esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on the Application of THz-TDS in Coal-Rock Interface Recognition
## 4 Conclusions  
In recent years, THz time-domain spectroscopy has developed rapidly, and research using its application in various fields is in progress. Specifically, research on THz detection system designed for outdoor and severe conditions is underway. The advantage of using THz waves to detect the coal-rock interface is the ability to perform timely, accurate and non-destructive material detection. The analysis of time and frequency domain signals, refractive index and absorption coefficient of coal and rock samples and coal and rock mixture samples can accurately identify the detected powders. The establishment of a database can enable the analysis of the shearers to judge whether the cutting face is coal, rock or coal-rock. When a coal mining machine encounters a coal-rock interface during cutting, THz time-domain spectroscopy can be employed to assess the working face, thus avoiding cutting into the rock portion. In addition, compared with the existing research, this study proposed a thickness measurement method combining the terahertz time-domain system with simulation software, and carried out related experiments and simulation. Through terahertz testing of 10 kinds of coal flakes with different thicknesses, and comparative analysis with the simulation data, the feasibility of this method was confirmed. It lays a foundation for the future study of coal seam thickness detection using the terahertz system. The research in this work contributes to the application of THz time-domain spectroscopy technology in the identification of coal-rock interface, and also provides a reference for the study of THz wave detection of coal seam.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14041431/s1](https://www.mdpi.com/article/10.3390/app14041431/s1)]([https://www.mdpi.com/article/10.3390/app14041431/s1](https://www.mdpi.com/article/10.3390/app14041431/s1)), Figure S1: THz-TDS; Figure S2: TDS 1008 inside, laser beam path; Figure S3: Samples-a; Figure S4: Samples-b.  
Conceptualization, Z.J., T.M. and C.Y.; methodology, T.M. and H.L.; software C.Y. and W.H.; validation, L.H., Z.J. and H.L.; formal analysis, Z.J. and W.H.; investigation, W.H.; resources, H.L.; data curation, C.Y., H.L. and W.H.; writing-original draft preparation, Z.J., T.M. and L.H.; Writing-review and editing, Z.J. and H.L; visualization, C.Y.; supervision, H.L.; project administration, W.H.; funding acquisition, H.L. and W.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2021 YFB3200100; Applied Basic Research Projects of Shanxi Province, grant number 202203021221212; Research Project Supported by Shanxi Scholarship Council of China, grant number 2020-135; the Key R&D Project of Datong City, grant number 2020019; and Special Project of Shanxi Datong University, grant numbers 2020 YGZX003, 2020 YGZX004, 2021 YGZX33, and 2022 YGZX001.
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Analysis of the Final Solution  
The final analysis has been conduced on the best solution found by SNO. The geometry of this solution is shown in Figure 9.  
An interesting feature of this solution is the regularity of the patch size in the central parts of the reflector. This feature is important because it leads to a higher reliability of the model used in the optimization with respect to a full wave analysis.  
The patch size distribution is less regular in the corners. This is a common feature of the solutions found with optimization procedures because these patches have a lower impact on the final radiation pattern: in fact, the incident field intensity is much lower in these parts of the reflector.  
Figure 8: Convergence curves of mBBO, nBBO, DE, and SNO: each thin line corresponds to a single optimization trial, while the thick red line is the average one. (**a**) mBBO; (**b**) nBBO; (**c**) DE; (**d**) SNO.  
Finally, the solution has been assessed using a full wave simulation. The results are presented in Figure 10, the radiation patterns computed in E- and H- planes with the aperture field method (indicated in the legend with _optimization_) and of the full-wave are compared for all the analyzed scan angles.  
Figure 10: _Cont_.  
Figure 9: Optimal geometry obtained by SNO.  
It is important to notice that the radiation patterns are very similar, especially in the areas close to the main beam: there are some slight variations because the full-wave model takes into account also the interaction of the reflected field with the feeder.
