esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Previous studies have provided no clear statements or solutions regarding the non-detection zone in low-frequency ultrasonic guided waves. However, the existence of the non-detection zone cannot be ignored in NDT. In this study, we located the non-detection zone in long-distance conventional ultrasonic guided wave inspections of a square steel bar and investigated the reasons and conditions for the appearance of the non-detection zone through simulations. Further, by analyzing the characteristics of the simulated pulse wave when the non-detection zone appears, the range of the non-detection zone and a solution are proposed. The simulation envelope is used for a relative comparison; by suppressing the interference of the ultrasonic signal trailing using the envelope calculation, the flaw is located by a comparison with the simulation results of the multi-pulse superimposed envelope in the extended time domain (the monitoring time Intervals I and II). The simulation results are verified with actual measurements. The results of this study provide a feasible solution for locating the non-detection zone and serve as a powerful supplement for conventional long-distance ultrasonic guided wave inspections. This type of non-detection zone can also occur in the ultrasonic flaw detection of long rod-like structures; therefore, this study has certain applicability and practical significance for other structures. However, due to the influence of actual field factors on the actual detection of the pulse energy loss, the amplitude deviation between the equivalent simulation and the actual measurement is large in this study and is not conducive to the prediction of the depth (\(D0\)) of the flaw. For this reason, it is necessary to improve the approximate model of the low-frequency ultrasonic guided wave and the estimation of various types of flaws to provide a new approach that is widely applicable.  
Figure 12: Location of a triangle flaw. (**a**) The triangle type of the flaw on the steel square bar; (**b**) The comparison of multiple envelopes in the effective comparison interval for the triangle flaw.  
**Acknowledgments:** This work was supported by the Key Research and Development Program of Shaanxi Province (Grant No. 2017 ZDXM-GY-130), Xi'an City Science and Technology Project (Grant No. 2017080 CG/RC043 (XALG009)).  
**Author Contributions:** [PERSON] and [PERSON] performed the theoretical analysis, conceived and designed the experiments; [PERSON], [PERSON] and [PERSON] performed the experiments and analyzed the data; [PERSON] wrote the paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# An Ultra-Stable Polysaccharide Gel Plugging Agent for Water Shutoff in Mature Oil Reservoirs
## 3 Results
### Thermal Stability of the CTS/MBA Gel System  
To screen for the optimum formula of the CTS/MBA gel system, the thermal stability of the gel system was evaluated. The experimental data from 90 days of observation are shown in Table 1. It can be observed that most of the gel samples presented syneresis  
Figure 8: Degradation rate at different shearing times (shear rate: 6000 r/min).  
Figure 9: Effect of shear degradation on the gelation time.  
phenomena within 30 days, even the gel samples at a higher MBA concentration of 1.0 wt%. However, the gel samples showed better stability with a higher CTS concentration of 3.5 wt%, which indicates that the polymer concentration has the dominant effect on the stability of the gel. The gel samples that contain 3.5 wt% CTS and 0.6 or 0.8 wt% MBA presented favorable thermal stability, which kept stable for 90 days without syneresis (Figure 10). According to the research of [PERSON] et al. [43], the PAM/PEI gel system can keep stable at 120 \({}^{\circ}\)C for more than 2 weeks when the concentration of PAM is no less than 7.0 wt%. The lower CTS usage of the CTS/MBA gel system may result from the better temperature resistance of CTS. The rigid structure of CTS can resist the high-temperature degradation more effectively than the flexible structure of PAM. The lower CTS usage can improve the in-depth injectivity of the gelant by decreasing the initial viscosity, thus saving the cost. A higher MBA concentration is unnecessary when the gel is stable enough, so the gel system that consists of 3.5 wt% CTS and 0.6 wt% MBA is recommended as a water shutoff agent in high-temperature reservoirs.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \multicolumn{2}{c}{**Gel Composition**} & \multicolumn{8}{c}{**Strength Code of Gel (d)**} \\ \hline
