esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Summary and Perspective
### Sensor Fusion and Development  
Sensor fusion implies using more than one sensor output to achieve an improved process understanding and thus control. The fusion of various data such as meltpool size, temperature, and acoustic emission can be used as an information source for internal and surface defects. It might also serve as an efficient approach to control the L-PBF process and account for corrective actions. Very limited research discusses the applicability of sensor fusion to monitor the L-PBF processes. For example, [PERSON] et al. [164] proposed using a photodetector, high-speed visible camera, and short-wave infrared thermal camera for online defect detection of the L-PBF process. It was noted that low-fidelity sensors such as photodetectors might be more cost-effective than thermal cameras. The authors also noted that an array of photodetectors might enhance defect detection accuracy. Clearly, there is a need for more studies on different ML algorithms that can fuse different data collected and benefit from these additions instead of analyzing the output of each sensor individually.  
Moreover, to use the ML model in online control of the L-PBF, a relatively fast response is needed to ensure a timely control signal is given to take corrective action [102]. Therefore, it was also suggested to develop computation-enabled intelligent sensors capable of being application-targeted [165]. These sensors will benefit from \"Big Data\" analytics and the Internet of Things (\"IoT\") to help place L-PBF in its expected position in Industry 4.0.
## 4 Simulation Analysis
### Simulation Results Analysis  
#### 4.4.1 Comparison Algorithms  
This study conducts a comparative analysis of the proposed algorithm against the Data Rate Benchmark (DR-BM) algorithm [25; 26] and the Q-routing algorithm [27]. The DR-BM algorithm, a conventional routing approach widely applied in diverse network scenarios, serves as a representative benchmark for performance evaluation. The DR-BM algorithm prioritizes data rate in routing decisions, making it highly relevant to satellite network routing challenges. Utilizing DR-BM as a benchmark enables a comprehensive evaluation of the proposed algorithm's performance in leveraging link data rates, thereby demonstrating the efficacy and robustness of the proposed algorithm.  
The Q-routing algorithm represents a seminal application of reinforcement learning in satellite routing. As one of the pioneering algorithms that introduced reinforcement learning to routing problems, Q-routing has established a substantial research foundation and exerted significant influence in both the academic and industrial domains. Therefore, a comparison with Q-routing enables a comprehensive evaluation of the proposed algorithm's performance in addressing LEO satellite routing challenges, while also providing valuable insights for future research directions. This comparative analysis highlights the advancements and innovations of the proposed algorithm over traditional reinforcement learning-based routing approaches.
## 5 Simulation Study
### Results and Discussions
#### 5.3.3 Average Delay  
An evaluation of the average end-to-end delay is carried out for all application traffic from the smart meters to the data concentrator, as well as the DR traffic traversing from the data concentrator to the smart meters. As mentioned earlier, the IEEE 802.11b standard with a maximum data rate of 11 Mbps is employed in this study. Other standards with higher bandwidth and data rates may provide higher throughput and lower delay. The average end-to-end delay for all application traffic on the different OLSR versions is presented in Figure 11.  
Figure 11: Average end-to-end delay for all application traffic types.  
Figure 10: Blocking probability for all application traffic types.  
It can be seen that ML has the lowest average end-to-end delay of all the existing OLSR versions. In addition, the \(OLSR_{MM}\) results showed that average end-to-end delay can be further reduced by 23%.
