esa-sceva/satcom-chunk-collection huggingface dataset:
# Sound Localization Framework for Construction Site Monitoring
## 4 Construction Sound Localization Framework (CSLF)
### Construction Sound Localization Framework (CSLF)  
Herein, we discuss the overall process involved in the CSLF. The TDOA localization technique is used to measure the relative coordinates of the microphones, whereas the GCC-PHAT is used to calculate the relative arrival time delay of the standard microphone and other microphones. LSM is used to estimate the location of the sound signal by calculating the approximation repeatedly based on the data produced by TDOA and GCC-PHAT (Figure 3).  
Owing to the nature of the construction site, there are restrictions on wireless communications and a high risk of physical damage. Therefore, we determined that the hardware and components should be inexpensive and appropriate for use on actual construction sites. Therefore, we employed a least-squares method for the estimated position error correction method, considering it does not require high computational power [36]. The algorithm enables the easy computation of approximate locations in the relevant space by analyzing the spatial characteristics of the construction site.  
The test system for verifying the framework was developed as follows: RaspberryPi3b+ was used as the main development board. A wireless router, network connection hub, and an omnidirectional microphone (miniDSpsi-1) were installed for recording in all directions. Four client Pi and one server Pi were created. The server Pi transmitted commands to the client Pi and analyzed the data gathered from the client Pi to calculate the sound location values.  
The specific framework comprises six parts: config.python, thread.python. client.python, record.python, server. python, and analyze.python, as shown in Figure 4. config.python stores constant values required for additional estimate calculations, including the IP address of the server Pi used in the array thread connection, sampling information for record.python, task options, iteration and error limits, and the relative coordinates of the microphones for analyze.python. client.python transmits the connected socket data to the server Pi when the client Pi is connected to the system. If a request is received, the recorded sound data are transmitted along with config.python.  
Figure 4: System architecture of CSLF.  
The Pyaudio function is used by record.python to record ambient audio in an array format. server.python performs thread connections and synchronizes the local time of the client Pi. The data received from the client Pi are analyzed, and the estimates are calculated. The analyze.python method calculates the arrival time difference based on the data recorded by the client Pi using the GCC PHAT method. Then, LSM is used to return the estimated sound-source location values.
## 3 Methodology
### Grey Wolf Optimization
#### 3.2.1 Key Steps in the Grey Wolf Optimizer Algorithm  
The position of each search agent is updated by a mechanism inspired by the hunting behavior of grey wolves. The update rule is based on the current position of the agent and its distance from the three best agents (\(\alpha\), \(\beta\), \(\delta\)). This ensures that the search agents converge towards the optimal solution while also exploring the search space. The position update is mathematically formulated as:  
\[X_{i}^{t+1}=X_{\alpha}-A\cdot\left|C\cdot X_{\alpha}-X_{i}^{t}\right. \tag{6}\]Here, \(X_{i}^{t}\) represents the current position of the agent, and \(X_{\alpha}\) is the position of the best search agent (the \(\alpha\) wolf). The parameters \(A\) and \(C\) are coefficient vectors that guide the agent's movement towards the optimal solution.  
The coefficients \(\alpha\), \(A\), and \(C\) are updated during each iteration. The parameter \(\alpha\) decreases linearly, shifting the algorithm from exploration (initial stages) to exploitation (later stages). The vectors \(A\) and \(C\) are computed using random values, ensuring that the search agents move towards the prey (optimal solution) while maintaining randomness to prevent local optima stagnation. Specifically, \(A\) is updated as:  
\[\left\{\begin{array}{l}A=2\alpha\cdot r_{1}-\alpha\\ C=2\cdot r_{1}\end{array}\right. \tag{7}\]  
Each search agent's fitness is evaluated at every iteration, determining how well it performs according to the problem's objective function. The three best solutions--\(\alpha\), \(\beta\), and \(\delta\)--are then identified. The \(\alpha\) wolf represents the global best solution, while the \(\beta\) and \(\delta\) wolves represent the second and third best solutions, respectively. The positions of the \(\alpha\), \(\beta\), and \(\delta\) wolves guide the other agents and ensure that the search is directed towards the optimal solution.  
