esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigation of Ti65 Powder Spreading Behavior in Multi-Layer Laser Powder Bed Fusion
## 4 Results and Discussion
### Variation in Powder Bed Quality over Successive Layers
#### 4.1.2 Microscopic Properties  
In addition to the macroscopic properties of spread powder beds on printed regions, corresponding microscopic properties are also analyzed. Figure 9 presents particle rearrangement within the powder beds in the printed region under different spreading velocities. In this analysis, particles with diameters \(d<30\)\(\upmu\)m are classified as small, those with \(d>60\)\(\upmu\)m as large, and the rest as medium-sized particles. As shown, with the increase in layer number, the percentage of small particles in the powder bed in the printed region decreases, while the percentage of medium- and large-sized particles increases. This trend is primarily due to the low preset spreading height (\(H_{\text{set}}\)) in the initial several layers, which limits the deposition of large particles due to the boundary effects imposed by the blade. \(H_{\text{real}}\) increases with each subsequent layer; thus, this boundary constraint is alleviated, allowing more medium- and large-sized particles to be released in the printed region.  
Figure 8: Uniformity (**a**) and \(H_{\text{real}}\) (**b**) of the powder bed across successive layers at different spreading velocities.  
Figure 9: Particle rearrangement of each layer in the printed region when \(V=0.075\) m/s (**a**), \(V=0.100\) m/s (**b**), and \(V=0.125\) m/s (**c**).  
Figure 10a demonstrates the distribution of coordination number (CN), defined as the number of particles in contact with the given particle, across different layers. As can be seen, starting from the second layer, the peak of each CN curve shifts from 2 to 3, implying the poor quality of the first-layer powder bed. In addition, the CN evolutions of large particles with the layer number under different conditions are also analyzed and presented in Figure 10b. As can be seen, with the increase in layer number, the peak of the CN distribution curve shifts to the right and stabilizes after the fourth layer, reflecting a more densely packed powder bed. Furthermore, a reduction in spreading velocity leads to a more pronounced rightward shift in the CN peak, further confirming that lower spreading velocities contribute to denser powder beds.  
To further analyze the powder bed structure, pores within the powder bed are quantitatively characterized using the Radical Tessellation (RT) technique, as shown in Figure 11a. Figure 11b gives the normalized pore size distributions of powder beds obtained under different spreading velocities. It can be observed that with the increase in printed layer number, the peak of the pore size distribution progressively moves to the left, indicating a reduction in large pores within the powder bed. This trend tends to be stable after the fourth layer. Figure 11b also shows that small pores are usually accompanied by lower spreading velocity, suggesting a much denser powder bed structure.
## 2 Materials and Methods
### Asphaltene Extraction (Deasphaltenization)  
The asphaltenes were extracted from bitumen by exploiting their differential solubility. The protocol carried out has also been used for a previous study [12], and is a slight modification of ASTM standard protocol D2007-80 of asphaltene extraction [15]. A specific amount of bitumen was weighed into a tube and then dissolved in equal part chloroform, i.e., 1 g of bitumen in 1 mL of CCl4. Upon complete dissolution of the bitumen in the solvent, 40 times its volume of n-pentane was added and the tube was vortexed to homogenize the solution. The asphaltenes started to precipitate in the solution because n-pentane is a differential solvent of bitumen. The tube was sealed with a stopper and parafilm and kept  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Unit** & **Value** & **Method** \\ \hline Penetration @25 \({}^{\circ}\)C & dmm & 70–100 & EN 1426:2015 \\ \hline Softening point & \({}^{\circ}\)C & 43–51 & EN 1427:2015 \\ \hline Dynamic viscosity @135 \({}^{\circ}\)C & Pa.s & \(\geq\)0.23 & EN 13702:2018 \\ \hline Flash point & \({}^{\circ}\)C & Min 250 & EN ISO 2592:2017 \\ \hline Fraass breaking point & \({}^{\circ}\)C & Max \(-\)10 & EN 12593:2015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Some of the given characteristics of the base virgin 70/100 bitumen.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Characteristic** & **Unit** & **Recycling Agent 1 (R1)** & **Recycling Agent 2 (R2)** \\ \hline Aspect & - & Liquid & Liquid \\ \hline Color & - & Yellow & Yellow \\ \hline Viscosity & cP & 25–50 & 20–30 \\ \hline Pour point & \({}^{\circ}\)C & \(\leq\)\(-\)5 & \(\leq\)0 \\ \hline Chemical nature & - & Mix of amino derivatives & Blend of vegetable esters  
\end{table}
Table 2: Some of the given properties of the recycling agents.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Testing Step** & **Testing Bitumen** \\ \hline
1 & Virgin bitumen \\ \hline
2 & RTFO + PAV bitumen \\ \hline
3 & (RTFO + PAV bitumen) + R\({}_{\text{i}}\) \\ \hline
4 & (RTFO + PAV bitumen + R\({}_{\text{i}}\)) + PAV \\ \hline
5 & (RTFO + PAV bitumen + R\({}_{\text{i}}\) + PAV) + R\({}_{\text{i}}\) \\ \hline
6 & (RTFO + PAV bitumen + R\({}_{\text{i}}\) + PAV + R\({}_{\text{i}}\)) + PAV \\ \hline R\({}_{\text{i}}\) refers to recycling agent 1 and 2. \\ \end{tabular}
\end{table}
Table 3: Testing steps and the corresponding samples.  