**CTS Concn wt\%** & **MBA Concn wt\%** & **1** & **2** & **3** & **4** & **5** & **10** & **20** & **30** & **45** & **60** & **75** & **90** \\ \hline \multirow{4}{*}{1.5} & 0.4 & D & de & - & - & - & - & - & - & - & - & - & - \\  & 0.6 & G & de & - & - & - & - & - & - & - & - & - & - \\  & 0.8 & I & I & de & - & - & - & - & - & - & - & - & - \\  & 1.0 & I & I & de & - & - & - & - & - & - & - & - & - \\ \hline \multirow{4}{*}{2.0} & 0.4 & I & I & I & de & - & - & - & - & - & - & - & - \\  & 0.6 & I & I & I & I & de & - & - & - & - & - & - & - \\  & 0.8 & I & I & I & I & de & - & - & - & - & - & - & - \\  & 1.0 & I & I & I & I & de & - & - & - & - & - & - & - \\ \hline \multirow{4}{*}{2.5} & 0.4 & I & I & I & I & de & - & - & - & - & - & - & - \\  & 0.6 & I & I & I & I & I & de & - & - & - & - & - & - \\  & 0.8 & I & I & I & I & I & de & - & - & - & - & - & - \\  & 1.0 & I & I & I & I & I & de & - & - & - & - & - & - \\ \hline
# Construction and Application of Safety Management Scenarios at Construction Sites
## 4 Discussion  
### Scenario Construction and Safety Evaluation of Foundation and Basement Construction Phases  
Through the analysis of natural and man-made conditions in Section 3.2 for the construction of the foundation and basement of the production base project, and considering the time limit of the spring construction, the simulation of the outdoor wind environment in the spring of the construction phase (Figure 6), the mechanical buffer zone (Figure 7a), and the material buffer zone (Figure 7b) are superimposed to construct a safety management scenario for the construction of the foundation and basement of the production base project (Figure 17).  
During the foundation and basement construction stage, the production base project is in a dangerous area, specifically in the construction Area Nos. 1 and 2. In this area, the wind speed is high under the set climate conditions, and it is within the range of the secondary buffer zone for machinery and materials at the construction site. Therefore, onsite personnel should avoid staying in this area for long periods and be aware of potential dangers such as falling objects and overturning materials. Wind speeds are higher in Area Nos. 1 and 2, and if flammble materials such as formwork wood squares are present in material stacking Area Nos. 2 and 3, fire extinguishers should be placed upwind of their material stacking areas to prevent fire accidents from occurring downwind. On the other  
Figure 16: Mechanical buffer (**a**) and Material buffers (**b**) during the construction phase of the renovation.  
hand, the construction site 3 area is a relatively safe area in this stage of construction. It is less affected by the wind environment and is not within the first and second level buffer zones for machinery at the construction site. Additionally, no material stacking occurs here, rendering it a suitable temporary refuge for construction site personnel. The site management personnel can arrange for the orderly evacuation of personnel according to the situation onsite.  
In the foundation and basement construction stage of the site, there are many new entrants, so to implement production base project worker entry education and training, a personnel safety education system in the safety management system is important, especially machinery operation and special operators training, so that the project department can form a perfect personnel entry process. In this construction phase, the onsite materials are mostly templates, steel pipes, reinforcing bars, etc., and there are many types of materials, so the project department should establish a job responsibility system, where the personnel in each position are jointly responsible for the safety control of project materials. The project department should supervise all kinds of mechanical equipment operators to seriously study the \"safety technical regulations for the use of construction machinery\" (JGJ33-2012), and mechanical equipment operators need to be licensed to work.
## 2 Materials and Methods--Implementation, Integration, and Testing of the IDEASSat Flight Model
### IDEASSat Satellite Subsystems
#### 2.1.1 Structure and Mechanisms (STR)  
The IDEASSat spacecraft structure was designed and fabricated in-house. The main material of the IDEASSat structure was made of aluminum alloy 6061-T6. The deployment mechanism of the solar panels was realized by metal 3D printing technology. The UHF antenna deployment module was made with Delrin based on the design of MinXSS-2.  
