esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Physicochemical Characterisation of the Feedback  
The total solids analysis showed that the steam explosion pre-treatment affected the solids concentration in WS. More specifically, the TS content decreased from 36% in WS to 21.5% (_w/w_) in SE (Table 1). Similarly, SE had a volatile solids (VS) content of 19.37% (_w/w_), while in WS, VS accounted for 33.38% (_w/w_) of the total biomass. Prior to the initiation of steam explosion, water was added to the pre-treatment vessel to allow the feedstock to be pumped out after the steam explosion process. It is likely that the increased temperature during SE pre-treatment (155 \({}^{\circ}\)C) allowed the pores of the substrate to open and absorb moisture. Similar results have been reported in previous studies examining the effect of steam explosion pre-treatment on WS [32]. DDGS and RM were found to have similar VS contents of 86.1% (_w/w_) and 86.36% (_w/w_), respectively, whereas the inoculum contained 2.53% VS (_w/w_).  
Furthermore, the steam explosion pre-treatment seemed to increase the percentage of cellulose content in the WS from 37.41% to 47.9% (_w/w_) of the total dry biomass. These results are not in agreement with previous studies where it was pointed out that cellulose content in lignocellulosic residues does not follow a specific trend after steam explosion pre-treatment [32; 33]. However, differences in the configuration of cellulose (e.g., crystalline and amorphous structures) and the crystallinity index (the relative amount of crystalline structures in cellulose) within different lignocellulosic residues might affect the response of this material to steam explosion pre-treatment [34]. Unlike cellulose, the percentage of hemicellulose in WS samples did not seem to be affected by steam explosion. This result can be attributed to the fact that the applied steam explosion temperature (155 \({}^{\circ}\)C) was relatively low for hemicellulose hydrolysis to occur; this is reported to happen in temperature treatments between 150 and 230 \({}^{\circ}\)C [35]. However, a total breakdown of hemicelluloses of WS into oligosaccharides is not necessary for biogas production, since microbial clusters in AD, such as _Caldicoprobacter_ sp. and _Cosridium_ sp., inherently possess hemicellulose-degrading enzymes [34; 35].  
The analytical measurements also confirmed the high protein content of DDGS and RM (28% and 25%, _w/w_, respectively). At the same time, crude protein content was lower than 1% (_w/w_) for both straw samples. As expected, both DDGS and RM contained significantly lower concentrations of lignin than WS samples (Table 1).
## 2 Lattice Discrete Particle Model
### LDPM Cyclic Behavior  
Realistic energy dissipation is crucial in the cyclic loading context to reproduce the behavior of concrete subjected to seismic loading. Two parameters, \(k_{t}\) and \(k_{c}\), dominate in controlling the energy dissipated under cyclic tension and compression loading, respectively, in the Lattice Discrete Particle Model (LDPM).  
The parameter kt drives the residual plastic strain and form of the unloading and reloading curves due to tensile cycling. If a specimen is unloaded from the tensile envelope curve, the residual plastic strain is given as:  
\[\epsilon_{tpl}=k_{t}\Big{(}\epsilon_{N,max}-\frac{\sigma_{N,max}}{E_{N}}\Big{)} \tag{10}\]where \(\epsilon_{N,max}\) denotes the maximum normal strain, \(\sigma_{N,max}\) corresponds to the maximum stress, and \(E_{N}\) denotes the elastic modulus.  
Increasing the \(k_{t}\) value increases the residual plastic strain, shifting the reloading curve along the strain axis. This shift makes reloading intersect the envelope curve later, decreasing the hysteresis loop's area. Dissipated energy under cyclic loading is therefore decreased. On the other hand, this decreases the residual plastic strain, making reloading occur earlier and leading to a larger hysteresis loop, increasing the dissipated energy.  
The parameter \(k_{c}\) governs the reloading strain in compression after unloading. The reloading limit strain is given as:  
\[\epsilon_{cr}=k_{c}\epsilon_{cpl} \tag{11}\]  
where \(\epsilon_{cpl}\) denotes the residual plastic strain in compression.  