in a dark chamber for 24 h. The solution was then filtered using a pre-weighed Whatman 1002-090 filter paper with a diameter and pore size of 90 mm and 8 \(\upmu\)m, respectively. The asphaltenes remained on the filter paper, which was then placed in the oven at 60 \({}^{\circ}\)C for 2 h in order to allow evaporation of the n-pentane solvent. The filter paper was then weighed again to determine the percentage of asphaltenes obtained from the specific quantity of bitumen weighed as this percentage varies from one bitumen to another. The asphaltenes were then collected and analyzed. The whole desphaltenization process was carried out at room temperature.
## 1 Introduction
Therefore, before their implementation on an industrial or pilot scale, it is necessary to assess the methane potential of the biomass through laboratory-scale assays, such as the Biochemical Methane Potential (BMP) test, which allows the identification of substrate biodegradability, activity of the inoculum, and methane yield, providing some insights about the potential toxicity of the substrate to the AD process due to the presence of certain metabolites [14]. The use of _P. oceanica_ for methane production by AD has been scarcely investigated in the literature. In an earlier study, the methane yield obtained was rather low (62 L\({}_{\text{CH4}}\) kg\({}^{-1}\) VS\({}_{\text{Pos}}\)), most likely, due to the high lignin content (>30% VS) and the presence of minerals and salts [4]. Accordingly, the effect of thermal pre-treatment (132 \({}^{\circ}\)C; 2.3 bar; 30 min) coupled with the removal of salts and the use of acid (HCI) during hydrolysis was also assessed by [PERSON] and [PERSON] [4]. However, despite the applied pre-treatments, the methane production did not improve and even the yield was sometimes reduced by up to 73%. Another alternative could be the use of other substrates for an anaerobic co-digestion (co-AD) process. The co-AD process has important advantages, such as: (i) avoiding the use of chemicals or extra energy; (ii) the mixed feedstock could be prepared to present an optimal C/N ratio (25-30); (iii) mixing two co-substrates can dilute the presence of toxicants for the anaerobic process, such as salts or phenols; and (iv) it could enhance the valorization of other biomasses otherwise wasted [15; 16]. Lignocellulosic biomasses, such as _P. oceanica_, are usually subjected to co-AD processes due to the lack of the necessary amount of nitrogen and, thus, the balanced C/N ratio to allow a stable performance. However, the bioavailability of the nitrogen source provided may influence the final methane production, although no sound conclusions can be drawn due to the significant lack of specific studies [17]. This study aimed to assess the influence of different nitrogen-rich substrates, which represent different nitrogen bioavailability, as co-substrates of the co-AD process of aegagropiles (ashore biomass) of the marine phanerogam _P. oceanica_, through BMP tests. This study represents a breakthrough in research into the valorization of _P. oceanica_ waste, as it is the first time this biomass has been proposed to be used as a co-substrate in a co-AD process. In addition, it aims to further investigate the importance of the type of nitrogen source in anaerobic co-digestion systems. Another objective of this study was to assess the kinetics of anaerobic digestion of single _P. oceanica_ biomass and mixtures of this biomass with different co-substrates rich in nitrogen, such as peptone, casein, synthetic casein (composed of a mixture of amino acids), urea, and the microalga _Raphidocelis subcapitata_.
