esa-sceva/satcom-chunk-collection huggingface dataset:
# Engineering Properties and Environmental Impact of Soil Mixing with Steel Slag Applied in Subgrade
## 3 Results and Discussion
### Environmental Impact Assessment Methods
#### 3.3.2 Permeability Test  
Figure 13 shows the permeability coefficients of the test soils. The permeability coefficient of intact soil was 6.9 \(\times\) 10\({}^{-8}\) cm/s-9.69 \(\times\) 10\({}^{-8}\) cm/s, and was within the range of the permeability coefficient of typical clay. For SSL, its permeability coefficient was much higher than that of intact soil, with a value of 4.25 \(\times\) 10\({}^{-4}\) cm/s-1 \(\times\) 10\({}^{-2}\) cm/s, similar to the permeability coefficient of silty sand and medium sand. Therefore, SL can also be used as a permeable structure and material. Considering the potential trace elements of SL, a lower permeability coefficient means safety from dangerous elements. Therefore, silty clay and clay are often used for edge wrapping of contaminated subgrade filler. As shown in Figure 13, the addition of 50% SL increased the permeability coefficient of soil, and the test soil increased by 743%, consistent with the permeability coefficient range of clay and silty clay. Therefore, the permeability of potentially polluting elements can still be controlled; especially after adding 2% cement, the permeability coefficient of the test soil was close to 1.72 \(\times\) 10\({}^{-7}\) cm/s. In addition, the permeability coefficient of the test soils decreased with increasing compactness. For practical engineering applications, permeability can also be reduced by increasing compactness [34; 35].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Element**} & \multicolumn{3}{c}{**Samples**} & \multicolumn{3}{c}{**Surface**} & \multirow{2}{*}{**Ground Water**} & \multirow{2}{*}{**Detection**} \\  & & & & **Water** & & & & **Limit** \\ \cline{2-2} \cline{5-8}  & **SL** & **SSL** & **SSLC** & **Class I** & **Class I** & **Class II** & **Class III** \\ \hline Cadmium & \textless{}0.9 & \textless{}0.9 & \textless{}0.9 & 1 & 0.1 & 1 & 5 & 0.9 \(\mu\)g/L \\ Lead & \textless{}0.6 & \textless{}0.6 & \textless{}0.6 & 10 & 5 & 5 & 10 & 0.6 \(\mu\)g/L \\ Zinc & \textless{}0.05 & \textless{}0.05 & \textless{}0.05 & 0.05 & 0.05 & 0.5 & 1 & 0.06 mg/L \\ Arsenic & \textless{}0.10 & 0.62 & 0.54 & 50 & 1 & 1 & 10 & 0.10 ug/L \\ Mercury & \textless{}0.02 & \textless{}0.02 & \textless{}0.02 & 0.05 & 0.1 & 0.1 & 1 & 0.02 \(\mu\)g/L \\ Selenium & \textless{}0.10 & 0.54 & 0.46 & 10 & 10 & 10 & 10 & 10 & 0.10 ug/L \\ Copper & \textless{}0.01 & \textless{}0.01 & \textless{}0.01 & 0.01 & 0.01 & 0.05 & 1 & 0.02 mg/L \\ Nickel & \textless{}0.03 & \textless{}0.03 & \textless{}0.03 & - & 0.002 & 0.002 & 0.02 & 0.03 mg/L \\ Total chromium & \textless{}0.03 & \textless{}0.03 & \textless{}0.03 & 0.01 & 0.005 & 0.01 & 0.05 & 0.03 mg/L \\ Hexavalent chromium & \textless{}0.004 & 0.009 & 0.008 & 0.01 & 0.005 & 0.01 & 0.05 & 0.004 mg/L \\ Barium & \textless{}0.1 & \textless{}0.1 & \textless{}0.1 & 0.7 & 0.01 & 0.1 & 0.7 & 0.1 mg/L \\ \hline \hline \end{tabular}
\end{table}
Table 5: The composition analysis results of infiltration fluid.