When \(k_{c}\) is increased, the reloading strain is greater, and the stress will be zero for a greater range of strain before reloading. This influence minimizes the area within the stress-strain loop and, thereby, the energy dissipation. On the other hand, reduction shifts the reloading curve nearer to the unloading point, decreasing the extent of strains throughout, wherein there is zero stress. This redaction increases the area beneath the loop and dissipates energy with the loading cycle.  
Both \(k_{c}\) and \(k_{t}\) influence the size and shape of the hysteresis loops in cyclic tension and compression, which are necessary measures of the energy dissipation capability of concrete under seismic loading. The calibration of these parameters is essential to properly reflect experimental tests' plastic deformation and energy dissipation behavior. The modified LDPM model [24] integrates these parameters to eliminate shortcomings in the original cycling law to enhance the simulation of the behavior of concrete subjected to different loading states, such as uniaxial tension-compression, hydrostatic pressure, and triaxial compression.
## 5 Results and Discussion  
This section presents and critically analyzes the results obtained from both two-dimensional (2D) and three-dimensional (3D) finite element (FE) simulations of single-segment, three-segment, and five-segment cables. The primary focus is on comparing the AC-to-DC resistance ratio (\(\text{R}_{\text{AC}}/\text{R}_{\text{DC}}\)) across different segmentations and frequencies, assessing the accuracy of 2D models relative to their 3D counterparts, and evaluating the computational efficiency of each modeling approach. Additionally, the impact of segmentation and frequency on current distribution and magnetic field density is examined to provide comprehensive insights into the electromagnetic behavior of segmented cables.
## 3 Materials and Methods
### Statistical Analyses  
The main objective of the data analysis was to transform the collected data into information and knowledge that could be used to act. The initial step involved an evaluation of the significance of the influence of the process parameters (X1 to X9) on the occurrence of various defects. To achieve this, statistical methods such as the Kruskal-Wallis test and the Chi-square test were employed.  
The Kruskal-Wallis test [35; 36], a nonparametric statistical method, was used to compare medians in more than two independent groups. This test is appropriate when the assumption of normal data distribution is untenable or when the sample sizes vary across groups. It is an extension of the Mann-Whitney U test for cases involving more than two groups and is particularly effective for analysing ordinal or continuous data that do not meet the assumptions of normality. The null hypothesis (H\({}_{0}\)) of the Kruskal-Wallis test posits that all groups originate from populations with the same median, whereas the alternative hypothesis (H\({}_{1}\)) suggests that at least one group comes from a population with a different median.  
The test is used to classify the data in all groups and to facilitate a comparison of the sums of ranks between the groups. The test statistic, designated as \(H\), is calculated using the following formula:  
\[H=\frac{12}{N(N+1)}\sum_{i=1}^{k}\frac{R_{i}^{2}}{n_{i}}\;-\;3(N\,+\,1) \tag{1}\]  
where  
* \(N\) is the total number of observations;
* \(R_{i}\) is the sum of ranks for the \(i\)-th group;
* \(n_{i}\) is the number of observations in the \(i\)-th group;
* \(k\) is the number of groups.  
If the resulting \(p\)-value is small (typically \(p\) < 0.05), it indicates that there are statistically significant differences between the medians of the groups.  
The Chi-square (\(\chi^{2}\)) test is used to examine the relationship between two categorical variables by comparing the observed and expected frequency of occurrence in different categories [27; 28]. The null hypothesis (H\({}_{0}\)) of the Chi-square test assumes no association between the variables, thereby indicating that they are independent. In contrast, the alternative hypothesis (H\({}_{1}\)) suggests a dependency between the variables. The Chi-square statistic is calculated using the following formula:  
\[\chi^{2}=\sum\frac{\left(\text{O}-E\right)^{2}}{E} \tag{2}\]  
where \(Oij\) is the observed value and \(E_{ij}\) are theoretical values.  
If the \(p\)-value obtained from this test is less than 0.05, the null hypothesis is rejected, indicating a statistically significant association between the variables.  
