, trading extra latency for better isolation. This also helps in reducing the amount of code, by making the online analysis a special case of offline processing.  
Performance is another major concern when dealing with Web-scale data. We used per-resource calculations implemented in the map phase everywhere we could. When a corpus was needed, we chose to process small batches in the reducer with a constant number of documents independent of their size. The triple upload to the ARCOMEM knowledge base is done in batches when applicable. In addition, we profiled the online analysis and optimized it drastically, including making some quality/time trade-offs.
# The Progress of Cobalt-Based Anode Materials for Lithium Ion Batteries and Sodium Ion Batteries
## 2 Li\({}^{+}\)/Na\({}^{+}\) Storage Mechanism
### Li+/Na+ Storage Mechanism in Co\({}_{x}\)A\({}_{y}\) (A=O, S, P and Se)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Active Materials** & **Li\({}^{+}\) Storage Mechanism** & **Reference** \\ \hline Co\({}_{3}\)O\({}_{4}\) & Co\({}_{3}\)O\({}_{4}+8\)Li\({}^{++}+8e^{-}\to 3\)Co +4 Li\({}_{2}\)O & [15] \\ Co\({}_{2}\)P & Co\({}_{2}\)P + 3 Li\({}^{++}+3e^{-}\to 2\)Co + Li\({}_{3}\)P & [46] \\ CoS\({}_{2}\) & CoS\({}_{2}+4\)Li\({}^{++}+4e^{-}\to \)Co + 2 Li\({}_{2}\)S & [14] \\ CoSe\({}_{2}\) & CoSe\({}_{2}+4\)Li\({}^{++}+4e^{-}\to \)Co + 2 Li\({}_{2}\)Se & [20] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Li\({}^{+}\) storage mechanism of some Co\({}_{x}\)A\({}_{y}\)-active materials. The sodium storage reaction mechanism of CoSe\({}_{2}\) was investigated by [PERSON]'s group with a CV test, as shown in Figure 3d [48]. During the first anodic scan, two adjacent peaks at 1.7 and 1.87 V were attributed to stepwise conversion reactions from Co and Na\({}_{2}\)Se to an intermediate Na\({}_{x}\)CoSe and a final CoSe\({}_{2}\), respectively. In the second anodic scan, three reduction peaks appeared at 1.4, 1.1 and 0.68 V, corresponding to the uptake of Na\({}^{+}\) and a stepwise sedation of CoSe\({}_{2}\). The corresponding reactions can be seen as follows:  
Discharge process:  
\[\mathrm{CoSe_{2}+xNa^{+}+xe^{-}\to Na_{x}CoSe_{2}} \tag{14}\]  
\[\mathrm{Na_{x}CoSe_{2}+(2-x)Na^{+}+(2-x)e^{-}\to CoSe+Na_{2}Se} \tag{15}\]  
\[\mathrm{CoSe+2 Na^{+}+2e^{-}\to Co+Na_{2}Se} \tag{16}\]  
Charge process:  
\[\mathrm{Co+2 Na_{2}Se\to Na_{x}CoSe_{2}+(4-x)Na^{+}+(4-x)e^{-}} \tag{17}\]  
\[\mathrm{Na_{x}CoSe_{2}\to CoSe_{2}+xNa^{+}+xe^{-}} \tag{18}\]  
Figure 3: CV curves of **(a)** shale-like Co\({}_{3}\)O\({}_{4}\) tested at 0.1 mV/s from 0.005 to 2.9 V vs. Na/Na\({}^{+}\)[35]. **(b)** CoP@C-RGO-NF tested at 0.1 mV/s in the voltage range of 0.01–3 V vs. Na/Na\({}^{+}\)[27]. **(c)** CoS2/rGO at a scan rate of 0.5 mV/s from 0.01–3 V vs. Na/Na\({}^{+}\)[49]. **(d)** CoSe\({}_{2}\)@C/carbon nanotubes (CNTs) tested at 0.1 mV/s from 0.5–2.9 V [48]. The Li\({}^{+}\)/Na\({}^{+}\) storage mechanism is decided by components that composited with cobalt. Similarly, other cobalt-based composites like Co\({}_{2}\)SnO\({}_{4}\), CoMn\({}_{2}\)O\({}_{4}\) and Sb-Co-P have more complex Li\({}^{+}\)/Na\({}^{+}\) storage mechanisms, due to more components in composites [39; 50; 51; 52]. Nevertheless, the Li\({}^{+}\)/Na\({}^{+}\) storage mechanisms are still composed of basic alloying and conversion reactions.
