esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Path Planning Multiple Mobile Robots Based on the LAPGWO Algorithm
###### Abstract  
Given that the traditional optimization algorithm (GWO) often encounters problems like local optimum, and the convergence efficiency is not satisfactory in the path planning task of multiple mobile robots, an improved grey wolf optimization algorithm (LAPGWO) based on the combination of the logistic chaotic mapping and the artificial potential field method (APF) is proposed. Firstly, the LAPGWO algorithm uses logistic chaotic mapping to initialize the scale of grey wolves, improving the diversity of the population distribution. Secondly, the potential field function of APF is introduced to guide the individual grey wolves to move towards the low potential energy area. By adjusting the angle between the resultant force direction of the possible field and the movement direction, the global search ability is enhanced, and the algorithm is prevented from falling into the local optimum. At the same time, in the later iterations, it gradually decreases to increase the local search ability and accelerate the search efficiency. Finally, a repulsive force correction term function is proposed to solve the problem of unreachable targets. An independent potential field is constructed for each robot during the driving process to reduce path conflicts. To verify the performance of the improved algorithm, this paper will verify and analyze two different improved grey wolf algorithms based on the warehouse environment. The results show that, compared with the GWO algorithm, the shortest path and calculation time of the LAPGWO algorithm is shortened by 22.09%, 34.12%, and 47.75%, respectively. It has better convergence and stability. A physical verification platform is built to verify the practical effectiveness of the method proposed in this paper.  
multi-robot task allocation; spatiotemporal coupling task package segmentation; dynamic task adaptation; computational efficiency optimization; path optimization
## 3 Results and Discussion
### Unconfined Compressive Strength
#### 3.3.1 Effect of Variation in Fly Ash-Stabilized Loess on Stress
The UCS stresses of FA-treated loess specimens were in the range of 227.17-241.25 kPa for the curing time of zero days under non-submergence condition. Then, the value increased as curing time increased. Specifically, the UCS stresses were in the range of 635.43-664.35 kPa at 14 days of curing time and reached the range of 678.93-705.33 kPa at 28 days of curing time. However, the UCS stress of FA-treated loess specimens decreased under submergence condition. The UCS stresses of FA-treated loess specimens were only in the range of 81.51-112.70 kPa for the curing time of zero days, 99.34-137.88 kPa at 14 days of curing time, and 89.09-146.61 kPa at 28 days of curing time. Compressive strength reached its highest value when the FA ratio was 30% at 28 days of curing time. Therefore, curing time had an important effect on the strength results, especially for samples under long curing time. These results were consistent with those of recent studies [83; 84]. A high FA ratio could lead to increased pozzolanic reactions; as a result, a large amount of cementitious materials combined with the soil particles as time progressed [85; 86]. With a high amount of stabilizer, a large amount of CaO could contact with silica and alumina from the soil and thus form the main short-term product, which was mainly influenced by dilution effects [87; 88]. As time progressed, a large amount of CaO could react with a large amount of silica and alumina released from the stabilizer; accordingly, the formation of C-S-H accelerated, which played a major role in strength degradation as time progressed [80; 83; 87; 88]. However, this result was completely opposite to that of [PERSON] (2000), who suggested that the strength of specimens increased up to seven days of curing time and then UCS decreased for all the mixtures after 7-14 days [76]. Strength degradation probably occurred because of fast hydration and consequent shrinkage cracking or possible formation of ettringite [76]. The UCS stress under non-submergence condition was higher than that under submergence condition. This behavior was probably due to that the submerged soil and FA mixtures had the same FA ratio as the non-submerged mixtures. However, the increase in water content might decrease the sodium concentrations, which resulted in low UCS stress [81; 83]. Therefore, the strength of FASL enhanced with the increase in curing time. Overall, non-submergence showed a downward trend, whereas submergence showed an upward trend for all FA ratios and curing times. The UCS stress of both conditions had some fluctuations. Although submergence had lower stress, it outperformed non-submergence in improving the effect of FA on loess.
