esa-sceva/satcom-chunk-collection huggingface dataset:
# Wireless Positioning in IoT: A Look at Current and Future Trends
## 3 Designing an IoT Positioning System
### Positioning Domains
#### 3.1.2 Time Domain  
Positioning estimation based on timing information is based on estimating the time-of-arrival (TOA) or the time-difference-of-arrival (TDOA) from three or more fixed access nodes and then converting those timing estimates into distances. For example, 3D location based on TOA is possible with three synchronized measurements from three known devices. The goal is to solve the following set of equations and find out the node's location, \(\xi_{n}=(x_{n},y_{n},z_{n})\), assuming that \(\xi_{a}=(x_{a},y_{a},z_{a})\) are known coordinates:  
\[\mathbf{L}_{n}=\sqrt{(\xi_{a}-\xi_{n})^{2}}. \tag{3}\]  
For TDOA, the range is now a difference of ranges, based on the TOA at the measurement device. Hence, the TDOA from a node \(n\) to a measurement device \(m\) would be written as  
\[\mathbf{L}_{nm}=\mathbf{L}_{n}-\mathbf{L}_{m}. \tag{4}\]  
Due to this difference, the range measurement is free of errors imposed by the measurement device's clock, since it cancels out when subtracting the two TOA measurements.  
Overall, the time measurements require synchronized clocks, either at the receiver or at the transmitter side, leading to a significant burden on device cost. This does not play well for IoT applications, which are driven by the need of having low-cost devices.  
It is also important to keep in mind the relationship between bandwidth and accuracy for TOA measurements. This is illustrated in Figure 3, where the positioning error is plotted against the available channel bandwidth at different Signal-to-Noise Ratio (SNR) values. Clearly, sub-m positioning accuracy with time-based approaches is achievable only with high bandwidths (of the order of 100 MHz), but it is very challenging for narrowband and ultra-narrowband systems even at very high SNR.  
Figure 2: Ideal radio signal propagation at 0.5, 2.4, 30 and 60 GHz.
Table 1: Multichannel management versus omnichannel management. tegration and customer satisfaction in the setting of a service station located in a semi-rural region of a developing country. The study focuses on three core dimensions of omnichannel interaction--data usage, digital payment options, and logistics coordination--and assesses how each influences user perceptions of value and service adequacy. Figure 1 presents the progression of e-commerce adoption across selected LATAM countries from 2017 to 2020, offering an empirical context for the digital environment in which omnichannel service systems are being implemented. The central research question guiding this study is the following: To what extent does omnichannel integration affect customer satisfaction, understood as a proxy for Quality of Experience (QoE), in a service environment located in a digitally emerging context? To answer this, the following specific objectives were defined:  
1. To assess the level of customer satisfaction in relation to the service station's current omnichannel strategy;
2. To evaluate the correlation between customer satisfaction and (a) the use of data for personalization, (b) the availability of digital payment options, and (c) the flexibility of logistical arrangements;
3. To interpret the findings within the conceptual framework of Net-Living and digital well-being, linking empirical results to broader issues of service inclusion, user autonomy, and experience optimization. By addressing these objectives, the study seeks to contribute to the broader understanding of how omnichannel service systems function as socio-technical tools for enhancing quality of life, particularly in digitally constrained environments, where seamless and inclusive digital interactions are critical for everyday well-being. Furthermore, it extends the current discourse by situating QoE not only as a performance metric but as a lived, perceptual outcome influenced by the synergy between technology, behavior, and expectation. This article is structured as follows. Section 2 outlines the theoretical framework, drawing from customer journey theory, expectancy-disconfirmation paradigms, and omnichannel service design literature. Section 3 details the methodology used in this empirical study, including sample characteristics, data collection tools, and statistical techniques. Section 4 presents the main results, both descriptive and inferential. Section 5 offers a discussion  
Figure 1: Use of e-commerce in Latin America as a percentage of the population (adapted from [10]). of the findings in relation to the existing scholarship and their implications for inclusive digital transformation. Finally, Section 6 concludes with key insights and directions for future research.
## 6 Conclusions  
A numerical method for simulating rocket explosion on the launch pad was developed in order to study the propagation law of near-ground explosion and quantify the enhancement effect on peak overpressure. From analysis and discussion of results, the following main conclusions were drawn:  
1. The existence of the ground will change the propagation law and conform to the reflection law of the shock wave. Rigid ground absorbs no energy and reflects all of it, while concrete ground absorbs and reflects some of energy, respectively. However, the shock waves are all enhanced at the near-ground of different materials.
