esa-sceva/satcom-chunk-collection huggingface dataset:
# A State-of-the-Art Review on the Study of the Diffusion Mechanism of Fissure Grouting
## 5 Current Status of Experimental Research on Grouting Modeling of Fissured Rock Bodies
### Development and Testing of the Multi-Hole Grouting Diffusion Model Test System  
The effective selection of grouting materials, grouting pressure, grouting volume, and the arrangement of grouting holes are crucial factors in achieving economic and practical goals during the grouting process. The next crucial step is to arrange grouting holes, including their shape and spacing, after choosing the appropriate type of grout. In addition to producing the desired strengthening effect, a sensible grouting hole and spacing design can also prevent needless financial expenditures. Therefore, some scholars have conducted research on this and achieved certain results.  
Figure 7: Schematic diagram of the monitoring system [82].  
[PERSON] et al. [84] conducted experiments on porous grouting for soil reinforcement. They found that injecting a small amount of grout into multiple holes can achieve better reinforcement compared with a single hole. The reinforcement effectiveness decreased with the increase in grouting interval time in the consolidated soil layer, but in the over-consolidated soil layer, the reinforcement effect was independent of interval time. Their study also examined the effects of different hole layouts. [PERSON] et al. [85] investigated the interaction problem of multi-hole grouting in seepage grouting. They used natural boundary element theory and the Fourier series method to derive an analytical solution for the seepage pressure of an external well, considering the wellbore wall as an impermeable formation. [PERSON] et al. [86] introduced an optimization approach for multi-hole grouting in fissured rock masses. Their method involved strategically placing multiple holes along the longer sides of the rock masses in different directions and cycles to prevent potential issues of porous crossing. The effectiveness of this optimization scheme was confirmed by calculating the water yield in the grouting area and analyzing the composition of the core cement. In a related study, [PERSON] et al. [87] developed a diffusion model that considers both single-hole and porous grouting. This model was based on fractal geometry and the filtration effect. The researchers applied the grouting hole parameters derived from the model to the actual site and evaluated the reinforcement effect of grouting based on these specific hole parameters.
## 3 Results and Discussion
### Fluorescence Imaging in Cells  
The application of ITEC for the detection of Al\({}^{3+}\) in HeLa cells was studied. The cells were incubated with 5 umol/L of ITEC and 0 umol/L, 2 umol/L, and 5 umol/L Al\({}^{3+}\) solution, respectively, in PBS (2% DMSO, pH = 7.4) for 20 min at 37 \({}^{\circ}\)C, then washed three times with PBS to remove the excess ITEC and Al\({}^{3+}\). As depicted in Figure 5, no obvious fluorescence was observed in the cells incubated only with ITEC. However, the fluorescence was founded in Figure 5 (2a-3a) after adding Al\({}^{3+}\). Moreover, it displayed enhanced fluorescence with the increased concentrations of Al\({}^{3+}\) (2 umol/L, 5 umol/L) in cells. Therefore, the results in cells indicated that ITEC could be used as an effective Al\({}^{3+}\) imaging agent.  
Figure 4: **(a)** The [PERSON]’s plot for ITEC-Al\({}^{3+}\)complex, [ITEC] + [Al\({}^{3+}\)] = 10 μm; (b) \({}^{1}\)H NMR (400 MHz) spectra of ITEC, ITEC with Al\({}^{3+}\) (1.0 equiv.), and ITEC with Al\({}^{3+}\) (2.0 equiv.) in DMSO; (c) The proposed binding mechanism of ITEC-Al\({}^{3+}\) and the color change of ITEC solution (20 μm) after adding Al\({}^{3+}\) (40 μm).  
As we all know, Al\({}^{3+}\) is a non-essential element of the human body. It will accumulate in the body from water or food containing Al\({}^{3+}\). Considering the complicated process, we mimic the phenomenon of bioaccumulation and performed cell imaging (Figure 6). The cells were cultured with 7.4 umol/L Al\({}^{3+}\) daily for 3 days and followed by normal culture for 5 days to achieve natural concentration, then incubated with ITEC solution (5 umol/L) for 20 min. The cell has been successfully imaged, which demonstrates the applicability of the technique.
