esa-sceva/satcom-chunk-collection huggingface dataset:
# A LiDAR-Based Backfill Monitoring System
## 4 Experiment
### Results of Each Step  
According to the backfill progress computation workflow described in Section 3, the processing of a set of LiDAR scans includes three steps, representing one complete cycle of computing the current backfill progress. Algorithms were tested in the simulation environments of backfill (see Table 2), and results of LiDAR with ranges of 100 m were selected to illustrate the results of each step, as shown in Figure 11.  
In step 1, the intersection boundary of the tunnel and the backfill slurry surface was extracted. For the purpose of improving the computing speed, the initial tunnel point cloud has been downsampled, making the intersection boundary sparser. As a result, the surface point cloud generated by the moving sampling method in step 2 is sparser, but this does not affect the subsequent surface reconstruction and volume calculation results. In step 3, the mesh of the filling body generated by Poisson surface reconstruction is illustrated above the point cloud result, which shows the filling condition inside the tunnel. Dragging the  
Figure 10: Diagram of point cloud data in the backfill monitor.  
Figure 9: Simulation of backfill progress: (**a**) external simulation environment before backfill; (**b**) internal simulation environment before backfill; (**c**) internal simulation environment of the earlier backfill process; (**d**) internal simulation environment of the later backfill process; (**e**) external simulation environment of the interim backfill process.  
left mouse button allows for monitoring the filling progress from different angles, but the computation results are always oriented towards the user.  
The computation results of step 3 are presented using the visualization tool Rviz [41]. The visualization results include the volume of the slurry and the initial tunnel and the division of slurry volume and tunnel volume. For each set of LiDAR scans processed, the slurry volume and backfill progress are updated.
# Fuzzy Echo State Network-Based Fault Diagnosis of Remote-Controlled Robotic Arms
## 1 Introduction
The model is capable of identifying diverse fault types, including sensor faults, actuator faults, and communication faults, under continuous operational conditions without requiring task interruption. The use of a fuzzy ESN further enables efficient processing of smaller three-dimensional datasets, ensuring robust performance under practical operating conditions. Experimental results on a leader-follower robotic arm system validate the effectiveness of the proposed technique, demonstrating its ability to accurately diagnose faults in real time. The remainder of this paper is organized as follows. Section 2 provides a comprehensive overview of the system of remote-controlled robotic arms and details the types of faults considered in the study. Section 3 presents the DFESN for fault classification, including an explanation of the architecture, training algorithm, the use of CMA-ES for optimizing hyperparameters, and an analysis of the computational complexity compared to other methods. Section 4 showcases the experimental setup and results, validating the effectiveness of the proposed method. The paper concludes with Section 5, which discusses the conclusion.
# On the Optimal Calculation of the Rice Coding Parameter
## 3 General Selection of the Golomb Parameter for Rice Coding
### Function Bounds  
Since \(g(r)\) is a derivable function, its first derivative is:  
\[\frac{\partial g(r)}{\partial r}=N-\ln 2\cdot\frac{\sum n_{i}}{2^{r}}. \tag{16}\]  
Since \(g(r)\) is convex, we can find the value of \(r_{g}^{*}\) yielding the minimum value of the function by setting the function's first derivative to zero:  
\[N=\ln 2\cdot\frac{\sum n_{i}}{2^{r_{g}^{*}}}, \tag{17}\]  
which means that:  
\[r_{g}^{*}=\log_{2}\left(\frac{\ln 2}{N}\sum n_{i}\right). \tag{18}\]  
Since \(h(r)\) is a derivable function, we can proceed with the same approach again to find its optimal value. In fact, its first derivative is the same as the first derivative of \(g(r)\), which means that:  
\[r_{h}^{*}=\log_{2}\left(\frac{\ln 2}{N}\sum n_{i}\right). \tag{19}\]  
For nomenclature convenience, let us define \(S(\mathbf{n})\) as the right-hand side expression of (18) and (19):  
\[S(\mathbf{n})=\log_{2}\left(\ln(2)\mu\right)\text{, where} \tag{20}\]  
\(\mu\) is the arithmetic mean of the values in \(\mathbf{n}\). Please note the similarities of (20) with (3), and the different assumptions concerning the probability distributions of the input data for deriving these two expressions.