# AI-Assisted Multi-Operator RAN Sharing for Energy-Efficient Networks
## 5 Energy-Saving Features and AI-Assisted Radio Resource Management
### Hierarchical Reinforcement Learning (HRL)  
HRL is an extension of traditional reinforcement learning where several agents are hierarchically connected and work together to achieve a common objective. HRL employs two techniques, namely, _task decomposition_ and _temporal abstraction_. Task decomposition breaks down the reinforcement learning problem into smaller subproblems wherein solving each of them will be more beneficial than solving the entire problem as seen in Figure 14 [31, 32]. On the other hand, temporal abstraction involves a higher-level agent learning to perform the task by choosing optimal subtasks as higher-level actions [33, 34]. This approach helps us to deal with exponentially growing memory requirements of high-dimensional complex reinforcement learning problems while improving their convergence probability and speed.  
Moreover, HRL enables a flexible credit assignment to agents in a hierarchy based on their functionality [35]. The system designer can define separate reward functions for each subtask. Agents higher in the hierarchy can have a simpler and sparser reward function. Furthermore, this approach provides more structured exploration patterns throughout the training process leading to agents trained on subtasks and being able to transfer their experience to similar environments, something that most state-of-the-art algorithms tend to fail due to overspecialization. Additionally, it allows agents with different objectives and policies to coexist and coordinate with each other to achieve a common goal [23].  
In our case, HRL provides an elegant approach to combine and implement different network optimization strategies operating on different time scales. By having the strategies as actions of a higher-level agent, we learn the sequence and the most suitable optimization strategies under different network scenarios. We delve further into the hierarchical aspects of this work in Section 5.3.  
Figure 14: Hierarchical reinforcement learning.
# Biologically Derived Gels for the Cleaning of Historical and Artistic Metal Heritage
## 4 Organogels for Organic Coatings Removal
### Bio-Based Solvents
#### 4.2.4 Gamma-Valerolactone  
\(\gamma\)-valerolactone (GVL) is one of the most common lactones. It is a natural polar chemical that can be obtained from levulinic acid, eco-friendly derived from lignocellulosic and carbohydrates biomass [90; 108]. Besides being bio-sourced, the spectrum of its green features also includes biodegradability and low toxicity. This latter quality brought GVL to become a valid candidate to replace dipolar aprotic solvents nowadays outlawed because of their toxicity [108]. In the industrial world, \(\gamma\)-valerolactone is experimented as a green fossil-fuel additive and it is notable for its use in food and cosmetic goods because of its safety and pleasant scent [99].  
In the perspective of metal conservation, GVL's polarity makes it a suitable solvent to treat equally polar media. In parallel, the very low volatility and the non-toxicity shows compelling advantages for restorers and conservators, yielding a safer workplace with scarce volatile organic compounds (VOC) emissions.  
Thanks to these characteristics, a few years ago, \(\gamma\)-valerolactone was exploited for the first time in the field of cultural heritage. The research involved the cleaning of paintings through the removal of natural (i.e., dammar) and synthetic (i.e., Paraloid(r)) varnishes from the surface [81]. Due to its low vapor pressure and potential harmfulness on such sensitive works of art [107], \(\gamma\)-valerolactone was applied in a gelled formulation, to better control its activity and release. The encouraging results have recently driven research to further investigate the potentiality of this solvent in art conservation with the use of a cutting-edge delivery material [109].
## 1 Introduction
RNNs capture temporal dependencies in time series effectively. However, due to the typical sequential structure of RNNs, RNN-based methods require multiple iterations to model long-term temporal correlations, which can lead to error accumulation and gradient explosion issues [16]. Unlike RNNs, CNN-based methods have advantages such as parallel operations and gradient stabilization. However, CNNs perform implicit temporal modeling. The time steps are not visible, which leads to a lack of flexibility [17]. Several research studies have employed Transformer-based architectures to extract the temporal dependencies [18; 19]. These approaches have demonstrated promising capabilities in modeling long-term temporal correlations. However, Transformers rely on positional encoding to capture the order information within a sequence, which leads to limited effectiveness in capturing local temporal correlations [20]. Figure 1: Transportation systems. Considering the multifaceted aspects and complexities of the matter at hand, we propose a method named DSTGFCN based on an encoder-decoder framework to achieve traffic prediction. In particular, the DSTGFCN captures dynamic spatio-temporal features from observed data to construct dynamic adjacency matrix. With the graph convolution gated recurrent network and the dynamic adjacency matrix, the model achieves simultaneous modeling of dynamic spatio-temporal correlations and adds residual connections between the graph convolution gated recurrent layers to address problems such as error accumulation and gradient explosion. The main contributions of this paper can be summarized as follows:  
* A multi-step ahead prediction model is proposed to achieve accurate traffic prediction. A dynamic graph fusion module can extract spatial information from observed data without prior knowledge and fuse dynamic spatial features from adjacent time steps to generate a dynamic adjacency matrix. * We effectively modeled the dynamic spatio-temporal correlations by combining the Graph Convolutional Gated Recurrent Unit (GC-GRU) with the dynamic adjacency matrix. Residual connections were added between the GC-GRU layers to propagate gradients and extract long-term temporal dependencies efficiently. * The proposed model was tested against multiple baselines on six real-world traffic datasets and showed superior predictions. In addition, ablation experiments validated the effectiveness of each component. This paper is organized as follows: Section 2 presents a comprehensive review of related works. Section 3 introduces the preliminary content and formulates the research problem. Next, in Section 4, we provide a detailed description of our proposed approach. The experiments, including comparative experiments, ablation experiments, and visualization of predictions, are presented in Section 5. Section 6 discusses the advantages of the proposed method. In the end, Section 7 summarizes the paper and presents plans.
