esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Case Study: Analysis of a Device
### Choice of the Device to Validate the Proposed Methodology  
For study purposes, we performed the physical data acquisition of a Samsung SM-A105M/DS smartphone (Galaxy A10). The choice for this device took into account four main criteria.  
The first was the scope of the device and the global sales volume. According to (the market analysis and business consulting company Counterpoint)[58], Samsung Galaxy A10 (A105M) was the best-selling Android smartphone in the world in the third quarter of 2019, and placed second in overall sales volume, just behind the iPhone XR.  
The second criterion was the Android version on the device because the more recent the smartphone, the more layers of security are implemented, and the more secure the system tends to be. In this case, the SM-A105 model runs Android version 9.0 (Pie), which runs on most Android devices (41.9%) [59].  
The third criterion was the difficulty in carrying out the welds on the board, considering that the device has tiny components. Therefore, without using a VR-Table for the realization of an ISP, it is still possible to weld the conductors on the TAPs.  
The fourth criterion is the fact that the SM-A105 data has device encryption enabled automatically (Direct Boot).
# Retrofit of Existing Reinforced Concrete (RC) Buildings: Steel vs. RC Exoskeletons
## 2 Case Study Building
### Summary and Discussion of Results  
Overall, both the steel and RC exoskeleton solutions adopted in the present study proved to offer effective contributions to the bare RC building from a seismic point of view (Figure 11).  
The retrofit intervention based on the use of a steel exoskeleton is certainly more interesting, both from the point of view of mechanical effectiveness (strength and ductility) and from the point of view of in-field construction. Moreover, the steel exoskeleton adds high stiffness to the composite system, compared to the RC one (35; 36). From a quantitative point of view, the different behaviour of examined solutions can thus be clearly distinguished in Figure 11. In this regard, it is worth noting--for both cases--that the fixing joints between the exoskeleton and the existing building have been considered as rigid, while their role could be maximized in efficiency by means of dissipative elements.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Push-Over** & \(F_{max}\) & \(\Gamma\) & \(F_{max}^{*}\) & \(\alpha_{u}\)/\(\alpha_{1}\) & \(S_{e}\)(\(T^{*}\)) & \(q^{*}\) & \(C\) & \(D\) & \(S\) \\ \hline  & **(kN)** & & **(kN)** & & **(g)** & & **(cm)** & **(cm)** & \\ \hline Mode + \(X\) + \(0.3Y\) & 3687 & 1.41 & 2606 & 1.70 & 0.317 & 1.1 & 10.67 & 10.21 & 1.04 \\ Mode + \(X\) – \(0.3Y\) & 5736 & 1.44 & 4055 & 1.43 & 0.373 & 1.0 & 9.69 & 8.70 & 1.11 \\ Mode \(-X\) + \(0.3Y\) & 4494 & 1.41 & 3177 & 1.34 & 0.352 & 1.0 & 11.11 & 9.20 & 1.20 \\ Mode \(-X\) – \(0.3Y\) & 3697 & 1.41 & 2614 & 1.23 & 0.327 & 1.1 & 11.08 & 9.92 & 1.11 \\ Mode + \(Y\) + \(0.3X\) & 5980 & 1.32 & 4527 & 1.18 & 0.368 & 1.0 & 12.41 & 8.80 & 1.41 \\ Mode + \(Y\) – \(0.3X\) & 9588 & 1.32 & 7260 & 1.23 & 0.461 & 1.0 & 13.03 & 7.03 & 1.85 \\ Mode \(-Y\) + \(0.3X\) & 6101 & 1.32 & 4619 & 1.33 & 0.361 & 1.0 & 13.41 & 8.99 & 1.49 \\ Mode \(-Y\) – \(0.3X\) & 4359 & 1.32 & 3301 & 1.32 & 0.310 & 1.0 & 12.55 & 10.46 & 1.20 \\ Mass + \(X\) + \(0.3Y\) & 6859 & 1.41 & 4849 & 1.93 & 0.402 & 1.0 & 8.76 & 8.05 & 1.08 \\ Mass + \(X\) – \(0.3Y\) & 6738 & 1.41 & 4764 & 1.00 & 0.412 & 1.0 & 10.38 & 7.87 & 1.31 \\ Mass \(-X\) + \(0.3Y\) & 6614 & 1.41 & 4676 & 1.67 & 0.418 & 1.0 & 11.32 & 7.75 & 1.46 \\ Mass \(-X\) – \(0.3Y\) & 6359 & 1.41 & 4496 & 0.87 & 0.409 & 1.0 & 11.13 & 7.92 & 1.40 \\ Mass + \(Y\) – \(0.3X\) & 7918 & 1.32 & 5995 & 1.94 & 0.427 & 1.0 & 12.65 & 7.58 & 1.66 \\ Mass + \(Y\) – \(0.3X\) & 7893 & 1.32 & 5976 & 1.63 & 0.451 & 1.0 & 13.40 & 7.19 & 1.86 \\ Mass \(-Y\) + \(0.3X\) & 8167 & 1.32 & 6184 & 1.70 & 0.413 & 1.0 & 14.34 & 7.78 & 1.84 \\ Mass \(-Y\) – \(0.3X\) & 7387 & 1.32 & 5593 & 1.10 & 0.396 & 1.0 & 10.41 & 8.17 & 1.27 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of PO analysis for the post-intervention stage (RC exoskeleton).  