# Vibration Response Analysis of Overhead System Regarding Train-Track-Bridge Dynamic Interaction
## 2 System Dynamics Theory of Train-Track Overhead System
### Vibration Analysis Model of Track Structure  
The track type is a ballasted track. The Ritz method is used to introduce the normal mode coordinates of rail. Considering the vertical (\(q_{Vk}(t)\)), transverse (\(q_{Lk}(t)\)) and torsion (\(q_{Tk}(t)\)) modes of the rail, the two order ordinary differential equations of rail vibration are obtained as shown in Equation (7):  
\[\begin{array}{c}\ddot{q}_{Vk}(t)+\frac{EI_{T}}{m_{T}}\Big{(}\frac{k\pi}{l} \Big{)}^{4}q_{Vk}(t)=-\sum\limits_{i=1}^{N}F_{Vi}Z_{k}(x_{si})+\sum\limits_{j= 1}^{4}P_{j}Z_{k}(x_{wj})(k=1\sim N_{V})\\ \ddot{q}_{Lk}(t)+\frac{EI_{Z}}{m_{T}}\Big{(}\frac{k\pi}{l}\Big{)}^{4}q_{Lk}(t)= -\sum\limits_{i=1}^{N}F_{Li}Z_{k}(x_{si})+\sum\limits_{j=1}^{4}Q_{j}Y_{k}(x_{ wj})(k=1\sim N_{L})\\ \ddot{q}_{Tk}(t)+\frac{G_{r}I_{l}}{p_{r}I_{0}}\Big{(}\frac{k\pi}{l}\Big{)}^{2} q_{Tk}(t)=-\sum\
olimits_{i=1}^{N}M_{si}\Phi_{k}(x_{si})+\sum\
olimits_{j=1}^{4}M_{wj} \Phi_{k}(x_{wj})(k=1\sim N_{T})\\ \end{array} \tag{7}\]  
where: \(I_{Y}\), \(I_{Z}\) = the moment of inertia of rail section to y and z axes, respectively  
\(I_{l}\)= the moment of inertia of rail torsion  
\(I_{0}\) = the polar inertia moment of the rail section  
\(\rho_{r}\) = the rail density  
\(G_{r}\) = the rail shear modulus  
\(F_{Vi}\), \(F_{Li}\) = the vertical and horizontal reaction force of the \(i\)-th fulcrum  
\(P_{j}\), \(Q_{j}\) = the vertical and transverse load of the \(j\)th wheel acting on the rail  
\(M_{si}\) = the rail reaction moment at the \(i\)th fulcrum  
\(M_{wj}\) = the moment of the \(j\)th wheel acting on the rail  
\(x_{wj}\) = the \(j\)th coordinate  
\(x_{si}\) = the coordinate of the ith fulcrum  
\(N_{V}\), \(N_{L}\) and \(N_{T}\) = the highest order of rail vertical, lateral, and torsional,  
\(L\) = the rail length \(Z_{k}\), \(Y_{k}\), and \(\Phi_{k}\) = the vertical, transverse, and torsional mode functions of rail, respectively.
# Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
## 3 Results
### Laser Repetition Rate  
If a pulse repetition rate is lowered at the same laser average power, the energy per pulse increases so the ablation tendency is generally strong. On the other hand, if a pulse repetition rate is increased or a CW mode is used, the energy per pulse is reduced, but the heat energy is evenly distributed over the entire area, which is advantageous for welding or sintering. As the equipment used in this experiment did not support the CW mode and only supported a variable pulse mode of 20-90 kHz, the results were observed whilst changing each pulse condition. As a result, when the pulse repetition rate was 40 kHz or less, as shown in Figure 5, the energy per single pulse was high and machining by laser ablation mainly occurred, making it impossible to form a conductive pattern. When the pulse repetition rate was increased to 60 kHz, the ablation tendency was reduced and an interconnection between the aluminum powders by sintering occurred, but damage to the edge of the line occurred due to the high energy per pulse. However, at a 90 kHz pulse repetition rate, a neat conductive pattern was obtained based on the proper polymer binder ablation and the aluminum powder interconnection by sintering.  
Figure 3: SEM images of laser irradiated areas with different laser scanning directions.
# Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
## 1 Introduction
### Related Work  
We here briefly review recent literature which is close to our approach. We start by noting the work of Reference [10], in which a mixture of GAN models is proposed as a data clustering model. Although clearly related, this is somewhat different to our approach, in which we use a single GAN generator, linked to the hyperparameters of the posterior mixture model, rather than a mixture of GAN models. Furthermore, our goal is forecasting rather than unsupervised data classification. The approaches advocated in Reference [11, 12] propose a latent space, used for sampling latent vectors in the GAN, formulated via a Gaussian mixture. The latter replaces, in these papers, the single Gaussian distribution used for such generation in standard GAN models. In both these papers, the generator and discriminator have a similar structure to a standard GAN. In Reference [13], a mixture model is used, but for the discriminator alone, with the generator being that of a standard GAN model; we note the difference to our approach, in which the generator is a GMM. The work of [PERSON] [14] proposes a model to best approximate the joint distribution over a set of static and temporal features, associated with a time series. The authors use a GAN to propagate these features so as to model the dynamics of the time series. We note that the outputs from the model are point estimates and that, in the case of a 1-d system, the method is the same as CGAN albeit with an added autoencoder. Finally, Reference [15] compares (standard) GAN models to GMMs for image generation. The authors show that GANs are superior in their ability to generate sharp images, but note that mixture models offer more efficient inference. They propose a combination of the two and introduce a GAN model in which the GAN generator is a mixture model. However, the sample generator still makes point estimates from the multi-modal distribution in order to retain a discriminator function the same as that of a standard GAN model. We offer discriminator extensions which allow the GAN process to operate on the full (multi-modal) posterior distribution.