In this study, the Kruskal-Wallis test was used to compare the medians of multiple groups when normality assumptions were not met. Meanwhile, the Chi-square test was used to assess the relationship between categorical variables by comparing the observed and expected frequencies. These methods were used to determine the importance of the influence of the process parameters (X1 to X9) on the appearance of various defects (D1 to D6) in the production process.
# Helmet Wearing Detection Algorithm Based on YOLOv5s-FCW
## 5 Conclusions  
To handle the issues regarding existing safety HD methods, such as high parameter counts, complex neural networks, intensive computation requirements, limited suitability for deployment in embedded and other devices, and insufficient accuracy in detecting small targets, we propose an optimized lightweight algorithm called YOLOv5s-FCW for HD. Firstly, to enhance the efficiency of the YOLOv5s algorithm, we replaced backbone network with FasterNet for FE, resulting in a reduction in both the parameters and the computational complexity of the network; secondly, we integrated a CBAM module into the YOLOv5 model to enhance the model's capacity for detecting small targets, such as SH, by increasing its focus on them; finally, to enhance the convergence of the model, we replaced the GIoU_loss loss function with the WIoU_Loss loss function. Experimental results demonstrate that our proposed YOLOv5s-FCW method enhances mAP by 2.5% and accuracy by 4.6% compared to the baseline algorithm. This optimized algorithm also improves the recognition of small and obscured targets and reduces the computational requirements of the YOLOv5s model by 20% while maintaining a higher average accuracy of detection. As a result, our algorithm reduces hardware costs and fulfills the performance standards for the detection of SH in construction zones.  
Although the proposed method improves the detection accuracy and detection speed of the safety helmet target to a certain extent, there is still room for further improvement in the parameters and lightweight of the network. Whether it can be applied to videos of safety helmet scenes still needs to be studied in the future work.  
Conceptualization, J.L. and H.Z.; Data curation, H.Z. and P.L.; Formal analysis, J.L.; Funding acquisition, D.X.; Investigation, D.X.; Methodology, G.L. and P.L.; Resources, G.L. and P.L.; Software, P.L.; Validation, H.Z., S.H. and D.X.; Writing--original draft, G.L. and S.H.; Writing--review & editing, H.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 52074064.  
Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **mAP** & \multirow{2}{*}{**AP**} & \multirow{2}{*}{**R**} & **Parameters (10\%)** & **GFLOPs** \\  & **(IoU = 0.5)** & & & & \\ \hline YOLOv7 & 0.769 & 0.826 & 0.787 & 9.3 & 23.2 \\ YOLOv8 & 0.798 & 0.843 & 0.796 & 8.6 & 20.4 \\ YOLOv9 & 0.828 & 0.865 & 0.805 & 10.4 & 26.0 \\ Transformers & 0.836 & 0.891 & 0.798 & 36.7 & 84.97 \\ Ours & 0.837 & 0.889 & 0.813 & 5.62 & 13.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with other SOTA methods.  
Data Availability Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare no conflicts of interest.
## 5 Conclusions  
In this paper, the numerical simulation and shaking table test are carried out for an isolation system with MR damper and rubber bearing by using a high-order single-step algorithm. Both the continuity and switch control strategies are examined. The main findings are summarized as follows:  
1. The proposed isolation system can be applied to different site categories. The deformation corresponding to isolation layer can be effectively limited. Simultaneously, acceleration response of the superstructure can be reduced. Therefore, the smart isolation control system is shown to exhibit better control performance as compared with the traditional passive control method.
2. Energy dissipation paths can be adapted by MR dampers in accordance with varying earthquake input excitations, and variable damping can be provided for the isolation layer. From the acceleration reductions and the hysteresis curves, the seismic energy can be found to be effectively dissipated. Therefore, the design objective concerning the mitigation of secondary damages can also be fulfilled.
3. Restoring force can be provided by the rubber bearings to the isolation layer, thus the requirement of deformation limit of the isolation layer can be achieved under rare earthquakes.
4. The high-order single-step algorithm has the capability of real-time calculation of structural response. Therefore, time delay issues can be effectively addressed. The entire control process, including data acquisition, real-time calculation and the results updating can be achieved by the control system. The control effect is not affected by changes associated with external input.