## 4 Conclusions  
After experimental verification, we could draw the following conclusions:  
* Taking the insulation resistance drop by two orders of magnitude as the cable failure criterion, it was concluded that the failure time of the cable could be delayed to 1.57 to 14.86 times the blank control. The fire protection effects of the 12 cases were ranked from good to bad as follows: case 8 \(>\) case 9 \(>\) case 11 \(>\) case 10 \(>\) case 3 \(>\) case 12 \(>\) case 4 \(>\) case 5 \(>\) case 7 \(>\) case 2 \(>\) case 6 \(>\) case 1. The failure time of the cable had a certain relationship with the temperature rise rate in the tray.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Case Number** & **Types of Material** & **Coating Thickness (mm)** & **Failure Time (h:min:s)** \\ \hline
3 & Brand B IFC (W) & 4.4 & 00:23:20 \\
8 & Brand C NFC & 40 & 01:09:20 \\
9 & Brand C NFC & 30 & 00:40:00 \\
10 & Brand C NFC & 20 & 00:25:50 \\
11 & Brand D FC & 50 & 00:27:40 \\
12 & Brand D FC & 30 & 00:15:10 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Failure time data for some cases.  
* For IFC, the water-based coatings with better adhesion, expansion effect and more compact char structure should be selected. When IFC were used to protect the cables in the trays, a certain initial thickness of the coatings was necessary, and the thicker the IFC were, the better the fire protection effect was.
* For NFC, the thicker the coating was, the better the fire protection effect was. The initial temperature rise of a specimen coated with NFC was relatively slow, and the temperature rise rate slowly increased with time.
* For FC, the thicker the material was, the better the fire protection effect was. The initial temperature rise rate of a specimen wrapped with FC was low, but the temperature rise rate in the medium-term suddenly increased rapidly, resulting in a rapid temperature rise.
* Among the materials with a qualified failure time, the order of the fire protection effect at the same thickness was: IFC > NFC > FC. There were advantages and disadvantages to the use of the three fire protection materials for fire protection transformation, and the materials needed to be selected according to actual needs.  
Conceptualization, D.L.; methodology, D.L. and F.J.; investigation, L.W.; resources, J.L. and J.Y.; experiment, F.J. and W.Y.; data curation, F.J.; writing--original draft preparation, F.J.; writing--review and editing, D.L.; supervision, D.L.; project administration, F.J. and J.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Funds of Guangdong Provincial Key Laboratory of Fire Science and Technology (No:39000-42080092).  
Not applicable.  
Data available on request due to restrictions eg privacy or ethical.  
The authors declare no conflict of interest.
