esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multilevel Adaptive Path-Planning Model in Off-Road Environments
## 2 Overview of Ant Colony Algorithm and Improvements  
### Basic Mathematical Model for the Ant Colony Algorithm  
An ant colony algorithm (ACA) is an abstract expression of the foraging behaviors of ants in the natural world. In an ACA, the path pheromone concentration between nodes decides the motion of ants. An ant at a given node \(i\) calculates the probability to move towards the next optional node, and if an ant \(k\) moves from node \(i\) to another node \(j\) at the time \(t\), then its motion transfer probability is expressed as follows:  
\[P_{i,j}^{k}(t)\ =\ \begin{cases}\frac{\pi_{i,j}^{k}(t)\cdot\eta_{ij}^{k}(t) \cdot\eta_{ij}^{k}(t)}{\sum_{j\in allowed,k}\frac{\pi_{ij}^{k}(t)\cdot\eta_{ij}^ {k}(t)}{\eta_{ij}^{k}(t)}},j\in A_{k}\\ 0,else\end{cases} \tag{1}\]  
where \(A_{k}\) is the set of selectable nodes that the ant \(k\) can move to at the next time point; \(\eta_{ij}(t)\) is the expectation heuristic function, which represents the expectation value that the ant moves from \(i\) to \(j\). \(\eta_{ij}(t)\) can be calculated by Equation (2), where \(E\) is the location of the target point and \(d(i,E)\) is the distance between node \(i\) and target node:  
\[\eta_{ij}(t)\ =\ \frac{1}{d(i,E)} \tag{2}\]  
\(\tau_{ij}(t)\) is the pheromone concentration on the route from node \(i\) to node \(j\) at the time point \(t\). After each round of global updating, the pheromone concentration of the route will be updated by a given strategy, and the update equation is as follows:  
\[\tau_{i,j}(t+n)\ =\ (1-\rho)\cdot\tau_{i,j}(t)+\Delta\tau_{i,j} \tag{3}\]  
where \(1-\rho\) is the residual coefficient of pheromone, and a larger value of \(1-\rho\) indicates less volatility of the pheromone; \(\Delta\tau_{i,j}\) represents the increment of pheromone along the \(i-j\) route during iteration, which is expressed as follows:  
\[\Delta\tau_{i,j}\ =\ \sum_{k\ =\ 1}^{m}\Delta\tau_{i,j}^{k} \tag{4}\]  
where \(\Delta\tau_{i,j}^{k}\) is the pheromone that the ant \(k\) releases along the \(i-j\) route during iteration. The calculation methods vary as the pheromone update rules differ. The ant cycle system is the most popular update rule, which updates the pheromone after a feasible solution is found after iteration. \(\Delta\tau_{i,j}^{k}\) is calculated by Equation (5):  
\[\Delta\tau_{i,j}^{k}\ =\ \begin{cases}\frac{Q}{L_{k}},\text{when ant $k$ passes through route $i$-$j}\\ 0,\text{else}\end{cases} \tag{5}\]  
where \(Q\) is the pheromone concentration and \(L_{k}\) is the length of the route that the ant \(k\) moves during the iteration.
## 1 Introduction
This device achieved high-precision emissivity measurements through precise control of the sample surface temperature and high-accuracy calibration. The device was evaluated for its performance in measuring the emissivity of silicon carbide at 1073 K, with an uncertainty of less than 2.9% reported [21]. In 2023, [PERSON] et al. developed a direct method for measuring the spectral directional emissivity of optically thick materials at temperatures ranging from 40 \({}^{\circ}\)C to 450 \({}^{\circ}\)C and angles from 0\({}^{\circ}\) to 80\({}^{\circ}\). They estimated the accuracy of calibration and uncertainty assessment during the measurement process, with uncertainties ranging from 2% to 20% under optimal and worst-case configurations, respectively [22]. In 2024, [PERSON], [PERSON] et al. developed a device capable of covering a temperature range from 50 \({}^{\circ}\)C to 1000 \({}^{\circ}\)C with a spectral range of 3-14 \(\upmu\)m. They investigated the normal spectral emissivity of silicon carbide over the temperature range from 50 \({}^{\circ}\)C to 100 \({}^{\circ}\)C. An analysis of the uncertainty in emissivity measurements at different temperatures and wavelengths revealed uncertainties better than 5% within the measurement range [23]. However, the aforementioned measurement device either exhibits relatively high uncertainty in the measurement of sample emissivity or can achieve uncertainty of less than 1% only under specific conditions. Therefore, based on the definition of material emissivity, this paper presents the design and construction of a measurement device for material emissivity aimed at addressing these limitations.