###### Abstract  
LoRa technology, renowned for its low-power, long-range capabilities in IoT applications, faces challenges in real-world scenarios, including fading channels, interference, and environmental obstacles. This paper aims to study the reliability of LoRa in Non-Line-of-Sight (NLoS) conditions and in noisy and mobile environments for Industrial IoT (IIoT) applications. Experimental measurements consider factors like vegetation and infrastructure, introducing mobility to replicate NLoS conditions. Utilizing an open-source LoRa Physical Layer (PHY) Software-Defined Radio (SDR) prototype developed with GNU Radio, we assess communication reliability through metrics such as Block Error Rate (BLEER), Signal-to-Noise-Interference-plus-Noise Ratio (SINR), and data rate. The study reveals the estimated overall reliability of the LoRa signal at 90.23%, emphasizing specific configuration details. This work contributes to the broader field of LoRa communication, encompassing hardware, software, protocols, and management, enhancing our understanding of LoRa's dependability in challenging IIoT environments.  
IoT, Wireless Sensor Networks; LoRa; NLoS; wireless communication; reliability +
Footnote †: journal: Applied Sciences  
Article  
Improving the Reliability of Long-Range Communication against Interference for Non-Line-of-Sight Conditions in Industrial Internet of Things Applications
## 5 Results
For all calibration parameters sets, the model predicts reduced ureolytic activity and, thus, precipitation very regularly and evenly for all even-numbered mineralization-medium injections. The experimental measurements however do not show a similarly consistent, repetitive decrease of the ureolytic activity for even-numbered mineralization-medium injections. Thus, during some injection cycles, the model underpredicts ureolysis and precipitation, while in general overestimates both, leading to an overestimation of the final volume fraction of precipitated calcite (Figure 5). Interestingly, the sum of the concentration residuals for column #2 (0.66 M) is lowest for the sorption-coefficients-only case and increases for all other parameter sets in which additional parameters were calibrated. The residuals for the final calcite of column #2 (0.66 M) behave very similarly to those of column #1 (0.33 M), as they are lowest for the parameter set including the ad- and desorption rate coefficients and the injection concentrations and the second lowest for the parameter set including only the ad- and desorption rate coefficients. Another measure to assess the predictions of precipitated CaCO\({}_{3}\) is a comparison of the amount of the injected calcium and the amount precipitated CaCO\({}_{3}\) for each set of calibrated parameters. Therefore, we express all the injected calcium in terms of CaCO\({}_{3}\) for easier comparison with the  
Figure 5: Predicted volume fractions of final calcite compared to the experimental data of column experiment #2 (0.66 M mineralization medium concentration): Note that, during this experiment, urease was only injected before every odd-numbered mineralization-medium injection. The error bars represent the standard deviation calculated from triplicate measurements. None of this experiment’s data were used for calibration. Figure 6: Prediction of concentration measurements at 10.16 cm from the inlet compared to the experimental data of column experiment #2 (0.66 M mineralization medium concentration): Note that, during this experiment, urease was only injected before every odd-numbered mineralization-medium injection. None of this experiment’s data were used for calibration. precipitated CaCO\({}_{3}\). For column #1 (0.33 M), enough calcium to precipitate a total of 82.7 g CaCO\({}_{3}\) was injected; the case with the fitted, reduced injection concentration predicted calcium for 66.1 g of CaCO\({}_{3}\) was injected. Integrating the measured calcite along the experiment column's length results in 28.5 g for column #1 (0.33 M). In the sorption-coefficients-only case, a total of 50.3 g calcite is predicted to precipitate; in the case with the injection concentrations included, it was 45.7 g; in the case with the urease activity, it was 50.8 g; and in the case with the multiplier to pKa, it was 53.7 g. Note that, for all model calibrations, the simulation results predict that more than 60% of the injected calcium precipitates while, in the experiment, only 34% of the injected calcium precipitated.
## 3 Materials and Methods
### Hybrid User Study with Domain Experts
#### 3.3.2 Participant Groups and Roles  
The participants were divided into two groups: (1) VR-participants, who were present in the virtual environment and were able to interact with it, and (2) Teams-participants who watched a streamed video from the perspective of one of the VR-participants via Teams desktop application. Teams-participants were able to communicate verbally with VR-participants and performed tasks outside of the VE using their laptop tools. In total, 5 remote sessions with 20 participants were organized: ten VR-participants, wearing HMD, and ten Teams-participants (referred to as VR and T). Each session was planned to have four participants: two VR and two Teams participants. However, due to a scheduling conflict, one Teams participant skipped their session and joined a later one, causing one session to have 3 participants, and another session 5 participants.  