## 3 Crack Detection and Crack Localization with Multiple PD Measurements
### Crack Detection in Round Bars
Both the raw data and the angle values, smoothed with a moving average over 100 points, indicate the formation of a crack at an angle of \(0^{\circ}\), i.e., at potential probe P1. The reduction in scatter of the angle \(\phi\) seems to be a suitable criterion for crack initiation. In Figure 9 the data of an experiment without a temperature stabilization prior to the experiment are shown. At the beginning of the experiment a steep increase of all three relative potentials due to a heating-up of the specimen is visible (Figure 9a). A first crack on the fracture surface near potential probe 3 occurs after 115,000 cycles. After that point, a second increase of all potentials takes place, where potential 3 shows the steepest increase. The angle \(\phi\) shows a pronounced scatter around a nearly constant mean value up to about 120,000 cycles, thereafter the scatter decreases significantly,  
Figure 8: Results of an experiment with a crack initiation at the position of potential probe 1. The crack front was marked by introducing overloads (colored lines); (**a**) the relative potential P1 shows a steeper increase compared to the values of P2 and P3; (**b**) the angle calculated with the geometrical model shows a high scatter in the beginning of the experiment. After crack initiation the scatter decreases, and the position of the initiated crack can be determined. The colored lines correspond to the crack extensions shown in the crack surface in Figure 8a. and the mean value indicates the crack formation at an angle of about 103\({}^{\circ}\). The following change of the mean value indicates that the further crack propagation is mainly in direction of higher angles and then again to lower angles which corresponds well with the observation on the fracture surface. The measurements show that multiple potential drop measurements on notched round specimens evaluated with a simple geometric model allow the determination of the crack location even for relatively short cracks. The scatter of the calculated angles can be used as a criterion for crack detection. On the one hand, this method improves the sensitivity of the DCPDM in the case of short crack lengths and eliminates potential changes due to temperature fluctuations. On the other hand, however, the method presented here only provides reliable values for the location of single cracks. In case of multiple cracks, only the center of gravity of the cracks is displayed by the geometric model.
# Predicting Player Churn of a Free-to-Play Mobile Video Game Using Supervised Machine Learning
## 4 Research Methodology
### Metrics for Model Evaluation  
Determining which of the selected machine learning models (previously fitted with the appropriate data) yields the best performance on a given task was accomplished by comparing the selected models using different evaluation metrics. Metrics used for model evaluation include the following: F1-score, area under the ROC curve (AUC curve), and the AP curve. The F1 score of a model is the harmonic mean of the models' raw accuracy and recall. It is considered a more reliable metric for model evaluation then its raw components, but further comparisons using AUC and AP metrics are also useful. The AUC metric is defined as the area under the ROC curve. The ROC curve is a graphical representation of the recall value of a model in relation to the FPR. Drawing the ROC curve and calculating the area underneath, we obtain the AUC metric. The AUC provides an aggregate measure of performance across all possible classification thresholds. The larger the area the ROC curve covers, the higher the model performance. Similarly to the AUC metric, the AP metric is the area covered by the precision-recall curve. Again, the bigger the area, the higher the model's performance.
## 5 Bench-Marking Models
### Bidirectional LSTM  
Bi-LSTM is also an RNN. It utilizes information in both the previous and following directions in the training phase [45]. The fundamental principle of the Bi-LSTM model is that it examines a specific sequence from both the front and back. Which uses one LSTM layer for forward processing and the other for backward processing. The network would be  
Figure 7: Overview of the LSTM model.  
able to record the evolution of energy that would power both its history and its future [46]. This bidirectional processing is achieved by duplicating the hidden layers of the LSTM, where one set of layers processes the input sequence in the forward direction and another set of layers processes the input sequence in the reverse direction. As illustrated in Figure 8, the hidden state (\(h_{i}^{f}\)) and the cell state (\(c_{i}^{f}\)) in the \(i\)th forward LSTM cell are calculated as similar as the Equation (9). On the contrary, each LSTM cell in the backward LSTM takes the following hidden state (\(h_{i+1}^{b}\)), and following cell state (\(c_{i+1}^{b}\)), and \(\mathbf{x}_{i}\) as input. Therefore, the hidden state (\(h_{i}^{b}\)) and cell state (\(c_{i}^{b}\)) of the \(i\)th backward LSTM cell are calculated as Equation (10).  
\[(h_{i}^{b},c_{i}^{b})=C(h_{i+1}^{b},c_{i+1}^{b},\mathbf{x}_{i}) \tag{10}\]  
After the calculations of both forward and backward LSTM cells, the hidden states of the two directions could be concatenated or combined in some way to obtain the output. The common combination is the sigmoid function, as noted in Figure 8. The output is fed into the Dense layer to obtain the final prediction. Similar to the LSTM model, we also compare the proposed model with univariate and multivariate Bi-LSTM models.
