esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion
### Implications for Simulating Truck Haulage Systems in Underground Mines  
Mine development aims to maximize production profits while investing minimal capital and operating costs over the lifetime of a mine [24]. Therefore, optimal operating methods and equipment usage strategies are required to increase the productivity of mines and minimize operating costs. In particular, efforts are being made to efficiently operate ore loading and implement material-handling operations that account for more than 50% of the total mine-operation cost [25]. Because the efficiency of the truck haulage system varies depending on the combination of the equipment used, operation patterns, and operation methods, it is necessary to operate an optimal truck haulage system that can maximize ore production and minimize the cost and time required for transport operations [26; 27].  
Recently, several discrete event simulation techniques [28; 29; 30; 31; 32; 33; 34; 35; 36] have been proposed to design an effective truck transport system and realize efficient system operation. For these simulations, a simulation algorithm is designed based on unit operations constituting the truck transport system, such as traveling, spotting, loading, dumping, and queuing [37], and the time required for unit operations is inputted as an input factor in the simulation. Existing studies have assumed that the traveling, spotting, loading, dumping, and queuing times of trucks are constant, and simulations have been performed by entering values for a specific time point or period. However, as examined in this study, it was confirmed that the characteristics and types of truck travel time differ in each transport section or period, and the distribution and fluctuation of truck transport time data by section also vary.  
Therefore, to improve the prediction accuracy and confidence level of the transport system simulation, it is necessary to perform a simulation considering the uncertainty caused by the variability of the truck transport time data. The traveling time inputted for the simulation must be divided into several sections, instead of inputting one value of a single section. That is, in the case of a stable section with little variability in the transport time of the truck, the transport time can be calculated using a deterministic method and used as the simulation input data. However, in the case of an unstable section  
Figure 11: Comparison result of existing log data (11 to 15 weeks, ’21.01.19\(\sim\)’21.02.21) for truck transport times and newly acquired log data (transport time per truck) at week 16 (’21.02.22\(\sim\)’21.02.27).  
with large fluctuations, it would be more appropriate to determine the input value using a stochastic method.
## 4 Results
### Evaluation of Satellite Precipitation Products
#### 4.1.3 Monthly Precipitation  
The daily precipitation estimates from IMERG and 3B42V7 at the five gauges were aggregated to monthly time series, and they were compared with the observed monthly precipitation. As shown in Figure 7, both IMERG and 3B42V7 products generally followed the variation patterns of monthly precipitation that were similar to those recorded by the rain gauges. Although the aggregation of the satellite precipitation estimates from a daily scale to a monthly scale cannot change the magnitudes of their systematic errors (BIAS), the linear correlations between the satellite precipitation and the observations were significantly improved (Figure 7). The CC values were increased in the range of 0.882-0.954 for 3B42V7 and 0.903-0.939 for IMERG. The possible reason for this improvement is that although both IMERG and 3B42V7 were comparatively inaccurate to characterize daily precipitation variability, they generally captured precipitation seasonality in the Chindwin River basin, which is indicated in Figure 4. Overall, 3B42V7 performed better than IMERG in estimating monthly precipitation at the Hkamti, Homalin, and Kalewa stations, whereas IMERG had higher accuracy than 3B42V7 at the Mawlaik and Monywa stations.
# The Full Lifecycle Evolution Model of Accidents: A Case Study of Underground Metal Mines in China
## 2 Methodology
### Application of the FLEM  
FLEM primarily has two applications: accident cause analysis and the formulation of enterprise-level preventive measures. The application process and interaction relationships are illustrated in Figure 3.  
Application method I: Accident traceability based on investigation reports is the first application method, which involves identifying the hazard sources that led to the accident, categorizing the control measures, and analyzing their failure mechanisms. Additionally, this approach allows for the evaluation of the response at various stages of accident progression, further tracing the preconditions of unsafe behavior and ultimately identifying targeted control measures and recommendations for improvements in management systems.  
Application method II: systematic analysis of enterprise-wide accident evolution pathways, with the following primary steps:  
1. Hazard source identification: identify potential accident types and the specific categories of hazard sources that could lead to such accidents within the enterprise, based on case studies, the relevant literature, or industry consensus.