## 1 Introduction
(2018); [PERSON] et al. (2018) conducted a series of centrifuge tests to study the toppling failure mechanism of anti-dip rock slopes and the development process of the rupture surface. The results indicate that toppling failure is controlled by the planes of the rock layer and the structure planes and deformation failure often occurs from the foot of the slope, which can be regarded as cantilever beam toppling and bending freely to the free face under gravity. The internal friction  
Figure 1: Typical toppling deformation slope, (**a**) The toppling slope of the Yuqu River Zhala Hydropower Station, (**b**) The toppling slope on the left bank of Wulongnong Hydropower Station. angle controls the failure mode of the anti-dip slope, and the cohesion has little effect on the toppling deformation or failure. [PERSON] and [PERSON] [11] analysed the influence of the tensile strength of the rock stratum on toppling deformation by centrifuge model testing. The results indicated that toppling deformation was mainly controlled by the tensile strength of the rock stratum. The failure mechanism of block-flexure toppling failure using a centrifuge model was reported by [PERSON] et al. [19]. The results of the centrifuge test indicates that block-flexure toppling failure occurs instantaneously and that the total failure surface is stepped. The deformation and failure zone can be divided into three subzones: a toppling failure zone, a crack zone, and a deformation zone [20]. The causes and conditions for the toppling deformation of counter-tilt layered rock slopes are complicated ([PERSON] et al. [21]). The author took the toppling deformation body in front of the dam of the Lancang River Gushui Hydropower Station as a prototype slope to construct a physics model slope and conducted a centrifuge simulation test. The author revealed the disaster mode for deep-seated toppling of anti-dip rock slopes and proposed that the change in the slope angles was the key hazard factor for the deformation and failure of anti-dip rock slopes. However, the conclusion is required further verification and improvement because of the limitations of the test samples. This study is based on the above-mentioned understanding, taking the toppling deformation body in front of the dam of the Lancang River Gushui Hydropower Station as a prototype slope for the construction of three physics models with slope angles of 55\({}^{\circ}\), 65\({}^{\circ}\) and 75\({}^{\circ}\) and attempts to perform a comparative analysis on the centrifuge tests of the models with different slope angles to investigate the influence of the change in the free-face conditions on the toppling deformation of anti-dip rock slopes. Additionally, the author would like to obtain the relationship between the change in the slope angle and the evolution of the toppling deformation on the basis of centrifuge tests of multiple samples. In this way, the author can establish a basis for further understanding the failure mode of the toppling deformation and to establish a reliable method for calculating the depth of the toppling-bending zone.
## 2 Materials and Methods
### Antioxidant Capacity Determination  
2.6.1. ABTS Radical Assay  
ABTS radical scavenging activity assay was achieved according to [PERSON] et al. [40], with some modifications [41]. The oxidation of ABTS with potassium persulfate was needed to generate the ABTS radical, forming a deep green solution. ABTS (7 mM) and potassium persulfate (2.45 mM) were combined and left in the dark for at least 16 h to obtain the ABTS stock solution. Fresh daily ABTS** working solution, with an absorbance around 0.7 at 734 nm, was made by mixing ABTS** stock solution and 5 mM phosphate buffer (pH 7.4). Next, 10 \(\upmu\)L of extract (50 mg sample/mL) was mixed with 990 \(\upmu\)L of fresh ABTS** working solution. Finally, the reaction mixture was kept in the dark for 45 min at room temperature, and absorbance was determined at 734 nm. ABTS radical scavenging capacity was determined by the following equation:  
\[\text{ABTS radical scavenging capacity}\left(\%\right)=\left(\frac{Abs\;blank- Abs\;sample}{Abs\;blank}\right)\times 100 \tag{2}\]  
where _Abs blank_ was the absorbance of 10 \(\upmu\)L of the NaDES with 990 \(\upmu\)L ABTS** working solution, and _Abs sample_ was the absorbance of 10 \(\upmu\)L of the extract with 990 \(\upmu\)L ABTS** working solution.  