## 3 Results  
### Method for Initial Estimation of the Trajectory and Accuracy of Tracking of the Target  
This subsection is devoted to solving the fourth problem of the study, which includes creating a method for forming a final smoothed estimate of the target trajectory and  
Figure 9: Typical scenario of adaptation of the tracking system in conditions of deterioration of measurement reliability: (**a**) change in the prediction of reliability \(P_{meas}\); (**b**) adaptation of the measurement noise parameter R; and (**c**) reduction in the influence of measurements when \(P_{meas}\) drops.  
assessing the effectiveness of its tracking by key metrics. The developed system provides integration of the results of the Kalman filter, GERT forecast, and measurement reliability estimates in order to obtain a holistic assessment of the object's state. The initial conditions included: the possibility of partial measurement losses, changing tracking modes, and the need for a prompt response. The method for dynamically adjusting weight coefficients according to the level of confidence in the sources of the estimate is proposed, and a procedure for assessing the quality of tracking by indicators, such as RMSE, probability of loss (Plost), and response delay (Dresponse), is formalized. This subsection presents the algorithm for combining estimates, describes the rules for switching support modes, and demonstrates the results of the simulation evaluation of the system's effectiveness in various conditions.  
To enhance the understanding of how jamming is modeled and how it affects the simulated environment, Figure 10 illustrates a range-angle map of the radar scene. A broadband jammer (red marker), centered at 600 m and 0\({}^{\circ}\), emits noise energy forming a Gaussian-shaped suppression field. A radar target (cyan marker) located at 650 m and 20\({}^{\circ}\) falls partially within this field. This configuration emulates the masking effect of non-coherent barrage jamming, which is widely used in electronic countermeasure scenarios to reduce the signal-to-noise ratio and interfere with target detection. The principle of jamming-induced spatial suppression is also reflected in more advanced models such as the frequency-diverse array (FDA)-based 2D jamming described in [23; 24; 25]. Although our implementation is simpler and does not simulate deceptive structures, it captures the essential mechanism of interference spread in the range-angle domain, allowing for a realistic performance evaluation of the proposed method.
## 4 Conclusions  
In this study, uniaxial compression tests were conducted on fracture-grouted rock specimens after different numbers of wet and dry cycles by simulating the operation circumstances of the abandoned mine pumped storage. The test was carried out to investigate the rock damage mechanism of the roadway. The mechanical and acoustic properties of the specimens were also analyzed and studied, with three main conclusions:  
1. The wave velocity, peak strength, and elastic modulus of the fissure-grouted rock specimens decrease in a linear connection as a result of wet and dry cycle action on the rock specimens. This linear relationship is negatively connected with the number of wet and dry cycles. Additionally, the reduction rate slows down as the quantity of wet and dry cycles rises, indicating that the impact of wet and dry cycles on specimens of fracture-grouted rock gradually diminishes.
2. The damage variables after wet and dry cycling can better reflect the damage evolution process of rock specimens after wet and dry cycling; the change in damage variables went through a smooth phase, a slowly increasing phase, and a steeply rising phase, which is consistent with the change law of the stress-strain curve.
3. A damage intrinsic model for fracture-grouted rock specimens is introduced, considering the impact of wet and dry cycles. The model uses the rock material parameters \(m\) to reflect the impact of rock fractures, laminations, anisotropy, and other characteristics on its mechanical properties. The established damage constitutive model can describe the damage characteristics of the surrounding rock of the abandoned mine pumped storage, and can provide a model reference for the understanding of the instability mechanism of the abandoned mine pumped storage.  
All authors contributed to this paper. Conceptualization, B.W. and Q.L.; methodology, Q.F.; validation, B.W., Q.F. and Q.C.; formal analysis, B.W., Q.Y. and Q.C.; investigation, B.W.; writing--original draft preparation, B.W.; writing--review and editing, Q.L.; supervision, Q.L.; funding acquisition, Q.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Institute of Energy, Hefei Comprehensive National Science Center, under Grant (No. 21 KZS215), Open Research Grant of Joint National-Local Engineering Research Centre for Safe and Precise Coal Mining (Grant No. EC2021014), 2021 Anhui Province Key Research and Development Program (No. 202104a07020009), and Graduate innovation fund of Anhui University of Science and Technology in 2021 (No. 2021 CX2013).  
