esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Range of Motion  
An analysis of the obtained values of the angular range of motion in the thoracolumbar spine showed that in the case of P, the highest mobility value was obtained for the Th7-Th8 segment (to which a load-simulating flexion was directly applied) and for the Th12-L1 segment (in which the thoracic spine transitions to lumbar spine). It was noted that a fracture of the Th12 vertebra disrupted the transmission of loads through the spinal column, which may be a contributing factor to spinal instability. In the case of F, increased mobility values were observed across all segments. The obtained values were, on average, approximately 25% higher than those obtained for the P (Figure 6A). A significant increase in mobility was also observed for the Th10-Th11 segment, located above the fracture, where the obtained value was approximately 42% higher than the value of the physiological system.  
When using LSF4 TC2, the mobility of the Th8-Th9 and Th9-Th10 segments was approximately 7% higher, and for the Th10-Th11 and Th11-Th12 segments, it was approximately 27% higher than the values obtained for the same segments in the case of P. For the other segments, the obtained mobility values were lower in the physiological case. It was also shown that the introduction of LSF4 TC2 caused an increase in mobility in almost all segments of the thoracolumbar junction by an average of approximately 15% compared to the values obtained for the LSF4 fixation without connectors. In the case of the Th12-L1 segment, the mobility value decreased by approximately 32% compared to the value obtained for fixation without transverse connectors.  
Figure 5: An example distribution of total displacement (**A**) and engineering principal strain 1 (**B**) and maximum values (**C**) obtained for configurations with four-segment fixation (LSF4) and with four-segment fixation with two connectors (LSF4 TC2).  
Figure 6: Range of motion of the individual functional spinal units (**A**), intradiscal pressure in individual intervertebral discs (**B**), and stresses on the articular facets (**C**) for considered configurations: physiological (P), with a fracture of the Th12 vertebra (F), with four-segment fixation (LSF4), with four-segment fixation with two connectors (LSF4 TC2).
###### Abstract  
Global Navigation Satellite System Real-time Kinematic (GNSS-RTK) is an indispensable source for the absolute positioning of autonomous systems. Unfortunately, the performance of the GNSS-RTK is significantly degraded in urban canyons, due to the notorious multipath and Non-Line-of-Sight (NLOS). On the contrary, LiDAR/inertial odometry (LIO) can provide locally accurate pose estimation in structured urban scenarios but is subjected to drift over time. Considering their complementarities, GNSS-RTK, adaptively integrated with LIO was proposed in this paper, aiming to realize continuous and accurate global positioning for autonomous systems in urban scenarios. As one of the main contributions, this paper proposes to identify the quality of the GNSS-RTK solution based on the point cloud map incrementally generated by LIO. A smaller mean elevation angle mask of the surrounding point cloud indicates a relatively open area thus the correspondent GNSS-RTK would be reliable. Global factor graph optimization is performed to fuse reliable GNSS-RTK and LIO. Evaluations are performed on datasets collected in typical urban canyons of Hong Kong. With the help of the proposed GNSS-RTK selection strategy, the performance of the GNSS-RTK/LIO integration was significantly improved with the absolute translation error reduced by more than 50%, compared with the conventional integration method where all the GNSS-RTK solutions are used.  
adaptive GNSS-RTK/LIO fusion; GNSS-RTK selection; globally urban positioning; factor graph optimization +
Footnote †: journal:  
_Applied_  
_sciences_
## 3 Detection of Coal Burst Risk Active-Passive Seismic CT
### Implementation of Active-Passive CT
#### 3.2.2 Active-Passive Seismic Wave CT Inversion at the \"Square Effect\" Area  
On 10 May 2022, the LW23908 had mined forward 150 m and was approaching the \"square effect\" location, which is about 190 m from the setup entry by the theoretical analysis. Meanwhile, field practice has demonstrated that the influence of the \"square effect\" began about 50 m ahead square location, so the second active-passive CT detection was implemented. The data of mining tremors from 7 May 2022 to 10 May 2022 were selected as the focus, and after pretreatment, a total of 183 tremors with clear P-wave arrival were picked, accompanied by artificial blasting sources. The density of the active-passive seismic rays can ensure the accuracy requirements. Figure 7 shows the isoline of the inversed P-wave velocity. Two high P-wave velocity areas are identified, which demonstrate two high-stress zones in the coal seam. It is not difficult to see that these two high-stress zones (high-stress area 1 and high-stress area 2) correspond to the rear and front foundations of the hard-thick roof, which indicates that the hard roof is about to rupture and cave soon, the deformation and subsidence of the roof strata causing the stress  
Figure 5: Seismic ray grid generated by both blasting and mining-induced tremors at the beginning stage of LW23908.  