2. Pathway mapping and construction: Based on legal regulations, industry standards, and operational procedures, determine the specific control measures for each hazard source and establish corresponding control systems at different stages of accident evolution. This forms the enterprise's accident evolution pathway, where non-compliance with any part of the system represents a potential hazard.
3. Accident case statistical analysis: Map the causes identified in the individual accident analysis process to their corresponding points in the enterprise's accident evolution pathway. Through statistical analysis of the frequency at each node, identify the critical weak points that require focused management and develop improvement measures or institutional adjustments to enhance the overall safety management level of the enterprise.
4. Model refinement and improvement: continuously improve the enterprise's accident evolution pathway by repeating steps (1)-(3) as new production accidents occur during the implementation of new control measures.  
Method I primarily focuses on post-accident causality analysis, which aligns with the primary application of existing accident causation theories for analyzing single accident types. Method II, however, adopts a proactive approach by mapping enterprise-wide risk  
Figure 3: Application of FLEM.  
factors from the source and is more geared towards developing comprehensive accident risk control plans for enterprises.
## 3 Experimental Results
### Wave Characteristics at the Same Strain under Different Loading Rates  
The strain data were analyzed at the strain level of 0.006 under the three loading rates. Figure 5a-c present the 3D strain diagrams under the loading rates of 0.1, 0.5, and 1 mm/min, respectively. The vertical views are compared to the strain diagrams along the sample surface's central line, as shown in Figure 5.  
Comparison of the 3D strain diagrams under the three loading rates indicated spatial-temporal heterogeneity and disorderedness. The fluctuations were more significant on the 3D strain diagram under the loading rate of 0.1 mm/min (Figure 5a) than under the other two loading rates. A comparison of the vertical views showed that the deformation waves' peaks were denser on the strain diagrams under the loading rates of 0.1 and 1 mm/min  
Figure 4: Vertical view of the strain component \(\varepsilon_{xx}\) at different time points at loading rates of 0.5 and 1 mm/min.  
Figure 5: Strain diagrams at the same strain under different loading rates.  
(Figure 5a,c). By contrast, there were much fewer peaks in Figure 5b, indicating more regular and consistent strain fluctuations on the sample surface. The strain diagrams along the central line of the sample surface were analyzed under the three loading rates. The fluctuation is obvious from the left boundary to 65 mm in Figure 5a. However, the fluctuations barely existed in the second half, indicating that the deformation wave had not yet propagated to the second half. By contrast, the fluctuation in Figure 5c is not so regular, and there is no obvious fluctuation period. As compared with the cases of the other two loading rates, the waves undulate gently. Thus, the deformation waves' fluctuations were much more regular in the red sandstone samples under the loading rate of 0.5 mm/min. The propagation rate was the lowest at the loading rate of 0.1 mm/min. At 1 mm/min, the deformation wave propagates in the form of broken lines.
## 3 Results
### Effects of Electrical Treatment on N. closterium f. minutissima Growth
Electrical stimulation may alter the chemical properties of the culture medium, indirectly affecting microalgal growth. To determine whether the effects of electrical stimulation on microalgal growth are direct or mediated through changes in the culture medium's properties, this study conducted experiments with the highest voltages. The culture medium was subjected to a brief electrical stimulation treatment before inoculation, followed by cultivation under the same conditions. The results indicated that the growth status of _N. closterium_ f. _minutissima_ cultivated in the electrically treated medium did not differ significantly from the control group (
## 4 Simulation Results and Discussions
### Multi-Objective OPF Problem  
In the MOOPF problems, two or three objective functions are simultaneously set as the objective functions. The MOOPF problems are solved by MaMPA in both IEEE 30- and 118-bus systems, and the results are compared with the compared algorithms based on the hypervolume indicator in the presented subsections.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case 5** & **Min** & **Max** & **Average** & **Std.** \\
**Algorithms** & & & & \\ \hline SSA [78] & 129,675.00 & 129,788.80 & 129,734.60 & 34.79 \\ Rao-3 [81] & 129,220.68 & 129,440.35 & 129,331.60 & 4.09 \\ PSO [60] & 129,703.48 & 131,573.7 & 129,867.01 & 610.70 \\ BBO [60] & 129,734.06 & 132,268.8 & 129,985.25 & 995.20 \\ ABC [60] & 129,677.83 & 130,896.6 & 129,732.46 & 446.80 \\ TLBO [60] & 129,550.49 & 129,691.0 & 129,607.63 & 72.14 \\ AGTLBO [60] & 129,545.56 & 129,562.3 & 129,552.92 & 8.53 \\ MaMPA & 128,088.69 & 128,669.94 & 128,370.72 & 141.68 \\ \hline
**Case 6** & **Min** & **Max** & **Average** & **Std.** \\
**Algorithms** & & & & \\ \hline
**Case 7** & **Min** & **Max** & **Average** & **Std.** \\
**Algorithms** & & & & \\ \hline SSA [78] & 12.193 & 13.498 & 12.301 & 0.6600 \\ MaMPA & 11.1950 & 13.8413 & 12.3769 & 0.6550 \\ \hline
**Case 8** & **Min** & **Max** & **Average** & **Std.** \\
**Algorithms** & & & & \\ \hline MaMPA & 0.05959 & 0.05967 & 0.05962 & \(2.095\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Statistical comparison of MaMPA and other algorithms in the IEEE 118-bus system.