The battery replacement procedure was performed collaboratively during the test; every participant had their own role and related set of tasks, represented in Table 1, thus mimicking their real work tasks and role. The battery replacement task was chosen because it is a fairly complex procedure consisting of identifying the components and parts, opening and closing lids, releasing fixings, and disconnecting and reconnecting cables. As the task is performed in a high-risk environment, the replacement also involves several safety measures both before and after the replacement. The tasks were further split into two scenarios: (1) 3D CAD model exploration in the Showroom space and (2) Assembly/Disassembly of a 3D CAD model in a simulated context (Lab VE). The VR-participant 1 acted as the eyes for the Teams participants while performing assigned tasks and followed the instructions from Teams participants to find the best view. VR-participant 2 was mostly responsible for inter acting with 3D models and virtual tools. Teams-participant 1 took notes during the whole process as well as asked for more details, e.g., additional measures of the 3D components. Teams-participant 2 instructed VR-participant 2 to find the best possible position and took screenshots from their view. Overall, the tasks were designed to perform the collaboration between VR-participants as well as collaboration between VR and Teams participants.
## 3 Results and Discussion
### Environmental Indexes  
Figure 5 shows the results of the environmental indexes. The geoaccumulation index for samples obtained from the non-mining sector in the Aconcagua River basin indicates no contamination, except for one sample each of As, Cu, and Zn, which show low contamination levels. For soil samples from mining areas in the Aconcagua River basin, 2 samples of As, 13 of Cu, 5 of Pb, and 3 of Zn are slightly contaminated; 2 samples of Cu and 1 of Pb are moderately to heavily contaminated; and 4 samples of Cu are highly contaminated. The rest of the samples are not contaminated. In contrast, five samples of As were found with a moderate degree of contamination for the Casablanca River basin and one sample of As, two of Pb, and one of Zn with a slight degree of contamination.  
For the contamination factor, in the Aconcagua River basin, samples from areas without mining activity showed 12 samples of As, 6 of Cu, and 3 of Zn with moderate contamination. For soil samples including mining activity, 14 samples of As, 32 of Cu, 22 of Pb, and 12 of Zn were highly contaminated. In contrast, the Casablanca River basin showed five samples of As with considerable contamination, while the rest were shallowly to moderately contaminated for all elements.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Factor 1** & **Factor 2** & **Factor 3** \\ \hline Location/Basin & Arsenic & Lead and Zinc & Copper \\ \hline Vegetables from & Cooper, Lead, and & & \\ Casablanca & Zinc & Arsenic & All (residually) \\ \hline Fruits from & & & \\ Casablanca & Arsenic & Lead & Copper and Zinc \\ \hline \end{tabular}
\end{table}
Table 6: Summary of the metals contributing to each factor at each location.
## 5 Discussion
The data supported the hypothesis that the perceived value of systems engineering directly affects systems engineering process performance (H3), thus implying that as the perceived value of systems engineering increases, the performance of the systems engineering process increases. Therefore, perceiving and understanding the value of the process would be likely to enhance the performance of the process. The results of this study suggest several implications for launch vehicle organizations and for systems engineering in general. Prior research has shown that systems engineering affects cost, schedules, technical performance, and customer satisfaction [9; 10; 20; 32; 41]. The present study identified that systems engineering process performance in launch vehicle organizations is influenced by organizational commitment, the value of systems engineering, top management support, and systems engineering support. Improving these factors could improve systems engineering process performance. For launch vehicle organizations, improving systems engineering process performance could decrease technical issues and launch failures, and ultimately result in time and cost savings. After recognizing these factors influencing systems engineering, leaders of an organization could target these areas for improvement to enhance the performance of their organization's systems engineering process. For example, the value of systems engineering has a significant direct effect on systems engineering process performance. The leader of an organization could choose to implement protocols to educate and ensure that the individuals in the organization recognize the value of systems engineering. This would in turn improve the performance of the organization's systems engineering and ultimately improve the launch vehicle organization's ability to meet cost, schedule, and technical performance goals.
## 5 Results and Discussion
### Data Normalization and Parameter Setting
#### 5.1.2 Model Parameter Settings  
Input temperature, humidity, stripping volume, wind speed, and wind direction variables to output PM2.5 or PM10. Then, establish a GA-LSSVM prediction model. The input and output layer index data are detailed in Table 3. The root mean square error (\(RMSE\)) is used as the fitness function, representing the correlation ratio between the actual and predicted values of the model. It is combined with the sample standard deviation (\(STD\)) and the correlation coefficient (\(R^{2}\)) to evaluate the model's prediction effectiveness.  