concentration in the coal seam. The distribution of the P-wave velocity is highly consistent with the theoretical analysis of the \"square effect\" and verified the existence of the \"square effect\" again. For LW23908, the influencing area of the \"square effect\" is about 150 m, and the stress concentration degree in front of the workforce is higher than that behind the workforce. Compared with the inversion results on 18 March 2022, shown in Figure 6, the maximum of the P-wave increases notably from 4.7 km/s to 5.5 km/s, indicating a much more dangerous coal burst during the \"square effect\" phase than the beginning stage; the everyday total energy and numbers of mining-induced tremors of LW23908 also proved that the \"square effect\" stage is the biggest challenge of coal burst prevention.
# Power-Efficient Random Access Design for Machine Type Communication
## 4 Link Budget Analysis and Simulation Results
### Random Access Capacity  
From the previous analysis, the number of devices per random access opportunity, \(N\), of the current design for the bandwidth of \(W=180\) kHz should be controlled below 7. For the random access duration of about 0.25 s, the random access period is set to be 2.5 s to maintain a 10% overall overhead (cf. Figure 7). Therefore, a total of 60 s/2.5 s \(\times\) 7 = 168 devices can be supported per minute, per band (of width 180 kHz), and per cell. The random access capacity of a cell is thus 168 devices per band for the device wake up period of 1 min, and 2520, 5040, and 10,080 devices for the periods of 15, 30, and 60 min, respectively. The control mechanism of the number of random access devices per random access occasion can be found in ref. [26].
## 3 Results
### Impact of Meteorological Factors on GPP
#### 3.3.2 Major Controlling Factors of GPP  
To facilitate the interpretation of ridge regression coefficients (\(a_{i}\)), we categorized them into four levels: high-intensity negative effects (\(-1.6\leq a_{i}<-0.5\)), indicating a statistically significant inhibitory effect of the independent variable on GPP; low-intensity negative effects (\(-0.5\leq a_{i}<0\)); low-intensity positive effects (\(0<a_{i}\leq 0.5\)), reflecting limited positive predictive power of the independent variable on GPP; and high-intensity positive effects (\(0.5<a_{i}\leq 1.6\)), identifying the independent variable as a strong driving factor with substantial explanatory power for GPP changes.  
Overall, the impacts of TEM and PRE on grassland productivity were overwhelmingly positive, with only minimal areas exhibiting negative effects (TEM: low-intensity negative \(0.01\%\), high-intensity negative \(0.69\%\); PRE: low-intensity negative \(0.16\%\), high-intensity negative \(13.18\%\)). In contrast, the positive and negative effects of VPD and downward SSR were more balanced (VPD: low-intensity negative \(10.99\%\), high-intensity negative \(7.47\%\), low-intensity positive \(35.89\%\), high-intensity positive \(45.65\%\); SSR: low-intensity negative \(3.64\%\), high-intensity negative \(3.38\%\), low-intensity positive \(42.58\%\), high-intensity positive \(50.40\%\)), suggesting that changes in these meteorological factors play a significant role in the decline of grassland productivity. Notably, the effects of VPD showed a distinct regional distribution, with positive effects primarily concentrated in the northern Altai Mountains and central Tianshan Mountains, while negative impacts were noticeably concentrated in the northwestern Junggar Basin and some northern areas. The spatial distribution characteristics of VPD effects were highly consistent with the regions of positive and negative correlation observed in the partial correlation analysis. Additionally, there was  
Figure 4: Spatial distribution of partial correlation coefficients of meteorological factors in Xinjiang from 2001 to 2020. (**a**) Vapor pressure deficit (VPD). (**b**) Temperature (TEM). (**c**) Downward surface shortwave radiation (SSR). (**d**) Precipitation (PRE). Proportion of significance levels of each meteorological factor: (**e**) VPD, (**f**) TEM, (**g**) SSR, (**h**) PRE.  
overlap between regions where SSR had negative impacts and areas where VPD exerted positive effects.  