## 2 Mathematical Modeling
### Spatial Representation
\begin{pmatrix}\left(N(\Lambda)+h \right)\cos\left(\Lambda\right)\cos\left(\lambda\right)\\ \left(N(\Lambda)+h\right)\cos\left(\Lambda\right)\sin\left(\lambda\right)\\ \left(N(\Lambda)\big{(}1-e_{\oplus}^{2}\big{)}+h\right)\sin\left(\Lambda \right)\end{pmatrix}  \tag{4}\]  
\[N(\Lambda)=\frac{a_{\oplus}}{1-e_{\oplus}^{2}\sin\left(\Lambda\right)} \tag{5}\]  
The most common ellipsoidal Earth model is the WGS84 model [31], but, in this study, a spherical model with a radius of \(r_{\oplus}=6371.000\) km is considered, so \(e_{\oplus}=0\), \(a_{\oplus}=N(\Lambda)=r_{\oplus}\), and (4) becomes as represented in (6). \[\mathbf{x}_{\text{E}}\big{|}_{\text{ECEF}}= \begin{pmatrix}\left(r_{\oplus}+h \right)\cos\left(\Lambda\right)\cos\left(\lambda\right)\\ \left(r_{\oplus}+h\right)\cos\left(\Lambda\right)\sin\left(\lambda\right)\\ \left(r_{\oplus}+h\right)\sin\left(\Lambda\right)\end{pmatrix}  \tag{6}\]  
The conversion from ECEF to ECI is reported in (7), where \(\alpha_{\text{GR}}\) is the position of Greenwich meridian and it is a function of time \(t\). \[\mathbf{x}\big{|}_{\text{ECI}}=\mathbf{R}_{(a_{\text{GR}}(t),3)}\mathbf{x} \big{|}_{\text{ECEF}} \tag{7}\]  
Both the signal emitter and the satellites have to be expressed in the same reference system to write the optimization problem. The particular coordinate system for the calculations did not matter for the purposes of this study, and ECI was chosen simplicity because it is assumed to be inertial for the time span of a satellite mission and because both the transformations LLH-to-ECI for the signal emitter and PQW-to-ECI for the satellites are well known in the literature. It is noted for the reader that transformation between coordinate systems introduces computational errors: it could be possible to determine a new coordinate system that minimizes these computational errors, but this task is beyond the purposes of this study and, to the knowledge of the authors, it is an open problem that is never addressed for these kinds of applications. In this study, the influence of dynamics in terms of forces/torques is absent, i.e., it is supposed that they are balanced and that the control system compensates for all the disturbances, and residual values are expected to have a secondary order effect.
# Performance Analysis of an Improved Gravity Anchor Bold Expanded Foundation
## 4 Response of the Foundation
### Foundation Stress Analysis
#### 4.2.2 Damage Analysis of Concrete  
It is necessary to understand the development process of foundation damage in order to analyze the safety performance of structures. This can highlight the relatively weak parts of the structure. To study the failure response of the new wind turbine foundation under load, this paper studies the distribution of the tensile damage factor (DAMAGET) of the foundation under different loads based on the plastic damage constitutive model of concrete. The evolution and development process of damage at different positions of the foundation with the increase in load was revealed.  
During the loading process, the damage development distribution of the new foundation is as shown in Figure 15. It can be seen from Figure 15 that, during the loading process of the new wind turbine foundation, the damage first appears in the upper part of the inner side of the foundation ring wall. With the increase in the load to 50%, the damage also occurred at the neck of the foundation and the corbel. When the load is further increased to 75%, the damage is further extended to the lower part of the foundation ring wall and the bottom surface of the foundation. When the load is fully applied, the damage area of the above parts is further expanded, and the maximum value of the tensile damage factor appears at the foundation corbel, with a maximum value of 0.98.  