# Lightweight Anomaly Detection in Digit Recognition Using Federated Learning
## 7 Experimental Results and Analysis
### Evaluation of Autoencoder Performance on ESP32-CAM: Embedded Deployment
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training Configuration**} & \multicolumn{4}{c}{**Training Results**} & \multicolumn{4}{c}{**Anomaly Detection Rate (\%)**} \\ \cline{2-13}
**Model** & **Autoencoder** & **Batch** & **Epches** & **Training** & **Training (\%)** & **Inference** & **MNIST** & **MNIST-C** & **MNIST-C** & **MIST-C** & **Dataset** & **MNIST-C** & **EMNIST** & **F1 Score** \\
**Number** & **Architecture** & **Size** & **Epochs** & **Training** & **Loss** & **Time (\%)** & **Time (\%)** & **Inference** & **MNIST** & **Fog** & **Motion** & **Dotted** & **Dataset** & **MNIST-C** & **EMNIST** & **F1 Score** \\ \hline \(1\) & [196, 16, 196] & 50 & 60 & 0.0124 & 727.18 & 9.34 & 11.50 & 93.65 & 10.30 & 54.30 & 20.95 & 85.65 & 0.8699 \\ \(2\) & [196, 32, 196] & 50 & 61 & 0.0074 & 1720.17 & 16.41 & 17.10 & 97.95 & 23.55 & 87.60 & 40.90 & 88.55 & 0.8612 \\ \(3\) & [196, 64, 196] & 10 & 30 & 0.0038 & 2574.34 & 32.21 & 11.95 & 99.10 & 27.25 & 91.20 & 37.90 & 86.00 & 0.8689 \\ \(4\) & [196, 32, 32, 196] & 50 & 115 & 0.0144 & 3585.85 & 21.49 & 11.35 & 91.65 & 11.25 & 46.45 & 18.78 & 85.65 & **0.8695** \\ \(5\) & [196, 32, 8, 32, 196] & 50 & 111 & 0.0196 & 3577.33 & 16.47 & 12.30 & 88.20 & 7.85 & 33.35 & 15.15 & 81.95 & 0.8438 \\ \(6\) & [196, 46, 16, 46, 196] & 50 & 158 & 0.0172 & 12618.32 & 35.48 & 12.30 & 88.80 & 11.20 & 42.35 & 18.10 & 86.10 & 0.8679 \\ \(7\) & [196, 64, 32, 32, 64, 196] & 10 & 42 & 0.0437 & 4281.15 & 42.07 & 6.90 & 62.60 & 0.85 & 14.55 & 5.05 & 53.10 & 0.6638 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Autoencoder training performance and anomaly detection results on MNIST, MNIST-C, and EMNIST (on ESP32-CAM). Figure 6: Performance comparison between FC and ESP32-CAM across all autoencoder models: (a) final training loss; (b) F1 score in anomaly detection.
## 6 Conclusions  
In this paper, an in-depth analysis of discrete-skew methodology was investigated, and an approach proposed to better understand the effect of the skewing angle and its determination during the design phase of SynRM. Following a literature review discussing the topic, the reduction factor for each harmonic component was introduced and derived in general form to estimate the resulting amplitude of each torque ripple component as a function of the skew. As a result, the overall torque ripple waveform was estimated considering the reduction of each harmonic component.  
To assess the validity of the proposed method, two SynRMs were evaluated and a torque ripple reduction of up to 70% was obtained. The outcomes from the analytical expressions and FEA evaluation showed good agreement. Moreover, the proposed harmonic component reduction factor showed promising results, being able to roughly estimate the reduction of specific harmonic components of the torque ripple. The proposed skewing technique was used to mitigate the torque ripple on a previously optimized triple-barrier SynRM with acceptable results: a 65% ripple reduction was observed from 3D FEA simulations results.  
By analyzing the harmonic distribution of the torque ripple, the best skewing strategy can be selected as an input of the proposed skewing technique: for mostly-purely-sinusoidal torque waveforms, a two-step or three-step skewing is advised, whilst for machines that have multiple high-magnitude harmonic components, a multi-step skewing can be recommended.  
Conceptualization; methodology; software; validation; formal analysis; writing--original draft preparation, C.G. and C.M.; writing--review and editing, J.A.T. and M.D.; visualization, C.G. and C.M.; supervision, J.A.T. and M.D.; funding acquisition, J.A.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded in part by the Agencia Nacional de Investigacion y Desarrollo (ANID), Chile through grant ANIDPFCHA/Doctorado Nacional/2020-21200527, grant ANIDPFCHA/Doctorado Nacional/2020-21200350, project FONDECYT REGULAR #1201667, and project FONDECF ID21110099.  