## 2 Related Work and Contributions
### Existing Studies of the Distribution of RSSI  
In contrast to the common assumption that RSSI follows a Gaussian distribution [19; 20; 21; 22], there are many circumstances causing RSSI to form bimodal or even multi-modal probability distributions. The works in [23; 24] simply mention this phenomenon without specifying causes. The work in [25] suggests that fading is related to the irregular RSSI distribution. More concrete causes for this phenomenon are stated in [26], including the orientation of antenna, the presence of moving objects or humans. These studies have one thing in common: the research is driven by RSSI-based localization in Wi-Fi networks. Hence, the signal strength is computed based on successfully decoded Wi-Fi packets, which are primary beacons from access points. While these observations are certainly true, they are limited to the Wi-Fi technology and focus on alleviating the instability in signal strength rather than investigating it.  
The RSSI study in this paper is based on raw in-phase and quadrature (IQ) samples captured by a software-defined radio (SDR) device. It proves that the multi-modal probability distribution of RSSI is also related to the characteristics of the signal itself, causing the distribution to form stable and distinctive patterns. From this aspect RSSI can be used for technology recognition, which is one of the contributions of this paper.
# Decreasing the Rail Potential of High-Speed Railways Using Ground Wires
## 3 Rail Potential Problem in High-Speed Railways  
### Characteristics  
High-speed railways are different from conventional speed railways in the following aspects:  
1. **Large train current.** In a 350 km/h EMU with 16 cars, the peak current of the train can exceed 800 A, which is about twice the current of the most powerful electric locomotive.
2. **Large short-circuit current in traction networks.** In China, most substations of high-speed railways obtain electric power from 220 kV grids, which normally have a high short-circuit level. When a fault takes place near the substation, the short-circuit current will be limited only by the impedance of the traction transformer. For a 63 MVA single-phase transformer with a 10.5% impedance, this current will reach up to 20 kA.
3. **High rail-ground leakage resistance.** High-speed railways mainly adopt a ballastless track, which normally has high rail-ground leakage resistance. As an example, in terms of the Japanese material, a value of 100-500 \(\Omega\)-km has been reported for the Shinkansen, compared to 2-5 \(\Omega\)-km for normal lines [19].  
These factors will lead to high rail potentials in high-speed railways, which may become a danger to the passengers and maintenance personnel and can influence the operation of the signal equipment. Therefore, technical countermeasures must be taken to suppress the rail potentials in high-speed railways.
## 3 Results  
### Laboratory Studies  
According to the results of laboratory studies, the following petrophysical parameters of the carbonate rocks were obtained: longitudinal and transverse (or P- and S-) wave velocities, bulk density, and porosity. Moreover, the porosity was obtained in two different ways: using the gas permeameter (\(=\varphi 1\)) and with computed tomography. According to the results of tomography, two porosity values were also obtained: on a standard sample with a diameter of 30 mm (\(=\varphi 2\)) and on a small sample with a diameter of 6 mm (\(=\varphi 3\)). For two sample sizes (standard and small), the mode of aspect ratio was estimated from probability density functions (PDFs) shown in Figure 4. The results of laboratory studies are presented in Table 1.  
When comparing the porosity obtained using different methods and for different rock volumes, it follows that the pores in carbonate rocks were distributed unevenly. This can be also observed on volumetric images provided by the computed tomography (Figures 5 and 6). Figure 6 (left parts) shows the porosity distribution obtained with two different resolutions of computed tomography (around 12 and 37 \(\upmu\)m/voxel). The dashed contours on the left indicate the area of the small sample. Voxels for the sample with a 6 mm diameter are shown in green. Voxels for the sample with a diameter of 30 mm are indicated in blue. However, as seen from the PDFs shown in Figure 4, the pore shape distribution was rather similar for different resolutions. The representative elementary volume (REV) was estimated as a volume beginning from which the porosity stayed stable. To find the REV, a volume of the cubic shape was considered inside the sample. We started from the cube with an edge length of 1 mm. We gradually increased the edge length up to 17 mm and calculated the porosity for each cubic volume.  