Figure 14: Relationship between reinforcement stress and load level.  
Figure 15: Foundation damage distribution.  
To obtain a more detailed change rule of new foundation damage, this paper selects several feature points of different parts of the foundation, and we gradually increase the load ratio (from 0 to 100%). The change in the tensile damage factor at the feature points was observed. The feature points and damage changes are shown in Figure 16.  
From Figure 16, it can be seen that when the wind load is transmitted to the new foundation, the upper part of the foundation ring wall (point a) and the lower part of the foundation ring wall (point c) are first damaged. After the wind load exceeds 10%, the damage at points a and c increases slowly. When the wind load is applied to 60%, the damage begins to occur at the neck of the foundation (point b). The tensile damage factor increases sharply to the maximum at this time. When the wind load is applied to 70%, the cantilever root (point d) of the foundation slab is destroyed. The tensile damage factor increases rapidly here.  
The above analysis shows that the damage of the foundation is mainly concentrated in the plate of the foundation and the foundation ring wall. The tensile failure coefficient of the inner side of the bottom plate reaches the maximum value of 0.98 when the load is fully applied. When the external load gradually increases from 10% to 100%, the damage inside the foundation slab starts from scratch and eventually spreads throughout the whole foundation. Therefore, it is very necessary to increase the stress reinforcement here when reinforcing the reinforcement.
# A Novel Approach to Enhance Crude Oil Recovery Ratio Using Selected Bacterial Species
## 5 Conclusions  
Local isolates of _Pseudomonas putida_, _Bacillus_ species, and _Nocardiia cyiacigeorgica_ were able to grow at higher concentrations of heavy crude oil, effectively converting heavy fractions of crude oil to lighter fractions and indicating their potential application in microbial-enhanced oil recovery processes. Bacterial biotransformation of Bouri crude oil showed significant dependence on temperature as an important parameter.  
Viscosity was studied versus shear rate and temperature. The control sample showed that the use of these bacteria can alter crude to a significant extent. The three isolates considerably improved oil production and reduced oil viscosity. Isolates _Pseudomonas putida_ and _Bacillus_ species were both demonstrated to be promising candidates for MEOR applications. The extra recovery of crude heavy oil in the sand-packed column experiments and migration of bacteria in porous sandstone cores further confirmed this.  
In conclusion, MEOR is an eco-friendly cost-effective technique with various benefits over other chemical EOR methods and the isolated strains can result in additional oil as a part of the tertiary oil recovery process.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app112110492/s1](https://www.mdpi.com/article/10.3390/app112110492/s1)]([https://www.mdpi.com/article/10.3390/app112110492/s1](https://www.mdpi.com/article/10.3390/app112110492/s1)). The aligned sequences of the representative strains retrieved from the GenBank (NCBI, USA) database and test strain were used to generate phylogenetic tree and similarity matrices using the PHYLIP.  
Author conceptualization, H.A.A., data curation, H.A.A. and I.M.E., formal analysis, methodology, H.A.A. and I.M.E.; writing--original draft, H.A.A. and I.M.E.; review and editing, H.A.A. and I.M.B., Supervision, H.A.A. All authors have read and agreed to the published version of the manuscript.  
This work was financed by Libyan Petroleum Institute (LPI).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
Data Availability Statement: All datasets generated or analysed for this study are included in the manuscript and the Supplementary Materials. The data for the three genomes of _bacterial_ spp. isolates were deposited at NCPI/GenBank in the Bioproject PRJNA433829.  
We are extremely grateful for the help of the staff at the Reservoir Rocks lab & [PERSON] at GC lab, and the technical assistance from [PERSON] at Rheology lab (Tripoli Libya).  
The authors declare no conflict of interest.