2. Ground may have certain effect on the pressure-time curve of the shock wave. The pressure-time curves of shock wave in the air show a single-peak feature. If the ground exists, the pressure-time curve of the gauge whose incident angle is less than 40\({}^{\circ}\) presents a bimodal feature, namely, incident wave and reflected wave. However, when the incident angle of the gauge is greater than 40\({}^{\circ}\), the pressure-time curve shows a single-peak feature, and the peak value is Mach reflected pressure.  
Figure 12: The peak overpressure factor of the explosion above different ground.  
* For gauges at different heights, different ground may have different effects on the peak overpressure. For gauges whose height is not greater than 4 m, when \(x\) is small, the peak overpressure above the rigid ground is the highest. With the increase of \(x\), the peak overpressure above the three kinds of ground gradually tends to be the same. As for \(h\) greater than 4 m, the peak overpressure curves of different ground are the same when \(x\) is small. With the increase of \(x\), the peak overpressure of rigid ground is higher than that of concrete and soil ground, and the peak overpressure of concrete and soil ground is higher than that of the air.
* Grounds have the enhancement effect on peak overpressures, but different grounds have different ways. For rigid ground, the enhancement effect of peak overpressure at different heights is nearly the same and the peak overpressure factor is about 2. However, for the concrete and soil ground, the enhancement effect of the peak overpressure at different heights is quite different, among which the peak overpressure factor of 6 m is about 2.1, and the peak overpressure factor of 10m is the smallest, about 1.43.  
Methodology, Y.W., H.W.; software, Y.W., B.Z.; formal analysis, C.C.; investigation, Y.W.; resources, Y.W., H.W., C.C.; data curation, Y.W.; writing--original draft preparation, Y.W.; writing--review and editing, Y.W.; visualization, Y.W.  
This research received no external funding.  
The authors greatly appreciate the comments from the reviewers, whose comments helped to improve the quality of the paper.  
The authors declare no conflict of interest.
###### Abstract  
Based on the Zhuyuan-Bailonggang sewage interconnection pipe project in Shanghai, the ABAQUS finite element software was used in numerical simulations to study the fine control of stratum disturbances caused by pipe jacking parameter deviation in soft soil areas. Combining the simulation results with onsite measured data, the Peck formula was used to predict surface settlement. The results indicate the following: (1) The jacking speed and face pressure are negatively correlated with surface settlement. Under the maximum positive deviation and negative deviations in the jacking speed, after the tail passes through the monitoring section D\({}_{0}\) 16 ring, the maximum value of settlement at point B8 increases by 21.6% and decreases by 12.8%, respectively. Increasing the jacking speed increases the area with stress change ratio \(R<0\) at monitoring section D\({}_{0}\), and the arch foot at the tail of the pipe jacking machine decreases the surface settlement. In contrast, when the face pressure deviates from its average value, the variation range is less than 1%. (2) The pipe slurry coefficient and surface subsidence are positively correlated. Under the maximum positive deviation and the maximum negative deviation, the tail passes through the monitoring section D\({}_{0}\) 16 ring, and the maximum settlement value at B8 decreases by 4.9% and increases by 16.5%, respectively. The increase in the coefficient reduces the area with \(R<0\) at D\({}_{0}\) and increases the surface settlement. (3) In the order of descending strength, surface settlement is affected by the jacking speed, slurry friction coefficient, and face pressure. (4) To predict the maximum surface settlement value due to deviations in the jacking parameters, the Peck formula was modified using a correction factor \(\alpha\) ranging from 0.6 to 3.0 and a settlement trough width correction factor \(\beta\) ranging from 1.6 to 4.0. The modified prediction curve is in closer agreement with the actual conditions.  
Keywords:sewage connecting pipe; field measurement; numerical simulation; jacking parameter deviation; surface subsidence; correlation; fine control; Peck formula
# Buckling of Composite Structures with
## 4 Variational Formulations of Contact Problems with Local Buckling  
The composite structure represents a 3D body (laminates or functionally graded material, FGM) that is a space occupied by layered structures \(\sum_{k=1}^{N}V_{k}\), where \(V_{k}\) is a space occupied by the individual \(k\)-th layer description of the layered structures. The functional characterizing composite structures \(J\) can be divided into three parts:  
\[J=J_{en}+J_{bbc}+J_{ubc} \tag{6}\]  
The first part, \(J_{en}\), corresponds to the strain (internal) energy. The classical approaches are presented below:  
- Hu-Washizu  
Figure 8: Single, axisymmetric delamination—the division of the structure into sub-laminates. Figure 7: The effects of the delamination length a on the buckling pressures p.  
