esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Failure Mode  
Figure 7 shows the failure modes of DTT specimens under different loading angles. Most specimens were significantly influenced by anisotropy, exhibiting tensile-shear mixed failure. For specimens at \(\beta=0^{\circ}\) and \(\beta=75^{\circ}\), an approximately vertical fracture along the bedding plane was observed. For specimens at \(\beta=15^{\circ}\) and \(\beta=30^{\circ}\), the fractures deviatedunder the influence of the bedding plane. For specimen at \(\beta=45^{\circ}\), the fracture occurred primarily along the bedding plane. For specimens at \(\beta=60^{\circ}\) and \(\beta=90^{\circ}\), the failure modes demonstrated more pronounced tensile failure within the rock matrix. Combined with the AE monitoring results, it is evident that specimens at \(\beta=60^{\circ}\) and \(\beta=90^{\circ}\) did not generate significant AE energy or ringing counts during the loading process, with energy accumulating continuously until the stress level reached 100%. In contrast, under other loading conditions, the AE energy and ringing counts increased progressively during loading due to the influence of bedding plane failure, exhibiting varying degrees of progressive failure characteristics.  
Figure 8 shows the failure modes of BST specimens under different loading angles. It is evident that the failure modes varied significantly with the loading angle. For specimens at \(\beta=0^{\circ}\), the failure mode was tensile failure along the bedding plane. For specimens at \(\beta=15\)-\(45^{\circ}\), the failure mode was tensile-shear mixed failure, with shear failure along the bedding plane being dominant. For specimens at \(\beta=60\)-\(75^{\circ}\), the failure mode remained tensile-shear mixed failure, but tensile failure across the bedding plane became dominant. For specimens at \(\beta=90^{\circ}\), the failure mode was tensile failure across the bedding plane. Thus, anisotropy significantly influenced the BST specimens, affecting both their tensile strength and failure modes.  
Overall, the failure modes of BST specimens can be categorized into three types: tensile failure along the bedding plane, tensile-shear mixed failure, and tensile failure across the  
Figure 8: Failure modes of BST specimens under different loading angles.  
Figure 7: Typical failure modes of DTT specimens under different loading angles.  
bedding plane. In contrast, the failure modes of DTT specimens are limited to tensile failure across the bedding plane and tensile-shear mixed failure.
# Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods
## 7 Evaluation Results
### Summary of the Experimental Evaluation Results  
In this paper, the multi-label classification results of the UWB data were quantified based on two metrics, i.e., the F1-score and confusion matrix. The F1-score is typically used in the literature to quantify the performance of ML-based classifiers because it provides a convenient single value score [11; 42; 49]. However, it can sometimes overlook the insightful information of some classes. Table 5 gives the typical summary of the two evaluated scenarios using both the F1-score and overall accuracy. The individual F1-score for each class (LOS, NLOS, and MP) is also given in the table.  
Based on the data given in Table 5, both the macro-averaging F1-score and overall accuracy showed that the SVM classifier gave the best performance when the training and test environments  
Figure 10: Comparison of the multi-class classification results for the MLP classifier using confusion matrices in two scenarios: (**a**) the test dataset obtained from the same conditions as the training phase; (**b**) the test dataset collected in a different condition from the training. In the evaluation, three fully-connected hidden layers with 100 neurons in each layer were used for the MLP classifier.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Scenarios**} & \multirow{2}{*}{**Classifiers**} & \multicolumn{2}{c}{**Individual F1-Scores**} & \multicolumn{2}{c}{**Macro-Averaging**} & \multirow{2}{*}{**Overall**} \\ \cline{3-4} \cline{6-7}  & & **LOS** & & & **NLOS** & & **F1-Scores** \\ \hline Training and test & SVM & 0.77 & **0.78** & 0.70 & **0.75** & **75.35** \\ environments are different & RF & 0.74 & **0.76** & 0.71 & 0.73 & 73.52 \\  & MLP & 0.72 & **0.75** & 0.71 & 0.73 & 72.86 \\ \hline Training and test & SVM & 0.81 & 0.81 & **0.86** & 0.83 & 82.80 \\ environments are the same & RF & 0.91 & **0.93** & **0.93** & **0.92** & **91.90** \\  & MLP & 0.90 & **0.92** & **0.92** & 0.91 & 91.20 \\ \hline \hline \end{tabular} The bold numbers in the table refer to the best performance scores for each class (LOS, NLOS, and MP) and each classifier based on the metrics and scenarios.  