Figure 11: PO analysis results: comparison of bare RC building and existing building retrofitted with steel or RC exoskeletons.
## 3 Results and Discussion
### Relationships between Oils Variables and (E)-2-nonenal  
Since the PCA not only evidenced the effect of storage conditions on the composition of the oil, but also the effect of storage time on it; (E)-2-Nonenal was selected as a marker of oil quality degradation; as oil deterioration is associated with the appearance of this volatile compound over time. Thus, in order to establish general relationships between the different components of the oil and (E)-2-nonenal, a partial least squares (PLS) regression model was built to visualize how this compound is a function of the independent variables. According to the cross-validation, 4 components were selected, which explained 77% of the variability in Y. Figure 4 shows the graph of the samples and variables jointly (biplot) for component 1 and 2.  
As shown in Figure 4, the same 11 variables previously described in PCA stand out in relation to the levels of (E)-2-nonenal in oils. The samples were distributed along the first component in a pattern that considers milder storage conditions and shorter storage times (left half) towards more aggressive conditions and longer storage times (right half). The first group was associated with the presence of \(\alpha\)-tocopherol, non-oxidized phenolic  
Figure 4: Relationships between oils variables and (E)-2-nonenal by PLSA. Plots of samples and variables. Sample are labeled by storage conditions. Abbreviations see Figure 3.  
compounds, DAOD, AOAH, and DALD. The second group was associated with oxidized phenolic compounds, elenolic acid, DAODO, DALDO, and AOAHO. This relationship is corroborated when analyzing the regression coefficients of the PLS model (Figure 5) for the first component, where the first group of compounds correlates inversely with the content of (E)-2-nonenal and the latter group correlates directly. This group includes the total volatile compounds associated with (E)-2-nonenal. As previously mentioned, the increase in volatile compounds during storage could be explained by the decomposition of hydroperoxides formed during the autoxidation of olive oil subjected to temperature and light conditions. Of particular relevance are the oxidized phenolic compounds that increase in tandem with the volatiles, such as (E)-2-nonenal. This correlation is likely due to the formation of the oxidation product 9- or 10- hydroperoxylinoleic acid, which by decomposition would generate (E)-2-nonenal, as noted earlier.
## 1 Introduction  
Reinforcement learning techniques are usually experience-based rather than data-based. Hence, to train RL agents, they should be left to explore either in a sandboxed real environment or a simulation. While sandboxed real environments might not always be made available, simulations are an optimal solution [1]. However, this itself requires explicit effort to be made at times. It might not be possible to bring in a variety in the training environment for RL agents. These environments are only as good and as varied as the mind(s) behind the environment design can fathom. Generative models are useful for this, particularly generative adversarial networks. generative adversarial networks (GANs) were originally used as generative models for unsupervised learning. However, they have now found use cases in semi-supervised learning and fully supervised learning, as well as reinforcement learning. GANs have been conventionally used to generate new plausible examples for data sets and related tasks, majorly revolving around images and text. Generative adversarial networks, developed and launched in 2014, are a part of neural networks that do not require any supervision for learning. GANs have a system structure comprising two neural networks models, which are the generator and discriminator. The communication between these two models helps them to analyze the variations in the data set provided. The generator makes an attempt to mislead the discriminator by generating fake data samples (Figure 1). The discriminator tries to demarcate between the fake and real data samples. This is a repetitive process and hence improves the efficiency of the generator and discriminator. Some of the real-world applications of GANs include: creatingnew image samples from existing samples, text or image translation, image editing and applications in the field of AI for generating video games scenes.  
