esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Theory of Rock-Breaking for a High-Pressure Water Jet  
### Rock-Breaking Mechanism of High-Pressure Water Jet  
Many studies have reported that the failure of rock subjected to water jets mainly results from the water-hammer pressure and stagnation pressure [18; 19; 20]. At the initial stage, the water-hammer pressure is responsible for the majority of rock damage. Then, the stagnation pressure further breaks the rock and leads to crack initiation and propagation in the damaged rock. Therefore, it is very necessary to analyze the impact pressure generated by high-pressure water jets.  
Figure 1: Technical sketch of coal seam permeability improvement by water jet slotting. (**a**) is the process of high-pressure water jet slotting in coal seams; (**b**) is the crack condition surrounding the slots.  
Once a high-pressure water jet impacts the rock, water-hammer pressure forms owing to the liquid jet compression. According to existing studies [21; 22; 23], the water-hammer pressure at the central area of a solid, and the duration time, are expressed as follows:  
\[P_{vth}=\frac{v\rho_{w}c_{w}\rho_{s}c_{s}}{\rho_{w}c_{w}+\rho_{s}c_{s}} \tag{1}\]  
\[t_{vth}=\frac{Rv}{2c_{w}^{2}}, \tag{2}\]  
where \(v\) and \(R\) are the impact velocity and diameter of the water jet, respectively; \(\rho_{w}\) and \(\rho_{s}\) denote the densities of the water and rock, respectively; \(c_{w}\) and \(c_{s}\) are the shock velocities of the impact stress wave in the water and sandstone, respectively. The duration of the water hammer pressure is very short, being only a few microseconds. However, the pressure is enormous and causes the main damage to the rock, which results in the formation of a cavity.  
When steady impact is established, the pressure on the central axis decreases to the much lower Bernoulli stagnation pressure, as follows:  
\[Ps=\frac{\rho_{w}v^{2}}{2}. \tag{3}\]  
On one hand, some micro cracks are generated under the impact of enormous water-hammer pressure. On the other hand, natural rock, particularly coal, contains defects and has its own cracks. Therefore, the effect of stagnation pressure on rock failure cannot be ignored.
## 4 Discussion and Conclusions
### The Temperature and Porosity Effects on Groundwater Quality  
The results of the present study have demonstrated that temperature was the most significant factor influencing groundwater pathogen-microbiota indicators. Mesophilic microbes, such as bacteria and coliforms, exhibit optimal growth within specific temperature ranges. Coliforms, which are commonly found in the intestines of animals and humans, demonstrate optimal growth at temperatures ranging from 30 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, with the most rapid reproduction occurring at 37 \({}^{\circ}\)C. The quantity, metabolism, and activity are subject to change when the temperatures are not conducive to their survival [44; 45; 46; 47; 48]. This is consistent with our finding that TBC and TEC values are lower below 30 \({}^{\circ}\)C. In summer, with high temperatures, the average groundwater TEC values are approximately seven times higher than in other seasons. This suggests that during summer, the microbial TBC and TEC exceedance in soil surface karst zone groundwater is severe, rendering it unsuitable as a source of potable water for human use.  
Soil consists of particles of varying sizes, and its particle size distribution, coupled with porosity, constitutes one of its critical physical properties. These aspects significantly influence the evolution of groundwater microbes. Research by scholars such as [PERSON] and [PERSON], using saturated soil columns with varied particle sizes, has demonstrated that as particle size diminishes, microbial concentration at the column outlet decreases, while retention at the inlet increases [32; 33]. Furthermore, [PERSON]'s experiments on agricultural grassland soil revealed that particles of different sizes possess distinct adsorption surface energies. For instance, only 2% of _Escherichia coli_ were retained in soil particles \(\geq\)31 \(\upmu\)m, compared to 65% in particles <2 \(\upmu\)m [49]. In studies involving Bacillus subtilis spores in soil, [PERSON] found that doubling the soil particle size could increase the migration speed of spore microbes by 82%, likely due to the impact of larger pores in sandy soil on both adsorption capacity and hydrophobic interactions [50]. This is consistent with our finding that in denser soil or with smaller soil particle sizes, the groundwater TBC and TEC values increase. This phenomenon underscores that in rocky desertification areas under agricultural conditions, more frequent tilling and looser soil quality correlate with higher groundwater TBC levels, with the smallest and most stable fluctuations.