\end{table}
Table 5: Summary of the results based on the F1-scores and overall accuracy.  
were different. The consistent outcome in SVM also revealed the reason why it is one of the most frequently used classifiers in the literature [9; 10; 17; 21]. In contrast, RF performed the best among the three evaluated classifiers regarding the same training and test environments.  
However, it was hard to clarify using Table 5 that the predicted accuracy of MP condition was significantly low compared to the other two classes in all evaluated classifiers in a scenario when the training and test were in different environments. This phenomenon could be detected using the confusion matrix as previously described in Section 7.2.
## 3 Methodology
### Model Complexity and Lightweight Analysis  
In alignment with the emphasis on \"lightweight\" design in our study, we assess the model complexity of the employed architectures--MobileNetV2 and VGG16--under both centralized and decentralized federated learning setups. The term \"lightweight\" refers to models with reduced computational burden, fewer parameters, and lower inference latency, making them suitable for deployment on resource-constrained edge devices [32]. MobileNetV2, specifically designed for efficiency, has approximately 3.4 million parameters and a model size of ~14 MB, making it significantly smaller than traditional CNNs like VGG16, which has ~138 million parameters and a model size exceeding 500 MB. In our decentralized simulation, MobileNetV2 offered faster convergence and reduced communication overhead due to its compact architecture, confirming its suitability for real-time, privacy-preserving fall detection on embedded or mobile devices [33; 34]. Although VGG16 achieved competitive accuracy, its significantly larger footprint makes MobileNetV2 the more viable lightweight alternative for edge AI integration.
## 3 Results and Discussion
### Residual Stresses  
The near-surface residual stresses in the rebar were measured at the location between two transverse non-parallel ribs and are shown in Figure 9. Both the longitudinal (axial) and tangential stresses are compressive, and they vary in magnitude almost in a linear fashion from 10 MPa on the surface to 90 MPa up to a depth of 100 um. Beyond this depth the magnitude remains the same for up to a depth of about 300 um. Compressive stresses of about 50 MPa near the surface of the rebar were also observed by [PERSON] et al. [10], but were documented only to a depth of 50 um. The stresses shown in Figure 9 are compressive and much lower than the tensile yield strength of tempered martensite at room temperature, which is the predominant microstructure constituent near the surface of the rebar. No stress reversal of compressive to tensile stresses was observed in the studied depth range. The measurement uncertainty depicted is evaluated in the course of the linear regression analysis of the lattice spacing to the \(\sin^{2}\psi\) values according to the \(\sin^{2}\psi\)-method [36; 43].  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **C** & **Si** & **Mn** & **Cu** & **Ni** & **Cr** & **P** & **S** \\ \hline CCT & 0.19 & 0.29 & 0.67 & - & - & - & 0.007 & 0.011 \\ Rebar & 0.19 & 0.26 & 0.83 & 0.31 & 0.12 & 0.09 & 0.02 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of the sample from the CCT diagram compared with that of the rebar (wt%) [42].  
Figure 8: Continuous cooling transformation diagram for a composition matching the B500B rebar steel. Numbers in circles denote the Vickers hardness HV30. Reproduced by permission of the authors of [42].  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \(\mathbf{A_{c1}}\) & \(\mathbf{A_{c3}}\) & \multicolumn{4}{c}{**a**} & \multicolumn{4}{c}{**b**} & \multicolumn{4}{c}{**c**} \\ \cline{3-11}  & & & **HV30** & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & **HV30** & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) & **HV30** & \(\mathbf{T_{1}}\) & \(\mathbf{T_{2}}\) \\ \hline CCT & 715 & 835 & 229 & 650 & 520 & 199 & 690 & 570 & 178 & 715 & 590 \\ Rebar & 730 & 820 & 235 & 610 & 531 & 192 & 676 & 564 & 177 & 700 & 600 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of dilatometric tests of rebar samples to the corresponding curves in the CCT diagram. Temperatures are given in \({}^{\circ}\)C.