From Figure 11, it can be seen that the root mean square error (\(RMSE\)) of the LSSVM model before optimization is 13.484 (PM2.5) and 15.351 (PM10), and the coefficient of determination (\(R^{2}\)) is 0.482 (PM2.5) and 0.466 (PM10), respectively. After genetic algorithm (GA) optimization, the RMSE indicators for PM2.5 and PM10 have decreased by 36% and 53%, respectively, compared with before optimization, the \(R^{2}\) is increased by 81% and 96%, respectively.  
Figure 11: The effect of GA-LSSVM on predicting PM2.5 and PM10.  
Figure 10: The fitness curves of PM2.5 and PM10 predicted by GA-LSSVM model.  
Figure 9: Effect of LSSVM in predicting PM2.5 and PM10.  
and the final fitting degree is 0.872 and 0.913, respectively. The optimization effect has been significantly improved.
# Acoustic Localization for a Moving Source Based on Cross Array Azimuth
## 4 Experiments
### Data Length for Correlation  
Since the signal collected was not even during the interval while the source moved, it is necessary to extract part of the whole signal periodically for short-time correlation. To ensure efficiency, the extraction must cover one whole period in each short-time correlation.  
As the sound source was being actuated by a pulley, the speed of motion was relatively slow, which should be less than 5 m/s.  
\[fr=(\frac{v_{0}\pm v_{t}}{v_{0}\mp v_{s}})f \tag{35}\]  
According to the Doppler effect formula, the difference in value between Doppler frequency \(f\) and original frequency \(f\) was about 0.01 \(f\). Meanwhile, sound velocity \(v_{0}\) was 343 m/s, velocity \(v_{t}\) of the observer was zero and velocity of source \(v_{s}\) took the maximum velocity 5 m/s. Therefore, data bias resulting from the Doppler effect can be ignored.  
The longest distance that one acoustic wave travels in the single array is 2 \(D\), such that the maximum travel time is 2 \(D/c\). The sampling interval is \(T_{N}=1/F\) when the sampling frequency is assumed to be \(F\). The data length \(n\) for short-time correlation describes the theoretical length of each correlation in Equation (36):  
\[n\geq\frac{1/f+2D/c}{1/F} \tag{36}\]
# Advances in Finite Element Modeling of Fatigue Crack Propagation
## 3 Advanced Techniques in Finite Element Modeling for Fatigue Crack Propagation
### Virtual Crack Closure Technique (VCCT)  
VCCT is used to calculate the energy release rate, which is crucial for predicting crack growth in different modes. By virtually closing the crack and calculating the forces and displacements, this method provides a reliable way to estimate crack propagation rates. Unlike some other methods, VCCT does not require special singularity elements or highly refined meshes around the crack tip, simplifying the modeling process [128; 129]. This can handle mixed-mode loading conditions, providing flexibility in analyzing different types of stress scenarios [130]. Moreover, VCCT exclusively depends on the statuses of elements directly linked to crack fronts, rendering it apt for parallel computing [131]. [PERSON] and [PERSON] [132] introduced a method for computing the stress intensity factor (SIF) utilizing the Virtual Crack Closure Technique (VCCT) with tetrahedral finite elements. A notable aspect of this method is its ability to utilize tetrahedral element meshes that do not have stringent shape prerequisites for determining the SIF using VCCT. As a result, structures containing cracks can be effortlessly and automatically meshed using arbitrary tetrahedral elements. Although VCCT does not require highly refined meshes, the accuracy of the results can still be influenced by the mesh size and quality [133; 134; 135]. Furthermore, this technique requires the presence of an initial crack, which may not always be available or easy to define in some practical scenarios [130]. This technique is also primarily based on LEFM, which may limit its applicability to materials and conditions where plasticity or other non-linear effects are significant [136].