# TFBN: A Cost Effective High Performance Hierarchical Interconnection Network
## 3 Routing Algorithm for TFBN  
The routing algorithm is vital to be defined in supercomputer systems to clarify the mechanism of sending and receiving messages between any two systems. Thus, it simplifies the path selection by a message inside the network to move from a source node to a destination node. The performance of the interconnection network, which is a vital mainstay in creating the massively parallel computer system that is affected widely by the proficiency of message routing and by the router design.  
The routing procedure is divided into three phases to forward a message from its source to its destination in TFBN [30]. TFBN is a hierarchical interconnection network; therefore, forming any higher-level network will apply the same coordinate locations of the immediate lower level. In phase-1,  
Figure 2: \(4\times 4\) higher level network of a TFBN.  
the message will be routed in the source BM, and it will be directed to the proper gate node to be sent to the higher-level network. In the second stage which is called Phase-2, the message will be routed through the higher-level network. Once it reaches the highest-level which leads to the destination BM, the routing operation will be reversed from the higher to the lower levels until the packet stabilizes in the final destination BM. In Phase-3, the message will be routed from the gate node in the destination BM to reach its final station in the destination node. The routing mechanism of Phase-1 and Phase-3 is almost the same. One is in the source BM and another one is in the destination BM.  
A usual deterministic dimension-order routing protocol will be applied to ease the routing process in this network. In this protocol, the message will be routed first vertically and then it will be routed horizontally. In the source node, when the packet is generated, the destination will be checked. If the source, as well as the destination nodes, are in the same BM, then the procedure of routing will perform within the BM only. In this case, only Phase-1 will be applied. Otherwise, if the source and the destination nodes are in different BMs, the message shall be sent to a proper gate node that connects the BM to the level where the routing is carried out. In this situation, the three routing phases will be applied. The routing of messages by these three phases on TFBN is illustrated in the following Figure 3.
## 3 Using LTE/5G-based Public Mobile Land Networks for Autarkic Microgrid Blackout Recovery
### Measurement of Latency  
Latency measurements between two MAS MTC devices (USB dongles connected with the Raspberry PIs in the above test setup) were performed by pinging from one device to the other and capturing and logging the traffic on both devices with tcpdump. The packet size was 56 bytes plus 8 byte of header = 64 bytes to be sent between the UEs via the mobile network nodes shown in Figure 3, so that the latency includes that of the private radio access network and private mobile core network outside the lab's radio access network. There was no other traffic (public users' traffic) on the network during the test, which reduces the latency to the minimum possible. Each test was done with 20,000 packets, with a periodicity of 1, 10 or 100 ms.  
Figure 4 shows that average latency achieved is 20 ms. With periodicity of 10 ms, more than 3000 packets were received within 22 ms delay. The maximum latency observed is 40 ms. This includes (Figure 3) the latencies of the two radio links, the core network and the message flow of packets over the private LTE network.  
Figure 3: Packet Flow in Latency Tests.  