Figure 11: Harmonic components of the electromagnetic torque for the optimum design applying five-step skewing: (**a**) current density 5 A/mm\({}^{2}\); (**b**) current density 7.5 A/mm\({}^{2}\); (**c**) current density 10 A/mm\({}^{2}\); (**d**) current density 12.5 A/mm\({}^{2}\).
## 3 Results and Discussion  
### MIC Determination  
In our previous studies, we used an MIC of 10 mg/L of BAC to differentiate between BAC resistance and sensitivity phenotypes of isolates of _L. monocytogenes_[13, 14]. According to this criterion, the S2\({}^{\text{BAC}}\) included in the present study would be considered as BAC-R (MIC 20 mg/L) and S2-1 as BAC-S (MIC 2.5 mg/L) (Table 1). Susceptibility testing also revealed that S2-1 and quality control strains were similar in terms of the MIC of BAC, and that the BAC-R isolate also showed increased MICs of different compounds, especially EB and CIP, two well-known substrates for different multidrug-resistant efflux pumps [13, 14, 15], when compared to its parent strain S2-1 (Table 1). Reduced susceptibility to BAC, EB and CIP should have the same molecular mechanism [8, 13]. Small changes were also observed in the MICs of other quaternary ammoniums such as CTAB (Table 1). For S2\({}^{\text{BAC}}\), the overexpression of _Listeria_ efflux pumps similar to MdrL [13, 15] may be related to its multidrug resistance phenotype.  
To determine efflux pump activity, changes in the MICs of BAC were observed both in the presence and absence of resperine [14]. The MICs of BAC revealed an effect of resperine on the MIC of BAC in both BAC-S and BAC-R isolates (Table 1). The effect of resperine and the increased MICs of EB and CIP are not observed in BAC-R _qacH_-harboring strains [14]. The change observed in the MICs suggests that the resistance of S2\({}^{\text{BAC}}\) was related to endogenous efflux pumps of the MFS class [13, 14, 15].  
In food production environments, _L. monocytogenes_ efflux pumps do not confer resistance to disinfectants at the concentrations commonly used. In resistant strains, the MICs of BAC (20 mg/L) (Table 1) were much lower than the concentrations used in practice (200 mg/L or greater) [13]; thus, QACs are still considered an effective way to control BAC-R strains of _L. monocytogenes_[13]. Nevertheless, low-level BAC resistance in these _L. monocytogenes_ strains may contribute to their adaptation and survival [13, 18]; hence, the resistant strains may have the potential to persist in food ecosystems.
# Application of Digital Twin in the Industry of Axial Hollow-Wall Pipes
## 2 Establish a Digital Twin System of Axial Hollow-Wall Pipes  
For numerous high-value engineering applications, particularly in fields, such as \"aerospace\", the digital twin's main emphasis lies in the real-time interaction (measured in seconds) between the physical twin and the digital twin via data exchange. In contrast, for low-value engineering applications, such as pipelines, the role of the digital twin is more focused on predicting future conditions that the product may encounter, based on changes in the digital twin. These predictions are then utilized to adjust the product design accordingly. According to pertinent research [1], digitalized industries that rely on digital twins typically comprise three main components: digital twin systems, Cyber-Physical System (CPS) interaction systems, and digital production lines. The digital twin system is composed of a manufacturing process model, a product definition model, and measurement and detection models. Essentially, it is a type of simulation model. On the other hand, the digital production line mainly consists of digital processing and assembly systems, along with digital measurement and detection systems. These two components interact through CPSs to achieve intelligent industrial production, as illustrated in Figure 1.  
Based on this digitalized industrial system, this study designs a digital twin loop system suitable for axial hollow-wall pipes. The digital twin system comprises three sub-loops and one overall loop, as depicted in Figure 2.
## 5 Numerical Procedure  
This section presents the numerical methods applied in order to examine the viscoelastic turbulence model against the available DNS data identified within the literature. A new finite volume C++ computational solver was developed in the OpenFOAM software by modifying the \(k-\varepsilon\) sub-class files and introducing the FENE-P viscoelastic quantities such as: the polymer stress to the momentum equation; conformation tensor transport equation; and modified damping function to include elastic effects.  