## 6 Conclusions  
Through theoretical analysis and experimentation, we conducted experiments on electro-osmotic drainage and the electrochemical modification of mudstone, with a focus on elucidating the differences in the modification effects of electrolyte solutions and electrode materials on the liquid and plastic limits and shear strength of mudstone. The following conclusions were reached.  
1. From the standpoint of theoretical analysis, the mechanism of the modification effect of electrochemical technology on mudstone is explained, focusing primarily on electro-osmotic drainage consolidation and electrochemical reaction cementation. After testing and comparing the liquid and plastic limits of mudstone samples before and after modification, it was found that the liquid limit of the modified mudstone samples decreased by 7.874%, while the plastic limit increased by 9.499%.
2. Using a method with controlled variables, the laws of the electrolyte solution and electrode materials on the shear strength of modified mudstone were investigated. It was found that the type of ions added to the electrolyte solution affected the cementation strength of the soil. The concentration of ions in the electrolyte solution affected the electro-osmotic efficiency and the amount of residual ions in the soil after electrolysis. The best solution to match the mudstone samples in the test was an AlCl\({}_{3}\) solution with a mass fraction of 10%.
3. Three common electrode materials were chosen for electrochemical modification tests: graphite, iron, and aluminum. The electrolytically oxidizable metal electrodes (iron and aluminum) produced passivation, resulting in higher water content in the modified mudstone specimens compared to the graphite electrode group. The internal friction angle of the graphite electrode-modified mudstone specimen was 26.7\({}^{\circ}\) and the cohesion was 34.4 kPa, which was nearly three times that of the original mudstone specimen with 15% water content. For real-world applications involving electrochemical changes to mudstones in the study area, it is suggested to use electrodes made of non-metallic materials and 10% AlCl\({}_{3}\).  
Conceptualization and methodology, X.C.; writing--original draft preparation, X.C. and L.H.; writing--review and editing, X.C. and G.S.V.T.; validation, G.S.V.T.; formal analysis, L.H.; data curation, J.K.; supervision, H.Y. and L.H. All authors have read and agreed to the published version of the manuscript.  
This work was financially supported by the National Natural Science Foundation of China (No. 52374144), Natural Science Foundation of Liaoning Province (CN) (Grant No. 2022-KF-23-10), and Xinjiang Uygur Autonomous Region Science and Technology Major Program (No. 2023A01002).  