The result was that 95% of packets were received within 23 ms and the maximum latency was 40 ms, satisfying the use case's latency requirement.
## 1 Introduction
[PERSON] and [PERSON] [23] investigated the load transfer mechanism of the lime- and cement-solidified soil, which is used as a load transfer platform in piled embankments. The results of the two-dimensional numerical analysis show that the solidified soil platform behaves like a beam. In their study, the heights of the solidified soil platform ranged from 0.4 to 1.0 m, which is much lower than the height of the embankment. The theoretical analysis models for pile-supported embankments are also mainly based on coarse materials or cohesionless soils [24]. [PERSON] et al. [25] pointed out that in the fill materials, the peak internal friction angle, particle size, and cohesion have a significant effect on the soil arching. [PERSON] et al. [26] stated that, in cohesionless soils, the load is transferred primarily through frictional forces, whereas in cohesive soils, the load is transferred through both friction and cohesion, resulting in a more stable soil arching effect. However, the effects of solidified soil-on-soil arching mechanisms in pile-supported embankments have not yet been adequately studied, especially under dynamic load, and require further investigation. Solidified soil, due to its economic and high performance, has been used for road and railway embankments. In this work, the load transfer mechanism and total settlements of gravel embankments on piled foundations when replaced with solidified soil embankments under static and dynamic loads were investigated. A comparative analysis of gravel embankment and solidified soil embankment under static and dynamic traffic loads was also performed. In addition, a comprehensive parametric study was conducted to investigate the effects of various factors, such as the cohesions (5-75 kPa) and the friction angles (20-45 deg) of solidified soil, the dynamic vehicle wheel loads (25-137.5 kN), the velocities (30-120 km/h), the embankment heights (5.5-9.5 m) and the pile spacings (2.5-5.5 m) on the total settlements and the load transfer mechanism.
# A Method for Borehole Image Reverse Positioning and Restoration Based on Grayscale Characteristics
## 4 Borehole Image Error Analysis and Restoration
### Borehole Image Restoration Process  
Based on the aforementioned research, a borehole image restoration process has been formulated, detailed as follows:  
1. Preprocess the borehole image by cropping the blank areas and removing the image ruler, focusing solely on the valid portion of the borehole image.
2. Conduct three-dimensional positioning analysis of the panoramic imaging probe based on the grayscale features of the borehole image, obtaining the probe's three-dimensional trajectory coordinates.
3. Analyze the grayscale errors in the image and calculate the grayscale error matrix based on the probe's three-dimensional trajectory coordinates. Perform grayscale restoration on the image to obtain the grayscale-restored borehole image.
4. Analyze the perspective errors in the image, calculate the perspective error matrix based on the probe's three-dimensional trajectory, and perform perspective restoration on the grayscale-restored image to obtain the grayscale- and perspective-restored borehole image.
## 3 Results and Discussion
### Volatile Organic Compounds of Cane Wines  
Chromatographic analysis of the headspace of the cane wines identified a total of 29 compounds, with their distribution varying by sample (Table 6).  
Nineteen compounds were detected in the SC15 trial, 16 in SC16, 15 in SC12, and 13 in SC13. The lowest number of VOCs was recorded in the spontaneously fermented trials, nine in SC9 and ten in SC11. Among the esters, decanoic acid, ethyl ester had the highest relative peak area values in SC11 (29.38%) and SC16 (28.79%), while dodecanoic acid, ethyl ester had the highest in SC13 (52.31%). Ethyl ester is one of the most abundant esters in commercial runs [80], as dodecanoic acid [81]. Fruity and floral notes in some commercial rums are linked to decanoic acid, ethyl ester [75], while sweet and waxy odors are emitted by dodecanoic acid, ethyl ester in some unaged rums [74; 82]. Notably, hexadecanoic acid, ethyl ester was only detected in the inoculated trials (SC12, SC13, SC15, and SC16), and octadecanoic acid, ethyl ester was only present in SC15 and SC16. Both esters, derived from fatty acids, enhance the complexity and depth of the rum's aromatic bouquet, imparting waxy and sweet notes [81].  