# Research on the Symbolic 3D Route Scene Expression Method Based on the Importance of Objects
## 1 Introduction
In order to save time in terrain simplification, an algorithm using ROAM to pre-process the LOD emerged, and the Chunked LOD model [37] was constructed to alleviate the pressure on the GPU, but the model took up a large amount of hard disk space. According to the characteristics of 3D model data, [PERSON] et al. [38] constructed a 3D model data structure based on point indexing that uses a multi-level LOD management model for image textures, thus constructing a 3D model with LOD structure. [PERSON] et al. [39] used an efficient compression of 3D scenes as well as scheduling and rendering algorithms to enhance the rendering performance of the scenes. Although this method alleviates many of the problems associated with data visualisation due to network transmission, its data format model is not uniform, thus making interactive sharing between users impossible. Although the above-mentioned studies have solved the problem of constructing 3D line scenes to a certain extent, they have mainly focused on the study of large-scale urban 3D scene representation or the study of 3D tile data organisation and loading patterns [40]. These studies lack the design of representation rules for 3D route scenes dominated by the spatial cognitive needs of users and the importance evaluation models applied to 3D symbolic representations [41; 42; 43; 44]. Thus, there are problems with the selection of key targets and the prominent expression of objects in the study of symbolic 3D route scenes. These limit the promotion and application of 3D maps. To address these issues, this research proposes a method for symbolic 3D route scenes that takes into account the importance of physical objects. First, the spatial importance of similar elements in the 3D route scene needs to be calculated, and then these elements are processed according to semantic factors in order to create a corresponding importance evaluation model. This gives us the importance distribution in the study area. The 3D symbols are then classified and modelled using a symbolic approach based on the importance of each entity in the 3D route scene. Finally, all entities are displayed hierarchically at different scales, depending on their importance. The research proposes a new form of 3D route scene representation that is based on user perception and highlights important objects. The method uses 3D symbols instead of real 3D models to represent 3D route scenes. Not only can it effectively emphasise the key objects in the scene, but it also makes the loading of the scene more stable and efficient. In addition, the construction method of the object importance evaluation model may also be useful for research in other application areas. In summary, the research proposes a new idea for the representation of 3D route scenes.
## 2 Materials and Methods
### In Vitro Release of Fe\({}^{2+}\)  
The study of in vitro release was carried out to investigate the amounts of Fe\({}^{2+}\) released from the hybrid glass materials. In particular, the release of Fe\({}^{2+}\) from different materials was analysed at different release times using the normative reference A3500-Fe B, phenanthroline method. Powders of hybrid materials were soaked in 10 mL of SBF solution. Afterwards, 1.00 mL of this mixture containing the hybrid materials was added to a solution composed of 10% hydroxylamine hydrochloride, 10% acetate sodium, 0.1% 1,10-phenanthroline, and water. Release measurements, taken by means of a Shimadzu UV 700 Double-Beam Scanning UV-Vis spectrophotometer (Shimadzu, Kyoto, Japan), were repeated three times on each hybrid. The absorbance of Fe\({}^{2+}\) released was obtained at a wavelength of \(\lambda\) = 520.0 nm. Means \(\pm\) standard deviation were calculated for each measure. The calibration curve of absorbance versus concentration was determined in the range 0-4.99 mg L\({}^{-1}\). Over this interval, the data fitted the Beer-Lambert law:  
\[\text{A}=\varepsilon\,\text{1C}=1.265\,\text{C} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Specimen** & **PEG Content (wt.\%)** & **Fe(II)C Content (wt.\%)** \\ \hline SiO\({}_{2}\)/PEG6/Fe(II)C20 & 6 wt\% & 20 wt\% \\ \hline SiO\({}_{2}\)/PEG12/Fe(II)C20 & 12 wt\% & 20 wt\% \\ \hline SiO\({}_{2}\)/PEG24/Fe(II)C20 & 24 wt\% & 20 wt\% \\ \hline SiO\({}_{2}\)/PEG50/Fe(II)C20 & 50 wt\% & 20 wt\% \\ \hline SiO\({}_{2}\)/PEG50/Fe(II)C15 & 50 wt\% & 15 wt\% \\ \hline SiO\({}_{2}\)/PEG50/Fe(II)C10 & 50 wt\% & 10 wt\% \\ \hline SiO\({}_{2}\)/PEG50/Fe(II)C5 & 50 wt\% & 5 wt\% \\ \hline \end{tabular}
\end{table}
Table 1: List of samples with the various percentage contents of PEG and Fe(II)C studied.  
Figure 1: Flow chart of sol-gel synthesis depicting the steps that lead to the formation of a gel from a liquid.  
where A is absorbance, \(\varepsilon\) is the molar absorptivity coefficient (M\({}^{-1}\) cm\({}^{-1}\)), 1 is the path length in cm, and C is concentration (M).