**Institutional Review Board Statement:** Not applicable.
## 3 Results
### Consistency of Precise Products from Different Analysis Centers
#### 3.3.5 QZSS  
Due to the limited tracking of ground stations as the BDS-2 GEO and IGSO satellites, the epoch-wise orbit and clock differences of QZSS GEO and IGSO satellites between GFZ and WHU precise products on DOY 122 of 2020 also show large fluctuations (see Figure 8). The orbit differences are usually within 0.3 m, except for the GEO satellite J07 in the along-track direction (with an average value of 1.318 m), and the clock difference series even have obvious trends. The RMSs of epoch-wise differences of precise satellite orbit and clock corrections among different ACs as well as SISRE statistics for QZSS IGSO (J01, J02 and J03) and GEO (J07) satellites are listed in Table 7. It is noted that the CNES real-time precise products do not support QZSS satellites, and it is also the case for the ESA precise products in terms of the QZSS GEO satellite. Similar to the BDS-2 non-MEO satellites, especially for the GEO satellites, the consistency of the post-processed precise products among different ACs for the QZSS IGSO and GEO satellites is worse than that for the GPS, GLONASS, Galileo, BDS-2 and BDS-3 MEO satellites, which may be attributed to the worse accuracy of precise products of non-MEO satellites. The differences in radial orbits, along-track orbits, cross-track orbits, clocks and SISREs between GFZ and WHU precise products for QZSS GEO satellite are 5.6, 328.0 and 116.8 cm, 0.24 ns and 32.4 cm, respectively, and the corresponding five statistics between GFZ and ESA precise products for QZSS IGSO satellites are15.4, 6.0 and 6.8 cm, 0.14 ns and 14.8 cm, respectively. Compared with the differences between GFZ and ESA precise products for QZSS IGSO satellites, the corresponding differences between WHU and ESA, and WHU and GFZ precise products seem to be at a similar level in terms of radial orbits and SISRE, but are enlarged by approximately two times in terms of along-track orbits, cross-track orbits, and clocks. Regarding SISRE, the inconsistency of the post-processed precise products among different ACs for the QZSS GEO satellite is twice larger than that for the QZSS IGSO satellites. In comparison to the BDS-2 GEO and IGSO satellites, the SISRE differences among different ACs are increased by about 10 and 5 cm for the QZSS GEO and IGSO satellites, respectively.  
Figure 8: Epoch-wise orbit and clock differences for QZSS GEO and IGSO satellites between GFZ and WHU precise products on DOY 122 of 2020.
## 5 Performance Evaluations
### Packet Loss Rate
#### 5.2.1 Packet Loss Rate with Different Network Topologies
The difference in results for the E-trickle algorithm is also closely related to the results in Section 5.1. If a mobile node transmits DIS messages more frequently per unit time, the neighboring router nodes also respond more frequently, which reduces the packet loss rate. While the proposed algorithm maintains network consistency using fewer messages than the E-trickle algorithm, it cannot accurately match the time interval for parent node selection owing to the random speed and direction of motion of the mobile node. Hence, the proposed algorithm exhibits an equal or slightly higher packet loss rate than the E-trickle algorithm. The results in Figure 10 varied more significantly in random network topologies than in network topologies with a certain pattern. However, as shown in Figure 10b,c, a change in the number of router nodes in a random network topology did not lead to a significant variation in the results. Therefore, it was hypothesized that the proposed and existing algorithms are affected more by the speed of the mobile node than the number of router nodes. An experiment was conducted to test this hypothesis.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 2 Related Work
As they use a connection state determined by using heartbeat messages, those methods consider all mobile nodes with the left state to be a failed node. However, most of those methods do not consider a situation in which a mobile node can leave the monitoring region. Therefore, those methods erroneously consider a mobile node that has left the monitoring region without failures to be a failed node. To overcome this shortcoming, the classification method presented in this work distinguishes a faulty node from mobile nodes, which normally disconnects from the wireless sensor network.
# Application of Multi-System Combination Precise Point Positioning in Landslide Monitoring
## 4 Overview of the Study Area  
The study area is located in the northwest of Quanzhou City, Fujian Province (Figure 1). It is a medium-low mountain landform of tectonic erosion, with the highest elevation of 957 m, the lowest elevation of 290 m and the height difference of 650 m. The landslide area is located at the foot of the middle and front slope. Topographically, the boundary of 470 m and 350 m constitutes the junction of steep and slow slopes. The elevation of 290 m-350 m is the gentle slope, and the slope is about 5\({}^{\circ}\)-10\({}^{\circ}\). The elevation of 350 m-470 m is the area where villagers gather in Yaoshan. The human engineering reconstruction is intense. The overall slope is about 15\({}^{\circ}\)-20\({}^{\circ}\), and the slope of some artificial slopes behind the house can reach 50\({}^{\circ}\)-70\({}^{\circ}\). The elevation of 470 m-957 m is a steep slope, and the slope is about 35\({}^{\circ}\)-40\({}^{\circ}\). Slope body planting of tea, vegetables and other cash crops, and dense vegetation, has a coverage rate of 70-80%.