We recognize the use of GANs in reinforcement learning as an area of opportunity for training environment synthesis and augmentation. Augmented experience training refers to training RL agents by augmenting their environment exploration experiences. Technically, this translates to an RL agent being trained on variations in a domain environment so that the agent can converge with a good decision strategy sooner and can be more robust when variations and exceptions occur in real time in the actual environments. For example, imagine the case of autonomous driving. Some training environments would be:  
1. A straight road with a starting point and ending point;
2. A curved road with a starting point and ending point.  
It seems obvious to also train the agent for sharp turns as well as gentle turns. One way to achieve this could be by developing environments manually for each use case: sharp turns as well as gentle turns. However, for a more creative solution, GANs can be used to introduce variations in our environments. This is more scalable than a manual solution.
# A Contrastive Learning Method for the Visual Representation of 3D Point Clouds
## 2 Materials and Methods
### Three-Dimensional Point Cloud Momentum Contrast Learning Framework  
The three-dimensional point cloud momentum contrast learning framework is shown in Figure 1. The whole framework is divided into upper and lower parts, and also divided into a self-attention point cloud capsule network, \(f(\cdot)\), and a momentum-based self-attention point cloud capsule network, \(g(\cdot)\). These are both self-attention point cloud capsule networks with the same structure, but different parameters. The design of the self-attention point cloud capsule network is introduced in Section 2.2. The input of \(f(\cdot)\) is the point cloud \(x\), and the input of \(g(\cdot)\) is the positive sample \(x^{+}\) of the point cloud \(x\) obtained by three data augmentation and mixing methods. In order to maintain the consistency of the feature of each iteration of \(g(\cdot)\), the parameters of \(g(\cdot)\) are updated using momentum. In order to use the sample information of past small batches, a dynamic feature queue of first-in, first-out is maintained to store the features of positive and negative samples. For each iteration of training, the feature queue will store the features of the positive sample \(x^{+}\) of the current iteration sample \(x\). As the iteration continues, the features in the feature queue will continue to increase on a first-in, first-out principle. The digital capsule layer \(P\) output is compressed by the backbone network to obtain \(P_{t}\), which is used to improve the feature queue's ability to store features, and at the same time, makes features more focused on point cloud transformation operations. Finally, for the equivariance of the capsule network, the Jaccard coefficient is used to measure the similarity between the capsules, thus constructing the Jaccard contrast loss as the loss function of contrastive learning.
# Integrating BIM and GIS for an Existing Infrastructure
## 7 Conclusions and Future Developments  
The AECO sector is in an era of digital transformation, marked by the ongoing advent of the use of data-driven technologies. BIM and GIS are two of these technologies, with each employing a different but complementary approach, providing a global view of infrastructure and the surrounding environment. With this in mind, collaboration and access to information from any device through proprietary applications that meet the needs of each project becomes essential. These must be linked to information, data analytics, and virtual representations of infrastructure, enabling the use of BIM viewers, GIS viewers, or joint viewers to provide global models with all the detail of BIM in large GIS environments.  
The use of open standards allows integration from BIM to GIS. However, further research is needed to achieve a stronger link in this interoperability, being necessary to strengthen the semantic linkage, as the geometric linkage seems to have been overcome. Each project is unique, and the associated information needs to be organized and structured according to a project's specific needs. The use of external DBs compensates for the shortcomings of the use of these standards and facilitates the implementation of other data technologies such as IoT or AI.  
In the development of the case study in the present research, some limitations have been identified for future lines of research. Firstly, the use of the standard SLPK format may generate certain shortcomings and uncertainty since its usability is presently restricted to certain commercially licensed software. Therefore, the use of this standard in open-source applications should be addressed. The integration of a BIM model into a GIS environment has been explored here, so the inverse direction should be investigated, i.e., from a data model in the SLPK standard to the IFC. Finally, another line of research should be to assess the efficiency of the BIM-GIS management application versus traditional management methods in the O&M of this road.  