The data used for conducting classifications are available from the corresponding author upon request.  
The authors declare no potential conflict of interest with respect to the research, authorship, and publication of this article.
# Uncertainty Analysis of 3D Line Reconstruction in a New Minimal Spatial Line Representation
###### Abstract  
Line segments are common in urban scenes and contain rich structural information. However, different from point-based reconstruction, reconstructed 3D lines may have large displacement from the ground truth in spite of a very small sum of reprojection error. In this work, we present a method to analyze the uncertainty of line reconstruction and provide a quantitative evaluation of accuracy. A new minimal four-vector line representation based on Plucker line is introduced, which is tailed for uncertainty analysis of line reconstruction. Each component of the compact representation has a certain physical meaning about the 3D line's orientation or position. The reliability of the reconstructed lines can be measured by the confidence interval of each component in the proposed representation. Based on the uncertainty analysis of two-view line triangulation, the uncertainty of multi-view line reconstruction is also derived. Combining the proposed uncertainty analysis with reprojection error, a more reliable 3D line map can be obtained. Experiments on simulation data, synthetic and real-world image sequences validate the feasibility of the proposed method.  
line reconstruction; line representation; uncertainty analysis; Plucker coordinates +
Footnote †: journal: Applied Sciences
## 4 Improved Routing Protocol  
The routing protocols of WSN are diverse so that projects can choose a routing strategy according to their applications. To meet the need of mobile ad hoc networks (MANETs), the conventional routing protocol in WSNs is mostly derived from Ad hoc On-demand Distance Vector (AODV) routing protocol [27], which has the advantages of being on-demand and spontaneous. It initiates the route discovery when an application requests data transmission. Namely, when a route becomes invalid because of poor link quality or node damaging in natural disasters, this kind of reactive protocol initiates a next round of route discovery process to establish a new route from the source to the destination. Under this circumstance, it is the large consumption that the route discovery process introduces, especially when the network increases with the power transmission scale. However, in the application of RCC for protection, only a few hops may be broken due to the external circumstance, but others are still sound. Therefore, the representative reactive routing protocol like AODV and Dynamic Source Routing (DSR) is not suitable for protection.  
In this section, a routing protocol is proposed for RCC. The advantage of this routing protocol is that routes are locally rebuilt without discovery packets flooding in the overall network. Moreover, the route assures the directional propagation of data packets will get a lower number of hops and thus result in low end-to-end delay.
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 4 The Implication of Fluoride Toxicity on Human Health
### Oxidative Stress  
Under normal physiologic conditions, glutathione (GSH) regulates many functions such as reactive oxygen species (ROS) scavenging, maintaining cell membrane integrity, signal transduction, etc. Intracellular oxidative stress may have functional consequences such as increased mitochondrial superoxide production and a chronic inflammatory response. Excessive intracellular oxidative stress may contribute to several body dysfunctions like diabetes, neurological diseases, cancer, and rapid aging. The imbalance between oxidants and antioxidants is regulated by the GSH redox system in the human body. Some authors suggest that oxidative stress is the key mechanism of fluoride toxicity [95; 97; 98; 99]. Fluoride causes the increased generation of ROS such as superoxide anion, reactive hydroperoxides (ROOH), and hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)) (Figure 4). Fluoride exposure can reduce the cellular level of GSH, often resulting in excessive production of ROS [97; 98; 99]. Fluoride inhibits the activity of antioxidant enzymes such as catalase, glutathione peroxidase (GPx), GSH reductase (GR), and superoxide dismutase (SOD) (see Table 1).  
It has been found that a high amount of fluoride can trigger oxidative stress by sabotaging the intracellular anti-oxidative defense system. The levels of ROS were significantly increased, while the activity of SOD was decreased in the blood of subjects from endemic fluorosis areas in China [5].  
A close association between chronic fluoride toxicity and increased oxidative stress has been reported in children with skeletal fluorosis [95; 100]. A significantly lower redox ratio of GSH to GSSG, as the marker of increased oxidative stress, has been reported in children with autism spectrum disorder (ASD). Increased vulnerability to oxidative stress may contribute to the development and clinical manifestation of ASD [101; 102].  