All self-made hardware was connected with the PC-104 connector, while ADCS and UHF modules needed to use micro or nano D-sub connectors as the control interface. The assembly was divided into the \(-\)Z group and \(+\)Z group with ADCS as the midpoint. The assembly process was formulated to consider the configuration of the cables and harnessing, and the steps of staking the cabling to prevent unwanted motion during launch. The IDEASSat flight model underwent more than 10 fit-checks during the integration phase to check for tolerances, as well as to formulate the final FM assembly procedure. Considering the time required for the epoxy to cure in a 50 \({}^{\circ}\)C bake out, it took a total of 3 days to complete the assembly of the spacecraft FM.
## 4 Experimental Results
### Automatic Solar Tracking Results  
The solar tracking accuracy in the automatic mode was evaluated by measuring the ISTE and the RSTA with the sensor detailed in Section 3.3. The analysis of the tracking results is shown in the next subsections.
## 1 Introduction
However, high computational complexity is the major disadvantage. The models also cannot be used for estimating the performance of battery packs consisting of multiple batteries in electric vehicles. On the other hand, equivalent circuit models can describe battery SoC, SoH, and impedance. Together with actually measured currents and voltages, batteries' non-linear dynamic behaviors can be shown [36; 37]. In recent years, machine learning has also been employed for calculations to analyze battery states [38]. Through a great deal of training data, the error between predicted and measured battery performance is minimized. The typical algorithm is an artificial neural network (ANN). In terms of precision, it gives stellar results. Parameters can be calibrated to enable preserving fine performance of the battery in its life cycle [39; 40; 41; 42]. However, since machine learning is based on mathematical optimization, battery behaviors and potential physical phenomena cannot be linked together. This research proposes a comprehensive model to describe the discharging behavior of LiFePO\({}_{4}\) batteries. In the model, parameters of the discharging behaviors and electrochemical phenomena are intimately related. The proposed model is based on a modified Thevenin circuit, Butler-Volmer kinetics, the Arrhenius equation, Peukert's law, and a back-propagation neural network (BPNN). The model estimates the CE and the remaining capacity of the battery in various environments. The simplifications of the electrochemistry equations make it possible to integrate it into equivalent circuit models in order to predict the battery's states during real operation. The structure of the paper is as follows: Section 2 describes the experimental framework, procedures, and the experimental data. Section 3 introduces the battery models and terminology used throughout this paper. The work including battery modeling and its parameter identification are given in Section 4. A comprehensive battery model is presented to simulate the discharging behavior of the batteries. Curve fitting was employed to estimate the SoC and battery's parameters, while the BPNN estimated the SoH. Section 5 describes that battery capacity tests were conducted to verify the accuracy and robustness of the proposed method in various environments. The SoC estimation results are shown and analyzed. Lastly, the conclusions are drawn in Section 6.
## 1 Introduction  
Opportunistic networks (ONs) are networks supported by mobile devices that meet sporadically. As data can only be transmitted during encounters, nodes must carry it with them for potentially prolonged periods until an opportunity to pass on the data arises. Unlike a traditional data network, ONs are formed by multiple network partitions that change continuously, and data delivery depends on multiple hops that do not necessarily form an end-to-end path at any given moment.
# Development on Solid Polymer Electrolytes for Electrochemical Devices
## 1 Introduction
Various polymer electrolytes have been developed from polymers, such as poly(ethylene oxide) (PEO) [1; 4; 5], cellulose [6], polyvinylidene fluoride (PVDF) [7], poly(vinyl alcohol) (PVA) [8], polyacrylonitrile (PAN) [9], chitosan [10], poly(methyl methacrylate) (PMMA) [11], starch [12] and poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP) [13], to name a few. This review presents some achievements on both the synthetic polymers and biopolymers as polymer host in SPEs used, along with working and counter electrodes in various electrochemical devices namely lithium-ion batteries, sodium-ion batteries, DSSCs, QDSCs, fuel cells, supercapacitors, EDLCs, ECDs, proton-conducting batteries and proton-exchange membrane fuel cells (PEMFCs). Here, we pay special attention to the conductivity aspect of SPEs in these electrochemical devices. Different types of approaches taken by researchers to improve the ionic conductivity of SPEs are also discussed.