Conceptualization, J.J.C., M.G.A. and R.M.P.; Methodology, M.G.A.; Software, J.J.C., M.G.A., R.M.P. and J.A.C.; Validation, J.J.C., M.G.A. and J.A.C.; Formal analysis, J.J.C., M.G.A., R.M.P. and J.A.C.; Investigation, J.J.C., M.G.A. and J.A.C.; Resources, J.J.C. and M.G.A.; Data curation, J.J.C., M.G.A., R.M.P. and J.A.C.; Writing--original draft, J.J.C. and J.A.C.; Writing--review & editing, M.G.A. and R.M.P.; Visualization, M.G.A. and R.M.P.; Supervision, M.G.A. and R.M.P.; Project administration, M.G.A.; Funding acquisition, M.G.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Enterprise University Chair [PERSON].  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.
# NOMA-Based VLC Systems: A Comprehensive Review
## 3 Integration of NOMA-Based VLC with Emerging Technologies
### NOMA-Based Hybrid RF/VLC Systems
An online method was developed by the authors of [100] to reduce the energy used by a hybrid VLC-RF network in a low-light environment while still meeting the need for the available light. Similarly, in [101], the paper analyzed optimum categorization for a NOMA hybrid VLC-RF network in the presence of perfect CSI. To the same end, [102] tackled the issue of efficient resource allocation in NOMA-aided hybrid VLC-RF with shared backhaul to optimize the possible data rate. In order to ensure that vehicular communications are both ultra-reliable and low-latency, the researchers in [103] presented a powerful hybrid VLC/RF paradigm for resource management. In addition, a heterogeneous RF/VLC model for optimizing RF/VLC link selection has been presented [104]. Last but not least, the NOMA-aided hybrid VLC-RF system's performance was examined in the context of reconfigurable intelligent surface (RIS) by scientists [105]. Figure 6: An overview of Hybrid RF/VLC wireless network.
# Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model
## 2 Pose Estimation Approach
### Humanoid Robot Model  
The human body must be represented using a humanoid robot model that mimics the organization of human-like links and joints in order to reconstruct 3D human poses from 2D joint data collected from MPP. Therefore, arbitrary 3D poses can be reconstructed from 2D images taken at arbitrary viewing angles and distances from a camera by measuring link lengths in pixels and estimating joint angles of the humanoid model using an optimization method.  
In general, humanoid robots are described with links and joints based on the Denavit-Hartenberg (DH) method [27] in which a reference coordinate frame is placed on the supporting foot. Since the goal of the current approach is to generate and estimate poses of humanoid that are as human-like as possible, we improved our previous humanoid model [28] to generate arbitrary poses as follows:  
* Locating the origin of the reference frame at the center of the body, i.e., the root joint, to create arbitrary poses.
* Adding three DoF lumbar spine joints at the center of the pelvis to create poses where only the upper body moves separately.
* Redefining the rotational polarity of all joint variables to match the Vicon motion capture system for better interoperability of the joint data measured by the system.  
As shown in Figure 2, the proposed humanoid model is composed of a total of 23 joint variables. That is, it is composed of 12 joint angles which rotating axes are perpendicular to the sagittal plane \(\left(\theta_{hd},\,\theta_{sh}^{J,r},\,\theta_{el}^{J,r},\,\theta_{tr},\, \theta_{hp}^{J,r},\,\theta_{in}^{J,r},\,\theta_{an}^{J,r}\right)\), 7 joint angles which rotating axes are perpendicular to the frontal plane \(\left(\phi_{sh}^{J,r},\,\phi_{tr},\,\phi_{hp}^{J,r},\,\phi_{an}^{J,r}\right)\), and 4 joint angles which rotating axes are perpendicular to the transverse plane \(\left(\psi_{hd},\,\psi_{tr},\,\psi_{hp}^{J,r}\right)\), where the subscripts _hd_, _sh_, _el_, _tr_, _hp_, _kn_, and _an_ indicate joint names of the head, shoulder, elbow, torso, hip, knee, and ankle, respectively, and the superscripts \(l\) and \(r\) denote the left and right parts, respectively.  
Figure 1: Definition of landmarks in MediaPipe Pose [22].  
Figure 2: The 3D humanoid robot model.
# Improving a PGNAA Technique to Detect Heavy Metals in Solid Samples
###### Abstract  
Prompt gamma-ray neutron activation analysis (PGNAA) is a useful approach for determining the concentrations of a variety of elements in natural materials, either online or in situ, without affecting their chemical forms in matter. The current research aimed to improve the yield of a portable PGNAA setup using a dc beam of 2.5 MeV neutrons and a CeBr\({}_{3}\) detector to record gamma rays from neutron inelastic scatterings. It is impossible to avoid the superimposition of heavy metal gamma rays and those from the detector's element. However, tests were carried out to improve the signal-to-background ratio. By assessing the minimum detectable concentrations (MDC) of chrome, titanium, and zinc in soil samples, the effectiveness of the new optimization was confirmed. The study shows an improvement in the MDC values.  