5. The switch control strategy is found to be simple and reliable. The displacement response of isolation layer can be effectively controlled. Moreover, continuous regulated damping force can be achieved through a continuity control strategy. It is appropriate to consider the characteristics of MR dampers when instantaneous variable damping is needed.  
The research is supported by the National Natural Science Foundation of China (Project No. 51678322, 51650110509 and 51578347), Natural Science Foundation of Heilongjiang Province (Project No. E2016053) and the Taishan Scholar Priority Discipline Talent Group program funded by the Shan Dong Province.  
[PERSON] and [PERSON] developed the algorithm, conceived and designed the experimental works; [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] investigated the control strategy and experimental implementation, [PERSON] and [PERSON] analyzed the experimental data; [PERSON] drafted the manuscript, [PERSON], [PERSON] and [PERSON] undertook the revision and editing.  
The authors declare no conflict of interest.
# Analyzing the Long-Term Phenological Trends of Salt Marsh Ecosystem across Coastal LOUISIANA
## 5 Conclusions
Further, their spatial coverage/swaths are much less than MODIS; hence, combining scenes from multiple days may create biases in the analysis itself. Hence, for analysis at such broader landscape scale, MODIS is clearly a robust sensor. This is the first time that such a comprehensive analysis of the phenological trends of the LA salt marsh ecosystem has been studied in terms of the seasonality parameters. Using the suggested methods in this study, the monitoring of salt marsh ecosystem in LA, the southeast US, and elsewhere in the world with similar environmental settings will be possible, following the necessary re-parameterization of the GBM model. Through continuous monitoring of the ecosystem, it will be possible to ascertain whether salt marshes will continue to be a major carbon sink in the environment, or changes in the environment will force them to become a net carbon source. As mentioned before, salt marshes are dynamic and critical ecosystems in terms of carbon sequestration. Therefore, continuous monitoring of these critical ecosystems is crucial for effective restoration and management practices and the formulation of global change policies. We would like to thank and acknowledge our project partners at Mississippi State University, University of Nebraska-Lincoln, and University of New Orleans. This research was partially funded by the National Aeronautics and Space Administration (NASA) Gulf of Mexico program (Grant # NNX10 AE65G), National Science Foundation (NSF) Division of Environmental Biology (Grant # 1050500), and Gulf of Mexico Research Initiative (GoMRI) (Grant # GRI-0012). [PERSON] is grateful to the University of Georgia Graduate School Dean's Award for Social Science for carrying out this research. [PERSON] helped with processing of image data, designed the study and wrote the manuscript. [PERSON] provided invaluable advice during the analysis phase and helped with editing the manuscript. The authors declare no conflict of interest.
# The Bearing Performance and Sectional Design Method of Yielding U-Shaped Steel Support
## 1 Introduction
[25] investigated the influence mechanism of friction effects at support joints under impact loading, developing a friction joint model and testing system to guide support design. [PERSON] et al. [26], to solve the problem of excessive deformation of the surrounding rock of the roadway in the process of coal longwall mining, proposed the joint support measure of \"U-shaped steel support + anchor\", and the field application results showed that the deformation of the surrounding rock of the roadway was effectively improved. [PERSON] et al. [27] studied the mechanical deformation characteristics of U-shaped steel supports under different loads through laboratory tests and numerical simulations, which provided guidance for the selection of on-site arch structures and roadway excavation conditions. The above studies have improved the bearing performance and stress state of the yielding U-shaped steel support by optimizing the form of support nodes and adopting combined support measures. However, due to the complexity of deep soft rock projects, the influence mechanism of the bearing performance of the yielding U-shaped steel support is still unclear, and the sectional design remains at the stage of on-site experience, without fully considering the influence of the sectional position on the yieldable and bearing performance. Therefore, it is necessary to conduct research on the influence mechanism of the bearing performance of the yielding U-shaped steel support and its sectional design under complex environments. Based on this, taking the mining gateway of Tangkou Coal Mine in a kilometer-deep well as the engineering background, this paper conducts a statistical analysis of the failure forms of U29 steel support on site. By using the independently developed full-scale support mechanical test system, indoor tests on full-scale yielding U-shaped steel support with different sectional methods are carried out, as well as numerical tests on the bearing performance of yielding U-shaped steel support under different load patterns and different cross-sectional forms. The deformation and internal force distribution laws of the support under different conditions are analyzed, and a sectional design method for yielding U-shaped steel support is proposed to guide the on-site design.