## 3 Results and Discussion
### Water Activity, Density, Mechanical Properties, and Acoustic Emission
Samples with Psylium contents of 3% and 5% generated sounds with the highest energy (Table 3). It was observed that the increase in the additional amounts of maltodextrin, MCG, HPMC, and Psyllium caused a significant increase in the number of AE events, but the amplitude and energy of a single EA event were similar to the other samples (Table 3). The reason for such results may be due to the high fat content in the samples with Psyllium additions. Moreover, the addition of carboxymethyl cellulose improved the gluten structure and formed a matrix with the gluten proteins where starch granules became embedded and decreased the solid loss in cooking [45]. The high number of AE events registered in the case of the tested instant noodles acted not only due to the breaking of the instant noodles' ribbons, but also due to their mutual friction during the compression test. The smallest sound amplitude was recorded for the hardest samples of instant noodles (with Psyllium). The amplitude depends on the structure of the material--the more anisotropic the structure, the higher the amplitude of the sound [46]. Acoustic emission is strongly dependent on the formulation of the product. A small sound amplitude indicates a low crispness of the instant noodle, which may be related to an increase of the moisture in the instant noodle [47], an increase in fat content together with structural changes [44], or a high inulin content [48]. Mechanical parameters such as the force (p < 0.001) and the work (p < 0.001) significantly depend on the type of additive (Table 3). The hardness is a measure of the compression force of the noodles. Work is the energy needed to compress samples to 40% of the original height. The control and HPMC samples were characterized by the lowest hardness value, while the samples with Psyllium 3 and 5% were characterized by the highest hardness value. The hardness, like acoustic emission, strongly depends on the density of the material and its chemical composition [31; 44; 49]. Therefore, the samples from Psyllium were the hardest because they had high densities and the lowest water contents. Both the hardness and the compression work were characterized by high variability, as evidenced by the value of the standard deviation. This is due to the uniformity of the samples.
## 3 Experiment and Analysis
### Comparison with Other Methods
The y coordinate of the average shoreline is the average value of y coordinates calculated by the two types of shorelines, and the coordinate points of the average shoreline can then be calculated. Considering the points of the average coastline as the standard and passing a parallel line of the y-axis through the x coordinate of each point, the parallel line intersects the coastline extracted by the proposed method and the isoline tracking method. The accuracy of the two methods was estimated according to the accuracy estimation method [5], which is to calculate the x, y standard deviation and variance of the whole coastline by calculating the difference between each point and the average value; the data is shown in Table 1. Error range refers to the interval segment of the y coordinate error value, and error range (points) refers to the number of points where the y coordinate error value is in this error interval. It can be seen from the table that the variance and standard deviation of the shoreline extracted by this method are 0.1528 and 0.0233, and the variance and standard deviation of the isoline tracking method are 0.3638 and 0.1338. Therefore, the shoreline extracted by this method has less error and higher accuracy. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Total No. of**} & \multicolumn{5}{c}{**Error Range (Points)**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{1}{c}{**Variance**} \\  & **Points** & **\textgreater{}1** & **0.8–1.0** & **0.6–0.8** & **0.4–0.6** & **0.2–0.4** & **0.0–0.2** & **Deviation (m)** & **(m\({}^{2}\))** \\ \hline Proposed Contour tracking & 254 & 0 & 0 & 21 & 62 & 54 & 117 & 0.1528 & 0.0233 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The error and accuracy data of the two methods. Figure 16: (a) The superposition of point cloud of the two methods, and point cloud of cliff segment; (b) The superposition of coastline of the two methods, and point cloud of cliff segment.
## Results
### Multivariate Analysis Based on Metabaanalyst 4.0  
Using Metabaanalyst algorithms on the matrix M, the PCA analysis (scores and loadings) was performed again (see Figure 5), as well as the PLSDA score plot coupled with the dendrogram derived from cluster analysis (Figure 6).  
Figure 5: Graphics of PCA analysis: scores (**left**) and loadings (**right**) plots for the 8 groups of oils (I to VIII). For the group individual affiliation and abbreviations see Table 1.  
Figure 6: Partial least squares with discriminant analysis (PLSDA) score plot and dendrogram cluster analysis for the 8 groups of oils (I to VIII). For the group individual affiliation and abbreviations see Table 1.  