Analysis of TEM and PRE impacts demonstrated that both factors primarily exhibited a strong positive correlation with GPP, evidenced by the high prevalence of high-intensity positive effects (TEM: 82.00%, PRE: 71.19%). Unlike VPD, TEM and PRE did not display widespread significant negative impacts (Figure 5b,d,f,h). This finding, quantified by the effect size distribution, was consistent with the results from the partial correlation analysis, further validating that the dominant effects of TEM and PRE on GPP were strongly positive.
## 1 Introduction  
As global trade has grown in recent decades, the shipping industry has grown dramatically in response. Safety requirements have become increasingly important [1]. To ensure the safety of maritime transportation, scientific measures should be taken to respond to and prevent the various types of maritime accidents instead of putting ambiguous anticipations of possible human errors into place [2; 3; 4]. Navigator error was involved in 79% of accidents in the Republic of Korea over the past five years, based on a recent assessment [5]. Moreover, inappropriate behaviors accounted for 49% of all maritime accidents [6]. Although the majority of maritime accidents were directly related to navigator error, evidence of the navigator error has not yet been explored objectively [7]. Most existing assessments of navigators' lookout behaviors have depended on indirect data-collection methods such as diaries, surveys, and questionnaires [8; 9; 10]. [PERSON] et al. used self-reported diaries and questionnaires to investigate the situational awareness of navigators related to lookout activity patterns [11]. The use of such indirect approaches remains a subjective limitation for the accurate monitoring of the lookout behavior of navigators during navigation. This limitation could be addressed using more objective data-collection measures for navigator behavior, such as eye trackers and optical sensors. [PERSON] conducted eye tracker-based research to investigate the association between the allocation of navigational equipment and navigation safety in the simulated environment [12]. Moreover, the eye tracker was also utilized to evaluate the navigational workload and concentration in the given navigational environment [13; 14]. However, there is still a lack of investigation into navigators' physical behavior to measure lookout activity patterns. The purpose of this study was to establish a classification model for lookout behaviors using the Kinect sensor in a simulated navigation environment.  
The proposed sensor-based lookout activity classification model was employed with 27 senior cadets at a maritime university in the Republic of Korea. In the standard navigation scenario, participants performed seven standard lookout activities in navigation simulation. The infra-red images collected by the Kinect system were then utilized to develop a machine-learning activity classification model, which was validated in a non-scenario navigation experiment that used direct observation as ground truth. The two main contributions of this study are a scientific research framework for the investigation of the navigator error and objective evidence of how navigators physically performed navigational lookout.
## 5 Numerical Experiment
### Sensitivity Analysis
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Attendance Rate of** & \
\begin{tabular}{c} **The Average Percent** \\ **of Working Regular** \\ **Employees** \\ \end{tabular} &
\begin{tabular}{c} **The Average Percent** \\ **of Working Irregular** \\ **Employees** \\ \end{tabular} & **Total Cost (USD)** \\ \hline
100\% & 63.5\% & 36.5\% & 176,860 \\
95\% & 64.6\% & 35.4\% & 177,035 \\
90\% & 66\% & 34\% & 177,205 \\
85\% & 67.7\% & 32.3\% & 177,405 \\
80\% & 69.1\% & 30.9\% & 177,575 \\
75\% & 70.8\% & 29.2\% & 177,770 \\
70\% & 72.1\% & 27.9\% & 177,945 \\
65\% & 73.8\% & 26.2\% & 178,190 \\ Under 60\% & &
\begin{tabular}{c} Infeasible \\ \end{tabular} & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Changes in the percent of regular and irregular employees and total cost. Figure 7: The difference between an expected number and the assigned number of irregular employees depending on attendance rate (the percent of irregular employees’ absences). As mentioned ahead, the average percent of regular employee attendance is increased as the average percent of irregular employee absence is decreased. This results in increasing stability in performing the job. On the other hand, the total labor cost keeps moving up, since the cost of regular employees is normally higher than the cost of irregular employees. In other words, labor costs can be decreased by increasing the number of irregular employees, and the stability of staff schedules can be increased by decreasing the number of irregular employees. Therefore, it is important to make staff schedules considering factors such as labor costs and stability of staff schedules after taking the conditions and situations of a construction company without being biased as one factor. Thus, this research is expected to help a manager with decision-making for a staff schedule by providing various proactive cases depending on the change of the average percent of irregular employee absence through sensitivity analysis in the numerical experiment.