2.6.2. Capacity to Inhibit the Formation of a Hydroxyl Radical Assay  
The capacity to inhibit the formation of hydroxyl radicals was evaluated, following the method described by [PERSON] et al. [42] with some modifications [41]. Hydroxyl radicals are obtained through the Fenton reaction because of the oxidation of Fe (II) to Fe (III) by H\({}_{2}\)O\({}_{2}\). Phenolic compounds will inhibit the formation of hydroxyl radicals by preventing this oxidation reaction. To achieve this, 50 \(\upmu\)L of 3 mM 1,10-phenanthroline in 0.1 M phosphate buffer (pH 7.4) was combined with 50 \(\upmu\)L of 3 mM ferrous sulfate, 50 \(\upmu\)L of peel extract (extract concentrations: 50 mg sample/mL for the assays made during the selection of NaDES and the molar relation of NaDES components and 5 mg sample/mL for the assays carried out during extraction optimization and conventional extraction), and 50 \(\upmu\)L of 0.01% H\({}_{2}\)O\({}_{2}\). To start the reaction, the mixture was incubated for 1 h at 37 \({}^{\circ}\)C and 700 rpm. Next the absorbance corresponding to the complex Fe(II)-phenanthroline was measured at 536 nm. The results were expressed as % of hydroxyl radical formation inhibition using the following equation:  
\[\%=\left(\frac{Abs\ sample-Abs\ blank}{Abs\ control-Abs\ blank}\right)\times 100 \tag{3}\]  
where _Abs sample_ was the absorbance of the sample extracts, _Abs blank_ was the absorbance of the NaDES, and Abs control was the absorbance of a control solution prepared by adding water instead of H\({}_{2}\)O\({}_{2}\) and NaDES.
## 5 Conclusions  
This study analyzed the spatial-temporal variation characteristics of WUE and its influencing factors in the YRB and in different ecosystems from 1982 to 2018. The results show that: (1) From 1982 to 2018 the WUE exhibited a significant increase trend (0.56 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\)) in the YRB. The area showing a significant increase in WUE (47.07%, Slope \(>\) 0, \(p\)\(<\) 0.05) was higher than the area with a significant decrease (14.64%, Slope \(<\) 0, \(p\)\(<\) 0.05). The region of significant increase (Slope \(>\) 0, \(p\)\(<\) 0.05) in WUE in 2000-2018 (45.35%) was higher than that of 1982-2000 (8.23%), which was 37.12% higher in comparison. (2) Forest WUE (1.267 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) Cropland WUE (0.972 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) \(>\) Grassland WUE (0.805 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O) under different LUCC from 1982 to 2018. Forest had the highest WUE (1.267 gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O). The WUE of forest ecosystems (0.79 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\)) increased at a higher rate than that of farmland(0.55 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\)) and grassland (0.44 \(\times\) 10\({}^{-2}\) gC\(\cdot\)kg\({}^{-1}\cdot\)H\({}_{2}\)O\(\cdot\)a\({}^{-1}\)) from 2000 to 2018. (3) Precipitation (37.98%, R > 0, \(p\) < 0.05) and soil moisture (10.30%, R > 0, \(p\) < 0.05) are the main climatic factors affecting WUE in the YRB. A total of 45.55% of the WUE exhibited a significant increase trend (Slope > 0, \(p\) < 0.05), which is mainly attributed to the simultaneous increase in GPP and ET. This study provides important scientific support for water resource management and ecological protection in the Yellow River Basin.  
Conceptualization, J.Y., Y.W. and F.Q.; methodology, J.L. and X.Z.; data curation, J.L. and X.Z.; visualization, J.L. and M.Y.; writing--original draft, J.L. and S.C.; writing--review and editing, J.L., J.J. and L.W.; supervision, J.Y., Y.W. and F.Q. All authors have read and agreed to the published version of the manuscript.  