The positions of the \(\alpha\), \(\beta\), and \(\delta\) wolves are updated regularly, and if any agent surpasses the current best solution, it will replace the respective wolf in the hierarchy. This process ensures that the best solutions remain as the reference for other agents. Over time, the algorithm moves closer to the global optimum by updating these best positions, leading to an effective search for optimal solutions in complex problem spaces.  
The computational complexity of the IGWO algorithm is primarily determined by the number of search agents \(N\), the number of iterations \(T\), and the dimensionality of the problem \(D\). In each iteration, the fitness of all search agents is evaluated (\(O(N)\)), and their positions are updated based on three leader wolves (\(O(N\cdot D)\)). Therefore, the overall complexity of the algorithm is approximately \(O(T\cdot N\cdot D)\). Since fitness evaluation is model-based (e.g., training a neural network), its cost dominates the computation, and reducing training time or using early stopping can significantly improve efficiency.
## 3 Results
### Performance Evaluation of Models and Analysis of the Results  
The most important factor involved in the evaluation of the models was the analysis based on the area under the curve (AUC), which represents a measure of the separability with values ranges from 0 (lower) to 1 (higher) [68]. Higher values of AUC reflect the highest performance. A model is considered reliable if the AUC values are higher than 0.7 [69]. The AUC values of these models calculated using validation data are shown in Figure 9.  
Figure 9: ROC curves of modeled results using validation data.  
All the investigated ML models produced results with AUC values above 83%, which highlights the reliability of these models in providing good results in the study area. However, the variations in AUC values among all the models was relatively high, with AUCs of 0.969 in SVM, 0.967 in RF, 0.872 in maxENT, 0.967 in GBM, and 0.836 in LR (Figure 9). Keeping in view the assessment and validation of the models' output, it is not recommended to investigate using only one parameter (i.e., AUC) for ML approaches, as this may not reflect the true prediction ability of models with varying and closer values. Therefore, the model results were further scanned through the other evaluation procedures based on the calculation of indices, and the results of these indices-based analyses are provided in Figure 10a. Figure 10a shows that across all five ML algorithms, the results of RF and SVM were found to be consistent in terms of providing higher performance accuracies than the rest of the models. The achieved values of these indices for ACC, precision, recall, F1 score, and MCC in RF were 0.87, 0.75, 0.98, 0.85, and 2655, respectively, whereas in SVM these values were 0.87, 0.77, 0.96, 0.85, and 2667.99, respectively (Figure 10a). However, in addition to SVM and RF as the top two models, GBM was also one of the leading models, providing results with higher accuracies, and it can be seen in Figure 10a that the difference between the performances of this model and the performances of SVM and RF was almost negligible, with ACC 0.86, precision 0.77, recall 0.94, F1 score 0.84, and MCC 2621.99. Therefore, it can be stated that no significant performance difference was found between these three models (RF, SVM, and GBM). LR and maxENT provided an averaged result with an ACC of above 0.65; a recall value of above 0.86; and the lowest values in precision, F1 score, and MCC (Figure 10a). To evaluate the best performer, the POA was calculated, which indicated that the SVM (POA = 2669) was the best model and placed RF (POA = 2656) and GBM (POA = 2623) second and third, respectively (Figure 10b). The models in this study were trained with the best possible configurations; therefore, the performance rankings of some of the models in this study followed the results of previous research [34]. However, the importance of the GBM model for LSM cannot be neglected, as it stands with SVM and RF in terms of the evaluation measures.
## 4 Conclusions and Outlook  
In conclusion, our experiments showed that it can be beneficial to change the typically used noble gas in an HCF setup to a molecular gas in case of performing pulse-shaping experiments to generate high harmonics. Using temporally shaped pulses and an HCF for HHG the XUV yield could be doubled and the spectral window for selective optimization of a single harmonic could be increased when using SF\({}_{6}\) for spectral broadening. Furthermore, it was shown that the characteristic of the XUV spectrum can be easily influenced by simply changing the gas which is used for spectral broadening without any further change of a single optical element. Thus, nonlinear fiber optics with molecular media offer large potential in ultrafast science. This can be of great interest when building up XUV beamlines for spectroscopy. Since using an HCF for spectral broadening is a standard technique in ultrafast science and nowadays a large variation of pulse-shaping devices becomes increasingly common to be implemented in laser systems, the general idea of switching to a molecular gas can be easily realized and offers, as in case of HHG, a lot of potential.  