# Energy-Efficient Hosting Rich Content from Mobile Platforms with Relative Proximity Sensing
## 4 On-Demand Wi-Fi Connection to Nearby Mobile Devices
### Predicting Wi-Fi Connectivity between Two Mobile Devices  
One important building block of the above mechanism is the algorithm to predict whether two mobile devices are within Wi-Fi range. As discussed, we cannot find the natural way of determining Wi-Fi connectivity, i.e., by using Wi-Fi beacons, or by using probe requests/responses, because during normal operation the mobile devices' Wi-Fi interface is turned off. Although there are several energy impact which affects the battery life time like security mechanisms for the mobile devices [36; 37] in a realistic scenario, periodically turning it on and off would result in a prohibitive battery drain. Furthermore, we want to maximize the accuracy of the _T-Wing_ proxy server's prediction about which mobile devices are within Wi-Fi range of a requestor's location. For this purpose, using standard, simple localization techniques based on 3G/4G LTE (e.g., based on cell tower that you are located in, or even sophisticated localization techniques based on received signal power strengths and triangulations) is not sufficiently accurate to predict Wi-Fi connectivity between two mobile devices.  
Our solution is based on the observation that we do not need to determine the exact 'location' of any mobile device, but instead we only need an estimate for the'relative RF distance' between the mobile devices. In other words, we are merely interested in 'the similarity of locations of two mobile devices locations'. As it turns out, this is a structurally easier problem, and it allows us to achieve much more accurate results than those based on estimating the proximity of the given two nodes' measurements.  
We have implemented and evaluated two algorithms for Wi-Fi connectivity prediction. When predicting the existence of Wi-Fi connectivity between two mobile devices \(A\) and \(B\), both algorithms compare the received signal strengths from base stations as measured at \(A\) and \(B\), but they differ in the exact prediction metric. In the first metric (Metric \(A\)), each mobile device reports its \(X\) strongest base stations, and the algorithm computes the pair-wise differences of RSSI values on the two mobile devices of these \(X\) strongest base stations. We have tested different values for \(X\). In the second metric (Metric \(B\)), each mobile device reports RSSI values from all base stations whose RSSI exceeds \(-15\) dBm. The algorithm then computes for each such base station the difference between the two RSSI values and takes the maximum or average of these values to predict connectivity. The above metrics are still work in progress and we are investigating other approaches as well.
## 1 Introduction
Existing methods often treat short-term occlusion of a target by other objects as target disappearance. When the target reappears, a trajectory is reinitialized, which destroys the integrity of the target trajectory. To address the above problems this paper proposes a multi-objective tracking algorithm (REACTrack) based on Re-ID enhancement and association correction. The main contributions of this paper are as follows:  
* In CenterNet network, a predicted distance difference loss (PDDL) is designed for a center point detection heat map--suppress the response values of non-true Ground Truth (GT) locations in the predicted heat map. Make the high response value closer to the target truth position to improve the detection effect;
* Build an expanded learnable Effective Features GCN (EFGCN). Build a graph model for the relationship between the target GT position and the position around the GT. Ensure Re-ID feature quality and robustness;
* Design of Trajectory-Associated Correction Module (ACM). Under the influence of complex background and frequent occlusion of pedestrian targets, the target trajectory is corrected to improve the trajectory integrity. The rest of the paper is organized as follows: Section 2 provides the related work on MOT. Section 3 explains REACTrack with detailed description for each stage. Section 4 gives the ablation studies and baseline comparison results and visualization. Finally, Section 5 concludes the paper by providing perspectives for future work.