The porosity-based REV analysis performed for the 3D images showed that Samples 1 and 3 exhibited porosity curves that converged to a plateau when the cube edge became close to 4 mm. Sample 2 had a higher REV than the sample's size and was characterized by a high degree of heterogeneity of the porous structure, which is reflected in Figure 7. Meanwhile, the porosity of this sample stayed stable for the sample size varying from 4 to 12 mm.  
Due to the presence of voids of different size, a significant difference in porosity values obtained with various methods was observed.  
Figure 4: Histograms and PDFs of pore’s aspect ratio for small samples (**left**) and big samples (**right**): (**a**,**b**) for Sample 1; (**c**,**d**) for Sample 2; (**e**,**f**) for Sample 3.
## 3 Results  
### Effect of GMP on Skin Lesions in DNCB-Treated BALB/c Mice  
As shown in Figure 2A,B, after applying DNCB to the back skin for six weeks, the clinical skin symptoms of the DNCB group was significantly more severe than the NC group. In contrast, the skin scores were reduced significantly in the 0.1% GMP and 1% GMP treated groups compared to the DNCB group (Figure 2A,B). In addition, the spleen index was reduced significantly in the DNCB group compared to the NC group, which was significantly lower in the GMP-treated groups (Figure 2C). The topical application of GMP decreased DNCB-induced epidermal hyperplasia significantly in BALB/c mice (Figure 2D). Toluidine blue and Congo red staining results showed that eosinophil and mast cell infiltrations in the skin lesions were less severe in the 0.1% GMP and 1% GMP groups than in the DNCB group (Figure 2D).  
Figure 2: Effect of GMP on AD-like symptoms in DNCB-treated BALB/c mice. (**A**) Images of dorsal skin lesions from each group were taken at the end of the experiment. (**B**) The total skin score of dorsal skin lesions (**C**) Spleen index was measured. (**D**) Histological and immunohistochemical staining of dorsal skin samples. Scale bar: 100 μm. Data represent the means \(\pm\) SDs. * \(p\) < 0.05 compared to the NC group, # \(p\) < 0.05 compared to the DNCB group. AD, atopic dermatitis; GMP, Gomokpi; DEX, dexamethasone; DNCB, 2,4-dinitrochlorobenzene.  
Effect of GMP on Serum IgE Levels and Inflammatory Cytokine Levels in Skin Lesions in DNCB-Treated BALB/c Mice  
The effects of GMP on the levels of serum IgE and inflammatory cytokines in the skin tissues were evaluated using an enzyme-linked immunosorbent assay (ELISA). The results show that the serum IgE level was significantly increased in the DNCB group, compared with the NC group (Figure 3A). In contrast, treatment with 1% GMP reduced the IgE level in the serum significantly. The levels of IFN-\(\gamma\), VEGF, and IL-10 in the skin lysates were significantly higher in the DNCB group than in the NC group. On the other hand, these increases were lowered by treatment with GMP (Figure 3B-D).
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 3 Results
### Test Results  
This section presents the results of the sine vibration tests, categorized according to the predefined density classes A, B, C, and D. The analysis focuses on identifying the dynamic response characteristics of each specimen under control vibrational input. As shown in Figures 12-15, the modal frequency responses exhibit distinct groupings corresponding to each density class.  
Overall, most specimens demonstrated consistent and repeatable vibrational behavior, particularly in the first and second resonant modes. This consistency suggests reliable material properties within each density class and validates the classification method used.  
However, three specimens deviated significantly from the expected pattern, exhibiting notably lower acceleration amplitudes in both the first and second modes. These anomalies may indicate variations in internal structure, potential defects, or experimental inconsistencies and warrant further investigation. The reduced response could be due to increased internal damping or localized heterogeneities affecting the modal behavior.  
Table 3 provides a consolidated overview of the vibration test results, listing the first and second resonance frequencies along with their corresponding peak acceleration amplitudes for all eleven specimens. This tabular summary enables a clear comparison across samples and density classes. While most specimens exhibit consistent resonance behavior, three tests stand out due to notably reduced acceleration amplitudes in both modes. Despite maintaining resonance frequencies above 200 Hz--comparable to the rest--the significant  
Figure 14: Modal frequency response for density class C.  