Tailored harmonic spectra are of immediate interest and importance for the field of stationary or time-resolved spectroscopy in the XUV and soft-X-ray regions [27]. A number of applications have demonstrated the use of this laboratory-scale light source for probing matter properties in spectral and temporal domain with XUV photon energies and femtosecond time resolution, e.g., XUV scanning microscopy at 13 nm [28] or time-resolved photoemission spectroscopy of surface chemistry [29].  
The possibility to shape high harmonics greatly extends the field of applications and broadens the level of information retrieved from such measurements. A lot of applications originally performed at synchrotrons become also available for tabletop HHG sources, e.g., studying absorptions edges of transitions metals [30; 31]. This is also true for XUV imaging demanding for monochromatic illumination. This is usually achieved using spectral filtering when using an HHG spectrum as a source and obviously selection of a single harmonic by pulse-shaping can be advantageous compared to afterwards removal of unwanted wavelengths by XUV multilayer optics [32]. The mentioned applications may all be of benefit when a molecular gas is used for pulse-shaping.
### Requirements of a Solution Based on Technologies for the Home  
The main characteristics of any solution that seeks to alleviate the needs of the elderly and achieve a high degree of acceptance are framed in the economy, usability, adaptability, and functionality of the proposals. Solutions must be economically affordable. Systems must be easy to install, maintain, and use. They must be flexible and scalable, so that future service expansion or reduction is transparent [24]. Their functionality must be well-known and adapted to the existing needs. Furthermore, a key aspect is that these devices must be customizable to each user.  
Domotic systems allow us to solve the needs of the elderly in an isolated way or through integrating joint solutions that manage to include all the individual solutions. Currently, the most popular technological solution implemented in homes is the remote alarm service, which allows people to activate an alarm in the event of an accident [35]. This system has evolved into a complete telecare service. In addition to managing the alarm, it offers a social support service to older adults. Most service providers offer the management of other types of home help as well, such as repairs or maintenance. Proposed technologies allow the automation and coordination of all the devices at home that can be controlled to simplify and improve the quality of life of its residents [36]. It is all about integrating device management, programming actions, and interacting in a friendly way with the system, so that the environment can be easily controlled [37].  
Home automation provides comfort, communication, and security, and efficiently manages energy use, ensuring water, electricity, and fuel savings [38]. The demand in the market continues to grow, and the functions of each home are changing. Wireless sensor networks for the home aim to reduce global energy consumption and CO\({}_{2}\). Theoretically, by integrating a home automation system, up to 60% of electricity consumption can be saved [39]. In addition to the need to reduce energy consumption to decrease pollution, the factor must be considered economical.
# Clustering Approach for Reliable Wireless Communication
## 4 Results and Discussion
### Scenario 5: Four Antennas at BS/20, 40, and 70 Users/MUSA Code Length of 31  
The results achieved with Scenario 4 confirm the previous conclusions that the allocation of low-correlation codes increases the system performance. In Figure 15(a), 200 random MUSA codes were divided into 20 and 40 clusters, respectively, using the method presented in Section 2, by imposing a higher correlation threshold for the sequences of the same cluster. The combination of an increased number of users and large data is not suitable for performance improvement. A higher correlation threshold will lead to a higher number of clusters (equal to 40), with fewer codewords inside, which will maximize the system's BER, for an SNR higher than 25 dB. In Figure 15(b), a lower correlation threshold was used for the simulation of a system with 40 users. Thus, the algorithm grouped the sequences into 10 clusters. A lower correlation threshold leads to a greater number of users using codewords from the same cluster. The performance of the system slightly decreases in this scenario because the sequences assigned to various users are often part of the same cluster, and thus, they are correlated.
# Fourier Neural Operator for Fluid Flow in Small-Shape 2D Simulated Porous Media Dataset
## 5 Discussion  
The FNO model is underpinned by a rigorous mathematical methodology, as described. Furthermore, the statistical/graphical pressure prediction results associated with the small-size grids simplistically simulating fluid flow in a subsurface reservoir indicate promising prediction accuracy, which outperforms that of a CNN model. However, there is a drawback associated with the FNO model applied to these small-size grids. As with other data-driven ML/DL methods, FNO relies on the number of data samples it is provided with, and it may require a large number of small-size grids to adequately train it to fully learn the full range of possible variations in complex subsurface systems. For this reason, FNO was applied to a relatively simple example dataset, i.e., based on relatively limited assumptions of \(K_{\mathrm{m}}=1\) md, \(K_{\mathrm{f}}=1000\) md, and \(N_{\mathrm{f}}=5\). As opposed to data-driven neural networks such as FNO, which rely exclusively on the provided data points, Physics-Informed Neural Networks (PINNs) use the PDE itself as a data source. In PINNs, the PDEs are explicitly encoded into the NN via automatic differentiation algorithms. The weighted summation of the MSE of the PDE residuals, BCs, ICs, and possibly known solution points could then be minimized as a loss function based on the NN parameters. Therefore, it could be beneficial to combine PINN and FNO to find out how the performance changes compared to a stand-alone FNO. In this sense, the model uses available data and/or physics constraints to  
Figure 5: A comparison between the actual pressure distributions and those obtained by FNO for three representative testing subset samples. The pressure difference is based on a point-by-point absolute error. Outputs are displayed as rectangles rather than squares due to a scaling issue.  
learn the solution operator, conquering the limitations of purely data-driven and physics-based techniques.
## 5 Results and Discussion
### Sandy Soils  
A similar non-linear regression analysis was applied for sandy soil materials. Out of the 265 data points, 139 were identified to be of sandy soil. The proposed correlation for sandy soil types is  
\[\mathrm{V_{s}}=45.07\mathrm{N^{0.5534}} \tag{16}\]  
This proposed correlation has a coefficient of regression value of \(\mathrm{R^{2}}=0.7922\). This value is relatively higher than the coefficient for all soil types and can be regarded as a strong correlation [68]. Figure 5 shows the plotted \(\mathrm{V_{s}}\) and N-value for sandy soil and the corresponding proposed empirical relation. The empirical correlations for sandy soils proposed by other researchers were also compiled. Thirteen correlations follow the power law form \(\mathrm{V_{s}}=\mathrm{a}\times\mathrm{N^{b}}\) while utilizing the uncorrected SPT-N values in the analysis, while [PERSON] et al. (1999) [22] and [PERSON] and [PERSON] (2007) [28] utilized corrected N-values for their statistical analysis. From the compiled curves, the a coefficient ranges from 31.7 to 162, while the b coefficient ranges from 0.17 to 0.54. On the other hand, [PERSON] and [PERSON] (1985) [56] and [PERSON] (1996) [27] proposed an empirical relation with the form \(\mathrm{V_{s}}=\mathrm{a}(\mathrm{N}+\mathrm{b})^{c}\).  
Figure 5: **(A).** Uncorrected SPT-N and \(\mathrm{V_{s}}\) pairs for sandy soils and the determined empirical correlation. **(B).** Comparison of the proposed empirical correlation for sandy soil types and other published works [10; 11; 14; 18; 22; 24; 28; 30; 35; 55; 59; 60; 62].  
When the proposed empirical relation is compared to the works of other researchers, it can be observed that the relations proposed by [PERSON] et al. (1989) ([PERSON] et al. (1989) and [PERSON] (1970) ([PERSON] et al. (1970)) can act as the upper and lower limits for empirical relationships for sandy soil types, respectively. The other eleven published empirical relations lie between these constraints, including the one proposed by this study. As the curve of this present study lies in the middle of all the other curves, this indicates a very good agreement with respect to the previous works.
## 2 Cable Model and Effective Length in 3D Twisted Conductors
### The Effective Length \(l_{\text{twist}}\) in 3D Models
#### 2.3.2 Practical Computation of the Effective Length \(l_{\text{twist}}\)  
The \(l_{\text{twist}}\) in (2) represents the actual length of a line (no volume) passing at a distance \(r\) from the z-axis. However, each conductor occupies a finite cross-sectional area, and by assuming the radial distance \(r\) to be from the z-axis to the center of that area, calculation error will be introduced. Accurately calculating the effective length numerically for complex twisted geometries is challenging. Therefore, instead of individually summing each conductor's helix length, an efficient approach leverages the full 3D geometry within a finite-element (FE) framework:  
1. Model the cable, including twists, in the FE environment using tetrahedral elements that cover the entire conductor volume;
2. Compute the total cable volume V and the total cross-sectional area A for each model;
3. From \"volume equals to length times cross section area\", infer the effective twisted length \(l_{\text{twist}}\).  
This methodology captures the geometric complexity introduced by twisting without the need for numerical summation of infinite helical paths. The resulting \(l_{\text{twist}}\) accurately reflects the influence of the twisted geometry on the cable's actual traveled length.
## 4 Numerical Simulation and Results Analysis
### Numerical Results Analysis
Figure 18: The stress distribution characteristic around the vertical unfilled joint for the time of 70 \(\upmu\)s: (**a**) S11 (normal stress in the x direction), (**b**) S22 (normal stress in the y direction), (**c**) S12 (in-plane shear stress). The stress evolution at the end of the joint is shown in Figure 21. The points P1 and P2 are located at the top and bottom end of the unfilled joint, respectively, as shown in Figures 16a and 19a. It can be seen that the normal stresses at both end of the vertical joint presented the same variation characteristic, whereas the sign of in-plane shear stress showed an inverse trend with the incidence of the blast wave. This is the reason why the sign of \(K_{\rm II}^{d}\) is different for the initiation of wing crack VW-1 and VW-2. Combined with the crack fracture characteristic, it can be concluded that the negative sign of shear stress could induce the wing crack initiated in a clockwise direction, whereas the positive sign of shear stress could induce the wing crack initiated in a counterclockwise direction. In addition, the stresses are much higher at the near end of the oblique joint than that at the far end of the oblique joint. This also well clarified that why the wing crack is more easy to initiate at the near end of the oblique joint. Figure 21: Stress evolution of the point at the end of the joint: (**a**) vertical unfilled joint; (**b**) oblique unfilled joint. Figure 13:0 The stress distribution characteristic around the oblique unfilled joint for the time of 55 \(\upmu\)s: (**a**) S11 (normal stress in the x direction), (**b**) S22 (normal stress in the y direction), (**c**) S12 (in-plane shear stress).
# An Insight into the Chemistry of Cement--A Review
## 3 Hydration Step Involving OPC
### Mechanism and Heat of Hydration of OPC
#### 3.3.2 Induction (Dormant) Period  
After the fast hydration step of cement, the general hydration rate slows down for some hours. The explanation of this sharp slowdown is probably attributable to two main theories, considering both the apparent low reactivity of C\({}_{3}\)S and its high solubility calculated from the enthalpy of formation. The first theory is named as the theory of the protective membrane, and it considers the decrease in the hydration rate attributable to the formation of metastable C-S-H layers covering the reacting surface of cement particles, thus preventing the fast dissolution ability of C\({}_{3}\)S [72]. Several studies reported in the literature evidenced the tendency of C-S-H to rapidly form once it has interacted with water [73]. In principle, the formation of a low-permeable layer of hydrates, with enough density and coverage, should be able to slow down the ion diffusion rate from the anhydrous cement grains. However, experimental evidence confirms that C-S-H does not form a continuous layer around the grains. In this way, there is still contact with the pore solution, thus weakening this theory [74].  
The second theory, the theory of dissolution, instead suggests that the slowdown rate is principally due to the decrease in the kinetic of dissolution of both C-S-H and alite when the system is evolving towards the equilibrium condition. In this context, it should be highlighted that the dissolution of C\({}_{3}\)S has been mostly simplistically considered in all models addressing the hydration kinetics of cementitious matrices. This is probably caused by the dissolution step, which in most cases has been studied independently from the other mechanisms that characterize the hydration step. As dissolution occurs together with the precipitation of hydrate phases at the surface, this might interfere with the dissolution process involving the cement components. Hence, this theory remains valid, but the role of the kinetics of dissolution should be highlighted [75].