## 4 Discussion  
The purpose of this study was to evaluate the maxilla-mandibular interoc occlusal registration accuracy on quadrant scans obtained by IOSs. Though most restorations were designed in quadrant scans in current digital workflow, there were few studies about the accuracy of VIRs in quadrant scans. In this study, the accuracy of VIRs was assessed by comparing the signed and unsigned differences of the linear distance measuring five pairs of interarch markers corresponding to the canine, premolar, and molar. In addition, the trueness and precision of scan data by various IOSs were assessed together. The unsigned difference of the three IOSs showed statistical differences with the model scanner (T500) at most positions. Only Trios showed no significant difference when compared to the T500 in the FPS group except one position. The signed difference also showed a statistical difference when compared to T500 in multiple positions. Both findings revealed that the VIR using IOSs had a more deviation than that obtained with the laboratory scanner. The first hypothesis stated that there is no difference in terms of accuracy when comparing the VIR of the IOS with that of the laboratory scanner. It was partially rejected. While T500 showed uniformly low unsigned and signed differences, regardless of the dedentulous conditions, the unsigned differences in the PPS, SSDEB, and LSDEB groups of Trios increased compared to those in the FPS group. In the case of the LSDEB group in Trios, (-) signed differences of all positions suggested that the maxillary and mandibular arches were in overclosure state. In the case of CS3600, when compared with the FPS group, the signed and unsigned differences in the PPS and SSDEB groups increased, and there might be more deviation in the posterior position. For i500, the signed and unsigned difference increased in the SSDEB and LSDEB groups without posterior teeth support, and the deviation in the posterior position increased. The second hypothesis stated that there is no difference in the accuracy of the VIR according to the dedentulous condition. It was also partially rejected. In our study, the (+) signed difference at any position indicated that the VIR was recorded with an overestimated interoclusal distance; the (-) signed difference meant the opposite.
Sensors_**2012**, _12_, 2026-2061; doi:10.3390/s120202026  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Review_  
**Localization Algorithms of Underwater Wireless Sensor Networks: A Survey**  
**[PERSON] \({}^{1,2,\star}\), [PERSON] \({}^{1,2}\), [PERSON] \({}^{3}\), [PERSON] \({}^{4}\) and [PERSON] \({}^{5}\)**  
\({}^{1}\) Department of Information and Communication Systems, Hohai University, 200 North Jinling Road, Changzhou 213022, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Changzhou Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou 213022, China  
\({}^{3}\) Department of Multimedia Engineering, Graduate School of Information Science and Technology, Osaka University, 1-1 Yamadaoka, Suita, Osaka 565-0871, Japan; E-Mail: [EMAIL_ADDRESS]  
\({}^{4}\) Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road Zhongguancun, Beijing 100190, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{5}\) Naval Academy of Armament, Beijing 100161, China; E-Mail: [EMAIL_ADDRESS]  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS].  
_Received: 4 January 2012; in revised form: 20 January 2012 / Accepted: 30 January 2012 / Published: 13 February 2012_  
**Abstract:** In Underwater Wireless Sensor Networks (UWSNs), localization is one of most important technologies since it plays a critical role in many applications. Motivated by widespread adoption of localization, in this paper, we present a comprehensive survey of localization algorithms. First, we classify localization algorithms into three categories based on sensor nodes' mobility: stationary localization algorithms, mobile localization algorithms and hybrid localization algorithms. Moreover, we compare the localization algorithms in detail and analyze future research directions of localization algorithms in UWSNs.  
**Keywords:** underwater wireless sensor networks; localization algorithms; centralized; distributed; hybrid