This study was supported by The International Partnership Program of Chinese Academy of Sciences, grant number 177 GJH2022020 BS, The National Natural Science Foundation of China, grant number 32301564; The National Natural Science Foundation of China, grant number 42430514; The Science and Technology Projects in Guangzhou, grant number E3309.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 4 Results and Discussion
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**Lengthuihe Auxiliary CSGRD**} & \multicolumn{3}{c}{**Shaping I Hydropower CSGRD**} \\ \hline \multirow{2}{*}{**Margins**} & **Test Age** & **Pearson** & \multirow{2}{*}{**Pearson**} & \multirow{2}{*}{**t-Statistics**} & \multirow{2}{*}{**\(p\)-Value**} & **Test Age** & **Pearson** & \multirow{2}{*}{**t-Statistics**} & \multirow{2}{*}{**\(p\)-Value**} \\  & **(Days)** & **Correlation (r)** & & & & **(Days)** & & **Correlation (r)** & \\ \hline \multirow{3}{*}{Paste} & 07 & 0.172 & 0.741 & \(4.67\times 10^{-1}\) & 14 & 0.844 & 6.785 & \(1.07\times 10^{-5}\) \\  & 28 & 0.197 & 0.853 & \(4.04\times 10^{-1}\) & 28 & 0.787 & 5.252 & \(1.06\times 10^{-4}\) \\ Margin (\(\alpha\)) & 90 & 0.221 & 0.784 & \(4.43\times 10^{-1}\) & 90 & 0.850 & 6.986 & \(7.96\times 10^{-6}\) \\  & 180 & 0.418 & 1.382 & \(1.84\times 10^{-1}\) & 180 & 0.787 & 5.252 & \(1.06\times 10^{-4}\) \\ \hline \multirow{3}{*}{Mortar} & 07 & \(-0.269\) & \(-1.185\) & \(2.51\times 10^{-1}\) & 14 & \(-0.544\) & 2.568 & \(1.96\times 10^{-2}\) \\  & 28 & \(-0.216\) & \(-0.936\) & \(3.62\times 10^{-1}\) & 28 & \(-0.628\) & 3.222 & \(5.3\times 10^{-3}\) \\ Margin (\(\beta\)) & 90 & \(-0.003\) & \(-0.011\) & \(9.91\times 10^{-1}\) & 90 & \(-0.565\) & 2.748 & \(1.46\times 10^{-2}\) \\  & 180 & 0.204 & 0.624 & \(5.40\times 10^{-1}\) & 180 & \(-0.628\) & 3.222 & \(5.30\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 21: Completed Pearson correlation coefficients and statistical significance of both CSGRD study cases. Figure 4: _Cont._  
Figure 3: Relationship between paste and mortar margins and compressive strength development.
## 3 Results and Discussion
### Evaluation of Automated Laser Point Detection Algorithm
Figure 14: Evaluation of the automated initial grid detection algorithm including an analysis of the initially suggested laser grid (**top**) and the success with respect to the desired, objective grid (**bottom**). With regard to an effective application in the clinical routine, we further evaluated the duration required to process the high-speed video recordings. Figure 16 shows the respective processing durations of the previous detection algorithm [20] (blue) and the present implementation (green). Please note, that the initial grid detection by [20] is not automated in any way, but had to be done completely manually. The automation of the initial grid detection saves 41.1% of the required time and the advances in the tracking algorithm save 42.2% in the tracking duration. Over all, the duration could be reduced from 46.5  to 27.0 min, which corresponds to a reduction of 41.9%. In summary, it can be concluded that the advances in the laser point detection algorithm increased the robustness and effectiveness while decreasing the total duration. Please, note that the evaluation of the processing duration has to be considered an estimation rather than a measurement. An extensive analysis of the proposed method is presented in the supplementary material. Figure 15: Evaluation of the automated laser point tracking algorithm compared to its previous version by [20] including the sensitivity (**top**) and the specificity (**bottom**). The depicted bars represent the average value for each series while the vertical error bars indicate the standard deviation within each series. The horizontal lines represent the average value over all frames of all series and the error bars indicate the corresponding standard deviation. of algorithms including a detailed run-time analysis will not be expedient until the automation is completed and the clinical application is imminent.
# Effective Selfish Mining Defense Strategies to Improve Bitcoin Dependability
## 3 Dynamic Difficulty Adjustment Algorithm (DDAA)  
In the bitcoin protocol, a difficulty parameter controls the overall mining difficulty. To counter the selfish mining attack, we propose the DDAA, which increases the mining difficulty (i.e., requiring higher computing power requirements) by adjusting the difficulty parameter when a successful selfish mining attack is committed. The increasing cost of the malicious attack because of the increasing computing power requirement disincentivizes any future attacks.  