# BPET: A Unified Blockchain-Based Framework for Peer-to-Peer Energy Trading
## 7 Discussion
### Security  
In the BPET system, all validators are entities with their identities verified. A malicious validator voting on false transactions will be detected and voted out of the validators' quorum by all other honest validators through the QBFT consensus. Moreover, the immediate finality property of QBFT eliminates threats of long-range attacks aiming to revise the transaction history of the ledger and selfish mining attacks in which the attacker holds discovered blocks privately and then attempts to fork a private chain.  
To eliminate smart contract security vulnerabilities, all BPET smart contracts were fully tested using the Mocha test framework. We also used industrial best practices, including Openzeppelin's [41] secure ERC20 [35] token, access control, and smart contract audit tools (e.g., Slither [42], Securify [43], and Mythril [44]) to ensure the security of BPET smart contracts. However, these security solutions primarily address vulnerabilities in decentralized finance, such as reentrancy, frontrunning, and access control. In contrast, security for energy trading smart contracts heavily relies on self-audit. Therefore, there is significant potential for future research to develop automated solutions for detecting and remedying energy domain-specific smart contract vulnerabilities [45].
## 4 Discussion
### Emission Dynamics of Nitro Oxide and Ammonia Gas  
Changes in N2O emissions were mainly driven by nitrification and denitrification processes [73]. Following the temperature dynamics, high and low N2O emissions resulted from high and low temperatures, respectively. A high temperature ensures rapid bio-oxidative activities, which result in high N2O emissions, but low temperatures slow down bio-oxidative activities, which reduce N2O emissions [8]. The slight increases in N2O emissions observed at certain periods of the composting process can be attributed to incomplete denitrification processes that convert NH4+-N into N2O [67]. However, the emission of N2O remained high for the MC at 45% due to the high NO3\({}^{-}\)-N production by the treatment. Compost mixtures with high NO3\({}^{-}\)-N concentrations release more N2O through denitrification process [29]. Likewise, the temporal reduction in pH might have inhibited the activities of N2O reductase, giving rise to the discharge of N2O [20; 74].  
Nitrogen transformation, which involves the processes of mineralization of organic nitrogen compounds, nitrification and denitrification, is an important benchmark for evaluating NH\({}_{3}\) emission during composting [47]. The varying influence of moisture, temperature, organic matter content and pH contributed to the emission of NH\({}_{3}\)[75]. As expected, high and low emissions were observed at the initial thermophilic period and the maturation phase, respectively. High temperatures are responsible for converting NH\({}_{4}\)+-N to NH\({}_{3}\)[31], while low temperatures slow the decomposition of organic matter, promoting its stabilization [76]. These findings may have explained the observed correspondence of NH\({}_{3}\) losses with increasing temperatures (>60 \({}^{\circ}\)C). In the present study, moisture contents of 55% and 65% retained more total nitrogen than the MC of 45%, which could be the reason for the low reduction in NH\({}_{3}\) by the treatments. Losses in total nitrogen have been shown to promote NH\({}_{3}\) emission during composting [67].