## Appendix A Framework for Physital Cultural Heritage Tourism Application Development
### Applying the Framework to the Chilly Mo VR Application
* Design high-resolution digital twins of cultural artifacts and environments, such as Persepolis. Iterative Development:
* Regularly test prototypes with toddlers and parents, gathering feedback to improve usability and engagement. Marketing Strategy:
* Promote the app through partnerships with cultural institutions like the Louvre. * Highlight the educational and gamified aspects in campaigns targeting parents. Stage 6: Testing and Refinement Functional Testing:
* Ensure seamless operation of the VR experience across devices, particularly smartphones. * Test NFT minting, redemption processes, and in-app integrations. User Feedback:
* Use semi-structured interviews and questionnaires with parents, educators, and toddlers to evaluate engagement and usability. * Address feedback to refine intuitive navigation and interactive features. Compliance Check:
* Verify alignment with cultural authenticity, age-appropriate design, and accessibility standards. Stage 7: Deployment and Support Launch Strategy:
* Provide live tech support during the app launch to assist users with installation, navigation, and troubleshooting. NFT Placement and Accessibility:
* Display digital artifacts in-app, with physical representations available at museum gift shops. Compatibility and Maintenance:
* Regularly update the app to include new civilizations and quests. * Ensure compatibility with emerging XR devices and blockchain platforms. Stage 8: Evaluation and Iteration Performance Metrics:
* Track user engagement through metrics such as VR session durations, completed quests, and NFT redemptions. * Measure educational outcomes using feedback from parents and educators. Long-Term Improvements:
* Expand content offerings by incorporating additional civilizations and festivals. * Explore partnerships with other museums and cultural institutions to scale the application. By applying the integrated framework, the Chilly Mo app can evolve into a comprehensive DCH XR platform, blending educational value with gamified tourism and physgital offerings. The inclusion of NFTs and blockchain provides new dimensions for personalization and rewards, while the iterative design process ensures the app meets the needs of its young audience. This approach not only enriches the cultural learning experience for toddlers but also creates innovative opportunities for museums and cultural institutions to engage with the next generation.
## 2 Materials and Methods
### Analysis of the In Vitro Extract Release
The results showed that the release was more efficient at pH 2.0 due to the higher solubilization tendency of Ch in this medium. The release decreased as the concentration of Ch in the matrix increased [82]. Similarly, rutin was encapsulated in microparticles of casein and pectin by the complex coacervation method, which released 81.82% \(\pm\) 9.58 of rutin in a simulated intestinal environment, within 10 h [77]. Microspheres loaded with Ch and Na-Alg, as well as polyphenols extracted from _Camelia sinensis_ L., underwent an in vitro release test. It was found that, after 6 h of exposure to an alkaline medium (pH 7.4), they released over 90% of the extract, whereas exposure to an acidic medium with pH 1.2 resulted in insignificant release [86]. In a different study, the release of _Viola odorata_ L. extract from Ch and Na-Alg microcapsules occurred in two phases in both acidic (pH 1.5) and alkaline (pH 7) environments. Rapid release occurred in the first 40 min, followed by sustained release until the end of the experiment. The microparticles released a higher amount of extract in the pH 7 medium (95.39%) compared with the pH 1.5 medium, where they released 43.21%. In the acidic environment, the carboxyl groups in the Na-Alg structure were transformed into acid due to the acidic pH. This transformation favored the formation of alginic acid that prevented the release of the basic substance from the microcapsule [87]. Regarding the release capacity of the extract from microspheres, analysis of variance (ANOVA) was used to statistically evaluate the samples in terms of the main effect (Figure S15), taking into account certain statistical factors, including the preparation method, the concentration of chitosan, the swelling time, and the samples. According to the ANOVA analysis, the dotted red line denotes the mean design of experiments (DOE) for the release parameter. Among the factors analyzed, the time factor generated the highest variance in DOE (method 2), followed by the method factor, and, finally, the Chitosan factor. Another observation was that the microspheres prepared by method 2 presented the best results among all the tests to which they were subjected to and were the most suitable for future in vivo tests. The results of the non-linear regressions, whose statistical performances were the best, are presented in Table 6. Among the non-linear regression functions, the one accepted by the statistical comparison procedure was the sigmoidal allosteric one. All samples showed very high values of the coefficient of determination (R\({}^{2}\) > 0.998), denoting the accuracy of the practical execution of the in vitro release experiment.