## 5 Sensitivity Analysis
### Grouting Pressure  
The vertical displacements of the surface above the tunnel axis and tunnel top at y = 60 m in the model were selected to monitor, and the developments of the displacements of the surface and tunnel within 90 days under different grouting pressures were obtained, as shown in Figure 9.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Items** & **Values** \\ \hline Grouting pressure, \(p_{\text{g}}\) (kpa) & 280 (240; 260; 280; 300) \\ \hline Grouting filling rate, \(GFR\) (\%) & 200 (100; 150; 200; 250; 300) \\ \hline Thickness of underlying soft soil, \(H_{\text{s}}\) (m) & 8 (6; 8; 10;12) \\ \hline Average seepage volume, \(v_{\text{s}}\) (L/(m\({}^{2}\)-d)) & 2 (0.05; 0.5; 2; 3) \\ \hline \hline \end{tabular} Note: the values outside the brackets are the basic values of the corresponding items for sensitivity analysis of other parameters, and the values in the brackets are the variables of the corresponding items for themselves sensitivity analysis.  
\end{table}
Table 6: Parameters for sensitivity analysis.  
Figure 8: Simulated and monitored surface subsidence trough.  
The results showed that the surface displacement during the construction period and consolidation period were less affected by the grouting pressure applied during construction as shown in Figure 9a. Compared with the surface settlement, the displacement of the tunnel top was slightly affected by the grouting pressure, especially the settlement during the construction period. As shown in Figure 9b, the floating amount of the tunnel increased with the grouting pressure increasing, while the settlement during the consolidation period was basically the same, which led to a positive correlation between the cumulative settlement of the tunnel and its settlement during the construction period.
## 7 Multi-Objective Optimization in Wireless Sensor Networks: Trends
### Focus of Research with Respect to Nature of Optimization Problem  
In various applications of wireless sensor networks, the desirable objectives including but not limited to maximization of coverage, maximization of battery life, maximization of energy efficiency, minimization of cost, minimization of delay, maximization of throughput and minimization of packet  
Figure 6: Trend of research community w.r.t. multi-objective optimization techniques. Where, EC = Epsilon constrained, Lex = Lexicographic, WSS = Weighted sum of square, WCN = Weighted chevyshev norm, NBI = Normal boundary intersection, PAES = Pareto archived evolution strategy, WAVG = Weighted average, WSUM = Weighted sum and PO = Pareto optimal.  
Figure 7: Trend of research community w.r.t. optimization objectives.  
error rate are formulated by using different optimization formulations. Different practical scenarios related to optimization give rise to different nature of optimization problem. Figure 8 shows a glimpse of the trend relating to different optimization formulations. It is evident that most of the desirable scenarios culminate in NP-Hard optimization formulations. For example in [207], optimization of connectivity, coverage, cost, network lifetime and service quality has been formulated as NP-Hard optimization problem. The problem of optimal channel assignment to maximize the throughput, improve fairness and handoff experience of the users have been formulated as NP-Hard problem in [195]. The other commonly used optimization formulations are combinatorial, non-convex, convex, mixed-integer linear programming, linear programming, non-linear programming, NP-Complete, mixed-integer non-linear programming, integer linear programming and concave.
## 4 Discussion
### Limitations of the Study  
This study used only the YOLO series, but no comparison was made with different segmentation models (e.g., U-Net, Mask R-CNN). This shortcoming limits the comparative power of the study between methods. Although performed by experts in the field, the manual process of meniscal ROI selection may lead to some labeling errors. Although the study results were tested by external validation, the diversity of these datasets is limited across different clinical protocols or devices. The model's performance on datasets from different MRI devices or imaging protocols has not yet been evaluated. In addition, evaluations on a single slice (sagittal) are another study limitation.  