(**a**) Partial least squares with discriminant analysis (PLSDA) score plot (**b**) and dendrogram cluster analysis for the 8 groups of oils (I to VIII)The scores plot shows a good discrimination and classification of oil groups as well as similarities between some groups, such as VII-VIII (see buckthorn and olive oils), I-II-V (sun flower, pumpkin and soybean oils), IV-VI (hempesed and walnut oils) and separately group III (linseed oils). The explained variance was 49% (PC1) and 24% (PC2), covered by a total variance of 73% (Figure 5 left). According to the loadings plot (Figure 5, right) the wavenumbers which may explain this classification are located in 2 main domains, one including the wavenumbers 719, 790, 866, 1118, 1396, 1651, and 3007 cm\({}^{-1}\), and the other including the wavenumbers 846, 1026, 2852, and 2922 cm\({}^{-1}\). These data are in good agreement with the statistical values mentioned in Table 3.  
The PLSDA scores plot showed even a better discrimination between the oils groups I to VIII, with a similar variance of 47.4% (PC1) and 12.2% (PC2) covering a total variance of 59.6% (Figure 6a).  
The oil groups VII and VIII (sea buckthorn and olive oils) were better discriminated, the group I-II-V (sunflower, pumpkin and soybean oils) were split, the soybean group being better differentiated. The groups IV and VI (hempesed and walnut oils) remained non differentiated while group III (linseed oils) remained singular. The dendrogram (Figure 6b) shows good and significant clustering between groups VII and VIII, groups I, II and V, and groups IV and VI, exempting one sample of pumpkin oil (PO1) and one sample of hemp oil (HO2). All these data confirm the initial statistical related to the discrepancy of some samples against the authentic ones.
## 6 Conclusions and Future Work  
In this study, forest penalizing attributes (FPA) and its enhanced novel variants were deployed for the detection of Android malware. Specifically, cascade generalization (Cas_FPA) and rotation-forest-based FPA (RoF_FPA) were implemented and empirically evaluated on imbalanced and SMOTE-balanced Android malware detection datasets. Experiments were carried out to investigate the effectiveness and usefulness of the proposed approaches. Results from empirical evaluation revealed that FPA produced a significantly improved detection performance when compared with the baseline classifiers, such as NB, BN, CR, DTable, ADT, and DS, on the original and SMOTE-balanced Malgenome and Drebin datasets. This observation supports the applicability of FPA for Android malware detection. Additionally, the enhanced variants of FPA (Cas_FAP and RoF_FPA) convincingly outperformed FPA on original and balanced datasets, demonstrating their effectiveness in Android malware detection. It is worth noting that the deployment of a data sampling method (SMOTE) in this research work did not only resolve the latent class imbalance problem but positively enhanced the detection performance of the proposed models. For generalizability, the proposed models (FPA, Cas_FPA, RoF_FPA, Cas_FPA+SMOTE, and RoF_FPA+SMOTE) also outperformed the state-of-the-art models in the existing literature on the Malgenome and Drebin malware datasets. Therefore, we recommend the proposed models for use in Android malware detection and the use of the data sampling method for addressing the class imbalance problem.  
In the future, we plan to extend this work by introducing ensemble methods to the model and by considering hybrid (dynamic and static) features of malware applications. More real-life datasets should also be considered in future work. The high dimensionality of Android malware datasets is another issue that needs to be addressed. Specifically, the combination of data sampling and feature selection methods as solutions to class imbalance and high-dimensionality problems in Android malware detection will be investigated.  
**Author Contributions:** Conceptualization, A.G.A., A.O.B., L.F.C. and F.E.U.-H.; Data curation, A.O.B., S.A.S. and Z.O.A.; Formal analysis, A.G.A., A.O.B., S.A.S. and Z.O.A.; Funding acquisition, L.F.C. and S.B.; Investigation, A.G.A., A.O.B., H.A.M., F.E.U.-H. and G.B.B.; Methodology, A.O.B. and F.E.U.-H.; Project administration, L.F.C. and S.B.; Resources, H.A.M., S.B., S.A.S., P.O.S. and Z.O.A.; Software, H.A.M., P.O.S. and G.B.B.; Supervision, L.F.C. and S.B.; Validation, L.F.C., S.B. and P.O.S.; Visualization, S.A.S., P.O.S. and G.B.B.; Writing--original draft, A.O.B. and H.A.M.; Writing--review & editing, L.F.C., F.E.U.-H., G.B.B. and Z.O.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Available on request.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Active Equalization of Lithium-Ion Battery Based on Reconfigurable Topology
###### Abstract  
The equalization technique is a key technique in the secondary utilization of retired batteries. In this paper, a double-layer equalization method is proposed, which combines the reconfigurable topology with the converter active equalization method. The inner layer uses the reconfigurable topology to have a balanced set of battery cells. Thanks to isolating the lowest SOC (state of charge) cell in the battery group, the energy transfer loss among cells is avoided. In addition, this topology can reduce cost and control complexity and the number of components. In the outer layer, a Buck-Boost converter is added for each battery group, and the outputs of the converters are connected in series. The output voltage of the converter varies as the SOC of the group varies while the total output voltage is stable. In order to validate the proposed method, an equalization circuit consisting of 12 battery cells is built on Matlab/Simulink. Simulation results show that the proposed method can effectively balance the battery pack and maintain a stable output voltage. Compared to the conventional active equalization method, the proposed method has significantly improved the equalization efficiency.  