###### Abstract  
This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer-Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment.  
underwater acoustic wireless sensor network (UWSN); target localization; received signal strength (RSS); Cramer-Rao lower bounds (CRLBs) Article
## 4 Selective Area Growth Applied on InPSi Wafers
### Demonstration of Selectively Grown MQW Structures  
Selectively grown MQW active regions on InPSi were thoroughly characterized. In order to cover a broad PL range, mask's width W\({}_{\text{m}}\) of ranging from 5 \(\upmu\)m to 30 \(\upmu\)m by step of 5 \(\upmu\)m were used, while the distance between the masks was set to be 30 \(\upmu\)m. Therefore, AlGaInAs-MQW based active regions surrounded by two undoped InP layers were selectively grown on InPSi and analyzed by HRXRD, PL measurements and TEM. The SAG regions are 30 \(\upmu\)m-large, which require specific means of characterizations to investigate their crystal quality.  
First, PL measurements were done using a Horiba-Labram set-up equipped with a microscope to focus the laser excitation spot on the surface of each SAG region (Figure 8a). A 160 nm-wide wavelength extension ranging 1490 to 1650 nm was achieved. The full width at half maximum (FWHM) of the PL signals is almost constant over the entire spectral range. The latter is a clear signature of high-crystal quality of the MQW for the different SAG regions. A significantly lower signal-to-noise ratio is measured for the PL peaks at 1650 nm because of the limited responsivity of the InGaAs detector of our PL setup for wavelengths above 1600 nm.  
Figure 7: Description of the specific SAG process developed on InP-SOI and InPSi wafers.  
XRD is particularly relevant to characterize the thickness enhancement induced in the selectively regrown MQW structures. Therefore, HRXRD was performed using a D8 Discover diffractometer from Bruker equipped with a microfocus X-ray source (I\(\mu\)S) delivering a sub millimeter spot size and down to 50 \(\upmu\)m. The HRXRD diffractograms of the selectively grown MQW structures obtained for W\({}_{\text{m}}\) = 5, 15, 20 and 30 \(\upmu\)m are presented in Figure 8b. The lowering distances between the satellite peaks account for the enhancement of the thickness of the quantum wells and barriers with the SAG mask width. The quantum well and barrier period ranges from 16.4 nm for W\({}_{\text{m}}\) = 5 \(\upmu\)m to 20.4 nm for W\({}_{\text{m}}\) = 30 \(\upmu\)m, with a reference period value determined outside SAG regions of 14.2 nm. One can notice the enlargement of the satellite peaks. The latter is due the contribution of lateral variations in the analyzed region between the masks [42]. However, for a given SAG region, the central zone thickness/composition variation is negligible. Therefore, we can consider that if the component is fabricated from materials grown in the central zone of the SAG region, it will not suffer from thickness variation. The cross-sections of the SAG regions obtained from SAG masks' width of 5 \(\upmu\)m and 30 \(\upmu\)m were analyzed by TEM (Figure 8c,d). The latter confirm the thickness variation when W\({}_{\text{m}}\) increases and the excellent material quality of the selectively grown MQW active regions.