1-butanol, 3-methyl-, acetate, and octanoic acid, ethyl ester were detected in all cane wines, with the highest relative peak area values in SC9 and SC11, respectively. The positive aromatic impact of these esters in rum has been identified in rum distillates [10; 83]. Octanoic acid, ethyl ester, butanoic acid, ethyl ester, and hexanoic acid, ethyl ester play key roles in the overall aromatic expression of rum. 1-butanol, 3-methyl-, acetate is present in various alcoholic beverages, including rum, but its role in defining the overall flavor profile is secondary and varies by the geographical origin of the rum [79; 84].  
Phenylethyl alcohol was only observed in the SC9 trial, and it is important in defining the aroma of rums aged in oak barrels [83]. Acetic acid, 2-phenylethyl ester, identified in SC9 and SC13, is also present in high quantities in aged rums [79; 83]. These results confirm the importance of specific esters and alcohols in defining the flavor profile of rum. However, variations in fermentation protocols can significantly influence the VOC composition and, consequently, the flavor profile of the respective cane wines.
## 3 Experimental and Calculation Procedures
### Investigation Methods
#### 3.3.5 Thermal Analysis  
Thermal analysis was carried out on a TG/DTA/DSC apparatus (STA 449 Jupiter F1, Netzsch, Selb, Germany). Samples of mass 20 mg placed in alumina crucibles and heated from 30 to 1200 \({}^{\circ}\)C under helium atmosphere with flow rate 50 mL-min\({}^{-1}\) and heating rate 10 \({}^{\circ}\)C-min\({}^{-1}\). The gaseous products emitted during decomposition of materials after adsorption were analysed by Quadrupole Mass Spectroscopy (QMS 403C Aeolos, Selb, Germany). The QMS data in the range from 10 to 300 amu were recorded.
# Towards Mitigating Jellyfish Attacks Based on Honesty Metrics in V2X Autonomous Networks
## 5 Result Analysis and Performance Evaluation
### Under Jellyfish Delay Variance Attacks
#### 5.2.2 End-to-End Delay  
The end-to-end delay ratio for different numbers of delay variance attacker nodes is demonstrated in Figure 14. Since this type of attack introduces some delay before transmitting packets, the end-to-end delay values are slightly higher than in periodic drop attacks. With a ratio of 7.92 ms, the proposed protocol with the lowest delay outperforms [37], 2017 (13.84 ms), and [20], 2018 (9.34 ms), for three malicious nodes. The end-to-end delay of [37], 2017, significantly increases to 23.14 ms when the number of attacker nodes is inflated to nine, whereas [20], 2018, has an end-to-end delay of 15.09 m; our proposed protocol maintains the best end-to-end delay of 11.28 ms. The results show that SecE-V2X outperforms [37], 2017, and [20], 2018, in terms of end-to-end delay, proving that our developed protocol is still successful in the face of Jellyfish delay variance attacks.
## 3 Aim, Materials, and Methods
### Mixed Transport Scheduling Model under Complex Disasters  
The transportation management system primarily encompasses functions such as transport route planning, vehicle scheduling, and on-the-way monitoring. Leveraging technologies like RFID and GPS from the Internet of Things (IoT), available vehicle resources are collected [48], while real-time information regarding vehicles, materials, and disaster areas is integrated with the transport planning system through network technology. By employing cloud computing, data mining, and other advanced techniques, intelligent analysis is conducted on the aforementioned vehicle resources, human resources, and material resources to devise optimal emergency material transport routes. Through communication technology and GIS technology integration, real-time position updates for transportation vehicles along with transport status information and road conditions are transmitted back to the transportation management system in order to achieve the real-time monitoring of transportation vehicles, as well as tracking management of emergency materials, while providing feedback on optimal paths among other relevant details (Figure 5).
## 4 Experiments
### Results and Discussion  
#### 4.4.1 View Classification  
First of all, we investigated view classification performance. View classification results for the test data are shown in Table 3. The whole accuracy of view classification was 91.39%. Focusing on the results for each angle, classification for frontal and profile views was fully successful. On the other hand, misclassification was found in the diagonal views, particularly at 45\({}^{\circ}\). In conclusion, the performance of our view classification was acceptable. However, the last fact also indicates that it is required for the following VSR models to carry out recognition successfully even for the miscategorized sequences.