Figure 15: Modal frequency response for density class D.  
drop in acceleration suggests either measurement anomalies or material inconsistencies, such as internal damping variations or structural defects. This observation reinforces the importance of considering both frequency and amplitude when evaluating dynamic response characteristics.  
After completing the experimental testing, a finite element analysis (FEA) was performed. The damping coefficients calculated for each specimen based on the test results were then incorporated into the FEA to ensure accurate correlation between the experimental and numerical data.  
The damping ratios obtained using the half-power bandwidth method are summarized in Figure 16.
# A Simplified Matrix Formulation for Sensitivity Analysis of Hidden Markov Models
## 4 Sensitivity Analysis of Smoothing Probability  
In this section, the sensitivity analysis of smoothing probability for variation of the transition, initial and observation parameters are presented using simplified matrix formulation. The sensitivity function \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:T})(\theta)\) where \(t<T\) and \(\theta\) is one of the model parameters, can be computed using the recursive probability expression shown in Equation (2) and (3).  
\[p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:T})(\theta)=p(x_{v}^{t},\mathbf{y}_{ \mathbf{e}}^{1:t},\mathbf{y}_{\mathbf{e}}^{t+1:T})(\theta)=p(\mathbf{y}_{ \mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta)\cdot p(x_{v}^{t},\mathbf{y}_{\mathbf{e} }^{1:t})(\theta)\]  
The first term in this product can be computed using the backward procedure as shown in Equation (3); the second term is the filtering probability that is computed using the forward procedure. Consequently, the sensitivity function for the smoothing probability \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:T})(\theta)\) is the product of the polynomial sensitivity functions computed using the forward and backward procedures. The sensitivity function for the filtering probability \(p(x_{v}^{t},\mathbf{y}_{\mathbf{e}}^{1:t})(\theta)\) is discussed in Section 3. From Equation (3), the backward recursive probability expression, it follows that  
\[p(\mathbf{y}_{\mathbf{e}}^{t+1:T}|x_{v}^{t})(\theta)=\begin{cases}\sum_{z=1}^{ n}o_{z,\mathbf{e}^{\mathbf{T}}}(\theta)\cdot a_{v,z}(\theta)&\text{if }t=T-1\\ \sum_{z=1}^{n}o_{z,\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^ {\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{ \mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{\mathbf{e}^{ \mathbf{\mathbf{e}}}}}}}}}}}}}}}}}(\theta)\cdot p(\mathbf{y}_{\mathbf{e}}^{ t+2:T}|x_{z}^{t+1})(\theta)&\text{if }t<T-1,\end{cases} \tag{10}\]  
where  
\[a_{v,z}(\theta)=\begin{cases}a_{v,z}(\theta_{a})&\text{for }transition\text{ } parameter\text{ }variation\text{ }\theta=\theta_{a}\\ a_{v,z}&\text{otherwise,}\end{cases}\]  
Figure 3: Time in seconds to compute the sensitivity coefficients for an observation sequence length from 1 to 1000 with a step size of 10.  
\[o_{z,\textbf{e}^{t}}(\theta)=\begin{cases}o_{z,\textbf{e}^{t}}(\theta_{o})&\text{ for \emph{observation parameter variation }}\theta=\theta_{o}\\ o_{z,\textbf{e}^{t}}&\text{\emph{otherwise}}\end{cases}\]  
To compute the coefficients of the sensitivity function \(p(\textbf{y}_{\textbf{e}}^{t+1:T}|x_{v}^{t})(\theta)\), a series of Backward matrices \(B^{k},k=t,...,T-1\), based on the simplified matrix formulation as presented in Section 3 are used. In the next subsections, the sensitivity functions for the smoothing probability \(p(x_{v}^{t},\textbf{y}_{\textbf{e}}^{1:T})(\theta)\) for transition, initial and observation parameter variations are discussed.