## 1 Introduction  
Grouting is a technique for sealing fissures in the rock, concrete, and masonry structures. Several studies have used a wide range of grout materials, including cement substituted by a silica fume (SF), metakaolin (MK), and fly ash (FA), as well as cement treated with polymers [1; 2; 3; 4]. The main purpose of the grouting process is to cover the sand particles with a high flowable grout material. The component's compressive and bonding strengths will be greatly improved as a result [5]. Water reducer polymers are widely used in the cement grout (CG), cementitious materials (CM), and concrete industries to reduce the content of water and improve the physical and structural properties of cement [6; 7; 8].  
Coarse sand, probably due to its high grain size, permitted better particle interlocking, enhancing \(\sigma_{\rm f}\). Several parameters, including cement to sand ratios, w/c, raw materials utilized, sand grading, and sand particle morphology, influence the strength properties of CM [9; 10].  
No study in the literature observed that two separate standards were used to measure the CS of CG modified with water reducer polymer to provide high flowable and high strength cement grout.  
To the best of the authors' knowledge, formulation of a computational model in terms of an analytical function for estimating the compressive strength of cementitious materials as a function of the parameters involved in the problem, has not been proposed before in the literature. This can be attributed mainly to the complex and highly non-linear behavior exhibited by the compressive strength in relation to the parameters involved, such as the water-to-cement ratio, the polymer content (P), and the testing age (t) of samples. Based on the above, the salient objectives of the work presented herein can be summarized as follows:  
1. Assess the effect of different water reducer polymer additive surfaces on CG characteristics using scanning electron microscopy testing (SEM).
2. Investigate the effect of five grains of sand with various sizes on the flowability of CG modified with water reducer polymer.
3. Calculate the maximum compression and flexural stress of self-compacting CG using linear and nonlinear techniques.
4. Correlating prismatic compressive strength to cylindrical compressive strength followed by the American Society for Testing and Materials and British Standards, and prismatic compressive strength to the flexural strength of a CG.
# Toward Secure Smart Grid Systems: Risks, Threats, Challenges, and Future Directions
## 2 Background and Preliminaries
### Smart Grid Interoperability Layers  
SGAM establishes a tiered structure to facilitate the smooth integration and interoperability of various smart grid components and services (see Table 5). The component, communication, information, function, and business layers are the five main layers of this architecture. Together, these layers facilitate the integration of IoT technologies at both the strategic and operational levels of the smart grid, each playing a distinct role in providing safe, scalable, and standards-compliant grid operations.  
The component layer comprises the hardware assets and physical equipment that make up the smart grid, including substations, sensors, smart meters, control units, and DER components [32]. It provides real-time data capture and control by establishing the physical infrastructure on which IoT devices function. Since physical tampering or malfunction might cause the entire system to malfunction, it is imperative to provide security and resilience at this layer. The communication layer specifies the communication technologies, networking protocols, and media (both wired and wireless) used to transfer data throughout the grid [26]. Protocols such as IEC 61850, DNP3, Modbus, Zigbee, and LTE are widely used, depending on the domain and application. This layer is crucial to IoT technologies as it enables low-latency, secure, and dependable data flow between systems and devices. The information layer manages the syntactic rules, semantics, and data models that organize and interpret the data that is transferred [33]. It guarantees that information from various devices and fields is consistent, comprehensible, and useful for processing. This layer is essential to enabling analytics and machine-to-machine interoperability in an IoT-enabled smart grid. The function layer reflects the application logic and services, such as demand-response functions, forecasting, control algorithms, and grid monitoring, that use data to make operational decisions [34]. It connects unprocessed data with useful procedures, frequently with the help of AI/ML models for optimization. IoT integration provides automation and distributed intelligence at the grid's edge. Finally, the business layer addresses the strategic, financial, and regulatory aspects of smart grid operations, including energy trading, billing, stakeholder relations, and compliance [35]. It analyzes information and performs tasks related to generating value and providing services. This layer is enhanced by IoT, which facilitates new business models such as dynamic pricing, real-time service adaptation, and prosumer energy exchange.