Figure 4: The glutathione (GSH) redox system. Hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), the immediate precursor of hydroxyl radical, is metabolized via the action of catalase and glutathione peroxidase (GPx). GPx also metabolizes reactive hydroperoxides (ROOH) and oxidizes reduced GSH to its disulfide form (GSSH), which is recycled back to GSH by the action of glutathione reductase (GR). A cofactor for GR is the reduced nicotinamide adenine dinucleotide phosphate (NADPH, which is supplied by the action of glucose-6-phosphate dehydrogenase).  
The increased activity of the enzyme glutathione S-transferase (GST) was found in a group of schoolchildren with the higher levels of dental fluorosis drinking water with 1.8 ppm fluoride in Mexico [103]. The authors suggested that the increased GST activity most likely was the result of the body's need to inactivate free radicals produced by exposure to fluoride.
# Novel Techniques for Microbiological Safety in Meat and Fish Industries
## 2 Factors Affecting Microbial spoilage of Meat, Fish and Derived Products
### Composition of Meat, Fish and Derived Products  
The growth of specific spoilage strains of microorganisms is supported by the nutrient composition of food. Proteolytic organisms have substantial potential to attack protein-rich foods, lipolytic organisms feed on fat-rich foods, while fermentative organisms are more liable to attack carbohydrate-rich foods [7]. Enzymatic degradation of lipids free fatty acids and produce derivatives such as aldehydes or ketones that cause rancid and unpleasant flavors and aromas in fat-based foods. Lipid decomposition mostly occurs in meat-based foods and oil groups such as olive oil derivatives. Naturally present antimicrobial substances and added inhibitory substances have the potential to control this spoilage in such foods [8]. For example, the egg albumin enzyme lysozyme has the potential to disrupt the cell walls of Gram-positive bacteria that can hydrolyze glycosidic bonds present in the peptidoglycan layer of the cell wall, while the avidin protein binds with biotin and forms conalbumin with iron and protease, which also limits protein degradation. Lactoferrin is also known as a natural inhibitor present in milk that seizes the iron needed for microbial growth, such as conalbumin which is found in eggs [9].  
Commonly used antifungal compounds, such as citrate and nisin and butylated hydroxyanisol (BHA) and butylated hydroxytoluene (BHT), used as antioxidants act as inhibitors against microbial growth in meat, while the sorbates and benzoates show more sensitivity to bacterial growth. Various media concentrated with high sugar or salt content are considered as the preferred media for yeast growth; for example, _Debaryomyces hansenii_ has more salt tolerance, while _Hemimysis anomalous_ and _Candida pseudotropicalis_ has lower salt tolerance up to 25% and 11%, respectively, in NaCl solution (_w/v_) [7]. Saling also helps to control the bacterial growth of _Enterobacteriaceae_ spp. by reducing salted meat water content to >0.9 [10]. However, 3% salt addition has resulted in significant reduction in meat water activity to 0.95, which will subsequently reduce the growth of pathogenic microorganisms such as _Salmonella typhimurium_, _Staphylococcus aureus_, _Listeria monocytogenes_ and _Bacillus_ spp. [11].
## 2 Experimental Design and Setup
### Testing Conditions  
The water depth and wave-current conditions in the flume were designed to realistically simulate the conditions found in the Xhiounen Strait of China. Based on the model scale, the experiments simulated the conditions of regular waves and currents at the corresponding constant water depth of \(d\) = 1.0 m.  
Over a 100-year return period in the Xhiounen Strait, the wave heights at cumulative frequencies of 1%, 5%, and 13% and the average wave height were 7.80 m, 6.37 m, 5.32 m, and 3.36 m, respectively; the average wave period was 10.4 s and the current rate was 3.3 m/s. Based on these wave and current conditions, 20 different test cases of wave and current combinations acting in the same direction were designed, and the wave and current parameters of the model and prototype are given in Table 1.  