active equalization; reconfigurable topology; double-layer model; retired battery; secondary utilization; buck-boost Article
# Blockchain Based Secure Routing and Trust Management in Wireless Sensor Networks +
## 4 System Model
### Registration  
All nodes have unique Media Access Control (MAC) addresses. The identity of the SNs, ANs and BSs is marked as \(SN_{ID}\), \(AN_{ID}\) and \(BS_{ID}\), respectively. The ANs are registered using the smart contract of the public blockchain. The smart contract verifies  
Figure 1: Proposed system model.  
the existence of ANs. Moreover, the validity of ANs' identities and MAC addresses is also checked by the BSs. In the registration process, the public blockchain keeps a record of ANs' identities when the above steps are successfully performed. When the identities of ANs are stored in the blockchain, no one can maliciously tamper with these identities. In this way, the blockchain provides a reliable authentication mechanism in our WSN. In contrast, when the verification of ANs' identities fails, then these ANs are revoked from the network. After completing the registration process, SNs are permitted to join the blockchain. The SNs, after their deployment, are associated with the corresponding ANs. External attacks are reduced by registering the nodes.
## 2 Materials and Methods
### Data Acquisition and Measurements  
CBCT was performed with a CS 9600 Carestream Dental. The exposure parameters were as follows: 120.00 KV, 6.3 mA, an exposure time of 20.00 s, and a dose of 814.18 mGy/cm\({}^{2}\). The Frankfurt plane was parallel to the floor. CS 3D Imaging (Carestream Dental LLC) was used for measurements. The measurements were performed twice independently by two investigators (experienced orthodontists with many years of experience), and the mean result was used in the statistical analysis. Intra- and inter-observer reliability tests were performed for CBCT measurements, \(p<0.005\).  
The following measurements were taken:  
2. Horizontal plane separately for each level--L1, L2, and L3: 1. [label=A.]
3. Incisive canal width-- | Cl-Cl|.
4. Antero-posterior IC-- | Ca-Cp|.
5. Distance between the most mesial point of the root and the tangent passing through the most anterior point of the incisive canal-- | Rm-Cat|.
6. Distance from Cl to the posterior edge of the incisor root-- | Cl-Rpt|.
7. Distance between roots | Rm-Rm|.
8. Sagittal plane: 1. [label=B.]
9. Angle formed by the long axis of the incisor and the palatine plane--CP angle.
10. Angle formed by the long axis of the incisive canal and the palatine plane--IP angle.
11. Coronal plane: 1. [label=C.]
12. Angle formed by the long axes of the central incisors (values above zero for convergent roots, negative values for divergent roots).  
L1, L2, and L3 were determined in sagittal planes as follows: L1--the lowest point of the incisive canal on the palatal wall, L2--half the distance between L1 and L3, and L3--the height of the top of the maxillary central incisor (Figure 1).  
Measurement landmarks:  
Rm--the most mesial point of the root U1.  
Rp--the most posterior point of the root U1.  
Cl--the most lateral point of the incisive canal.  
Ca--the most anterior point of the incisive canal.  
Cat--tangent to Ca.  
Rpt--tangent to Rp.  
The long axis of the incisor was determined by connecting the incisal edge with the root apex (Figure 2).  
Figure 1: Measurement scheme.  
The long axis of the canal was determined by connecting a point midway between the labial and palatal walls of the incisive foramen and a point midway across the width of the canal in the predetermined palatal plane (Figure 3).