# Identification of Underwater Structural Bridge Damage and BIM-Based Bridge Damage Management
## 4 Bridge Underwater Structural Health Management System Based on BIM Technology  
The Bridge Health Monitoring System (SHM) has made great progress, but there is still a lack of external detection of underwater bridge structures. There are few applications for underwater structure monitoring, and some existing independent detection methods cannot be well combined with the SHM system. The characteristics of BIM can make up for this deficiency. BIM is an open platform with information integration and engineering characteristics. It can provide a visual and developable digital expression environment for health monitoring, and effectively improve the visualization and information sharing of monitoring information. The above two damage identification algorithms are integrated into the bridge underwater health inspection system, and the high exploitability of the BIM platform is used to realize the visualization of bridge underwater structural defect detection.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{**Damage Number**} & **a** & **b** & **c** \\ \hline \multirow{3}{*}{Volume/cm\({}^{3}\)} & true value & 8.08 & 362.69 & 420.28 \\  & calculated value & 8.52 & 372.49 & 439.61 \\  & relative error \(e\) & 5.5\% & 2.7\% & 4.6\% \\ \hline \multirow{3}{*}{Area} & true value & 19.78 & 202.03 & 214.11 \\  & calculated value & 20.71 & 207.28 & 227.60 \\  & relative error \(e\) & 4.7\% & 2.6\% & 6.3\% \\ \hline \multirow{3}{*}{Depth} & true value & 1.05 & 4.85 & 6.14 \\  & calculated value & 1.11 & 5.01 & 6.29 \\  & relative error \(e\) & 6.2\% & 3.3\% & 2.5\% \\ \hline \multirow{3}{*}{X-axis width} & true value & 4.54 & 13.30 & 13.13 \\  & calculated value & 4.78 & 13.41 & 13.55 \\  & relative error \(e\) & 5.4\% & 0.8\% & 3.2\% \\ \hline \multirow{3}{*}{Y-axis width} & true value & 4.45 & 12.95 & 12.99 \\  & calculated value & 4.67 & 13.38 & 13.31 \\ \cline{1-1}  & relative error \(e\) & 5\% & 3.3\% & 2.4\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Calculation and Error of Damage Geometry Information.  
Users can visualize the system to view disease information on the bridge substructure, the depth, length, width, and volume of the disease, as well as 3D images of the disease. It greatly facilitates researchers to understand the health status of bridges.
# A Critical Review of the Modelling Tools for the Reactive Transport of Organic Contaminants
## 2 Processes
### Isotope Fractionation  
As early as the late 1960s, Lebedev et al. [267] demonstrated that the oxidation of hydrocarbons leads to an accumulation of \(\delta^{13}\)C in the remaining substrate. Stahl [268] investigated the biodegradation of crude oil and observed a similar trend, indicating significant isotope fractionation during the degradation of the aliphatic fraction. Additionally, maturation processes can influence the isotopic composition of organic compounds [269; 270]. In recent years, isotopic fractionation has been the focus of numerous studies aimed at monitoring and quantifying the outcomes of biodegradation processes under both aerobic and anaerobic conditions [271; 272]. Findings from these studies have indicated that even in laboratory settings, different isotope fractionation factors can be obtained for the same substrates but with different enzymes, suggesting that predicting this process is challenging [273]. Furthermore, while the isotopic fractionation of aromatic hydrocarbons is relatively well understood, similar bond cleavage reactions in the degradation of volatile alkane chains present more complexities [274; 275; 276].  
In general, the isotopic signature undergoes kinetic and equilibrium fractionation. While both processes can impact the isotopic fingerprint of hydrocarbons, kinetic fractionation tends to have a more pronounced effect on residues, resulting in an enrichment of substrates with heavier isotopes and products with lighter isotopes (known as kinetic isotope effect--KIE) [277]. The most significant isotope effects are associated with substitutions that influence reaction rates [278; 279]. Determining KIE has become a standard measurement, with a wealth of KIE data available in the literature [280]. The disparity between theoretical and observed KIE aids in confirming or rejecting reaction pathways, including their rates, mechanisms, and intermediates [281]. However, in many cases, neither theoretical nor observed KIE is conclusive enough to support or refute hypotheses regarding reaction mechanisms. In such instances, complementary calculations with density functional theory (DFT) are necessary [280; 282].  