Specifically, let \(K\) be the difficulty controlling parameter, \(H\) be the hash rate that is determined by the available computing power and the value of \(K\), \(\beta\) be the difficulty adjustment parameter, and \(\lambda\) be the state transition rate. In the proposed DDAA, \(H\) is negatively correlated with \(K\) because an increase in \(K\) means a decrease in the hash rate \(H\) (_H_new_). More specifically, a larger \(K\) means that more computation and power resources are needed to mine a block; given the same available resources, the hash rate decreases. In other words, to maintain the same hash rate, when \(K\) increases, an MM needs to upgrade their computing hardware, which reduces his/her attack reward, thus disincentivizing the attack.  
Because a too big \(\beta\) will heavily decelerate the new block generation speed, thus affecting the normal mining process, to control the variance and reduce the impact on the bitcoin network, in our study we set a range for parameter \(\beta\) as \(1<\beta<2\) (meaning that the increase in the computing power requirement cannot exceed the twice of the original computing power requirement). In the context of the quantitative dependability analysis, the adjustments in \(K\) and \(H\) are reflected in the decrease in all the \(\lambda\) transition rates. Equation (2) gives the updating formula for \(K,H\), and \(\lambda\).  
\[K\_new=\beta\times K,\ H\_new\ =\ H/\beta,\text{and}\lambda\_new\ =\ \lambda/\beta. \tag{2}\]  
To analyze the effectiveness and performance of the suggested DDAA, we evaluate the bitcoin dependability before and after the difficulty adjustment. Table 1 presents the transition rates related to the MM's computing power (\(\lambda_{01}\), \(\lambda_{01}\), \(\lambda_{12}\), \(\lambda_{23}\), \(\lambda_{34}\)) and the transition rates related to the HM's recovery capability (\(\mu_{00}\), \(\mu_{10}\), \(\mu_{21}\)). The values of those transition rates are designed based on the statistics and studies from [35]. Specifically, values in sets \(a\), \(b\), and \(c\) are the original values corresponding to the low, medium, and high computing power of the MM. Using Equation (2) with \(\beta=1.25\) (selected for the illustration purpose), the adjusted values of all \(\lambda\) are presented in sets \(a^{\prime}\), \(b^{\prime}\), and \(c^{\prime}\) in Table 1.
## 6 Experimental Evaluation
### Ama
#### 6.3.2 Experiment 2: Mapping from Related Multiple Dimensions  
The aim of this experiment is to study how mapping from related independent sensory modalities can lead to an improved mapping performance by overcoming some of the limitations of generating a map from a single sensory modality.  
In this experiment, ten Toradex Oak USB Sensor [35] nodes equipped with humidity and temperature sensors were deployed on the brass sheet. Cold water was sprayed onto the brass sheet to increase the humidity in order to make the relationship between the temperature and humidity more visible. After \(30\) s of applying heat, thermal and humidity measurements from the sensor nodes were recorded. Then, for each node, the temperature reading was removed from the collected data set one at a time. Both the distributed mapping service and the AMA were used to calculate the removed temperature reading using the rest of the data set. Figure 18 plots the two calculated and the measured temperature readings.  
Figure 17: (**a**) Detailed section of Figure 15 showing the area around the obstacle with artefacts marked in red. (**b**) Detailed section of Figure 16 showing the area around the obstacle with reduced artefacts.  
Figure 18: The temperature values calculated by the distributed mapping service and by AMA compared to the actual reading.  
With the presence of only one surface discontinuity, AMA reduced the percent error by \(11.8\%\) compared to the distributed mapping service. The percent error is the relative change calculated from the absolute change between the actual (measured) and determined (accepted) values divided by the accepted value [36].  
AMA uses the scale-based local distance metric to further reduce the effect of unrelated and uncertain measurements. It weights independent temperature measurements and adjusts the support set by changing the relative importance of nearby points using the humidity readings. If a data value does not satisfy the temperature-humidity relationship, it will be removed from the support set. Noise that often exists in sensor measurements during the gathering process also contributes to the relative change in the estimated temperature reading.  
This experiment shows that mapping from related multiple dimensions can increase mapping accuracy and reduce the performance requirements of a single sensor