## 3 VRIS Image Steganography Model
### VRIS Model Architecture and Design  
The VRIS model consists of three key components:  
Visual-Masker Module: This module extracts characteristics of the hidden image utilizing a multi-scale convolutional kernel. It leverages multi-layer convolution and batch normalization techniques to enhance feature integration [37]. Ultimately, these features are seamlessly mapped onto the cover image, producing a visually indistinguishable first-level encrypted image.  
Hidden-Insight Module: This module extracts features from the second-level encrypted image, processes them through activation functions and batch normalization, and subsequently retrieves the information embedded within the hidden image. These refined features are then transformed into the secretly reconstructed image.  
AI-Evasion Shield Module: Serving as a discriminator, this module distinguishes between noisy second-level encrypted images and cover images. Through adversarial training, it refines the steganography strategy to bolster the model's resilience against machine learning-based deception.  
To elevate the complexity and confidentiality of the first-level encrypted image, random noise is introduced prior to decoding. This gives rise to the second-level encrypted image, characterized by randomness, controllability, and diversity. Randomness ensures that each noise instance is unique, while controllability facilitates experimental adjustments. By varying the noise intensity, a range of experimental conditions can be explored. The incorporation of random noise serves to obscure image details, enhance the stealth of the hidden image, and fortify the model against potential attacks.
## 3 Theoretical Framework
### Inverse Problem  
To obtain the effective elastic moduli of the interlayer HSQ-NW, the sample parameters (the matrix of elastic moduli \(C\), the density \(\rho\) and the thickness \(h\) of each sublayer), which are inputs to calculate \(K\) within the multilayered mathematical model used, vary to match the experimental points. This goal is achieved through the minimization of a certain objective function that can be constructed in various forms. A natural way is to minimize the discrepancy \(\Delta\) between the measured (\(d_{j}^{m}\)) and calculated (\(d_{j}^{c}\)) SAW dispersion characteristics (e.g., between the SAW wavelengths or group velocities at the measured resonance frequencies \(f_{j}\)):  
\[\Delta(C,\rho,h)=\sum_{j}(1-d_{j}^{c}/d_{j}^{m})^{2} \tag{6}\]  
To search for the global minimum of such objective functions, genetic algorithms are usually employed [21]. For example, a similar approach was successfully applied to the evaluation of the effective elastic properties of layered composite fiber-reinforced plastic plates [22].  
Figure 7: Second validating example for the sample Ni/HSQ400/Si without NWs. Experimental points in the (\(\Lambda,f\)) plane (**a**); \(|K_{33}(\Lambda,f)\) levele-line image (**b**); SAW dispersion curves and experimental points (bold dots) in the (\(\Lambda,f\)) and frequency-slowness planes (**c,d**); low-frequency markers (\(f<250\) MHz) in part (**a**) are hardware interference.  
However, the calculation of dispersion characteristics at each step is unreasonably time-consuming, because, to obtain each of them, the matrix \(K\) are to be computed hundreds or even thousands of times. Drastic cost reduction is achieved by using the objective function  
\[F(C,\rho,h)=\sum_{j}|K_{33}^{-1}(\Lambda_{j},f_{j})|, \tag{7}\]  
where \((\Lambda_{j},f_{j})\) are experimentally obtained points in the wavelength-frequency plane. The calculation of this function requires only one call of the procedure \(K_{33}\) for each experimental point. The appropriateness of function (7) as an objective function is explained by the fact that it becomes equal to zero when the variable set of sample parameters \(C\), \(\rho\), and \(h\) yields, together with the fixed known parameters, the SAW characteristics \(d_{j}^{c}\) corresponding to the measured resonance points \((\Lambda_{j},f_{j})\). A similar approach was also recently proposed for real-time assessment of anisotropic plate properties using elastic guided waves [23]. To obtain the set of parameters providing the minimal value of function \(F(C,\rho,h)\) (i.e., to find the effective sample's parameters), we use the method of coordinate-wise minimization within prescribed ranges of parameter variations. It usually requires no more than 50 iterations, which is computationally cost free even as compared with the plotting of the dispersion curves obtained.