## 2 Modeling of a Multi-Dimensional Evaluation Indicator System
### Basis and Selection of Evaluation Indicators
In this study, we mainly focused on the in-depth monitoring of soil moisture through six indicators: soil bulk density, the angle of internal soil friction, soil shear strength, soil moisture content, the porosity ratio, and the Topographic Wetness Index. The authors of [17; 18] showed that human activities play a significant role in the risk assessment of slope landslides in large mountainous hydropower station reservoir areas, mainly reflected in the effect of reservoir water levels on slopes. The kinetic effects caused by cyclical changes in water levels in a reservoir area will concentrate soil shear strength within the soil body and reduce the porosity ratio and soil shear strength. This will cause the slope body to deform continuously and increase the potential risk of slope instability. Slope deformation is a direct reflection of changes in the internal geologic stress state of the slope, and landslide hazards can be visualized through surface deformation. When the degree of slope deformation progressively increases, the geological stresses in the slope will continuously adjust, causing the shear strength to decrease. Landsides occur when the stress required for sliding exceeds the ultimate shear strength of the soil. In summary, this study systematically analyzed various factors affecting slope stability across multiple temporal scales and geospatial dimensions. Consequently, a comprehensive slope landslide risk evaluation indicator system comprising 20 influencing factors, as delineated in Table 1, was developed. This table also presents empirical correlation coefficients between each indicator and landslide occurrences, providing a robust foundation for assessing slope stability and associated risks. Our slope landslide risk evaluation indicator system covers six aspects and multiple dimensions: types of terrain morphology, meteorological conditions, the ecological environment, soil conditions, human activity, and external manifestations. With rich data types and strong logical correlation and through the real-time monitoring of these indicators, this system can comprehensively grasp slope landslide risk dynamics from various perspectives over a long period. This enables a more scientific and accurate assessment of slope landslide risk and advances the lead time for early warnings, providing long-term guidance for the prevention and control of landslides.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 6 Irradiation-Induced Properties
### Magnetic Properties
It stated that grain boundaries or defects in the nanosheets were responsible for the ferromagnetism of MoS\({}_{2}\) nanosheets. Figure 34: Magnetization curves of crystalline MoS\({}_{2}\) lamellae with a thickness of 100 microns after various electron irradiation [74]. Left panel: under low-energy electron irradiation. L(i): 150 kGy and 0.7 MeV; L(ii): 300 kGy and 0.7 MeV; L(iii): 600 kGy and 0.7 MeV. Right panel: under high-energy electron irradiation. H(i): 100 kGy and 2.0 MeV; H(ii): 250 kGy and 2.0 MeV. Magnetic fields were parallel to _ab_-plane or _c_-plane of the lamellae. Reprinted from Reference [74], with the permission of AIP Publishing. Recent work stated that zigzag edges of MoS\({}_{2}\) few-layers can induce ferromagnetism. Therefore, defects, such as sulfur vacancies, can convert diamagnetic 2H-MoS\({}_{2}\) nanosheets ferromagnetism [193]. Irradiation could create numerous defects in MoS\({}_{2}\) 2D materials as discussed in the previous sections. So irradiation should affect magnetic properties of MoS\({}_{2}\) few-layers significantly. More fruitful outputs are expected in irradiated MoS\({}_{2}\) few-layers.
## 3 Material and Methods
### Lipid Extraction and Fatty Acid Analysis  
Lipid extraction was carried out according to the Folch method [44]. Direct trans-esterification of dried samples was performed for determining the content of fatty acids. The fatty acid methyl esters (FAME) were analyzed using a gas chromatography-mass spectrometry (GC-MS) machine (Agilent Technologies 7890/5975) equipped with a DB-5 (30 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m, Agilent) column (Agilent, Santa Clara, CA, USA). Helium was used as carrier gas (constant flow: 1.2 mL/min), the injector was kept at 250 \({}^{\circ}\)C with an injection volume of 1 \(\upmu\)L under a split ratio of 20:1, and the column temperature was first set at 80 \({}^{\circ}\)C for one minute and then increased at a rate of 10 \({}^{\circ}\)C per minute to 250 \({}^{\circ}\)C, the rate was then changed to 8 \({}^{\circ}\)C per minute until 280 \({}^{\circ}\)C was reached and maintained for 5 minutes. Identification of individual FAME was based upon comparing the mass to charge ratio with that of standard FAME mixtures and NIST database.