CeBr\({}_{3}\) detector; 2.5 MeV neutrons; neutron inelastic scattering; minimum detection concentrations +
Footnote †: journal: Physics Letters A
###### Abstract  
Automatic urban sound classification is a desirable capability for urban monitoring systems, allowing real-time monitoring of urban environments and recognition of events. Current embedded systems provide enough computational power to perform real-time urban audio recognition. Using such devices for the edge computation when acting as nodes of Wireless Sensor Networks (WSN) drastically alleviates the required bandwidth consumption. In this paper, we evaluate classical Machine Learning (ML) techniques for urban sound classification on embedded devices with respect to accuracy and execution time. This evaluation provides a real estimation of what can be expected when performing urban sound classification on such constrained devices. In addition, a cascade approach is also proposed to combine ML techniques by exploiting embedded characteristics such as pipeline or multi-thread execution present in current embedded devices. The accuracy of this approach is similar to the traditional solutions, but provides in addition more flexibility to prioritize accuracy or timing.  
urban sound classification; machine learning; embedded system; environment sound recognition; audio feature extraction; edge computing Article
## 5 Conclusions  
In this study, we address the problem of maximizing energy efficiency in an IRS-assisted HST communication system by considering essential physical constraints. We formulate a multi-variable-coupled energy efficiency optimization problem and employ an alternating optimization strategy to decouple the variables. The Dinkelbach and SCA algorithms are utilized to convert the non-convex problem into convex ones to solve for BS transmit beamforming. The RCG algorithm is used to optimize the IRS phase shifts. The simulation results demonstrate that the IRS-assisted HST communication system indeed enhances system energy efficiency. Specifically, when the number of IRS reflecting elements is 64, the system EE is improved by approximately 12.41%, 35.26%, and 37.96% compared to the semi-definite relaxation algorithm, the random phase-shift approach, and the scheme with no IRS, respectively. Compared to traditional HST communication systems that increase energy efficiency by adding more BS antennas, increasing the number of IRS elements can economically and effectively boost the system energy efficiency, making the HST communication system greener and more energy-efficient.  
In future research, considering the complexity of railway environments, we suggest investigating the use of advanced techniques such as deep reinforcement learning and other AI methods to optimize the energy efficiency of IRS-assisted HST communication systems. Given the difficulty of acquiring accurate information about the ideal CSI, future research should look towards investigating the performance of IRS-assisted HST communication systems in the presence of an imperfect CSI.  
Conceptualization, C.L., Y.L., J.X. and Z.Z.; methodology, C.L., Y.L., J.X. and Z.Z.; validation, C.L., Y.L., J.X. and Z.Z.; writing--original draft preparation, Y.L.; writing--review and editing, C.L. and J.X.; supervision, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62161016), the Science and Technology Plan of Gansu Province (20 JR10 RA273), and the Beijing Engineering Research Center of High-Speed Railway Broadband Mobile Communications (BHRC-2022-1), Beijing Jiaotong University.  
Not applicable.  
Figure 7: EE versus the number of N/M in different schemes.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Numerical and Experimental Investigations of Laser Metal Deposition (LMD) Using STS 316L
###### Abstract  
This study aimed to understand the effect of heat accumulation on microstructure formation on STS 316L during multilayer deposition by a laser metal deposition (LMD) process and to predict the microstructure morphology. A comprehensive experimental and numerical study was conducted to quantify the solidification parameters (temperature gradient (G) and growth rate (R)) in the LMD multilayer deposition process. During deposition, the temperature profile at a fixed point in the deposit was measured to validate the numerical model, and then the solidification parameters were quantified using the model. Simultaneously, the microstructure of the deposit was investigated to confirm the microstructure morphology. Then, a relationship between the microstructure morphology and the G/R was proposed using a solidification map. The findings of this study can guide the design of scanning paths to produce deposits with a uniform structure.  
laser metal deposition (LMD); solidification parameters; microstructure; heat accumulation; STS 316L +
Footnote †: journal: Applied Physics Letters