In these experiments, the wave and current conditions were generated using the same methods as those used by [PERSON] et al. (2020) and [PERSON] et al. (2020). In the wave-only case, regular waves were generated by the reciprocal motion of the wave maker; in the current-only case, a uniform current was generated by adjusting the flow rate of the circulation pump system until the velocity of the current was stable; in the wave-current case, the current was generated first, then the wave generator was run using the same control parameters as those in the wave-only case. The wave-current forces on the pile were measured when the waves and the current had been combined stably; each case was repeated three times to reduce random experimental errors, and the test results presented herein are the average values of those three tests.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Model**} & \multicolumn{3}{c}{**Prototype**} \\ \hline \(\mathbf{H_{0}}\) [cm] & \(\mathbf{T}\) [s] & \(\mathbf{U}\) [m/s] & \(\mathbf{H_{p}}\) [m] & \(\mathbf{T_{p}}\) [s] & \(\mathbf{U_{p}}\) [m/s] \\ \hline
5.6 & 1.35 & 0, 0.1, 0.2, 0.3, 0.4 & 3.36 & 10.4 & 0, 0.8, 1.6, 2.5, 3.3 \\
8.9 & 1.35 & 0, 0.1, 0.2, 0.3, 0.4 & 5.32 & 10.4 & 0, 0.8, 1.6, 2.5, 3.3 \\
10.6 & 1.35 & 0, 0.1, 0.2, 0.3, 0.4 & 6.37 & 10.4 & 0, 0.8, 1.6, 2.5, 3.3 \\
13.0 & 1.35 & 0, 0.1, 0.2, 0.3, 0.4 & 7.80 & 10.4 & 0, 0.8, 1.6, 2.5, 3.3 \\ \hline \hline \end{tabular} Notes: \(\mathbf{H_{0}}\)—model wave height [cm] under wave action without current; \(\mathbf{T}\)—model wave period [s] under wave action without current; \(\mathbf{U}\)—model current velocity [m/s] under current-only action.  
\end{table}
Table 1: Wave and current parameters.  
Figure 4: Installation of pile model: (**a**) photograph of model in wave flume; (**b**) measurement arrangement (unit: cm).
# Mercury Uptake and Transport by Plants in Aquatic Environments: A Meta-Analysis
## 3 Results and Discussion
### TF for Hg in Plants  
The effect sizes (In TF) of plants belonging to the same family were combined to calculate mean and 95% CI values. In this regard, 17 plant families were included in the meta-analysis, and a simplified forest plot was drawn (Figure 4). It was observed that the total average effect size considering all the species was less than zero (mean \(=-0.278\), CI: \(-0.515\)--\(-0.043\)), indicating that for most plants, the roots accumulated more Hg than the aboveground parts. Specifically, the effect sizes corresponding to Cymodocaceae (mean = 0.061, CI: 0.045-0.077) and Potamogetonaceae (mean = 1.986, CI: 1.704-2.206) were  
Figure 3: Biological accumulation factors (BAFs) of plant roots based on families, presented as the mean effect size and 95% CI. (**a**) Absorption of Hg from water by plant roots. (**b**) Absorption of Hg from sediments by plant roots. The numerals after the family names represent the number of observations. “Mean” indicates the overall effect considering all families.  
significantly greater than zero, indicating that these plants accumulated more Hg in their aboveground parts relative to their roots. There were no significant differences between the Hg accumulation capacities of the roots and aboveground parts of Acoraceae, Cyperaceae, Juncaginaceae, Juncaceae, Onagracaceae, Poaceae, Posidoniaceae, and Typhaceae plants. However, Hg accumulation in the roots of Amaranthaceae, Aracee, Convolvolvalaceae, Hydrocharitaceae, Plantaginaceae, Pontederiaceae, and Salviniaceae was considerably higher than that in their aboveground parts.  
The diverse differences that exist between plants and changes in environmental conditions may lead to different heavy metal ion transfer coefficients between the roots and aboveground parts of plants [63; 64]. After entering the plant root, Hg\({}^{+}\) is either stored in the root tissue or it continues to move upward [65]. For this upward transportation to continue, Hg\({}^{+}\) needs to be moved from the root symplast to the xylem apoplast [64; 65]. During this process, most Hg\({}^{+}\) remains in the root cell wall given that the plant cell wall can precipitate heavy metal ions [63]. Furthermore, negatively charged heavy metal ions have greater mobility, whereas positively charged heavy metal ions tend to accumulate on the negatively charged root surface [66]. Therefore, the transfer of absorbed Hg by roots to other plant organs is difficult [67].