## 5 Performance Validation
### Simulation Setup and Node Deployment  
To mimic actual underwater conditions, we have used an actual SSP from the 2013 World Ocean Database (WOD) [30]. Specifically, we employ the Leroy model [14] on the eXpendable Bathythermograph (XBT) data was collected by the ship NOAA OKEANOS Explorer on June 5 th, 2009 and May 25, 2010 to obtain the two different SSPs shown in Figure 11. Since the temperature readings obtained by the ship for M-SSP1 terminate at 1617.5 m while the maximum measured seabed depth is 3249 m, the SSP after 1617.5 m is treated as depth-dependent only. The selection of M-SSP1 is due to its close resemblance to the Munk SSP profile which is deemed as an idealized SSP and is widely used in underwater simulation [31]. Furthermore, M-SSP2 inhibits a different SS gradient along the depth and provides an underwater environment that is quite far from the Munk profile and hence acts as a good testing baseline.  
In our simulation, we first select one of the shown SSPs in Figure 11 and then deploy nodes randomly within a region spanning the depth of the SSP and up to a horizontal distance of 5 km. We then utilize such a plane as the equivalent vertical plane chosen in the 3D environment. We then run our ray categorization algorithm and identify direct ray, rays experiencing a single LEP and those undergoing multiple LEPs. We then select the direct rays in validating the performance of the proposed ray  
Figure 11: The (Sound Speed Profiles) (SSP) extracted from World Oceanic Database (WOD) database with part (**a**) showing the first SSP (labeled M-SSP1) measured at coordinate of N40\({}^{\circ}\) 31\({}^{\prime}\) 24.179\({}^{\prime\prime}\) W127\({}^{\circ}\) 35\({}^{\prime}\) 52.27\({}^{\prime\prime}\), and (**b**) signifies the second SSP (labeled M-SSP2) measured at N20\({}^{\circ}\) 7\({}^{\prime}\) 18.0624\({}^{\prime\prime}\) W 175\({}^{\circ}\) 56\({}^{\prime}\) 17.3436\({}^{\prime\prime}\).
## 3 Mathematical Model  
The considered objectives of multi-user load scheduling are cost (\(J_{\text{C}}\)), user inconvenience (\(J_{I}\)), and peak load (\(J_{P}\)). The total cost of all users is given by the following [15; 16]:  
\[J_{C}=\sum_{u}^{U}\sum_{h}^{H}\sum_{a}^{A}p_{h}^{u}.\beta_{h}.x_{a,h}^{opt,u} \frac{60\text{ (min)}}{\gamma\text{ (min)}} \tag{1}\]  
where \(u\) is the index for instantaneous users for up to \(U\) users, \(h\) is the time index for the 24 h horizon, and \(a\) is the indication for the user's appliance for the overall count of \(A\). \(P_{h}^{u}\) is the entire consumption of user \(u\) for a given time slot \(h\) for all available shiftable user loads of \(A\). \(\beta_{h}\) is the price rate of energy consumption that is given from utility for a given time slot \(h\). \(x_{a,h}^{opt,u}\) is the optimal operation status ON/OFF of a given device \(a\) within time slot \(h\) for one user \(u\). \(\gamma\) is the time slot considered in the given model (15 min).  
The second objective is the user inconvenience that considers the difference (late or advance) for the operation time of a user appliance between base (preferred user time operation) and optimal time operation. The user inconvenience can be expressed as  
\[J_{I}=\sum_{u}^{U}\sum_{h}^{H}\sum_{a}^{A}\Big{(}x_{a,h}^{pre,u}-x_{a,h}^{opt,u }\Big{)}\forall a\in A,\forall h\in H,u\in U \tag{2}\]  
where \(x_{a,h}^{pre,u}\) and \(x_{a,h}^{opt,u}\) are the status of preferred and optimal user appliances' operation time, respectively. The values of \(x_{a,h}^{pre,u}\) and \(x_{a,h}^{opt,u}\) are within \([0,1]\), where 1 indicates ON and 0 indicates OFF. To ensure that the optimal operation time is sufficient to complete their normal operation time, the following equation must be true:\[\sum_{B_{n}^{u}}^{E_{n}^{u}}x_{a,h}^{u}\geq H_{a}^{u}\forall a\in A \tag{3}\]  
where \(B_{n}^{u}\) and \(E_{n}^{u}\) are the beginning and ending optimal operation time for a given user appliance a. \(H_{a}^{u}\) is the normal operational time for a given user's appliance \(a\).  
The third objective function is the peak load of user consumption, which can be given as below:  
\[J_{P}=Max(P_{h}) \tag{4}\]  
where \(P_{h}\) is the total consumption of all users for all appliances within a given time slot \(h\).