\[ \begin{array}{l}J_{en}(\sigma,e,u)=\sum\limits_{k=1}^{N}\left[\int\limits_{y^{(k) }}\left(\frac{1}{2}\Big{[}\epsilon^{(k)}\Big{]}^{Tr}\Big{[}Q^{(k)}\Big{]}\Big{[} \epsilon^{(k)}\Big{]}-\Big{[}\sigma^{(k)}\Big{]}^{Tr}\Big{[}\epsilon^{(k)} \Big{]}\right.\\ \left.+\Big{[}\sigma^{(k)}\Big{]}^{Tr}\Big{[}e^{(k)}\Big{]}^{Tr}\Big{[}\Big{[} e^{(k)}\Big{]}\Big{(}u_{a}^{(k)}\Big{]}\Big{]}\right)\right)dV\end{array}  \tag{7}\]  
* [PERSON] \[ \begin{array}{l}J_{en}(\sigma,e)=\sum\limits_{k=1}^{N}\left[\int\limits_{y ^{(k)}}\left(-\frac{1}{2}\Big{[}\sigma^{(k)}\Big{]}^{Tr}\Big{(}\Big{[}Q^{(k) }\Big{]}^{-1}\Big{)}^{Tr}\Big{[}\sigma^{(k)}\Big{]}\right.\\ \left.+\Big{[}\sigma^{(k)}\Big{]}^{Tr}\Big{[}e^{(k)}\Big{(}u_{a}^{(k)}\Big{]} \Big{]}\Big{)}\right)dV\end{array} \] (8)
* Lagrange \[J_{en}(u)=\sum\limits_{k=1}^{N}\left[\int\limits_{y^{(k)}}\left(\frac{1}{2} \Big{[}\Big{[}e^{(k)}\Big{]}\Big{(}u_{a}^{(k)}\Big{)}\Big{]}^{Tr}\Big{[}Q^{(k) }\Big{]}\Big{[}e^{(k)}\Big{(}u_{a}^{(k)}\Big{)}\Big{]}\right)\right)dV\] (9)  
where \(\sigma\) is the stress tensor, \(e\) is the strain tensor and \(u\) denotes the components of the displacements. The relation between stresses and strains is described by the linear elastic relation. For laminated composites, the traditional relation takes the classical form presented, e.g., by [PERSON] [60]. For FGMs, the physical relation takes the following form:  
\[[Q(z)]=\frac{E(z)}{(1+v)}\left[ \begin{array}{ccccc}\frac{1-v}{1-2v}&\frac{v }{1-2v}&\frac{v}{1-2v}&0&0&0\\ \frac{v}{1-2v}&\frac{1-v}{1-2v}&\frac{v}{1-2v}&0&0&0\\ \frac{v}{1-2v}&\frac{1-v}{1-2v}&\frac{1-v}{1-2v}&0&0&0\\ 0&0&0&0.5&0&0\\ 0&0&0&0.5&0\\ 0&0&0&0&0.5\\ \end{array} \right] \tag{10}\]  
where:  
\[E(z)/E_{b}=\left[(E_{t}/E_{b}-1)find(z)+1\right]find(z)=\left(\frac{z}{h}+ \frac{1}{2}\right)^{n} \tag{11}\]  
The second term in the functional (6) corresponds to the bilateral boundary conditions formulated in the equality form:  
\[\sigma^{(k)}_{(a\beta)}n^{(k)}_{\beta}=s_{\rm a},u^{(k)}_{j\mu}=W_{j\mu}\ \mbox{on}\ S,k=1\ \mbox{or}\ {\rm k}={\rm N} \tag{12}\]  
They determine values of the displacement field \(W\) or the distributed external loads \(s\).
# Indoor Positioning by Double Deep Q-Network in VLC-Based Empty Office Environment
## 4 Proposed Indoor Positioning Method
### Double Deep Q-Network  
In this subsection, the use of the double deep Q-Network for positioning is explained. The double deep Q-Network model is developed to improve the issue of overestimation encountered in conventional DQN models [25]. Briefly, the problem of overestimations is that the agent always chooses the non-optimal action in any given state only because it has the maximum Q-value. In basic Q-learning, the optimal policy of the agent is always to choose the best action in any given state. The assumption behind the idea is that the best action has the maximum expected/estimated Q-value. However, the agent knows nothing about the environment in the beginning, so it needs to estimate Q(s,a) at first and update it at each iteration. Such Q-values have lots of noise, and we are never sure whether the action with the maximum expected/estimated Q-value is really the best one. Unfortunately, the best action often has smaller Q-values than the non-optimal ones in most cases. According to the optimal policy in basic Q-learning, the agent tends to take the non-optimal action in any given state only because it has the maximum Q-value. Such a problem is called the overestimation of the action value (Q-value). When such a problem occurs, the noises from the estimated Q-value will cause large positive biases in the updating procedure. As a consequence, the learning process will be very complicated and messy. Hence, the authors proposed a solution to decouple the action selection during evaluation from the action taken by the agent. Consequently, instead of using the Bellman equation used in the DQN algorithm, (4) is used:  
\[Q(s,\ a;\ \theta)=r+\gamma Q(s^{\prime},\ \underset{a^{\prime}}{\text{argmax }}Q(s^{\prime},\ a^{\prime};\theta);\theta^{\prime}) \tag{4}\]  
As can be seen from (4), the main parameter of the neural network determines the optimal next action among all the available next actions. The target neural network can then evaluate that action to know the Q-value. This result can solve the problem of overestimation and provide a better final policy. Based on these advantages, this paper proposes indoor positioning by implementing the double deep Q-Network instead of the DQN.  
Now, the factors considered in this study for reinforcement learning--the agent, state, action, and reward--will be explained. The agent predicts the location of the UE based on the RSS. The state represents the fingerprint data for the predicted point location. At this time, the initial state is determined based on the UE's RSS. Action refers to the agent moving the predicted point location up, down, left, or right. The reward represents the agent's reward for its actions. Next, state, action, and reward are explained in detail.
# Verification Methodology for Simulation Models of the Synchronous Generator on Transients Analysis
## 5 Conclusions  
Power system disturbances are becoming more frequent and faster due to continuous decreases in inertia. Therefore, it is important to have accurate models of all power system elements, especially synchronous generators, which are last-standing inertia contributors. This paper proposes a methodology for accurate fine adjustment of the dynamic model of synchronous generators by applying a combination of two independent algorithms.  
In calculating the transients in synchronous generators and the network, it is possible to apply a simulation model or the FEM to an actual generator model. The application of the simulation model is simpler and faster, but it requires accurate knowledge of the parameters of the generator-equivalent diagram. These parameters are not constant, due to the nonlinear magnetizing characteristic of the core from which the magnetic circuit is made, and also due to the complex form of the magnetic circuit of the generator. Calculations  
Figure 17: The comparison of phase current “w” obtained by the FEM calculation and by the dynamic model.  
using the FEM require knowledge of the actual form of the machine and all the built-in materials. However, these calculations provide results that are more complex and require significantly more time.  
To obtain more accurate results of transients using a standard simulation model, it is recommended that calculations be performed on the model by the FEM for a certain system, and that the parameters be subsequently corrected in the equivalent diagram in the simulation model. Such a finely adjusted simulation model will provide more precise results in the analysis of other possible disturbances of the observed system.  
Conceptualization, D.S. and B.T.; methodology, B.T. and A.S.; validation, D.S. and J.N.; formal analysis, B.T. and J.N.; resources, A.S.; data curation, B.T.; software, B.T. and A.S.; writing--original draft preparation, B.T. and A.S.; writing--review and editing, D.S.; visualization, D.S., B.T. and A.S.; supervision, D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementData is contained within the article.  
The authors declare no conflict of interest.
## 4 Heterogeneous Systems: Ozonation
### Modeling for Photo-Fenton Processes  
A kinetic investigation of photo-Fenton processes in a microreactor has been presented very recently [54]. In this work, the adopted experimental set-up is of the same type as the one independently used for UVC/H\({}_{2}\)O\({}_{2}\) investigations, but with some substantial differences. The reactor consists of a PTFE (Teflon) tube with an internal diameter of 500 \(\upmu\)m, coiled on a fluorescent light. In this case, however, the refractive index of the polymer was substantially different from the one of water and it is estimated that 70% of the light from the light source could not reach the inner surface of the channel. As a result, the light dependence of the constants could not be assessed. Moreover, the larger diameter combined with the lower flow rates and the longer residence time (10-40 min) would require a further evaluation of the dispersion. Despite the fact that no tracer experiment is provided, the high aspect ratio and the long residence time allow to estimate a characteristic time of radial diffusion much smaller than diffusion in the axial direction. However, this does not exclude the presence of axial dispersion effects.  
A detailed kinetic model was derived for carbofuran degradation according to the following reaction mechanism:  
\[Fe^{2+}+H_{2}O_{2}\to Fe^{3+}+OH^{-}+HO^{\bullet}\] (r.9)  
\[Fe^{2+}+HO^{\bullet}\to OH^{-}+Fe^{3+}\] (r.10)  
\[Fe^{2+}+HO^{\bullet}_{2}\to Fe^{3+}+HO^{-}_{2}\] (r.11)  
\[Fe^{3+}+H_{2}O_{2}\to Fe^{2+}+HO^{\bullet}_{2}+H^{+}\] (r.12)  
\[Fe^{3+}+HO^{\bullet}_{2}\to Fe^{2+}+O_{2}+H^{+}\] (r.13)  
\[H_{2}O_{2}+HO^{\bullet}\to HO^{\bullet}_{2}+H_{2}O\] (r.14)  
\[A+HO^{\bullet}\to R(CO_{2})\] (r.15)  
\[Fe^{3+}-R(CO_{2})\xrightarrow{hy}Fe^{2+}+R(CO^{\bullet}_{2})\] (r.16)  
and a genetic algorithm was used to estimate kinetic constants. The results were compared to the ones obtained in a batch reactor. This is the first reported kinetic investigation of photo-Fenton processes with low iron concentrations in microreactors. Nonetheless, future efforts should address the issue of the light dependence of the constants for a more reliable and scalable estimation of the parameters.
## 4 Results and Discussion
### Execution Time Comparison  
We obtain a measurement of the time required to conclude the simulation using several parameters for each application's deployment device. Table 6 shows the obtained measurements for each device. We compute each measurement using only one thread environment because a multithread environment was not conceived in the original application. We choose the following simulation times: 4 days, one week, two weeks, and three weeks. A Polaroid tablet device obtained the biggest execution time, while a Galaxy Tab 10.1 device obtained the shortest one. The rows in Table 6 start from the slower to the higher simulation time. The mobile devices used for the application's deployment were not recent models, because even when some of them were featured as high-end devices when released, they are old devices and, with recent advances in mobile technology, many of them can be featured as low or middle-end devices when compared with the existing technology available at the time of writing this paper.
###### Abstract
In this paper, we prove the usefulness of CPW combined with the envelope spectrum to extract frequency-domain features from the (CWRU) signals database that, combined with time-domain features, have yield accuracies over 97% for some ML models. The former frequency-domain condition indicators are the impulse responses in the envelope spectrum, whose amplitudes are the maximum of all impulse responses' amplitudes spaced at each fault's characteristic frequency for every signal and their log ratios (Ball Pass Frequency of the Inner Race (BPFI) Amplitude, Ball Pass Frequency of the Outer Race (BPFO) Amplitude, Ball Spin Frequency (BSF) Amplitude, Log (BPFI Amplitude/BPFO Amplitude), Log (BPFI Amplitude/BSF Amplitude), Log (BSF Amplitude/BPFO Amplitude)). The latter time-domain indicators are basic statistics (mean, standard deviation, Root Mean Square (RMS) and shape factor), high-order statistics (kurtosis and skewness) and impulsive metrics (peak value, impulse factor, crest factor and clearance factor). In particular, the log ratio between the maximum amplitude of the impulse responses spaced at the BPFI and the maximum amplitude of the impulse responses spaced at the BPFO in the envelope spectrum has been proven a crucial feature. The significance of the aforementioned condition indicators has been assessed by applying two statistical methods: one-way Analysis of Variance (ANOVA) and the Kruskal-Wallis test. The ML models trained with these features are Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbors (k-NN) and Naive Bayes (NB), developed by the MATLAB(r)-powered _Statistics and Machine Learning Toolbox 11.71_. Therefore, this paper focuses on the extraction, analysis and demonstration of feature validity, and not on developing application-specific ML models. Their performance has been compared with that of a CNN trained with greyscale images of raw time signals, as this is a simple method that has delivered outstanding results in previous studies. Furthermore, we provide our MATLAB(r) functions and processed datasets for a proper assessment of the merits of our method. This manuscript is organised as follows: Section 2.1 summarises the CWRU's experimental set-up. Section 2.2 explains the theoretical basis for frequency- and time-domain feature extraction. Section 2.3 covers the statistical analysis of these features and concludes by explaining the method for training ML models. Section 2.4 summarises the process for obtaining greyscale images from the raw CWRU's time signals. Section 2.5 summarises the proposed CNN's architecture and defines its hyper-parameters. Section 3.1 presents the statistical analysis results and the different ML algorithms' validation accuracies. Section 3.2 presents CNN's validation accuracies. Section 4 discusses the results, comparing them with some of the works referred to above. Finally, Section 5 concludes by presenting the main advantages and disadvantages of the proposed method.