A fully-developed channel flow using half of the channel height, \(h\), is applied given the symmetry of the governing geometry. We assigned 100 cells in the transverse (wall) direction with approximately 10 cells located inside the viscous sublayer. This is to provide mesh independent results, with errors within 0.5% for the mean velocity and the friction factor compared with a very fine mesh, similarly with [30]. The initial state of the simulation is the Newtonian solution until a steady-state solution was reached for each run case, except for HDR where a similar IDR developed case is applied to reduce computational time. Relaxation factors for the additional conformation tensor field are set to 0.2, along with residual control set to \(10^{-5}\). To improve numerical stability, an artificial diffusion term is added to the RACE of the form, \(\kappa\partial_{k}^{2}C_{ij}\), where \(\kappa\) denotes a constant, isotropic, artificial numerical diffusivity. In earlier studies [10], the dimensionless artificial numerical diffusivity is taken to be \(\kappa/hu_{\tau}\sim O(10^{-2})\). Here, \(\kappa/hu_{\tau}\sim O(10^{-3})\) and has negligible effect on mean values.  
A pressure gradient is forced in the stream-wise direction to be unity, with periodic boundary conditions for all other flow fields, mimicking the DNS procedure of [PERSON] et al. [22]. No-slip boundary conditions were imposed on the solid wall for the velocity field \(U\), along with \(k\) and \(\tilde{\epsilon}\) set to zero (or very small, \(\sim 10^{-15}\)). A Dirichlet boundary condition for \(C_{ij}\) is reported in Appendix A (similar to [26], but for all components), which is imposed within OpenFOAM under the swak4 Foam library using the groovyBC functionality developed by [PERSON] [45].  
When normalizing the governing equations and inherently the various physical quantities, the velocity scale is taken to be the friction velocity (leading to the use of superscript +) and the length scale is the viscous length, \(x_{i}=x_{i}^{+}\
u_{0}/U_{\tau}\). The conformation tensor is already in dimensionless form.
# Developing and Field Testing Path Planning for Robotic Aquaculture Water Quality Monitoring
## 4 Results  
The GPS coordinates of each of the ponds were recorded and normalized between 0 and 1 to facilitate the computation of the routes. These normalized list of coordinates were input into each of the three path planning methods, the output of each being three lists of the GPS coordinates of each pond to be visited by each drone. These three routes were then loaded into the drones using our mission control software for flight testing. During the flight, GPS, compass, battery, and temperature data were recorded. The routes for each method are simulated and visualized in Figure 9.
# Hybrid Methods for Selecting Precast Concrete Suppliers Based on Factory Capacity
## 2 Literature Review
### Gap in Knowledge  
There are notable knowledge gaps in the existing literature concerning the selection of supplier materials in the construction industry. The first gap involves a failure of existing studies to address the issue of supplier selection for precast components. Over the years, the precast construction method has become one of the most sustainable and cost-effective. Therefore, research-based guidance is lacking in the decision-making processes associated with selecting suitable precast suppliers.  
Secondly, most of the available studies generally assume that supplier productivity is the primary criterion for evaluation. Comprehensive evaluation criteria are needed to identify the most suitable supplier for the precast construction method. The existingliterature fails to conclusively explore and identify the multi-layered factors that should be coupled with supplier productivity during the selection process.  
Thirdly, the efforts and time required to construct a CQW matrix, as utilized in the WASPAS method, can be significantly reduced by leveraging knowledge of resource capacity. Traditional methods heavily rely on expert judgment, which can be time-consuming and resource-intensive. Therefore, there is a pressing need to streamline and optimize this process to minimize costs and time investment.  
Fourthly, there is a need to assess the integration of SWARA, WASPAS, and VE in the supplier process. Current studies do not combine the listed methods and techniques to develop a comprehensive framework for assessing and selecting precast suppliers.  
Finally, a significant gap exists regarding the operational efficiency and consistency of existing hybrid MCDM frameworks. Many established models, such as those combining AHP with TOPSIS or VIKOR, place a \"dual subjective burden\" on experts: they are required to first weight the criteria and then score each alternative against every subjective criterion. This process is not only resource-intensive but can also introduce inconsistencies, especially with a large number of alternatives. Furthermore, these models often lack a systematic and automated mechanism to translate a key objective performance indicator, such as factory capacity, into qualitative performance scores. This gap highlights the need for a framework that not only integrates multiple criteria but also automates key evaluation steps to enhance consistency and reduce the subjective workload of decision-makers.
# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 9 Ultrasonic Method
### Introduction and Principle of the Application
#### 9.1.3 Brick Masonry Members  
[PERSON] et al. [126] have used ultrasonic pulse velocity (UPV) and Impulse Excitation of Vibration (IEV) to evaluate the dynamic modulus of elasticity and shear modulus of brick masonry members. They found that both NDT methods could be employed to determine the dynamic properties of the brick samples. However, it was observed that the UPV method was faster and simpler in comparison with the corresponding IEV method.
## 2 Materials and Methods
### Hybrid (GP-XGBoost)
The hybrid GP-XGBoost approach combines the best features of two powerful methods and can offer distinct advantages. First, it improves model interpretability by using symbolic equations from GP which clearly show the relationships between input variables and the target. This is especially useful in geotechnical engineering where understanding soil behavior is key to making informed decisions. Second, the hybrid model boosts predictive accuracy by using XGBoost's ability to handle complex, nonlinear patterns and residual errors. GP-derived features enhance the dataset and allow XGBoost to work with a richer, more meaningful input space. This often leads to better generalization and reduced overfitting. The method is also highly adaptable and makes it suitable for various datasets and applications where both precision and clarity are important. The GP-XGBoost model is particularly effective for problems that involve complex nonlinear relationships and require both high accuracy and easy interpretation. GP's symbolic equations help engineers understand the key factors driving these properties, while XGBoost ensures dependable predictions. Beyond geotechnics, the hybrid model is valuable in areas like environmental science where interactions between soil, water, and plants are critical. By combining symbolic reasoning with advanced machine learning, this hybrid approach bridges the gap between explainable and high-performance predictive models.
## 4 Numerical Investigation
### High-Rise Structural Configuration  
In order to establish the robustness of the novel device and its ability to reduce wind vibration response, it is important to evaluate its performance on realistic high-order systems for which constraints such as actuator force-power demands and damper strokes can be taken into account. To achieve this, the 76-storey benchmark wind-sensitive sway structure proposed by [PERSON] et al. [3] is used in this study. The building has a square 42 m \(\times\) 42 m cross-section, with a height to width aspect ratio of 7.3 and a low natural frequency that lends it the wind sensitivity attribute. A simplified planar finite element model of the structure is constructed by considering the portion of the building between two adjacent rigid floors as a classical beam element of uniform thickness, leading to 76 rotational and 76 translational degrees of freedom. From these, all the rotational degrees of freedom have been removed using static condensation, leading to a lumped mass sway model with degrees of freedom, representing the displacement of each floor in the lateral direction. The resulting simulated structure has a total mass of 153,000 tons, with the first five frequencies at 0.16, 0.765, 1.992, 3.790 and 6.395 Hz, and corresponding modal structural damping ratios of 1% calculated using [PERSON]'s approach. In this study, four alternatives, namely: passive (TMD), semi-active (STMD), active (ATMD) and semi-active hybrid (SHMD) controlled structures are used for the establishment of the comparison metrics. The assemblage of the different control configurations is depicted in Figure 11.  
In every control configuration, the dynamic absorber comprises an inertial mass of 500 tons that corresponds to 0.356% of the total structural mass, limited by realistic structural design constraints. For DVA configurations that require tuning of the device (i.e., TMD, SHMD and STMD), appropriate spring stiffness, \(k_{p}\), is chosen such that the device is tuned to the fundamental frequency of the structure (i.e., \(\approx\) 0.16 Hz).  
Figure 10: (**a**) Steady-state; and (**b**) Peak frequency acceleration response of the different structural configurations.  
In order to ensure the fairness of the comparison, it was deemed necessary to restrain the maximum damper stroke of each of the alternatives by increasing the damping coefficient of the device appropriately so as to limit strokes to a maximum of 95 cm. Because control configurations that damper strokes are not a cause of concern, such as the case of the TMD, the damping ratio is numerically optimised (and kept low, approximately to the value calculated using [PERSON]'s equations [19]) for maximum rms acceleration response reduction. The resulting damping coefficients that equalise the maximum strokes at maximum rms acceleration response reduction are outlined in Table 1 below: