esa-sceva/satcom-chunk-collection huggingface dataset:
# Settlement Prediction of Footings Using \(V_{s}\)
## 2 Development of Settlement Prediction Method Based on \(V_{s}\)
### Stress Distribution Beneath Footings
Hence, the \(I_{z}\) and vertical strain are identical under elasticity theory with \(E\) of 1, and \(I_{z}\) equals the net stress increment in the vertical direction for a vertical deflection. In order words, the \(I_{z}\) represents the ratio of the vertical stress distributed to the subsoil along the depth to the vertical stress applied at the surface. Using Equations (7)-(9) under unit stress (\(q=1\)) and equivalent diameter, \(d=(4 LB/\pi)^{0.5}\), for square or strip foundations, the strain influence factors are calculated and presented in Figure 1 for various Poisson ratios (\(\
u\)). Compared with [PERSON]'s triangle, the peak values from the elastic approach occur at shallower depths, and the values are much higher than those for [PERSON]'s triangles, especially for low Poisson ratios (\(\
u\leq 0.3\)), despite the peak values of triangles increasing with increasing applied pressure. This discrepancy could lead to significant errors in settlement prediction because low stiffness near the surface governs the deformation behavior. Figure 1: Strain influence factor for: (**a**) Square footing (\(L/B=1.0\)); (**b**) Strip footing (\(L/B\geq 10.0\)). [PERSON]'s triangles were derived by comparing deformation profiles beneath footings obtained from analysis using finite element method (FEM) and experiments. Poisson ratios used in the FEM study ranged from 0.3 to 0.48. However, after recent developments in experimental apparatus and measurement techniques, sophisticated local strain measurement (excluding experimental errors) have become possible, and consequently it was reported that Poisson ratios less than 0.2 are adequate for all the soil types under the working load condition [18; 19]. It seems that the abovementioned inaccuracies for [PERSON]'s triangular \(I_{z}\) occur not only for shallow depths (Poisson ratio) but also for deep depths (simplification), and hence the triangular \(I_{z}\) can be improved for more accurate settlement calculation. In this study, feasibility of \(I_{z}\) derived from the elasticity theory was verified and compared to the [PERSON]'s triangular \(I_{z}\).
# Reinforcement Learning for Energy Optimization with 5G Communications in Vehicular Social Networks
## 2 Materials and Methods
It helped D2D transmitters decide on efficient mode selection and power control to maximize the energy efficiency of the D2D links. In a VSN, vehicles move constantly with high mobility. This high mobility results in frequent handovers in cellular communication and changes the channel states accordingly. However, none of the above studies considered vehicle mobility and changing channel states. In addition, there is a large amount of communication between adjacent vehicles in a VSN. D2D communication has distance limitations that have a significant impact on performance; thus, vehicle mobility should be considered. Therefore, any appropriate mode-selection and power-control method must consider the network dynamics that come with vehicle mobility. This mobility causes signaling overhead and data latency because of frequent changes in the channel information state. Typical optimization methods incur high complexity if they consider the various relevant features. To make optimal decisions in a dynamic network environment, reinforcement learning can make recommendations according to the various states. In D2D communication applications, deep Q-learning has the advantage of being able to learn directly using network data and process high-dimensional data [15]. However, deep Q-learning is more complex than Q-learning, and the available data for vehicles are limited. So, it brings high complexity and overhead problems. In this paper, we propose a mode-selection and power-control algorithm using reinforcement learning for the H-CRAN architecture in a VSN. Our algorithm consists of two parts: centralized Q-learning and distributed Q-learning. In the centralized part, the agent in a BBU pool manages one Q-table to maximize the system's energy efficiency and guarantee the QoS constraints. It recommendsan appropriate communication mode and transmission power for the vehicles, based on the average SINR and available resources. To satisfy the outage probability as a QoS constraint, the target SINR that determines the states is adjusted. In the distributed part, the agents in each vehicle manage each table and learn to maximize the received SINR. The agents choose their actions by comparing the actions recommended by the BBU with their own actions.
# Selection of Features Based on Electric Power Quantities for Non-Intrusive Load Monitoring
## 5 Feature Selection  
Feature selection is the procedure performed to reduce the number of input variables when developing a model in order to facilitate the learning and the interpretation, reduce the computational cost and often improve the model performance. The addition of unrelated or redundant features influences model accuracy: redundant features increase the model complexity enhancing the risk of overfitting. Moreover, they could introduce spurious correlations between the features and the class labels.  
Many feature extraction algorithms map the parameters in a lower dimensional feature space, suffering from the lack of interpretation of the projection dimensions. In this case, the two methods chosen in this work retain a subset of original features allowing further interpretation of physical meaning behind the selection.  
The first method is based on neighborhood component analysis (NCA) [27], which implements a feature weighting approach to optimize the nearest neighbor classifier performance by maximizing an objective function that evaluates the average leave-one-out classification accuracy over the training data. However, the cost function of NCA is prone to overfitting, and an improved version of NCA with a regularization term was proposed by [PERSON] et al. [28], where the regularization term was selected empirically.  
In the second method, the ranking is done applying minimum redundancy maximum relevance (MRMRR) algorithm. The MRMR algorithm [29] minimizes the redundancy of  
Figure 2: The MLP architecture.  
a feature set while maximizing the relevance of the feature set to the target. Redundancy and relevance are expresses by the mutual information of features and mutual information of a feature and the target.  
It is worth noting that there is no guarantee that the feature selection algorithm will supply a feature ranking applicable to a generic classifier, based on a different algorithm. However, in practice, the significance of features can often be generalized, especially when both algorithms show good performance in classification. The outcome of the feature ranking was used to design a set of tests employing an increasing number of features.
## 2 State of the Art
### Technologies
#### 2.3.3 Category 3 (Auxiliary Functionalities)  
Resources management, safety systems, cooling equipment, heating, ventilation, and air conditioning (HVAC), augmented reality (AR) and virtual reality (VR) are only a few of the aspects that interact with the process; however, the implications of their use are not always evident. Examples that can easily be mentioned here are the effects on the process performance because of the feedstock delivery system's existence, or the quality restrictions due to maintenance management. Furthermore, chillers in the case of laser cutting may interfere with optimization with respect to energy efficiency [82], while in the case of human-in-the-loop optimization, the existence of AR visualization techniques may slow down the cycle time [83].
# Two-Dimensional Digital Beam Steering Based on Liquid Crystal Phase Gratings
## 1 Introduction  
Pointing a laser beam accurately is relevant for many applications, such as optical tweezers [1], free space optical communications [2], and light detection and ranging [3; 4; 5]. To achieve this control, beam steering (BS) systems have been developed, both mechanical and non-mechanical [6].  
There is a plethora of mechanical methods where movable micro-mirrors or microlenses steer a light beam by using piezo-actuators, rotating prisms, or microelectromechanical systems [7; 8; 9]. The main disadvantage of all these mechanical systems is that they need continuous adjustments to maintain alignment, and they can suffer from external noise [10]. Additionally, micromechanical systems with movable parts are scarcely acceptable for space applications because the harsh conditions during take-off and orbital steps easily jeopardize their stability and proper functioning.  
Non-mechanical methods are widely used nowadays, such as acousto-optic and electro-optic deflectors [11; 12] and liquid crystal spatial (LC) light modulators [13; 14]. When compared to mechanical devices, non-mechanical devices offer a higher reliability, which makes them suitable for space applications [15]. For these devices, large steerable angles and large apertures are desirable.  
LCs have been demonstrated to be a good candidate to implement non-mechanical beam steering devices due to their optical properties and swift response to moderate external electric fields. Optical Phased Arrays [16], Pancharatnam-Berry [17], Liquid Crystal on Silicon (LCoS) systems [18; 19], and Polarization Gratings [20; 21; 22] are some solutions provided by LCs. LC devices are interesting when easy electronic driving, small size, light weight, non-mechanical parts, low power consumption, and low cost are considered as relevant requirements of the intended device.  
LC beam steerers are usually prepared as phase-only devices. They are optical systems that interact with light, only modifying their phase and leaving other characteristics, suchas wavelength or intensity, unchanged [23; 24]. The aim of this work is to present a high fill factor LC BS device, capable of redirecting an impinging light beam in two dimensions (Figure 1). The steering angle is electronically tunable by spatially varying the retardation pattern of the liquid crystal.
# A Fault-Signal-Based Generalizing Remaining Useful Life Prognostics Method for Wheel Hub Bearings
## 4 Validation
### Analysis of Experimental Results
Table 2.5 The RUL prognostics results with the energy of the accumulative signal utilizing the proposed method: (**a**) prognostics result based on 25% of the accumulative data; (**b**) prognostics result based on 50% of the accumulative data; (**c**) prognostics result based on 75% of the accumulative data; (**d**) prognostics result based on 100% of the accumulative data. Table 2.6 The RUL prognostics results with kurtosis of the accumulative signal utilizing the proposed method: (**a**) prognostics result based on 25% of the accumulative data; (**b**) prognostics result based on 50% of the accumulative data; (**c**) prognostics result based on 75% of the accumulative data; (**d**) prognostics result based on 100% of the accumulative data. From the above analysis, we can see that: (a) Compared with GM and GA, the proposed method GG gains similar satisfactory accuracies of RUL prognostics for all the amplitude, energy, and kurtosis features with fatigue damage types. (b) The proposed method GG can process inaccurate fault signals with different kinds of noise in the actual working environment. (c) The proposed method GG has good accuracy of RUL prognostics in the early and middle stages of the wheel hub bearing's running stage, that is, the proposed method GG of RUL prognostics is conducted in the long term, which ensures maximum safety and reliability, especially in the early stage of the wheel hub bearing's running stage. As can be seen from the above, the proposed method GG has good generalization.
# Remote Sensing and Machine Learning for Safer Railways: A Review
## 4 Discussion
### Application Classification  
Table 5 categorizes the publications based on their application objective. The category of track defect detection included subcategories such as assessing rail surface deterioration and detecting rail cracks. It is evident that a majority of the studies focused on defect detection on the rail tracks or their fasteners.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Objective** & **Publications** \\ \hline \multirow{7}{*}{\begin{tabular}{c} Track Defect \\ Detection \\ \end{tabular} } & [PERSON] et al. (2022) [30], [PERSON] et al. (2022) [22], [PERSON] et al. (2019) [32], [PERSON] et al. (2022) [62], [PERSON] et al. (2021) [40], [PERSON] et al. (2020) [60], [PERSON] et al. (2016) [23], [PERSON] et al. (2021) [33], [PERSON] et al. (2017) [24], [PERSON] et al. (2021) [51], [PERSON] et al. (2017) [58], [PERSON] et al. (2021) [57], [PERSON] et al. (2019) [53], [PERSON] et al. (2022) [20], [PERSON] et al. (2020) [42], [PERSON] et al. (2010) [38], [PERSON] et al. (2022) [26], [PERSON] et al. (2022) [68], [PERSON] et al. (2020) [59], [PERSON] (2021) [63], [PERSON] et al. (2021) [18], [PERSON] et al. (2018) [36], [PERSON] et al. (2020) [69], [PERSON] et al. (2022) [70] \\ \hline \multirow{7}{*}{\begin{tabular}{c} Fastener \\ Inspection \\ \end{tabular} } & [PERSON] et al. (2023) [43], [PERSON] et al. (2019) [52], [PERSON] et al. (2020) [44], [PERSON] et al. (2019) [34], [PERSON] et al. (2020) [71], [PERSON] et al. (2021) [21], [PERSON] et al. (2021) [35], [PERSON] et al. (2019) [61], [PERSON] et al. (2021) [55], [PERSON] et al. (2021) [25], [PERSON] et al. (2015) [72]. \\ \hline \multirow{2}{*}{\begin{tabular}{c} Object \\ Detection \\ \end{tabular} } & [PERSON] et al. (2021) [39], [PERSON] et al. (2022) [41], [PERSON] et al. (2013) [14], [PERSON] et al. (2019) [19], [PERSON] et al. (2022) [50], [PERSON] et al. (2022) [37] \\ \hline \multirow{2}{*}{\begin{tabular}{c} Surrounding \\ Inspection \\ \end{tabular} } & [PERSON] et al. (2019) [31], [PERSON] et al. (2018) [49], [PERSON] et al. (2019) [65], [PERSON] et al. (2017) [73] \\ \hline \multirow{2}{*}{\
\begin{tabular}{c} Defect \\ Segmentation \\ \end{tabular} } & [PERSON] et al. (2017) [12], [PERSON] et al. (2021) [28], [PERSON] et al. (2020) [56], [PERSON] et al. (2022) [54] \\ \hline \multirow{2}{*}{
\begin{tabular}{c} Concrete \\ Inspection \\ \end{tabular} } & [PERSON] et al. (2016) [29], [PERSON] et al. (2022) [27], [PERSON] et al. (2021) [74] \\ \hline \hline \end{tabular}
\end{table}
Table 5: Application method categories.
## 3 Results
### FTIR and Raman Spectroscopy  
The chelation among the elements of the composite material developed and the coating formed over the surface were analyzed through FTIR and Raman spectroscopy techniques. As depicted in Figure 5a, the composite developed after coating showed similar FTIR peaks as that of pure aloe vera and PLA. The composite revealed the peak of C-O and C-H at 1823 cm\({}^{-1}\) and 1919 cm\({}^{-1}\), respectively. Furthermore, the composite sample had a collection of peaks at 600 cm\({}^{-1}\) and 651 cm\({}^{-1}\), nominal to HPO\({}_{4}\)\({}^{2-}\). These peaks corresponded to the results presented in previous studies [53; 54]. The native materials were assessed to compare the available peaks with the developed composite peaks. The bonding mode can be determined by analyzing the presence of C=O and C-H bending and stretching vibrations, and their peak positions. The C=O peaks for aloe vera were observed at 1601 cm\({}^{-1}\), whereas for PLA, they were slightly shifted to a higher frequency range and developed a sharp peak at 1746 cm\({}^{-1}\). Meanwhile, a broad peak at 3445 cm\({}^{-1}\) was observed for aloe vera and the synthesized composite, corresponding to the terminal hydroxyl group. Also, a characteristic C-H stretching frequency was observed for all of the samples, which agrees with the results obtained from the literature [55; 56; 57; 58].  
Raman spectroscopy was used to provide insight into the coating developed over the porous aloe vera-loaded substrate through dip-coating. According to Figure 5b, the PLA-coated sample showed characteristic bands at 2995 cm\({}^{-1}\) and 2990 cm\({}^{-1}\), which are in agreement with the results obtained in [59; 60]. These bands are attributed to the symmetric and asymmetric stretching vibration of the C-H (\(\mathbf{v}_{\text{as/CH3}}\)) bond of the PLA chain [61]. The stretching vibration of C=O (\(\mathbf{v}_{\text{C-O}}\)) can be observed at 1763 cm\({}^{-1}\), whereas an asymmetric deformation vibration of the CH3 (\(\mathbf{g}_{\text{aCH3}}\)) bond and stretching vibration of the C-COO (\(\mathbf{v}_{\text{C-COO}}\)) bond were seen at 1445 cm\({}^{-1}\) and 888 cm\({}^{-1}\), respectively. The presence of hydroxyapatite (with 15% conc.) in the sample highlighted an additional PO\({}_{4}\)\({}^{-3}\) band at 1200 cm\({}^{-1}\). The spectrum covered approximately all of the polymer-specific bands of PLA along with some minor bands of the substrate material. Another spectrum for non-coated material was also observed under Raman analysis, which showed no such intense peaks due to the presence of zero polymeric bonds. Although some microbands of PO\({}_{4}\)\({}^{-3}\) were observed at 585 cm\({}^{-1}\) and 425 cm\({}^{-1}\), their intensity was relatively low.
## 3 Result  
### Experiment Results  
A total of 36 participants completed the preliminary experimental survey. The correct rates for the cognitive survey of guidance, positioning, and warning-type functional signs were 94.44%, 91.67%, and 94.44%, respectively, indicating that the subjects were generally proficient in recognizing the guidance signage (Table 1).  
In the experimental scenarios at Pudong Airport (Test1) and Xi'an North Railway Station (Test2), 26 and 31 human subjects participated, respectively. The analysis of basic attributes revealed correlations with the degree of cognition. However, the coefficient characterizing the significance of the correlation, with a \(p\)-value > 0.05, was not statistically significant.
## 3 Results and Discussion
### Expansive PU Resins Injections  
Injection of expansive resins under the buildings affected, performed in 2013, showed that the procedure was quite effective, recording minimal settlement and movements according to the topographic control. However, the injected area in the street was not enough. Relative subsidence in the center of the area affected by a sinkhole of about 12 to 15 cm was observed (Figure 7). This is explained by the depth of the injection in the street area, of only 4 m. Considering the extent of the apparently sunken area (more than 20 m) and the depth of the area affected by dissolution processes (between 14 and 20 m), this depth was clearly inadequate.  
Regarding resin injections performed in the protection embankment in 2015, overconsumption reaching more than 80% was recorded in some areas below the embankment. This was especially observed at a depth between 9 and 15 m, being those areas (Figure 7) were the ones where cracks were seen at the surface and where the highest differential settlements were recorded. This indicates the presence of voids or strongly undermined areas. Considering the extent of the apparently sunken zone (more than 20 m) and the depth of the zone affected by dissolution and undermining processes, the treatment resulted in being insufficient. In this case, the main limiting factor was the injection method and equipment itself, which had many difficulties in reaching high depths before the resins started to solidify in the tubes.  
Figure 7: Settlement observed in the street after performing the PU resin injection treatment: (**a**) general view; (**b**) piezometric pipe (corresponding to borehole #2) used as a reference point; this borehole had a depth of 25 m and the pipe rested on firm ground. Note how the relative descent of the surrounding ground causes the pipe to protrude from the ground.  
The use of expansive PU resin injection [1; 28] showed to be useful as a simple and fast method to control and delay the sinkhole phenomenon. This technique induced minimal settlement and movement issues in the buildings. Due to the great extent of the problem, the technique was not enough to totally control the sinkhole phenomenon: late subsidence was observed. However, in both cases analyzed (buildings affected and street), the PU resin injections generated an advantageous effect: due to the flexibility given by the resins, the dissolution processes on the ground tended to generate a \"general bending\" effect at the surface more than a sudden sinkhole (collapse) phenomenon. At least, this effect delayed the collapse. In fact, after the injection of expansive PU resins, a slight decrease in subsidence rates was observed in all the areas under study.
## 3 Results and Discussion
#### 3.2.2 Copolymerization of Ethylene with Propylene
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & **Content of** & **M\({}_{\mathbf{n}}\)** & **M\({}_{\mathbf{w}l}\)** & **(\(\sum\)CH\({}_{3}\))/** & **Bu/** & **(hexene)\({}_{\mathbf{pol.}}\),** \\  & **Fraction,** & **wt.\%** & **kg/mol** & **M\({}_{\mathbf{n}}\)** & **1000 C** & **1000 C** & **mol.\%** \\ \hline Initial copolymer 1 & - & 22 & 4.5 & 21.6 & 20.3 & 4.1 \\ F1 & 25.3 & 8 & 4.0 & - & 26.7 5 & 5.3 \\ F2 & 18.5 & 19 & 1.7 & 24.8 & 23.3 & 4.7 \\ F3 & 19.2 & 38 & 1.6 & 17.7 & 17.0 & 3.4 \\ F4 & 22.0 & 74 & 1.6 & 13.3 & 12.9 & 2.6 \\ F5 & 15.0 & 180 & 1.9 & 10.0 & 9.84 & 2.0 \\  & & & & & F1/F5 & 2.65 \\ \hline Initial copolymer 2 & - & 22 & 5.2 & 18.0 & 16.7 & 3.3 \\ F1 & 19.7 & 8 & 3.0 & 29.6 & 27.3 5 & 5.5 \\ F2 & 18.6 & 20 & 1.6 & 20.7 & 19.3 & 3.9 \\ F3 & 20.0 & 41 & 1.4 & 17.7 & 17.0 & 3.4 \\ F4 & 24.4 & 77 & 1.5 & 12.5 & 12.0 & 2.4 \\ F5 & 17.2 & 190 & 2.0 & 9.1 & 9.0 & 1.8 \\  & & & & & F1/F5 & 3.1 \\ \hline Initial copolymer 3 & - & 23 & 4.2 & 22.6 & 21.4 & 4.3 \\ F1 & 27.1 & 11 & 3.6 & - & 34.0 5 & 6.8 \\ F2 & 18.8 & 23 & 1.6 & 19.6 & 18.4 & 3.7 \\ F3 & 22.2 & 49 & 1.6 & 14.4 & 13.8 & 2.8 \\ F4 & 20.8 & 98 & 1.7 & 10.3 & 10.0 & 2.0 \\ F5 & 11.1 & 210 & 1.8 & 8.2 & 8.0 & 1.6 \\  & & & & & F1/F5 & 4.25 \\ \hline Initial copolymer 4 & - & 38 & 3.7 & 20.0 & 19.2 & 3.84 \\ F1 & 23.1 & 13 & 4.1 & - & 32.0 5 & 6.4 \\ F2 & 40.3 & 44 & 2.2 & 17.7 & 17.1 & 3.7 \\ F3 & 24.3 & 100 & 1.9 & 11.1 & 11.1 & 2.3 \\ F4 & 10.7 & 187 & 1.9 & 7.0 & 6.85 & 1.37 \\ F5 & 1.6 & 320 & 1.9 & - & - & - \\  & & & & F1/F4 & 4.7 \\ \hline \hline \end{tabular} 1
[FOOTNOTE:1]Footnote 1: Obtained on the TMC-1 catalyst at a molar ratio C\({}_{6}\)/C\({}_{2}\) = 2.3 (exp. 3 in Table 2). 2 Obtained on the TMC-1-2 catalyst at a molar ratio C\({}_{6}\)/C\({}_{2}\) = 4.5 (exp. 10 in Table 2). 4 Obtained on the TMC-3 catalyst at a molar ratio C\({}_{6}\)/C\({}_{2}\) = 5.2 (exp. 14 in Table 2). 5  
\end{table} 
Table 3: Data on the molecular weight characteristics of individual fractions and the content of branchings in these fractions for copolymers produced over TMC-1, TMC-1-2, TMC-2, and TMC-3 catalysts. Supposedly, the enhanced copolymerization ability of the TMC-1 catalyst in the copolymerization of ethylene with 1-hexene and propylene may be related to the formation of the active component in this catalyst as the AlCl\({}_{3}\)-TiCl\({}_{2}\)-AlCl\({}_{3}\) structure, in which the active site is probably more accessible for coordination and insertion of comonomers having methyl (propylene) or butyl branchings (1-hexene) near the double bond.