The extent of fractionation depends on geological conditions, including species, geochemical background, temperature, nutrients, and electron acceptors [272; 283]. Isotope fractionation also depends on the nature of the chemical reaction, the mass of the respective isotopes, and molecular factors such as uptake of the reactant into the cell, transport of the reactant to the enzyme, or binding to the enzyme [42].
## 3 Study Area and Data  
### Study Area  
The KKH is one of the highest mountainous highways in the world. Its section within China starts from Kashi in the north and ends at Khunjerab in the south (Figure 3a-c). We define the 10 km area flanking both sides of this KKH section as the study area, and its total length is 415 km. It passes through the Kunlun Mountain and the Karakoram Mountain, which are located at the northwestern edge of the Qinghai-Tibet Plateau [43]. The area along the highway is mainly mountainous with large topographic relief, and the altitude ranges from 1200 to 7000 m. The overall altitude along the highway is relatively high, with about seventy percent of the area having an altitude exceeding 3000 m. The stratum lithology along the highway is complex, including Variscan granite, Carboniferous limestone and shale, Triassic sandstone and conglomerate, and Quaternary loose sediments. Due to active tectonic processes, there are many faults along the highway, and earthquakes occur frequently [13]. Moreover, the climate along the highway shows typical characteristics of aridity. Precipitation in the study area is scarce and is concentrated from May to September. About seventy-eight percent of the study area has annual average precipitation of no more than 100 mm. Furthermore, only about three percent of the whole area has average annual precipitation of more than 200 mm.
# Prediction and Optimization of Open-Pit Mine Blasting Based on Intelligent Algorithms
## 4 Methods
### Multi-Objective Particle Swarm Optimization  
Particle swarm optimization (PSO) is an optimization technique based on swarm intelligence and is characterized by its simplicity, high precision in optimization results, and fast convergence [41]. PSO simulates the movement of particles in the search space to find the optimal solution [42]. Multi-objective particle swarm optimization (MOPSO) was developed based on conventional particle swarm optimization algorithms, initially proposed by [PERSON] et al. [43]. MOPSO has the advantages of efficient search and a well-distributed Pareto front, and it is widely used to optimize mathematical problems in engineering applications [28]. The basic principle involves particles dynamically adjusting their velocity and position for the next step based on their own optimal position and the globally known optimal position, with the fitness function determining the fitness value of particles.  
In this study, blast fragmentation (P\({}_{50}\)) and blasting cost (C) are optimized. Therefore, the GA-LSSVM model serves as one of the proxy models. Additionally, the blasting cost calculation model serves as another proxy model, represented by Equation (7) [44]. The optimization process of MOPSO is shown in Figure 4.  
\[C=\frac{L_{m}\cdot(H+H_{0})}{S\cdot B\cdot H\cdot\rho_{r}}+\frac{q\cdot E_{m}}{ \rho_{r}}+\frac{N_{D}\cdot D_{m}}{S\cdot B\cdot H\cdot\rho_{r}}+R_{1}+R_{2} \tag{7}\]  
Among the variables, \(L_{m}\) is the unit price of drilling, \(\rho_{r}\) is the density of ore, \(E_{m}\) is the unit price of explosives, \(N_{D}\) is the average number of electronic detonators used per blast hole, \(D_{m}\) is the unit price of detonators, \(R_{1}\) is the labor cost for drilling, and \(R_{2}\) is the labor cost for loading explosives.  
The collected mining production data and the constants in Equation (7) are shown in Table 2.  
Figure 3: GA optimization LSSVM model process.