Although the proposed method shows strong segmentation performance, some limitations should be noted. The model performs exceptionally well in cases where meniscal tears have clear boundaries. However, for meniscal tears with irregular shapes or low contrast in MRI scans, the segmentation accuracy decreases slightly. This is probably due to the fact that, in these cases, it is difficult to distinguish meniscal tissue from the surrounding structures.
# Detection of Sensitive Data to Counter Global Terrorism
## 2 Research Methodology
### Singular Value Decomposition (SVD)  
At this point, we have constructed the term-document matrix M, which can be processed further for factorization and dimensionality reduction. The SVD takes the \(m\times n\) matrix as the input and returns three different matrices, i.e., term matrix, diagonal matrix, and document matrix.  
\[M=U\ S\ V^{T} \tag{5}\]  
* U is a \(m\times k\) matrix where the terms are represented by rows, while columns correspond to concepts.
* S is a \(k\times k\) diagonal matrix whose entries represent the strength of the individual concepts. The magnitude of the individual values is signified by the importance of the concepts, and this importance can be shown by the variance in the data. A key perception of the LSI can be explained by the small number of concepts that are used to describe the data. The entries in the diagonal matrix S are directly related to the importance of each concept.
* V is a \(k\times n\) matrix where documents are represented by documents, while the concepts are characterized by columns.  
In Latent Semantic Analysis (LSA), m represents the number of terms, while n represents several documents. The decomposition of the matrix is parameterized with a number k, which is less than or equal to the original number n, which indicates how many concepts should be considered during analysis. If \(k=n\), the product of the matrices constitutes the original matrix. However, if \(k<n\), the product of the matrices generates a low-rank approximation of the original matrix. Normally, k is selected to be much lower than n in order to explore the concepts signified in the documents.  
As we had RDD data to analyze, we implemented RowMatrix API to calculate Singular Value Decomposition. The snippet of the codes is presented in Appendix F. A RowMatrix is backed up by rows of an RDD, where each row represents a local vector. This is a row-oriented distributed matrix that has no meaningful row indices, and it was used to calculate the column summary statistics and decomposition. The computeSVD is a dimension reduction function that is used to decompose a matrix into its three different constituent matrices.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 4 Microwave Irradiation
### Microwave-H\({}_{2}\)O\({}_{2}\) Combined Treatment  
It was observed that in WAS the combined MW/H\({}_{2}\)O\({}_{2}\) treatment could effectively solubilize several intra- and extracellular compounds, for instance, proteins and sugars [51]. The reason behind the combinability of these two treatments is that H\({}_{2}\)O\({}_{2}\) is a very heat-sensitive compound and undergoes thermal decomposition, which results in nascent OH-radicals, and so increasing the effect of oxidization [52].  
It was shown that in the temperature range of 60-120 \({}^{\circ}\)C achieved by MW irradiation, the addition of H\({}_{2}\)O\({}_{2}\) could increase the solubility of ammonia in sludge [53]. In the study of [PERSON] et al. it was shown that the extent of soluble COD in sludge was significantly higher (around 10,000 mgL\({}^{-1}\)) when using MW/H\({}_{2}\)O\({}_{2}\) treatment than that obtained for the standalone MW or H\({}_{2}\)O\({}_{2}\) (approx. 5000 mgL\({}^{-1}\) and 2500 mgL\({}^{-1}\), respectively) [54]. [PERSON] et al. concluded that the application of hybrid MW-H\({}_{2}\)O\({}_{2}\) pre-treatment in a WAS and fruit-waste mixture increased the solubilization of sludge by 33%, and enhanced the biogas yield as well [55]. [PERSON] et al. shown that combining H\({}_{2}\)O\({}_{2}\) with microwave irradiation resulted in a significant increment in solubilization from 30 to 50%, when the specific energy investment was 18.600 kJ/kg TS. When acidic conditions were applied during the H\({}_{2}\)O\({}_{2}\)-assisted MW treatment of the sludge samples, the methane production reached 323 mL/gVS [56].  
Table 1 summarizes the results of these studies in regards to different efficiency indicators while applying microwave irradiation alone or combined.