# Cyber-Secure IoT and Machine Learning Framework for Optimal Emergency Ambulance Allocation
## 3 Methodology
### Experimental Settings
\begin{table}
\begin{tabular}{c c c} \hline
**Data Source** & **Data Type** & **Example Variables** \\ \hline National Fire Agency & & Monthly dispatch volume, response \\ Emergency Activity & Dispatch Records & time, on-site arrival time, hospital \\ Information System & & transfer time, etc. \\ \hline National Fire Agency & & Patient severity level, transfer \\ Emergency Rescue & Patient Characteristics & status, age group, etc. \\ Standardization System & & Number of ambulances available, \\ National Fire Agency & Station Operation Data & number of staff, equipment \\ Ambance Operation & & operational status, etc. \\ Management System & & Hospital type, transfer distance, \\ Emergency Patient Transfer & & admission capacity, etc. \\ Institution Data & & Total population, proportion of \\ \hline Ministry of the Interior and & & elderly, scale of floating \\ Safety, Statistics Korea & & population, etc. \\ \hline Ministry of Land, & & Average driving speed, traffic \\ Infrastructure and Transport & Traffic Indicators & congestion level, road traffic \\  & & volume, accident frequency, etc. \\ \hline Emergency Medical & & Number of emergency medical \\ Information Portal (E-GEN) & Medical Infrastructure & institutions, hospitals, hours of \\  & & operation, etc. \\ \hline Public Data Portal, KOSIS & \
\begin{tabular}{c} Socio-environmental \\ Factors \\ \end{tabular} &
\begin{tabular}{c} Number of food establishments, \\ alcohol consumption rate, \\ distribution of public facilities, etc. \\ \end{tabular} \\ \hline National Spatial Data & & GIS-based administrative boundary \\ Infrastructure & Geospatial Information & coordinates, etc. \\ \hline \end{tabular}
\end{table} 
Table 1: Summary of data sources. Each analytical block was designed as an independent module while maintaining interoperability to allow for integrated simulation-based cross-validation. Since factors influencing ambulance deployment arise from multiple domains--including demographic characteristics, socio-environmental conditions, hospital accessibility, traffic dynamics, and patient severity--variables were organized using a multidimensional taxonomy. This structure enhanced model interpretability and minimized multicollinearity during the machine learning-based variable weighting process. Key features were selected through a combination of expert Delphi surveys and Lasso regression-based importance estimation. By adopting this architecture, this study advances beyond simple statistical analysis to develop a predictive model grounded in real-world structured data that captures the heterogeneity of regional ambulance demand and the complexity of operational environments. Incorporating spatial and temporal data diversity further improves the scalability of the model and its applicability to practical policy design and implementation.
# Towards a Decentralized Collaborative Framework for Scalable Edge AI
## 6 Experimental Evaluation
### Efficacy of the Proposed Approach
We note then that if the individual parties (**IND**) request the FL model and distill their local models with the FL model, then the result is their local models improve in accuracy by 10 accuracy points (i.e., **MDD**). Next, we observe in Figure 5b that in the RNN-Reddit case, the model trained by the FL group (**FL**) on its own shows very low accuracy values on the test set of the clients. In contrast, the model trained on the individual clients (**IND**) improves in accuracy over the training epochs. This might be due to the difficulty of text-based datasets such as Reddit, which is difficult to generalize well, especially with a small client population. Therefore,  
Figure 4: Performance comparison in the LR-Synthetic scenario. the FL model could not perform well when tested on the **IND** group, which confirms observations about FL in non-IID settings [19]. Further, we observe improvement in the individual clients' model accuracy by 6 accuracy points, which are achieved when they discover and distill the FL model with their own local model (i.e., the **MDD** approach). In summary, the results obtained using a variety of benchmarks and case studies demonstrate that the developed MDD approach is effective. However, there are technical limitations and research gaps that require further attention:  
* **Discovery mechanism:** The effectiveness of the MDD approach largely depends on the efficacy of the discovery protocol. In this work, we have relied on assumptions of the availability of discovery information. However, we have not explored the overhead of the discovery method and how to implement such a protocol at the Internet scale. We believe this can be overcome by borrowing approaches from content (or information)-centric network [40]. * **Distillation method:** Similarly, the effectiveness of the MDD approach largely depends on the efficiency of the distillation methods. In this work, we have used a simple model fusion based on average to achieve knowledge distillation. However, we have not explored the effectiveness of various distillation methods and their computational overhead. We believe there is a recent line of work on achieving efficient knowledge distillation between pre-trained models [43]. With this work, we set the first steps toward additional research into some technical areas that are required for this strategy to become practically viable.