# Collaborative Allocation Method of En-Route Network Resources Based on Stackelberg Game Model
## 5 Experiment Analysis
As the overall controlling party, ATC considers the overall situation at this time, but it also takes into account the choosing preferences of the airlines, reflecting the collaborative  
Figure 8: Probability of Eastern Airlines choosing each plan. Figure 7: Probability of Shenzhen Airlines choosing each plan. decision-making process. From the optimal option of each airline shown in Table 3, it can be seen that the preference of each airline is different. At this time, ATC needs to make a decision while adequately considering the demands of the airlines and combing the overall situation. From the perspective of ATC, the comparison situation of the three plans is shown in Figure 9. It can be seen from Figure 9 that plan 1 has the lowest total delay cost and is the optimal strategy overall, which can fully satisfy the demand of Juneyao Airlines. At the same time, combing the probability results of selecting each plan in the game, it can be seen that plan 1 is the suboptimal option for Shenzhen Airlines and Eastern Airlines, which can also meet their preferences. Therefore, the final optimal solution is plan 1. Through the game analysis on the six initial allocation plans, the preferences of each airline are obtained, and ATC makes the final decision on this basis. The total delay costs for plan 1, plan 3, and plan 4 are 267.7 min, 287.7 min, and 321.7 min, respectively. When the ATC prefers minimizing the overall delay cost, the optimal allocation plan is plan 1. However, if ATC intends to prioritize the flights of the airline because it is the main base airline, it will choose the optimal plan which this airline prefers. For example, Eastern Airlines has a comparatively large operating scale in the East China region, and therefore, ATC may select the plan which is preferred by Eastern Airlines as the final allocation plan. The delay costs of Eastern Airlines choosing plan 1, plan 3, and plan 4 are 94.8 min, 90.2 min, and 104.2 min, respectively. And the delay cost of choosing plan 3 is the lowest, that is, Eastern Airlines prefers plan 3. Therefore plan 3 is taken as the optimal allocation plan. In order to test the effectiveness of the final allocation plan, the total delay cost of the allocation plan obtained from the game is compared with the results of the allocation plan obtained from the commonly used RBS algorithm. The total delay cost of the plan allocated according to the results of the Ration-by-Schedule (RBS) algorithm is 351.5 min. Compared to the results of the RBS algorithm, the total delay cost of plan 1, plan 3, and plan 4 decreased by 23.84%, 18.15%, and 8.47%, respectively, which are all better than the traditional RBS algorithm. So, this method can provide the ATC department with an alternative and better allocation plan.
## 3 Development of New Applications for DIC
#### 3.1.2 Experimental Work  
To employ DIC it is necessary to have a random pattern with correctly sized speckles and good density on the composite tow surface. The main challenge in using DIC for strain measurement in this application was to make a fine speckle pattern that can survive high temperatures and pressures,  
Figure 15: Axial strain (\(\varepsilon_{xx}\)) side view just before (**a**) and after (**b**) failure. Axial strain (\(\varepsilon_{xx}\)) top view just before (**c**) and after (**d**) failure [16].  
as the composite tow would pass the heat source and would go under the compaction roller during AFP. Furthermore, due to the small width of the tow (one quarter of an inch), creating a correctly sized random pattern was a challenging task. In order to overcome these challenges, a high-temperature, abrasion-resistant paint with the commercial name of CP4040-S1 of Aremco Products, Valley Cottage, NY, USA was used. The random pattern was generated using a stiff bristle brush and a mesh metal sheet. The paint particles were thrown to the composite tow from a distance of 8 inches using the bristle brush, while a fine metal mesh was used to make sure only small paint particles could reach the tow surface. The size of speckles ranged from 0.1 mm (5 pixels) to 0.4 mm. The pattern density was about 50% (i.e., overall area covered by paint was about 50%). The random pattern was created on a composite tow made of carbon fiber and a poly-ether-ether-ketone (PEEK) matrix (commercial name TenCate Cetex TC1200). After applying the pattern on the tow, the paint was cured in an oven between two aluminum sheets for 45 min (Figure 16).  
The test setup including the AFP head and the DIC system is shown in Figure 17. The cameras were distanced 8 inches apart and were positioned at about a 40-degree stereo angle. The light source was placed between the cameras to create proper lighting conditions without creating reflections in the pictures. As AFP laid the composite tows, a series of photos were taken at a rate of 10 pictures per second, providing the opportunity to measure transient strain from the initial layup moment until it was cooled down to about room temperature.  
Figure 16: Preparation of carbon/poly-ether-ether-ketone (PEEK) tape with random pattern.  
Figure 17: Setup for strain measurements during AFP manufacturing: (1) AFP head mounted on a robotic arm, (2) hot gas torch, (3) compaction roller, (4) DIC camera system, and (5) halogen lamp [19].