# Dependence of the Staphylococcal Volatilome Composition on Microbial Nutrition
## Results and Discussion  
### Staphylococcal Volatile Metabolites Produced in Rich Media  
Aerobic growth of _S. aureus_ and _S. epidermidis_ in four complex media (BHI, LB, MHB, and TSB) yielded 337 non-redundant peaks (Table S1 in the Supplementary Materials), 85 of which we were able to assign names based on mass spectral and retention index matches to the NIST library and prior published analyses of _Staphylococcus_ spp. volatiles (Table 1). We assigned chemical classifications to an additional 61 volatiles that were identified to a Metabolomics Standards Initiative (MSI) level 3 (Table S1). Of the level 2 or level 3 volatiles, 102 were detected in _S. aureus_ culture filtrates, 110 were detected in _S. epidermidis_ filtrates, and 66 volatiles were produced by both species. We detected seven volatiles in this study, which have been frequently detected in prior analyses of _S. aureus_, _S. epidermidis_, and other staphylococci: 3-methylbutanoic acid, 3-methylbutan-1-ol, 3-methylbutanal, 2-phenylacetaldehyde, butan-2-one, butane-2,3-dione, and undecan-2-one. Sixty-eight compounds from this study were named for the first time as volatile metabolites of either _S. aureus_ (n = 9), _S. epidermidis_ (n = 37), or both (n = 22), and 12 of them have not been previously described for any bacterium (Table 1).  
We identified 36 volatiles that were specific to _S. aureus_ and 44 to _S. epidermidis_. Two _S. aureus_-specific volatiles that were detected in the greatest relative abundance in BHI and TSB filtrates were acetic acid and 3-methylbut-3-enyl acetate (isopentenyl alcohol acetate). We posited that the former was resulting from a process called overflow metabolism through the reversible Pta-AckA pathway, in which bacteria will excrete acetate during rapid growth via glycolysis [1; 37]. Upon glucose depletion, acetate is assimilated by the same pathway for energy production, and, therefore, the absence of similar amounts of acetate in _S. epidermidis_ BHI and TSB cultures may be related to growth kinetics (i.e., sampling at only one time point). 2,4-Dimethyl-1H-pyrrole, a constituent of porphobilinogen, the major precursor of heme and other bacterial siderophores, was detected exclusively in _S. epidermidis_ filtrates of BHI and LB in high abundance. We were unable to specify this metabolite as a product of anabolism or catabolism, but one possibility is that the propionic acid portions of porphobilinogen are stripped off as easily available carbon and energy sources by _S. epidermidis_, and the 2,4-dimethyl-1H-pyrrole is discarded as a catabolic byproduct. Additional compounds detected solely in the filtrates of _S. epidermidis_ were benzaldehyde (MHB only), 2-phenylacetaldehyde (all media), and (methyldisulfanyl)methane (BHI and LB), though all three compounds have been reported in prior analyses of _S. aureus_ volatilomes [38; 39; 40; 41].
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 4 ACA Integrated with the APF Method
### Algorithm Execution Process  
Step 1: Establish a grid environment map model. Set the free grids as white grids and the obstacle grids as black grids. Define the starting map point as S and the end as E.  
Step 2: Initialize relevant parameters. Set the initial values of parameters such as the maximum number of iterations \(K\) in the algorithm, the number of ants \(M\) in the ant colony,  
Figure 2: Example of path optimization.  
the importance factor \(\alpha\) of PC, the importance factor \(\beta\) of the HF, and the evaporation factor \(\rho\) of pheromone.  
Step 3: Perform the selection operation of path nodes. Based on the probability transfer formula, the stop should select the next node to visit from the available nodes and record the visited nodes in the taboo list. After each node selection, determine in real time whether the current ant has reached the target node. Continue following the operation steps just now until all the ants in this iteration have completed their path search.  
Step 4: Record path length and ant quantity. Record the path length and the nodes passed by each ant in each iteration, and record the best path length, the worst path length and the number of ants passing through this path.  
Step 5: Pheromone update. According to the data recorded in Step 4, update the PC according to the pheromone update mechanism.  
Step 6: End of iteration process. Determine whether all iterations have been completed. If not, return to Step 3 to continue the operation; otherwise, store and output the optimal path obtained from this operation.  
Step 7: Pruning optimization. Perform pruning operation on the optimal path obtained in Step 6 to remove redundant turning points and output the optimized path. The algorithm flowchart is shown in Figure 3.